Multi-machine interconnection control method and system for network sealing operation vehicle

Abstract

The present invention relates to the technical field of network sealing operations, and in particular to a multi-machine interconnection control method and system for network sealing operation vehicles, which ensure the movement consistency and synchronization of all operation vehicles in the network sealing operation; the method comprises: acquiring image data information of multiple transmission cables in a network sealing target area; extracting cable surface features for the image data information of each transmission cable to obtain a transmission cable surface feature set; inputting the transmission cable surface feature set into a pre-constructed operation vehicle sliding evaluation model to obtain an operation vehicle sliding evaluation index; combining the operation vehicle sliding evaluation index with a preset network sealing operation speed to obtain an operation vehicle movement feature vector; inputting the operation vehicle movement feature vector into a pre-constructed operation vehicle movement control analysis model to obtain an operation vehicle movement control strategy; when performing a network sealing operation, controlling the corresponding operation vehicle to perform the network sealing operation according to the obtained multiple operation vehicle movement control strategies.

Description

Technical Field

[0001] The present invention relates to the technical field of network sealing operations, and in particular to a multi-machine interconnection control method and system for a network sealing operation vehicle. Background Art

[0002] In the field of power maintenance, especially in the maintenance of high-voltage transmission lines, network sealing operation is a vital task; traditional network sealing operations mainly rely on manual work, which has problems such as high labor intensity, high operation risks, and low efficiency; in recent years, with the development of intelligent robot technology, network sealing operation vehicles, as a new type of automated equipment, have gradually begun to be used in the network sealing maintenance of transmission lines, greatly improving the safety and efficiency of operations.

[0003] However, when facing the network sealing operation of large-scale power transmission networks, a single network sealing operation vehicle often cannot meet the operation requirements; therefore, multi-machine collaborative operation has become an inevitable trend; however, the collaborative operation of multiple network sealing operation vehicles faces many challenges, the most critical of which is how to ensure the synchronization and coordination between multiple machines; especially when the operation vehicle needs to perform network sealing operations on different transmission cables, due to the differences in the physical properties of the cables (such as surface smoothness, tilt angle, material, etc.), each operation vehicle may slide differently during movement, thus affecting the synchronization and overall progress of the operation. Therefore, a multi-machine interconnection control method for network sealing operation vehicles is urgently needed. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a multi-machine interconnection control method and system for network sealing operation vehicles, which ensures the movement consistency and synchronization of all operation vehicles in the network sealing operation and reduces the operation errors caused by sliding differences.

[0005] In a first aspect, the present invention provides a multi-machine interconnection control method for a network blocking operation vehicle, the method comprising:

[0006] Acquire image data information of multiple power transmission cables in the target area of ​​network blocking;

[0007] Based on the image data information of each transmission cable, cable surface features are extracted to obtain a transmission cable surface feature set;

[0008] Input the surface feature set of the transmission cable into the pre-built working vehicle sliding evaluation model to obtain the working vehicle sliding evaluation index;

[0009] The sliding evaluation index of the working vehicle is combined with the preset network closing operation speed to obtain the moving characteristic vector of the working vehicle;

[0010] Inputting the working vehicle movement feature vector into the pre-built working vehicle movement control analysis model to obtain the working vehicle movement control strategy;

[0011] When performing the network sealing operation, the corresponding working vehicles are controlled to perform the network sealing operation according to the obtained movement control strategies of multiple working vehicles.

[0012] Furthermore, the transmission cable surface feature set includes texture features, geometric features and material features;

[0013] Wherein, the texture features also include the texture density, roughness and smoothness of the cable surface;

[0014] The geometric characteristics also include the diameter, curvature and wear of the cable;

[0015] The material characteristics also include the material type of the cable and the cable surface coating.

[0016] Furthermore, the feature extraction method of the transmission cable surface feature set comprises:

[0017] De-noising, contrast enhancement and normalization of image data information of power transmission cables

[0018] For each transmission cable, an image segmentation algorithm is used to separate the transmission cable area from the background;

[0019] Performing texture analysis on the separated transmission cables to extract the texture features of the transmission cables; performing geometric measurement on the separated transmission cables to extract the geometric features of the transmission cables; performing spectral analysis on the separated transmission cables to extract the material features of the transmission cables;

[0020] The extracted texture features, geometric features and material features are normalized and integrated to generate a surface feature set of the transmission cable.

