A method for intelligent control of collaborative autonomous linkage inspection of unmanned equipment in power grid

Abstract

This invention discloses an intelligent control method for collaborative autonomous linkage inspection of unmanned equipment in a dedicated power grid. The method includes: opening a task sharing channel for each inspection party, through which the inspection parties share shared inspection tasks in real time; marking the shared inspection tasks in a power grid visualization information model; sharing the power grid visualization information model with each inspection party; identifying the inspection tasks dispatched by the inspection parties and marking them accordingly in the power grid visualization information model; determining the timeliness requirements of each inspection task and the shared inspection task in the power grid visualization information model; marking the inspection tasks and the shared inspection tasks with corresponding classification tags according to the timeliness requirements; performing real-time collaborative inspection analysis of the unmanned inspection equipment based on the classification tags to obtain the inspection control mode, and sharing task completion data in real time with each unmanned inspection equipment of the inspection parties during the inspection process.

Description

Technical Field

[0001] This invention belongs to the field of power grid inspection technology, specifically a collaborative and autonomous linkage intelligent control method for power grid unmanned equipment inspection. Background Technology

[0002] Against the backdrop of the power industry's continued deepening of digital transformation and intelligent upgrading, the operation and maintenance inspection of dedicated power grids is facing multiple challenges in terms of efficiency improvement, safety assurance, and resource optimization. Currently, power grid inspection still heavily relies on manual methods, especially in high-voltage lines, substations, and areas with complex terrain. Inspection personnel must climb towers or enter high-risk environments, posing safety risks such as falls and electric shocks. Furthermore, they are limited by extreme weather (such as heavy rain and high temperatures) or geographical conditions (such as mountains and swamps), resulting in long inspection cycles and numerous blind spots. At the same time, traditional inspection methods are "single-specialty, single-task" oriented, with transmission, substation, and distribution operations belonging to different teams. The procurement and application of intelligent equipment such as drones are also fragmented, leading to redundant equipment configuration, dispersed manpower, and low resource utilization. For example, the inspection of high-voltage lines and adjacent distribution lines in the same area requires dispatching different teams, resulting in a double waste of time and cost. Although drone and unmanned vehicle technologies have been gradually applied to power line inspection, enabling functions such as equipment defect identification and thermal imaging detection by equipping them with sensors such as visible light, infrared, and lidar, their application is still limited to single-point breakthroughs and lacks cross-professional and cross-task collaborative capabilities.

[0003] Based on this, in order to achieve efficient inspection of the power grid, this invention provides an intelligent control method for collaborative autonomous linkage inspection of unmanned equipment in the power grid. Summary of the Invention

[0004] To address the problems of the above solutions, this invention provides an intelligent control method for collaborative autonomous linkage inspection of unmanned equipment in power grids.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A method for intelligent control of collaborative autonomous linkage inspection of unmanned equipment in a dedicated power grid, the method comprising:

[0007] Step 1: Identify the inspection parties corresponding to the dedicated power grid area, open a task sharing channel for each inspection party, and allow the inspection parties to share the shared inspection tasks that need to be inspected in the dedicated power grid area in real time through the task sharing channel; summarize and store the shared inspection tasks.

[0008] Furthermore, the determination of the inspection team includes:

[0009] Obtain power grid information for the dedicated power grid area, and identify in real time the various inspection parties that need to conduct inspections of the dedicated power grid area based on the power grid information.

[0010] Furthermore, the completion data of each shared inspection task is acquired in real time, and the stored shared inspection tasks are adjusted based on the completion data.

[0011] Step 2: Establish a power grid visualization information model for the dedicated power grid, and mark the corresponding tasks in the power grid visualization information model according to the shared inspection tasks; share the power grid visualization information model with all inspection parties;

[0012] Step 3: Identify the unmanned inspection information of the unmanned inspection equipment dispatched by the inspection party, identify each inspection task, and mark the inspection tasks accordingly in the power grid visualization information model.

