Substation inspection robot management method based on company Internet of Things platform

Abstract

The invention discloses a transformer substation patrol robot management method based on a company Internet of Things platform, and the method comprises the steps: collecting multi-modal data in a transformer substation, carrying out the fusion and analysis of the collected multi-modal data, and generating equipment state information based on a deep learning algorithm; according to the equipment state information and the routing inspection task priority, task allocation is performed on the routing inspection robot through a dynamic scheduling algorithm, according to a task allocation result, an optimal routing inspection path for the robot to execute tasks is formulated by using a path planning algorithm, and the path is adjusted through real-time data; constructing an equipment health management model, and predicting an equipment operation state based on the time sequence data and a machine learning algorithm; real-time monitoring is carried out through an Internet of Things platform, and data is stored in a centralized database. According to the invention, the inspection efficiency and accuracy of the transformer substation can be obviously improved, the potential risk caused by equipment failure or inspection omission is reduced, and the operation maintenance mode of the unattended transformer substation is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of substation management, and in particular to a substation patrol robot management method based on a company's Internet of Things platform. Background Art

[0002] With the rapid development of the power industry, the scale of substations continues to expand, and the popularity of unmanned substations has increased year by year. However, the traditional manual inspection mode can no longer meet the requirements of efficient and intelligent management of modern power equipment due to problems such as long time consumption, low efficiency, and difficulty in ensuring inspection quality. In recent years, the application of patrol robots based on Internet of Things technology in substation inspection has gradually emerged, which has greatly improved the inspection efficiency and accuracy through real-time data collection, status monitoring and intelligent analysis. However, the current patrol robot management system generally has problems such as data isolation, rigid task allocation, inflexible path planning, and insufficient equipment operation status prediction ability, which limits the further promotion and efficiency improvement of unmanned operation and maintenance of substations. Therefore, it is urgent to develop a unified management method that can realize multimodal data fusion, dynamic task allocation, intelligent path planning and health management to solve the many technical bottlenecks in the current substation inspection. Summary of the invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides a substation patrol robot management method based on the company's Internet of Things platform, aiming to solve the key technical problems in the management of substation patrol robots, specifically including the following aspects: First, how to use multimodal data fusion technology to uniformly process multi-source data such as visible light images, infrared thermal images, and equipment operating parameters to generate accurate equipment status information; second, how to design a dynamic task allocation algorithm based on equipment priority and real-time status to achieve efficient matching between tasks and patrol robots; third, how to construct an intelligent inspection path planning algorithm based on three-dimensional environment modeling and dynamic path adjustment to improve the flexibility and accuracy of robot inspections; fourth, how to establish an equipment health management system based on time series models and machine learning algorithms to accurately predict the equipment operating status and dynamically adjust the inspection plan according to the equipment health status. The solution to these technical problems will significantly improve the intelligent management level of substation patrol robots.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions, a substation patrol robot management method based on the company's Internet of Things platform, including:

[0006] Use patrol robots and IoT sensing devices to collect multimodal data in the substation, fuse and analyze the collected multimodal data, and generate equipment status information based on deep learning algorithms;

[0007] According to the equipment status information and inspection task priority, the patrol robot is assigned tasks through a dynamic scheduling algorithm. Based on the task assignment results, the path planning algorithm is used to formulate the optimal inspection path for the robot to perform the task, and the path is adjusted through real-time data;

[0008] Build equipment health management models, predict equipment operating status based on time series data and machine learning algorithms, and update equipment health records;

[0009] The robot's execution status, task completion status and inspection data are monitored in real time through the Internet of Things platform, and the data is stored in a centralized database.

[0010] As a preferred solution of the substation patrol robot management method based on the company's Internet of Things platform described in the present invention, the multimodal data collected in the substation includes visible light images, infrared thermal images, sound signals, environmental sensor data and equipment operating parameters.

