CN114348887A

CN114348887A - Intelligent monitoring and early warning system and method based on tower crane rotation action model

Info

Publication number: CN114348887A
Application number: CN202210274748.5A
Authority: CN
Inventors: 陈德木; 陆建江
Original assignee: Hangzhou JIE Drive Technology Co Ltd
Current assignee: Hangzhou JIE Drive Technology Co Ltd
Priority date: 2022-03-21
Filing date: 2022-03-21
Publication date: 2022-04-15
Anticipated expiration: 2042-03-21
Also published as: CN114348887B

Abstract

The application provides an intelligent monitoring and early warning system based on tower crane gyration action model. Wherein, intelligent monitoring early warning system based on tower crane gyration action model includes: a controller, a gravity sensor and a wind sensor; the gravity sensor is used for collecting weight information of the material hung by the lifting hook; the wind sensor is used for acquiring wind power information around the intelligent tower crane; the controller is used for calculating a theoretical rotation angle of the intelligent tower crane after preset time according to a rotation angle calculation parameter, comparing the theoretical rotation angle with an actual rotation angle of the intelligent tower crane, and performing early warning on rotation action of the intelligent tower crane according to a comparison result, wherein the rotation angle calculation parameter comprises the weight information and the wind power information. According to the scheme, the abnormity detection of the rotation action of the intelligent tower crane can be realized so as to carry out intelligent early warning, and further the safety of the intelligent tower crane is improved.

Description

Intelligent monitoring and early warning system and method based on tower crane rotation action model

Technical Field

The application relates to the technical field of intelligent tower cranes, in particular to an intelligent monitoring and early warning system and an abnormity perception method based on a tower crane rotation action model.

Background

With the development of the building industry, the mechanization degree of building construction is improved year by year, and a tower crane (for short, a tower crane) is widely applied to the building industry as a machine capable of realizing vertical and horizontal material transportation, particularly due to the characteristics of high lifting height, large lifting weight, large working amplitude and the like.

Along with the frequent emergence of tower crane incident, in order to protect tower crane operating personnel, the personal safety of company of serving as an emergency and reduce the incident that human error leads to, unmanned tower crane promptly intelligence tower crane becomes new research and development direction, wherein, how to realize the unusual detection in order to carry out intelligent early warning to intelligent tower crane gyration action, becomes the current problem that awaits the solution urgently.

Disclosure of Invention

The application aims at providing an intelligent monitoring and early warning system based on a tower crane rotation action model.

The first aspect of this application provides an intelligent monitoring early warning system based on tower crane gyration action model, includes: a controller, a gravity sensor and a wind sensor; wherein the content of the first and second substances,

the gravity sensor is arranged at a lifting hook of the intelligent tower crane and used for collecting weight information of a material lifted by the lifting hook and sending the weight information to the controller;

The wind power sensor is arranged at the top of the intelligent tower crane and used for acquiring wind power information around the intelligent tower crane and sending the wind power information to the controller;

the controller is used for calculating a theoretical rotation angle of the intelligent tower crane after preset time according to a rotation angle calculation parameter, comparing the theoretical rotation angle with an actual rotation angle of the intelligent tower crane, and performing early warning on rotation action of the intelligent tower crane according to a comparison result, wherein the rotation angle calculation parameter comprises the weight information and the wind power information.

The second aspect of the present application provides an anomaly sensing method for an intelligent monitoring and early warning system based on a tower crane rotation action model provided by the first aspect of the present application, the method includes:

the gravity sensor collects weight information of the material hung by the lifting hook and sends the weight information to the controller;

the wind sensor collects wind power information around the intelligent tower crane and sends the wind power information to the controller;

the controller calculates the theoretical gyration angle of the intelligent tower crane after the preset duration according to the gyration angle calculation parameter, compares the theoretical gyration angle with the actual gyration angle of the intelligent tower crane, and carries out early warning on the gyration action of the intelligent tower crane according to the comparison result, wherein the gyration angle calculation parameter comprises the weight information and the wind power information.

The third aspect of the application provides an intelligent tower crane, the intelligent tower crane is provided with the intelligent monitoring and early warning system based on the tower crane gyration action model that this application first aspect provided.

Compared with the prior art, the intelligent monitoring and early warning system based on the tower crane rotation action model is provided with the controller, the gravity sensor and the wind sensor; the gravity sensor is arranged at a lifting hook of the intelligent tower crane and used for collecting weight information of a material lifted by the lifting hook and sending the weight information to the controller; the wind power sensor is arranged at the top of the intelligent tower crane and used for acquiring wind power information around the intelligent tower crane and sending the wind power information to the controller; the controller is used for calculating a theoretical rotation angle of the intelligent tower crane after preset time according to a rotation angle calculation parameter, comparing the theoretical rotation angle with an actual rotation angle of the intelligent tower crane, and performing early warning on rotation action of the intelligent tower crane according to a comparison result, wherein the rotation angle calculation parameter comprises the weight information and the wind power information. Therefore, the theoretical rotation angle can be calculated by utilizing the rotation angle calculation parameters such as weight and wind power, whether the rotation action is abnormal or not is judged by comparing with the actual rotation angle, and early warning is performed, so that the intelligent tower crane rotation action is detected in an abnormal manner to perform intelligent early warning, and the safety of the intelligent tower crane is improved.

Drawings

Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:

fig. 1 shows a schematic structural diagram of an intelligent monitoring and early warning system based on a tower crane slewing motion model according to some embodiments of the present application;

FIG. 2 shows a schematic structural diagram of an intelligent tower crane provided by some embodiments of the present application;

fig. 3 illustrates a flow chart of an anomaly awareness method provided by some embodiments of the present application.

Detailed Description

Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which this application belongs.

In addition, the terms "first" and "second", etc. are used to distinguish different objects, rather than to describe a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.

The embodiment of the application provides an intelligent monitoring and early warning system based on a tower crane rotation action model, and the following description is exemplarily combined with the embodiment and the attached drawings.

Referring to fig. 1, which illustrates a schematic structural diagram of an intelligent monitoring and early warning system based on a tower crane slewing motion model according to some embodiments of the present application, the following exemplary description may be understood with reference to fig. 2, and as shown in fig. 1, the intelligent monitoring and early warning system based on a tower crane slewing motion model may include a controller 101, a gravity sensor 102, and a wind sensor 103; wherein the content of the first and second substances,

The gravity sensor 102 is arranged at a lifting hook of the intelligent tower crane and used for collecting weight information of a material lifted by the lifting hook and sending the weight information to the controller 101;

the wind power sensor 103 is arranged at the top of the intelligent tower crane and used for acquiring wind power information around the intelligent tower crane and sending the wind power information to the controller 101;

the controller 101 is used for calculating a theoretical rotation angle of the intelligent tower crane after a preset time length is calculated according to a rotation angle calculation parameter, comparing the theoretical rotation angle with an actual rotation angle of the intelligent tower crane, and performing early warning on rotation action of the intelligent tower crane according to a comparison result, wherein the rotation angle calculation parameter comprises the weight information and the wind power information.

Compared with the prior art, the intelligent monitoring and early warning system based on the tower crane rotation action model provided by the embodiment of the application is provided with the controller 101, the gravity sensor 102 and the wind sensor 103; the gravity sensor 102 is arranged at a lifting hook of the intelligent tower crane and used for collecting weight information of a material lifted by the lifting hook and sending the weight information to the controller 101; the wind power sensor 103 is arranged at the top of the intelligent tower crane and used for acquiring wind power information around the intelligent tower crane and sending the wind power information to the controller 101; the controller 101 is used for calculating a theoretical rotation angle of the intelligent tower crane after a preset time length is calculated according to a rotation angle calculation parameter, comparing the theoretical rotation angle with an actual rotation angle of the intelligent tower crane, and performing early warning on rotation action of the intelligent tower crane according to a comparison result, wherein the rotation angle calculation parameter comprises the weight information and the wind power information. Therefore, the theoretical rotation angle can be calculated by utilizing the rotation angle calculation parameters such as weight and wind power, whether the rotation action is abnormal or not is judged by comparing with the actual rotation angle, and early warning is performed, so that the intelligent tower crane rotation action is detected in an abnormal manner to perform intelligent early warning, and the safety of the intelligent tower crane is improved.

The controller 101 refers to a controller 101 of an intelligent tower crane, and can be implemented by a computer host, a microcontroller, a Programmable Logic Controller (PLC), and the like, and the gravity sensor 102 and the wind sensor 103 can be implemented by directly adopting a finished product sensor provided by the prior art, and the embodiment of the application does not limit the specific model and specification of the sensor, wherein the gravity sensor 102 is used for acquiring weight information of hoisted materials, and the wind sensor 103 is used for acquiring wind information of the hoisted materials, including wind direction, wind speed, and the like.

In some modification implementation manners of the embodiment of the application, the rotation angle calculation parameter of the intelligent tower crane further comprises the output torque, the amplitude variation position and the windward sectional area of the material of the rotation mechanism of the intelligent tower crane.

On the basis of any of the above embodiments, in some modified embodiments, the controller 101 is specifically configured to calculate wind force applied to the material according to a cross-sectional area in a windward direction of the material and wind speed information in the wind force information, calculate a wind resistance torque of the material according to the wind force applied to the material and the amplitude variation position, calculate a rotational torque of the material according to the output torque and the wind resistance torque, and calculate a theoretical rotation angle within a preset time period according to the rotational torque and the weight of the material.

For example, the wind force on the material can be calculated according to the following formula:

the wind force of the material = windward area and windward area coefficient and wind speed resistance.

Wherein wind speed drag = coefficient K wind speed.

The windward area coefficient and the coefficient K may be determined by experiments, and the embodiment of the present application is not limited.

Through the embodiment, the theoretical rotation angle can be obtained through mathematical relation calculation so as to judge whether the rotation action of the intelligent tower crane is abnormal.

Because the material can rotate in the hoisting process, the windward area of the material is not convenient to measure, and the corresponding windward area coefficient is difficult to determine, therefore, the windward sectional area of the material is preferably adopted to calculate as the windward area, and a more accurate calculation result can be obtained. In some modifications, the controller 101 is further configured to determine the windward cross-sectional area of the material according to a three-dimensional image of the material acquired in advance, orientation information of the material acquired in real time, and wind direction information in the wind force information.

Wherein, the three-dimensional image of above-mentioned material can gather in advance and obtain, and is specific, in some embodiments, intelligent monitoring early warning system based on tower crane gyration action model still includes: the intelligent auxiliary robot is used for detecting the working condition of the intelligent tower crane;

The intelligent auxiliary robot is in communication connection with the controller 101, and is configured to move around the material on the ground to capture a three-dimensional image of the material, and send the three-dimensional image to the controller 101.

On the basis of any of the above embodiments, in some modified embodiments, the intelligent monitoring and early warning system based on the tower crane rotation motion model further includes: a global camera connected to the controller 101;

the global camera is arranged on the intelligent tower crane boom downwards and used for shooting a global image of a working scene of the intelligent tower crane and sending the global image to the controller 101;

the controller 101 is further configured to identify orientation information of the material in the working scene in real time according to the global image.

On the basis of any of the above embodiments, in some modified embodiments, the controller 101 is specifically configured to normalize the rotation angle calculation parameter, input the normalized rotation angle calculation parameter into a preset rotation motion model, and output a theoretical rotation angle after a preset duration by using the rotation motion model, where the rotation motion model is obtained by pre-training with normal tower crane operation data based on a neural network.

Considering that the windward area coefficient and the coefficient K can change frequently in practical application to influence the accuracy of mathematical calculation, the embodiment of the application realizes the determination of the theoretical rotation angle by means of the neural network instead, so that the accurate theoretical rotation angle can be obtained, and the accuracy of rotation motion detection and the accuracy of early warning are improved.

On the basis of the above embodiment, in some modified embodiments, the controller 101 is further configured to obtain multiple sets of operation data of the normal tower crane, where each set of operation data includes a rotation angle calculation parameter of the intelligent tower crane acquired at any time and an actual rotation angle after a preset time duration at any time, and each rotation angle calculation parameter in each set of operation data is normalized and serves as a set of training samples together with the actual rotation angle in the set of operation data, and a neural network is trained by using multiple sets of training samples to obtain a rotation motion model, where the rotation angle calculation parameter after normalization in each set of training samples serves as an input factor of the neural network, and the actual rotation angle in each set of training samples serves as an output factor of the neural network.

Through the embodiment, a relatively accurate rotation action model can be obtained through training, and a relatively accurate theoretical rotation angle is further ensured to be obtained.

In the embodiment, an intelligent monitoring and early warning system based on a tower crane rotation action model is provided, and correspondingly, the application further provides an abnormity perception method. The anomaly sensing method provided by the embodiment of the application can be realized based on the above intelligent monitoring and early warning system based on the tower crane rotation action model, please refer to fig. 3, which shows a flow chart of the anomaly sensing method provided by some embodiments of the application. Since the method embodiment is basically similar to the system embodiment, the description is simple, and the relevant points can be referred to the partial description of the system embodiment. The method embodiments described below are merely illustrative.

As shown in fig. 3, an anomaly sensing method can be executed by the intelligent monitoring and early warning system based on the tower crane rotation motion model provided in any of the above embodiments, and may include the following steps:

step S101: and the gravity sensor collects the weight information of the material hung by the lifting hook and sends the weight information to the controller.

Step S102: and a wind sensor collects wind power information around the intelligent tower crane and sends the wind power information to the controller.

Step S103: the controller calculates the theoretical gyration angle of intelligent tower crane after presetting duration according to gyration angle calculation parameter to will theoretical gyration angle with the actual gyration angle of intelligent tower crane compares, and is right according to the contrast result the gyration action of intelligent tower crane carries out the early warning, wherein, gyration angle calculation parameter includes weight information with wind-force information.

In some modification implementation manners of the embodiment of the present application, the theoretical rotation angle of the intelligent tower crane after the controller calculates the preset duration according to the rotation angle calculation parameter includes:

the controller calculates the wind power borne by the material according to the cross section area of the material in the windward direction and the wind speed information in the wind power information, calculates the wind resistance torque of the material according to the wind power borne by the material and the amplitude variation position, calculates the rotation torque of the material according to the output torque and the wind resistance torque, and calculates the theoretical rotation angle within a preset time length according to the rotation torque and the weight of the material.

In some modified embodiments of the embodiment of the present application, before the theoretical rotation angle of the intelligent tower crane after the controller calculates the preset duration according to the rotation angle calculation parameter, the method further includes:

the controller determines the sectional area of the material in the windward direction according to the pre-collected three-dimensional image of the material, the orientation information of the material acquired in real time and the wind direction information in the wind power information.

In some modified embodiments of the embodiment of the present application, before the controller determines the windward cross-sectional area of the material according to the pre-acquired three-dimensional image of the material, the orientation information of the material acquired in real time, and the wind direction information in the wind force information, the method further includes:

the intelligent auxiliary robot moves around the material on the ground to shoot a three-dimensional image of the material, and sends the three-dimensional image to the controller.

In some modifications of the embodiments of the present application, before determining the windward cross-sectional area of the material according to the pre-acquired three-dimensional image of the material, the orientation information of the material acquired in real time, and the wind direction information in the wind force information, the controller further includes:

The global camera shoots a global image of the working scene of the intelligent tower crane and sends the global image to the controller;

and the controller identifies the orientation information of the material in the working scene in real time according to the global image.

In some modifications of the embodiments of the present application, the theoretical rotation angle of the intelligent tower crane after the controller calculates the preset duration according to the rotation angle calculation parameter includes:

the controller normalizes the rotation angle calculation parameters, inputs the normalization processed rotation angle calculation parameters into a preset rotation action model, and outputs a theoretical rotation angle after the preset time length by using the rotation action model, wherein the rotation action model is obtained by utilizing normal tower crane operation data to train in advance based on a neural network.

In some modifications of the embodiments of the present application, before the controller normalizes the rotation angle calculation parameter and inputs the normalized rotation angle calculation parameter into a preset rotation motion model, the method further includes:

the controller obtains multiunit operational data of normal tower crane, every group operational data includes the gyration angle calculation parameter of the intelligent tower crane of gathering at any moment, and is in actual gyration angle after the arbitrary moment presets duration, and with every in the operational data each gyration angle calculation parameter after carrying out the normalization processing with in this group operational data actual gyration angle is as a set of training sample together, utilizes the multiunit training sample training neural network obtains gyration action model, wherein, in every group training sample gyration angle calculation parameter after the normalization processing is as neural network's input factor, in every group training sample actual gyration angle is as neural network's output factor.

The anomaly sensing method provided by the embodiment of the application has the same inventive concept as the intelligent monitoring and early warning system based on the tower crane rotation action model provided by the embodiment of the application, and has the same beneficial effects.

The embodiment of the application also provides an intelligent tower crane corresponding to the intelligent monitoring and early warning system based on the tower crane rotation action model provided by the embodiment, which can be understood by referring to fig. 2, the intelligent tower crane is provided with the intelligent monitoring and early warning system based on the tower crane rotation action model provided by any embodiment, and the abnormity perception method provided by any embodiment can be executed to automatically perform abnormity detection and early warning on the rotation action of the intelligent tower crane.

The intelligent tower crane provided by the embodiment of the application has the same inventive concept as the intelligent monitoring and early warning system based on the tower crane rotation action model provided by the embodiment of the application, and has the same beneficial effects.

In addition, in order to further improve the intellectualization and the unmanned of the intelligent tower crane, the intelligent tower crane can also reduce the safety accidents of tower crane hoisting by configuring the following sensing equipment for the automatic grabbing process of the tower crane hook, and the following description is combined with the example.

In some embodiments, the sensing device for the automatic grabbing process of the tower crane hook may include: the device comprises a controller, an automatic lifting hook, a lifting hook driving mechanism, a visual sensor and a sensor driving mechanism; wherein the content of the first and second substances,

the automatic lifting hook is connected with the lifting hook driving mechanism, the visual sensor is connected with the sensor driving mechanism, and the lifting hook driving mechanism, the sensor driving mechanism and the visual sensor are all connected with the controller;

the controller is passing through lifting hook drive mechanism control during automatic lifting hook motion, still pass through sensor drive mechanism control vision sensor follows automatic lifting hook motion to control vision sensor orientation automatic lifting hook place region gathers vision sensing signal, with the basis vision sensing signal control automatic lifting hook snatchs the goods.

The controller can be realized by a computer host, a microcontroller, a Programmable Logic Controller (PLC) and the like, and the automatic lifting hook can be realized by any automatic lifting hook provided by the prior art, so that the embodiment of the application is not limited.

It should be noted that, if above-mentioned tower crane is unmanned tower crane, then this controller can locate on the control platform on ground, should control the bench and be provided with the display screen to the live picture around the lifting hook is looked over through the display screen to the tower crane control personnel, corresponding, the aforesaid is according to vision sensing signal control automatic lifting hook snatchs the goods, can be for playing this vision sensing signal through the display screen of locating the control platform, makes the tower crane control personnel accurately know the lifting hook circumstances around to control automatic lifting hook and hang and get the goods.

In addition, the controller, the vision sensor and the sensor driving mechanism can be connected in a wireless mode or in a wired mode, considering that the stability of a wireless signal is relatively poor, and safety accidents are possibly caused by signal interruption and errors, in some embodiments, the vision sensor and the sensor driving mechanism are connected with the controller by cables, and specifically, the cables can be connected to a console on the ground along a crane boom and a standard joint and connected with the controller on the console, so that the quality and the stability of the signals are improved, and the safety accidents caused by signal problems are avoided.

Through being used for the automatic sensing equipment who snatchs the process of tower crane lifting hook for intelligent tower crane configuration, can make through setting up controller, automatic lifting hook, lifting hook actuating mechanism, visual sensor and sensor actuating mechanism, automatic lifting hook with lifting hook actuating mechanism connects, visual sensor with sensor actuating mechanism connects, lifting hook actuating mechanism with the visual sensor all with the controller is connected, and the controller is when passing through the lifting hook actuating mechanism control automatic lifting hook motion, still through sensor actuating mechanism control the visual sensor follows automatic lifting hook motion, and control the visual sensor orientation automatic lifting hook regional collection visual sensing signal in place, with according to visual sensing signal control automatic lifting hook snatchs the goods to can make visual sensor follow automatic lifting hook motion and closely gather visual sensing signal, compared with the mode of installing the zoom camera in the prior art, the method can avoid the problem that the manual zooming influences the operation of tower crane operators or the automatic zooming misalignment causes blurred pictures, can automatically acquire high-definition and accurate vision sensing signals without additional operation of the tower crane operators, so that the operator of the tower crane can observe the conditions of the lifting hook, the surrounding environment, the obstacles and the like according to the visual sensing signal, solves the potential safety hazard of dead zones such as 'isolating mountain crane' and the like, ensures the hoisting safety of the dead zones, can further utilize an automatic lifting hook to automatically grab goods based on the visual sensing signal, reduces the problems of inaccurate hook hoisting and the like, does not need a dragger to hoist the goods to the lifting hook by adopting a manual operation mode, the participation of staffs such as a householder, a commander and the like can be reduced, so that the probability of accidental injury of the staffs by goods is further reduced, and the incidence rate of safety accidents is reduced.

In some variations of embodiments of the present application, the hook driving mechanism includes a first trolley, the sensor driving mechanism includes a second trolley, and the first trolley and the second trolley are both disposed on a boom of a tower crane and move along the boom.

Specifically, in some embodiments, the vision sensor is suspended on the second trolley by a rope pulley assembly, and moves in the horizontal direction according to the movement of the second trolley along the crane boom, and moves in the vertical direction according to the retracting action of the rope pulley assembly.

In addition, the first trolley and the second trolley can also adopt two sets of different amplitude-variable steel wire ropes to respectively carry out traction movement, so that the distance between the first trolley and the second trolley can be adjusted, the distance from a visual sensor to an automatic lifting hook can be conveniently adjusted, and a better observation visual field can be obtained.

In addition, the first trolley and the second trolley need to adopt two sets of different lifting steel wire ropes to respectively pull the automatic lifting hook and the vision sensor to lift, so that the vision sensor can be leveled with the automatic lifting hook, and can also be higher than the automatic lifting hook or lower than the automatic lifting hook to acquire signals, and the vision sensor can be applied to various working conditions to obtain a better observation visual field.

Through setting up the independent drive vision sensor of second dolly, can be according to the nimble following relation of adjusting between vision sensor and the automatic lifting hook of operating condition, for example, can adjust and keep 3 meters intervals along the width of cloth direction between vision sensor and the automatic lifting hook, keep a meter interval along direction of height, perhaps, adjust and keep a meter interval along the width of cloth direction between vision sensor and the automatic lifting hook, keep parallelly (the interval is zero) etc. along direction of height to obtain the observation field of vision of preferred.

After the following relationship is determined, the controller can automatically control the vision sensor to follow the movement according to the following relationship when controlling the movement of the automatic lifting hook so as to keep the same observation visual field. In addition, the operator can also adjust the following relationship according to actual requirements, and the embodiment of the present application does not limit specific values thereof.

It should be noted that, what relate to in this application embodiment follows means that certain distance and angle are kept with automatic lifting hook when moving to obtain the same observation field of vision, improve the tower crane and control personnel and observe experience, avoid the field of vision transform and influence the tower crane and control personnel and observe.

The vision sensor that this application embodiment provided can be connected with the controller through the cable, the cable can be receive and release through the winder, the winder can be located on the second dolly, the winder can keep the cable in the state of tightening up, avoids the cable slack to rock and influence other parts and move.

In other modified embodiments, the tower crane is provided with a variable amplitude sensor and a height sensor, the variable amplitude sensor is used for detecting variable amplitude position information of the automatic lifting hook, and the height sensor is used for detecting height position information of the automatic lifting hook;

and the controller controls the vision sensor to move along with the automatic lifting hook according to the amplitude variation position information and the height position information of the automatic lifting hook.

