CN117572863A - Path optimization method and system for substation robot - Google Patents
Path optimization method and system for substation robot Download PDFInfo
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Abstract
The invention discloses a path optimization method and a path optimization system for a transformer substation robot, which relate to the technical field of robots and comprise the following steps: obtaining inspection task information of a target substation robot; planning a path of a target substation robot to obtain routing inspection path information; transmitting the inspection path information to a target substation robot; the target substation robot is subjected to inspection, real-time monitoring is carried out on the target substation robot, and dynamic monitoring data of the robot are obtained; performing obstacle recognition based on the dynamic monitoring data of the robot to obtain a dynamic obstacle recognition result; and carrying out path optimization according to the patrol task information and the patrol path information based on the dynamic obstacle recognition result. The invention solves the technical problem that path planning in the prior art lacks adaptability to environmental changes, and achieves the technical effect of improving the adaptability and flexibility to environmental changes through dynamic adjustment and optimization of the routing inspection path.
Description
Technical Field
The invention relates to the technical field of robots, in particular to a path optimization method and a path optimization system for a transformer substation robot.
Background
With the continuous expansion of the scale and the increase of the complexity of the transformer substation, the robot is increasingly widely applied to the inspection and the monitoring of the transformer substation, and a plurality of obstacles and dynamic variation factors such as transformers, wires, insulators and the like exist in the environment of the transformer substation, so that the robot needs to safely and accurately avoid the obstacles and efficiently finish the inspection task. However, in the prior art, robot path optimization mainly depends on preset rules, algorithms or models, and these methods cannot well cope with complex dynamic changes and uncertainties in the substation environment. The prior art has the technical problem that path planning lacks adaptability to environmental changes.
Disclosure of Invention
The path optimization method and the path optimization system for the substation robot effectively solve the technical problem that path planning in the prior art lacks adaptability to environmental changes, and achieve the technical effect of improving the adaptability and the flexibility to the environmental changes through dynamic adjustment and optimization of the routing inspection path.
The application provides a path optimization method and a path optimization system for a substation robot, wherein the technical scheme is as follows:
in a first aspect, embodiments of the present application provide a path optimization method for a substation robot, the method including:
obtaining inspection task information of a target substation robot;
carrying out path planning on the target substation robot according to the routing inspection task information to obtain routing inspection path information;
transmitting the routing inspection path information to the target substation robot;
the target substation robot performs inspection according to the inspection path information and the inspection task information, and monitors the target substation robot in real time to obtain dynamic monitoring data of the robot;
performing obstacle recognition based on the dynamic monitoring data of the robot to obtain a dynamic obstacle recognition result;
and carrying out path optimization according to the routing inspection task information and the routing inspection path information based on the dynamic obstacle recognition result.
In a second aspect, embodiments of the present application provide a path optimization system for a substation robot, the system comprising:
the inspection task information acquisition module is used for acquiring inspection task information of the target substation robot;
the routing inspection path information acquisition module is used for planning a path of the target substation robot according to the routing inspection task information to acquire routing inspection path information;
the information transmission module is used for transmitting the inspection path information to the target substation robot;
the robot dynamic monitoring data acquisition module is used for carrying out inspection on the target substation robot according to the inspection path information and the inspection task information, and carrying out real-time monitoring on the target substation robot to acquire robot dynamic monitoring data;
the dynamic obstacle recognition result obtaining module is used for carrying out obstacle recognition based on the dynamic robot monitoring data to obtain a dynamic obstacle recognition result;
and the path optimization module is used for performing path optimization according to the patrol task information and the patrol path information based on the dynamic obstacle recognition result.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
according to the method, the routing inspection task information of the target substation robot is obtained, then the path planning is carried out on the target substation robot according to the routing inspection task information, routing inspection path information is obtained, and the routing inspection path information is transmitted to the target substation robot. The target substation robot performs inspection according to the inspection path information and the inspection task information, monitors the target substation robot in real time to obtain dynamic robot monitoring data, performs obstacle recognition based on the dynamic robot monitoring data to obtain a dynamic obstacle recognition result, and performs path optimization according to the inspection task information and the inspection path information based on the dynamic obstacle recognition result. The method effectively solves the technical problem that path planning in the prior art lacks adaptability to environmental changes, and achieves the technical effect of improving the adaptability and flexibility to environmental changes through dynamic adjustment and optimization of the routing inspection path.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a path optimization method for a substation robot according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a path optimization system for a substation robot according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a patrol task information acquisition module 1, a patrol path information acquisition module 2, an information transmission module 3, a robot dynamic monitoring data acquisition module 4, a dynamic obstacle recognition result acquisition module 5 and a path optimization module 6.
