CN113911915B

CN113911915B - Sensing Internet of things system and method for sensing abnormal lifting state of intelligent tower crane

Info

Publication number: CN113911915B
Application number: CN202111071062.8A
Authority: CN
Inventors: 陈德木; 蒋云; 赵晓东; 陆建江; 陈曦
Original assignee: Hangzhou Dajie Intelligent Transmission Technology Co Ltd
Current assignee: Hangzhou Dajie Intelligent Transmission Technology Co Ltd
Priority date: 2021-09-13
Filing date: 2021-09-13
Publication date: 2023-06-02
Anticipated expiration: 2041-09-13
Also published as: CN113911915A

Abstract

The application provides a sensing Internet of things system and method for sensing abnormal lifting state of an intelligent tower crane and the tower crane. Wherein, sensing thing networking system includes: a controller and an attitude sensor in communication with the controller; the attitude sensor is fixedly arranged on the tower crane lifting hook and is used for acquiring the attitude data of the tower crane lifting hook in real time and sending the attitude data to the controller; the controller determines inclination information and 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 according to the inclination information and the swing information. The attitude data of the tower crane lifting hook is utilized to accurately judge whether the lifting state of the tower crane is abnormal or not, so that targeted treatment is timely carried out when the abnormality is detected, the scattering of cargoes is avoided, workers are injured by smashing, and the safety accident occurrence rate caused by the scattering of the lifted cargoes in the lifting stage of the tower crane is reduced.

Description

Sensing Internet of things system and method for sensing abnormal lifting state of intelligent tower crane

Technical Field

The application relates to the technical field of intelligent tower cranes, in particular to a sensing Internet of things system and method for sensing abnormal lifting states of an intelligent tower crane and the intelligent tower crane.

Background

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

However, because the traditional tower crane has higher height, the visual blind area of a tower crane driver is more, a plurality of operations are required to be completed by means of ground commanders, the tower crane driver is required to hoist materials, and the operations can be completed only by repeatedly coordinating and communicating with the tower crane driver through interphones, when goods are hoisted, the problems of falling of a lifting hook, falling of a rope and the like often occur in the lifting process due to the reasons of loose binding, inaccurate hooking and the like of the goods, so that the goods are scattered to injure ground staff such as the cable worker, the command and the like, and safety accidents are caused.

Therefore, a technical scheme capable of effectively detecting lifting abnormality of the tower crane to reduce lifting safety accidents of the tower crane is needed.

Disclosure of Invention

The application aims to provide a sensing Internet of things system and method for sensing abnormal lifting state of an intelligent tower crane and the tower crane.

The first aspect of the application provides a sensing internet of things system for sensing abnormal lifting state of an intelligent tower crane, which comprises: a controller and an attitude sensor in communication with the controller;

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

the controller determines inclination information and 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 according to the inclination information and the swing information.

The second aspect of the application provides an intelligent tower crane, wherein the intelligent tower crane is provided with the sensing internet of things system for sensing the abnormal lifting state of the intelligent tower crane.

A third aspect of the present application provides a method for sensing an abnormal lifting state of an intelligent tower crane, including:

acquiring attitude data of a tower crane lifting hook acquired by an attitude sensor, wherein the attitude sensor is fixedly arranged on the tower crane lifting hook;

determining inclination information and swing information of the tower crane lifting hook according to the attitude data;

judging whether the lifting state of the tower crane lifting hook is abnormal or not according to the inclination information and the swing information.

Compared with the prior art, the sensing internet of things system for sensing the abnormal lifting state of the intelligent tower crane is provided, the controller is arranged, the attitude sensor is in communication connection with the controller, the attitude sensor is fixedly arranged on the tower crane lifting hook and is used for collecting the attitude data of the tower crane 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 tower crane lifting hook according to the attitude data and judges whether the lifting state of the tower crane lifting hook is abnormal according to the inclination information and the swing information. Because the lifting hook often generates a larger inclination angle or shakes greatly before the lifting hook falls off and the rope falls off, the lifting hook can accurately judge whether the lifting state of the tower crane is abnormal or not by utilizing the attitude data of the lifting hook of the tower crane, so that targeted treatment is timely carried out when the abnormality is detected, the scattering of cargoes is avoided, the injury to workers is avoided, and the safety accident occurrence rate caused by the scattering of the cargoes in the lifting stage of the tower crane is reduced.