[0013] Furthermore, the unmanned inspection information includes the inspection party, equipment information, inspection task, and inspection route.

[0014] Furthermore, inspection tasks marked in the power grid visualization information model cannot be viewed by other inspection parties without authorization from the corresponding inspection party.

[0015] Step 4: Determine the timeliness requirements of each inspection task and shared inspection task in the power grid visualization information model; mark the inspection tasks and shared inspection tasks with corresponding classification labels according to the timeliness requirements. The classification labels include Category I labels and Category II labels.

[0016] Furthermore, based on timeliness requirements, appropriate classification labels are assigned to inspection tasks and shared inspection tasks, including:

[0017] Establish a classification and evaluation model. The expression for the classification and evaluation model is as follows:

[0018] ;

[0019] In the formula: (s, FB) are the input data, s is the timeliness requirement corresponding to the inspection task or shared inspection task, and FB is the second-class standard; s→FB means that the corresponding timeliness requirement meets the second-class standard; the output data is the classification evaluation value FD(s, FB), and the classification evaluation value is 1 or 0;

[0020] Two categories of standards are determined, and the two categories of standards and the timeliness requirements of inspection tasks or shared inspection tasks are integrated into the input data and input into the classification and evaluation model for analysis to obtain the corresponding classification and evaluation values ​​of inspection tasks or shared inspection tasks.

[0021] When the classification evaluation value is 1, the inspection task or shared inspection task is marked with a second-class label.

[0022] When the classification evaluation value is 0, a category label is assigned to the inspection task or shared inspection task.

[0023] Step 5: Perform real-time collaborative inspection analysis on unmanned inspection equipment based on the classification tags of inspection tasks and shared inspection tasks, obtain the inspection control mode of unmanned inspection equipment, control the unmanned inspection settings according to the inspection control mode, and share task completion data with each unmanned inspection equipment of the inspection party in real time during the inspection process.

[0024] Furthermore, based on the classification tags of inspection tasks and shared inspection tasks, real-time collaborative inspection analysis is performed on unmanned inspection equipment, including:

[0025] Identify the inspection tasks of the unmanned inspection equipment, and determine the basic tasks set by the unmanned inspection equipment in real time based on the inspection tasks; when the unmanned inspection equipment completes the inspection and performs regression, generate a regression inspection task, and determine the classification label of the regression inspection task.

[0026] Segment the basic tasks to obtain the corresponding task nodes; identify the classification labels of the basic tasks;

[0027] When the classification label is a type of label, the inspection analysis is carried out according to the type of inspection mode to obtain the corresponding inspection control method of the unmanned inspection equipment.

[0028] When the classification label is a Class II label, the inspection analysis is carried out according to the Class II inspection mode to obtain the corresponding inspection control method of the unmanned inspection equipment.

[0029] Furthermore, the basic tasks are divided according to the inspection positions corresponding to each task process step, and task process steps that do not belong to the same inspection position range are divided into different task nodes.

[0030] Furthermore, one type of inspection mode includes:

[0031] Based on the location of the unmanned inspection equipment, real-time inspection analysis is performed to estimate whether there is spare time to complete the basic task.

[0032] No inspection or adjustment will be performed if there is no available time.

[0033] When there is estimated spare time, collaborative task analysis is performed based on the spare time to obtain inspection adjustment methods. Inspection adjustments are then made based on these methods to obtain inspection control methods.

[0034] Furthermore, collaborative task analysis is performed based on available time, including:

[0035] Step SA1: Mark each task node of the basic task as a basic task node; determine the basic screening distance based on the available time; identify the additional inspection task points associated with each basic task point based on the basic screening distance.

[0036] Identify the task characteristics of each additional inspection task point, estimate the time required to complete the additional inspection task point based on the task characteristics, filter the additional inspection task points based on the time, and mark the remaining additional inspection task points as associated nodes.