[0011] As a preferred solution of the substation patrol robot management method based on the company's Internet of Things platform described in the present invention, the multimodal data includes: image data collected by the visible light camera and infrared thermal imager on the patrol robot, including equipment appearance images and infrared temperature distribution maps; sensor data collected by the Internet of Things sensing equipment, including environmental temperature and humidity, voltage, current and gas concentration information; operating status data collected by the robot and monitoring platform, including the real-time position, switch status and vibration signal of the equipment; all data are uploaded to the centralized database of the Internet of Things platform via wireless communication for storage and processing.

[0012] As a preferred solution of the substation patrol robot management method based on the company's Internet of Things platform described in the present invention, the fusion and analysis include: extracting features of visible light images and infrared thermal images through convolutional neural networks, converting them into structured multi-dimensional feature vectors, and processing sensor data and operating status data through recurrent neural networks to capture changing trends and correlations in time series, using the attention mechanism to assign weights to feature vectors of different modalities, and inputting the fused features into the fully connected layer to generate a unified device status feature vector.

[0013] As a preferred solution of the substation patrol robot management method based on the company's Internet of Things platform described in the present invention, the dynamic scheduling algorithm includes: the equipment status characteristics are generated by the data fusion module, the task priority is set according to the importance level and fault risk of the equipment; the current state of the robot includes its position, remaining power and progress of executing the task;

[0014] The dynamic scheduling algorithm adopts a reinforcement learning model to optimize the matching relationship between robots and tasks, and achieves load balancing in the execution of multi-robot collaborative tasks through a distributed scheduling strategy.

[0015] As a preferred solution of the substation patrol robot management method based on the company's Internet of Things platform described in the present invention, the reinforcement learning model includes inputting device status characteristics, task priority, robot current position and power information, outputting task allocation plan, and constructing a reward function based on minimizing task completion time and optimizing robot resource utilization through repeated training, and using a scheduling algorithm to dynamically adjust task allocation in the system.

[0016] As a preferred solution of the substation patrol robot management method based on the company's Internet of Things platform described in the present invention, the path planning algorithm includes using lidar and visual SLAM technology to build a three-dimensional environmental model of the substation, which includes equipment locations, obstacle areas and drivable paths, and calculates the shortest path between the patrol robot's task points based on the A algorithm to generate an initial patrol path. During the inspection process, if dynamic obstacles or other path change factors are detected, the reinforcement learning algorithm adjusts the path planning through a real-time path optimization module.

[0017] As a preferred solution of the substation patrol robot management method based on the company's Internet of Things platform described in the present invention, the three-dimensional environment model includes, constructed by laser radar, camera and digital twin technology, the model contains the spatial position information of the equipment, obstacle area identification and the robot's driving path, and is updated in real time on the Internet of Things platform. The model interacts with the platform database and dynamically records the inspection data.

[0018] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, it implements the steps of a substation patrol robot management method based on a company's Internet of Things platform.

[0019] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of a substation patrol robot management method based on a company's Internet of Things platform are implemented.

[0020] Beneficial effects of the invention: The invention proposes a substation patrol robot management method based on the company's Internet of Things platform, generates equipment status information through multimodal data fusion, realizes reasonable scheduling of patrol tasks through dynamic task allocation algorithm, optimizes the driving path of patrol robots through path planning algorithm, and provides operation status prediction and patrol plan adjustment through equipment health management system, ultimately forming an intelligent, automated and efficient patrol management platform. The invention can significantly improve the efficiency and accuracy of substation patrol, reduce the potential risks caused by equipment failure or patrol omissions, optimize the operation and maintenance mode of unmanned substations, and provide strong support for the intelligent management of substations. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0022] Figure 1 A schematic flow chart of a method for managing a substation patrol robot based on a company's Internet of Things platform is provided as an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0024] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0025] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0026] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0027] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0028] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0029] Example 1, reference Figure 1 , which is the first embodiment of the present invention, and provides a substation patrol robot management method based on the company's Internet of Things platform, including:

[0030] S1: Use patrol robots and IoT sensing devices to collect multimodal data in the substation, fuse and analyze the collected multimodal data, and generate equipment status information based on deep learning algorithms.