Detailed Description
The application provides a path optimization method and a path optimization system for a substation robot, which are used for solving the technical problem that path planning in the prior art lacks adaptability to environmental changes.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present application based on the embodiments herein.
It should be noted that the terms "comprises" and "comprising," along with any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus, but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
As shown in fig. 1, the present invention provides a path optimization method for a substation robot for improving adaptability and flexibility to environmental changes by dynamically adjusting and optimizing a patrol path, the method comprising:
the method comprises the steps of obtaining patrol task information of a target substation robot, wherein the target substation robot is a robot for carrying out patrol tasks in a specific substation and can finish tasks such as autonomous navigation, equipment patrol, data acquisition and the like in a complex environment of the substation, and the patrol task information is information such as task content, requirements, operation sequence and the like related to the patrol of the substation and comprises an equipment list, equipment states, inspection items, operation steps, safety notes and the like of all equipment to be patrol. The method for acquiring the inspection task information of the target substation robot includes communication with a substation automation system (the substation automation system stores and manages the inspection task information of the substation), or manual input of the inspection task information into the robot system, for example, an operator may input specific content of the inspection task on a console or a mobile device and then transmit the specific content to the target substation robot.
And planning a path of the target substation robot according to the routing inspection task information, wherein the path planning refers to planning one or more optimal paths from a starting point to a terminal point according to the information such as the position and the operation sequence of equipment to be inspected in the routing inspection task, and generating corresponding routing inspection path information. Firstly, analyzing and analyzing the acquired inspection task information, extracting information such as equipment position, operation sequence and the like which need inspection, then calculating one or more optimal paths according to the analyzed information such as equipment position, operation sequence and the like by utilizing optimization algorithms such as a graph theory algorithm, a genetic algorithm and the like, wherein the paths are optimal solutions obtained after constraint on factors such as limiting conditions of movement of a robot, safety and efficiency of operation and the like. And generating corresponding path information including information such as a starting point, an ending point, a path length, equipment needing to be operated on the path and the like of the path according to the calculated optimal path, storing the generated routing inspection path information into a robot system, and transmitting the routing inspection path information to a target substation robot through a communication network.
The target substation robot performs inspection according to the inspection path information and the inspection task information, that is, after the robot receives the inspection task information, the inspection task information is stored in a memory, and starts to inspect the task, the robot performs autonomous or semi-autonomous navigation by using a navigation system (such as a laser radar, a GPS and the like) of the robot according to the inspection path information, and goes to a position of equipment to be inspected, and after the robot reaches the position of the equipment, the robot performs operations such as detection, photographing, reading and the like on the equipment according to operation steps in the inspection task information, and simultaneously, the robot monitors the state of the equipment in real time through a built-in sensor (such as a temperature sensor, a humidity sensor and the like). In the process of inspection, the robot monitors the state (such as battery power, motion state and the like) and the change of the surrounding environment (such as the occurrence of obstacles, gas leakage and the like) in real time through the sensor of the robot, and collects and processes the data as dynamic monitoring data. The robot stores the collected dynamic monitoring data in a memory and transmits the dynamic monitoring data to a robot system in real time through a communication network.