[0021] Furthermore, the method for constructing the working vehicle sliding evaluation model comprises:

[0022] Collect actual operation data of historical network sealing operations, including the movement of the network sealing vehicle under different transmission cable surface features, including texture features, geometric features, and material features;

[0023] Preprocess the actual operation data of historical network blocking operations collected, including data cleaning, feature extraction and labeling;

[0024] According to the physical characteristics of the transmission cable and the movement of the work vehicle, the input characteristics and output characteristics of the work vehicle sliding evaluation model are determined;

[0025] Based on the determined input characteristics and output characteristics, a model framework of the working vehicle sliding evaluation model is determined;

[0026] The working vehicle sliding evaluation model is trained using the preprocessed actual working data, and the performance of the model is optimized by adjusting the parameters and structure of the working vehicle sliding evaluation model; and the working vehicle sliding evaluation model is evaluated and optimized.

[0027] Furthermore, the calculation formula of the working vehicle sliding evaluation model is: in, represents the sliding evaluation index of the working vehicle, n is the number of features in the surface feature set of the transmission cable, and They are The weights and eigenvalues ​​of the surface features of the transmission cable, 𝑏 is the bias term; where, The value range of is constrained between 0 and 1, where values ​​close to 0 indicate low sliding risk and values ​​close to 1 indicate high sliding risk.

[0028] Furthermore, the work vehicle movement control strategy includes the actual moving speed of each work vehicle on the transmission cable where the work vehicle is located.

[0029] Furthermore, the calculation formula of the working vehicle movement control analysis model is: in, represents the mobile control speed of the i-th work vehicle in the work vehicle mobile control strategy, The preset net sealing operation speed is displayed. In the same net sealing operation, the preset net sealing operation speeds of multiple operation vehicles are the same. represents the vehicle sliding evaluation index of the i-th vehicle, is a positive proportional constant used to adjust right impact.

[0030] On the other hand, the present application also provides a multi-machine interconnection control system for a network blocking operation vehicle, the system comprising:

[0031] An image data acquisition module is used to acquire image data information of multiple power transmission cables in the target area of ​​network blocking;

[0032] The cable surface feature extraction module is used to receive the transmission cable image data from the image data acquisition module; and extract the cable surface features from the image data information of each transmission cable to generate a transmission cable surface feature set;

[0033] The working vehicle sliding evaluation module is used to receive the transmission cable surface feature set output by the cable surface feature extraction module; and input the feature set into the pre-built working vehicle sliding evaluation model to calculate and output the working vehicle sliding evaluation index; the sliding evaluation index is used to characterize the relative sliding condition of the working vehicle when moving on the transmission cable;

[0034] The moving feature vector generating module is used to receive the sliding evaluation index output by the working vehicle sliding evaluation module; and generate the moving feature vector of the working vehicle in combination with the preset network sealing operation speed;

[0035] The working vehicle movement control analysis module is used to receive the working vehicle movement feature vector output by the movement feature vector generation module; and input the feature vector into a pre-built working vehicle movement control analysis model to calculate and output the working vehicle movement control strategy;

[0036] The network sealing operation control module is used to receive multiple operation vehicle movement control strategies output by the operation vehicle movement control analysis module; and according to the obtained movement control strategies, the corresponding network sealing operation vehicle is controlled in real time to perform network sealing operations.

[0037] In a third aspect, the present application provides an electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and the computer program, when executed by the processor, implements the steps of any one of the above methods.

[0038] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps in any one of the above-mentioned methods when executed by a processor.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows: by acquiring and analyzing the image data information of the transmission cable in real time, the sliding conditions of each work vehicle on different cables are accurately evaluated, and then the corresponding mobile control strategy is formulated; it helps to ensure the movement consistency and synchronization of all work vehicles in the network sealing operation, reduce the operational errors caused by sliding differences, and improve the safety of the operation; based on the work vehicle sliding evaluation index and the preset network sealing operation speed, the generated work vehicle movement feature vector can guide the work vehicle to perform network sealing operations at the optimal speed, avoid unnecessary waiting and adjustment time, and improve the overall efficiency of multi-machine collaborative operations; by considering the differences in the physical properties of the transmission cables, the movement strategy of the work vehicle can be dynamically adjusted to adapt to various complex operating environments, enhance the adaptability to different operating scenarios and the flexibility of the operation; by automatically controlling multiple network sealing operation vehicles, the need for manual intervention is reduced, the labor intensity and operation risks are reduced, and the accuracy and reliability of the operation are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a flow chart of the present invention;