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 designate like parts throughout the figures. In the drawings:

fig. 1 illustrates a schematic structural diagram of a sensor internet of things system for sensing abnormal lifting states of an intelligent tower crane according to some embodiments of the present application;

FIG. 2 illustrates a schematic view of a rotational direction of a smart tower crane hook provided by some embodiments of the present application;

FIG. 3 illustrates a flow chart of a method for intelligent tower crane lifting anomaly state awareness provided by some embodiments of the present application;

fig. 4 illustrates a schematic structural diagram of an intelligent tower crane according to 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 noted that unless otherwise indicated, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs.

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

The embodiment of the application provides a sensing internet of things system and method for sensing abnormal lifting states of an intelligent tower crane and the tower crane, and the sensing internet of things system and method and the tower crane are described in an exemplary mode with reference to the embodiment and the accompanying drawings.

Please refer to fig. 1, which illustrates a schematic structural diagram of a sensing internet of things system for sensing abnormal lifting states of an intelligent tower crane according to some embodiments of the present application, as shown in fig. 1, the sensing internet of things system for sensing abnormal lifting states of an intelligent tower crane may include: a controller 101 and an attitude sensor 102 communicatively connected to the controller 101;

As can be understood by combining the schematic structural diagram of the tower crane shown in fig. 4, the attitude sensor 102 is fixedly disposed on the tower crane hook, and is configured to collect the attitude data of the tower crane hook in real time and send the attitude data to the controller 101;

the controller 101 determines inclination information and 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 according to the inclination information and the swing information.

The controller 101 may be implemented by a host computer, a microcontroller, a programmable logic controller PLC, etc., and the gesture sensor 102 may be implemented by a motion sensor including but not limited to a tri-axis gyroscope, a tri-axis accelerometer, a tri-axis electronic compass, etc., which is not limited in this embodiment.

It should be noted that, if the tower crane is an unmanned tower crane, the controller 101 may be disposed on a console on the ground, and a display screen and/or a sound connected to the controller 101 is disposed on the console, so as to broadcast whether the lifting state of the tower crane lifting hook is abnormal in an image and/or voice manner, so that a tower crane operator can know whether the lifting state of the tower crane lifting hook is abnormal.

In addition, the controller 101 and the attitude sensor 102 may be connected in a wireless manner or in a wired manner, so that the safety accident may be caused by signal interruption and error in consideration of relatively poor stability of wireless signals, in some embodiments, the attitude sensor 102 and the controller 101 are preferably connected in a wired manner by using a cable, and the cable may be specifically connected to a console on the ground along a crane arm and a standard section and connected to a controller on the console, thereby improving signal quality and stability, and avoiding abnormal lifting state of a tower crane hook which cannot be found in time due to signal problems, thereby causing the safety accident.

Compared with the prior art, the sensor internet of things system for sensing the abnormal lifting state of the intelligent tower crane is provided, the controller 101 is arranged, the attitude sensor 102 is in communication connection with the controller 101, the attitude sensor 102 is fixedly arranged on the tower crane lifting hook and is used for collecting the attitude data of the tower crane lifting hook in real time and sending the attitude data to the controller 101, and the controller 101 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 according to the inclination information and the swing information. Because the lifting hook often generates a larger inclination angle or shakes greatly before the lifting hook falls off and the rope falls off, the lifting hook can accurately judge whether the lifting state of the tower crane is abnormal or not by utilizing the attitude data of the lifting hook of the tower crane, so that targeted treatment is timely carried out when the abnormality is detected, the scattering of cargoes is avoided, the injury to workers is avoided, and the safety accident occurrence rate caused by the scattering of the cargoes in the lifting stage of the tower crane is reduced.

The attitude sensor 102 that this application embodiment provided can be connected with the controller 101 through the cable, the cable can 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 in the state of tightening, avoids the cable to loosen and rocks and influence other part operations.

In some modified implementations of the embodiments of the present application, the controller 101 stores no-load attitude data collected by the attitude sensor 102 when the tower crane hook is no-load, and determines inclination information and swing information of the tower crane hook by comparing the load attitude data with the no-load attitude data after receiving load attitude data collected by the attitude sensor 102 when the tower crane hook is loaded.

The empty load attitude data are basic attitude data which are acquired in a static state that the tower crane lifting hook is empty and the surrounding is windless and used for comparison, and after the tower crane is hooked to a load, the load attitude data and the empty load attitude data are compared, so that the inclination information and the swing information of the tower crane lifting hook can be obtained.