Performing obstacle recognition based on the dynamic monitoring data of the robot to obtain a dynamic obstacle recognition result, wherein the dynamic obstacle recognition result comprises the steps of preprocessing the dynamic monitoring data collected by the robot (such as data cleaning, filtering, noise reduction and the like), processing the preprocessed data by utilizing technologies such as computer vision, image processing and the like, recognizing an obstacle or potential fault on a robot inspection path, for example, detecting whether an object blocks the moving path of the robot or not by analyzing an image captured by a camera of the robot. The detected obstacles are classified and identified according to their characteristics and types, for example, the obstacles are classified into fixed obstacles (e.g., equipment, buildings, etc.) and dynamic obstacles (e.g., people, moving objects, etc.), or specific obstacle types (e.g., vehicles, packages, etc.) are identified. Generating a corresponding dynamic obstacle recognition result according to the classification and recognition result of the obstacle, for example, recording information such as the position, the size and the like of the detected fixed obstacle in a robot memory for avoiding during subsequent path planning; and analyzing and processing the information such as the motion trail, the speed and the like of the dynamic obstacle, and predicting the future position and the possible motion direction of the dynamic obstacle.
Analyzing the identified obstacle based on the dynamic obstacle recognition result, including information such as the type, position and size of the obstacle, the distance and relative direction between the robot and the obstacle, and the like, and performing path optimization according to the inspection task information and the inspection path information, namely, re-planning the inspection path of the robot by using an optimization algorithm such as a graph theory algorithm, a genetic algorithm, and the like according to the analysis result of the obstacle and the dynamic monitoring data of the robot, for example, avoiding the fixed obstacle, and selecting another safe and efficient path to reach the target equipment; for dynamic obstacles, predicting future positions and possible movement directions of the dynamic obstacles, and selecting proper time and paths for inspection. And evaluating and comparing the re-planned path, and selecting the optimal path as a new path for robot inspection, wherein evaluation standards comprise path length, safety, execution efficiency and the like. Updating the optimized path information into a robot system, transmitting the path information to a target substation robot through a communication network, carrying out a routing inspection task by the robot according to the new path information, continuously monitoring the surrounding environment in real time when the robot executes the new routing inspection path, finding new obstacles or changes in time, and carrying out path optimization again. The technical effects of improving the adaptability and the flexibility to environmental changes through dynamic adjustment and optimization of the inspection path are achieved.
In a preferred implementation manner provided in the embodiments of the present application, path planning is performed on the target substation robot according to the routing task information, so as to obtain routing path information, including:
the initial position information of the target substation robot is obtained by presetting initial position information in the robot system or by external input (e.g., GPS signals, encoder readings, etc.) at the start of the robot.
And carrying out feature recognition according to the inspection task information, namely recognizing key features and target positions in an inspection area, including identifiable features such as the shape, the size, the color, the labels and the like of equipment, positioning and recognizing the features in the inspection area by a robot through technologies such as image processing, pattern recognition and the like, and constructing an inspection position distribution diagram according to the positions and the distribution of the features.
Based on the initial position information and the patrol position distribution diagram, the robot system performs patrol path analysis by utilizing a graph theory algorithm, artificial intelligence and other technologies to obtain patrol path information, wherein the patrol path information comprises information such as a starting point, an ending point, path length, equipment needing to be operated on a path and the like. According to the preferred embodiment, the initial position information of the target substation robot is obtained, so that the robot is ensured to be in a correct position when the inspection task starts, the problem of lost or deviated paths in the inspection process is avoided, and the technical effect of ensuring accurate navigation and path planning of the robot is achieved.
In another preferred implementation manner provided in the embodiments of the present application, performing inspection path analysis based on the initial position information and the inspection position distribution map, to obtain the inspection path information includes:
and carrying out inspection position priority analysis on the inspection position distribution map based on the inspection task information, namely evaluating and analyzing each position in the inspection position distribution map according to the importance or priority requirement of the inspection position in the inspection task information, for example, adjusting the priority of the corresponding position according to the requirement of the inspection task information on critical equipment, high-risk area or equipment with higher failure rate, and obtaining the optimized inspection position distribution map after evaluating, analyzing and priority adjustment.