[0037] Step SA2: Determine the priority of each associated node and mark the associated node with the highest priority as a cooperative node;

[0038] Step SA3: Filter the remaining associated nodes based on the collaborating nodes;

[0039] If there are no associated nodes, proceed to step SA4;

[0040] If there are associated nodes, return to step SA2;

[0041] Step SA4: Determine the inspection adjustment method based on the obtained collaborative nodes.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] This invention effectively solves the efficiency, safety, and resource optimization challenges faced by power grid maintenance and inspection during the digital transformation and intelligent upgrading of the power industry. By breaking the reliance on the power grid in the traditional manual inspection mode, it significantly reduces the safety risks such as falls and electric shocks faced by inspection personnel when climbing towers or entering high-risk environments. It also avoids the limitations imposed by extreme weather and geographical conditions on inspection work, greatly shortens the inspection cycle, and reduces coverage blind spots. Furthermore, it fully utilizes inspection resources to achieve collaborative inspection of the power grid, improving inspection efficiency and timeliness. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0046] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0047] like Figure 1 As shown, a collaborative autonomous linkage intelligent control method for inspection of unmanned equipment in a dedicated power grid is provided. The method includes:

[0048] Step 1: Obtain power grid information for the dedicated power grid area, including power equipment, lines, inspection requirements, historical records, and other relevant information; identify the various inspection parties that need to inspect the dedicated power grid area based on the power grid information. Generally, a dedicated power grid has various power equipment and lines, which may correspond to different power departments or teams, resulting in multiple inspection parties for the dedicated power grid area depending on the situation; open a task sharing channel for each inspection party, and each inspection party can share shared inspection tasks that need to be inspected in the dedicated power grid area in real time through the task sharing channel; summarize and store the obtained shared inspection tasks.

[0049] The task sharing channel is used for data transmission by the relevant inspection parties and can be implemented using various existing data transmission methods.

[0050] In one embodiment, the inspector may also determine this through various other existing methods, such as manual settings or case references.

[0051] In one embodiment, sharing can be done in the cloud.

[0052] In one embodiment, to prevent the leakage of sensitive task information, data and other related information of each inspection party, various existing methods can be used for protection, such as de-hiding, encryption, and blockchain; that is, to provide security protection for the shared inspection tasks shared by the inspection parties.

[0053] In one embodiment, the completion data of each shared inspection task is acquired in real time. This data can be partially or fully completed, and feedback is provided by the corresponding inspection party. Alternatively, intelligent identification can be performed based on the sharing status of each inspection party. The stored shared inspection tasks can be adjusted based on the completion data. For example, if a task is partially completed, that part of the task no longer needs to be inspected and can be marked or removed.

[0054] Step Two: Establish a power grid visualization information model of the power private network based on power private network information and visualization technology. The power private network can be displayed intuitively through power visualization information, and the power equipment corresponding to each shared inspection task can be marked subsequently; identify each stored shared inspection task, and mark the corresponding shared inspection task in the power grid visualization information model, that is, mark the shared inspection task and the power equipment that needs to be inspected for the shared inspection task; share the power grid visualization information model with each inspection party; that is, each inspection party can access and use the power grid visualization information model.

[0055] Step 3: Identify the unmanned inspection information of one or more unmanned inspection devices dispatched by the inspection party. This information includes the inspection party, equipment information, inspection task, inspection route, and other relevant details. Unmanned inspection devices include drones and unmanned vehicles. Identify each inspection task, which may differ from shared inspection tasks. For example, some inspection tasks may not be shared by the inspection party for various reasons and may only be carried out by their own unmanned inspection devices; therefore, these tasks may not be included in the shared inspection tasks. Identification can be performed directly based on the individual unmanned inspection information. Mark the inspection tasks accordingly in the power grid visualization information model, making them inaccessible to other inspection parties.

