CN114818312A

CN114818312A - Modeling method, modeling system and remote operation system for hoisting operation

Info

Publication number: CN114818312A
Application number: CN202210427114.9A
Authority: CN
Inventors: 郭轶; 丁平; 俞晓斌
Original assignee: Sany America Inc
Current assignee: Sany America Inc
Priority date: 2022-04-21
Filing date: 2022-04-21
Publication date: 2022-07-29

Abstract

The invention provides a modeling method, a modeling system and a remote operation system for hoisting operation, wherein the method comprises the following steps: acquiring a first characteristic value and a second characteristic value of the target object, wherein the first characteristic value is a characteristic acquired by the unmanned aerial vehicle, and the second characteristic value is a characteristic acquired by an acquisition terminal; constructing a digital twin model according to the first characteristic value; planning a hoisting decision according to the second characteristic value and the digital twin model. According to the modeling method, the modeling system and the remote operation system for the hoisting operation, provided by the invention, the multi-dimensional data acquisition is carried out through the acquisition terminal and the unmanned aerial vehicle, the full coverage of the visual field of an operation area is realized, the existence of monitoring dead angles is avoided, and the hoisting strategy is formulated according to the monitored data, so that the hoisting operation requirements under different scenes are met.

Description

Modeling method, modeling system and remote operation system for hoisting operation

Technical Field

The invention relates to the technical field of engineering machinery, in particular to a modeling method, a modeling system and a remote operation system for hoisting operation.

Background

The crane is an important engineering facility, can perform mobile transportation in the vertical direction and the horizontal direction on large materials through actions such as lifting, amplitude changing, rotation and the like, can be widely applied to scenes such as construction sites, and is low in intelligent degree because a driver is required to drive the crane in an operation cabin depending on the skill and experience of the driver.

In order to improve the intelligent degree of hoisting equipment, the existing tracking monitoring of an intelligent crane depends on a camera, a sensor and the like arranged at the positions of a crane boom and the like to track and monitor the operation process, state parameters and environmental parameters, but dead angles of a visual field and a monitoring range exist, so that the acquired information is incomplete, and the judgment of the crane operation process has deviation.

Disclosure of Invention

The invention provides a modeling method of hoisting operation, which is used for solving the defects that the existing hoisting operation monitoring system cannot meet the operation requirements and has potential safety hazards, multi-dimensional data acquisition is carried out through an acquisition terminal and an unmanned aerial vehicle, the full coverage of the visual field of an operation area is realized, the existence of monitoring dead corners is avoided, and hoisting strategies are formulated according to the monitored data, so that the hoisting operation requirements under different scenes are met.

The invention also provides a modeling system for the hoisting operation.

The invention also provides a remote operation system.

According to a first aspect of the present invention, there is provided a modeling method for a lifting operation, comprising: the system is applied to a server, the server is connected with a hoisting device, an unmanned aerial vehicle and an acquisition terminal in an operation area, the hoisting device is used for hoisting a target object in the operation area, and the acquisition terminal is arranged on the hoisting device;

the method comprises the following steps:

acquiring a first characteristic value and a second characteristic value of the target object, wherein the first characteristic value is a characteristic acquired by the unmanned aerial vehicle, and the second characteristic value is a characteristic acquired by an acquisition terminal;

constructing a digital twin model according to the first characteristic value;

planning a hoisting decision according to the second characteristic value and the digital twin model.

According to an embodiment of the present invention, the step of obtaining the first feature value corresponding to the target specifically includes:

acquiring a first characteristic parameter and a second characteristic parameter corresponding to the target object, wherein the first characteristic parameter is an image parameter acquired by the unmanned aerial vehicle, and the second characteristic parameter is a depth parameter acquired by the unmanned aerial vehicle;

and generating the first characteristic value according to the first characteristic parameter and the second characteristic parameter.

Specifically, the embodiment provides an implementation method for obtaining a first characteristic value corresponding to the target object, and the first characteristic value is generated according to a first characteristic parameter and a second characteristic parameter acquired by an unmanned aerial vehicle, so that a basis is provided for building a virtual model of a hoisting device and a surrounding operating environment.