The amplitude sensor and the height sensor can be realized by sensors provided by the prior art, and can be mechanical sensors, infrared sensors or laser sensors, which can achieve the purpose of the embodiment of the application, and the embodiment of the application is not limited.

The amplitude-variable position information can comprise the horizontal distance from the automatic lifting hook to the standard knot along the amplitude-variable direction (namely the horizontal direction of the cargo boom), the height position information can comprise the vertical distance from the automatic lifting hook to the cargo boom along the vertical direction, and according to the amplitude-variable position information and the height position information, as the visual sensor and the automatic lifting hook are also driven by the trolley and the rope, the amplitude-variable position information and the height position information of the position where the visual sensor is located can be determined according to the amplitude-variable position information and the height position information of the automatic lifting hook and by combining the predetermined following relation, and the visual sensor is controlled to move to the position where the visual sensor is located, so that the following movement with the automatic lifting hook is realized.

Specifically, in some embodiments, the controller further determines a rough relative position relationship between the vision sensor and the automatic lifting hook according to the amplitude variation position information and the height position information of the automatic lifting hook, and coarsely adjusts the relative position relationship according to the rough relative position relationship, and turns the vision sensor to the area where the automatic lifting hook is located. Because the amplitude variation position information and the height position information of the automatic lifting hook and the visual sensor are obtained when the automatic lifting hook and the visual sensor move along, the visual sensor can be quickly and coarsely adjusted to turn to the area where the automatic lifting hook is located according to the existing data through the implementation mode.

In view of the fact that the automatic hook is not necessarily located at a preferred position in the visual sensor field after coarse adjustment, and the visual sensor may swing with air disturbance in the high altitude and fail to accurately capture a desired picture, in some modified embodiments, on the basis of the above embodiment, the controller further identifies the automatic hook in the visual sensing signals collected by the visual sensor after coarse adjustment of the visual sensor to the area where the automatic hook is located, determines a fine relative position relationship between the visual sensor and the automatic hook, and fine-adjusts the visual sensor according to the fine relative position relationship, so that the visual sensor after fine adjustment collects visual sensing signals that are in line with the desired visual sensor. In this embodiment, the image recognition technology provided by the prior art can be used to recognize the automatic lifting hook in the visual sensing signal, so as to determine the fine relative position relationship between the visual sensor and the automatic lifting hook, and the visual sensor is finely adjusted according to the fine relative position relationship, so that the visual sensor after fine adjustment acquires the visual sensing signal which meets the expectation, wherein the expectation can be that the automatic lifting hook is located in the middle position of the picture of the visual sensing signal, or the automatic lifting hook and the lifted cargo are located in the middle position of the picture of the visual sensing signal as a whole, and the embodiment of the application is not limited. Through the implementation mode, the vision sensing signal which is in line with the expectation can be recorded through fine adjustment on the basis of coarse adjustment, and the precision of the vision sensing signal is improved, so that the vision sensing signal is used for accurately controlling the action of the automatic lifting hook.

In aforementioned arbitrary embodiment, above-mentioned vision sensor can include cloud platform camera or laser scanner, and it all can gather and obtain accurate vision sensing signal to the staff accuracy is known lifting hook operating mode is controlled to the help tower crane, and the automatic lifting hook hoist and mount goods of accurate control.

It is easy to understand if vision sensor weight is lighter, can swing along with air disturbance in the high altitude, influence the shooting effect, consequently, in some change implementation modes, still can for vision sensor configuration gesture stable control ware to help vision sensor stabilize the gesture in the high altitude, reduce and rock, improve the shooting effect, and then help the tower crane control personnel to accurately know the lifting hook operating mode, and the automatic lifting hook hoist and mount goods of accurate control.

The attitude stabilization controller can be realized by at least one of a counterweight, a flywheel and a control moment gyro, and can be realized by one of the counterweight, the flywheel and the control moment gyro or by a plurality of the counterweight, the flywheel and the control moment gyro. Wherein, the counterweight is most easily realized and the implementation cost is lowest; if the flywheel is additionally arranged, the flywheel should be horizontally placed, and the generated angular momentum can help to keep the posture of the vision sensor stable; in addition, the principle of the control moment gyro is that when a torque perpendicular to the rotation axis of the gyro is given to the gyro, a precession moment perpendicular to the rotation axis and perpendicular to the torque axis is generated.

In consideration of the problems that a lifting hook falls off, a rope falls off and the like often occur in the lifting process due to the reasons that goods are not bound tightly, hooked inaccurately and the like when the goods are lifted, so that the goods fall off to injure ground workers such as a cable businessman and the like, and safety accidents are caused, and therefore the abnormal lifting of the tower crane needs to be further detected to reduce the lifting safety accidents of the tower crane. On the basis of any of the above embodiments, in some modification embodiments, the sensing device for the automatic grabbing process of the tower crane hook may further include: an attitude sensor in communicative connection with the controller;

the attitude sensor is fixedly arranged on the automatic lifting hook and used for acquiring attitude data of the automatic lifting hook in real time and sending the attitude data to the controller;

and the controller determines the inclination information and the swing information of the automatic lifting hook according to the attitude data and judges whether the lifting state of the automatic lifting hook is abnormal or not according to the inclination information and the swing information.

The attitude sensor may be implemented by motion sensors such as, but not limited to, a three-axis gyroscope, a three-axis accelerometer, and a three-axis electronic compass, and the embodiments of the present application are not limited thereto.

It should be noted that, if above-mentioned tower crane is unmanned tower crane, then this controller can locate on the platform is controlled on ground, should control and be provided with display screen and/or stereo set be connected with the controller on the platform for whether the play to rise state that is used for reporting automatic lifting hook through image and/or voice mode is unusual, so that the tower crane control personnel know the play to rise state of automatic lifting hook and whether unusual.

In addition, the controller and the attitude sensor can be connected in a wireless mode or in a wired mode, considering that the stability of a wireless signal is relatively poor and safety accidents are possibly caused due to signal interruption and errors, in some embodiments, the attitude sensor and the controller are preferably connected in a wired mode by using a cable, and specifically, the cable can be connected to a console on the ground along a crane boom and a standard joint and is connected with the controller on the console, so that the signal quality and the stability are improved, and the safety accidents caused by the fact that the lifting state of the automatic lifting hook cannot be found in time due to signal problems are avoided.

Compared with the prior art, the above-mentioned sensing equipment that is used for automatic process of snatching of tower crane lifting hook through further set up with controller communication connection's attitude sensor, just attitude sensor is fixed to be set up on automatic lifting hook for gather in real time automatic lifting hook's attitude data sends for the controller, the controller basis attitude data confirms automatic lifting hook's slope information and swing information, and according to slope information and swing information judge whether automatic lifting hook's the state of lifting to rise is unusual. Because before the lifting hook drops, the rope drops, the lifting hook often can produce great inclination, perhaps shake etc. unusually by a wide margin, consequently, utilize automatic lifting hook's gesture data can judge the tower crane hoisting state more accurately and whether unusual to in time carry out the pertinence when detecting unusually, avoid the goods to scatter and injure the workman, reduce the tower crane hoisting stage because of the incident incidence that the goods of hoist and mount scatter and lead to.

The gesture sensor that this application embodiment provided can be connected with the controller through the cable, the cable can be receive and release through the winder, the winder can be located and hang on the dolly of automatic lifting hook, the winder can keep the cable to be in the state of tightening up, avoids the cable to relax and rocks and influence other part operations.

In some modifications of the embodiments of the present application, the controller stores no-load attitude data collected by the attitude sensor when the automatic hook is no-load, and determines inclination information and swing information of the automatic hook by comparing the load attitude data with the no-load attitude data after receiving load attitude data collected by the attitude sensor when the automatic hook is loaded.

The no-load attitude data is basic attitude data which is collected in a static state that the automatic lifting hook is no-load and no wind exists around and is used for comparison, and after the automatic lifting hook hangs a cargo, namely a load, the inclination information and the swing information of the automatic lifting hook can be obtained by comparing the load attitude data with the no-load attitude data.

The inclination information refers to information such as an inclination angle generated by rotation of the automatic lifting hook with the automatic lifting hook serving as a reference, the swing information refers to information such as a swing angle generated by swing of the automatic lifting hook with a trolley for suspending the automatic lifting hook serving as a reference, the radius of a circle can be calculated according to a path (a section of arc line on the circle) which the automatic lifting hook passes through in the swing process, the swing angle can be calculated according to the length of the arc line, and whether the state of the automatic lifting hook is abnormal or not can be judged and predicted according to the inclination information and the swing information.

Specifically, the controller may determine inclination change information and swing change information of a unit time window according to the inclination information and the swing information by using a sliding time window method, and determine whether the lifting state of the automatic lifting hook is abnormal according to the inclination change information and the swing change information.

Wherein the tilt change information includes at least one of a tilt change width and a tilt angle at the end of the unit time window, and the wobble change information includes at least one of a wobble change width and a wobble angle at the end of the unit time window.

The inclination change amplitude is a difference value between an inclination angle at the last stage of a unit time window and an inclination angle at the initial stage, the inclination angle of the automatic lifting hook inclining to the unhooking direction can be defined to be positive, the inclination angle at the opposite direction is negative, if the difference value is a positive value and is greater than a preset threshold value, the abnormal probability is greater, and the lifting abnormity can be directly judged or whether the lifting abnormity exists is further judged by combining other factors.

The swing change amplitude is a difference value between an inclination angle at the last stage of a unit time window and an inclination angle at the initial stage, the swing angle of the automatic lifting hook swinging towards the unhooking direction can be defined to be positive, the swing angle in the opposite direction is negative, if the difference value is a positive value and is greater than a preset threshold value, the abnormal probability is greater, and the lifting abnormity can be directly judged or whether the lifting abnormity exists is further judged by combining other factors.

In some modification embodiments, in order to improve the determination accuracy, the controller may input the inclination change information and the swing change information into a first neural network model trained in advance, and determine whether the lifting state of the automatic lifting hook is abnormal according to the first neural network model.

The first neural network model can be obtained by training a large number of training samples, the training samples comprise a plurality of groups of training data determined by experiments, each group of training data comprises inclination change information and swing change information, and whether abnormal labels exist or not is judged, through training, the first neural network model can output whether abnormal labels exist or not according to the input inclination change information and swing change information, and then the first neural network model can be used for judging whether the lifting state of the automatic lifting hook is abnormal or not.

The input data of the first neural network model comprises inclination change information and swing change information, the output data is whether an abnormal label (a binary label) exists or not, and the whole input parameters and the output are simple, so that the neural network can be realized by adopting a BP neural network, a convolutional neural network CNN and other neural networks with simple structures, can be composed of an input layer, a hidden layer and an output layer, and the purpose of the embodiment of the application can be realized without complex design, thereby reducing the implementation difficulty and obtaining a more accurate judgment result. The BP neural network and the convolutional neural network CNN are both mature neural network models, and those skilled in the art can refer to the prior art and combine actual requirements to flexibly construct the first neural network model to achieve the purpose of the embodiment of the present application.

Through the embodiment, whether the lifting state of the automatic lifting hook is abnormal or not can be accurately judged by utilizing the neural network model, and compared with a mode of judging according to a threshold value, the accuracy is higher.

Consider that environmental wind also can influence the hoist and mount firmness of goods, if wind direction is the same with the unhook direction, can increase the probability that goods unhook, and the wind speed is big more, and the unhook probability is big more, and is opposite, if wind direction is opposite with the unhook direction, can reduce the probability that goods unhook, for more accurate judgement whether the rising state of automatic lifting hook is unusual, in some change implementation modes, the above-mentioned sensing equipment that is used for the automatic process of snatching of tower crane lifting hook still includes: the wind direction sensor and the wind speed sensor are arranged on the tower crane;

the wind direction sensor and the wind speed sensor are both connected with the controller and are respectively used for acquiring wind direction information and wind speed information around the automatic lifting hook and sending the wind direction information and the wind speed information to the controller;

the controller is further used for comprehensively judging whether the lifting state of the automatic lifting hook is abnormal or not according to the inclination change information, the swing change information, the wind direction information and the wind speed information.

The specific determination method may be as described above, based on the preset threshold, to comprehensively determine whether the lifting state of the automatic lifting hook is abnormal, or a neural network may be used to determine whether the lifting state of the automatic lifting hook is abnormal, for example, in some embodiments, the controller may input the tilt change information, the swing change information, the wind direction information, and the wind speed information into a second neural network model trained in advance, and determine whether the lifting state of the automatic lifting hook is abnormal according to the second neural network model.

The second neural network model can be obtained by training a large number of training samples, the training samples comprise a plurality of groups of training data determined by experiments, each group of training data comprises inclination change information, swing change information, wind direction information and the wind speed information, and whether an abnormal label exists or not is output through training, and then whether the lifting state of the automatic lifting hook is abnormal or not can be judged by using the second neural network model according to the input inclination change information, swing change information, wind direction information and the wind speed information.

Similar to the first neural network model, the input data of the second neural network model includes inclination change information, swing change information, wind direction information and wind speed information, the output data is a label (binary label) whether an abnormality exists, and the whole input parameters and output are simple, so that the neural network can be realized by adopting a neural network with a simple structure such as a BP neural network and a convolutional neural network CNN, and the neural network can be composed of an input layer, a hidden layer and an output layer, and the purpose of the embodiment of the application can be realized without complex design, so that the implementation difficulty is reduced, and a more accurate judgment result is obtained. The BP neural network and the convolutional neural network CNN are both mature neural network models, and those skilled in the art can refer to the prior art and combine actual requirements to flexibly construct the first neural network model to achieve the purpose of the embodiment of the present application.

Through the embodiment, whether the lifting state of the automatic lifting hook is abnormal or not can be accurately judged by using the second neural network model, the influence of wind power on the abnormal unhooking is considered, the neural network model is used for judging, the accuracy is high, and whether the lifting state of the automatic lifting hook is abnormal or not can be accurately judged.

On the basis of any of the above embodiments, in another modified embodiment, the sensing device for the automatic grabbing process of the tower crane hook may further include: the alarm device is arranged on the automatic lifting hook;

the alarm device is connected with the controller, and the controller broadcasts abnormal alarm information through the alarm device when detecting that the lifting state of the automatic lifting hook is abnormal.

The alarm device can comprise a buzzer, a sound box and other voice alarm devices, and can warn surrounding workers to evacuate by broadcasting abnormal alarm information to the surrounding, so that the condition that the goods are unhooked to hurt the nearby workers is avoided, and the accident loss is reduced.

In addition, after the goods are lifted, the controller can comprehensively judge whether the lifting state of the tower crane lifting hook is abnormal according to the visual sensing signal and the attitude data acquired by the attitude sensor, for example, taking the visual sensing signal as a real-time picture shot by a pan-tilt camera as an example, the lifting hook and the rope can be identified through image identification, and whether the abnormality exists can be judged through the relative positions of the lifting hook and the rope in the pictures shot successively and the movement trend of the rope, for example, if the rope moves to a preset range at the outlet of the lifting hook and has a trend of continuing moving towards the unhooking direction, the unhooking risk is judged, namely the lifting state of the tower crane lifting hook is judged to be abnormal; otherwise, the unhooking risk can be judged, namely the lifting state of the tower crane lifting hook is judged to be abnormal. Among them, the image recognition technology is a mature technology in the prior art, and those skilled in the art can directly apply the prior art to the present application to achieve the purpose of the embodiments of the present application.

It should be noted that, if the lifting state of the tower crane lifting hook is comprehensively judged to be abnormal according to the attitude data and the visual sensing signal, the specific judgment mode may be: if the lifting state of the tower crane lifting hook is judged to be abnormal by adopting any one of the attitude data and the visual sensing signal, the lifting state of the tower crane lifting hook is judged to be abnormal on the whole, and if not, the lifting state of the tower crane lifting hook is judged to be abnormal. Therefore, whether the lifting state of the tower crane lifting hook is abnormal or not is comprehensively and accurately judged by utilizing the attitude data and the visual sensing signals, and the accuracy is improved.

In addition, in order to further improve the intellectualization and the unmanned of the intelligent tower crane, the intelligent tower crane can also reduce the safety accidents of tower crane hoisting by configuring the following sensing internet of things system for sensing the abnormal hoisting state of the intelligent tower crane, and the following description is combined with the example.

In some embodiments, the sensing internet of things system for sensing the abnormal lifting state of the intelligent tower crane may include: the device comprises a controller and an attitude sensor in communication connection with the controller;

the attitude sensor is fixedly arranged on the tower crane hook and used for acquiring attitude data of the tower crane hook in real time and sending the attitude data to the controller;

And the controller determines the inclination information and the swing information of the tower crane lifting hook according to the attitude data and judges whether the lifting state of the tower crane lifting hook is abnormal or not according to the inclination information and the swing information.

The controller can be realized by a computer host, a microcontroller, a Programmable Logic Controller (PLC) and the like, the attitude sensor can be realized by motion sensors such as a triaxial gyroscope, a triaxial accelerometer, a triaxial electronic compass and the like, and the embodiment of the application is not limited.

It should be noted that, if above-mentioned tower crane is unmanned tower crane, then this controller can locate on the platform is controlled on ground, should control the bench and be provided with display screen and/or stereo set be connected with the controller for whether play to rise the state of tower crane lifting hook is unusual through image and/or voice mode broadcast, so that the tower crane control personnel know the play to rise the state of tower crane lifting hook and whether unusual.

In addition, the controller and the attitude sensor can be connected in a wireless mode or in a wired mode, considering that the stability of a wireless signal is relatively poor and safety accidents are possibly caused due to signal interruption and errors, in some embodiments, the attitude sensor and the controller are connected by a cable in a preferred wired mode, specifically, the cable can be connected to a control platform on the ground along a crane boom and a standard joint and is connected with the controller on the control platform, so that the signal quality and the stability are improved, and the problem that the lifting state abnormity of the tower crane lifting hook cannot be timely found due to signal problems and further safety accidents are caused is avoided.

Compared with the prior art, the sensing thing networking systems that is used for intelligence tower crane to play to rise abnormal state perception that this application embodiment provided is through setting up the controller and with controller communication connection's attitude sensor, just attitude sensor is fixed to be set up on the tower crane lifting hook, is used for real-time acquisition the attitude data of tower crane lifting hook sends for the controller, the controller basis attitude data confirms the slope information and the swing information of tower crane lifting hook, and according to slope information and swing information judge whether the play to rise state of tower crane lifting hook is unusual. Because before the lifting hook drops, the rope drops, the lifting hook often can produce great inclination, perhaps shake etc. unusually by a wide margin, consequently, utilize the attitude data of tower crane lifting hook can judge the tower crane hoisting state more accurately and whether unusual to in time carry out the pertinence when detecting unusually, avoid the goods to scatter and injure the workman, reduce the tower crane hoisting stage because of the incident incidence that the goods scatters and leads to.

The attitude sensor that this application embodiment provided can be connected with the controller through the cable, the cable can be receive and release through the winder, the winder can be located and hang on the dolly of tower crane lifting hook, the winder can keep the cable to be in the state of tightening up, avoids the cable to relax and rocks and influence other part operations.

In some change implementation modes of this application embodiment, the controller storage has when the tower crane lifting hook is unloaded the unloaded attitude data that attitude sensor gathered, and receiving when the tower crane lifting hook is loaded behind the load attitude data that attitude sensor gathered, through the comparison load attitude data with unloaded attitude data confirms the slope information and the swing information of tower crane lifting hook.

The no-load attitude data is basic attitude data which is collected in a static state that the tower crane lifting hook is no-load and no wind exists around and is used for comparison, and after goods, namely loads, are hung on the tower crane lifting hook, the inclination information and the swing information of the tower crane lifting hook can be obtained by comparing the load attitude data with the no-load attitude data.

Wherein, above-mentioned inclination information is that the tower crane lifting hook uses self to rotate and information such as inclination that produces as the reference, and above-mentioned swing information is that the tower crane lifting hook is with the dolly that hangs this tower crane lifting hook as information such as the swing angle that the reference swing produced, wherein, according to the route (one section pitch arc on the circle) that the tower crane lifting hook passed through at the swing in-process, can calculate the radius of circle, and then calculate the swing angle according to pitch arc length, according to this inclination information and swing information, can judge, predict whether the state of tower crane lifting hook is unusual.

Specifically, the controller can adopt a sliding time window method, determine inclination change information and swing change information of a unit time window according to the inclination information and the swing information, and judge whether the lifting state of the tower crane lifting hook is abnormal or not according to the inclination change information and the swing change information.

The inclination change amplitude is a difference value between an inclination angle at the last stage of a unit time window and an inclination angle at the initial stage, the inclination angle of the tower crane lifting hook inclined towards the unhooking direction can be defined to be positive, the inclination angle at the opposite direction is negative, if the difference value is a positive value and is greater than a preset threshold value, the abnormal probability is greater, and the lifting abnormity can be directly judged or whether the lifting abnormity exists is further judged by combining other factors.

The swing change amplitude is a difference value between an inclination angle at the last stage of a unit time window and an inclination angle at the initial stage, a swing angle of the tower crane lifting hook swinging towards the unhooking direction can be defined to be positive, a swing angle in the opposite direction is negative, if the difference value is a positive value and is greater than a preset threshold value, the abnormal probability is high, and the lifting abnormity can be directly judged or whether the lifting abnormity exists is further judged by combining other factors.

Considering that whether the lifting abnormality exists or not is judged only by comparing the threshold value, and the probability of misjudgment exists, in order to improve the judgment accuracy, in some modification embodiments, the controller may input the inclination change information and the swing change information into a first neural network model trained in advance, and judge whether the lifting state of the tower crane lifting hook is abnormal or not according to the first neural network model.

The first neural network model can be obtained by training a large number of training samples, the training samples comprise multiple groups of training data determined by experiments, each group of training data comprises inclination change information and swing change information, and whether abnormal labels exist or not is judged, through training, the first neural network model can output whether abnormal labels exist or not according to the input inclination change information and swing change information, and then the first neural network model can be used for judging whether the lifting state of the tower crane lifting hook is abnormal or not.

Through above-mentioned embodiment, can utilize the comparatively accurate judgement of neural network model whether the lifting state of tower crane lifting hook is unusual, compare in the mode of judging according to the threshold value, the precision is higher.

Considering that environmental wind also can influence the hoist and mount firmness of goods, if wind direction is the same with unhook direction, can increase the probability of goods unhook, and the wind speed is big more, and the unhook probability is big more, and is opposite, if wind direction is opposite with unhook direction, can reduce the probability of goods unhook, for more accurate judgement the tower crane lifting hook play to rise the state and whether unusual, in some change implementation modes, above-mentioned a sensing thing networking systems for intelligent tower crane plays to rise unusual state perception still includes: the wind direction sensor and the wind speed sensor are arranged on the tower crane;

the wind direction sensor and the wind speed sensor are both connected with the controller and are respectively used for acquiring wind direction information and wind speed information around the tower crane hook and sending the wind direction information and the wind speed information to the controller;

the controller is further used for comprehensively judging whether the lifting state of the tower crane lifting hook is abnormal or not according to the inclination change information, the swing change information, the wind direction information and the wind speed information.

The specific judgment mode is as described above, whether the lifting state of the tower crane lifting hook is abnormal or not can be comprehensively judged based on the preset threshold value, and whether the lifting state of the tower crane lifting hook is abnormal or not can also be judged by adopting a neural network, for example, in some embodiments, the controller can input the inclination change information, the swing change information, the wind direction information and the wind speed information into a pre-trained second neural network model, and judge whether the lifting state of the tower crane lifting hook is abnormal or not according to the second neural network model.

The second neural network model can be obtained by training a large number of training samples, the training samples comprise a plurality of groups of training data determined by experiments, each group of training data comprises inclination change information, swing change information, wind direction information and the wind speed information, and whether abnormal labels exist or not is output through training, so that whether the abnormal labels exist or not can be judged by the second neural network model according to the input inclination change information, swing change information, wind direction information and the wind speed information, and then whether the lifting state of the tower crane lifting hook is abnormal or not can be judged by the second neural network model.