[0056] In one embodiment, for unmanned inspection equipment dispatched by the inspection party, the inspection planning is carried out based on its supporting or existing inspection system according to the initial inspection task, and relevant information such as the inspection route is determined.

[0057] Step 4: Determine the timeliness requirements of each inspection task and shared inspection task in the power grid visualization information model; mark the inspection tasks and shared inspection tasks with corresponding classification labels in real time according to the timeliness requirements. The classification labels include Category 1 labels and Category 2 labels.

[0058] In one embodiment, the timeliness requirements of each inspection task and shared inspection task in the power grid visualization information model are determined. For those with preset timeliness requirements, they are directly identified. For those without timeliness requirements, they can be determined by combining relevant specifications, regulations, historical cases, etc. Intelligent identification can be performed directly by building an intelligent model based on machine learning, deep learning algorithms, etc.

[0059] In one embodiment, inspection tasks and shared inspection tasks are labeled with corresponding classification tags according to timeliness requirements, including:

[0060] Establish a classification and evaluation model. The expression for the classification and evaluation model is as follows:

[0061] ;

[0062] In the formula: (s, FB) are the input data, s is the timeliness requirement corresponding to the inspection task or shared inspection task, and FB is the second-class standard; s→FB means that the corresponding timeliness requirement meets the second-class standard; the output data is the classification evaluation value FD(s, FB), and the classification evaluation value is 1 or 0; the corresponding training set is set using the corresponding historical data for training.

[0063] Two categories of standards are determined, and the two categories of standards and the timeliness requirements of inspection tasks or shared inspection tasks are integrated into the input data and input into the classification and evaluation model for analysis to obtain the corresponding classification and evaluation values ​​of inspection tasks or shared inspection tasks.

[0064] When the classification evaluation value is 1, the inspection task or shared inspection task is marked with a second-class label.

[0065] When the classification evaluation value is 0, a category label is assigned to the inspection task or shared inspection task.

[0066] In one embodiment, the second-class standard is determined based on the inspection and analysis efficiency of the tasks corresponding to the second-class labels, and can be set manually by the inspection party.

[0067] In one embodiment, historical inspection analysis data corresponding to tasks based on two types of tags are acquired in real time. The analysis timeliness corresponding to the tasks is statistically analyzed based on the historical inspection analysis data. The analysis timeliness can also be estimated based on the inspection analysis method of the application. Two types of standards are set based on the analysis timeliness. For example, if the timeliness requirement is equal to or lower than the analysis timeliness, the analysis timeliness is 8 seconds. Then, the timeliness requirement is ≥8 seconds, which meets the two types of standards.

[0068] In one embodiment, the timeliness of analysis can be determined more accurately based on the location of unmanned inspection equipment. For example, a timeliness analysis model can be established based on machine learning, deep learning algorithms, etc., and the corresponding training set can be labeled with relevant historical data. The training set can be used to train the model so that the timeliness analysis model can predict the timeliness of analysis for each task based on the location of each unmanned inspection device of the inspection party.

[0069] Step 5: Based on the classification tags of inspection tasks and shared inspection tasks, perform real-time collaborative inspection analysis on unmanned inspection equipment to obtain the inspection control mode of the unmanned inspection equipment, and control the unmanned inspection settings according to the inspection control mode; during the inspection process, share task completion data in real time with each unmanned inspection equipment of the inspection party, so that other unmanned inspection equipment can understand the inspection tasks of other unmanned inspection equipment, the completion status of each task node, and the task nodes that are being inspected in real time. Initially, this is shared among unmanned inspection equipment within the inspection party, and later, the overall task completion status of the inspection party will be summarized and shared through task sharing channels to achieve timely updates to the power grid visualization information model.

