Anti-collision monitoring method and system for operation scenes near live objects

Abstract

The present disclosure relates to the field of electric power technology, and discloses a method and system for anti-collision monitoring in a scenario of operation near a live body. The specific scheme is as follows: in the case of a collision between a substation crane and a transmission line, based on the point cloud data obtained by scanning the laser radar of the substation crane in the collision space range, the point cloud density of the collision space range corresponding to the collision point is determined. If the point cloud density does not meet the preset point cloud density, the target laser scanning angle is searched within the preset laser scanning angle range with the goal of minimizing the scanning blind area of ​​the laser radar. Then, during the continued operation of the substation crane, the laser radar is controlled to scan at the target laser scanning angle to obtain the laser radar scanning blind area and determine the potential collision danger zone; thus, based on the potential collision danger zone, the attitude parameters of the variable arm of the substation crane are adjusted to avoid the collision between the substation crane and the transmission line.

Description

Technical Field

[0001] The present disclosure relates to the field of electric power technology, and in particular to a method, system, electronic device and storage medium for anti-collision monitoring in a scenario of working near a charged object. Background Art

[0002] During the operation of the substation crane, in order to avoid collision between the crane and the transmission line, it is necessary to obtain the three-dimensional point cloud data of the crane's surrounding environment in real time, and determine whether there is a collision risk based on the preset safety distance threshold.

[0003] However, due to the complexity of the crane's structure and the multi-degree-of-freedom characteristics of its motion, the laser radar scanning will have blind spots in certain specific postures, making the acquired point cloud data incomplete and difficult to fully reflect the spatial position relationship between the crane and the transmission line. Furthermore, it is difficult to accurately mark potential collision risk areas to avoid collisions between the crane and the transmission line. Summary of the invention

[0004] The present disclosure provides a method, system, electronic device and storage medium for anti-collision monitoring in a scenario of working near charged objects, which can solve at least one of the above problems.

[0005] The present disclosure provides a method for anti-collision monitoring in a scenario of operation near a charged object, comprising:

[0006] In the event of a collision between the substation crane and the transmission line, determining a collision space range corresponding to the collision point based on the position information of the collision point between the substation crane and the transmission line and the vehicle posture parameters at the time of the collision;

[0007] Determine the point cloud density of the collision space range based on the point cloud data obtained by scanning the collision space range by the laser radar of the substation crane;

[0008] When the point cloud density of the collision space does not meet the preset point cloud density requirement, the target laser scanning angle is searched within the preset laser scanning angle range with the goal of minimizing the scanning blind area of ​​the laser radar;

[0009] During the continued operation of the substation crane, the laser radar is controlled to scan at the target laser scanning angle to obtain a laser radar scanning blind area;

[0010] Determine a potential collision risk area based on the position information of the power transmission line in the laser radar scanning blind area;

[0011] Based on the potential collision risk zone, the attitude parameters of the luffing arm of the substation crane are adjusted to avoid the substation crane colliding with the transmission line in the potential collision risk zone.

[0012] According to another aspect of the present disclosure, there is provided a collision prevention monitoring system for a scene of operation near a charged object, characterized in that it comprises:

[0013] A collision range determination module, used to determine the collision space range corresponding to the collision point based on the position information of the collision point between the substation crane and the transmission line and the vehicle posture parameters at the time of collision when the substation crane collides with the transmission line;

[0014] A point cloud density determination module, configured to determine the point cloud density of the collision space range based on point cloud data obtained by scanning the collision space range by the laser radar of the substation crane;

[0015] A target angle determination module, used to search for a target laser scanning angle within a preset laser scanning angle range with the goal of minimizing a scanning blind area of ​​the laser radar when the point cloud density of the collision space range does not meet a preset point cloud density requirement;

[0016] A scanning blind area determination module, used to control the laser radar to scan at the target laser scanning angle to obtain the laser radar scanning blind area during the continued operation of the substation crane;

[0017] A collision risk zone determination module, used to determine a potential collision risk zone based on the position information of the power transmission line in the laser radar scanning blind area;

[0018] The posture adjustment module is used to adjust the posture parameters of the luffing arm of the substation crane based on the potential collision risk area to avoid the substation crane colliding with the transmission line in the potential collision risk area.