[0041] Figure 2 is a flow chart for collecting image data information of multiple transmission cables;

[0042] Figure 3 It is a structural diagram of a multi-machine interconnected control system used for network sealing operation vehicles. DETAILED DESCRIPTION

[0043] In the description of this application, those skilled in the art should know that this application can be implemented as a method, an apparatus, an electronic device, and a computer-readable storage medium. Therefore, this application can be specifically implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), a combination of hardware and software. In addition, in some embodiments, this application can also be implemented in the form of a computer program product in one or more computer-readable storage media, and the computer-readable storage medium contains computer program code.

[0044] The above-mentioned computer-readable storage medium may adopt any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or devices, or any combination of the above. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, optical disc read-only memories, optical storage devices, magnetic storage devices, or any combination of the above. In the present application, computer-readable storage media can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or device.

[0045] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws.

[0046] The present application describes the provided methods, devices, and electronic devices through flowcharts and / or block diagrams.

[0047] It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, and these computer-readable program instructions are executed by a computer or other programmable data processing device to produce a device that implements the functions / operations specified by the boxes in the flowchart and / or block diagram.

[0048] These computer-readable program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to work in a specific manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction device product including functions / operations specified in the blocks in the flowchart and / or block diagram.

[0049] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable data processing apparatus to provide a process for implementing the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0050] The present application is described below in conjunction with the drawings in the present application.

[0051] Embodiment 1: Figure 1 to Figure 2 As shown, the multi-machine interconnection control method for a network sealing operation vehicle of the present invention is applied to control multiple network sealing operation vehicles to keep synchronization when multiple network sealing operation vehicles perform network sealing operations, and specifically includes the following steps:

[0052] S1. Obtain image data information of multiple power transmission cables in the target area of ​​network blocking, wherein each power transmission cable corresponds to an operation vehicle;

[0053] Step S1 is used to obtain image data information of multiple power transmission cables in the target area of ​​network blocking, and ensure that the image data of each power transmission cable corresponds to a network blocking operation vehicle; Figure 2 As shown, the details are as follows:

[0054] S11. Image acquisition equipment configuration: High-precision image acquisition equipment, such as industrial-grade cameras, needs to be installed on each network sealing operation vehicle; the above equipment must have good imaging quality and the ability to adapt to various weather conditions to ensure that clear and accurate cable image data can be obtained in different operating environments;

[0055] S12. Target area positioning: determine the transmission cable area that needs to be sealed; when it involves long-distance transmission lines, accurately locate each sealing target location through the GPS positioning system or other navigation aids, use the GPS positioning system and other navigation tools to accurately locate the sealing target area, ensure that the image acquisition equipment can accurately align with the target cable, and reduce data deviation caused by inaccurate positioning;

[0056] S13, image data acquisition: start the image acquisition device and aim at the target cable to take pictures; during the acquisition process, the different angles and lighting conditions of the cable should be taken into consideration, and multiple shots are required to obtain an image set that fully reflects the surface characteristics of the cable, ensuring that the acquired image data can fully reflect the surface characteristics of the cable and improve the accuracy and completeness of the analysis; in addition, in order to improve the image quality and reduce noise interference, image enhancement technology is also required for post-processing;

[0057] S14, Data transmission and storage: The collected image data needs to be transmitted to the central processing unit or cloud server in a timely manner for the next step of cable surface feature extraction; data transmission should ensure security and timeliness to avoid data loss or delay; at the same time, the storage solution needs to consider the huge amount of data to ensure sufficient storage space and efficient access performance;

[0058] S15. Establishing the correspondence between image data: Since each transmission cable corresponds to a work vehicle, it is necessary to establish the correspondence between the image data and the work vehicle during the image data collection stage; this is achieved by embedding metadata in the image file or establishing a special data association table to ensure that the cable to which each image belongs and the corresponding work vehicle can be accurately identified in subsequent analysis.