It can be understood with reference to fig. 2 that the above-mentioned inclination information refers to information such as an inclination angle generated by the tower crane hook rotating by taking the tower crane hook as a reference, and the above-mentioned swing information refers to information such as a swing angle generated by the tower crane hook swinging by taking a trolley suspending the tower crane hook as a reference, where a radius of a circle can be calculated according to a path (a section of arc line on a circle) through which the tower crane hook passes in a swinging process, and further, the swing angle can be calculated according to a length of the arc line, and whether the state of the tower crane hook is abnormal can be determined and predicted according to the inclination information and the swing information.

Specifically, the controller 101 may determine the inclination change information and the swing change information of the 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 tower crane 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 amplitude 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 amplitude and a wobble angle at the end of the unit time window.

The inclination change amplitude is a difference between an initial inclination angle and an end inclination angle of the unit time window, as shown in fig. 2, an inclination angle of the tower crane hook inclined towards the unhooking direction can be defined as positive, an inclination angle of the opposite direction is defined as negative, if the difference is positive and greater than a preset threshold, the probability of abnormality is high, and lifting abnormality can be directly judged or whether lifting abnormality exists can be further judged by combining other factors.

The swing change amplitude is a difference value between the final inclination angle and the initial inclination angle of the unit time window, as shown in fig. 2, the swing angle of the tower crane hook swinging in the unhooking direction can be defined as positive, the swing angle of the tower crane hook swinging in the opposite direction is defined as negative, if the difference value is positive and is larger than a preset threshold value, the abnormal probability is larger, and the lifting abnormality can be directly judged or further combined with other factors to judge whether the lifting abnormality exists.

Considering that the probability of erroneous judgment exists when judging whether the lifting is abnormal only by comparing the threshold values, in order to improve the judgment accuracy, in some modified embodiments, the controller 101 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 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 which are determined through experiments, each group of training data comprises inclination change information and swing change information, and whether an abnormal label exists or not.

The input data of the first neural network model comprises inclination change information and swing change information, the output data is a label (two kinds of labels) with abnormality, and the overall input parameters and output are relatively simple, so that the input data can be realized by adopting BP neural networks, convolutional neural networks CNN and other neural networks with simple structures, the input data can be formed by 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 relatively accurate judgment results are obtained. Wherein, the BP neural network and the convolutional neural network CNN are mature neural network models, and a person skilled in the art can flexibly construct the first neural network model by referring to the prior art and combining with actual requirements to achieve the purpose of the embodiment of the present application.

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

Considering that the environmental wind force also can influence the hoist and mount firmness of goods, if the wind direction is the same with unhook direction, can increase the probability that the goods unhook, and the wind speed is bigger, and unhook probability is bigger, conversely, if the wind direction is opposite with unhook direction, can reduce the probability that the goods unhook, in order to more accurate judgement the lifting state of tower crane lifting hook is unusual, in some change embodiments, above-mentioned a sensing internet of things system for intelligent tower crane lifting 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 connected with the controller 101 and are respectively used for collecting wind direction information and wind speed information around the tower crane lifting hook and sending the wind direction information and the wind speed information to the controller 101;

the controller 101 is further configured to comprehensively determine whether a lifting state of the tower crane hook is abnormal according to the inclination change information, the swing change information, the wind direction information, and the wind speed information.

The specific judging mode of the method can comprehensively judge whether the lifting state of the tower crane lifting hook is abnormal based on the preset threshold value, or can judge whether the lifting state of the tower crane lifting hook is abnormal by adopting a neural network, for example, in some embodiments, the controller 101 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 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 which are determined through experiments, each group of training data comprises inclination change information, swing change information, wind direction information and wind speed information, and whether an abnormal label exists or not, through training, the second neural network model can output whether the abnormal label exists or not according to the input inclination change information, swing change information, wind direction information and wind speed information, and then whether the lifting state of the tower crane lifting hook is abnormal or not can be judged by using the second neural network model.

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 (two kinds of labels) with abnormality, and the overall input parameters and output are simpler, so that the input data can be realized by adopting BP neural networks, convolutional neural networks CNN and other neural networks with simple structures, and the input data can be formed by an input layer, a hidden layer and an output layer, and the aim of the embodiment of the application can be realized without complex design, thereby reducing implementation difficulty and obtaining more accurate judgment results. Wherein, the BP neural network and the convolutional neural network CNN are mature neural network models, and a person skilled in the art can flexibly construct the first neural network model by referring to the prior art and combining with actual requirements to achieve the purpose of the embodiment of the present application.