[0070] In one embodiment, real-time collaborative inspection analysis of unmanned inspection equipment is performed based on the classification tags of inspection tasks and shared inspection tasks, including:

[0071] Identify the inspection tasks of the unmanned inspection equipment and mark the inspection task as the basic task set by the unmanned inspection equipment, that is, the initial priority is the highest; if the unmanned inspection equipment has multiple inspection tasks, mark the inspection task to be inspected as the basic task; when the unmanned inspection equipment completes the inspection and performs regression, it is regarded as performing regression inspection task and is used as its inspection task. The classification label is determined according to the timeliness of the regression inspection task.

[0072] Segment the basic tasks to obtain the corresponding task nodes; identify the classification labels of the basic tasks;

[0073] When the classification label is a type of label, the inspection analysis is carried out according to the type of inspection mode to obtain the corresponding inspection control method of the unmanned inspection equipment.

[0074] When the classification label is a Class II label, the inspection analysis is carried out according to the Class II inspection mode to obtain the corresponding inspection control method of the unmanned inspection equipment.

[0075] In one embodiment, the basic task is segmented based on the location of each step in completing the basic task. If all the steps in completing the basic task are within the same location range, then there is only one task node. If there are multiple location ranges, then there are multiple task nodes. The system uses a preset distance to determine whether a task belongs to the same location range. If it does, the task is considered to belong to the same location range. Otherwise, it does not belong to the same location range. The corresponding distance can be set manually, such as 5 meters, 6 meters, 4 meters, etc. Alternatively, the distance can be set differently according to the differences in inspection tasks. For example, a distance detail table for various inspection tasks can be set in advance, and then matched later.

[0076] In one embodiment, a type of inspection mode includes:

[0077] Based on the location of the unmanned inspection equipment, real-time inspection analysis is performed to estimate whether there is spare time to complete the basic task. That is, based on the location and inspection route, it is estimated whether there is spare time to complete the basic task under the timeliness requirement.

[0078] When there is no estimated spare time, no inspection adjustment is made, that is, the inspection is carried out according to the original inspection control method. Because it is a collaborative inspection, some task nodes of the basic task may be completed by other unmanned inspection equipment. Therefore, for real-time dynamic inspection analysis, it is possible that there was no estimated spare time before, but there is spare time later.

[0079] When there is estimated spare time, collaborative task analysis is performed based on the spare time to obtain inspection adjustment methods. Inspection adjustments are then made based on these methods to obtain inspection control methods.

[0080] In one embodiment, collaborative task analysis based on available time includes:

[0081] Step SA1: Mark each task node of the basic task as a basic task node; determine the basic screening distance based on the idle time; that is, determine a distance based on the idle time and the inspection speed of the unmanned inspection equipment, and mark it as the screening distance. To further improve the accuracy of the basic screening distance, the minimum task completion time can be subtracted from the idle time; identify the additional inspection task points associated with each basic task point based on the basic screening distance. The additional inspection task points are task nodes of other inspection tasks or shared inspection tasks, that is, other task nodes whose distance from the basic task node is within the basic screening distance; identify the task characteristics of each additional inspection task point, that is, what task needs to be completed, estimate the time required to complete the task node based on the task characteristics, and then update the basic screening distance to evaluate whether the additional inspection task points are within the distance range, thereby achieving the screening of additional inspection task points; mark the remaining additional inspection task points as associated nodes.

[0082] Step SA2: Determine the priority of each associated node. Generally, the priority of each task node is determined in real time based on the power grid visualization information model. This allows the priority of each associated node to be determined directly during the inspection and analysis process, improving analysis efficiency. The analysis can be performed by other servers, the cloud, or by unmanned inspection equipment. Mark the associated node with the highest priority as a collaborative node.

[0083] Step SA3: Filter the remaining associated nodes based on the collaborative nodes. That is, take into account the time required to complete the collaborative nodes, and filter the remaining associated nodes again in the above manner, removing the associated nodes that can no longer be completed.

[0084] If there are no associated nodes, proceed to step SA4;

[0085] If there are associated nodes, return to step SA2;

[0086] Step SA4: Determine the inspection adjustment method based on the obtained collaborative nodes.