[0059] In this step, step S1 not only provides high-quality data support for the subsequent cable surface feature extraction and operation vehicle control strategy formulation, but also ensures the efficiency and reliability of the multi-machine interconnection control method of the entire network sealing operation vehicle through precise positioning, comprehensive image acquisition, image quality optimization and efficient data management, thereby improving the safety, efficiency and accuracy of the network sealing operation.

[0060] S2. Extracting the surface features of each transmission cable from the image data information to obtain a surface feature set of the transmission cable;

[0061] In step S2, in order to accurately evaluate the sliding of the network sealing vehicle on different cables, it is necessary to extract key indicators that can reflect the surface characteristics of the cables from the acquired image data to form a transmission cable surface feature set; the transmission cable surface feature set has specific features including:

[0062] Texture features: texture density, roughness and smoothness of the cable surface; image processing techniques such as grayscale co-occurrence matrix are used to analyze the texture structure of the cable surface to quantify its complexity and uniformity; specifically, texture density reflects the density of the cable surface texture. High density usually means a higher friction coefficient, which is beneficial to the grip stability of the work vehicle; roughness is used to measure the degree of unevenness of the cable surface. The higher the roughness, the stronger the grip ability of the work vehicle, but it may also cause higher wear; smoothness is the opposite of roughness. A cable surface with high smoothness may make it difficult for the work vehicle to maintain a stable position;

[0063] Geometric features: cable diameter, curvature, and any potential damage or wear. Use image segmentation and edge detection techniques, such as the Canny operator or Hough transform, to identify and measure the basic geometry and defects of the cable. Specifically, the diameter directly affects the design and adjustment of the gripping mechanism of the work vehicle to accommodate cables of different thicknesses. The curvature reflects the curvature of the cable, affecting the path planning of the work vehicle and the selection of gripping points. By identifying damage or wear, potential cable failure points can be discovered in a timely manner to avoid accidents during the operation.

[0064] Material characteristics: Identify the material type of the cable, such as copper, aluminum or composite materials, as well as any coatings or protective layers that may exist; different materials have different physical properties, such as conductivity, weight and hardness, which affect the contact method and force control of the work vehicle; at the same time, special treatment of the cable surface can change its friction characteristics, corrosion resistance or insulation performance; by analyzing the color, reflectivity and spectral characteristics of the cable, combined with the material library in the database, the material composition and surface treatment process of the cable can be inferred, providing a reference for the adaptive adjustment of the work vehicle.

[0065] Furthermore, the extraction process of the above features is as follows:

[0066] S21, denoising, contrast enhancement and standardization of the original image to improve the accuracy of subsequent analysis;

[0067] S22. Use image segmentation algorithms, such as deep learning-based methods (e.g., U-Net), to accurately separate the cable area from the background, laying the foundation for feature extraction;

[0068] S23, according to the different feature categories listed above, respectively apply corresponding image processing algorithms and techniques, such as texture analysis, geometric measurement, spectral analysis, etc., to quantify the specific value of each type of feature;

[0069] S24. All extracted features are normalized to ensure comparability between different features, and then integrated into a complete feature set, namely, the transmission cable surface feature set.

[0070] In this step, the surface feature set of the transmission cable covers the three major features of the cable surface: texture, geometry, and material. It comprehensively evaluates the cable status from multiple dimensions, provides rich data support for the performance analysis of the network sealing vehicle, and can capture tiny but important details on the cable surface, such as slight wear and tear or subtle differences in material. It uses modern image processing and deep learning techniques, such as grayscale co-occurrence matrix, U-Net neural network, Canny operator, and Hough transform, to achieve highly accurate and automated feature extraction, which not only improves the accuracy of feature extraction, but also greatly reduces the need for manual intervention and improves overall work efficiency. By analyzing the diameter, curvature, material and other characteristics of the cable, The design and operation of the work vehicle can adapt to different types of cables in a more targeted manner, thereby improving the flexibility and adaptability of the operation; for example, adjusting the gripping force according to the roughness of the cable, or selecting appropriate contact materials according to the material of the cable, can significantly improve the safety and efficiency of the operation; through normalization processing, the comparability between different features is ensured, so that the feature set can be used in a variety of analysis and comparison scenarios; the standardized process also simplifies the complexity of subsequent data analysis and facilitates feature comparison across different cables and environments; by identifying potential damage or wear marks on the cable surface, it can provide early warning of the health of the cable, prevent possible operational accidents, and extend the service life of the cable.