According to an embodiment of the present invention, the step of obtaining the second feature value corresponding to the target specifically includes:

acquiring a third characteristic parameter and a fourth characteristic parameter corresponding to the target object, wherein the third characteristic parameter is an image parameter acquired by the acquisition terminal, and the fourth characteristic parameter is a depth parameter acquired by the acquisition terminal, and an acquisition included angle is formed between the acquisition direction of the acquisition terminal and the acquisition direction of the unmanned aerial vehicle;

and generating the second characteristic value according to the third characteristic parameter and the fourth characteristic parameter.

Specifically, the embodiment provides an implementation manner for acquiring a second characteristic value corresponding to the target object, and the second characteristic value is generated according to a third characteristic parameter and a fourth characteristic parameter acquired by an acquisition terminal, so that a basis is provided for building a virtual model of the hoisting equipment and the surrounding operating environment.

According to an embodiment of the present invention, the step of constructing the digital twin model according to the first feature value specifically includes:

acquiring a first feature vector and a second feature vector acquired by the unmanned aerial vehicle, wherein the first feature vector points to the regional feature of the hoisting equipment, and the second feature vector points to the regional feature of the target object;

generating a first starting equipment model according to the first characteristic vector, and acquiring a prefabricated hoisting equipment model according to the first starting equipment model;

coupling the first lifting equipment model with the prefabricated lifting equipment model to generate a second lifting equipment model;

generating a region environment model according to the second feature vector;

and constructing the digital twin model according to the second hoisting equipment model and the regional environment model.

Specifically, the embodiment provides an implementation method for constructing a digital twin model according to the first characteristic value, and the accuracy of the digital twin model is improved by generating a first lifting equipment model according to the first characteristic value acquired by the unmanned aerial vehicle and coupling the first lifting equipment model with the prefabricated lifting equipment model.

According to an embodiment of the present invention, the step of generating a first lifting equipment model according to the first eigenvector, and acquiring a prefabricated lifting equipment model according to the first lifting equipment model specifically includes:

acquiring coordinate parameters, contour parameters and model parameters of the hoisting equipment;

generating the first starting equipment model according to the coordinate parameters, the contour parameters and the model parameters;

and determining the corresponding prefabricated hoisting equipment model according to the coordinate parameters, the contour parameters and the model parameters.

Specifically, the present embodiment provides an implementation manner of obtaining the prefabricated hoisting equipment model according to the first equipment model, which is to facilitate building the first equipment model and determining the prefabricated hoisting equipment model by obtaining the coordinate parameters, the profile parameters and the model parameters of the hoisting equipment, so as to provide support for improving the accuracy of the first equipment model through the prefabricated hoisting equipment model.

According to an embodiment of the present invention, the step of generating the regional environment model according to the second feature vector specifically includes:

acquiring a first area form parameter and a second area form parameter acquired by the unmanned aerial vehicle, wherein the first area form parameter corresponds to a hoisting starting position parameter of the target object, and the second area form parameter corresponds to a hoisting ending position parameter of the target object;

and acquiring hoisting environment parameters, and generating the region environment model according to the hoisting environment parameters, the first region form parameters and the second region form parameters, wherein the hoisting environment parameters at least comprise wind speed, wind level, wind pressure and obstacles in the operation region.

Specifically, this embodiment provides an implementation manner for generating the regional environment model according to the second eigenvector, where a starting point and an end point of the target object in the operation region are determined by the hoisting start position parameter and the hoisting end position parameter, and the regional environment model is generated in cooperation with the hoisting environment parameter, so that the safety and accuracy of the hoisting operation are improved.

According to an embodiment of the present invention, the step of planning the hoisting decision according to the second characteristic value and the digital twin model specifically includes:

acquiring a third feature vector and a fourth feature vector acquired by the acquisition terminal, wherein the third feature vector points to the action feature of the hoisting equipment, and the fourth feature vector points to the regional feature of the target object;

and driving the digital twin model in real time according to the third eigenvector and the fourth eigenvector so as to display the action of the hoisting equipment in the corresponding actual environment in real time in the digital twin space.

Specifically, the embodiment provides an implementation manner for planning a hoisting decision according to the second characteristic value and the digital twin model, and the digital twin model is driven in real time according to the second characteristic value acquired by the acquisition terminal, so that the hoisting decision is planned.

According to an embodiment of the present invention, after the step of driving the digital twin model in real time according to the third eigenvector and the fourth eigenvector to display the motion of the lifting device in the actual environment in real time in the digital twin space, the method specifically includes:

so as to obtain a corresponding hoisting strategy;

and planning the hoisting decision according to the hoisting strategy.