Through the embodiment, whether the lifting state of the tower crane lifting hook is abnormal can be accurately judged by using the second neural network model, and whether the lifting state of the tower crane lifting hook is abnormal can be accurately judged by using the neural network model due to the fact that the influence of wind power on unhooking abnormality is considered.

Based on any of the foregoing embodiments, in other modified embodiments, the sensing internet of things system for sensing an abnormal lifting state of an 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 101, and when the controller 101 detects that the lifting state of the tower crane lifting hook is abnormal, abnormal alarm information is broadcast through the alarm device.

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 surrounding workers are injured due to unhooked cargoes is avoided, and accident loss is reduced.

Based on any of the foregoing embodiments, in some modified embodiments, the sensing internet of things system for sensing an abnormal lifting state of an 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 liquid crystal display device comprises a liquid crystal display device,

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;

When the controller controls the lifting hook to move through the lifting hook driving mechanism, the controller also controls the vision sensor to follow the lifting hook to move through the sensor driving mechanism, and controls the vision sensor to acquire vision sensing signals towards the area where the lifting hook is located, so that whether the lifting state of the tower crane lifting hook is abnormal or not is comprehensively judged according to the vision sensing signals and the gesture data acquired by the gesture sensor.

In addition, the controller can be connected with the vision sensor and the sensor driving mechanism in a wireless mode or in a wired mode, and the vision sensor and the sensor driving mechanism are preferably connected with the controller in a wired mode in some embodiments in consideration of relatively poor stability of wireless signals, and specifically, the cable can be connected to a console on the ground along a crane arm and a standard section and connected with the controller on the console, so that signal quality and stability are improved, and perception errors caused by signal problems are avoided.

Compared with the prior art, the sensor internet of things system for sensing the lifting abnormal state of the intelligent tower crane is provided, by further adding the visual sensor and the sensor driving mechanism, and controlling the lifting hook to move through the lifting hook driving mechanism, controlling the visual sensor to follow the lifting hook to move through the sensor driving mechanism, controlling the visual sensor to face the area where the lifting hook is located to collect the visual sensor signal, so that whether the lifting state of the lifting hook of the tower crane is abnormal or not is comprehensively judged according to the visual sensor signal and the gesture data collected by the gesture sensor, thereby enabling the visual sensor to follow the lifting hook to move and collect the visual sensor signal in a short distance, automatically collecting the high-definition and accurate visual sensor signal without additional operation of a tower crane operator, comprehensively and accurately judging whether the lifting state of the lifting hook of the tower crane is abnormal according to the visual sensor signal, further reducing the probability of being damaged by goods by mistake and reducing the occurrence rate of safety accidents.

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

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

The first trolley and the second trolley can share one set of variable-amplitude steel wire rope to draw and move, under the condition that the first trolley and the second trolley need to keep a fixed distance, such as 3 meters, 5 meters and the like, in addition, the first trolley and the second trolley can also adopt two sets of different variable-amplitude steel wire ropes to draw and move respectively, so that the distance between the first trolley and the second trolley can be adjusted, the distance from the vision sensor to the lifting hook can be adjusted conveniently, and a better observation field is obtained.

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

By arranging the second trolley to independently drive the vision sensor, the following relation between the vision sensor and the lifting hook can be flexibly adjusted according to the actual working condition, for example, the vision sensor and the lifting hook can be adjusted to keep 3 m intervals along the width-changing direction and keep 1 m intervals along the height direction, or the vision sensor and the lifting hook are adjusted to keep 1 m intervals along the width-changing direction and keep parallel (the interval is zero) along the height direction, and the like, so that a better observation field of view is obtained.

After the following relation is determined, the controller can automatically control the vision sensor to carry out following movement according to the following relation when controlling the lifting hook to move so as to keep the same observation field of view. In addition, the operator can also adjust the following relation according to the actual requirement, and the embodiment of the application is not limited to specific numerical values.

It should be noted that, the following related to the embodiment of the present application means that the visual sensor and the lifting hook keep a certain distance and angle when moving, so as to obtain the same observation field of view, so as to facilitate judging whether the lifting state of the tower crane lifting hook is abnormal through image comparison and recognition.