[0071] S3, inputting the surface feature set of the transmission cable into a pre-built working vehicle sliding evaluation model to obtain a working vehicle sliding evaluation index, where the working vehicle sliding evaluation index is used to characterize the relative sliding condition of the working vehicle when it moves on the transmission cable;

[0072] Step S3 is used to evaluate the sliding tendency of the network sealing vehicle on a specific cable, which is directly related to whether the vehicle can stably and efficiently complete the network sealing task; the vehicle sliding evaluation index (SCEI) reflects the stability and controllability of the vehicle when moving on different cable surfaces; the vehicle sliding evaluation model is based on machine learning technology and can predict the sliding tendency of the vehicle on a specific cable; the construction method of the vehicle sliding evaluation model includes:

[0073] S31. Collect a large amount of actual operation data, including the movement of the network sealing vehicle under different transmission cable surface features, including texture density, roughness, smoothness, diameter, curvature, material type and surface treatment; the above data is obtained through field tests, simulation experiments or historical operation records; pre-process the collected data, including data cleaning, feature extraction and labeling, so as to facilitate the training and evaluation of subsequent models;

[0074] S32. Select and design appropriate features based on the physical properties of the transmission cable and the movement of the work vehicle. The selected features should be able to fully and accurately describe the surface characteristics of the cable and the sliding conditions of the work vehicle when it moves on the cable. For example, the texture density, roughness, smoothness, diameter, curvature, etc. of the cable are selected as input features, and indicators related to the sliding conditions are designed as output features.

[0075] S33. After selecting appropriate features, it is necessary to select an appropriate model for training and prediction. Since the sliding evaluation problem is usually a complex nonlinear problem, some models with strong nonlinear fitting capabilities can be selected, such as neural networks, support vector machines, etc. In the model training process, it is necessary to use the collected data to train the operating vehicle sliding evaluation model, and optimize the performance of the model by adjusting the parameters and structure of the operating vehicle sliding evaluation model.

[0076] S34. After the training of the work vehicle sliding evaluation model is completed, the work vehicle sliding evaluation model needs to be evaluated and optimized; the performance of the work vehicle sliding evaluation model can be evaluated using methods such as cross-validation and hold-out method to check whether the performance of the work vehicle sliding evaluation model on the test set meets expectations; if the performance of the work vehicle sliding evaluation model is poor, it can be optimized by adjusting the model parameters, adding features, and improving the structure of the work vehicle sliding evaluation model; at the same time, the stability and robustness of the work vehicle sliding evaluation model also need to be evaluated to ensure that the work vehicle sliding evaluation model can maintain stable performance in practical applications.

[0077] In this step, by collecting a large amount of actual operation data and using machine learning technology for training, the accuracy and reliability of the work vehicle sliding evaluation model can be ensured; the data-driven method enables the work vehicle sliding evaluation model to more accurately reflect the actual operation situation and improve the stability and controllability of the network sealing work vehicle; in terms of feature selection, this method takes into account the physical properties of the transmission cable (such as texture density, roughness, smoothness, diameter, curvature, etc.) and the movement of the work vehicle, and can comprehensively and accurately describe the surface characteristics of the cable and the sliding of the work vehicle when moving on the cable; the application of the work vehicle sliding evaluation model can predict the sliding tendency of the work vehicle on different cables in real time.

[0078] More specifically, the core quantitative formula of the work vehicle sliding evaluation model is: in, represents the sliding evaluation index of the working vehicle, n is the number of features in the surface feature set of the transmission cable, and They are The weights and eigenvalues ​​of the surface features of the transmission cable, 𝑏 is the bias term; where, The value range of is constrained between 0 and 1, where values ​​close to 0 indicate low sliding risk and values ​​close to 1 indicate high sliding risk.

[0079] S4, combining the operating vehicle sliding evaluation index with the preset network closing operation speed to obtain the operating vehicle movement feature vector;

[0080] In step S4, the method for constructing the working vehicle movement feature vector includes:

[0081] Sliding Evaluation Index (SCEI): SCEI is a quantitative index obtained by analyzing the cable surface feature set (such as cable material, diameter, surface smoothness, etc.) in step S3, which is used to evaluate the sliding risk that the operating vehicle may encounter when operating on the cable. A higher SCEI means that the operating vehicle is more likely to slide on the cable, and additional measures need to be taken to ensure the safety of the operation.