According to an embodiment of the present invention, after the step of planning the hoisting decision according to the second characteristic value and the digital twin model, the method specifically includes:

extracting a preset hoisting point position of the hoisting device for hoisting the target object in the hoisting decision;

generating a simulated hoisting track corresponding to the target object according to the first area form parameter and the second area form parameter;

generating an environment digital mode according to the hoisting environment parameters;

generating a mimic hoisting decision corresponding to the target object according to the preset hoisting point position, the mimic hoisting track and the environment digital modality, and executing the mimic hoisting decision;

and after the mimicry hoisting decision is determined to be finished, the hoisting of the target object meets a preset hoisting condition, and the hoisting decision is sent to the hoisting equipment.

Specifically, the embodiment provides an implementation mode after a hoisting decision is planned according to the second characteristic value and the digital twin model, and the simulated hoisting decision is executed, so that the hoisting decision is simulated and hoisted before being executed, the hoisting safety is ensured, problems can be found in the simulation process, the problems are solved in time, the hoisting efficiency is improved, and the existence of hoisting errors and the occurrence of accidents are reduced.

acquiring the motion track characteristics of the target object in a continuous time acquisition node;

generating an instant hoisting track of the target object according to the motion track characteristics, and judging according to the instant hoisting track and the mimicry hoisting track;

and determining that the deviation rate between the instant hoisting track and the mimicry hoisting track is greater than a deviation threshold value, and sending an alarm.

Specifically, the embodiment provides another implementation manner after planning the hoisting decision according to the second characteristic value and the digital twin model, and the motion trajectory characteristic of the target object is obtained, so that the instant hoisting trajectory of the target object is generated, the monitoring of the hoisting decision of the target object is realized, and the safety of the hoisting operation is improved.

According to the modeling system for the hoisting operation, provided by the second aspect of the invention, the modeling is carried out by adopting the modeling method for the hoisting operation.

According to a third aspect of the present invention, there is provided a remote operation system, further comprising: the remote control platform is respectively connected with the server, the hoisting equipment, the unmanned aerial vehicle and the acquisition terminal so as to realize remote planning and hoisting decision;

wherein, the server is internally provided with the modeling system for the hoisting operation.

Specifically, the present embodiment provides an implementation manner of a remote control platform, which performs remote control operation by setting a digital twin space of the remote control platform and a server to cooperate with each other.

According to an embodiment of the present invention, further comprising: the cloud control platform is connected with a plurality of operation areas so as to realize planning and cooperative operation of a plurality of hoisting decisions;

wherein, every all have in the operation region the remote control platform, the server, hoisting equipment, unmanned aerial vehicle and collection terminal.

Specifically, the embodiment provides an implementation manner of a cloud control platform, and by setting the cloud control platform, configuration management and many-to-many comprehensive scheduling and monitoring of permissions, rules and the like of a driver, a vehicle and a remote operation platform are realized.

One or more technical solutions in the present invention have at least one of the following technical effects: according to the modeling method, the modeling system and the remote operation system for the hoisting operation, provided by the invention, the multi-dimensional data acquisition is carried out through the acquisition terminal and the unmanned aerial vehicle, the full coverage of the visual field of an operation area is realized, the existence of monitoring dead angles is avoided, and the hoisting strategy is formulated according to the monitored data, so that the hoisting operation requirements under different scenes are met.

Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.

Drawings

In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

FIG. 1 is a schematic flow diagram of a modeling method for lifting operation provided by the present invention;

FIG. 2 is a schematic diagram of a layout of a modeling system for lifting operation according to the present invention;

fig. 3 is a second schematic layout diagram of the modeling system for hoisting operation according to the present invention.

Reference numerals:

10. a server; 20. an unmanned aerial vehicle; 30. collecting a terminal; 40. a working area; 50. a remote control platform; 60. and a cloud control platform.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.

In the description of the embodiments of the present invention, it should be noted that the terms "center", "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience in describing the embodiments of the present invention and simplifying the description, but do not indicate or imply that the referred devices or elements must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the embodiments of the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.