[0019] According to another aspect of the present disclosure, there is provided an electronic device, comprising:

[0020] at least one processor; and

[0021] a memory communicatively connected to the at least one processor; wherein,

[0022] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any anti-collision monitoring method for an adjacent charged body operation scenario in the embodiments of the present disclosure.

[0023] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute any anti-collision monitoring method for a scenario of working near a charged object in the embodiments of the present disclosure.

[0024] By adopting the technical solution disclosed in the present invention, in the case of a collision between a substation crane and a transmission line, the collision space range corresponding to the collision point is determined based on the position information of the collision point between the substation crane and the transmission line and the vehicle posture parameters at the time of the collision. If the point cloud density of the spatial range does not meet the preset point cloud density requirements, the target laser scanning angle is searched within the preset laser scanning angle range with the goal of minimizing the scanning blind area of ​​the laser radar, and then, during the continued operation of the substation crane, the laser radar is controlled to scan at the target laser scanning angle to obtain the laser radar scanning blind area, and the potential collision danger area is determined based on the position information of the transmission line in the laser radar scanning blind area, and the posture parameters of the luffing arm of the substation crane are adjusted based on the potential collision danger area, so that the collision between the substation crane and the transmission line can be avoided in the future. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a flow chart of a method for anti-collision monitoring in a scenario of working near a charged object according to an embodiment of the present disclosure;

[0026] Figure 2 This is a structural block diagram of a collision prevention monitoring system for a scenario of working near a charged object according to an embodiment of the present disclosure;

[0027] Figure 3 is a block diagram of an electronic device for implementing an embodiment of the present disclosure. DETAILED DESCRIPTION

[0028] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0029] Figure 1 It is a flow chart of a method for anti-collision monitoring in a scenario of working near charged objects according to an embodiment of the present disclosure.

[0030] like Figure 1 As shown, the anti-collision monitoring method for the adjacent charged object operation scene may include:

[0031] S110, when a collision occurs between the substation crane and the transmission line, based on the position information of the collision point between the substation crane and the transmission line and the vehicle posture parameters at the time of the collision, determining a collision space range corresponding to the collision point;

[0032] S120, determining the point cloud density of the collision space range based on the point cloud data obtained by scanning the collision space range by the laser radar of the substation crane;

[0033] S130, when the point cloud density of the collision space range does not meet the preset point cloud density requirement, searching for a target laser scanning angle within a preset laser scanning angle range with the goal of minimizing the scanning blind area of ​​the laser radar;

[0034] S140, during the continued operation of the substation crane, controlling the laser radar to scan at a target laser scanning angle to obtain a laser radar scanning blind area;

[0035] S150, determining a potential collision risk area based on the position information of the power transmission line in the laser radar scanning blind area;

[0036] S160, adjusting the posture parameters of the luffing arm of the substation crane based on the potential collision danger zone to prevent the substation crane from colliding with the transmission line in the potential collision danger zone.

[0037] For example, one or more laser radars are provided on the substation crane, and the laser radars can be used to obtain point cloud data around the crane. Through the point cloud data around the crane and the location information of the surrounding transmission lines, it can be determined whether the substation crane collides with the transmission line. Alternatively, the probability of the substation crane colliding with the transmission line can be predicted. If the probability is too high, the collision space range of the collision point is predicted according to step S110.

[0038] For example, a camera is installed on the substation crane, or multiple cameras are installed around the substation crane and the power transmission line, and the operation process of the substation crane is monitored by the camera. For example, it can also be detected whether the substation crane collides with the power transmission line.

[0039] Exemplarily, the position information of the collision point may include three-dimensional coordinates.

[0040] Exemplarily, the vehicle posture parameters at the time of collision may include the vehicle's pitch angle, yaw angle, roll angle, boom angle, luffing arm length and lifting height.

[0041] Exemplarily, the point cloud density requirement may be that the point cloud density is greater than a preset density threshold.

[0042] For example, if there are multiple laser radars, the target laser scanning angle is determined for each laser radar, and then the laser radar scanning blind area is determined for each laser radar, and then the total laser radar scanning blind area is obtained, and then steps S150 and S160 are performed.