And acquiring routing inspection path planning records related to a specific transformer substation or routing inspection task through historical routing inspection data, robot operation records or other related data sources, and integrating the records to construct a routing inspection path planning record library, wherein the record library comprises information of successful routing inspection paths, encountered problems, obstacles and the like.
And carrying out data integration based on the routing inspection path planning record library, extracting useful information by cleaning, processing and integrating the data in the routing inspection path planning record library, and constructing a routing inspection path planning model which learns and simulates a routing inspection path planning method suitable for a specific transformer substation by utilizing technologies such as machine learning, artificial intelligence and the like.
And inputting the initial position information of the target substation robot and the optimized routing inspection position distribution diagram into the routing inspection path planning model, and generating one or more pieces of optimal routing inspection path information by the model according to the input information and the learned knowledge. According to the preferred embodiment, the importance and the priority order of different patrol positions are determined by carrying out priority analysis on the patrol task information, so that when the patrol path information is generated, the patrol path of the robot is planned according to the priority order, the priority of the patrol of the key equipment or the high-risk area is ensured, the technical effects of improving the patrol efficiency and the safety of the key equipment are achieved, the historical patrol data and the operation record of the robot are utilized, and the manual intervention and the errors are reduced by learning and simulating a successful patrol path planning method, so that the technical effects of improving the adaptability and the robustness of the robot in a complex environment are achieved.
In another preferred implementation manner provided in the embodiments of the present application, performing obstacle recognition based on the dynamic monitoring data of the robot, to obtain a dynamic obstacle recognition result, includes:
the method comprises the steps of carrying out feature recognition on dynamic monitoring data acquired by a robot by utilizing technologies such as machine learning, image processing and the like, extracting robot dynamic data and a plurality of target dynamic data from the dynamic monitoring data, wherein the robot dynamic data refer to various data which are acquired by the robot and are related to the movement of the robot, including the moving speed, the moving direction, the gesture and the like of the robot, reflect the moving state and the track of the robot on a patrol path, and the plurality of target dynamic data refer to various data which are acquired by the robot and are related to barriers, including the position, the moving speed, the moving direction, the size, the shape and the like of the barriers, and reflect the state and the movement characteristics of the barriers.
And obtaining a multidimensional dynamic influence analysis index, wherein the multidimensional dynamic influence analysis index comprises a dynamic influence factor and a dynamic influence degree. The dynamic influence factor refers to the influence degree of the obstacle on the robot, and is related to the relative motion, distance and physical characteristics of the obstacle between the obstacle and the robot, when the robot encounters the obstacle, the dynamic influence factor changes, for example, if the robot encounters an obstacle with larger volume, irregular shape and faster moving speed in the inspection process, the dynamic influence factor becomes larger, because the obstacle has larger influence on the aspects of the motion track, speed, gesture and the like of the robot, the influence degree of the obstacle on the robot is obtained by calculating the dynamic influence factor, and accordingly obstacle avoidance measures or inspection route adjustment are adopted. For example, if the dynamic impact factor is large, the robot needs to change the motion trajectory or slow down to avoid collision; if the dynamic influence factor is smaller, the robot can continue to carry out inspection according to the original plan. The dynamic influence degree refers to the degree of influence of the obstacle on the robot, and is calculated and analyzed based on the motion data of the robot and the dynamic data of the obstacle. For example, the dynamic influence factor and the dynamic influence degree are calculated by analyzing a movement locus, a speed change, a posture change, and the like of the robot when encountering an obstacle.
And respectively carrying out dynamic influence analysis on the dynamic data of the robot by using the calculated dynamic influence factors and dynamic influence degrees. And obtaining a plurality of associated dynamic effects by analyzing the influence degree of the obstacle on the robot and the influence degree of the obstacle on the robot, wherein the associated dynamic effects reflect the interrelation and dynamic changes between the robot and the obstacle, for example, if the robot identifies a moving obstacle in the inspection process, the corresponding associated dynamic effects are generated according to the characteristics of the moving speed, the moving direction, the moving size and the like of the obstacle, the distance between the robot and the obstacle, the relative speed and the like.