[0031] S2: According to the equipment status information and inspection task priority, the inspection robot is assigned tasks through a dynamic scheduling algorithm. Based on the task assignment results, the path planning algorithm is used to formulate the optimal inspection path for the robot to perform the task, and the path is adjusted through real-time data.

[0032] S3: Build a device health management model, predict the device operating status based on time series data and machine learning algorithms, and update the device health profile.

[0033] S4: The robot’s execution status, task completion status, and inspection data are monitored in real time through the IoT platform, and the data is stored in a centralized database.

[0034] The multimodal data collected in the substation includes visible light images, infrared thermal images, sound signals, environmental sensor data and equipment operating parameters.

[0035] The multimodal data includes: image data collected by the visible light camera and infrared thermal imager on the patrol robot, including equipment appearance images and infrared temperature distribution maps; sensor data collected by the Internet of Things sensing equipment, including environmental temperature and humidity, voltage, current and gas concentration information; operating status data collected by the robot and monitoring platform, including the real-time position, switch status and vibration signal of the equipment; all data are uploaded to the centralized database of the Internet of Things platform via wireless communication for storage and processing.

[0036] The fusion and analysis includes extracting features from visible light images and infrared thermal images through convolutional neural networks, converting them into structured multi-dimensional feature vectors, processing sensor data and operating status data through recurrent neural networks, capturing changing trends and correlations in time series, using an attention mechanism to assign weights to feature vectors of different modalities, and inputting the fused features into a fully connected layer to generate a unified device status feature vector.

[0037] The dynamic scheduling algorithm includes: the equipment status characteristics are generated by the data fusion module, the task priority is set according to the importance level and failure risk of the equipment; the current status of the robot includes its position, remaining power and progress of the task;

[0038] The dynamic scheduling algorithm adopts a reinforcement learning model to optimize the matching relationship between robots and tasks, and achieves load balancing in the execution of multi-robot collaborative tasks through a distributed scheduling strategy.

[0039] The reinforcement learning model includes inputting device state characteristics, task priority, robot current position and power information, outputting task allocation plan, constructing reward function based on minimizing task completion time and optimizing robot resource utilization through repeated training, and using scheduling algorithm to dynamically adjust task allocation in the system.

[0040] The path planning algorithm includes using lidar and visual SLAM technology to build a three-dimensional environmental model of the substation, which includes equipment locations, obstacle areas, and drivable paths, and calculates the shortest path between the patrol robot's task points based on the A algorithm to generate an initial patrol path. During the patrol process, if dynamic obstacles or other path change factors are detected, the reinforcement learning algorithm adjusts the path planning through a real-time path optimization module.

[0041] The three-dimensional environment model is constructed through lidar, camera and digital twin technology. The model contains the spatial location information of the equipment, obstacle area identification and the robot's driving path, and is updated in real time on the Internet of Things platform. The model interacts with the platform database and dynamically records inspection data.

[0042] The equipment health management model is built based on time series models and machine learning algorithms. Time series models (such as LSTM or GRU) are used to predict the changing trend of equipment operating parameters, and machine learning algorithms (such as random forest or XGBoost) evaluate the equipment health status by analyzing historical fault data and real-time operating parameters. The equipment health file contains the operating time, fault records and current health score, and is dynamically updated according to the model evaluation results.

[0043] The prediction results of the health management model are used to dynamically adjust the inspection plan. Specifically, when the equipment health score is lower than the preset threshold, the system automatically increases the inspection frequency of the equipment and prioritizes the inspection robot to inspect it; when the equipment status is stable, the system reduces its inspection frequency to optimize resource allocation. The updated inspection plan is sent to the inspection robot in real time through the IoT platform.

[0044] The IoT platform includes a centralized management interface, and the interface functions include real-time monitoring of patrol robot execution status, task completion status, and equipment status data. The platform classifies and stores patrol data by task and equipment dimensions, and supports retrieval of relevant data by time, equipment type, and task category. The platform also provides user authority management and data export functions, and supports displaying and analyzing historical data in the form of tables, graphs, etc.