[0087] In one embodiment, the priority of each associated node can be determined based on existing methods, such as comprehensively evaluating priority according to completion time, the inspection party, and timeliness requirements, or directly relying on a single parameter to determine priority, that is, only relying on the duration to determine priority. If the priorities are the same, other parameters are used to determine priority, such as first determining priority based on the inspection party, then determining priority according to timeliness requirements if priorities are the same, and then determining priority based on completion time if priorities are still the same. There are various ways to determine the priority of each task node.

[0088] In one embodiment, the second-class inspection mode includes:

[0089] This system identifies various inspection tasks and shared inspection tasks within the power grid visualization information model. Based on swarm intelligence (such as ant colony optimization and particle swarm optimization), it determines the optimal control method for completing the basic tasks. Because swarm intelligence algorithms require numerous iterations to achieve the global optimal solution, in power grid inspection scenarios, equipment may need to respond to sudden faults (such as line breaks) within seconds, which cannot meet timeliness requirements. Therefore, inspection tasks with two types of labels are analyzed and processed using classification labels. For example, in the emergency repair of tower collapses after a typhoon, the traditional ant colony algorithm takes 8-12 seconds to complete path planning, which may miss the golden repair window.

[0090] In one embodiment, the second-class inspection mode includes:

[0091] Determine the idle time of basic tasks, analyze collaborative tasks based on the idle time, obtain inspection adjustment methods, adjust inspections according to the inspection adjustment methods, and obtain inspection control methods.

[0092] The above formulas are all numerical calculations after removing dimensions. The formulas are obtained by software simulation based on a large amount of data and are closest to the real situation. The preset parameters and preset thresholds in the formulas are set by those skilled in the art according to the actual situation or obtained by simulation based on a large amount of data.

[0093] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for intelligent control of collaborative autonomous linkage inspection of unmanned equipment in a dedicated power grid, characterized in that, The methods include: Step 1: Identify the inspection parties corresponding to the dedicated power grid area and open a task sharing channel for each inspection party. The inspection parties can share the shared inspection tasks that need to be inspected in the dedicated power grid area in real time through the task sharing channel. The shared inspection tasks are summarized and stored. Step 2: Establish a power grid visualization information model for the dedicated power grid, and mark the corresponding tasks in the power grid visualization information model according to the shared inspection tasks; share the power grid visualization information model with all inspection parties; Step 3: Identify the unmanned inspection information of the unmanned inspection equipment dispatched by the inspection party, identify each inspection task, and mark the inspection tasks accordingly in the power grid visualization information model; Step 4: Determine the timeliness requirements of each inspection task and shared inspection task in the power grid visualization information model; mark the inspection tasks and shared inspection tasks with corresponding classification labels according to the timeliness requirements. The classification labels include Category 1 labels and Category 2 labels. Step 5: Perform real-time collaborative inspection analysis on unmanned inspection equipment based on the classification tags of inspection tasks and shared inspection tasks, obtain the inspection control method of unmanned inspection equipment, control the unmanned inspection equipment according to the inspection control method, and share task completion data with each unmanned inspection equipment of the inspection party in real time during the inspection process. Based on timeliness requirements, assign appropriate category tags to inspection tasks and shared inspection tasks, including: Establish a classification and evaluation model. The expression for the classification and evaluation model is as follows: ； In the formula: (s, FB) are the input data, s is the timeliness requirement corresponding to the inspection task or shared inspection task, and FB is the second-class standard; s→FB means that the corresponding timeliness requirement meets the second-class standard; the output data is the classification evaluation value FD(s, FB), and the classification evaluation value is 1 or 0; Two categories of standards are determined, and the two categories of standards and the timeliness requirements of inspection tasks or shared inspection tasks are integrated into the input data and input into the classification and evaluation model for analysis to obtain the corresponding classification and evaluation values ​​of inspection tasks or shared inspection tasks. When the classification evaluation value is 1, the inspection task or shared inspection task is marked with a second-class label. When the classification evaluation value is 0, a category label is assigned to the inspection task or shared inspection task.