In some embodiments of the present invention, as shown in fig. 1, the present disclosure provides a modeling method for a hoisting operation, including: the system is applied to a server 10, the server 10 is connected with hoisting equipment, an unmanned aerial vehicle 20 and an acquisition terminal 30 in an operation area 40, the hoisting equipment is used for hoisting a target object in the operation area 40, and the acquisition terminal 30 is arranged on the hoisting equipment;

the method comprises the following steps:

acquiring a first characteristic value and a second characteristic value of a target object, wherein the first characteristic value is a characteristic acquired by the unmanned aerial vehicle 20, and the second characteristic value is a characteristic acquired by the acquisition terminal 30;

constructing a digital twin model according to the first characteristic value;

and planning a hoisting decision according to the second characteristic value and the digital twin model.

In detail, the invention provides a modeling method for hoisting operation, which is used for solving the defects that the existing hoisting operation monitoring system cannot meet the operation requirements and has potential safety hazards, multi-dimensional data acquisition is carried out through an acquisition terminal 30 and an unmanned aerial vehicle 20, the full coverage of the visual field of an operation area 40 is realized, the existence of monitoring dead angles is avoided, and hoisting strategies are formulated according to the monitored data, so that the hoisting operation requirements under different scenes are met.

In some possible embodiments of the present invention, the step of obtaining the first feature value of the corresponding target specifically includes:

acquiring a first characteristic parameter and a second characteristic parameter corresponding to a target object, wherein the first characteristic parameter is an image parameter acquired by the unmanned aerial vehicle 20, and the second characteristic parameter is a depth parameter acquired by the unmanned aerial vehicle 20;

and generating a first characteristic value according to the first characteristic parameter and the second characteristic parameter.

Specifically, the embodiment provides an implementation method for obtaining a first characteristic value corresponding to a target object, and the first characteristic value is generated according to a first characteristic parameter and a second characteristic parameter acquired by the unmanned aerial vehicle 20, so that a basis is provided for building a virtual model of a hoisting device and a surrounding operating environment.

It should be noted that the first characteristic parameter acquired by the drone 20 includes image parameters of the hoisting device and the target object, and the second characteristic parameter includes depth parameters of the hoisting device and the target object.

In some possible embodiments of the present invention, the step of obtaining the second feature value of the corresponding target specifically includes:

acquiring a third characteristic parameter and a fourth characteristic parameter corresponding to the target object, wherein the third characteristic parameter is an image parameter acquired by the acquisition terminal 30, the fourth characteristic parameter is a depth parameter acquired by the acquisition terminal 30, and an acquisition included angle is formed between the acquisition direction of the acquisition terminal 30 and the acquisition direction of the unmanned aerial vehicle 20;

and generating a second characteristic value according to the third characteristic parameter and the fourth characteristic parameter.

Specifically, the embodiment provides an implementation manner for acquiring a second characteristic value corresponding to a target object, and the second characteristic value is generated according to a third characteristic parameter and a fourth characteristic parameter acquired by the acquisition terminal 30, so that a basis is provided for building a virtual model of the hoisting equipment and the surrounding operating environment.

It should be noted that the third characteristic parameter acquired by the acquisition terminal 30 includes image parameters of the hoisting device and the target object, and the fourth characteristic parameter includes depth parameters of the hoisting device and the target object.

In some possible embodiments of the present invention, the step of constructing the digital twin model according to the first feature value specifically includes:

acquiring a first feature vector and a second feature vector acquired by the unmanned aerial vehicle 20, wherein the first feature vector points to the regional feature of the hoisting equipment, and the second feature vector points to the regional feature of the target object;

coupling the first hoisting equipment model with the prefabricated hoisting equipment model to generate a second hoisting equipment model;

generating a region environment model according to the second feature vector;

and constructing a digital twin model according to the second hoisting equipment model and the regional environment model.

Specifically, the present embodiment provides an implementation manner of constructing a digital twin model according to the first characteristic value, and the accuracy of the digital twin model is improved by generating the first starting equipment model according to the first characteristic value acquired by the unmanned aerial vehicle 20 and coupling the starting equipment model with the prefabricated hoisting equipment model.

In a possible embodiment, the accuracy of the first hoisting equipment model generated by collecting the first characteristic value by the unmanned aerial vehicle 20 is different, and the simulation requirement of a digital twin model is difficult to meet, the pre-established prefabricated hoisting equipment model is a model of a crane real object constructed in equal proportion in a digital twin space, the digital twin is a model which is established in a digital space scene and corresponds to reality, and the position relation, the connection relation, the motion relation and the shape parameters of the real parts can be accurately displayed through the digital twin model.