Through the embodiment, the second neural network model can be utilized to accurately judge whether the lifting state of the tower crane lifting hook is abnormal or not, the influence of wind power on the abnormal unhooking is considered, the neural network model is utilized to judge, the accuracy is high, and whether the lifting state of the tower crane lifting hook is abnormal or not can be judged more accurately.

On the basis of any of the above embodiments, in another modified embodiment, the sensing internet of things system for sensing the abnormal lifting state of the intelligent tower crane may further include: the alarm device is arranged on the tower crane lifting hook;

the alarm device is connected with the controller, and the controller is detecting when the lifting state of the tower crane lifting hook is abnormal, the controller broadcasts abnormal alarm information through the alarm device.

On the basis of any of the above embodiments, in some modified embodiments, the sensing internet of things system for sensing the abnormal lifting state of the intelligent tower crane may further include: the device comprises a lifting hook, a lifting hook driving mechanism, a visual sensor and a sensor driving mechanism; wherein the content of the first and second substances,

the lifting hook is connected with the lifting hook driving mechanism, the visual sensor is connected with the sensor driving mechanism, and the lifting hook driving mechanism, the sensor driving mechanism and the visual sensor are all connected with the controller;

The controller is passing through lifting hook drive mechanism control during the lifting hook motion, still pass through sensor drive mechanism control vision sensor follows the lifting hook motion, and control vision sensor orientation the regional visual sensing signal of gathering in lifting hook place, with the basis visual sensing signal with the gesture data comprehensive judgement that gesture sensor gathered whether the lifting state of tower crane lifting hook is unusual.

In addition, the controller, the vision sensor and the sensor driving mechanism may be connected in a wireless manner or in a wired manner, and in view of relatively poor stability of wireless signals, in some embodiments, a wired manner is preferably adopted, and the vision sensor and the sensor driving mechanism are connected with the controller by cables, specifically, the cables may be connected to a console on the ground along a boom and a standard section and connected with the controller on the console, so as to improve signal quality and stability and avoid sensing errors caused by signal problems.

Compared with the prior art, the intelligent tower crane provided by the embodiment of the application can be used for sensing the sensing internet of things system for sensing the abnormal lifting state of the intelligent tower crane through configuration, can further add a visual sensor and a sensor driving mechanism, and can control the visual sensor to follow the lifting hook movement through the sensor driving mechanism when the lifting hook moves, and can control the visual sensor to acquire visual sensing signals towards the area where the lifting hook is located so as to comprehensively judge whether the lifting state of the lifting hook of the tower crane is abnormal according to the visual sensing signals and the attitude data acquired by the attitude sensor, so that the visual sensor can follow the lifting hook movement and can acquire the visual sensing signals closely, and the personnel can automatically acquire the high-definition and accurate visual sensing signals without extra operation of the tower crane control personnel, and whether the lifting state of the tower crane lifting hook is abnormal or not is comprehensively and more accurately judged according to the visual sensing signals, so that the probability of accidental injury of workers by goods is further reduced, and the safety accident rate is reduced.

In addition, the first trolley and the second trolley can also adopt two sets of different amplitude-variable steel wire ropes to respectively carry out traction movement, so that the distance between the first trolley and the second trolley can be adjusted, the distance from the visual sensor to the lifting hook can be conveniently adjusted, and a better observation visual field can be obtained.

In addition, the first trolley and the second trolley need to adopt two sets of different lifting steel wire ropes to respectively pull the lifting hook and the visual sensor to lift, so that the visual sensor can be leveled with the lifting hook, can be higher than the lifting hook or lower than the lifting hook to carry out signal acquisition, and can be applied to various working conditions to obtain a better observation visual field.

Through setting up the independent drive vision sensor of second dolly, can be according to the nimble following relation of adjusting between vision sensor and the lifting hook of operating condition, for example, can adjust and keep a meter interval along the width of cloth direction between vision sensor and the lifting hook, keep a meter interval along direction of height, perhaps, adjust and keep a meter interval along the width of cloth direction between vision sensor and the lifting hook, keep parallelly (the interval is zero) etc. along direction of height to obtain the observation field of vision of preferred.

After the following relationship is determined, the controller can automatically control the visual sensor to follow the following relationship when controlling the lifting hook to move so as to keep the same observation visual field. In addition, the operator can also adjust the following relationship according to actual requirements, and the embodiment of the present application does not limit specific values thereof.

It should be noted that the following referred to in the embodiment of the present application means that the vision sensor and the lifting hook keep a certain distance and angle during the movement to obtain the same observation visual field, so as to determine whether the lifting state of the tower crane lifting hook is abnormal or not through image comparison and identification.

For example, taking a real-time picture shot by a pan-tilt camera as an example of a visual sensing signal, through image recognition, a lifting hook and a rope can be recognized, and whether an abnormality exists can be judged through the relative positions of the lifting hook and the rope in the pictures shot successively and the movement trend of the rope, for example, if the rope moves to a preset range at an outlet of the lifting hook and has a trend of continuously moving towards an unhooking direction, it is judged that an unhooking risk exists, that is, it is judged that the lifting state of the tower crane lifting hook is abnormal; otherwise, the unhooking risk can be judged, namely the lifting state of the tower crane lifting hook is judged to be abnormal. Among them, the image recognition technology is a mature technology in the prior art, and those skilled in the art can directly apply the prior art to the present application to achieve the purpose of the embodiments of the present application.

In other modified embodiments, the tower crane is provided with a variable amplitude sensor and a height sensor, the variable amplitude sensor is used for detecting variable amplitude position information of the lifting hook, and the height sensor is used for detecting height position information of the lifting hook;

and the controller controls the vision sensor to move along with the lifting hook according to the amplitude variation position information and the height position information of the lifting hook.

The amplitude-variable position information can comprise the horizontal distance from the lifting hook to the standard knot along the amplitude-variable direction (namely the horizontal direction of the cargo boom), the height position information can comprise the vertical distance from the lifting hook to the cargo boom along the vertical direction, and according to the amplitude-variable position information and the height position information, as the visual sensor and the lifting hook are also driven by the trolley and the rope, the amplitude-variable position information and the height position information of the position where the visual sensor is located can be determined according to the amplitude-variable position information and the height position information of the lifting hook and by combining the predetermined following relation, and the visual sensor is controlled to move to the location where the visual sensor is located according to the amplitude-variable position information and the height position information, so that the following movement with the lifting hook is realized.

Specifically, in some embodiments, the controller further determines a rough relative position relationship between the vision sensor and the hook according to the amplitude variation position information and the height position information of the hook, and coarsely adjusts the rotation direction of the vision sensor to the region where the hook is located according to the rough relative position relationship. Because the amplitude variation position information and the height position information of the lifting hook and the visual sensor are obtained when the lifting hook and the visual sensor move along, the visual sensor can be quickly and coarsely adjusted to the area where the lifting hook is located according to the existing data through the implementation mode.

In view of the fact that the hook is not necessarily located at a preferred position in the visual sensor field after coarse adjustment, and the visual sensor may swing in the air due to air disturbance and fail to accurately capture a desired image, in some modified embodiments, on the basis of the above embodiment, the controller further identifies the hook in the visual sensing signal acquired by the visual sensor after coarse adjustment of the visual sensor to the area where the hook is located, determines a fine relative position relationship between the visual sensor and the hook, and fine-adjusts the visual sensor according to the fine relative position relationship, so that the visual sensor after fine adjustment acquires a visual sensing signal that meets the desired requirement. In this embodiment, the image recognition technology provided by the prior art can be used to recognize the hook in the visual sensing signal, so as to determine the fine relative position relationship between the visual sensor and the hook, and the visual sensor is finely adjusted according to the fine relative position relationship, so that the visual sensor after fine adjustment acquires the visual sensing signal which meets the expectation, wherein the expectation can be that the hook is located in the middle of the picture of the visual sensing signal, or the hook and the lifted cargo are located in the middle of the picture of the visual sensing signal as a whole, and the embodiment of the application is not limited. Through this embodiment, can further on the basis of coarse adjustment just remember the vision sensing signal that accords with the expectation through the fine setting, improve vision sensing signal precision to whether utilize this vision sensing signal accurately to judge the rising state of tower crane lifting hook is unusual.

In any of the foregoing embodiments, the visual sensor may include a pan-tilt camera or a laser scanner, which may acquire an accurate visual sensing signal to determine whether the lifting state of the tower crane hook is abnormal by combining attitude data.

Specifically, if the lifting state of the tower crane lifting hook is comprehensively judged to be abnormal according to the attitude data and the visual sensing signal, the specific judgment mode can be as follows: if the lifting state of the tower crane lifting hook is judged to be abnormal by adopting any one of the attitude data and the visual sensing signal, the lifting state of the tower crane lifting hook is judged to be abnormal on the whole, and if not, the lifting state of the tower crane lifting hook is judged to be abnormal. Therefore, whether the lifting state of the tower crane lifting hook is abnormal or not is comprehensively and accurately judged by utilizing the attitude data and the visual sensing signals, and the accuracy is improved.

It can be easily understood that if the weight of the vision sensor is light, the vision sensor can swing along with air disturbance in high altitude to influence the shooting effect, therefore, in some change implementation modes, the vision sensor can be also configured with an attitude stabilizing controller to help the vision sensor stabilize the attitude in high altitude, reduce the shaking, improve the shooting effect and further improve the accuracy of the abnormal judgment of the lifting state of the tower crane lifting hook.

In addition, in order to further perfect the intellectuality and the unmanned of above-mentioned intelligent tower crane, the three-dimensional augmented reality video control device that the intelligence tower crane can also be used for intelligent tower crane to control through the configuration below realizes comprehensive monitoring and discernment to the job site operating mode, need not the tower crane driver and carries out high altitude construction just can realize the control to the intelligence tower crane according to this three-dimensional augmented reality video, reduces staff's participation to can effectively reduce the accident rate and avoid staff's injures and deaths, combine the example below to explain.

In some embodiments, the three-dimensional augmented reality video control device for intelligent tower crane control may include: a controller, a global camera, and a plurality of local cameras;

the global camera and the local camera are both connected with the controller;

the global camera is arranged on the intelligent tower crane boom downwards and is used for shooting a global image of a working scene of the intelligent tower crane and sending the global image to the controller;

the plurality of local cameras are uniformly distributed on the periphery of a lifting hook of the intelligent tower crane and used for shooting local images from different directions on the periphery of the lifting hook and sending the local images to the controller;

the controller generates a three-dimensional augmented reality video representing the real-time working scene of the intelligent tower crane according to the global image and the local image, and controls the intelligent tower crane to operate according to the three-dimensional augmented reality video.

Compared with the prior art, the intelligent tower crane provided by the embodiment of the application can be provided with the controller, the global camera and the plurality of local cameras by configuring the three-dimensional augmented reality video control device for controlling the intelligent tower crane; wherein the global camera and the local camera are both connected with the controller; the global camera is arranged on the intelligent tower crane boom downwards and is used for shooting a global image of a working scene of the intelligent tower crane and sending the global image to the controller; the plurality of local cameras are uniformly distributed on the periphery of a lifting hook of the intelligent tower crane and used for shooting local images from different directions on the periphery of the lifting hook and sending the local images to the controller; the controller generates a three-dimensional augmented reality video representing the real-time working scene of the intelligent tower crane according to the global image and the local image, and controls the intelligent tower crane to operate according to the three-dimensional augmented reality video. Therefore, real-time images of the working scene of the tower crane can be acquired by the aid of the global camera and the local camera, a three-dimensional augmented reality video is generated, comprehensive monitoring and recognition of working conditions of a construction site are achieved, a tower crane driver is not required to carry out high-altitude operation, control over an intelligent tower crane can be achieved according to the three-dimensional augmented reality video, staff participation is reduced, and accordingly accident rate can be effectively reduced, and casualties of workers can be avoided.

Regarding the installation manner of the local camera, in some modified implementation manners of the embodiment of the present application, the three-dimensional augmented reality video control device for smart tower crane control may further include: a multi-branch support frame;

the multi-branch supporting frame is installed on the shell of the lifting hook and is opened in an umbrella shape, and the local cameras are installed at the tail ends of the branches of the multi-branch supporting frame.

In some variations, the multi-branch scaffold may include a bottom fixed portion, a sleeve, a plurality of branches, and an adjustable portion movable up and down the sleeve;

the bottom fixing part is arranged on the shell of the lifting hook, and the sleeve is sleeved on a steel wire rope of the lifting hook;

each branch comprises a supporting rod and a pull rod, one end of the supporting rod is connected with the bottom fixing part, and the other end of the supporting rod is used for installing the local camera;

one end of the pull rod is connected with the adjustable part, and the other end of the pull rod is connected with the middle part of the supporting rod.

Through setting up above-mentioned many branches support frame, can install local camera around the lifting hook, make local camera can accompany the lifting hook and remove, obtain stable, clear shooting picture, help generating accurate three-dimensional augmented reality video.

It should be noted that, the above is only a simple schematic structure of the multi-branch supporting frame, and in practical applications, the structure of the multi-branch supporting frame may be modified according to actual requirements to obtain better implementation effects, which do not depart from the inventive concept of the present embodiment, and all of which are within the protection scope of the present application.

On the basis of the above embodiment, in some modified embodiments, the outer surface of the sleeve is provided with an external thread, the adjustable part comprises a gear bearing provided with an internal thread and a driving motor, and the external thread is matched with the internal thread;

the driving motor is meshed with the gear bearing through a gear, is electrically connected with the controller and is used for driving the gear bearing to rotate around the sleeve to move up and down under the control of the controller.

Through above-mentioned embodiment, can realize the electric drive of adjustable portion, can drive the pull rod motion when adjustable portion reciprocates, and then drive local camera and reciprocate and be close to or keep away from the lifting hook to realize the automatically controlled regulation of local camera, help the tower crane control personnel to combine the position of the convenient, nimble regulation local camera of actual scene in order to obtain comparatively ideal shooting effect, and then generate accurate three-dimensional augmented reality video.

In addition, in order to improve the ease of use of local camera, in some change implementation modes, local camera pass through the cloud platform install in each branch end of many branches support frame, through setting up the cloud platform, required image is gathered to the local camera of control that can be more nimble, on the one hand, can when the deviation appears in the shooting angle, correct the angle deviation through the local camera of cloud platform control to the required image of more accurate collection, on the other hand, can control local camera and carry out the shooting of cruising, gather the image in the wider range on every side, so that further carry out the three-dimensional reconstruction of full scene, improve intelligent level.

In view of the balance problem and the shielding problem caused by the surrounding arrangement of the plurality of local cameras, more than one local camera may be generally arranged, and in view of the fact that the system load and the implementation cost for generating the three-dimensional augmented reality video are increased due to the excessive number of the local cameras, it is preferable that the number of the local cameras is one or more, so that the implementation cost and the implementation effect are both considered, and a high input-output ratio is obtained.

It should be noted that, the embodiment of the present application adopts a combination of a global camera and a local camera to perform image acquisition, wherein the global camera can capture a global image with a more comprehensive construction scene, but because the installation position of the global camera is higher, the defects of poor definition of objects at low positions in the captured image and shielding are existed, therefore, the image at the shielded position can be acquired by introducing the local camera surrounding the hook, the shielding problem is reduced, and because the local camera moves along with the hook, the image with higher definition can be acquired at a short distance, thus, through the cooperation of the global camera and the local camera, the global image and the local image are fused, the comprehensive, clear and accurate image data can be obtained, thereby ensuring that the generated three-dimensional augmented reality video can more accurately restore the real situation of the construction scene, the intelligent tower crane can realize accurate operation based on three-dimensional augmented reality video, and the intelligent level, the automation level and the operation accuracy of the intelligent tower crane are improved.

The controller and the local camera can be connected in a wireless mode or in a wired mode, considering that the stability of wireless signals is relatively poor, safety accidents are possibly caused by signal interruption and error, in some embodiments, the local camera and the controller are connected by cables in a preferable wired mode, specifically, the cables can be connected to a control platform on the ground along a crane boom and a standard section and connected with the controller on the control platform, so that the signal quality and the stability are improved, and the problem that the lifting state abnormity of a tower crane hook cannot be found in time due to signal problems is avoided, and further the safety accidents are caused. On this basis, above-mentioned a three-dimensional augmented reality video control device for intelligent tower crane is controlled can also include: the winder is arranged on the trolley for hanging the lifting hook; the local cameras are connected with the controller through cables, and the cables are stored and released through the winder. Through the embodiment, the wire winder can be used for keeping the wire in the tightening state, and the wire is prevented from loosening and shaking to influence the operation of other parts.

In some embodiments, the controller generates the three-dimensional augmented reality video representing the real-time working scene of the intelligent tower crane through three-dimensional reconstruction according to the global image and the local image.

For example, the controller determines, by using a dense reconstruction algorithm, position information of a three-dimensional point corresponding to each pixel point in a world coordinate system according to the camera position information of the global camera and the local camera and the pixel position information of each pixel point in the global image and the local image, and determines a three-dimensional augmented reality video of the real-time working scene of the intelligent tower crane according to a three-dimensional point cloud formed by the three-dimensional points. Three-dimensional reconstruction based on multiple images is a mature prior art, so the specific process is not described herein any more, and those skilled in the art can flexibly modify and implement the three-dimensional reconstruction based on the prior art.

In addition, a Building Information Modeling (BIM) tool may also be used to generate a three-dimensional augmented reality video based on the global image and the local image, which may also achieve the purpose of the embodiment of the present application and is also within the protection scope of the present application.

In addition, in order to further perfect the intellectuality and the unmanned of above-mentioned intelligent tower crane, the intelligent tower crane can also get the sensing thing networking equipment of putting motion detection through the following intelligent tower crane of being used for of configuration, realizes the automated inspection and the relative position calculation to lifting hook and goods, and then realizes the automation of lifting hook and gets the operation of putting, need not manual control and participation and can realize automatic hook and hang to effectively improve the automation of intelligent tower crane, intelligent level, reduce accident rate and avoid the staff casualties, the following combines the example to explain.

In some embodiments, the sensing internet of things equipment for intelligent tower crane picking and placing motion detection may include: a controller and a plurality of micro image sensors connected to the controller;

the plurality of miniature image sensors are all arranged on a lifting hook of the intelligent tower crane, at least one miniature image sensor is arranged on the inner side of a hook body of the lifting hook, and the plurality of miniature image sensors are respectively used for acquiring image information of a lifting part of goods to be loaded and unloaded at different positions of the lifting hook and sending the image information to the controller;

the controller is used for detecting the relative position information between the hoisting part and the lifting hook according to the image information and controlling the lifting hook to move according to the relative position information so as to take and place the goods to be loaded and unloaded.

Compared with the prior art, the intelligent tower crane provided by the embodiment of the application can be provided with the controller and the plurality of miniature image sensors connected with the controller by configuring the sensing internet of things equipment for detecting the taking and placing motion of the intelligent tower crane; the plurality of miniature image sensors are all arranged on a lifting hook of the intelligent tower crane, at least one miniature image sensor is arranged on the inner side of a hook body of the lifting hook, and the plurality of miniature image sensors are respectively used for acquiring image information of a lifting part of goods to be loaded and unloaded at different positions of the lifting hook and sending the image information to the controller; the controller is used for detecting the relative position information between the hoisting part and the lifting hook according to the image information and controlling the lifting hook to move according to the relative position information so as to take and place the goods to be loaded and unloaded. Thereby can realize the automated inspection and the relative position calculation to lifting hook and goods, help realizing that the automation of lifting hook is got and is put the operation, improve automation, the intelligent level of intelligent tower crane, reduce the accident rate and avoid the staff casualties.

The above-mentioned relative position information includes at least one kind of information such as a relative direction, a relative distance, a relative angle, etc., and the embodiment of the present application does not limit the specific content thereof, and those skilled in the art can flexibly select and use the information in combination with actual requirements.

In some variations of embodiments of the present application, at least one of the plurality of micro image sensors is disposed on a side of the handle of the lifting hook facing an opening of the lifting hook, and is configured to collect image information outside the lifting portion before the lifting portion enters the inside of the lifting hook; the controller is used for detecting the relative position information outside the hook of the lifting part outside the hook of the lifting hook according to the image information outside the hook, and controlling the lifting hook to be close to the lifting part according to the relative position information outside the hook.

Through this embodiment, can gather the outer image information of hook through the miniature image sensor who sets up outside the hook, and then utilize this outer image information of hook to confirm that hoist and mount portion is in the outer relative position information of hook of lifting hook to can ensure that the controller can control the lifting hook accurately moves to hoist and mount portion position, need not manual control and participates in and can realize automatic hook and hang.

In some modifications of the embodiment of the present application, the micro image sensor disposed inside the hook body is configured to acquire image information inside the hook of the hoisting part after the hoisting part enters the hook of the lifting hook;

the controller is used for detecting the relative position information in the hook after the hoisting part enters the hook according to the image information in the hook, and controlling the hoisting hook to hook the hoisting part according to the relative position information in the hook.

Through this embodiment, can gather the interior image information of hook through the miniature image sensor who sets up in the hook, and then utilize this interior image information of hook to confirm hoist and mount portion and be in relative position information in the hook in the lifting hook to can ensure that the controller can control the lifting hook fine setting makes hoist and mount portion accurately fall into the lifting hook, need not manual control and participates in and can realize automatic hook and hang.

For example, 4 miniature image sensors can be arranged on the lifting hook, wherein the miniature image sensors A and B are arranged outside the hook and used for collecting image information outside the hook, and the miniature image sensors C and D are arranged in the hook and used for collecting image information in the hook, so that the comprehensive monitoring of the working conditions inside and outside the lifting hook is realized.

In some variations of the embodiments of the present application, the controller is further configured to detect whether there is a risk of unhooking the cargo to be loaded or unloaded based on the relative position information in the hook after the cargo to be loaded or unloaded is lifted. With reference to the above example, the image information in the hook acquired by the miniature image sensor C disposed with the top end of the hook in the hook facing downward can be used to accurately determine whether the hoisting part is located in a safe area (for example, a preset range of the hook bottom is a safe area), if so, the hoisting can be performed, and if so, the situation that the hoisting part is in a safe area indicates that there is a risk of unhooking exists, and the position of the hook needs to be readjusted until the safety is ensured, and then the hoisting is performed.

In some modification embodiments of the embodiment of the present application, a transparent protection cover is disposed on an information acquisition end surface of the miniature image sensor, and the transparent protection cover is used for protecting the miniature image sensor from being polluted and/or damaged by impact of the hoisting part. The transparent protection cover can be realized by adopting glass or transparent acrylic, and the specific material of the transparent protection cover is not limited in the examples of the application. Consider that the building site environment is comparatively abominable, the dust is more, and the lifting hook collides with hoist and mount portion easily during hoist and mount, through this embodiment, can effectively protect miniature image sensor is by pollution such as dust, rainwater, and effectively protects miniature image sensor quilt hoist and mount portion striking damage.

When considering that the striking dynamics is great, transparent safety cover such as glass or ya keli is probably smashed and then damages miniature image sensor, consequently, in some modified implementation modes of this application embodiment, be provided with a plurality of recesses on the lifting hook, miniature image sensor imbeds in the recess, just the surface of transparent safety cover flushes or is less than the upper surface of recess. Through in embedding recess miniature image sensor and transparent safety cover, even hoist and mount portion and lifting hook take place the striking, the impact force that the striking produced also bears by the lifting hook body, and can not damage miniature image sensor to can effectively improve miniature image sensor's life.

In addition to any of the above embodiments, in some modified embodiments, the hoisting part of the cargo to be loaded and unloaded is provided with a preset pattern different from other parts;

the controller is used for identifying the hoisting part by detecting the preset pattern in the image information.