[0045] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0046] Example 2

[0047] The second embodiment of the present invention is different from the previous embodiment in that:

[0048] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0049] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0050] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0051] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0052] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0053] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A substation patrol robot management method based on the company's Internet of Things platform, characterized by: include, Use patrol robots and IoT sensing devices to collect multimodal data in the substation, fuse and analyze the collected multimodal data, and generate equipment status information based on deep learning algorithms; According to the equipment status information and inspection task priority, the patrol robot is assigned tasks through a dynamic scheduling algorithm. Based on the task assignment results, the path planning algorithm is used to formulate the optimal inspection path for the robot to perform the task, and the path is adjusted through real-time data; Build equipment health management models, predict equipment operating status based on time series data and machine learning algorithms, and update equipment health records; The robot's execution status, task completion status and inspection data are monitored in real time through the Internet of Things platform, and the data is stored in a centralized database.

2. The substation patrol robot management method based on the company's Internet of Things platform as claimed in claim 1 is characterized by: The multimodal data collected in the substation includes visible light images, infrared thermal images, sound signals, environmental sensor data and equipment operating parameters.

3. The substation patrol robot management method based on the company's Internet of Things platform as claimed in claim 2 is characterized by: The multimodal data includes: image data collected by the visible light camera and infrared thermal imager on the patrol robot, including equipment appearance images and infrared temperature distribution maps; sensor data collected by the Internet of Things sensing equipment, including environmental temperature and humidity, voltage, current and gas concentration information; operating status data collected by the robot and monitoring platform, including the real-time position, switch status and vibration signal of the equipment; all data are uploaded to the centralized database of the Internet of Things platform via wireless communication for storage and processing.

4. The substation patrol robot management method based on the company's Internet of Things platform as claimed in claim 3 is characterized by: The fusion and analysis includes extracting features from visible light images and infrared thermal images through convolutional neural networks, converting them into structured multi-dimensional feature vectors, processing sensor data and operating status data through recurrent neural networks, capturing changing trends and correlations in time series, using an attention mechanism to assign weights to feature vectors of different modalities, and inputting the fused features into a fully connected layer to generate a unified device status feature vector.

5. The substation patrol robot management method based on the company's Internet of Things platform as claimed in claim 4 is characterized by: The dynamic scheduling algorithm includes: the equipment status characteristics are generated by the data fusion module, the task priority is set according to the importance level and failure risk of the equipment; the current status of the robot includes its position, remaining power and progress of the task; The dynamic scheduling algorithm adopts a reinforcement learning model to optimize the matching relationship between robots and tasks, and achieves load balancing in the execution of multi-robot collaborative tasks through a distributed scheduling strategy.

6. The substation patrol robot management method based on the company's Internet of Things platform as claimed in claim 5 is characterized by: The reinforcement learning model includes inputting device state characteristics, task priority, robot current position and power information, outputting task allocation plan, constructing reward function based on minimizing task completion time and optimizing robot resource utilization through repeated training, and using scheduling algorithm to dynamically adjust task allocation in the system.

7. The substation patrol robot management method based on the company's Internet of Things platform as claimed in claim 6 is characterized by: The path planning algorithm includes using lidar and visual SLAM technology to build a three-dimensional environmental model of the substation, which includes equipment locations, obstacle areas, and drivable paths, and calculates the shortest path between the patrol robot's task points based on the A algorithm to generate an initial patrol path. During the patrol process, if dynamic obstacles or other path change factors are detected, the reinforcement learning algorithm adjusts the path planning through a real-time path optimization module.

8. The substation patrol robot management method based on the company's Internet of Things platform as claimed in claim 7 is characterized by: The three-dimensional environment model is constructed through lidar, camera and digital twin technology. The model contains the spatial location information of the equipment, obstacle area identification and the robot's driving path, and is updated in real time on the Internet of Things platform. The model interacts with the platform database and dynamically records inspection data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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