In some possible embodiments of the present invention, the step of generating a first starting equipment model according to the first eigenvector, and obtaining the prefabricated hoisting equipment model according to the first starting equipment model specifically includes:

acquiring coordinate parameters, contour parameters and model parameters of hoisting equipment;

generating a first equipment-resetting model according to the coordinate parameters, the contour parameters and the model parameters;

and determining a corresponding prefabricated hoisting equipment model according to the coordinate parameters, the contour parameters and the model parameters.

Specifically, the present embodiment provides an implementation manner of obtaining a prefabricated hoisting equipment model according to a first equipment model, which facilitates building the first equipment model and determining the prefabricated hoisting equipment model by obtaining coordinate parameters, profile parameters and model parameters of the hoisting equipment, and provides support for improving the accuracy of the first equipment model through the prefabricated hoisting equipment model.

In some possible embodiments of the present invention, the step of generating the regional environment model according to the second feature vector specifically includes:

acquiring a first area form parameter and a second area form parameter acquired by the unmanned aerial vehicle 20, wherein the first area form parameter corresponds to a hoisting starting position parameter of a target object, and the second area form parameter corresponds to a hoisting ending position parameter of the target object;

and acquiring hoisting environment parameters, and generating a region environment model according to the hoisting environment parameters, the first region form parameters and the second region form parameters, wherein the hoisting environment parameters at least comprise wind speed, wind level, wind pressure and obstacles in the operation region 40.

Specifically, the embodiment provides an implementation manner for generating the regional environment model according to the second eigenvector, the starting point and the ending point of the target object in the operation region 40 are determined by the hoisting starting position parameter and the hoisting ending position parameter, and the regional environment model is generated by matching with the hoisting environment parameter, so that the safety and the accuracy of the hoisting operation are improved.

In a possible embodiment, the first region shape parameter and the second region shape parameter are position parameters inputted by a human.

In a possible embodiment, the first and second regional form parameters are pre-marked work region 40 parameters.

In a possible implementation manner, the first area morphological parameter and the second area morphological parameter are input into the digital twin model by marking corresponding coordinates, and then planning of corresponding hoisting decisions is realized.

In a possible embodiment, the hoisting environment parameters including the wind speed, the wind level and the wind pressure in the working area 40 are loaded into the area environment model, so that the simulation of the environment of the working area 40 by the area environment model is more real.

In a possible implementation mode, the hoisting environment parameters can be loaded according to the average value in the continuous acquisition time node, and the accuracy of environment simulation is ensured.

In a possible implementation mode, the hoisting environment parameters can be updated in real time according to the acquired data, and the instantaneity of environment simulation is guaranteed.

In a possible implementation manner, for the acquisition of the hoisting environment parameters, setting of corresponding parameters can be performed, that is, a regional environment model under a specific environment, for example, extreme environments such as strong wind, heavy rain, ice and snow, can be simulated.

In some possible embodiments of the present invention, the step of planning the hoisting decision according to the second feature value and the digital twin model specifically includes:

acquiring a third feature vector and a fourth feature vector acquired by an acquisition terminal 30, wherein the third feature vector points to the action feature of the hoisting equipment, and the fourth feature vector points to the regional feature of the target object;

and driving the digital twin model in real time according to the third characteristic vector and the fourth characteristic vector so as to display the action of the hoisting equipment in the corresponding actual environment in real time in the digital twin space.

Specifically, the embodiment provides an implementation manner for planning a hoisting decision according to the second characteristic value and the digital twin model, and the digital twin model is driven in real time according to the second characteristic value acquired by the acquisition terminal 30, so that the hoisting decision is planned.

In a possible implementation mode, the real-time driving means that the model action in the digital twin space is consistent with the action of the crane in an actual scene, and the motion relation, the position relation and the connection relation among the crane model parts in the digital twin space are adjusted in real time according to detected data.

In a possible implementation mode, in an actual scene detected by a sensor, the jib rotates by 10 degrees in a variable amplitude manner, in a corresponding digital twin space, the corresponding variable amplitude of the jib is also rotated by 10 degrees, the crane rotation is detected by 5 degrees, a corresponding twin space crane model also rotates by 5 degrees correspondingly, the crane walking by 10m is detected, in a corresponding digital twin space, the crane model correspondingly walks by 10m, the lifting hook rises by 15m, and the lifting hook rises by 15m correspondingly to the twin space.