[0082] Preset closure speed (PFS): PFS is the speed set during the operation planning phase based on a variety of factors, including but not limited to the environmental conditions of the operation area, the time requirements of the operation task, and the maximum safe operating speed of the operation vehicle. PFS aims to find the best balance between efficiency and safety.

[0083] Constructing the moving characteristic vector of the working vehicle: The moving characteristic vector of the working vehicle is composed of two independent but interrelated elements, SCEI and PFS, which together determine the actual operating status of the working vehicle; although there is no direct causal relationship between PFS and SCEI, in the actual operation process, SCEI can be used as one of the reference bases for dynamically adjusting PFS; for example, when SCEI is high, the operator or the control system may appropriately reduce PFS to reduce the risk of sliding; conversely, when SCEI is low, the working vehicle can perform the operation according to the original PFS, and even moderately increase the speed in some cases to optimize the operation efficiency.

[0084] By combining SCEI and PFS into the moving feature vector of the working vehicle, it not only provides the working vehicle with real-time feedback on the sliding risk of the working environment, but also provides a scientific basis for the dynamic adjustment of the working speed; it not only helps to enhance the safety of the operation, but also optimizes the efficiency of the network sealing operation, ensuring that the automated network sealing operation can make timely, reasonable and safe responses when facing complex and changeable working environments.

[0085] S5, inputting the working vehicle movement feature vector into a pre-built working vehicle movement control analysis model to obtain the working vehicle movement control strategy;

[0086] Step S5 inputs the working vehicle movement feature vector generated in step S4 into the pre-built working vehicle movement control analysis model to obtain the actual movement speed of each working vehicle on the transmission cable where it is located, that is, the working vehicle movement control strategy; the purpose is to ensure that when multiple machines work together, each working vehicle can adjust the speed according to the characteristics of the cable to achieve synchronization and coordination, thereby improving the overall efficiency and safety of the network blocking operation;

[0087] Among them, the work vehicle movement control analysis model is designed based on the work vehicle movement feature vector. Its main function is to analyze the movement characteristics of the work vehicle on a specific cable and formulate an appropriate movement control strategy based on this. The calculation formula of the work vehicle movement control analysis model is as follows: in, represents the mobile control speed of the i-th work vehicle in the work vehicle mobile control strategy, Indicates the preset net sealing operation speed. In the same net sealing operation, the preset net sealing operation speeds of multiple operation vehicles are the same. represents the vehicle sliding evaluation index of the i-th vehicle, is a positive proportional constant used to adjust right In this model, when When it is smaller, it means that the vehicle can approach the preset speed. Move; and when When it is larger, it leads to Significantly lower than , to ensure the synchronization of movement of multiple work vehicles; by adjusting The value of can control the influence of the operating vehicle's sliding evaluation index on the actual moving speed, ensuring that under different cable conditions, the operating vehicle can operate efficiently and ensure the synchronization of the network sealing operation.

[0088] S6. When performing a network sealing operation, according to the obtained movement control strategies of multiple operation vehicles, control the corresponding operation vehicle to perform the network sealing operation;

[0089] Step S6 is the execution stage of the multi-machine interconnection control method for the network blocking operation vehicle, which is used to apply the operation vehicle movement control strategy generated in step S5 to the actual network blocking operation; this step ensures that the operation vehicle can dynamically adjust the speed according to the cable characteristics, realize multi-machine synchronous operation, thereby improving the operation efficiency and synchronization;

[0090] During the network sealing operation, the network sealing vehicle will automatically adjust the output of its traveling mechanism according to the speed command in the vehicle's mobile control strategy to achieve precise control on the transmission cables; at the same time, the network sealing vehicle also needs to ensure that it can adjust the speed or path in time during the operation to adapt to changes in cable conditions or respond to emergencies; in addition, in order to ensure the synchronization and coordination of multi-machine collaborative operations, it is necessary to monitor the status of each vehicle in real time and perform global scheduling and coordination as needed; for example, when a vehicle needs to suspend operations for some reason, it will promptly notify other vehicles to adjust the speed or change the path to ensure the smooth progress of the entire network sealing operation.

[0091] In summary, step S6 can ensure that each work vehicle can operate on its corresponding transmission cable at the optimal speed by controlling the corresponding work vehicle to perform network sealing operations according to the work vehicle movement control strategy, thereby improving the consistency of the operation and the overall progress.