Wherein, above-mentioned pattern of predetermineeing can adopt the spray gun to spray in hoist and mount portion, also can attach in hoist and mount portion in the form of sticker, perhaps will predetermine the pattern and locate on the signboard, pass through anchor clamps centre gripping again with this signboard in hoist and mount portion, this application embodiment does not limit its concrete implementation, the purpose of this application embodiment can all be realized to above-mentioned mode and combination, all should be within the scope of protection of this application.

Since the predetermined pattern is distinguished from other portions, the predetermined pattern can be detected by pattern matching recognition in the image information, and the hanger can be recognized in the image information. The pattern-based image recognition technology is a relatively mature technology at present, and therefore, the detailed description is omitted here, and a person skilled in the art can adopt any pattern-based image recognition technology disclosed in the prior art to achieve the purpose of the embodiments of the present application, which should be within the scope of the present application.

Through this embodiment, can utilize and predetermine the pattern and realize the differentiation sign to hoisting portion, help improving the controller and discern the rate of accuracy and the efficiency of hoisting portion, and then improve intelligent level and the security of intelligent tower crane.

In addition, since one of the purposes of the embodiments of the present application is to identify the relative position information between the hoisting part and the hook, in order to obtain the depth information and further determine the relative position information of the three-dimensional space, in some embodiments, the above-mentioned micro image sensor includes a binocular camera, and the controller is specifically configured to calculate the relative position information between the hoisting part and the hook by using a binocular camera ranging algorithm.

The distance measurement algorithm based on the binocular camera is a relatively mature technology at present, and therefore is not described herein any more, and a person skilled in the art can adopt any binocular camera distance measurement algorithm disclosed in the prior art to achieve the purpose of the embodiment of the present application, which all should be within the protection scope of the present application.

Through this embodiment, can utilize two mesh cameras and the range finding algorithm that corresponds thereof to accurately calculate the relative position information between hoist and mount portion and the lifting hook, and then accurately control the lifting hook motion and get in order to realize automatic getting and put, can effectively improve the lifting hook and get the precision and the efficiency of putting the motion.

It should be noted that the micro image sensor may be implemented by a CCD sensor or a CMOS sensor, which can achieve the purpose of the embodiments of the present application, and is not limited herein.

In addition, under considering the condition of miniature image sensor embedding recess, wireless communication signal can receive the shielding of lifting hook main part metallic structure and unable effective transmission, consequently, in order to improve the security of intelligent tower crane, in some embodiments, a sensing thing networking equipment for motion detection is got to intelligence tower crane, still includes: the winder is arranged on the trolley for hanging the lifting hook;

the miniature image sensors are connected with the controller through cables, and the cables are wound and unwound through the winder.

Specifically, the cable can be connected to the console on ground along jib loading boom, standard festival to be connected with the controller of controlling on the platform, thereby improve signal quality and stability, avoid leading to the incident because of the signal problem. Above-mentioned winder can keep the cable to be in the state of tightening up, avoids the cable slack to rock and influence other part operations, improves the security of intelligent tower crane.

In addition, the controller can be realized by a computer host, a microcontroller, a Programmable Logic Controller (PLC) and the like, and the lifting hook can be realized by any automatic lifting hook provided by the prior art, and the embodiment of the application is not limited.

In addition, in order to further perfect the intellectuality and the unmanned of above-mentioned intelligent tower crane, the intelligent tower crane can also realize the assistance-localization real-time of material through the following intelligent tower crane material location auxiliary device based on internet of things communication of configuration, and then realize the automation of material and get the operation of putting, need not manual control and participate in and can realize automatic hooking to effectively improve automation, intelligent level and the operating efficiency of intelligent tower crane, reduce the accident rate and avoid the staff casualties, explain below combining the example.

In some embodiments, the intelligent tower crane material positioning auxiliary device based on internet of things communication can include: the system comprises a controller, a radio frequency signal transmitter and a plurality of radio frequency signal receivers;

the radio frequency signal emitter is arranged on the material when in use and broadcasts radio frequency signals to the surroundings;

the radio frequency signal receivers are respectively arranged at a plurality of different positions on the intelligent tower crane and are in communication connection with the controller;

each radio frequency signal receiver is used for receiving a radio frequency signal broadcast by the radio frequency signal transmitter and sending the radio frequency signal and arrival time thereof to the controller;

And the controller is used for calculating the positioning information of the material by adopting a time difference of arrival (TDOA) algorithm according to the position information of each radio frequency signal receiver and the arrival time of the radio frequency signal transmitted by the radio frequency signal receiver.

Compared with the prior art, the intelligent tower crane material positioning auxiliary device based on the internet of things communication provided by the embodiment of the application is provided with the controller, the radio frequency signal transmitter and the plurality of radio frequency signal receivers, wherein the radio frequency signal transmitter is arranged on a material when in use and broadcasts radio frequency signals to the surroundings; a plurality of radio frequency signal receivers are located a plurality of different positions on the intelligent tower crane respectively, and all with controller communication connection, every radio frequency signal receiver all is used for receiving the radio frequency signal of radio frequency signal transmitter broadcast, and will radio frequency signal and arrival time send for the controller, the controller is used for according to every positional information that radio frequency signal receiver located and the arrival time of the radio frequency signal who sends adopt arrival time difference TDOA algorithm to calculate the locating information of material to can realize the location to the job site material, help realizing that the automation of lifting hook gets and puts the operation, improve automation, intelligent level and the operating efficiency of intelligent tower crane, reduce accident rate and avoid the staff casualties.

In the embodiments of the present application, a Time Difference of Arrival (TDOA) algorithm is used to calculate the location information of the material, and the TDOA is a method for performing location by using the Time Difference, and by measuring the Time when a signal (e.g., a radio frequency signal) arrives at a monitoring station (e.g., a radio frequency signal receiver in the embodiment), the distance of a signal source (e.g., a radio frequency signal transmitter in the embodiment) can be determined. The location of the signal can be determined by the distance from the signal source to the plurality of radio monitoring stations (taking the radio monitoring stations as the center and the distance as the radius to make a circle). By comparing the time difference of the signals arriving at a plurality of monitoring stations, a hyperbola with the monitoring stations as focuses and the distance difference as a long axis can be formed, and the intersection point of the hyperbolas is the position where the radio frequency signal transmitter is located, namely the position of the material. Since the TDOA algorithm is a relatively mature positioning algorithm, details are not described herein, and those skilled in the art can flexibly apply and modify the TDOA algorithm in combination with the prior art to achieve the purpose of the embodiments of the present application, which are all within the scope of the present application.

It should be noted that, in order to implement the TDOA algorithm, a plurality of rf signal receivers need to be clock synchronized, and specifically, in some modified embodiments, the plurality of rf signal receivers are connected to the controller in a wired manner, and the controller sends a clock synchronization signal to the plurality of rf signal receivers at preset time intervals, so that the plurality of rf signal receivers maintain clock synchronization. Therefore, clock synchronization with the radio frequency signal emitter is not needed, and the position of the radio frequency signal emitter, namely the positioning information of the material, can be accurately calculated according to the arrival time of the radio frequency signal by using the TDOA algorithm.

In order to solve the problems, in some modified embodiments of the present application, a radio frequency signal transmitted by a radio frequency signal transmitter carries identification information of an intelligent tower crane;

the controller is further used for screening out the radio frequency signals carrying the identification information of the intelligent tower crane of the current intelligent tower crane from all the received radio frequency signals, and calculating the positioning information of the material needing to be hoisted by the current intelligent tower crane according to the arrival time of the screened radio frequency signals so as to identify the material needing to be hoisted by the current intelligent tower crane from a plurality of materials on a construction site.

For example, can distinguish the sign to each intelligent tower crane, for example adopt the code, serial number etc. as intelligent tower crane identification information, when the operation, the user places or installs the radio frequency signal transmitter on the material, the radio frequency signal through the transmission of radio frequency signal transmitter carries intelligent tower crane identification information, can ensure that the intelligent tower crane screens out the radio frequency signal of this radio frequency signal transmitter according to this intelligent tower crane identification information and then fixes a position this radio frequency signal transmitter, thereby discern the material that needs current intelligent tower crane hoist and mount in a plurality of materials of follow job site.

The radio frequency signal transmitter can be manufactured into an electronic tag and is arranged on a material in the modes of attaching, clamping, binding and the like. The radio frequency signal transmitter can be bound with an intelligent tower crane in advance, and can also be temporarily paired during construction, for example, in some modification embodiments, an input module is arranged on the radio frequency signal transmitter;

the radio frequency signal transmitter is also used for generating intelligent tower crane identification information according to the intelligent tower crane identification input by the user through the input module.

The input module can be realized by a keyboard, a touch screen and the like, and the embodiment of the application is not limited. Through this embodiment, the workman can be in the on-the-spot real-time input intelligence tower crane sign in order to select suitable intelligent tower crane to hoist, and compatibility and flexibility are better.

In addition, if a plurality of materials need to be hoisted by the same intelligent tower crane on site, hoisting orders need to be distinguished, so in some modification embodiments, the radio frequency signal transmitted by the radio frequency signal transmitter carries information of the hoisting orders;

the controller is further used for determining the hoisting sequence of the materials needing to be hoisted by the current intelligent tower crane according to the hoisting sequence information, and sequentially hoisting the materials according to the hoisting sequence and the positioning information of the materials needing to be hoisted by the current intelligent tower crane.

The hoisting sequence information may include one of serial number information input by a user through an input module on the radio frequency signal transmitter, current power-on time information of the radio frequency signal transmitter, or starting broadcast time of the radio frequency signal.

If the hoisting sequence information is serial number information input by a user through an input module on the radio frequency signal transmitter, the user can flexibly set and adjust the hoisting sequence of each material through the input module, and the method has the advantages of convenience, rapidness, flexibility and adjustability.

If the hoisting sequence information is the current starting time information of the radio frequency signal transmitter or the starting broadcast time of the radio frequency signal, a user does not need to perform manual input operation, only needs to start the radio frequency signal transmitter or trigger broadcast, and can automatically generate corresponding hoisting sequence information according to the starting time or the broadcast trigger time and other time information, so that the use is more convenient and faster.

It should be noted that, in order to perform positioning by using the TDOA method, the number of the radio frequency signal receivers is at least 4, and the radio frequency signal receivers can be distributed at a plurality of positions on a crane arm, a tower body and a lifting hook of the intelligent tower crane. It is easy to understand that by measuring and recording the rotation angle, amplitude variation information and lifting information of the intelligent tower crane and combining the specific installation position of the radio frequency signal receiver, the real-time position information of the radio frequency signal receiver can be calculated at any time (specifically, the calculation can be carried out by combining the geometric relationship, and the description is omitted here), and then the TDOA algorithm is adopted to realize the positioning of the materials.

In addition, the radio frequency signal transmitter and the radio frequency signal receiver can be realized by any medium-long distance wireless communication module such as a GPRS/4G wireless communication module, a 2.4G wireless communication module, a WiFi wireless communication module and the like, and considering that the tower crane is higher in height, more on-site shelters are provided, the communication distance of the radio frequency signal needs to be as far as possible, and the radio frequency signal receiver has stronger penetrating capability and anti-jamming capability, therefore, the embodiment of the present application preferably uses 433M wireless module to implement the radio frequency signal transmitter and the radio frequency signal receiver, because the 433M wireless module has strong signal, long transmission distance, ideal transmission distance of about 3 kilometers, and the advantages of strong penetration and diffraction capability, small attenuation in the transmission process and the like, the tower crane can be well suitable for the working scene of an intelligent tower crane, therefore, a better signal transmission effect is obtained, and the implementation stability and reliability of the scheme are improved.

In addition, in order to further improve the intellectualization and the unmanned of the intelligent tower crane, the intelligent tower crane can automatically identify materials by configuring the following tower crane material classification and identification system based on image analysis, and further automatically select a reasonable mode for loading and unloading aiming at different types of materials, so that the occurrence of loading and unloading accidents is reduced, the unmanned and intelligent development of the tower crane is promoted, and the description is combined with an example below.

In some embodiments, the image analysis-based tower crane material classification and identification system may include:

the system comprises an image group acquisition module, a data acquisition module and a data processing module, wherein the image group acquisition module is used for acquiring a material image group acquired by a camera group arranged on the intelligent tower crane, the camera group comprises a plurality of cameras with different shooting angles, and the material image group comprises material images acquired by each camera aiming at materials;

the attribute information determining module is used for determining attribute information of the material according to the material image group, wherein the attribute information comprises shape information, size information and texture information;

and the material category matching module is used for matching the category of the material from a tower crane material database according to the attribute information of the material, wherein the tower crane material database stores the attribute information of different tower crane materials in advance.

Compared with the prior art, the tower crane material classification and identification system based on image analysis provided by the embodiment of the application comprises a material image group acquired by acquiring the camera group arranged on the intelligent tower crane, wherein the camera group comprises a plurality of cameras with different shooting angles, the material image group comprises every camera aiming at the material image acquired by the material, and according to the material image group, the attribute information of the material is determined, the attribute information comprises shape information, size information and texture information, and according to the attribute information of the material, the material type is obtained by matching the material in the tower crane material database, wherein the tower crane material database stores the attribute information of different tower crane materials in advance, and as the material type loaded and unloaded by the tower crane is clear and distinct, the system only needs to pertinently extract the shape, size and texture of the material, The intelligent tower crane has the advantages that the size, the texture and other attribute information, namely the categories of the materials can be quickly and accurately identified through database matching, the intelligent tower crane is facilitated to automatically select a reasonable mode for loading and unloading different categories of materials, the loading and unloading accidents are reduced, and the unmanned and intelligent development of the tower crane is promoted.

Wherein, the tower crane material classification identification system based on image analysis that this application embodiment provided can be realized by the controller of intelligent tower crane, realizes the automatic classification discernment to the tower crane material by the controller to further load and unload to the material automatic selection reasonable mode of different classes, for example, select suitable lifting hook to hoist, select different hoist and mount speed etc. to glass and steel, thereby reduce the emergence of loading and unloading accident, promote the unmanned, intelligent development of tower crane.

In some variations of the embodiments of the present application, the attribute information determination module includes:

the initial image query unit is used for querying an initial image corresponding to attitude information from an initial image database according to the attitude information of each camera for acquiring the material image, wherein the attitude information comprises shooting position information and shooting angle information of the camera, the initial image database stores the initial image acquired by each camera corresponding to the attitude information, and the initial image is acquired before the material enters the field;

the image comparison unit is used for comparing each material image in the material image group with the initial image which is acquired by the camera for acquiring the material image in advance under the same posture, and identifying a material main body in each material image;

And the attribute information determining unit is used for determining the attribute information of the material according to the material main body in each material image obtained by identification.

In addition to the above-described embodiments, in some modified embodiments of the present application, for the extraction of the shape information and the size information, the attribute information determination unit includes:

a coordinate conversion relation determining subunit, configured to determine a coordinate conversion relation between a pixel coordinate system corresponding to each camera and a world coordinate system;

the coordinate conversion subunit is used for converting the pixel coordinates of the material main body in each material image into world coordinates in a world coordinate system according to the coordinate conversion relation;

and the shape and size determining subunit is used for determining the shape information and the size information of the material according to the world coordinates.

The determination of the coordinate conversion relationship of the camera and the conversion of the pixel coordinate into the world coordinate are already the mature prior art, so the detailed process is not described herein, and those skilled in the art can refer to the prior art to flexibly change the implementation, and the embodiments of the present application are not limited and are all within the scope of the present application.

In addition to the foregoing embodiments, in some modifications of the embodiments of the present application with respect to the extraction of texture information, the attribute information determination unit includes:

and the texture determining subunit is used for identifying the texture information of the material body in each material image by adopting a texture identification algorithm.

The texture recognition algorithm may be implemented by any texture feature extraction algorithm provided by the prior art, for example, a Local Binary Pattern (LBP) algorithm, an OpenCV-based texture recognition algorithm, and the like, which may all achieve the purpose of the embodiments of the present application.

In practical application, materials commonly used for hoisting of the tower crane are mainly raw materials for building construction, such as steel bars, wood ridges, concrete, steel pipes, glass and the like, and the shapes, the sizes and the textures of the materials are greatly different, so that only attribute information, such as the shapes, the sizes, the textures and the like of the materials, is required to be pertinently extracted, the types of the materials can be quickly and accurately identified through database matching, the intelligent tower crane can be used for automatically selecting reasonable modes for loading and unloading aiming at different types of materials, loading and unloading accidents are reduced, and unmanned and intelligent development of the tower crane is promoted.

In some variation implementations of the embodiments of the present application, the camera includes a binocular camera, and the material image acquired by the binocular camera carries depth-of-field information;

the attribute information determination module includes:

and the depth-of-field-based determining unit is used for determining the attribute information of the material according to the depth-of-field information carried by each material image in the material image group.

In addition, in order to further improve the intellectualization and the unmanned of the intelligent tower crane, the intelligent tower crane can also be configured with a following intelligent tower crane maintenance management system based on a fault identification model to execute the arrival rate according to the daily instruction of the intelligent tower crane to predict the fault occurrence rate, and output a corresponding working condition detection strategy when exceeding a warning value, so that working condition detection is carried out on a component which possibly fails at any time and pertinently before the fault occurs, the fault is eliminated in a budding state, the occurrence of the fault can be effectively reduced, and the intellectualization level, the automation level and the safety of the intelligent tower crane are improved, which is described in combination with an example below.

In some embodiments, the intelligent tower crane maintenance management system based on the fault identification model may include:

The execution information acquisition module is used for acquiring execution monitoring information corresponding to a control instruction sent by the intelligent tower crane controller in real time;

the arrival rate calculation module is used for calculating the instruction execution arrival rate of the control instruction according to the execution monitoring information;

the fault recognition module is used for inputting the control instruction and the instruction execution arrival rate thereof into a pre-trained fault recognition model and obtaining the fault occurrence rate predicted by the fault recognition model;

and the maintenance strategy output module is used for inquiring and outputting the working condition detection strategy of the actuating mechanism corresponding to the control instruction if the fault occurrence rate is greater than a preset warning value.

Compared with the prior art, the intelligent tower crane maintenance management system based on the fault identification model provided by the embodiment of the application obtains the execution monitoring information corresponding to the control command sent by the intelligent tower crane controller in real time, then calculates the command execution arrival rate of the control command according to the execution monitoring information, inputs the control command and the command execution arrival rate thereof into the pre-trained fault identification model, and obtains the fault occurrence rate predicted by the fault identification model, if the fault occurrence rate is greater than the preset warning value, queries the working condition detection strategy of the execution mechanism corresponding to the control command and outputs the working condition detection strategy, and because most faults are caused by large product, before the fault occurs, slight abnormal expressions are often existed, such as the command execution is not in place, the command execution arrival rate is low, and the like, therefore, the fault occurrence rate can be predicted according to the daily command execution arrival rate of the intelligent tower crane, and when the fault exceeds the warning value, a corresponding working condition detection strategy is output, and further, before the fault occurs, working condition detection is performed on the component which possibly fails at any time and pertinently, the fault is eliminated in a sprouting state, the fault can be effectively reduced, and the intelligent level, the automatic level and the safety of the intelligent tower crane are improved.

Wherein, the operating mode detection strategy that this application embodiment inquired can be exported display equipment such as display screen, so that the tower crane control personnel maintain with the manual mode according to this operating mode detection strategy, in addition, the operating mode detection strategy that inquires also can export operating mode detection robot (be used for intelligent tower crane operating mode detection's intelligent auxiliary robot), utilize this operating mode detection robot to realize the automatic maintenance to intelligent tower crane, realize the automation of intelligent tower crane, unmanned, intelligent condition detects, promote the tower crane unmanned, intelligent development, the above-mentioned mode all can realize the purpose of this application embodiment, all should be within the protection scope of this application.

The execution monitoring information comprises action information of an execution mechanism corresponding to the control instruction, which is monitored from the sending of the control instruction, and the action information comprises at least one of action amplitude, action time and action speed. The action information can be used for judging the instruction execution condition of the execution mechanism and calculating the instruction execution position rate of the execution mechanism. The above-mentioned actuating mechanism includes hoisting mechanism, rotation mechanism and luffing mechanism, can monitor its action information to the corresponding sensor of above-mentioned actuating mechanism installation on the intelligent tower crane, for example, adopt image recognition analysis's mode to monitor its action information through installing the camera, if again, monitor its action information through installing sensors such as laser range finder, accelerometer, gyroscope, this application embodiment does not restrict its concrete implementation, and the collection of above-mentioned action information can be realized to the arbitrary mode that the prior art provided by technical personnel in the field.

After the action information is collected, it may be determined that the instruction execution arrival rate of the corresponding control instruction may be determined, in this embodiment of the present application, the instruction execution arrival rate may be calculated by an execution duration, and in some modified embodiments of this embodiment of the present application, the arrival rate calculation module includes:

the execution duration calculation unit is used for determining the actual execution in-place duration from the control instruction issuing to the control instruction execution condition according with the preset in-place condition according to the execution monitoring information;

and the arrival rate calculating unit is used for determining the instruction execution arrival rate of the control instruction according to the ratio of the actual execution arrival time length to the standard execution arrival time length corresponding to the control instruction.

For example, the control instruction is to lift the hook at a speed of 1 m/s, the standard execution in-place time length of the hook accelerated from static state to 1 m/s is 2 seconds, and if the actual execution in-place time length is 4 seconds, the corresponding instruction execution in-place rate is 2 seconds/4 seconds = 50%; for another example, the control instruction is to control the brake of the luffing trolley, the standard in-place execution time length is 0.5 second, and if the actual in-place execution time length is 0.5 second, the corresponding instruction execution in-place rate is 0.5 second/0.5 second = 100%. The above are exemplary illustrations, and those skilled in the art can flexibly set the specific value of the standard execution in-place duration and the specific calculation manner of the instruction execution in-place rate according to actual situations, which are all within the protection scope of the present application.

After calculating the instruction execution arrival rate, a pre-trained fault identification model can be input to predict the fault occurrence rate, and it should be noted that, in some modification embodiments of the embodiment of the present application, the intelligent tower crane maintenance management system based on the fault identification model further includes:

the historical information acquisition module is used for acquiring all historical control instructions of the intelligent tower crane before the fault occurs and corresponding historical execution monitoring information;

the historical arrival rate calculation module is used for calculating the historical execution arrival rate corresponding to the historical control instruction according to the historical control instruction and the historical execution monitoring information;

the historical arrival rate sorting module is used for sorting the historical execution arrival rates according to time to obtain a historical execution arrival rate set;

the fault rate assignment module is used for assigning the historical execution arrival rates in the historical execution arrival rate set according to the distance fault occurrence time, wherein the shorter the distance fault occurrence time is, the higher the assigned fault occurrence rate is;

the learning sample generation module is used for generating machine learning samples, wherein each machine learning sample comprises one historical control instruction and corresponding historical execution monitoring information and fault occurrence rate;

And the model training module is used for training a fault recognition model according to the machine learning sample to obtain a pre-trained fault recognition model.

In the embodiment, a large amount of historical data is collected to calculate the historical execution achievement rate (i.e. the historical instruction execution achievement rate) and sort the historical execution achievement rate according to the issuing time of the control instruction, then, for each actually occurring fault, the execution mechanism and the control instruction related to the fault are identified through fault detection, then, according to the time from the occurrence of the fault, the fault occurrence rate assignment is performed on the historical execution achievement rate corresponding to the control instruction related to the fault, the value of the assignment is between 0 and 1, the higher the value of the assignment is the closer the fault is, for example, the fault occurrence rate assignment corresponding to the historical execution achievement rate in one day from the fault is 0.9, the fault occurrence rate assignment corresponding to the historical execution achievement rate in 7 days from the fault is 0.5, and the like, the specific assignment mode is not limited in the embodiment of the present application, for example, the period from the historical execution achievement rate being lower than 100% to the occurrence of the fault can also be regarded as a fault period, the duration is L, and the time interval from the historical execution arrival rate corresponding to a certain control instruction to the occurrence of a fault is a, then the fault occurrence rate corresponding to the historical execution arrival rate can be calculated by using the following formula:

m ₁=1－a/L

In the above formula, the first and second carbon atoms are,m ₁and indicating the fault occurrence rate, a indicating the time interval from the historical execution to the bit rate corresponding to the control instruction to the fault occurrence, and L indicating the time interval from the historical execution to the bit rate lower than 100% to the fault occurrence.