[0043] It is understandable that steps S140-S160 may be performed at every moment, or may be performed at a preset frequency.

[0044] Exemplarily, when the point cloud density of the collision space meets the preset point cloud density requirement, the current scanning angle of the laser radar is used as the target laser scanning angle of the substation crane, that is, the scanning angle of the laser radar is not changed, and steps S140-S160 are continued.

[0045] According to the above implementation, in the case of a collision between the substation crane and the transmission line, the collision space range corresponding to the collision point is determined based on the position information of the collision point between the substation crane and the transmission line and the vehicle posture parameters at the time of the collision. If the point cloud density of the spatial range does not meet the preset point cloud density requirements, the target laser scanning angle is searched within the preset laser scanning angle range with the goal of minimizing the scanning blind area of ​​the laser radar, and then, during the continued operation of the substation crane, the laser radar is controlled to scan at the target laser scanning angle to obtain the laser radar scanning blind area, and the potential collision danger area is determined based on the position information of the transmission line in the laser radar scanning blind area, and the posture parameters of the luffing arm of the substation crane are adjusted based on the potential collision danger area, so that the collision between the substation crane and the transmission line can be avoided in the future.

[0046] In one embodiment, based on the position information of the collision point between the substation crane and the transmission line and the vehicle posture parameters at the time of collision, the spatial range corresponding to the collision point is determined, including: constructing a virtual geometric model of the substation crane based on the current position of the substation crane and the vehicle posture parameters at the time of collision; determining the collision part of the crane based on the three-dimensional coordinate information in the virtual geometric model of the substation crane and the three-dimensional coordinate information of the collision point; and determining the collision spatial range corresponding to the collision point based on the collision part of the crane.

[0047] Exemplarily, the corresponding three-dimensional coordinate point set including the substation crane can be determined according to the current position of the substation crane and the vehicle posture parameters at the time of collision; the three-dimensional coordinate point set is processed by a fast convex hull algorithm to obtain a virtual geometric model of the substation crane. Among them, the fast convex hull algorithm first selects the extreme point in the point set as the initial convex hull, and then gradually adds other points to update the convex hull structure. For example, the three-dimensional coordinate point set is input into the algorithm, and a minimum convex polyhedron containing all points is obtained through iterative calculation. The convex polyhedron is a virtual geometric model.

[0048] Exemplarily, based on the three-dimensional coordinate information in the virtual geometric model of the substation crane, a portion of the crane that coincides with the three-dimensional coordinate information of the collision point is determined as the collision portion of the crane.

[0049] Exemplarily, the spatial area where the collision site of the crane is located is determined as the collision spatial range corresponding to the collision point.

[0050] It can be understood that the collision space range is a three-dimensional convex polyhedron including the collision point and the collision part of the crane.

[0051] According to the above implementation, based on the current position of the substation crane and the vehicle posture parameters at the time of collision, a virtual geometric model of the substation crane is constructed; based on the three-dimensional coordinate information in the virtual geometric model of the substation crane and the three-dimensional coordinate information of the collision point, the collision part of the crane is accurately determined. Therefore, the collision part of the crane can be used to determine the collision space range corresponding to the collision point.

[0052] In one embodiment, based on the point cloud data obtained by the laser radar of the substation crane scanning the collision space range, the point cloud density of the collision space range is determined, including: constructing a three-dimensional polyhedron for the collision space range corresponding to the collision point; dividing the three-dimensional polyhedron into a plurality of cubic grid units; obtaining the point cloud data obtained by the laser radar of the substation crane scanning each cubic grid unit; determining the point cloud density of each cubic grid unit based on the point cloud data of each cubic grid unit and the volume of each cubic grid unit; determining the point cloud density of the collision space range based on the point cloud density of each cubic grid unit.

[0053] Exemplarily, a fast convex hull algorithm may be used to process point cloud data within the collision space range to obtain a three-dimensional polyhedron.

[0054] Exemplarily, the three-dimensional polyhedron may be divided into a plurality of cubic grid units of the same volume, or may be divided into a plurality of cubic grid units of different volumes.