[0092] Embodiment 2: Figure 3 As shown, the multi-machine interconnection control system for the network sealing operation vehicle of the present invention specifically includes the following modules:

[0093] An image data acquisition module is used to acquire image data information of multiple power transmission cables in the target area of ​​network blocking, and ensure that the image data of each power transmission cable corresponds to a network blocking operation vehicle;

[0094] The cable surface feature extraction module receives the transmission cable image data from the image data acquisition module; and extracts the cable surface features from the image data information of each transmission cable to generate a transmission cable surface feature set;

[0095] The working vehicle sliding evaluation module is used to receive the transmission cable surface feature set output by the cable surface feature extraction module; and input the feature set into the pre-built working vehicle sliding evaluation model to calculate and output the working vehicle sliding evaluation index; the sliding evaluation index is used to characterize the relative sliding condition of the working vehicle when moving on the transmission cable;

[0096] The moving feature vector generating module is used to receive the sliding evaluation index output by the working vehicle sliding evaluation module; and generate the moving feature vector of the working vehicle in combination with the preset network sealing operation speed;

[0097] The working vehicle movement control analysis module is used to receive the working vehicle movement feature vector output by the movement feature vector generation module; and input the feature vector into a pre-built working vehicle movement control analysis model to calculate and output the working vehicle movement control strategy;

[0098] The network sealing operation control module is used to receive multiple operation vehicle movement control strategies output by the operation vehicle movement control analysis module; and according to the obtained movement control strategies, the corresponding network sealing operation vehicle is controlled in real time to perform network sealing operations.

[0099] In this embodiment, through image data acquisition and cable surface feature extraction, the system can automatically identify and analyze the physical characteristics of the transmission cable, provide a scientific basis for the mobile control of the work vehicle, and avoid the subjectivity and uncertainty of manual judgment; the combination of the work vehicle sliding evaluation module and the mobile feature vector generation module enables the movement speed and stability of each network sealing work vehicle to be accurately controlled, effectively responding to the sliding challenges brought by different cables, and ensuring the consistency and overall progress of the operation; the network sealing operation control module can receive and analyze the mobile control strategy of each work vehicle in real time, realize the synchronous control and dynamic adjustment of multiple work vehicles, enhance the coordination and flexibility of the system, and adapt to complex and changeable working environments; the system realizes intelligent speed optimization of the work vehicle through the pre-built work vehicle mobile control analysis model, reduces the asynchrony of manual operation, and improves the efficiency and quality of network sealing operations; the application of multi-machine interconnection control system breaks through the limitations of a single work vehicle, can effectively cover and manage the network sealing operations of large-scale transmission networks, reduce overall maintenance costs, and improve the intelligence level of power maintenance work.

[0100] The various variations and specific embodiments of the multi-machine interconnection control method for a network sealing operation vehicle in the aforementioned embodiment 1 are also applicable to the multi-machine interconnection control system for a network sealing operation vehicle in this embodiment. Through the aforementioned detailed description of the multi-machine interconnection control method for a network sealing operation vehicle, those skilled in the art can clearly understand the implementation method of the multi-machine interconnection control system for a network sealing operation vehicle in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here.