In a possible embodiment, the collection terminal 30 includes an angle sensor, a temperature sensor, a wind speed sensor, and the like, to realize the collection angle, the collection position, the adjustment of the collection angle, and the measurement of the wind speed.

In a possible implementation mode, the planning of the hoisting strategy by the digital twin model is carried out in a space punctuation mode, and the space punctuation is divided into an operation collaboration point and a path smooth point. The operation cooperation point is a point position which needs to wait for a cooperation instruction after the automatic operation of the lifting hook arrives, and the path smoothing point is a path intervention point which is considered to be carried out by avoiding an obstacle and the like during operation planning. The operation planning simulation only temporarily considers the cooperative motion of the crane, the suspension arm, the suspension cable, the suspension hook and the like when the suspension hook moves along the planned path.

In some possible embodiments of the present invention, the driving the digital twin model in real time according to the third eigenvector and the fourth eigenvector to display the action of the hoisting device in the actual environment in real time in the digital twin space specifically includes:

so as to obtain a corresponding hoisting strategy;

and planning a hoisting decision according to a hoisting strategy.

In some possible embodiments of the present invention, after the step of planning the hoisting decision according to the second feature value and the digital twin model, the method specifically includes:

in the extraction and hoisting decision, a preset hoisting point position of a hoisting target object of the hoisting equipment is extracted;

generating a mimic lifting track corresponding to the target object according to the first area form parameter and the second area form parameter;

generating a mimic hoisting decision corresponding to the target object according to a preset hoisting point position, a mimic hoisting track and an environment digital mode, and executing the mimic hoisting decision;

and after the mimicry hoisting decision is determined to be executed, the hoisting of the target meets the preset hoisting condition, and the hoisting decision is sent to the hoisting equipment.

acquiring the motion track characteristics of a target object in a continuous time acquisition node;

Specifically, the embodiment provides another implementation mode after planning the hoisting decision according to the second characteristic value and the digital twin model, and the motion trajectory characteristic of the target object is obtained, so that the instant hoisting trajectory of the target object is generated, the monitoring of the hoisting decision of the target object is realized, and the safety of the hoisting operation is improved.

In a possible implementation mode, a plurality of instant nodes of the motion track characteristics of the target object are extracted, and an instant hoisting track of the target object is generated according to the plurality of instant nodes.

In some embodiments of the present invention, as shown in fig. 1 to 3, the present disclosure provides a modeling system for a hoisting operation, which performs modeling by using the above-mentioned modeling method for a hoisting operation.

In some embodiments of the present invention, the present solution provides a remote operation system, further comprising: the remote control platform 50 is connected with the server 10, the hoisting equipment, the unmanned aerial vehicle 20 and the acquisition terminal 30 respectively, so that a remote planning and hoisting decision is realized;

the server 10 includes the above-described modeling system for the lifting operation.

Specifically, the present embodiment provides an implementation of the remote control platform 50, which performs remote control work by setting the remote control platform 50 to cooperate with the digital twin space of the server 10.

In a possible embodiment, a display is also included, which displays the digital twin space on the server 10, the graphics and scenes obtained by the camera shooting in real time.

In a possible embodiment, the remote control platform 50 is in signal connection with a vehicle-mounted motion controller, such as a boom controller (controlling the motion of the boom), a chassis controller (controlling the walking motion of the chassis) to control the crane.

In a possible embodiment, the remote control platform 50 includes crawler crane remote control software, multi-channel video playing software, and the like.

In a possible embodiment, the collection terminal 30 is a data collection device disposed on the crane, such as a camera, a radar, and the like, various sensors, and the like.

In some possible embodiments of the present invention, the method further includes: the cloud control platform 60 is connected with the plurality of operation areas 40, so that planning and collaborative operation of a plurality of hoisting decisions can be realized; wherein, each operation area 40 is provided with a remote control platform 50, a server 10, a hoisting device, an unmanned aerial vehicle 20 and an acquisition terminal 30.

Specifically, the embodiment provides an implementation manner of the cloud control platform 60, and by setting the cloud control platform 60, configuration management and many-to-many comprehensive scheduling and monitoring of permissions, rules and the like of a driver, a vehicle and a remote operation platform are realized.