[0055] For example, an octree space partitioning algorithm may be used to recursively partition a three-dimensional polyhedron into a plurality of cubic grid units of different sizes. For example, a three-dimensional convex polyhedron in the collision area may be partitioned into cubic grid units with a side length of 0.5 meters.

[0056] Exemplarily, the number of point cloud data of the cubic grid unit is counted to obtain the number of point cloud data, and then the ratio between the number and the volume of the cubic grid unit is determined as the point cloud density of the cubic grid unit.

[0057] For example, the point cloud density of any cubic grid unit is used as the point cloud density of the collision space range. Alternatively, the minimum point cloud density is selected from the point cloud densities of each cubic grid unit as the point cloud density of the collision space range. Alternatively, the point cloud density of each cubic grid unit is averaged to obtain the point cloud density of the collision space range.

[0058] According to the above embodiment, the collision space range is divided into a plurality of cubic grid units, and then, the point cloud density of each cubic grid unit can be accurately determined by using the point cloud data based on each cubic grid unit and the volume of each cubic grid unit. Thus, based on the point cloud density of each cubic grid unit, the point cloud density of the collision space range is accurately determined.

[0059] In one embodiment, with the goal of minimizing the scanning blind area of ​​the laser radar, the target laser scanning angle is searched within the preset laser scanning angle range, including: determining the initial scanning angle and the preset step length within the preset laser scanning angle range; starting the first step with the initial scanning angle as the current scanning angle, and after executing the first step, increasing the current scanning angle by the preset step length and executing the first step until the increased scanning angle is no longer within the preset laser scanning angle range, then stopping the first step and stopping increasing the scanning angle; wherein the first step includes: in a pre-constructed three-dimensional scene model, controlling the simulated laser radar to scan according to the current scanning angle to determine the scanning blind area of ​​the simulated laser radar at the current scanning angle. Among the scanning blind areas of the simulated laser radar at each scanning angle, the scanning angle corresponding to the scanning blind area with the smallest area is selected as the target laser scanning angle.

[0060] For example, the preset laser scanning angle range may be 360 ​​degrees horizontally and -15 degrees to +15 degrees vertically. The preset step length may be 10 degrees horizontally and 1 degree vertically. In this way, multiple scanning angles may be obtained. For each scanning angle, the above-mentioned first step may be performed.

[0061] For example, the scanning blind area can be simulated by a ray tracing algorithm to simulate the laser radar scanning process and count the area that has not been scanned.

[0062] In practical applications, the three-dimensional scene model is constructed by using the pre-stored spatial coordinates of the substation transmission line and terrain elevation data, combined with the current position and posture of the crane. The octree algorithm is used to divide the scene spatially, and the scene is recursively divided into cubic units of 1m×1m×1m to improve the efficiency of subsequent calculations. According to the performance parameters of the laser radar, the scanning angle range is set to -180° to 180° horizontally, -45° to 45° vertically, and the angle granularity is 0.5°. The ant colony algorithm is used to search within the preset scanning angle range, and 100 ants are initialized. Each ant randomly selects a scanning angle. Then, the simulated laser radar is controlled to scan in the scene according to the angle, and the blind area at each scanning angle is calculated using the ray tracing algorithm. The blind area is defined as the area that light cannot reach. By comparing the blind area at different angles, the pheromone distribution is updated, and the pheromone volatility coefficient is set to 0.5. The calculation is iterated 500 times, and the top 10 solutions with the smallest blind area are selected as elite solutions in each iteration to enhance their pheromone concentration. Combined with the simulated annealing algorithm, local search is performed, the initial temperature is set to 100, the cooling coefficient is 0.95, and the elite solution is perturbed in a small range in each iteration. Through multiple iterative searches, the optimal laser radar scanning angle that minimizes the scanning blind area is obtained. The optimal angle parameters are transmitted to the laser radar control unit to adjust the scanning direction of the laser radar to achieve accurate scanning of the crane's surrounding environment.

[0063] According to the above implementation, the scanning angle of the laser radar is gradually changed within the laser scanning angle range with a preset step size, and the scanning blind area with the smallest area is targeted, so that the target laser scanning angle can be accurately searched.