[0101] In addition, the present application also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor. The transceiver, the memory, and the processor are respectively connected via a bus. When the computer program is executed by the processor, each process of the above-mentioned method for controlling output data is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0102] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A multi-machine interconnection control method for a network sealing operation vehicle, characterized in that: The method comprises: obtaining image data information of a plurality of transmission cables in a target area for network sealing; extracting cable surface features for each transmission cable image data information to obtain a set of transmission cable surface features; inputting the set of transmission cable surface features into a pre-constructed operating vehicle sliding evaluation model to obtain an operating vehicle sliding evaluation index; combining the operating vehicle sliding evaluation index with a preset network sealing operation speed to obtain an operating vehicle movement feature vector; inputting the operating vehicle movement feature vector into a pre-constructed operating vehicle movement control analysis model to obtain an operating vehicle movement control strategy; when performing a network sealing operation, controlling the corresponding operating vehicle to perform a network sealing operation according to the obtained plurality of operating vehicle movement control strategies; The surface feature set of the power transmission cable includes texture features, geometric features and material features; Wherein, the texture features also include the texture density, roughness and smoothness of the cable surface; The geometric characteristics also include the diameter, curvature and wear of the cable; The material characteristics also include the material type of the cable and the surface coating of the cable; The method for extracting the feature set of the transmission cable surface comprises: De-noising, contrast enhancement and standardization of image data information of power transmission cables; For each transmission cable, an image segmentation algorithm is used to separate the transmission cable area from the background; Performing texture analysis on the separated transmission cables to extract the texture features of the transmission cables; performing geometric measurement on the separated transmission cables to extract the geometric features of the transmission cables; performing spectral analysis on the separated transmission cables to extract the material features of the transmission cables; The extracted texture features, geometric features and material features are normalized and integrated to generate a surface feature set of the transmission cable; The method for constructing the working vehicle sliding evaluation model comprises: Collect actual operation data of historical network sealing operations, including the movement of the network sealing vehicle under different transmission cable surface features, including texture features, geometric features, and material features; Preprocess the actual operation data of historical network blocking operations collected, including data cleaning, feature extraction and labeling; According to the physical characteristics of the transmission cable and the movement of the work vehicle, the input characteristics and output characteristics of the work vehicle sliding evaluation model are determined; Based on the determined input characteristics and output characteristics, a model framework of the working vehicle sliding evaluation model is determined; The pre-processed actual operation data is used to train the operating vehicle sliding evaluation model, and the performance of the model is optimized by adjusting the parameters and structure of the operating vehicle sliding evaluation model; and the operating vehicle sliding evaluation model is evaluated and optimized; The calculation formula of the working vehicle movement control analysis model is: in, represents the mobile control speed of the i-th working vehicle in the working vehicle mobile control strategy, V p Indicates the preset net sealing operation speed. In the same net sealing operation, the preset net sealing operation speeds of multiple operation vehicles are the same. represents the vehicle sliding evaluation index of the i-th vehicle, k is a positive proportional constant used to adjust right impact.

2. The multi-machine interconnection control method for a network sealing operation vehicle according to claim 1, characterized in that: The calculation formula of the working vehicle sliding evaluation model is: Among them, I S represents the sliding evaluation index of the working vehicle, n is the number of features in the surface feature set of the transmission cable, and w i and x i are the weight and eigenvalue of the surface feature of the i-th transmission cable, and b is the bias term; where I S The value range of is constrained between 0 and 1, where values ​​close to 0 indicate low sliding risk and values ​​close to 1 indicate high sliding risk.

3. The multi-machine interconnection control method for a network sealing operation vehicle according to claim 2, characterized in that: The working vehicle movement control strategy includes the actual moving speed of each working vehicle on the transmission cable where the working vehicle is located.

4. A multi-machine interconnection control system for a network sealing vehicle, characterized in that: The system is applied to the multi-machine interconnection control method for a network sealing operation vehicle according to claim 1, and the system comprises: An image data acquisition module is used to acquire image data information of multiple power transmission cables in the target area of ​​network blocking; The cable surface feature extraction module is used to receive the transmission cable image data from the image data acquisition module; and extract the cable surface features from the image data information of each transmission cable to generate a transmission cable surface feature set; The working vehicle sliding evaluation module is used to receive the transmission cable surface feature set output by the cable surface feature extraction module; and input the feature set into the pre-built working vehicle sliding evaluation model to calculate and output the working vehicle sliding evaluation index; the sliding evaluation index is used to characterize the relative sliding condition of the working vehicle when moving on the transmission cable; The moving feature vector generating module is used to receive the sliding evaluation index output by the working vehicle sliding evaluation module; and generate the moving feature vector of the working vehicle in combination with the preset network sealing operation speed; The working vehicle movement control analysis module is used to receive the working vehicle movement feature vector output by the movement feature vector generation module; and input the feature vector into a pre-built working vehicle movement control analysis model to calculate and output the working vehicle movement control strategy; The network sealing operation control module is used to receive multiple operation vehicle movement control strategies output by the operation vehicle movement control analysis module; and according to the obtained movement control strategies, the corresponding network sealing operation vehicle is controlled in real time to perform network sealing operations.

5. A multi-machine interconnection control electronic device for a network sealing operation vehicle, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and characterized in that: When the computer program is executed by the processor, the steps in the method according to any one of claims 1 to 3 are implemented.

6. 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 in the method according to any one of claims 1 to 3 are implemented.

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