2. The intelligent control method for collaborative autonomous linkage inspection of unmanned equipment in a dedicated power grid according to claim 1, characterized in that, The determination of the inspection team includes: Obtain power grid information for the dedicated power grid area, and identify in real time the various inspection parties that need to conduct inspections of the dedicated power grid area based on the power grid information.

3. The intelligent control method for collaborative autonomous linkage inspection of unmanned equipment in a dedicated power grid according to claim 1, characterized in that, The system acquires the completion data of each shared inspection task in real time and adjusts the stored shared inspection tasks based on the completion data.

4. The intelligent control method for collaborative autonomous linkage inspection of unmanned equipment in a dedicated power grid according to claim 1, characterized in that, The unmanned inspection information includes the inspection party, equipment information, inspection task, and inspection route.

5. The intelligent control method for collaborative autonomous linkage inspection of unmanned equipment in a dedicated power grid according to claim 1, characterized in that, Inspection tasks marked in the power grid visualization information model cannot be viewed by other inspection parties without authorization from the corresponding inspection party.

6. The intelligent control method for collaborative autonomous linkage inspection of unmanned equipment in a dedicated power grid according to claim 1, characterized in that, Real-time collaborative inspection analysis of unmanned inspection equipment is performed based on the classification tags of inspection tasks and shared inspection tasks, including: Identify the inspection tasks of the unmanned inspection equipment, and determine the basic tasks of the unmanned inspection equipment in real time based on the inspection tasks; when the unmanned inspection equipment completes the inspection and performs regression, generate a regression inspection task, and determine the classification label of the regression inspection task. Segment the basic tasks to obtain the corresponding task nodes; identify the classification labels of the basic tasks; When the classification label is a type of label, the inspection analysis is carried out according to the type of inspection mode to obtain the corresponding inspection control method of the unmanned inspection equipment. When the classification label is a Class II label, the inspection analysis is carried out according to the Class II inspection mode to obtain the corresponding inspection control method of the unmanned inspection equipment.

7. The intelligent control method for collaborative autonomous linkage inspection of unmanned equipment in a dedicated power grid according to claim 6, characterized in that, The basic task is divided according to the inspection location corresponding to each task process step, and the task process steps that do not belong to the same inspection location range are divided into different task nodes.

8. The intelligent control method for collaborative autonomous linkage inspection of unmanned equipment in a dedicated power grid according to claim 6, characterized in that, One type of inspection mode includes: Based on the location of the unmanned inspection equipment, real-time inspection analysis is performed to estimate whether there is spare time to complete the basic task. No inspection or adjustment will be performed if there is no available time. When there is estimated spare time, collaborative task analysis is performed based on the spare time to obtain inspection adjustment methods. Inspection adjustments are then made based on these methods to obtain inspection control methods.

9. The intelligent control method for collaborative autonomous linkage inspection of unmanned equipment in a dedicated power grid according to claim 8, characterized in that, Collaborative task analysis is performed based on available time, including: Step SA1: Mark each task node of the basic task as a basic task node; determine the basic screening distance based on the available time; identify the additional inspection task points associated with each basic task point based on the basic screening distance. Identify the task characteristics of each additional inspection task point, estimate the time required to complete the additional inspection task point based on the task characteristics, filter the additional inspection task points based on the time, and mark the remaining additional inspection task points as associated nodes. Step SA2: Determine the priority of each associated node and mark the associated node with the highest priority as a cooperative node; Step SA3: Filter the remaining associated nodes based on the collaborating nodes; If there are no associated nodes, proceed to step SA4; If there are associated nodes, return to step SA2; Step SA4: Determine the inspection adjustment method based on the obtained collaborative nodes.

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