[0064] In one embodiment, with the goal of minimizing the scanning blind area of ​​the laser radar, a target laser scanning angle is searched within a preset laser scanning angle range, including: constructing an objective function with the scanning angle as an independent variable and the area of ​​the scanning blind area of ​​the laser radar at the scanning angle as a dependent variable; and using a genetic algorithm to solve the minimum value of the objective function to obtain the target laser scanning angle.

[0065] Exemplarily, the objective function may use scanning time and laser radar working energy consumption as constraints. For example, the scanning time is limited to be between an upper limit value and a lower limit value of the scanning time, and the laser radar working energy consumption is limited to be between an upper limit value and a lower limit value of the energy consumption.

[0066] It can be understood that the objective function is the fitness function in the genetic algorithm. In each iteration, the fitness function is used to calculate the fitness of each individual in the population. If the minimum fitness meets the preset conditions, the scanning angle corresponding to the individual corresponding to the minimum fitness is used as the target laser scanning angle. If the minimum fitness does not meet the preset conditions, crossover mutation is performed on each individual in the population to obtain a new population and enter the next iteration process. Each individual in the population corresponds to a scanning angle.

[0067] It can be understood that each time a crossover mutation is performed on a population, it is necessary to limit the constraints that satisfy the above objective function.

[0068] According to the above implementation, the target laser scanning angle can be quickly solved.

[0069] In one embodiment, based on the potential collision danger zone, the attitude parameters of the luffing arm of the substation crane are adjusted, including: rasterizing the potential collision danger zone to obtain a grid map; determining the movement range of the luffing arm of the substation crane based on the position information of the substation crane and the attitude information of the luffing arm; determining the target grid by taking the grid whose movement range overlaps with the grid map; and adjusting the attitude parameters of the luffing arm of the substation crane with the goal that the movement range of the luffing arm of the substation crane does not overlap with the target grid.

[0070] For example, an octree algorithm may be used to grid the three-dimensional space of the potential collision risk zone. The size of the grid may be determined according to the maximum working range of the luffing arm of the substation crane. For example, each grid may be a cube with a side length of 0.5 meters.

[0071] For example, the crane's range of motion is marked in the grid map using a breadth-first search algorithm. If the search radius reaches the crane's maximum operating radius, the search is stopped. The transmission line grid in the grid map is quickly retrieved using the R-tree spatial index structure, and the transmission line is simplified into a series of line segments and mapped into the grid space. The crane's range of motion grid and the transmission line grid are compared. If the grids overlap, the overlapping grids are marked as high-risk grids, i.e., target grids.

[0072] In practical applications, the crane's size parameters and current posture data are obtained in real time from the crane control system, including the boom length (20 meters), rotation angle (0°~360°), pitch angle (-5°~80°) and amplitude angle (0°~75°). The matrix transformation algorithm is used to calculate the precise position and occupied volume of the crane in three-dimensional space. Considering the bending deformation of the boom, the Bezier curve model is used to fit the shape of the boom. Based on the occupied volume information of the crane, the improved Bresenham algorithm is used to mark the crane's motion range in the grid map, and all grids that the crane may cover are marked as occupied. The number of occupied grids is about 10,000. The Kalman filter algorithm is used to predict the crane's posture, with a prediction time window of 0.5 seconds, to achieve real-time update of the motion range, and an update frequency of 10 Hz. The R-tree spatial index structure is used to quickly retrieve the transmission line grids in the grid map, with a retrieval time of less than 1 millisecond. Grids within the crane's movement range that overlap with the transmission line grid by more than 50% are marked as high-risk grids and marked in red.

[0073] Exemplarily, the attitude parameters of the luffing arm may include the telescopic length, pitch angle and yaw angle of the luffing arm.

[0074] Exemplarily, a fuzzy control rule base is constructed; the rule base includes input variables and output variables, the input variables are the relative distance and approach speed between the crane and the high-risk grid, and the output variables are the adjustment amounts of the luffing boom extension speed, pitch angular velocity, and yaw angular velocity. Thus, with the goal that the motion range of the luffing boom of the substation crane does not overlap with the target grid, the input variables are determined, and then the input variables are input into the above-mentioned fuzzy rule base to obtain the output variables, and the attitude parameters of the luffing boom of the substation crane are adjusted according to the output variables.