In addition, the above assignment may also be performed in combination with a specific historical execution achievement rate value, for example, the historical execution achievement rate when a fault occurs is b, and the historical execution achievement rate when an execution mechanism operates well is c, then the fault occurrence rate corresponding to a certain historical execution achievement rate d may be calculated by using the following formula:

in the above formula, the first and second carbon atoms are,m ₂the failure occurrence rate corresponding to the historical execution achievement rate d is shown, b shows the historical execution achievement rate when the failure occurs, and c shows the historical execution achievement rate when the execution mechanism runs well.

In addition, the failure occurrence rate may be calculated by summing the time factor and the historical execution arrival rate value factor, for example, according to the abovem ₁Andm ₂further determines the failure occurrence rate m, as follows:

m=m ₁×m ₂

in the above formula, m is the total failure occurrence rate,m ₁indicating the above-described failure occurrence rate assigned according to the time from the failure occurrence,m ₂the fault occurrence rate that the assignment is carried out according to the specific historical execution arrival rate numerical value is shown, through the embodiment, the total fault occurrence rate change range obtained after multiplying the two is larger, the difference of the fault occurrence rate can be shown numerically more obviously, the prediction accuracy of the fault is higher, the sensitivity and the accuracy of the fault recognition model trained after assignment are higher, and the integral implementation accuracy of the scheme is improved.

In addition, the fault recognition model is a model for predicting the fault occurrence rate according to the actual instruction execution arrival rate, and can be obtained by training a machine learning sample generated according to historical data.

The input data of the fault identification model comprises control instruction codes and instruction execution arrival rates thereof, the output data is fault occurrence rate, and the whole input parameters and output are simple, so that the fault identification model can be realized by adopting neural networks with simple structures such as a BP (back propagation) neural network and a Convolutional Neural Network (CNN), and can be composed of an input layer, a hidden layer and an output layer, the purpose of the embodiment of the application can be realized without complex design, the implementation difficulty is reduced, and a more accurate judgment result is obtained. The BP neural network and the convolutional neural network CNN are both mature neural network models, and those skilled in the art can refer to the prior art and combine actual requirements to flexibly construct the fault identification model to achieve the purpose of the embodiment of the present application, which are all within the protection scope of the present application.

After the failure occurrence rate is obtained through prediction, a corresponding working condition detection strategy can be further determined, in some modification implementation manners of the embodiment of the application, the intelligent tower crane maintenance management system based on the failure recognition model further comprises:

the strategy determining module is used for determining an actuating mechanism corresponding to the control instruction and a working condition detection strategy corresponding to the actuating mechanism according to various control instructions sent by the controller;

the mapping table generating module is used for generating a working condition detection strategy mapping table according to various control instructions and corresponding actuating mechanisms and working condition detection strategies;

the maintenance strategy output module comprises:

and the mapping table query module is used for querying and outputting the actuating mechanism and the working condition detection strategy corresponding to the control instruction from the working condition detection strategy mapping table.

By the embodiment, corresponding working condition detection strategies can be set in advance according to various situations, the mapping table is established for storage, and when the fault occurrence rate is detected to be greater than the preset warning value, the executing mechanism and the working condition detection strategies corresponding to the control command can be inquired from the mapping table and output, so that the targeted working condition detection can be conveniently carried out on the executing mechanism.

It should be noted that the working condition detection strategy may be flexibly set by a technician in combination with an actual situation, for example, a failure occurrence rate corresponding to a control instruction for controlling the braking of the amplitude-variable trolley exceeds a corresponding warning value, and the working condition detection strategy is to perform working condition detection for a braking system of the trolley; the fault occurrence rate corresponding to a control instruction for controlling the starting of the amplitude-variable trolley exceeds a corresponding warning value, and the working condition detection strategy is to detect the working condition of a driving system of the trolley; the embodiment of the present application does not limit the specific content of the above-mentioned operating condition detection strategy, and when it is used in the scheme of the present application, it should be within the protection scope of the present application.

In addition, in order to further improve the intellectualization and the unmanned performance of the intelligent tower crane, the intelligent tower crane can also realize the unmanned performance, the intellectualization and the automation of the selection of a lifting appliance by configuring the following tower crane lifting appliance selection device based on the three-dimensional material form model simulation, so that the intellectualization level, the automation level and the safety of the intelligent tower crane are improved, and the description is combined with an example below.

In some embodiments, the intelligent tower crane material positioning auxiliary device based on internet of things communication can include:

The system comprises a material image acquisition module, a material image acquisition module and a control module, wherein the material image acquisition module is used for acquiring a material image group acquired by a camera group arranged on an intelligent tower crane, the camera group comprises a plurality of cameras arranged at different positions of the intelligent tower crane, and the material image group comprises material images acquired by each camera aiming at materials;

the three-dimensional reconstruction module is used for performing three-dimensional reconstruction on the material according to the material image group to obtain a three-dimensional simulation material;

the lifting appliance matching module is used for matching a plurality of alternative three-dimensional simulation lifting appliances with the three-dimensional simulation material in sequence and selecting the three-dimensional simulation lifting appliance with the highest matching degree;

and the lifting appliance selecting module is used for controlling the intelligent tower crane to select a lifting appliance corresponding to the three-dimensional simulation lifting appliance from a lifting appliance pool.

Compared with the prior art, the tower crane lifting appliance selection device based on three-dimensional material form model simulation provided by the embodiment of the application acquires a material image group acquired by a camera group arranged on an intelligent tower crane, wherein the camera group comprises a plurality of cameras arranged at different positions of the intelligent tower crane, the material image group comprises material images acquired by each camera aiming at materials, then three-dimensional reconstruction is carried out on the materials according to the material image group to obtain three-dimensional simulation materials, then a plurality of alternative three-dimensional simulation lifting appliances are sequentially matched with the three-dimensional simulation materials, the three-dimensional simulation lifting appliance with the highest matching degree is selected, finally the intelligent tower crane is controlled to select the lifting appliance corresponding to the three-dimensional simulation lifting appliance from a lifting appliance pool, the three-dimensional simulation materials corresponding to the materials can be constructed through three-dimensional reconstruction, and the three-dimensional simulation lifting appliance is determined through simulation matching, then can directly, pertinence ground select suitable hoist from the hoist pond and hoist the material, can realize unmanned, intelligent and the automation that the hoist was selected, improve intelligent level, the automation level and the security of intelligent tower crane.

The tower crane lifting appliance selection method based on the three-dimensional material form model simulation can be realized by a controller of an intelligent tower crane, the controller can be realized by a computer host, a microcontroller, a Programmable Logic Controller (PLC) and the like, and the embodiment of the application is not limited.

In the embodiment of the present application, the three-dimensional reconstruction refers to establishing a mathematical model suitable for representation and processing by a computer for a three-dimensional object, which is a basis for processing, operating and analyzing the properties of the three-dimensional object in a computer environment, and is also a key technology for establishing a virtual reality expressing an objective world in the computer. In computer vision, three-dimensional reconstruction refers to a process of reconstructing three-dimensional information from a single-view or multi-view image, and may be implemented by calibrating a camera, i.e., calculating a relationship between a pixel coordinate system of the camera and a world coordinate system, and then reconstructing three-dimensional information using information in a plurality of two-dimensional images, for example, the following three-dimensional reconstruction process based on two-dimensional images is illustrated in (1) to (5):

(1) image acquisition: prior to image processing, a two-dimensional image of a three-dimensional object (e.g., an image of a material in the present application) is acquired by an imaging device (e.g., a camera).

(2) Calibrating a camera: an effective imaging model is established through camera calibration, and internal and external parameters of a camera are solved, so that three-dimensional point coordinates in a space can be obtained by combining the matching result of the image, and the purpose of three-dimensional reconstruction is achieved.

(3) Feature extraction: the features mainly include feature points, feature lines, and regions. In most cases, feature points are used as matching primitives, and the form of extracting the feature points is closely related to the matching strategy. The feature point extraction algorithm may include, but is not limited to: directional derivative based methods, image brightness contrast relationship based methods, mathematical morphology based methods, and the like.

(4) Stereo matching: stereo matching is to establish a corresponding relationship between different images according to the extracted features, that is, imaging points of the same physical space point in two different images are in one-to-one correspondence. Some factors in the scene, such as the illumination condition, noise interference, geometric distortion of the scene, surface physical characteristics, and camera characteristics, are considered in matching.

(5) Three-dimensional reconstruction: with the accurate matching result, the three-dimensional scene information can be restored by combining the internal and external parameters calibrated by the camera. Because the three-dimensional reconstruction precision is influenced by factors such as matching precision, internal and external parameter errors of a camera and the like, the work of the previous steps is needed to be done firstly, so that the precision of each link is high, the error is small, and the three-dimensional reconstruction can be realized more accurately.

The three-dimensional reconstruction process based on the two-dimensional image is exemplarily described above, and those skilled in the art can refer to the exemplary description above, and implement flexible change of an actual scene to perform three-dimensional reconstruction according to the material image to obtain a three-dimensional simulation material, so as to achieve the purpose of the embodiment of the present application, which is further described below with reference to examples:

in some variations of embodiments of the present application, the three-dimensional reconstruction module includes:

the camera position determining unit is used for determining the camera position information corresponding to each material image in the material image group;

and the three-dimensional reconstruction unit is used for performing three-dimensional reconstruction according to the position information of the camera corresponding to each material image and the position information of the pixel point corresponding to the material in each material image to obtain the three-dimensional simulation material corresponding to the material.

The position information of the camera can be calculated in real time according to the installation position of the camera and the amplitude variation, rotation and lifting information of the intelligent tower crane, for example, the camera can comprise a global camera arranged on a crane arm of the intelligent tower crane and can be used for shooting a global image of a working scene of the intelligent tower crane, so that the position of a material can be preliminarily positioned according to the global image to determine a material image, and the position information can be calculated and determined according to the rotation information and the installation position of the tower crane; for another example, the camera may further include a local camera installed near the spreader, where the local camera is used to shoot the material only by way of example to obtain a material image with higher definition and accuracy, and the position information of the local camera may be calculated and determined according to the rotation information, amplitude variation information, lifting information, and installation position of the tower crane; the calculation of the camera position information can be realized according to the geometric relationship, and the details are not repeated here.

In addition to the above-described embodiments, in some modified embodiments, the three-dimensional reconstruction unit includes:

and the dense reconstruction subunit is used for determining the position information of the three-dimensional point corresponding to each pixel point in the world coordinate system by adopting a dense reconstruction algorithm according to the position information of the camera corresponding to each material image and the position information of the pixel point corresponding to the material in each material image, and determining the three-dimensional simulation material corresponding to the material according to the three-dimensional point cloud formed by the three-dimensional points.

The method comprises the steps of calculating a three-dimensional point corresponding to each pixel point in an image one by one on the premise that the pose of a camera is known, and obtaining a dense three-dimensional point cloud on the surface of a scene object, wherein the dense reconstruction (MVS) algorithm is multiview solid geometry.

Through the embodiment, the three-dimensional reconstruction can be accurately and quickly realized, and the whole marking accuracy and marking efficiency are improved.

In some variations of embodiments of the present application, the spreader matching module comprises:

the lifting appliance matching unit is used for sequentially matching a plurality of alternative three-dimensional simulation lifting appliances with the three-dimensional simulation materials in BIM software of a building information model, and determining the matching degree according to the priority of the lifting appliances and the coupling degree of a lifting part, wherein different lifting appliances are preset with different priorities, and the coupling degree of the lifting part is determined according to the shape and size coupling information;

And the lifting appliance selecting unit is used for selecting the three-dimensional simulation lifting appliance with the highest matching degree from the plurality of three-dimensional simulation lifting appliances according to the matching degree.

In the implementation process, corresponding three-dimensional simulation lifting appliances can be preset in Building Information Model (BIM) software corresponding to each lifting appliance in a lifting appliance pool, attribute Information and priority Information are set for each three-dimensional simulation lifting appliance, after a three-dimensional simulation material is generated, the three-dimensional simulation lifting appliances can be sequentially matched with the three-dimensional simulation material in the BIM software, and then the matching degree of each three-dimensional simulation lifting appliance is determined.

It is easy to understand that different hangers can set different priorities, for example, the safety of the hook is higher, and the priority is greater than that of the hanging tongs, the clamp, the hanging beam, etc., and those skilled in the art can flexibly set the priority of each hanger according to actual needs, which is not limited herein.

In addition, the coupling degree of the hoisting part (i.e. the contact part of the spreader and the material) can be determined according to the shape and size coupling information, for example, the coupling degree between the circle and the circle is greater than the coupling degree between the circle and the rectangle, the coupling degree between the small-sized spreader and the large-sized material cannot be coupled, i.e. is zero, and the like.

By the mode, the BIM software can be used for automatically selecting the appropriate three-dimensional simulation lifting appliance for the material, so that the corresponding lifting appliance can be selected in a targeted manner, and the accuracy and the efficiency are high.

In some modified embodiments of the embodiment of the application, a spreader pool is configured for the intelligent tower crane, a plurality of different spreaders are arranged in the spreader pool, and each spreader is arranged at a preset position in the spreader pool according to a corresponding spreader identifier;

the module is selected to hoist includes:

and the lifting appliance selecting module unit is used for controlling the intelligent tower crane to select a corresponding lifting appliance from a preset position corresponding to the lifting appliance identification in the lifting appliance pool according to the selected lifting appliance identification of the three-dimensional simulation lifting appliance with the highest matching degree.

This embodiment can set up the hoist sign to the hoist of difference to locate the assigned position in the hoist pond with the hoist, like this, after matching and confirming three-dimensional emulation hoist, can be according to the quick hoist that selects of hoist sign corresponds, improve the hoist and select accuracy and efficiency.

It should be noted that the lifting device according to the embodiments of the present application includes, but is not limited to, a lifting hook, a lifting clamp, a lifting beam, a clamp, a steel plate lifting device, a lifting device, a steel ingot lifting device, a vertical rolled steel lifting device, a C-shaped lifting device, a round steel lifting device, an electric horizontal rolled lifting device, a container lifting device, a roll lifting device, and the like.

In addition, in order to further perfect the intellectuality and the unmanned of above-mentioned intelligent tower crane, the intelligent tower crane can also be used for the tower crane to maintain the intelligent auxiliary robot who maintains the maintenance through the configuration below, realize maintaining the automation of maintenance point and patrol and examine, improve the timeliness of maintaining the maintenance, can shoot the video of artifical maintenance and confirm simultaneously, and the automatic maintenance record that maintains that generates, help tracing back behind the accident, in addition, through shooting artifical maintenance and maintenance video, can encourage maintenance personnel to carefully, maintain the maintenance in charge, reduce and maintain the problem emergence that the maintenance is not in place, thereby improve intelligent level and the security of intelligent tower crane on the whole, the following description of combining the example.

In some embodiments, the intelligent auxiliary robot for tower crane maintenance may include: a control device, a drive device and an image pickup device;

the driving device and the camera device are both connected with the control device;

the driving device is used for driving the intelligent auxiliary robot to move along a preset overhaul path on the intelligent tower crane;

the control device is used for controlling the driving device to drive the intelligent auxiliary robot to patrol and maintain the maintenance points according to a preset maintenance schedule;

The camera device is used for shooting a manual maintenance video and evidence when the camera device stays at the maintenance point needing manual maintenance;

and the control device is also used for generating maintenance records aiming at each maintenance point on the maintenance schedule after the maintenance is finished.

Compared with the prior art, the intelligent auxiliary robot for tower crane maintenance provided by the embodiment of this application, through setting up controlling means, drive arrangement and camera device, wherein, drive arrangement with camera device all with controlling means connects, drive arrangement is used for driving intelligent auxiliary robot removes along the maintenance route of predetermineeing on the intelligent tower crane, controlling means is used for controlling according to predetermined maintenance schedule drive intelligent auxiliary robot patrols and examines and maintains the maintenance point, camera device is used for stopping need the manual maintenance when maintaining the maintenance point, shoots the manual maintenance video and demonstrates, controlling means still is used for after the maintenance is accomplished, aims at each maintenance point of maintaining on the maintenance schedule generates the maintenance record. Thereby can utilize intelligent auxiliary robot to maintain the automation of maintenance point and patrol and examine, improve the promptness of maintaining the maintenance, can shoot artifical maintenance video and certificate simultaneously, and the automatic maintenance record that generates helps going back after the accident, in addition, through shooting artifical maintenance video, can encourage maintenance personnel to be serious, maintain the maintenance in charge, reduce the problem emergence of maintaining the maintenance not in place, thereby improve intelligent level and the security of intelligent tower crane on the whole.

The control device can be implemented by a computer host, a microcontroller, a Programmable Logic Controller (PLC), and the like, and the camera device can be implemented by any camera provided by the prior art, which is not limited in the embodiment of the application.

In some modification implementation manners of the embodiment of the present application, the intelligent auxiliary robot for tower crane maintenance further includes: the electronic tag reader is connected with the control device;

the maintenance points are provided with electronic tags, and the electronic tags are used for distinguishing and identifying different maintenance points;

the electronic tag reader is used for detecting an electronic tag in the moving process of the intelligent auxiliary robot and sending tag information of the electronic tag to the control device when the electronic tag is detected;

the control device is also used for identifying the maintenance point according to the label information.

Through this embodiment, can utilize electronic tags to realize the sign to maintaining the maintenance point for intelligent auxiliary robot can be through reading this electronic tags discernment maintain the maintenance point, and then carry out the maintenance of pertinence to and accomplish and patrol and examine, avoid omitting simultaneously and maintain the maintenance point.

In some modification implementation manners of the embodiment of the present application, the intelligent auxiliary robot for tower crane maintenance further includes: the display device is connected with the control device;

the control device is also used for controlling the display device to play the maintenance guide information of the current maintenance point when the display device stays at the maintenance point needing manual maintenance.

By playing the maintenance guidance information, maintenance personnel can be guided to finish maintenance operation correctly and efficiently, and the problems of maintenance errors or inadequate maintenance and the like are avoided.

In some modification embodiments of the embodiment of the application, a steel chain is arranged along a preset overhaul path on the intelligent tower crane;

the driving device comprises a gear structure coupled with the steel chain and is used for driving the intelligent auxiliary robot to move along the steel chain.

Because to intelligent tower crane, there is a large amount of maintenance routes to need the climbing, consequently, through this embodiment, can make intelligent auxiliary robot can follow the maintenance route climbing reaches the maintenance point of each position, ensures the safety of this scheme, implements smoothly.

In some modification implementation manners of the embodiment of the present application, the intelligent auxiliary robot for tower crane maintenance further includes: a housing;

the control device, the driving device and the camera device are fixedly installed through the shell;

the surface of casing is provided with gyro wheel and rubidium indisputable boron magnet, ru indisputable boron magnet be used for with intelligent auxiliary robot inhales on intelligent tower crane, the gyro wheel be used for with intelligent tower crane contact so that intelligent auxiliary robot can follow the intelligence tower crane removes.

Through this embodiment, can ensure that intelligent auxiliary robot can adsorb and move on the intelligent tower crane tower body, avoid intelligent auxiliary robot in the emergence of the circumstances such as high altitude along with the wind swing, improve operation stability and security.

In some modification implementation manners of the embodiment of the present application, the intelligent auxiliary robot for tower crane maintenance further includes: a communication device;

the communication device is connected with the control device;

the control device is also used for sending the maintenance schedule of the polling to a terminal carried by a maintenance worker through the communication device before the polling so that the maintenance worker can patrol and examine maintenance points along with the intelligent auxiliary robot according to the maintenance schedule.

Through the embodiment, the maintenance personnel can be effectively reminded to maintain in time, and the maintenance personnel can know the maintenance content by sending the maintenance schedule, so that the maintenance efficiency is improved, and the condition that the maintenance is not in time is avoided.

In some modifications of the embodiment of the application, the control device is further configured to send warning information indicating that maintenance is being performed to the controller of the smart tower crane through the communication device during the period of polling, so that the controller avoids performing construction work during the maintenance.

Through this embodiment, can avoid intelligent tower crane construction operation during patrolling and examining, improve the security, avoid the construction to go on simultaneously with the maintenance and the incident emergence that leads to.

In some modification implementation manners of the embodiment of the application, the control device is further configured to control the camera device to collect a face image of a maintenance person when the camera device stays at the maintenance point where manual maintenance is needed, identify and authenticate the maintenance person according to the face image, and play warning information indicating authentication failure to the maintenance person after authentication failure.

Through the embodiment, the identity authentication of the maintenance personnel can be realized, the problems of error and not-in-place maintenance and the like caused by the situations that the maintenance personnel replace operation, illegal personnel (personnel without maintenance qualification) carry out maintenance and the like are avoided, and the maintenance quality and the arrival rate are improved.

In addition, in order to further improve the intellectualization and the unmanned of the intelligent tower crane, the intelligent tower crane can also realize the automatic judgment of whether the construction site is suitable for construction by configuring the following intelligent auxiliary robot for tower crane maintenance, so as to ensure that construction is restarted under the condition that no person exists around the material, avoid personal injury to the person caused by the person near the material during construction, integrally improve the intellectualization level and the safety of the intelligent tower crane, and the description is carried out by combining with an example.

In some embodiments, the intelligent auxiliary robot for detecting the working condition of the intelligent tower crane may include a housing, a mobile module, a control module, an environment scanning module, and a wireless communication module; wherein the content of the first and second substances,

the shell is arranged on the mobile module, the control module, the environment scanning module and the wireless communication module are all arranged on the shell, and the mobile module, the environment scanning module and the wireless communication module are all connected with the control module;

The moving module is used for driving the intelligent auxiliary robot to move on the ground around the intelligent tower crane under the control of the control module;

the environment scanning module is connected with the control module and used for scanning environment data around the intelligent tower crane under the control of the control module along with the movement of the moving module before the construction of the intelligent tower crane and sending the scanned environment data to the control module;

the control module is used for determining working condition information representing whether construction is suitable currently or not according to the environment data, and sending the working condition information to the controller of the intelligent tower crane through the wireless communication module, so that the controller determines whether construction is started or not according to the working condition information.

Compared with the prior art, the intelligent auxiliary robot for detecting the working condition of the intelligent tower crane provided by the embodiment of the application is characterized in that the intelligent auxiliary robot is provided with a shell, a mobile module, a control module, an environment scanning module and a wireless communication module, wherein the shell is arranged on the mobile module, the control module, the environment scanning module and the wireless communication module are arranged on the shell, the mobile module, the environment scanning module and the wireless communication module are all connected with the control module, the mobile module is used for driving the intelligent auxiliary robot to move on the ground around the intelligent tower crane under the control of the control module, the environment scanning module is connected with the control module and is used for scanning the environment data around the intelligent tower crane under the control of the control module along with the movement of the mobile module before the construction of the intelligent tower crane, and sending the environment data obtained by scanning to the control module, wherein the control module is used for determining working condition information representing whether the construction is suitable currently according to the environment data and sending the working condition information to a controller of the intelligent tower crane through the wireless communication module, so that the controller determines whether the construction is started according to the working condition information. Thereby make intelligent auxiliary robot can be before the construction automated inspection job site's material and personnel condition, and generate and show whether the operating mode information that is fit for the construction at present sends for the controller, and then make the controller basis whether the operating mode information is confirmed to begin the construction, can be so that whether the operating mode of intelligent tower crane understanding job site is fit for the construction, ensure to begin the construction again around the material under the personnel's condition, personnel cause bodily injury near the material when avoiding constructing, wholly improve intelligent level and the security of intelligent tower crane.