For example, taking a visual sensing signal as a real-time picture shot by a tripod head camera, the lifting hook and the rope can be identified through image recognition, whether an abnormality exists or not can be judged through the relative positions of the lifting hook and the rope and the movement trend of the rope in the pictures shot successively, for example, if the rope moves to a preset range at the outlet of the lifting hook and has a trend of continuing to move towards the unhooking direction, the unhooking risk is judged, namely the lifting state of the lifting hook of the tower crane 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. The image recognition technology is a mature technology in the prior art, and a person skilled in the art can directly apply the prior art to the present application to achieve the purpose of the embodiment of the present application.

The vision sensor that this application embodiment provided can be connected with the controller through the cable, the cable can receive and release through the winder, the winder can be located on the second dolly, the winder can keep the cable in tightening state, avoids the cable to loosen and rocks and influence other part operations.

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

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

The amplitude sensor and the height sensor can be realized by using a sensor provided by the prior art, and the amplitude sensor and the height sensor can be a mechanical sensor, an infrared sensor or a laser sensor, which can realize the purposes of the embodiment of the application, and the embodiment of the application is not limited.

The amplitude-changing position information can comprise the horizontal distance between the lifting hook and the standard joint along the amplitude-changing direction (namely the horizontal direction of the lifting arm), the height position information can comprise the vertical distance between the lifting hook and the lifting arm along the vertical direction, and the amplitude-changing position information and the height position information can be used for determining the amplitude-changing position information and the height position information of the position where the vision sensor should be positioned according to the amplitude-changing position information and the height position information of the lifting hook and combining the predetermined following relation and controlling the vision sensor to move to the position where the vision sensor should be positioned according to the amplitude-changing position information and the height position information.

When the vision sensor is controlled to move along with the lifting hook, the direction of the vision sensor is also required to be controlled (the vision sensor can be installed on the cloud deck through the cloud deck control, so that the direction is adjustable), the area where the lifting hook is located can be shot, specifically, in some embodiments, the controller also determines the rough relative position relation between the vision sensor and the lifting hook according to the amplitude position information and the height position information of the lifting hook, and the amplitude position information and the height position information of the vision sensor, and coarsely adjusts the area where the vision sensor is turned to the lifting hook according to the rough relative position relation. Because the amplitude position information and the height position information of the lifting hook and the visual sensor are already obtained during the following movement, the visual sensor can be quickly and coarsely adjusted to the area where the lifting hook is positioned according to the existing data through the implementation mode.

In view of the fact that the hook is not necessarily at a preferred position in the visual field of the vision sensor after coarse adjustment, and the vision sensor may swing along with air disturbance in the high altitude to fail to accurately capture an expected picture, in some modified embodiments, after coarsely adjusting the vision sensor to the area where the hook is located, the controller further determines a fine relative positional relationship between the vision sensor and the hook by identifying the hook in the vision sensor signal collected by the vision sensor, and fine-adjusts the vision sensor according to the fine relative positional relationship, so that the fine-adjusted vision sensor collects the vision sensor signal meeting the expected requirement. According to the embodiment, the lifting hook in the visual sensing signal can be identified by utilizing the image identification technology provided by the prior art, so that the fine relative position relationship between the visual sensor and the lifting hook is determined, and the visual sensor is finely tuned according to the fine relative position relationship, so that the visual sensor after fine tuning acquires the visual sensing signal which accords with the expectation, wherein the expectation can be that the lifting hook is positioned at the middle position of the picture of the visual sensing signal or that the lifting hook and the suspended goods are integrally positioned at the middle position of the picture of the visual sensing signal, and the embodiment of the application is not limited. Through this embodiment, can be on coarse tuning the basis further through fine setting and just recall and accord with anticipated vision sensing signal, improve vision sensing signal accuracy to utilize this vision sensing signal to judge accurately the play of lifting 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 collect accurate visual sensing signals, so as to determine whether a lifting state of a tower crane hook is abnormal by combining gesture data.

Specifically, if the lifting state of the tower crane lifting hook is comprehensively judged according to the gesture data and the visual sense signal, the specific judging mode can be as follows: if any one of the gesture data and the visual sensing signals is adopted to judge that the lifting state of the tower crane lifting hook is abnormal, the lifting state of the tower crane lifting hook is judged to be abnormal as a whole, otherwise, 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 comprehensively utilizing the attitude data and the visual sense signals, and the accuracy is improved.