[0075] According to the above implementation, the potential collision danger zone is gridded to obtain a grid map; based on the position information of the substation crane and the attitude information of the luffing arm, the movement range of the luffing arm of the substation crane is determined; the grid whose movement range coincides with the grid map is determined as the target grid; with the goal that the movement range of the luffing arm of the substation crane does not coincide with the target grid, the attitude parameters of the luffing arm of the substation crane are adjusted. Thus, the collision between the substation crane and the transmission line can be avoided.

[0076] Figure 2 It is a structural block diagram of an anti-collision monitoring system for a scenario of working near charged objects according to an embodiment of the present disclosure.

[0077] like Figure 2 As shown, the system may include:

[0078] A collision range determination module 210 is used to determine the collision space range corresponding to the collision point based on the position information of the collision point between the substation crane and the transmission line and the vehicle posture parameters at the time of collision when the substation crane collides with the transmission line;

[0079] A point cloud density determination module 220, configured to determine the point cloud density of the collision space range based on the point cloud data obtained by scanning the collision space range by the laser radar of the substation crane;

[0080] A target angle determination module 230 is used to search for a target laser scanning angle within a preset laser scanning angle range with the goal of minimizing a scanning blind area of ​​the laser radar when the point cloud density of the collision space range does not meet a preset point cloud density requirement;

[0081] A scanning blind area determination module 240 is used to control the laser radar to scan at the target laser scanning angle to obtain the laser radar scanning blind area during the continued operation of the substation crane;

[0082] A collision risk zone determination module 250, configured to determine a potential collision risk zone based on the position information of the power transmission line in the laser radar scanning blind area;

[0083] The posture adjustment module 260 is used to adjust the posture parameters of the luffing arm of the substation crane based on the potential collision risk area to avoid the substation crane colliding with the transmission line in the potential collision risk area.

[0084] In one implementation, the collision range determination module 210 includes:

[0085] A model determination unit, configured to construct a virtual geometric model of the substation crane based on the current position of the substation crane and the vehicle posture parameters at the time of the collision;

[0086] A collision location determination unit, configured to determine a collision location of the crane based on the three-dimensional coordinate information in the virtual geometric model of the substation crane and the three-dimensional coordinate information of the collision point;

[0087] A collision range determination unit is used to determine the collision space range corresponding to the collision point based on the collision position of the crane.

[0088] In one implementation, the point cloud density determination module 220 includes:

[0089] A polyhedron construction unit, used for constructing a three-dimensional polyhedron according to the collision space range corresponding to the collision point;

[0090] A polyhedron division unit, used for dividing the three-dimensional polyhedron into a plurality of cubic grid units;

[0091] A point cloud data acquisition unit, used to acquire point cloud data scanned by the laser radar of the substation crane at each of the cubic grid units;

[0092] A first density determination unit, configured to determine the point cloud density of each of the cubic grid cells based on the point cloud data of each of the cubic grid cells and the volume of each of the cubic grid cells;

[0093] The second density determination unit is used to determine the point cloud density of the collision space range based on the point cloud density of each of the cubic grid units.

[0094] In one implementation, the target angle determination module 230 includes:

[0095] An initialization unit, used to determine an initial scanning angle and a preset step length within a preset laser scanning angle range;

[0096] A scanning simulation unit, configured to start executing the first step with the initial scanning angle as the current scanning angle, and after executing the first step, increase the current scanning angle by the preset step length and execute the first step until the increased scanning angle is no longer within the preset laser scanning angle range, then stop executing the first step and stop increasing the scanning angle;

[0097] The first step includes: in a pre-constructed three-dimensional scene model, controlling the simulated laser radar to scan according to the current scanning angle to determine a scanning blind area of ​​the simulated laser radar at the current scanning angle;

[0098] The first angle determination unit is used to select a scanning angle corresponding to a scanning blind area with a minimum area among the scanning blind areas of the simulated laser radar at various scanning angles, as the target laser scanning angle.

[0099] In one implementation, the target angle determination module 230 includes:

[0100] An objective function determination unit, configured to construct an objective function using a scanning angle as an independent variable and an area of ​​a scanning blind zone of the laser radar at the scanning angle as a dependent variable;

[0101] The second angle determination unit is used to use a genetic algorithm to solve the minimum value of the objective function to obtain the target laser scanning angle.