The control module can be implemented by a computer host, a microcontroller, a Programmable Logic Controller (PLC), and the like, and the embodiment of the application is not limited.

The above-mentioned environment scanning module can adopt at least one of arbitrary laser scanner, binocular camera and the depth camera that prior art provided to realize, for example, above-mentioned environment scanning module can adopt any one of arbitrary laser scanner, binocular camera and the depth camera that prior art provided to realize, also can adopt wherein arbitrary two or three's combination to realize, and this application embodiment does not do the restriction.

Above-mentioned wireless communication module can adopt loRa wireless communication module, realization such as 433M wireless module, because loRa wireless communication module and 433M wireless module signal are strong, transmission distance is long, ideal transmission distance is about 3 kilometers, pierce through in addition, diffraction ability is strong, the transmission process decay is less, easy network deployment, advantages such as with low costs, consequently, the working scene who is applicable to intelligent tower crane that can be good, thereby obtain better signal transmission effect, the stability and the reliability of this scheme implementation improve, this application embodiment does not do the injecing.

In some modified embodiments of the embodiment of the present application, the control module is specifically configured to perform synchronous Mapping on the surrounding environment by using a Simultaneous Localization And Mapping (SLAM) algorithm corresponding to the environment scanning module according to the environment data, And determine working condition information indicating whether the current working condition is suitable for construction according to the constructed environment map.

Synchronous positioning and map building SLAM refers to a moving object carrying a sensor, realizes self positioning in the moving process, and synchronously builds a map of the surrounding environment in a proper mode. The specific explanation is that: a mobile robot starts to move from any place in a completely unknown environment, and continuously uses a sensor (such as the environment scanning module) to observe surrounding environment characteristics in the moving process so as to position the self pose, namely the position and the angle of the robot, and meanwhile, continuously updates and constructs an incremental map of the surrounding environment according to the relative pose information of the robot and the environment, so that the mobile robot is helped to construct a perception system for generating surrounding three-dimensional environment data, and the autonomous movement and environment perception of the mobile robot are realized.

At present, SLAM is a mature technology in the field of robots, and a person skilled in the art can implement the purpose of the embodiment of the present application by using any SLAM algorithm provided in the prior art directly or by changing the implementation, and the present application is not limited.

Considering that different SLAM algorithms need to be adopted for different data acquired by different environment scanning modules, in the embodiment of the present application, according to an actually adopted environment scanning module, an SLAM algorithm corresponding to the environment scanning module is adopted to synchronously map a surrounding environment, for example, if the environment scanning module adopts a laser scanner, an SLAM algorithm based on laser point cloud data corresponding to the laser scanner needs to be adopted to synchronously map the surrounding environment; if the environment scanning module adopts a binocular camera, synchronous map building is carried out on the surrounding environment by adopting a binocular vision image-based SLAM algorithm corresponding to the binocular camera; if the environment scanning module adopts a depth camera, synchronous map building is carried out on the surrounding environment by adopting a depth image-based SLAM algorithm corresponding to the depth camera; if the environment scanning module is realized by adopting a plurality of laser scanners, binocular cameras and depth cameras, synchronous map building needs to be carried out on the surrounding environment by adopting a corresponding fusion SLAM algorithm; the above are all mature technical means in the prior art, and those skilled in the art can implement the purpose of the embodiment of the present application by using any SLAM algorithm provided in the prior art directly or by changing the implementation, and details are not described here.

By the embodiment, the SLAM algorithm can be adopted to realize the real-time construction of the surrounding environment map, so that whether the current working condition information is suitable for construction or not is accurately determined.

On the basis of the above embodiment, in some modified embodiments, the control module is specifically configured to identify a material and a person in a constructed environment map, and generate working condition information indicating that construction is currently suitable under the condition that the material is identified and no person exists in a preset range around the material, otherwise generate working condition information indicating that construction is currently not suitable.

The materials are materials needing to be hoisted by the intelligent tower crane, such as reinforcing steel bars, wood ridges, concrete, steel pipes, glass and other raw materials for building construction, if the materials are recognized on a construction site, construction conditions of a foundation are met, in addition, whether personnel exist around the materials or not needs to be recognized in order to protect the personal safety of ground workers, if no personnel exist in a preset range around the materials, construction is suitable, and if the personnel exist, construction is not suitable. The preset range can be flexibly set according to actual needs, for example, 3 meters, 5 meters, and the like, and the embodiment of the present application is not limited.

Through this embodiment, can discern the material and produce the operating mode information that shows the current suitable construction and send for the controller under the condition that no personnel in the preset range around the material, avoid the controller to construct under unsuitable operating mode and the safety problem that leads to.

In addition to the above embodiments, in some modified embodiments, the material is provided with a material identification pattern;

the environment scanning module comprises a binocular camera or a depth camera, and the environment data comprises image data;

the control module is specifically configured to identify the material by detecting the material identification pattern in the image data.

The material identification pattern needs to be realized by adopting a pattern which can be distinguished from the surrounding environment, the material identification pattern can be sprayed on the surface of a material by a spray gun, and can also be attached to the surface of the material in the form of a sticker, or the preset pattern is arranged on an identification plate, and then the identification plate is clamped on the material by a clamp.

The material identification pattern is different from the surrounding environment, so that the material identification pattern can be detected through pattern matching identification in the image data, and the material can be identified in the image data. The pattern-based image recognition technology is a relatively mature technology at present, and therefore, the detailed description is omitted here, and a person skilled in the art can adopt any pattern-based image recognition technology disclosed in the prior art to achieve the purpose of the embodiments of the present application, which should be within the scope of the present application.

Through this embodiment, can utilize material identification pattern to realize the differentiation sign to the material, help improving the rate of accuracy and the efficiency that intelligent auxiliary robot discerned the material.

In addition, to different materials, can set up with this material correspond, different material identification pattern to make intelligent auxiliary robot can discern the classification of material according to this material identification pattern, with further help the controller to carry out more accurate decision-making, for example, when having multiple material at the job site, the controller can confirm the hoist and mount priority etc. of various materials according to the classification of the material of discernment, improves construction rationality and automatic efficiency of construction.

In addition, the identification of the materials can also be realized by adopting other embodiments, for example, in some modification embodiments, an electronic tag for identifying the materials is arranged on the materials;

the intelligent auxiliary robot further comprises: the electronic tag reader is connected with the control module;

the electronic tag reader is used for detecting the electronic tag in the moving process of the intelligent auxiliary robot and sending tag information of the electronic tag to the control module when the electronic tag is detected;

The control module is specifically used for identifying the material according to the label information.

Through this embodiment, can adopt electronic tags's mode to carry out the sign to the material, electronic tags can carry more material information, for example information such as classification, quantity, weight, hoist and mount priority, can make more reasonable planning construction plan of controller and carry out reasonable construction, improves the intellectuality and the automatic level of intelligent tower crane.

For the identification of the person, in some modified embodiments, the control module is specifically configured to detect the person in the image data by using a person identification algorithm, determine the position of the material and the person in the environment map, and determine whether the person is within a preset range around the material.

The image-based person recognition algorithm is a mature technology at present, and therefore is not described herein again, and those skilled in the art can adopt any image-based person recognition algorithm disclosed in the prior art to achieve the purpose of the embodiments of the present application, which all should be within the scope of the present application.

After the materials and the personnel are detected, the positions of the materials and the personnel can be identified in a constructed environment map, the distance between the materials and the personnel is calculated, and whether the personnel exist in a preset range around the materials is further determined.

Through this embodiment, can be comparatively accurate discernment material and personnel and confirm the distance between the two to whether there is personnel in the accurate judgement material within range of predetermineeing around, and then confirm whether suitable construction at present, improve the security of construction.

The moving module can be realized by any wheel type moving mechanism, foot type moving mechanism or crawler type moving mechanism provided by the prior art. For a mobile robot, a wheel type moving mechanism is the most applied structure, the wheel type moving mode is the most optimal on a flat ground, the efficient moving speed can be ensured, for a more complex ground, a crawler type moving mechanism can be adopted to obtain better penetrating performance and stability, and for a ground with large uneven fluctuation, a foot type moving mechanism can be adopted to realize. The purpose of the embodiments of the present application can be achieved by adopting an appropriate mobile module for different construction environments by those skilled in the art, and the present application does not limit the specific implementation manner of the mobile module.

In addition, in order to further improve the intelligent level and the safety of the intelligent tower crane, the intelligent tower crane can also monitor whether the motion state of the suspension arm is abnormal or not by configuring the internet of things sensing and monitoring system for the suspension arm condition of the intelligent tower crane, so that the automatic diagnosis of the abnormal state of the suspension arm is realized, and the automatic level, the intelligent level and the safety of the intelligent tower crane are improved.

In some embodiments, the intelligent tower crane boom condition internet of things sensing and monitoring system may include: a controller and an inertial sensor communicatively coupled to the controller, the inertial sensor including an accelerometer and a gyroscope;

the inertial sensor is fixedly arranged on a trolley of the intelligent tower crane and used for acquiring inertial motion data of the trolley in real time and sending the inertial motion data to the controller, the trolley is arranged on a suspension arm of the intelligent tower crane and moves along the suspension arm, and the inertial motion data comprises acceleration data and angular velocity data;

the controller is used for determining the inertial action information of the intelligent tower crane according to the inertial motion data and judging whether the motion state of the suspension arm is abnormal or not by comparing the inertial action information with the action information fed back by the driving mechanism of the intelligent tower crane, wherein the action information comprises rotation action information and/or amplitude variation action information.

Compared with the prior art, the intelligent tower crane boom condition internet of things sensing and monitoring system provided by the embodiment of the application is characterized in that the system comprises a controller and an inertial sensor which is in communication connection with the controller, the inertial sensor comprises an accelerometer and a gyroscope, the inertial sensor is fixedly arranged on a trolley of the intelligent tower crane and used for acquiring inertial motion data of the trolley in real time and sending the inertial motion data to the controller, the trolley is arranged on the boom of the intelligent tower crane and moves along the boom, the inertial motion data comprises acceleration data and angular velocity data, the controller is used for determining motion information of inertia of the intelligent tower crane according to the inertial motion data, and whether the motion state of the boom is abnormal or not is judged by comparing the motion information of the inertia and the motion information fed back by a driving mechanism of the intelligent tower crane, the motion information comprises rotation motion information and/or amplitude variation motion information, so that inertial motion data of the trolley on the suspension arm can be collected, inertial motion information is obtained through calculation, and then the inertial motion information is compared with the motion information fed back by the driving mechanism to judge whether the motion state of the suspension arm is abnormal or not, automatic diagnosis of the abnormal state of the suspension arm is achieved, and the automation and intelligence level of the intelligent tower crane is improved.

The controller can be realized by a computer host, a microcontroller, a Programmable Logic Controller (PLC) and the like, the inertial sensor can be realized by an accelerometer and a gyroscope, the embodiment of the application is not limited, and the suspension arm refers to a crane arm.

Above-mentioned controller with inertial sensor can adopt wired mode to connect, also can adopt wireless mode to connect, when adopting wireless mode to connect, the controller with inertial sensor can adopt loRa wireless communication module to carry out or 433M wireless module carries out communication connection, because loRa wireless communication module and 433M wireless module signal are strong, transmission distance is long, ideal transmission distance is about 3 kilometers, pierce through in addition, diffraction ability is strong, transmission process decay is less, easy network deployment, advantages such as with low costs, consequently, the working scenario that is applicable to intelligent tower crane that can be good, thereby obtain better signal transmission effect, improve the stability of this scheme implementation, reliability and network deployment flexibility, this application embodiment does not restrict.

The action information fed back by the driving mechanism is acquired by the controller from the driving mechanism, the driving mechanism comprises a variable amplitude driving mechanism and/or a rotary driving mechanism, and in some embodiments, the driving mechanism comprises a variable frequency motor and an encoder;

The variable frequency motor and the encoder are connected with each other and are both connected with the controller;

the variable frequency motor is used for driving an action mechanism of the intelligent tower crane to move under the control of the controller;

the encoder is used for measuring the action information output by the variable frequency motor and feeding back the measured action information to the controller.

Correspondingly, the variable frequency motor comprises a variable amplitude variable frequency motor and a rotary variable frequency motor, the encoder comprises a variable amplitude encoder and a rotary encoder, the variable amplitude encoder is used for measuring the variable amplitude position, the variable amplitude speed and other action information of the variable amplitude mechanism, and the rotary encoder is used for measuring the rotary angle, the rotary speed and other action information of the rotary mechanism.

In this embodiment, the variable frequency motor can be used to implement accurate control of the motion mechanism (including the swing mechanism and/or the luffing mechanism), and the encoder can be used to implement accurate measurement of the output of the variable frequency motor to obtain the output motion information.

In some variations, the driving mechanism includes a rotary driving mechanism, the driving mechanism includes a luffing driving mechanism, the motion information includes luffing motion information, and the luffing motion information includes a luffing distance and/or a luffing speed of the cart.

In some variations, the drive mechanism comprises a slewing drive mechanism, the motion information comprises slewing motion information comprising a slewing angle and/or a slewing speed of the boom.

In some modified embodiments, the controller is specifically configured to calculate a similarity between the motion information of the inertia and the feedback motion information, and determine that the motion state of the boom is abnormal if the similarity is smaller than a preset threshold.

The preset threshold value can be flexibly set according to actual requirements, and the embodiment of the application is not limited.

On the basis of the above embodiment, in some modified embodiments, if there are a plurality of items of motion information, the controller is specifically configured to calculate a similarity of each item of motion information, and determine that the motion state of the boom is abnormal when any one of the similarities is smaller than a preset threshold.

For example, if the motion information includes four items, namely a luffing distance, a luffing speed, a slewing angle and a slewing speed, the similarity between each inertial value and a feedback value is respectively calculated, and if any one of the similarities is smaller than a preset threshold value, it can be determined that the motion state of the boom is abnormal.

The inertial motion data comprise acceleration data collected by an accelerometer and angular velocity data collected by a gyroscope, the acceleration data are integrated according to time to obtain velocity data, the velocity data are integrated again to obtain displacement data, and the angular velocity data are integrated to obtain angle rotation data, so that inertial action information is obtained.

In some embodiments, the motion information may be calculated by using an inertial navigation algorithm, and correspondingly, the controller is specifically configured to calculate the motion information of the inertia of the intelligent tower crane by using the inertial navigation algorithm according to the inertial motion data.

The inertial navigation algorithm is a relatively mature technical means in the prior art, and in the process of integration, the inertial navigation algorithm can compensate the calculation data according to factors such as earth rotation and the like, so as to obtain a relatively accurate calculation result.

In some variation implementations of the embodiment of the application, the controller is further configured to control the trolley to move from the initial end to the tail end of the boom when the intelligent tower crane is unloaded, determine a motion trajectory of the trolley according to inertial motion data generated by the inertial sensor in a moving process, and determine whether the boom is deformed abnormally according to the motion trajectory.

The initial end refers to one end of the suspension arm close to the tower body, the tail end refers to one end of the suspension arm far away from the tower body, the position of the trolley at each moment can be calculated by adopting an inertial navigation algorithm according to inertial motion data collected in the moving process, the motion track of the trolley is further determined, the motion track is connected into a line segment, the deformation condition of the suspension arm can be accurately reflected by the bending condition of the line segment, therefore, the method can be used for calculating deformation information such as deformation rate of the suspension arm, and whether the deformation information such as the deformation rate meets a preset deformation standard is utilized to judge whether the suspension arm is deformed abnormally.

Through the embodiment, the deformation abnormity of the suspension arm can be detected, so that the state of the suspension arm can be comprehensively diagnosed, the abnormity can be found in time, and safety accidents caused by the abnormal operation of the suspension arm can be avoided.

In addition, in order to further improve the intelligent level and the safety of the intelligent tower crane, the intelligent tower crane can also realize the remote monitoring of the intelligent tower crane by configuring the following state data monitoring and transmitting system for the remote control of the intelligent tower crane, so that the automation and the intelligent level of the intelligent tower crane are improved, and the description is combined with the examples below.

In some embodiments, the state data monitoring and transmitting system for intelligent tower crane remote control may include: the system comprises a controller, a LoRa wireless communication module and a remote monitoring terminal;

the controller is connected with the LoRa wireless communication module, and the LoRa wireless communication module is connected with the remote monitoring terminal in a wireless communication mode;

the controller is used for acquiring state data of the intelligent tower crane and sending the state data to the remote monitoring terminal through the LoRa wireless communication module;

and the remote monitoring terminal is used for displaying the state data of the intelligent tower crane to a user.

Wherein, Long Range Radio (Long Range Radio) is meant to Long distance Radio, is an ultra-Long distance wireless transmission scheme based on spread spectrum technology, and its signal is strong, transmission distance is Long, ideal transmission distance is about 3 kilometers, and it is less to penetrate in addition, diffraction ability is strong, transmission process decay, easy network deployment, advantage such as with low costs, consequently, the working scenario that is applicable to intelligent tower crane that can be good to obtain better signal transmission effect, improve the stability and the reliability that this scheme was implemented, this application embodiment does not do the restriction.

Compared with the prior art, the state data monitoring transmission system for intelligent tower crane remote control provided by the embodiment of the application passes through the controller, the LoRa wireless communication module and the remote monitoring terminal, the controller is connected with the LoRa wireless communication module, the LoRa wireless communication module is connected with the remote monitoring terminal in a wireless communication mode, the controller is used for collecting the state data of the intelligent tower crane and sending the state data to the remote monitoring terminal through the LoRa wireless communication module, the remote monitoring terminal is used for displaying the state data of the intelligent tower crane to a user, so that the remote monitoring of the intelligent tower crane can be realized, as the LoRa wireless communication has the characteristics of long transmission distance, strong anti-interference capability, strong penetrating and diffracting capability and the like, the system is more suitable for complex tower crane construction scenes, so as to obtain better data transmission effect, the real-time performance and the stability of remote monitoring are improved, and the automation and the intelligent level of the intelligent tower crane are further improved.

In some modified embodiments of the embodiment of the present application, the working frequency band of the LoRa wireless communication module adopts a 470-510MHz frequency band. The 470-510MHz band is a free band for networking in small areas such as buildings, residential districts and villages, so that the implementation cost can be reduced, and the band has longer signal wavelength and better penetrating and diffracting performance, thereby obtaining better signal transmission effect.

In some modification implementation manners of the embodiment of the present application, the state data monitoring and transmitting system for intelligent tower crane remote control further includes: an LoRa gateway;

the LoRa gateway is arranged between the LoRa wireless communication module and the remote monitoring terminal;

the loRa wireless communication module of a plurality of intelligent tower cranes passes through the loRa gateway with remote monitoring terminal connects.

It should be noted that LoRa is a modulation technique of a physical layer, and may be used in different protocols, such as LoRaWAN protocol, CLAA network protocol, LoRa private network protocol, and LoRa data transparent transmission. When implementing, adopt loRa wireless communication connection between loRa wireless communication module and the loRa gateway, and the loRa gateway can adopt cellular network to be connected with remote monitoring terminal to obtain farther monitoring range, make the user monitor the intelligence tower crane at farther distance.

And pass through the loRa wireless communication module with a plurality of intelligent tower cranes the loRa gateway with remote monitoring terminal connects, can realize that a remote monitoring terminal to the remote monitoring of a plurality of intelligent tower cranes, realizes centralized control management, makes the user know the situation of job site comprehensively.

In some embodiments, the state data includes idle state data, amplitude variation action data, rotation action data and lifting action data of the intelligent tower crane, so that a user can know the real-time running state of the intelligent tower crane.

On the basis of the above embodiment, in some modified embodiments, the state data further includes the category and weight of the material hoisted by the intelligent tower crane each time;

the remote monitoring terminal is further used for counting the workload information of each intelligent tower crane within a preset time period according to the state data and displaying the workload information to a user, wherein the workload information comprises the category and the weight of the hoisted material within the preset time period.

Through this embodiment, can also realize the remote monitoring to intelligent tower crane work load, make the construction condition of understanding more comprehensive of user, manage the construction progress better.

For the acquisition of the material category information, on the basis of the above embodiment, in some modification embodiments, the intelligent tower crane is provided with a camera group connected with the controller, the camera group is used for acquiring a material image group of a material hoisted by the intelligent tower crane and sending the material image group to the controller, wherein the camera group comprises a plurality of cameras with different shooting angles, and the material image group comprises a material image acquired by each camera for the material;

The controller is further used for determining attribute information of the materials according to the material image group and obtaining categories of the materials through matching from a tower crane material database according to the attribute information of the materials, wherein the attribute information comprises shape information, size information and texture information, and the tower crane material database stores the attribute information of different tower crane materials in advance.

Because the material category loaded and unloaded by the tower crane is clear and has distinct characteristics, the materials which are usually hoisted by the tower crane are mainly raw materials for building construction, such as reinforcing steel bars, wood ridges, concrete, steel pipes, glass and the like, and the shapes, the sizes and the textures of the materials have great differences, so that the categories of the materials can be quickly and accurately identified through the database matching only by pertinently extracting the attribute information of the materials, such as the shapes, the sizes, the textures and the like. Through above-mentioned embodiment, the classification of discernment material that can be accurate, the user of being convenient for knows the classification of the material that intelligent tower crane hoisted through remote monitoring terminal to carry out reasonable management and control.

In some variations, the determining attribute information of the material according to the set of material images includes:

inquiring an initial image corresponding to the attitude information from an initial image database according to the attitude information of each camera for collecting the material image, wherein the attitude information comprises shooting position information and shooting angle information of the camera, the initial image collected by each camera corresponding to the attitude information is stored in the initial image database, and the initial image is collected before the material enters a field;

Identifying a material main body in each material image by comparing each material image in the material image group with the initial image which is acquired by a camera acquiring the material image in advance under the same posture;

and determining the attribute information of the material according to the identified material main body in each material image.

On the basis of the above embodiment, for extracting the shape information and the size information, in some modified embodiments, the determining the attribute information of the material according to the material body in each identified material image includes:

determining a coordinate conversion relation between a pixel coordinate system corresponding to each camera and a world coordinate system;

converting the pixel coordinates of the material main body in each material image into world coordinates in a world coordinate system according to the coordinate conversion relation;

and determining the shape information and the size information of the material according to the world coordinates.

On the basis of the foregoing embodiment, for extracting texture information, in some modified embodiments, the determining attribute information of the material according to the material body in each material image obtained by identification includes:

and identifying texture information of the material body in each material image by adopting a texture identification algorithm.

For the collection of the material weight information, on the basis of the above embodiment, in some modification embodiments, a guide pulley mechanism of the intelligent tower crane is provided with a force sensor;

the force sensor is used for collecting real-time force sensing data and sending the force sensing data to the controller;

The controller is further used for calculating the weight of the material hoisted by the intelligent tower crane according to the force sensing data.

Wherein, above-mentioned force transducer can adopt round pin axle force transducer to realize, utilizes power sensing data to calculate the weight of the material that intelligence tower crane hoisted is the comparatively ripe technical scheme in this field, and the no longer repeated here, the purpose that prior art realized this application embodiment can be referred to technical staff in this field.

Through this embodiment, can realize the measurement to material weight, the user of being convenient for knows the weight of the material that intelligent tower crane hoisted through remote monitoring terminal to carry out reasonable management and control.

In some change implementation modes of this application embodiment, remote monitoring terminal specifically is used for the basis the attribute information of intelligence tower crane with status data builds real-time simulation scene in building information model BIM software, and shows through the display simulation scene to make the user know through watching the simulation scene the operating condition of intelligence tower crane, wherein, attribute information includes position information, height information and the specification model information of intelligence tower crane.