In a possible implementation manner, the crane action in the actual scene may be controlled by the cloud control platform 60, or the crane action in the actual scene may be controlled by the remote control platform 50.

In a possible embodiment, the management authority of the cloud control platform 60 is greater than that of the remote control platform 50.

In a possible embodiment, the cloud control platform 60 is equivalent to a platform with authority management function, for example, a plurality of drivers respectively drive vehicles through a plurality of remote control platforms 50, and different driver authorities are different, for example, a driver with the highest driving authority can operate a plurality of vehicles, but the level is low and can only drive one or a few of the vehicles, here, taking a crane as an example, different types of cranes generally refer to cranes with different lifting capacities, and the higher the level is, the crane with a large lifting capacity can be operated, and the lower the level is, the lower the level is not.

In one application scenario, the modeling system for the hoisting operation is generally divided into two parts, namely a vehicle end (crawler crane) and a remote end (command center). Constructing a crawler crane and a digital twin body of an operation environment of the crawler crane by using a digital twin system, wherein the crawler crane adopts a priori modeling and parameter binding, and the operation environment of the crawler crane adopts an unmanned aerial vehicle 20 to carry out site surveying and mapping and construction; the digital twin content is presented in a real-time 3D rendering manner.

Further, the application system of the vehicle end (crawler crane) is divided into three parts of intelligent suspension arm, intelligent vehicle-mounted and unmanned aerial vehicle 20 surveying and mapping.

Furthermore, the intelligent suspension arm mainly comprises an intelligent holder, a laser radar, a vision camera, a power supply module, an IMU, an RTK and an AI computing unit, and is mainly used for sensing and computing the suspension arm, the lifting hook and the suspended object to obtain the accurate space state of the suspension arm, the lifting hook and the suspended object.

Furthermore, the intelligent vehicle-mounted system mainly comprises executing mechanisms such as a chassis controller and an upper arm controller for remotely controlling the crawler crane, 5G network equipment, a video gateway, a vehicle-mounted switch and the like for network communication, a panoramic camera for sensing the crawler crane, an IMU inertial navigation and the like, and the crawler crane is remotely controlled together.

Furthermore, the unmanned aerial vehicle mapping system mainly comprises an unmanned aerial vehicle 20, a high-precision mapping module, an aerial survey three-dimensional mapping computer and the like, and is used for topographic mapping and scene construction of a crawler crane operation site.

Further, the application system of the remote end (command center) is divided into a digital twin system, a remote control platform 50 and a cloud control platform 60. The digital twin system is mainly used for monitoring the operation of the crawler crane with high precision and high real-time performance, and lays a foundation for future intelligent operation by taking operation planning simulation as a demonstration. The remote control platform 50 is composed of a crawler crane console and a multi-channel display system, and is used for remotely controlling the crawler crane to operate.

Further, the cloud control platform 60 realizes configuration management and many-to-many comprehensive scheduling and monitoring of the authority, rules and the like of the driver, the vehicle and the remote operation platform.

In the description of the embodiments of the present invention, it should be noted that, unless explicitly stated or limited otherwise, the terms "connected" and "connected" are to be interpreted broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; may be directly connected or indirectly connected through an intermediate. Specific meanings of the above terms in the embodiments of the present invention can be understood by those of ordinary skill in the art according to specific situations.

In the description herein, reference to the description of the terms "one embodiment," "some embodiments," "a manner," "a particular manner," or "some manner" or the like, means that a particular feature, structure, material, or characteristic described in connection with the embodiment or manner is included in at least one embodiment or manner of an embodiment of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or mode. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or modes. Furthermore, various embodiments or modes described in this specification, as well as features of various embodiments or modes, may be combined and combined by those skilled in the art without contradiction.

Finally, it should be noted that: the above embodiments are merely illustrative of the present invention and are not to be construed as limiting the invention. Although the present invention has been described in detail with reference to the embodiments, it should be understood by those skilled in the art that various combinations, modifications or equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention, and the technical solution of the present invention is covered by the claims of the present invention.

Claims

1. A modeling method for hoisting operation is characterized by comprising the following steps: the system is applied to a server (10), the server (10) is connected with a hoisting device, an unmanned aerial vehicle (20) and an acquisition terminal (30) in a working area (40), the hoisting device is used for hoisting a target object in the working area (40), and the acquisition terminal (30) is arranged on the hoisting device;

the method comprises the following steps:

acquiring a first characteristic value and a second characteristic value of the target object, wherein the first characteristic value is a characteristic acquired by the unmanned aerial vehicle (20), and the second characteristic value is a characteristic acquired by an acquisition terminal (30);

and constructing a digital twin model according to the first characteristic value.