[0102] In one implementation, the posture adjustment module 260 includes:

[0103] A rasterization unit, used for rasterizing the potential collision risk area to obtain a raster map;

[0104] a motion range determining unit, configured to determine the motion range of the luffing arm of the substation crane based on the position information of the substation crane and the posture information of the luffing arm;

[0105] A target grid determination unit, configured to determine a target grid by taking the grid where the motion range coincides with the grid map;

[0106] The posture adjustment unit is used to adjust the posture parameters of the luffing arm of the substation crane with the goal of making the movement range of the luffing arm of the substation crane not coincide with the target grid.

[0107] For the description of specific functions and examples of each module and submodule of the device in the embodiment of the present disclosure, reference can be made to the relevant description of the corresponding steps in the above method embodiment, which will not be repeated here.

[0108] According to an embodiment of the present disclosure, the above method of the present disclosure may be applied to an electronic device and a readable storage medium.

[0109] Figure 3 A schematic block diagram of an example electronic device 600 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0110] like Figure 3 As shown, the device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0111] A number of components in the device 600 are connected to the I / O interface 605, including: an input unit 606, such as a keyboard, a mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a disk, an optical disk, etc.; and a communication unit 609, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0112] The computing unit 601 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 601 performs the various methods and processes described above, such as a method for anti-collision monitoring of a scene of an adjacent live body operation. For example, in some embodiments, a method for anti-collision monitoring of a scene of an adjacent live body operation may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the anti-collision monitoring method of a scene of an adjacent live body operation described above may be executed. Alternatively, in other embodiments, the computing unit 601 may be configured in any other appropriate manner (eg, by means of firmware) to execute a method for anti-collision monitoring in a scenario of operation near a charged object.

[0113] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0114] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0115] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0116] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0117] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0118] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0119] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0120] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the principles of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for anti-collision monitoring in a scene of operation near a live object, characterized in that: include: In the event of a collision between the substation crane and the transmission line, determining a collision space range corresponding to the collision point based on the position information of the collision point between the substation crane and the transmission line and the vehicle posture parameters at the time of the collision; Determine the point cloud density of the collision space range based on the point cloud data obtained by scanning the collision space range by the laser radar of the substation crane; When the point cloud density of the collision space does not meet the preset point cloud density requirement, the target laser scanning angle is searched within the preset laser scanning angle range with the goal of minimizing the scanning blind area of ​​the laser radar; During the continued operation of the substation crane, the laser radar is controlled to scan at the target laser scanning angle to obtain a laser radar scanning blind area; Determine a potential collision risk area based on the position information of the power transmission line in the laser radar scanning blind area; Based on the potential collision danger zone, adjusting the attitude parameters of the luffing arm of the substation crane to avoid the substation crane colliding with the transmission line in the potential collision danger zone; Wherein, the step of searching for a target laser scanning angle within a preset laser scanning angle range with the goal of minimizing the scanning blind area of ​​the laser radar includes: Within a preset laser scanning angle range, determining an initial scanning angle and a preset step length; Taking the initial scanning angle as the current scanning angle, starting to execute the first step, and after executing the first step, increasing the current scanning angle by the preset step length and executing the first step, until the increased scanning angle is no longer within the preset laser scanning angle range, then stopping executing the first step and stopping increasing the scanning angle; The first step includes: in a pre-constructed three-dimensional scene model, controlling the simulated laser radar to scan according to the current scanning angle to determine a scanning blind area of ​​the simulated laser radar at the current scanning angle; Among the obtained scanning blind areas of the simulated laser radar at various scanning angles, the scanning angle corresponding to the scanning blind area with the smallest area is selected as the target laser scanning angle.

2. The method according to claim 1, characterized in that The determining of the spatial range corresponding to the collision point based on the position information of the collision point between the substation crane and the transmission line and the vehicle posture parameters at the time of collision comprises: Based on the current position of the substation crane and the vehicle posture parameters at the time of the collision, construct a virtual geometric model of the substation crane; Determine the collision position of the crane based on the three-dimensional coordinate information in the virtual geometric model of the substation crane and the three-dimensional coordinate information of the collision point; Based on the collision position of the crane, a collision space range corresponding to the collision point is determined.