Building Information Modeling (BIM) software is a graphical tool for architecture, engineering and civil engineering. The core of BIM is to provide a complete building engineering information base consistent with the actual situation for a virtual building engineering three-dimensional model by establishing the model and utilizing the digital technology. The information base not only contains geometrical information, professional attributes and state information describing building components, but also contains state information of non-component objects (such as space and motion behaviors). By means of the three-dimensional model containing the construction engineering information, the information integration degree of the construction engineering is greatly improved, and therefore a platform for engineering information exchange and sharing is provided for related interest parties of the construction engineering project.

This embodiment can utilize BIM to establish the simulation scene of job site intelligence tower crane to the user knows the operating condition of intelligence tower crane more directly perceived, fast.

In addition, in order to further improve the intelligent level and the safety of the intelligent tower crane, the intelligent tower crane can also realize warning of personnel and vehicles on the operation site of the intelligent tower crane by configuring the following safety warning auxiliary system for the intelligent tower crane, so that the safety level of the intelligent tower crane is improved, and the description is combined with the example below.

In some embodiments, the safety warning auxiliary system for the intelligent tower crane may include: the intelligent tower crane comprises a controller of the intelligent tower crane and an intelligent auxiliary robot in communication connection with the controller;

the controller is used for determining the position information of the lifting hook of the intelligent tower crane according to the rotation information, the amplitude variation information and the lifting information of the intelligent tower crane and sending the position information of the lifting hook to the intelligent auxiliary robot;

the intelligent auxiliary robot is arranged on the ground near the intelligent tower crane and used for following the lifting hook of the intelligent tower crane according to the position information of the lifting hook and determining the warning area around the lifting hook and sending an alarm after the dynamic object is detected to enter the warning area.

Compared with the prior art, the safety warning auxiliary system for the intelligent tower crane provided by the embodiment of the application is characterized in that a controller and an intelligent auxiliary robot in communication connection with the controller are arranged, the controller is used for determining the position information of the lifting hook of the intelligent tower crane according to the rotation information, the amplitude information and the lifting information of the intelligent tower crane and sending the position information of the lifting hook to the intelligent auxiliary robot, the intelligent auxiliary robot is arranged on the ground near the intelligent tower crane and used for moving along with the lifting hook of the intelligent tower crane according to the position information of the lifting hook and determining the warning area around the lifting hook and sending an alarm after detecting that a dynamic object enters the warning area, so that dynamic objects such as personnel, vehicles and the like entering the warning area can be automatically detected and an alarm can be sent out in time, the safety of intelligence tower crane operation is improved, and because intelligent auxiliary robot follows the lifting hook and removes, can update warning region in real time, improve safety warning's accuracy and ageing.

In some embodiments, the intelligent-assisted robot comprises: the control module and the environment scanning module:

the environment scanning module is connected with the control module and used for scanning the environment data around the intelligent auxiliary robot and sending the scanned environment data to the control module;

the control module is used for detecting whether a dynamic object enters the warning area or not according to the environment data.

Through the embodiment, whether the dynamic object enters the warning area or not can be accurately detected in a mode of scanning surrounding environment data.

Furthermore, in some modified embodiments, the intelligent auxiliary robot further includes: the wireless communication module is connected with the control module;

the control module is in communication connection with the controller through the wireless communication module.

In some modification embodiments of the embodiment of the present application, the control module is specifically configured to perform synchronous mapping on the surrounding environment by using a synchronous positioning and mapping SLAM algorithm corresponding to the environment scanning module according to the environment data, determine a warning area around the hook according to the constructed environment map, and detect whether a dynamic object enters the warning area.

Synchronous positioning And Mapping (SLAM) refers to a moving object carrying a sensor, realizes self-positioning in the moving process, And synchronously maps the surrounding environment in a proper way, can be regarded as the combination of two technologies of self-positioning And Mapping, And is a mature algorithm applied to the field of robots. The specific explanation is that: a mobile robot starts to move from any place in a completely unknown environment, and continuously uses a sensor (such as the environment scanning module) to observe surrounding environment characteristics in the moving process so as to position the self pose, namely the position and the angle of the robot, and meanwhile, continuously updates and constructs an incremental map of the surrounding environment according to the relative pose information of the robot and the environment, so that the mobile robot is helped to construct a perception system for generating surrounding three-dimensional environment data, and the autonomous movement and environment perception of the mobile robot are realized.

By the embodiment, the SLAM algorithm can be adopted to realize the real-time construction of the surrounding environment map, so that the warning area around the hook is determined based on the constructed environment map, and whether a dynamic object enters the warning area or not is detected.

Considering that often there is a plurality of intelligent tower cranes work jointly at the job site, in order to make intelligent auxiliary robot can discern rather than the intelligent tower crane that pairs and carry out the warning of pertinence, in some change implementation modes of this application embodiment, a security warning auxiliary system for intelligent tower crane still includes: a robot warehouse paired with the intelligent auxiliary robot;

the robot warehouse is arranged below a tower body of the intelligent tower crane and used for parking the intelligent auxiliary robot and charging the intelligent auxiliary robot;

the intelligent auxiliary robot starts to move by taking the position of the robot warehouse as an initial position;

the control module is specifically used for determining the initial position as the tower body position of the intelligent tower crane, marking the tower body in the environment map according to the tower body position, marking the lifting hook in the environment map according to the lifting hook relative to the tower body position information of the lifting hook, and marking the preset area around the lifting hook as a warning area in the environment map.

On the basis of the above embodiment, the control module is specifically configured to update the environment map according to a preset time interval, and identify a dynamic object by comparing changes of the environment map before and after the update.

Wherein, above-mentioned dynamic object can be personnel, vehicles etc, above-mentioned time interval can be according to the nimble setting of actual demand, this application embodiment does not limit, through this implementation mode, can discern the dynamic object that appears in warning region through the mode that the map is compared, on this basis, in order to reduce the operational load of controller, improve the operating efficiency in order can be more real-time to send out the warning, can only compare with discerning the dynamic object to the environmental map of above-mentioned warning regional within range, thereby reduce the operational load of controller, improve the operating efficiency, thereby can be more timely send out the warning, improve the real-time of safety warning.

In addition, because the dynamic object entering the construction site is single and obvious in characteristics, for example, a person can wear a safety helmet and a tool, and the vehicle is limited to only vehicles with obvious characteristics and few types, such as a transport vehicle and a mixer truck, on the basis of the above embodiment, the characteristic information of the dynamic object can be extracted in advance, after the dynamic object is identified, the characteristic matching is performed according to the characteristic information, so that the dynamic object and the type thereof can be further determined definitely.

In some variations, the warning area may include a circular area centered on the hook and a convex area along a moving direction of the hook. The round area is determined as the warning area so as to avoid damage to people or vehicles due to unhooking of materials at the current position, when the lifting hook moves, the unhooking of the materials in the lifting process can continue to move forwards to damage the people or the vehicles in front, and certain reaction time is needed when the alarm is given out and the people leave after hearing the alarm, so that the protruding area is determined as the warning area, a certain advance can be provided, the people and the vehicles entering the protruding area can leave in sufficient time, and the effectiveness of the alarm is improved.

In addition, to implement the alarm function, in some embodiments, the intelligent auxiliary robot further includes: the alarm module is connected with the control module;

the control module is specifically used for controlling the alarm module to give an alarm to the dynamic object after detecting that the dynamic object enters the warning area.

The alarm module can be realized by adopting a sound box, a loudspeaker and the like, alarm content can be recorded in advance when the alarm module is actually implemented, for example, voice content such as 'please note that you have entered a tower crane construction dangerous area and please keep away from a lifting hook' can be recorded, and then the alarm module can be controlled to broadcast the voice content, so that an alarm is sent to a dynamic object. According to the embodiment, the alarm can be effectively performed in a voice mode or the like, and the effectiveness of the alarm can be improved.

In any of the above embodiments, the intelligent auxiliary robot may further include: and the mobile module is used for driving the intelligent auxiliary robot to move on the ground around the intelligent tower crane under the control of the control module.

In addition, in order to further improve the intelligent level and the safety of the intelligent tower crane, the intelligent tower crane can also realize prediction and warning of tower crane collision accidents by configuring the following safety warning auxiliary system for the intelligent tower crane, so that the safety level of the intelligent tower crane is improved, and the following description is combined with an example.

In some embodiments, the realization intelligence tower crane is supplementary to control security warning system can include: a controller and a safety monitoring terminal of the intelligent tower crane;

the controller is used for acquiring state data and an operation instruction of the intelligent tower crane and sending the state data and the operation instruction to the remote monitoring terminal;

the safety monitoring terminal is in communication connection with the controllers of the intelligent tower cranes and is used for simulating the running states of the intelligent tower cranes in three-dimensional simulation software according to the state data and the control instructions sent by the controllers, predicting running tracks and feeding back safety warning information aiming at the control instructions to the controllers according to prediction results.

Compared with the prior art, the auxiliary control safety warning system for realizing the intelligent tower crane provided by the embodiment of the application is characterized in that the controller and the safety monitoring terminal of the intelligent tower crane are arranged, the controller is used for collecting the state data and the control instruction of the intelligent tower crane, and sending the state data and the control instruction to the remote monitoring terminal, the safety monitoring terminal is in communication connection with the controllers of the intelligent tower cranes and used for simulating the running states of the intelligent tower cranes and predicting the running tracks according to the state data and the control instruction sent by the controllers in three-dimensional simulation software, and feeding back the safety warning information aiming at the control instruction to the controller according to the prediction result, so that the safety monitoring terminal can be used for predicting the collision accidents of the intelligent tower cranes, the controller can reasonably process the control command according to the safety warning information, the intelligent tower crane is safely controlled, the collision accident is avoided, and the safety and the intelligence level of the intelligent tower crane are improved.

In some modification implementation manners of the embodiment of the application, the safety monitoring terminal is specifically configured to construct a real-time simulation scene in building information model BIM software according to the attribute information of each intelligent tower crane, and simulate the operation states of the plurality of intelligent tower cranes and predict the operation tracks according to the state data and the control instructions of the intelligent tower cranes in the simulation scene.

The attribute information can include, but is not limited to, position information, height information and specification model information of the intelligent tower crane, a real-time simulation scene can be constructed in BIM software of a building information model by utilizing the attribute information, the state data comprises amplitude variation action data, rotation action data, lifting action data and other data describing the real-time working state of the intelligent tower crane, the state data can be set in the BIM software to simulate the real-time motion state of the intelligent tower crane, on the basis, the control instruction can be input, the control instruction can be further executed to predict the operation track of the intelligent tower crane executing the control instruction, and whether the intelligent tower cranes have the collision risk can be predicted under the condition that the operation tracks of the intelligent tower cranes are all predicted.

In some modified embodiments of the present application, the safety warning information includes safety indication information and danger indication information;

the safety monitoring terminal is specifically used for generating safety indication information aiming at the control command under the condition that the intelligent tower crane cannot generate a collision accident according to the prediction result, and generating danger indication information aiming at the control command under the condition that the intelligent tower crane cannot generate a collision accident according to the prediction result.

On the basis of any of the above embodiments, in some modified embodiments, the controller does not execute immediately after generating the manipulation instruction;

the controller is further configured to trigger execution of the control instruction after receiving the safety indication information for the control instruction fed back by the safety monitoring terminal.

It should be noted that, the controller is used as a control mechanism of the intelligent tower crane, and the control instructions for each motion mechanism (e.g., a hoisting mechanism, a slewing mechanism, a luffing mechanism, an automatic hook, etc.) are generated by the controller.

On the other hand, in some embodiments, the controller does not execute immediately after generating the manipulation instruction;

the controller is further used for changing the control instruction and re-sending the control instruction to the safety monitoring terminal for prediction after receiving the danger indication information which is fed back by the safety monitoring terminal and aims at the control instruction.

In this embodiment, after receiving the danger indication information, the controller does not execute the control instruction, but changes the control instruction and sends the control instruction to the safety monitoring terminal again for prediction, and executes the corresponding control instruction after receiving the safety indication information, so that the controller can operate after ensuring safety, and avoid accidents caused by operation under the condition of potential safety hazards.

It should be noted that the above danger indication information may carry information such as an identifier and a position of the intelligent tower crane colliding with the intelligent tower crane, and information such as a time and a position of the collision, and the controller may change the control command according to the above information, for example, change a luffing distance in the control command to enable the trolley to approach the tower body to avoid the collision, change a lifting height in the control command to enable the material to rise to avoid the collision, change a rotation speed in the control command to slow down the rotation speed to avoid the collision, and the like.

In other embodiments, the controller executes immediately after generating the manipulation instruction;

The controller is further configured to trigger to stop executing the control instruction after receiving the danger indication information for the control instruction fed back by the safety monitoring terminal.

In this embodiment, the controller can carry out immediately after generating control command, stop again after receiving danger indicating information, can remove the time of waiting for safety indicating information from like this, improve the work efficiency of intelligent tower crane, and because the intelligence tower crane has certain time delay from beginning the operation to bumping, as long as safety monitoring terminal can accomplish the operation orbit prediction and return safety warning information in the delay time, can effectively avoid the emergence of collision accident, can compromise work efficiency when improving the security.

On the basis of any embodiment, in some modified embodiments, the safety monitoring terminal is further configured to generate yielding indication information according to a preset yielding rule and send the yielding indication information to the controller when the prediction result is that the intelligent tower crane is in a collision accident;

the controller is further used for delaying or slowing down the execution of the control instruction according to the yielding indication information.

The line-giving rule can be flexibly set according to actual requirements, for example, lifting late to give early lifting, lifting low to give high lifting and the like, the embodiment of the application is not limited, delayed execution refers to stopping operation of the intelligent tower crane and resuming operation after waiting for a period of time (can be flexibly set according to actual requirements and can also be determined in real time according to collision time information fed back by a safety monitoring terminal), and slow execution refers to slowing down the movement speed of a movement mechanism to avoid collision. The purpose of the embodiment of the application can be achieved by the two implementation manners, wherein the safety of delayed execution is higher, and the influence of avoidance on the operation efficiency can be reduced by slowing down execution, so that the avoidance can be realized with higher operation efficiency.

On the basis of any of the above embodiments, in some modification embodiments, the control safety warning system for realizing the assistance of the intelligent tower crane further comprises: a LoRa wireless communication module;

the controller with the loRa wireless communication module is connected, the loRa wireless communication module with safety monitoring terminal adopts the loRa wireless communication mode to connect.

Wherein, Long Range Radio (Long Range Radio) is meant to the LoRa, is an ultra-Long distance wireless transmission scheme based on spread spectrum technique, and its signal is strong, transmission distance is Long, ideal transmission distance is about 3 kilometers, and also pierce through, diffraction ability is strong, transmission process decay is less, easy network deployment, advantages such as with low costs, consequently, the working scene that is applicable to intelligent tower crane that can be good realizes the network deployment of many intelligent tower cranes and safety monitoring terminal, thereby obtain better signal transmission effect, the stability and the reliability of this scheme implementation are improved, this application embodiment does not do the restriction.

In addition, in order to further improve the intelligent and automatic level of above-mentioned intelligent tower crane, the below electromagnetic positioning device who is used for intelligent tower crane lifting hook direction that can also be through the configuration of intelligence tower crane, realizes the detection to material hoist and mount portion hoist and mount direction and the automatic direction rotation of lifting hook, makes the lifting hook aim at material hoist and mount portion fast and hoist, improves hoist and mount efficiency and degree of accuracy, improves the safety level of intelligent tower crane, explains in combination with the example below.

In some embodiments, the electromagnetic positioning device for intelligent tower crane hook guidance may include a controller, an electric rotating hook, and an electromagnetic positioning system; wherein the content of the first and second substances,

the electric rotary lifting hook and the electromagnetic positioning system are both connected with the controller;

the material side emitter of the electromagnetic positioning system is detachably arranged on a hoisting part of a material to be hoisted and emits a magnetic signal outwards;

the system electronic unit of the electromagnetic positioning system is used for determining the pose information of the material side emitter according to the magnetic force signal and sending the pose information of the material side emitter to the controller;

the controller is used for determining the hook feeding direction of the hoisting part according to the pose information of the material side emitter and controlling the electric rotary lifting hook to adjust the hook lifting direction according to the hook feeding direction.

Compared with the prior art, the electromagnetic positioning device for guiding the lifting hook of the intelligent tower crane provided by the embodiment of the application is provided with the controller, the electric rotary lifting hook and the electromagnetic positioning system; the electric rotary lifting hook and the electromagnetic positioning system are both connected with the controller; the material side emitter of the electromagnetic positioning system is detachably arranged on a hoisting part of a material to be hoisted and emits a magnetic signal outwards; the system electronic unit of the electromagnetic positioning system is used for determining the pose information of the material side emitter according to the magnetic force signal and sending the pose information of the material side emitter to the controller; the controller is used for determining the hook feeding direction of the hoisting part according to the pose information of the material side emitter and controlling the electric rotary lifting hook to adjust the hook lifting direction according to the hook feeding direction. Thereby can utilize electromagnetic positioning system to realize the detection to material hoist and mount portion hoist and mount direction to control the electronic rotatory lifting hook adjustment hook and hang the direction, realize the automatic direction rotation of lifting hook, make the lifting hook aim at material hoist and mount portion fast and hoist, improve hoist and mount efficiency and degree of accuracy, and then improve the intellectuality and the automation level of intelligent tower crane.

The controller is a controller of the intelligent tower crane, and can be realized by a computer host, a microcontroller, a Programmable Logic Controller (PLC) and the like, the electric rotary lifting hook is a lifting hook of the intelligent tower crane and can be realized by any electric control rotary lifting hook assembly provided by the prior art, the electromagnetic positioning system can be realized by any electromagnetic type position tracking system provided by the prior art, and the embodiment of the application is not limited.

In some variations of embodiments of the present application, the electromagnetic positioning system comprises a system electronics unit, a receiver, and a material side transmitter; wherein the content of the first and second substances,

the system electronic unit and the receiver are both arranged on the fixing part of the electric rotary hook;

the receiver is connected with the system electronic unit and used for collecting the magnetic force signal emitted by the material side emitter and sending collected information to the system electronic unit;

the system electronic unit is used for calculating the pose information of the material side emitter according to the acquisition information and the position information of the receiver and sending the pose information to the controller.

The material side emitter is an emitter arranged on one side of the material, and the emitter can be arranged on the material hoisting part so as to determine the hook inlet direction of the hoisting part according to the pose information of the emitter.

Taking the electromagnetic positioning system to be realized by adopting a Polhemus LIBERTY LATUS wireless large-range tracking system as an example, the Polhemus LIBERTY LATUS wireless large-range tracking system is a wireless 6-degree-of-freedom magnetic force tracking solution, can track 12 independent markers (namely emitters) at most, has the characteristics of wide tracking range and high speed and simplicity in use, is provided with an intuitive user interface and a perfect software development kit, can output data to controllers such as a computer host, a microcontroller, a PLC and the like, has extremely high stability and extremely low signal-to-noise ratio, and can provide consistent high-quality data.

The specific manner of calculating the pose information of the material side emitter by the system electronic unit according to the acquired magnetic signal and the position information of the receiver can be realized by referring to a positioning algorithm based on the magnetic signal provided by the prior art, the specific algorithm is not limited in the embodiment of the application, and when the specific algorithm is specifically implemented, the specific algorithm can be directly realized by adopting the existing product (such as the Polhemus LIBERTY LATUS wireless large-range tracking system) without paying attention to the specific positioning algorithm.

Taking the above-mentioned Polhemus LIBERTY LATUS wireless wide-range tracking system as an example, in some modified embodiments, the material side transmitter includes a triplet electromagnetic source, a control circuit, and a power supply battery;

The power supply battery is used for supplying power to the triple electromagnetic source and the control circuit;

the control circuit is used for controlling the triple electromagnetic source to emit a magnetic force signal outwards.

Through above-mentioned embodiment, can adopt the transmitter of independent power supply and work to realize the transmission of magnetic signal, the transmitter need not to carry out the physics cable with system electronic unit and is connected for ground staff can make things convenient for at any time, swiftly install the transmitter in the hoist and mount portion of material, thereby has higher convenience and flexibility.

On the basis of the above embodiment, in some modified embodiments, the receiver includes a triplet electromagnetic receiving element for detecting the magnetic signal emitted by the material side emitter;

and the system electronic unit is used for calculating the 6-degree-of-freedom pose information of the material side emitter according to the magnetic force signal and the position information of the receiver.

Because the emitter and the receiver both adopt triple electromagnetic elements, the system electronic unit can calculate and obtain pose information with 6 degrees of freedom, including the position (X, Y, Z Cartesian coordinates) and the direction (azimuth angle, elevation angle and rolling angle) of the emitter, so that a plurality of modes are provided for determining the hook entering direction of the hoisting part, and abundant and flexible implementation modes are provided for being suitable for various complex scenes.

For example, if the material side emitter is mounted on the sling at a fixed angle (parallel or perpendicular to the hook-in direction), then the hook-in direction of the sling can be determined using the azimuth angle of one material side emitter; in addition, the hook-entering direction of the hoisting part can also be determined by utilizing the position information of a plurality of material side emitters and the respective emitters, such as:

in some variations, the number of material side emitters is two;

the two material side emitters are respectively arranged on two sides of a hoisting position on the hoisting part, and the connecting line of the two material side emitters is vertical to the hook inlet direction;

the controller is specifically used for determining a connecting line between the two material side emitters according to the pose information of the two material side emitters, making a vertical line from the position of the electric rotating lifting hook to the connecting line along the horizontal direction, and determining the hook inlet direction of the lifting part according to the direction of the vertical line.

Through this embodiment, can utilize two material side transmitters to realize the sign and the affirmation of the hook direction of advancing to hoist and mount portion, consider that material side transmitter has certain volume, be not convenient for stably be fixed in the rope, hoist and mount portions such as rings are last, the direction angle that is difficult to adopt the transmitter confirms the hook direction of advancing, to this kind of situation, can be at two material side transmitters of rope hoist and mount position both sides installation, thus, material side transmitter can bind with arbitrary gesture, hang and lean on, attached on the rope and need not to stabilize its gesture, then the position information who utilizes two transmitters can determine the hook direction of advancing, the operation is convenient, easy to implement, the higher advantage of the degree of accuracy.

On the basis of any of the above embodiments, in some variations, the electromagnetic positioning system further comprises a hook-side transmitter;

the lifting hook side emitter is fixedly arranged on the electric rotary lifting hook and used for emitting a magnetic signal outwards;

the receiver is also used for acquiring a magnetic signal transmitted by the emitter at the side of the lifting hook and sending acquired information to the system electronic unit;

the system electronic unit is used for calculating the pose information of the hook side emitter according to the magnetic force signal emitted by the hook side emitter and the position information of the receiver, and sending the pose information of the hook side emitter to the controller;

the controller is further used for determining the current hooking direction of the electric rotary lifting hook according to the pose information of the side emitter of the lifting hook, and controlling the electric rotary lifting hook to adjust the hooking direction to be consistent with the hooking direction according to the hooking direction.

Considering that the purpose of rotating the lifting hook is to make the lifting hook lifting direction consistent with the hook feeding direction of the lifting part, therefore, the real-time lifting hook direction of the lifting hook needs to be determined, and by the above manner, the current lifting hook direction of the electric rotating lifting hook can be determined by using the lifting hook side emitter, so that the electric rotating lifting hook is controlled according to the hook feeding direction to adjust the lifting hook lifting direction to be consistent with the hook feeding direction, and the automatic steering and alignment of the lifting hook are realized.

It should be noted that, according to the above-mentioned embodiment, the coordinate systems of the hooking direction and the hooking direction can be unified, that is, both the hooking direction and the hooking direction are obtained in a cartesian coordinate system, so that the pertinence and accuracy of the hook turning are improved, and the coincidence between the hooking direction and the hooking direction is ensured.