Based on the analysis results of the associated dynamic effects, dynamic obstacle recognition results are generated, including the type, position, movement track, etc. of the obstacle, and countermeasures (avoidance of the obstacle, adjustment of the inspection route, etc.) that the robot should take. For example, if the robot identifies a moving obstacle during the inspection, a corresponding dynamic obstacle identification result, such as the type, position, movement track, etc. of the obstacle is generated according to the characteristics of the moving speed, direction, size, etc. of the obstacle, and the distance, relative speed, etc. of the robot and the obstacle, and corresponding countermeasures, such as avoiding the obstacle or adjusting the inspection route, etc. are given. According to the preferred embodiment, the interaction and dynamic change between the robot and the obstacle can be accurately evaluated by analyzing the influence of the obstacle on the robot and the influence of the obstacle on the robot, so that safer and more reliable obstacle avoidance measures are adopted or the inspection route is adjusted, collision or dangerous situations are avoided, and the technical effects of enhancing the interaction and improving the safety are achieved.
In another preferred implementation manner provided in the embodiments of the present application, based on the dynamic obstacle recognition result, path optimization is performed according to the routing task information and the routing path information, including:
the method comprises the steps of obtaining a preset dynamic influence degree threshold, wherein the preset dynamic influence degree threshold is a preset value and is used for distinguishing the influence of the obstacle on the robot, and if the value of the dynamic influence degree is smaller than the threshold, the influence of the obstacle on the target substation robot is small, the target substation robot does not need to change the original path, and only needs to reasonably avoid the obstacle.
And screening the dynamic obstacle recognition result based on the preset dynamic influence degree threshold, and screening out characteristic association dynamic influence with the dynamic influence degree larger than or equal to the preset dynamic influence degree threshold by comparing the dynamic influence degree with the preset dynamic influence degree threshold, wherein the characteristic association dynamic influence represents an area or a target of the target substation robot which is greatly influenced.
And positioning the plurality of target dynamic data by utilizing the screened characteristic association dynamic influence to find out the target dynamic data with abnormality. And extracting real-time position data of the target substation robot in the inspection process according to the dynamic monitoring data of the robot. And then, combining the patrol task information and the patrol path information, and extracting real-time information such as the real-time state (real-time patrol task information) of each patrol task, the real-time position (real-time patrol path information) of the robot on the patrol path and the like.
And (3) real-time adjustment is carried out on the current inspection path by analyzing abnormal target dynamic data, real-time position data of the robot and real-time inspection task information to obtain an optimized inspection path, and the robot continues to inspect along the optimized inspection path. According to the preferred embodiment, the number of dynamic obstacle recognition results to be processed is reduced by setting the dynamic influence threshold, and the path is required to be further optimized only when the dynamic influence is greater than or equal to the preset threshold, so that the complexity of calculation and processing is reduced, and the technical effects of reducing the calculated amount and improving the efficiency are achieved.
In another preferred implementation manner provided in the embodiments of the present application, obtaining an optimized inspection path includes:
and acquiring a routing inspection path adjustment record library from the robot system, wherein the record library comprises response data of the robot to various obstacles and environments and corresponding path adjustment records in the past routing inspection process.
Training, testing and verifying according to the routing inspection path adjustment record library to obtain a routing inspection path optimization model, wherein the model aims at learning to generate an optimal routing inspection path according to abnormal target dynamic data, real-time position data, real-time routing inspection task information and real-time routing inspection path information.
And inputting the abnormal target dynamic data, the real-time position data, the real-time patrol task information and the real-time patrol path information into the patrol path optimization model, and generating an optimized patrol path by the model according to the real-time position of the robot, the dynamic change of the target transformer substation and other possible obstacle information. According to the preferred embodiment, the routing inspection path is dynamically optimized according to past experience and real-time data, so that the robot can adapt to environment and obstacle changes when performing routing inspection tasks, and the technical effects of improving routing inspection efficiency and safety are achieved.