2. The modeling method of hoisting operation according to claim 1, wherein the step of obtaining the first characteristic value corresponding to the target object specifically includes:

acquiring a first characteristic parameter and a second characteristic parameter corresponding to the target object, wherein the first characteristic parameter is an image parameter acquired by the unmanned aerial vehicle (20), and the second characteristic parameter is a depth parameter acquired by the unmanned aerial vehicle (20);

3. The modeling method of hoisting operation according to claim 2, wherein the step of obtaining the second characteristic value corresponding to the target object specifically includes:

acquiring a third characteristic parameter and a fourth characteristic parameter corresponding to the target object, wherein the third characteristic parameter is an image parameter acquired by the acquisition terminal (30), and the fourth characteristic parameter is a depth parameter acquired by the acquisition terminal (30), and an acquisition included angle is formed between the acquisition direction of the acquisition terminal (30) and the acquisition direction of the unmanned aerial vehicle (20);

4. The modeling method for hoisting operation according to claim 1, wherein the step of constructing the digital twin model according to the first characteristic value specifically comprises:

acquiring a first characteristic vector and a second characteristic vector acquired by the unmanned aerial vehicle (20), wherein the first characteristic vector points to the regional characteristics of the hoisting equipment, and the second characteristic vector points to the regional characteristics of the target object;

generating a region environment model according to the second feature vector;

5. The method for modeling a hoisting operation according to claim 4, wherein the step of generating a first lifting equipment model according to the first eigenvector and obtaining a prefabricated hoisting equipment model according to the first lifting equipment model specifically comprises:

6. The modeling method of hoisting operation according to claim 4, wherein the step of generating the regional environment model based on the second eigenvector specifically comprises:

acquiring a first area form parameter and a second area form parameter acquired by the unmanned aerial vehicle (20), wherein the first area form parameter corresponds to a hoisting starting position parameter of the target object, and the second area form parameter corresponds to a hoisting ending position parameter of the target object;

and obtaining hoisting environment parameters, and generating the region environment model according to the hoisting environment parameters, the first region form parameters and the second region form parameters, wherein the hoisting environment parameters at least comprise wind speed, wind level, wind pressure and obstacles in the operation region (40).

7. The modeling method of hoisting operation according to any one of claims 1 to 6, wherein the step of planning a hoisting decision according to the second characteristic value and the digital twin model specifically comprises:

acquiring a third feature vector and a fourth feature vector acquired by the acquisition terminal (30), wherein the third feature vector points to the action feature of the hoisting equipment, and the fourth feature vector points to the regional feature of the target object;

8. The modeling method of hoisting operation according to claim 6, wherein after the step of planning a hoisting decision based on the second characteristic value and the digital twin model, the method specifically comprises:

and generating a mimic hoisting decision corresponding to the target object according to the preset hoisting point position, the mimic hoisting track and the environment digital modality.

9. The modeling method of hoisting operation according to claim 8, wherein after the step of generating the pseudo-hoisting decision corresponding to the target object according to the preset hoisting point location, the pseudo-hoisting trajectory and the environmental digital modality, the method further comprises:

executing the mimicry hoisting decision;

10. The modeling method of hoisting operation according to claim 8, wherein the step of planning a hoisting decision based on the second characteristic value and the digital twin model comprises:

11. A modeling system for a lifting operation, characterized in that the modeling is performed by using the modeling method for a lifting operation according to any one of claims 1 to 10.

12. A remote operation system, characterized by further comprising: the remote control platform (50), the remote control platform (50) is respectively connected with the server (10), the hoisting equipment, the unmanned aerial vehicle (20) and the acquisition terminal (30) so as to realize remote planning and hoisting decision;

wherein the server (10) has therein a modeling system for the hoisting operation according to claim 11.

13. The remote operating system according to claim 12, further comprising: the cloud control platform (60) is connected with the plurality of operation areas (40) so as to realize planning and cooperative operation of the plurality of hoisting decisions;

wherein each of the work areas (40) has therein the remote control platform (50), the server (10), the lifting device, the drone (20) and the acquisition terminal (30).