In addition to the above-described embodiments, in some modified embodiments, the hook-side transmitter is fixedly attached to a hook body of the electric rotating hook;

the controller is specifically configured to calculate a current hooking direction of the electric rotating hook according to the relative pose information of the hook side emitter and the hook body and the pose information of the hook side emitter.

Through this embodiment, can with lifting hook side transmitter fixed mounting in on the coupler body of electrical rotary lifting hook to can be simple, the direct position appearance information calculation that utilizes lifting hook side transmitter obtains the current hook of lifting hook and hang the direction.

In view of the fact that the transmitter is easily damaged by a collision or the like if the transmitter is mounted on the hook body, in other modified embodiments, the hook-side transmitter is fixedly mounted on the fixing portion of the electric rotating hook;

The electric rotary lifting hook comprises an electric cabinet, a motor, a gear and a hook body which are arranged on the fixed part;

the gear is sleeved on the handle part of the hook body and is meshed with the output gear of the motor;

the electric cabinet is electrically connected with the motor and used for controlling the motor to rotate so as to drive the hook body to rotate through the gear, calculating relative orientation information of the hook body relative to the fixing part according to the rotation position information of the motor and the gear ratio of the output gear of the motor to the gear sleeved on the handle part of the hook body, and sending the relative orientation information to the controller;

the controller is specifically configured to calculate a current hooking direction of the electric rotating hook according to the relative orientation information, the pose information of the hook-side emitter, and the relative pose information between the hook-side emitter and the fixing portion.

The motor can be realized by a servo motor or a stepping motor, and the embodiment of the application is not limited.

Through this embodiment, also can realize the unanimity of hook hanging direction and advance hook direction under the same coordinate system to realize the accurate rotation and the counterpoint of lifting hook, and can effectively reduce the probability that the transmitter is damaged by the collision, improve life and system stability.

It should be noted that the electromagnetic positioning device for guiding the lifting hook of the intelligent tower crane provided in the embodiment of the present application may be matched with or compatibly implemented by the sensing internet of things device for detecting the taking and placing motion of the intelligent tower crane provided in the foregoing embodiment of the present application, for example, the following sensing internet of things device for detecting the taking and placing motion of the intelligent tower crane is first used to control the lifting hook (i.e., an automatic lifting hook, including the above-mentioned electric rotating lifting hook) to move to a position near the material lifting part, and then the electromagnetic positioning device for guiding the lifting hook of the intelligent tower crane provided in the embodiment of the present application is used to adjust the lifting direction of the lifting hook so as to align the lifting part, and then the lifting hook is controlled to automatically hook and lift the material.

In addition, in order to further improve the intellectuality and the automation level of above-mentioned intelligent tower crane, the automatic characteristic identification device of material that the intelligence tower crane can also be used for unmanned intelligent tower crane through the configuration below realizes the automated inspection of intelligence tower crane to the material to judge whether the material accords with the hoist and mount condition in order to carry out quick, accurate hoist and mount, and then improve the intellectuality of intelligence tower crane, automation level and security, explain below combining the example.

In some embodiments, the automatic material characteristic recognition device for the unmanned intelligent tower crane may include:

the image acquisition module is used for acquiring a global image of the working scene of the intelligent tower crane shot by a global camera;

the image identification module is used for identifying carriers and materials in the global image through an image identification algorithm;

the material type determining module is used for determining the type of the material according to preset carrier use conditions, wherein different carriers are used for loading different types of materials;

the shielding judgment module is used for judging whether the identified material is shielded or not according to the standard size of the type of material;

and the hoisting condition judgment module is used for determining that the material meets the hoisting condition under the condition that the material is judged not to be shielded.

In some variations of embodiments of the present application, the image recognition module includes:

the characteristic matching unit is used for identifying a carrier in the global image through image characteristic matching, wherein the carrier has image characteristics different from the material and the surrounding environment;

a growing point determining unit, configured to determine a growing point within the carrier bearing range of the global image;

And the region growing unit is used for dividing and determining the material in the global image by using a region growing method based on the growing point.

In some variations of the embodiments of the present application, the growth point determining unit includes:

a growing point determining subunit, configured to query a preset growing point selection mapping table for growing point selection information corresponding to the type of the material, where the growing point selection information corresponding to different material types is recorded in the growing point position mapping table, and the growing point positions, sizes, or numbers corresponding to different material types are different;

and determining growth points in the carrier bearing range of the global image according to the growth point selection information.

In some modifications of the embodiments of the present application, if the determined number of the growth points is plural, the region growing unit includes:

the multi-region growth subunit is used for dividing a plurality of growth regions in the global image by using a region growth method respectively by taking each growth point as a reference;

and the multi-region merging subunit is used for merging the plurality of growing regions to determine the material.

In some modifications of the embodiments of the present application, the occlusion determination module includes:

an actual size calculation unit for calculating the actual size of the identified material;

the standard size query unit is used for querying the standard size of the type of material from a preset material information base;

and the size comparison unit is used for comparing the difference between the actual size and the standard size, if the difference is smaller than a preset threshold value, judging that the material is not shielded, otherwise, judging that the material is shielded.

According to the automatic material characteristic recognition device for the unmanned intelligent tower crane, a global image of a working scene of the intelligent tower crane, which is shot by a global camera, is obtained, a carrier and a material are recognized in the global image through an image recognition algorithm, and the type of the material is determined according to preset carrier use conditions, wherein different carriers are used for loading different types of materials; judging whether the identified material is shielded or not according to the standard size of the type of material; and under the condition that the material is judged not to be shielded, determining that the material meets the hoisting condition. Thereby can be accurate discernment material and judge whether it is sheltered from, and then confirm whether the material accords with the hoist and mount condition in order to carry out quick, accurate hoist and mount, and then improve intelligent, the automation level and the security of intelligent tower crane.

In addition, in order to further improve the intellectualization and automation level of the above-mentioned intelligent tower crane, the intelligent tower crane can also realize the automatic detection of the intelligent tower crane to the material stacking state for fast and accurate hoisting by configuring the following material stacking space image recognition and analysis device for the intelligent tower crane, so as to improve the intellectualization, automation level and safety of the intelligent tower crane, which is described below by combining with examples.

In some embodiments, the material stacking space image recognition and analysis device for the intelligent tower crane may include:

the intelligent auxiliary robot is arranged on the ground near the intelligent tower crane and moves near the intelligent tower crane to acquire the working scene image;

the system comprises a global image acquisition module, a global camera and a control module, wherein the global image acquisition module is used for acquiring a global image of a working scene of the intelligent tower crane, which is shot by the global camera, and the global camera is arranged on a crane arm of the intelligent tower crane downwards;

the material identification module is used for identifying materials in each working scene image and the global image through an image identification algorithm and determining the size and the stacking position of each material;

And the three-dimensional reconstruction module is used for performing three-dimensional reconstruction after the working scene image and the global image are fused to obtain a three-dimensional simulation scene, and marking the material in the three-dimensional simulation scene according to the size and the stacking position of the material.

In some variations of the embodiments of the present application, the global image obtaining module includes:

the rotary shooting unit is used for controlling the crane boom to rotate and controlling the global camera to shoot a plurality of images in the rotating process;

and the image merging unit is used for merging the plurality of images to generate a global image of the working scene of the intelligent tower crane.

In some modified embodiments of the embodiment of the application, each pile of the materials is loaded by a corresponding carrier and then is stacked on a working site of the intelligent tower crane, and the carrier has image characteristics different from the materials and the surrounding environment;

the material identification module comprises:

the carrier identification unit is used for identifying the carrier in each working scene image and the global image through image feature matching according to the image features of the carrier;

and the spatial position determining unit is used for segmenting the stacked materials according to the positions of the carriers after the stacked materials are identified so as to determine the spatial position of each stack of the materials.

In some variations of the embodiments of the present application, the material identification module further includes:

a growing point determining unit, configured to determine a growing point within the carrier bearing range for each of the working scene images and the global image;

and the regional growth unit is used for respectively segmenting and determining the materials in the working scene image and the global image by using a regional growth method based on the growth point.

In some modified embodiments of the present application, the working scene image and the global image are both collected by using a binocular camera or a TOF depth camera and carry depth-of-field information;

the three-dimensional reconstruction module comprises:

the global image processing unit is used for constructing a three-dimensional simulation model corresponding to the global image in three-dimensional simulation software according to the depth of field information carried by the global image and the position of the global camera when the global image is shot;

and the scene image fusion unit is used for projecting each pixel point in the working scene image into the three-dimensional simulation model according to the depth of field information carried by the working scene image and the position of the camera of the intelligent auxiliary robot when the working scene image is shot, so as to obtain a three-dimensional simulation scene.

The material stacking space image recognition and analysis device for the intelligent tower crane, provided by the embodiment of the application, obtains the working scene image obtained by scanning the intelligent auxiliary robot on the ground, wherein the intelligent auxiliary robot is arranged on the ground near the intelligent tower crane and moves near the intelligent tower crane to collect the working scene image and acquire the global image of the working scene of the intelligent tower crane shot by a global camera, wherein the global camera is arranged on a crane arm of the intelligent tower crane downwards, materials are identified in each working scene image and the global image through an image identification algorithm, the size and the stacking position of each material are determined, performing three-dimensional reconstruction after the working scene image and the global image are fused to obtain a three-dimensional simulation scene, and marking the material in the three-dimensional simulation scene according to the size and the stacking position of the material. Thereby can realize that intelligent tower crane is in order to carry out quick, accurate hoist and mount to the automated inspection of material pile state, and then improve intelligent, the automation level and the security of intelligent tower crane.

In addition, in order to further improve the intellectualization and automation level of the intelligent tower crane, the intelligent tower crane can also realize the automatic planning of a material transmission path by configuring the following material transmission optimized path planning system for the intelligent tower crane, so as to avoid collision accidents, and further improve the intellectualization, automation level and safety of the intelligent tower crane, which is described in combination with an example below.

In some embodiments, the material transportation optimization path planning system for an intelligent tower crane may include:

the scene plan acquisition module is used for acquiring a plan of a working scene of the intelligent tower crane; wherein, the plan can be the global image of the working scene of the intelligent tower crane.

The gray layer determining module is used for determining a gray layer based on the plane map, wherein the gray layer comprises a plurality of gray areas, and the gray value of each gray area is in direct proportion to the height of the collision hidden danger object on the plane map;

the gray scale region selection module is used for selecting the gray scale region according to the priority of the gray scale value from low to high until the selected gray scale region is communicated with the starting point and the end point of material hoisting, and marking the selected combination region of the gray scale region as a safe transmission region;

and the transmission path determining module is used for determining the transmission path of the material according to the safe transmission area.

In some modified embodiments of the embodiment of the present application, the grayscale layer determining module includes:

a blank layer building unit, configured to build a blank layer on the planar graph;

the blank layer dividing unit is used for dividing each blank layer into a plurality of blank regions corresponding to coverage regions of the collision hidden danger objects on the plane diagram, wherein the collision hidden danger objects comprise buildings and other intelligent tower cranes around the intelligent tower crane;

And the gray value assignment unit is used for assigning gray values to the blank areas according to the heights of the collision hidden danger objects to form a plurality of gray regions, wherein the gray regions form a gray layer, and the gray value of the gray region corresponding to the collision hidden danger object with the higher height is higher.

In some variations of the embodiments of the present application, the apparatus further comprises:

and the collision hidden danger information determining module is used for determining the height and the coverage area of each collision hidden danger object below the lifting arm in the coverage range of the intelligent tower crane based on the plane diagram.

In some variations of the embodiments of the present application, the transmission path determining module includes:

and the transmission path determining unit is used for determining a material transmission path connecting a material hoisting starting point and a material hoisting end point according to a side line of the safe transmission area close to one side of the intelligent tower crane.

In some variation implementations of the embodiment of the present application, the material conveying path is located in the safe conveying area, a distance between the material conveying path and the side line is greater than a safe distance, and the safe distance is determined according to a radius of a circumscribed circle of the material.

The material transmission optimization path planning system for the intelligent tower crane, provided by the embodiment of the application, is used for obtaining a plan view of a working scene of the intelligent tower crane; determining a gray scale layer based on the plane map, wherein the gray scale layer comprises a plurality of gray scale regions, and the gray scale value of each gray scale region is in direct proportion to the height of the collision potential hazard on the plane map; selecting the gray areas according to the priority of the gray values from low to high until the selected gray areas are communicated with the starting point and the end point of material lifting, and marking the combined area of the selected gray areas as a safe transmission area; and determining a conveying path of the material according to the safe conveying area. Thereby can realize the automatic planning of material transmission route to avoid the collision accident, and then improve intelligent, the automation level and the security of intelligent tower crane.

In addition, in order to further improve the intellectualization and automation level of the intelligent tower crane, the intelligent tower crane can also realize centralized management and remote monitoring of the operation information of the intelligent tower crane by configuring the following cloud information system for the operation data of the intelligent tower crane, so that the automation and intelligence level of the intelligent tower crane is improved, and the following description is combined with an example.

In some embodiments, the cloud information system for intelligent tower crane operation data may include: the system comprises a controller, an LoRa wireless communication module, a LoRa gateway, a cloud server and a remote monitoring terminal, wherein the controller and the LoRa wireless communication module are arranged on an intelligent tower crane;

the controller is connected with the LoRa wireless communication modules, and the LoRa wireless communication modules of the multiple intelligent tower cranes are connected with the LoRa gateway based on a LoRa wireless communication protocol;

the LoRa gateway is arranged in a construction site of the intelligent tower cranes and is connected with the cloud server through a cellular network and/or a broadband network;

the controller is used for acquiring the operation data of the intelligent tower crane and sending the operation data to the cloud server for storage through the LoRa wireless communication module and the LoRa gateway;

the remote monitoring terminal is connected with the cloud server through a cellular network and/or a broadband network and used for acquiring the operation data of the intelligent tower crane from the cloud server and displaying the operation data.

Compared with the prior art, the cloud information system for the intelligent tower crane operation data provided by the embodiment of the application is provided with the controller, the LoRa wireless communication module, the LoRa gateway, the cloud server and the remote monitoring terminal; the controller is connected with the LoRa wireless communication modules, and the LoRa wireless communication modules of the multiple intelligent tower cranes are connected with the LoRa gateway based on a LoRa wireless communication protocol; the LoRa gateway is arranged in a construction site of the intelligent tower cranes and is connected with the cloud server through a cellular network and/or a broadband network; the controller is used for acquiring the operation data of the intelligent tower crane and sending the operation data to the cloud server for storage through the LoRa wireless communication module and the LoRa gateway; the remote monitoring terminal is connected with the cloud server through a cellular network and/or a broadband network and used for acquiring the operation data of the intelligent tower crane from the cloud server and displaying the operation data. Thereby can improve the automation and the intelligent level of intelligent tower crane to intelligent tower crane operation information's centralized management and remote monitoring.

In some modified embodiments of the embodiment of the present application, the controller is specifically configured to write the operation data into a JSON file and then send the operation data to the cloud server;

and the cloud server is used for analyzing the JSON file to obtain the operating data of the intelligent tower crane and storing the operating data.

In some modified embodiments of the embodiment of the present application, the working frequency band of the LoRa wireless communication module adopts a 470-510MHz frequency band.

In some modification implementations of the embodiment of the application, the operation data includes idle state data, amplitude variation action data, rotation action data, lifting action data of the intelligent tower crane, and the category and weight of the material hoisted each time.

In some change implementation manners of this application embodiment, high in the clouds server still is used for the basis work load information in the time quantum is predetermine to every intelligent tower crane of operational data statistics, and will work load information sends for remote monitoring terminal demonstrates, wherein, work load information includes the classification and the weight of the material of having hoisted the completion in the time quantum of predetermineeing.

In some modified implementation manners of the embodiment of the application, the intelligent tower crane is provided with a camera group connected with the controller, the camera group is used for acquiring a material image group of a material hoisted by the intelligent tower crane and sending the material image group to the controller, the camera group comprises a plurality of cameras with different shooting angles, and the material image group comprises material images acquired by each camera for the material;

In some modified embodiments of the embodiment of the present application, a guide pulley mechanism of the intelligent tower crane is provided with a force sensor;

In some change implementation modes of the embodiment of the application, the remote monitoring terminal is further used for establishing a real-time simulation scene in building information model BIM software according to the attribute information of the intelligent tower crane and the operation data, and displaying the simulation scene through the display, so that a user can know the operation information of the intelligent tower crane through watching the simulation scene, wherein the attribute information comprises the position information, the height information and the specification model information of the intelligent tower crane.

In addition, in order to further improve the intelligent and automatic level of the intelligent tower crane, the intelligent tower crane can also realize the cooperative control of the intelligent tower crane cluster to avoid collision accidents by configuring the following intelligent tower crane cluster cooperative control system for the task temporal model, so that the intelligent and automatic level and safety of the intelligent tower crane are improved, and the description is given by combining with an example.

In some embodiments, the intelligent tower crane cluster cooperative control system for the task temporal model may include:

the system comprises a to-be-executed task obtaining module, a task information collecting module and a task executing module, wherein the to-be-executed task obtaining module is used for obtaining task information of a latest to-be-executed task of each intelligent tower crane in an intelligent tower crane cluster to obtain a task information set, and the task information comprises task execution time and an action area of the intelligent tower crane executing the to-be-executed task;

the conflict information determining module is used for determining task conflict information of each intelligent tower crane according to the task information set;

the task ordering module is used for ordering the tasks to be executed of each intelligent tower crane according to the task conflict information to obtain a task queue;

and the task execution module is used for controlling each intelligent tower crane to execute the corresponding task to be executed according to the task queue.

In some modified embodiments of the present application, the task conflict information includes a task conflict amount;

the conflict information determination module includes:

and the conflict information determining unit is used for sequentially judging whether the action area corresponding to each intelligent tower crane exists in an intersection area or not with the action area corresponding to other intelligent tower cranes around, if the intersection area does not exist, determining that the task conflict quantity of the intelligent tower crane is zero, if the intersection area exists, determining that the task execution time of the intelligent tower crane and other intelligent tower cranes with the intersection area exists in the intersection area or not, if the intersection time does not exist, determining that the task conflict quantity of the intelligent tower crane is zero, and if the intersection time exists, recording the quantity of the intersection time as the task conflict quantity.

In some variations of the embodiments of the present application, the task ordering module includes:

the primary sequencing unit is used for primarily sequencing the tasks to be executed of each intelligent tower crane according to the sequence of the task conflict quantity from small to large, and grouping the tasks to be executed according to the difference of the task conflict quantity to obtain a task queue;

the secondary sequencing unit is used for carrying out secondary sequencing on the tasks to be executed according to the sequence of the time length of the cross time from short to long in the group aiming at the tasks to be executed, wherein the number of task conflicts in each group is nonzero and the same;

And the third sequencing unit is used for carrying out third sequencing on the adjacent tasks to be executed with the same cross time after the second sequencing according to the sequence of the task execution time of the tasks to be executed from short to long to obtain a task queue.

In some variations of the embodiments of the present application, the task execution module includes:

and the task execution unit is used for traversing each task to be executed in the task queue, if the task conflict quantity corresponding to the task to be executed is zero, triggering to immediately execute the task to be executed, if the task conflict quantity corresponding to the task to be executed is non-zero, judging whether other tasks to be executed which conflict with the task to be executed are executed in advance and are not executed completely, if not, triggering to immediately execute the task to be executed, and if not, temporarily not executing the task to be executed and skipping the task to be executed.

In some variations of embodiments of the present application, the apparatus further comprises:

the execution condition monitoring module is used for monitoring the execution condition of each task to be executed in the task queue;

and the execution completion processing module is used for deleting the tasks to be executed from the task queue after the completion of the execution of any one of the tasks to be executed is monitored, and triggering and executing the step of acquiring the task information of the latest task to be executed of each intelligent tower crane in the intelligent tower crane cluster.

According to the intelligent tower crane cluster cooperative control system for the task temporal model, a task information set is obtained by obtaining task information of a latest task to be executed of each intelligent tower crane in an intelligent tower crane cluster, wherein the task information comprises task execution time and an action area of the intelligent tower crane executing the task to be executed; determining task conflict information of each intelligent tower crane according to the task information set; sequencing the tasks to be executed of each intelligent tower crane according to the task conflict information to obtain a task queue; and controlling each intelligent tower crane to execute the corresponding task to be executed according to the task queue. Therefore, cooperative control of the intelligent tower crane cluster can be achieved to avoid collision accidents, and further the intelligence, automation level and safety of the intelligent tower crane are improved.

It should be noted that the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that contribute to the prior art in essence can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present disclosure, and the present disclosure should be construed as being covered by the claims and the specification.

Claims

1. The utility model provides an intelligent monitoring early warning system based on tower crane gyration action model which characterized in that includes: a controller, a gravity sensor and a wind sensor; wherein the content of the first and second substances,

2. The intelligent monitoring and early warning system based on the tower crane rotation action model as claimed in claim 1, wherein the rotation angle calculation parameters of the intelligent tower crane further comprise the output torque, the amplitude variation position and the windward sectional area of the material of the rotation mechanism of the intelligent tower crane.

3. The intelligent monitoring and early warning system based on the tower crane slewing motion model as claimed in claim 2, wherein the controller is specifically configured to calculate wind force borne by the material according to a cross-sectional area in a windward direction of the material and wind speed information in the wind force information, calculate a wind resistance torque of the material according to the wind force borne by the material and the amplitude variation position, calculate a rotational torque of the material according to the output torque and the wind resistance torque, and calculate a theoretical slewing angle within a preset time period according to the rotational torque and the weight of the material.

4. The intelligent monitoring and early warning system based on the tower crane rotation action model is characterized in that the controller is further used for determining the windward sectional area of the material according to the pre-collected three-dimensional image of the material, the orientation information of the material acquired in real time and the wind direction information in the wind power information.

5. The intelligent monitoring and early warning system based on tower crane rotation action model according to claim 4, further comprising: the intelligent auxiliary robot is used for detecting the working condition of the intelligent tower crane;

the intelligent auxiliary robot is in communication connection with the controller and used for moving around the material on the ground to shoot a three-dimensional image of the material and sending the three-dimensional image to the controller.

6. The intelligent monitoring and early warning system based on tower crane rotation action model according to claim 4, further comprising: a global camera connected to the controller;

the controller is further used for identifying orientation information of the materials in the working scene in real time according to the global image.

7. The intelligent monitoring and early warning system based on the tower crane slewing action model as claimed in claim 1, wherein the controller is specifically configured to normalize the slewing angle calculation parameter and input the normalized value into a preset slewing action model, and output a theoretical slewing angle after a preset duration by using the slewing action model, wherein the slewing action model is obtained by pre-training normal tower crane operation data based on a neural network.

8. The intelligent monitoring and early warning system based on the tower crane slewing motion model according to claim 7, it is characterized in that the controller is also used for acquiring a plurality of groups of operating data of the normal tower crane, each group of operating data comprises a rotation angle calculation parameter of the intelligent tower crane acquired at any moment, and the actual rotation angle after the time length is preset at any time, and after normalization processing is carried out on each rotation angle calculation parameter in each set of running data, the rotation angle calculation parameters and the actual rotation angle in the set of running data are used as a set of training samples, a neural network is trained by utilizing a plurality of sets of the training samples to obtain a rotation action model, and calculating parameters of the rotation angle after normalization processing in each group of training samples as input factors of the neural network, wherein the actual rotation angle in each group of training samples is used as an output factor of the neural network.

9. An anomaly perception method is characterized by being used for the intelligent monitoring and early warning system based on the tower crane slewing motion model according to any one of claims 1 to 8, and the method comprises the following steps:

10. An intelligent tower crane, which is characterized in that the intelligent tower crane is provided with the intelligent monitoring and early warning system based on the tower crane rotation action model according to any one of claims 1 to 8.