Example two
Based on the same inventive concept as the path optimization method for the substation robot in the foregoing embodiments, as shown in fig. 2, the present application provides a path optimization method for the substation robot, and the system and method embodiments in the embodiments of the present application are based on the same inventive concept. Wherein the system comprises:
the inspection task information acquisition module 1 is used for acquiring inspection task information of the target substation robot;
the inspection path information obtaining module 2 is used for planning a path of the target substation robot according to the inspection task information to obtain inspection path information;
the information transmission module 3 is used for transmitting the inspection path information to the target substation robot;
the robot dynamic monitoring data acquisition module 4 is used for the target substation robot to carry out inspection according to the inspection path information and the inspection task information, and carrying out real-time monitoring on the target substation robot to acquire robot dynamic monitoring data;
the dynamic obstacle recognition result obtaining module 5 is used for carrying out obstacle recognition based on the dynamic monitoring data of the robot to obtain a dynamic obstacle recognition result;
and the path optimization module 6 is used for performing path optimization according to the inspection task information and the inspection path information based on the dynamic obstacle recognition result by the path optimization module 6.
Further, the inspection path information obtaining module 2 is configured to execute the following method:
acquiring initial position information of the target substation robot;
performing feature recognition according to the patrol task information to obtain a patrol position distribution map;
and carrying out inspection path analysis based on the initial position information and the inspection position distribution diagram to obtain the inspection path information.
Further, the inspection path information obtaining module 2 is configured to execute the following method:
performing inspection position priority analysis on the inspection position distribution map based on the inspection task information to obtain an optimized inspection position distribution map;
obtaining a patrol path planning record library;
data integration is carried out based on the routing inspection path planning record library, and a routing inspection path planning model is obtained;
and inputting the initial position information and the optimized routing inspection position distribution diagram into the routing inspection path planning model to generate routing inspection path information.
Further, the dynamic obstacle recognition result obtaining module 5 is configured to execute the following method:
performing feature recognition based on the robot dynamic monitoring data to obtain robot dynamic data and a plurality of target dynamic data;
obtaining a multidimensional dynamic influence analysis index, wherein the multidimensional dynamic influence analysis index comprises a dynamic influence factor and a dynamic influence degree;
based on the multidimensional dynamic influence analysis index, respectively carrying out dynamic influence analysis on the robot dynamic data according to the target dynamic data to obtain a plurality of associated dynamic influences;
and generating the dynamic obstacle recognition result according to the plurality of associated dynamic influences.
Further, the dynamic obstacle recognition result obtaining module 5 is configured to execute the following method:
obtaining a preset dynamic influence threshold;
screening the dynamic obstacle recognition result based on the preset dynamic influence degree threshold value to obtain characteristic association dynamic influence which is larger than or equal to the preset dynamic influence degree threshold value;
positioning the plurality of target dynamic data based on the characteristic association dynamic influence to obtain abnormal target dynamic data;
extracting real-time position data of the target substation robot according to the dynamic robot monitoring data;
extracting real-time routing inspection task information and real-time routing inspection path information according to the routing inspection task information and the routing inspection path information;
and carrying out path adjustment on the real-time routing inspection path information based on the abnormal target dynamic data, the real-time position data and the real-time routing inspection task information to obtain an optimized routing inspection path.
Further, the dynamic obstacle recognition result obtaining module 5 is configured to execute the following method:
obtaining an inspection path adjustment record library;
training and testing according to the routing inspection path adjustment record library to obtain a routing inspection path optimization model;
and inputting the abnormal target dynamic data, the real-time position data, the real-time patrol task information and the real-time patrol path information into the patrol path optimization model to generate the optimized patrol path.
It should be noted that the sequence of the embodiments of the present application is merely for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing description of the preferred embodiments of the present application is not intended to limit the invention to the particular embodiments of the present application, but to limit the scope of the invention to the particular embodiments of the present application.