It is easy to understand that if the vision sensor is light, the vision sensor swings with air disturbance in the high air to affect the shooting effect, so that in some modification embodiments, the vision sensor can be further provided with a gesture stabilizing controller to help the vision sensor stabilize the gesture in the high air, so as to reduce shaking, improve the shooting effect and further improve the accuracy of abnormal judgment of the lifting state of the tower crane lifting hook.

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 counterweights, the flywheel and the control moment gyro or a plurality of the counterweights, the flywheel and the control moment gyro. Wherein, the addition of the counterweight is most easy to realize and the implementation cost is lowest; if the flywheel is additionally arranged, the flywheel should be horizontally arranged, and the angular momentum generated by the flywheel can be helpful for keeping the posture of the vision sensor stable; in addition, the principle of the moment gyro is that when a gyro is given torque perpendicular to the rotation axis of the gyro, a precession moment perpendicular to the rotation axis and perpendicular to the torque axis is generated, and by using the principle, the vision sensor can be helped to keep stable posture by installing the moment gyro, and the effect of stabilizing the posture in the mode is the best.

In the foregoing embodiment, a sensing internet of things system for sensing an abnormal lifting state of an intelligent tower crane is provided, and correspondingly, the application also provides a method for sensing the abnormal lifting state of the intelligent tower crane. The method for sensing the abnormal lifting state of the intelligent tower crane provided by the embodiment of the application can be realized based on the sensing internet of things system for sensing the abnormal lifting state of the intelligent tower crane, and please refer to fig. 3, which shows a flowchart of the method for sensing the abnormal lifting state of the intelligent tower crane provided by some embodiments of the application. Since this method embodiment is substantially similar to the system embodiment described above, the description is relatively simple, and reference will be made to the description of the system embodiment described above. The method embodiments described below are merely illustrative.

As shown in fig. 3, a method for sensing abnormal lifting states of an intelligent tower crane may be executed by the controller in the sensor internet of things system for sensing abnormal lifting states of the intelligent tower crane, and may include the following steps:

step S101: acquiring attitude data of a tower crane lifting hook acquired by an attitude sensor, wherein the attitude sensor is fixedly arranged on the tower crane lifting hook;

step S102: determining inclination information and swing information of the tower crane lifting hook according to the attitude data;

step S103: judging whether the lifting state of the tower crane lifting hook is abnormal or not according to the inclination information and the swing information.

In some embodiments, the step S102 may include:

the controller stores the empty load attitude data acquired by the attitude sensor when the tower crane lifting hook is empty, and determines the inclination information and the swing information of the tower crane lifting hook by comparing the load attitude data with the empty load attitude data after receiving the load attitude data acquired by the attitude sensor when the tower crane lifting hook is loaded.

In some embodiments, the step S103 may include:

the controller adopts a sliding time window method, determines the inclination change information and the swing change information of a unit time window according to the inclination information and the swing information, and judges whether the lifting state of the tower crane lifting hook is abnormal according to the inclination change information and the swing change information.

In some embodiments, the tilt change information includes at least one of a tilt change amplitude 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 amplitude and a wobble angle at the end of the unit time window.

In some embodiments, the determining whether the lifting state of the tower crane hook is abnormal according to the inclination change information and the swing change information may include:

the controller inputs the inclination change information and the swing change information into a first neural network model trained in advance, and judges whether the lifting state of the tower crane lifting hook is abnormal or not according to the first neural network model.

In some embodiments, the tower crane is further provided with a wind direction sensor and a wind speed sensor;

the determining whether the lifting state of the tower crane lifting hook is abnormal according to the inclination change information and the swing change information may include:

the controller acquires wind direction information and wind speed information around the tower crane lifting hook respectively acquired by the wind direction sensor and the wind speed sensor;

and the controller comprehensively judges whether the lifting state of the tower crane lifting hook is abnormal according to the inclination change information, the swing change information, the wind direction information and the wind speed information.

In some embodiments, the comprehensively judging whether the lifting state of the tower crane hook is abnormal according to the inclination change information, the swing change information, the wind direction information and the wind speed information may include:

the controller inputs 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 judges whether the lifting state of the tower crane lifting hook is abnormal or not according to the second neural network model.