3. The method according to claim 1, characterized in that The point cloud data obtained by scanning the collision space range by the laser radar of the substation crane to determine the point cloud density of the collision space range includes: Constructing a three-dimensional polyhedron for the collision space range corresponding to the collision point; Dividing the three-dimensional polyhedron into a plurality of cubic grid units; Acquire point cloud data obtained by scanning each of the cubic grid units by the laser radar of the substation crane; Determine the point cloud density of each of the cubic grid units based on the point cloud data of each of the cubic grid units and the volume of each of the cubic grid units; Based on the point cloud density of each of the cubic grid units, the point cloud density of the collision space range is determined.

4. The method according to claim 1, characterized in that: The method aims to minimize the scanning blind area of ​​the laser radar and searches for a target laser scanning angle within a preset laser scanning angle range, including: Taking the scanning angle as an independent variable and the area of ​​the scanning blind zone of the laser radar at the scanning angle as a dependent variable, constructing an objective function; A genetic algorithm is used to solve the minimum value of the objective function to obtain the target laser scanning angle.

5. The method according to claim 1, characterized in that The adjusting the attitude parameters of the luffing arm of the substation crane based on the potential collision risk area includes: Rasterizing the potential collision danger area to obtain a grid map; Determine the movement range of the luffing arm of the substation crane based on the position information of the substation crane and the posture information of the luffing arm; The grid where the movement range overlaps with the grid map is determined as a target grid; With the goal that the movement range of the luffing arm of the substation crane does not overlap with the target grid, the posture parameters of the luffing arm of the substation crane are adjusted.

6. A collision prevention monitoring system for a scene near a live object, characterized in that: include: A collision range determination module, used to determine the collision space range corresponding to the collision point based on the position information of the collision point between the substation crane and the transmission line and the vehicle posture parameters at the time of collision when the substation crane collides with the transmission line; A point cloud density determination module, configured to determine the point cloud density of the collision space range based on point cloud data obtained by scanning the collision space range by the laser radar of the substation crane; A target angle determination module, used to search for a target laser scanning angle within a preset laser scanning angle range with the goal of minimizing a scanning blind area of ​​the laser radar when the point cloud density of the collision space range does not meet a preset point cloud density requirement; A scanning blind area determination module, used to control the laser radar to scan at the target laser scanning angle to obtain the laser radar scanning blind area during the continued operation of the substation crane; A collision risk zone determination module, used to determine a potential collision risk zone based on the position information of the power transmission line in the laser radar scanning blind area; A posture adjustment module, used for adjusting the posture parameters of the luffing arm of the substation crane based on the potential collision risk area, so as to avoid the substation crane colliding with the transmission line in the potential collision risk area; Wherein, the target angle determination module includes: An initialization unit, used to determine an initial scanning angle and a preset step length within a preset laser scanning angle range; A scanning simulation unit, configured to start executing the first step with the initial scanning angle as the current scanning angle, and after executing the first step, increase the current scanning angle by the preset step length and execute the first step until the increased scanning angle is no longer within the preset laser scanning angle range, then stop executing the first step and stop increasing the scanning angle; The first step includes: in a pre-constructed three-dimensional scene model, controlling the simulated laser radar to scan according to the current scanning angle to determine a scanning blind area of ​​the simulated laser radar at the current scanning angle; The first angle determination unit is used to select a scanning angle corresponding to a scanning blind area with a minimum area among the scanning blind areas of the simulated laser radar at various scanning angles, as the target laser scanning angle.

7. The system according to claim 6, characterized in that The collision range determination module comprises: A model determination unit, configured to construct a virtual geometric model of the substation crane based on the current position of the substation crane and the vehicle posture parameters at the time of the collision; A collision location determination unit, configured to determine a collision location of the crane based on the three-dimensional coordinate information in the virtual geometric model of the substation crane and the three-dimensional coordinate information of the collision point; A collision range determination unit is used to determine the collision space range corresponding to the collision point based on the collision position of the crane.

8. An electronic device, comprising: at least one processor; as well as a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in any one of claims 1 to 5.

9. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-5.

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