The specification and drawings are merely exemplary of the application and are to be regarded as covering any and all modifications, variations, combinations, or equivalents that are within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (7)
1. A path optimization method for a substation robot, the method comprising:
obtaining inspection task information of a target substation robot;
carrying out path planning on the target substation robot according to the routing inspection task information to obtain routing inspection path information;
transmitting the routing inspection path information to the target substation robot;
the target substation robot performs inspection according to the inspection path information and the inspection task information, and monitors the target substation robot in real time to obtain dynamic monitoring data of the robot;
performing obstacle recognition based on the dynamic monitoring data of the robot to obtain a dynamic obstacle recognition result;
and carrying out path optimization according to the routing inspection task information and the routing inspection path information based on the dynamic obstacle recognition result.
2. The method of claim 1, wherein performing path planning on the target substation robot according to the inspection task information to obtain inspection path information, comprises:
acquiring initial position information of the target substation robot;
performing feature recognition according to the patrol task information to obtain a patrol position distribution map;
and carrying out inspection path analysis based on the initial position information and the inspection position distribution diagram to obtain the inspection path information.
3. The method of claim 2, wherein performing a patrol path analysis based on the initial position information and the patrol position profile to obtain the patrol path information comprises:
performing inspection position priority analysis on the inspection position distribution map based on the inspection task information to obtain an optimized inspection position distribution map;
obtaining a patrol path planning record library;
data integration is carried out based on the routing inspection path planning record library, and a routing inspection path planning model is obtained;
and inputting the initial position information and the optimized routing inspection position distribution diagram into the routing inspection path planning model to generate routing inspection path information.
4. The method of claim 1, wherein performing obstacle recognition based on the robot dynamic monitoring data to obtain a dynamic obstacle recognition result comprises:
performing feature recognition based on the robot dynamic monitoring data to obtain robot dynamic data and a plurality of target dynamic data;
obtaining a multidimensional dynamic influence analysis index, wherein the multidimensional dynamic influence analysis index comprises a dynamic influence factor and a dynamic influence degree;
based on the multidimensional dynamic influence analysis index, respectively carrying out dynamic influence analysis on the robot dynamic data according to the target dynamic data to obtain a plurality of associated dynamic influences;
and generating the dynamic obstacle recognition result according to the plurality of associated dynamic influences.
5. The method of claim 4, wherein performing path optimization based on the dynamic obstacle recognition result according to the patrol task information and the patrol path information comprises:
obtaining a preset dynamic influence threshold;
screening the dynamic obstacle recognition result based on the preset dynamic influence degree threshold value to obtain characteristic association dynamic influence which is larger than or equal to the preset dynamic influence degree threshold value;
positioning the plurality of target dynamic data based on the characteristic association dynamic influence to obtain abnormal target dynamic data;
extracting real-time position data of the target substation robot according to the dynamic robot monitoring data;
extracting real-time routing inspection task information and real-time routing inspection path information according to the routing inspection task information and the routing inspection path information;
and carrying out path adjustment on the real-time routing inspection path information based on the abnormal target dynamic data, the real-time position data and the real-time routing inspection task information to obtain an optimized routing inspection path.
6. The method of claim 5, wherein obtaining an optimized inspection path comprises:
obtaining an inspection path adjustment record library;
training and testing according to the routing inspection path adjustment record library to obtain a routing inspection path optimization model;
and inputting the abnormal target dynamic data, the real-time position data, the real-time patrol task information and the real-time patrol path information into the patrol path optimization model to generate the optimized patrol path.
7. A path optimization system for a substation robot, the system comprising:
the inspection task information acquisition module is used for acquiring inspection task information of the target substation robot;
the routing inspection path information acquisition module is used for planning a path of the target substation robot according to the routing inspection task information to acquire routing inspection path information;
the information transmission module is used for transmitting the inspection path information to the target substation robot;
the robot dynamic monitoring data acquisition module is used for carrying out inspection on the target substation robot according to the inspection path information and the inspection task information, and carrying out real-time monitoring on the target substation robot to acquire robot dynamic monitoring data;
the dynamic obstacle recognition result obtaining module is used for carrying out obstacle recognition based on the dynamic robot monitoring data to obtain a dynamic obstacle recognition result;
and the path optimization module is used for performing path optimization according to the patrol task information and the patrol path information based on the dynamic obstacle recognition result.
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