In some embodiments, the tower crane hook is provided with an alarm device, and the method further includes:

and when the controller detects that the lifting state of the tower crane lifting hook is abnormal, broadcasting abnormal alarm information through the alarm device.

The method for sensing the lifting abnormal state of the intelligent tower crane, which is provided by the embodiment of the application, has the same beneficial effects as the sensing internet of things system for sensing the lifting abnormal state of the intelligent tower crane, which is provided by the previous embodiment of the application, is based on the same inventive concept.

The embodiment of the application further provides an intelligent tower crane corresponding to the sensing internet of things system and the sensing method for sensing the abnormal lifting state of the intelligent tower crane provided by the foregoing embodiment, and please understand with reference to fig. 4, the intelligent tower crane is configured with the sensing internet of things system for sensing the abnormal lifting state of the intelligent tower crane provided by any of the foregoing embodiments, so that the sensing method for sensing the abnormal lifting state of the intelligent tower crane provided by any of the foregoing embodiments can be implemented.

The intelligent tower crane provided by the embodiment of the application has the same beneficial effects as the sensing internet of things system and method for sensing the abnormal lifting state of the intelligent tower crane provided by the previous embodiment of the application due to the same inventive concept.

In addition, in order to further perfect the intellectualization and unmanned of above-mentioned intelligent tower crane, the intelligent tower crane can also realize the comprehensive monitoring and the discernment to job site operating mode through the following three-dimensional augmented reality video control device that is used for intelligent tower crane to control through the configuration, need not the tower crane driver and carries out the overhead operation and just can realize the control to intelligent tower crane according to this three-dimensional augmented reality video, reduces staff's participation to can effectively reduce accident rate and avoid staff's casualties, the explanation is described below in connection with the example.

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 connected with the controller;

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

The plurality of local cameras are uniformly distributed on the periphery of the lifting hook of the intelligent tower crane, and are 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 controlled by arranging the controller, the global camera and the local cameras through 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 downwards arranged on the intelligent tower crane boom and is used for shooting a global image of the intelligent tower crane working scene and sending the global image to the controller; the plurality of local cameras are uniformly distributed on the periphery of the lifting hook of the intelligent tower crane, and are 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 using the global camera and the local camera, and then a three-dimensional augmented reality video is generated, so that the working condition of a construction site is comprehensively monitored and identified, a tower crane driver does not need to carry out overhead operation, the intelligent tower crane can be controlled according to the three-dimensional augmented reality video, the participation of staff is reduced, and the accident occurrence rate is effectively reduced, and the casualties of the staff are avoided.

Regarding the installation manner of the local camera, in some modified implementations of the embodiments of the present application, the three-dimensional augmented reality video control device for controlling an intelligent tower crane may further include: a multi-branch support;

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

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

the bottom fixing part is arranged on the shell of the lifting hook, and the sleeve is sleeved on the 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 support rod.

Through setting up above-mentioned multi-branch support frame, can install local camera around the lifting hook, make local camera can be along with the lifting hook removal, obtain stable, clear shooting picture, help generating accurate three-dimensional augmented reality video.

It should be noted that, the above is merely a simple schematic structure of the multi-branch support frame, and in practical application, the structure of the multi-branch support frame may be changed according to practical requirements to obtain a better implementation effect, which all do not depart from the inventive concept of the present embodiment, and all should be within the scope of protection of the present application.

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

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

Through the 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 reciprocates and be close to or keep away from the lifting hook to realize the automatically controlled regulation of local camera, help tower crane control personnel to combine the convenient, nimble position of adjusting 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 usability of local camera, in some change embodiments, the local camera pass through the cloud platform install in each branch end of multi-branch support frame, through setting up the cloud platform, can more nimble control local camera gathers required image, on the one hand, can be when the shooting angle appears deviating, correct the angle deviation through cloud platform control local camera to more accurate required image of gathering, on the other hand, can control local camera and cruise the shooting, gather the image in the bigger scope around, so that further carry out the three-dimensional reconstruction of full scene, improve intelligent level.

For the number of the local cameras, more than one local camera can be generally set in consideration of balance problems and shielding problems caused by surrounding arrangement of a plurality of local cameras, and in consideration of the fact that the number is too large, the system load and the implementation cost for generating the three-dimensional augmented reality video can be improved, preferably, the number of the local cameras is one or more, so that implementation cost and implementation effect are both considered, and a higher input-output ratio is obtained.

It should be noted that, the embodiment of the application adopts the mode that global camera and local camera combine together to carry out image acquisition, wherein, global camera can shoot and obtain the global image that construction scene is more comprehensive, but because its mounted position is higher, can exist and shelter from and shoot the less than of low object definition in the picture, consequently, through introducing the local camera that encircles the lifting hook setting, can gather the picture of shelter from the department, reduce shielding problem, and because local camera is along with the lifting hook removal, can closely gather the picture that the definition is higher, like this, through global camera and local camera's cooperation, with global image and local image fusion, can obtain comprehensive, clear, accurate image data, thereby ensure that the three-dimensional augmented reality video that generates can restore the true condition of construction scene more accurately, help the intelligent tower crane to realize accurate operation based on three-dimensional augmented reality video, the intelligent of intelligent tower crane, the automation level and operation precision are improved.

The controller and the local camera can be connected in a wireless mode or a wired mode, and the safety accidents caused by signal interruption and errors are possibly caused by the fact that the stability of wireless signals is relatively poor are considered. On this basis, the three-dimensional augmented reality video control device for intelligent tower crane control can further comprise: 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 wound and unwound through the winder. By the embodiment, the cable can be kept in the tightened state by the reel, and the cable is prevented from loosening and shaking to influence the operation of other parts.

The three-dimensional augmented reality video can be realized by adopting a three-dimensional reconstruction technology, and 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 specifically according to the global image and the local image.

For example, the controller determines the position information of each pixel point corresponding to a three-dimensional point in a world coordinate system by adopting a dense reconstruction algorithm 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 the three-dimensional augmented reality video of the real-time working scene of the intelligent tower crane according to the three-dimensional point cloud formed by the three-dimensional points. The three-dimensional reconstruction based on multiple images is already a mature prior art, so specific processes thereof are not repeated herein, and a person skilled in the art can flexibly alter and implement the three-dimensional reconstruction with reference to the prior art, and the embodiments of the present application are not limited and are all within the protection scope of the present application.

In addition, building information model (Building Information Modeling, BIM) tools may also be employed to generate three-dimensional augmented reality video based on the global and local images, which may also achieve the purposes of the embodiments of the present application, and should also be within the scope of the present application.

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

In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.

The units described as separate units may or may not be physically separate, and units shown 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 may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in 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 may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in 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, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solution 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 scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the embodiments, and are intended to be included within the scope of the claims and description.

Claims

1. A sensor thing networking system for intelligent tower crane plays to rise abnormal state perception, its characterized in that includes: a controller and an attitude sensor in communication with the controller;

the controller determines inclination information and 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 according to the inclination information and the swing information;

the controller stores the empty load attitude data acquired by the attitude sensor when the tower crane lifting hook is empty, and determines the inclination information and the swing information of the tower crane lifting hook by comparing the load attitude data with the empty load attitude data after receiving the load attitude data acquired by the attitude sensor when the tower crane lifting hook is loaded;

the controller adopts a sliding time window method, determines the inclination change information and the swing change information of a unit time window according to the inclination information and the swing information, and judges whether the lifting state of the tower crane lifting hook is abnormal according to the inclination change information and the swing change information;

Further comprises: 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 connected with the controller and are respectively used for collecting wind direction information and wind speed information around the tower crane lifting hook and sending the wind direction information and the wind speed information to the controller;

the controller is also 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 controller inputs the inclination change information, the swing change information, the wind direction information and the wind speed information into a first neural network model trained in advance, and judges whether the lifting state of the tower crane lifting hook is abnormal or not according to the first neural network model.

2. The system of the internet of things for sensing abnormal lifting state of an intelligent tower crane according to claim 1, wherein the inclination change information comprises at least one of an inclination change amplitude and an inclination angle of a unit time window end, and the swing change information comprises at least one of a swing change amplitude and a swing angle of a unit time window end.

3. The sensing internet of things system for sensing abnormal lifting states of an intelligent tower crane according to claim 1, further comprising: the alarm device is arranged on the tower crane lifting hook;

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

4. An intelligent tower crane, characterized in that the intelligent tower crane is provided with the sensing internet of things system for sensing the abnormal lifting state of the intelligent tower crane according to any one of claims 1 to 3.

5. A method for sensing abnormal lifting state of an intelligent tower crane using the sensing internet of things system for sensing abnormal lifting state of an intelligent tower crane according to any one of claims 1 to 3, comprising: