Power grid inspection scheduling method and system based on multi-level subdivision grid driving

Abstract

The application provides a power grid inspection scheduling method and system based on a multi-level split grid drive, relates to the technical field of inspection scheduling, and comprises the following steps: performing multi-level spatial splitting on target power grid resources and giving time-space coding; performing table structure design and resource data insertion based on the time-space grid coding set of the power grid resources and the target power grid resources; querying and determining the target grid coding task set of the to-be-inspected equipment set from the power grid resource splitting index large table, performing task scheduling and distribution on the target grid coding task set; respectively performing priority sorting on the inspection equipment branch scheduling task set; performing scheduling planning analysis based on the target branch inspection task sequence set, dynamically generating a target branch inspection path, and performing power grid inspection collaborative scheduling on the unmanned inspection equipment set through the target branch inspection path. Through the application, the technical effects of improving the inspection task space organization capability, optimizing the equipment scheduling strategy and enhancing the resource collaborative efficiency can be achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of inspection scheduling, and particularly relates to a power grid inspection scheduling method and system based on a multi-level split grid driving. BACKGROUND

[0002] With the continuous expansion of the scale of the power system and the continuous improvement of the operation complexity, the stable operation and safe management of the power grid equipment has become one of the core links in the energy security system. In order to realize the efficient management of key power grid resources such as substations, transmission lines, switch devices, etc., the inspection operation gradually introduces automation and intelligent means, among which the intelligent inspection mode represented by unmanned devices such as unmanned aerial vehicles and robots has been preliminarily applied in many power grid scenarios. However, the existing technology still faces great bottlenecks in the aspects of spatial management granularity, task scheduling efficiency and resource coordination.

[0003] At present, the spatial management of traditional power grid inspection tasks often relies on two-dimensional map labeling or point management mode with geographical coordinates as the core, and lacks a unified split standard and scalable spatial organization structure. This management mode is difficult to support efficient spatial indexing and resource classification when facing large-scale, multi-level and multi-type devices, especially in complex terrain areas such as mountainous areas, cross-city and cross-province areas. The coordinate labeling has the problems of repetition, vacancy or irregular distribution, which limits the accuracy and real-time performance of path planning and scheduling allocation. On the other hand, the current scheduling allocation of inspection tasks is mostly based on static strategies or preset rules, and lacks comprehensive consideration of device capacity, task urgency and environmental dynamic changes, resulting in lack of flexibility and responsiveness of task scheduling. For example, when the number of devices is limited or the task is temporarily changed, the system is difficult to quickly complete task priority adjustment, path reconstruction and dynamic coordination between devices, which easily causes some tasks to be ignored or repeatedly executed, reducing the overall operation efficiency.

[0004] In summary, the existing technology has the technical problem that due to the lack of a unified spatial split coding system and a dynamic scheduling mechanism, the power grid resources are difficult to realize efficient and accurate spatial organization and task allocation, which further affects the inspection coordination efficiency of unmanned inspection devices in complex environments and the intelligent level of the overall operation of the system. SUMMARY

[0005] The purpose of the present application is to provide a power grid inspection scheduling method and system based on a multi-level split grid driving, to solve the technical problem that due to the lack of a unified spatial split coding system and a dynamic scheduling mechanism in the existing technology, the power grid resources are difficult to realize efficient and accurate spatial organization and task allocation, which further affects the inspection coordination efficiency of unmanned inspection devices in complex environments and the intelligent level of the overall operation of the system.

[0006] In view of the above problems, the application provides a power grid inspection scheduling method and system driven by a multi-level split grid.

[0007] In a first aspect, the application provides a power grid inspection scheduling method driven by a multi-level split grid, which is implemented by a power grid inspection scheduling system driven by a multi-level split grid, and includes: performing multi-level spatial splitting and space-time coding assignment on target power grid resources by using an earth split grid coding system to obtain a set of power grid resource space-time grid codes; designing a table structure and inserting resource data based on the set of power grid resource space-time grid codes and the target power grid resources to establish a power grid resource split index large table; querying and determining a target grid code task set of a set of devices to be inspected from the power grid resource split index large table, performing task scheduling and distribution on the target grid code task set based on a set of unmanned inspection devices to determine a set of inspection device branch scheduling tasks; performing priority sorting on the set of inspection device branch scheduling tasks respectively in combination with historical power grid alarm data and environmental constraint conditions to obtain a set of target branch inspection task sequences; performing scheduling planning analysis based on the set of target branch inspection task sequences to dynamically generate a target branch inspection path, and performing power grid inspection collaborative scheduling on the set of unmanned inspection devices through the target branch inspection path.

[0008] Preferably, the power grid inspection scheduling method driven by a multi-level split grid further includes: obtaining a set of power grid resource attribute factors including resource type, location longitude and latitude, and size specification; performing attribute classification and identification on each resource in the target power grid resources according to the set of power grid resource attribute factors to obtain a set of power grid resource attribute parameters; determining a multi-level split level according to the inspection accuracy requirement of the target power grid resources; and performing multi-level spatial splitting and space-time coding assignment on the set of power grid resource attribute parameters based on the multi-level split level by using the earth split grid coding system to obtain a set of power grid resource space-time grid codes.

[0009] Preferably, the power grid inspection scheduling method driven by a multi-level split grid further includes: performing grid analysis on the multi-level split level based on the earth split grid coding system to determine multi-level split grid information; performing multi-level spatial splitting on the set of power grid resource attribute parameters based on the multi-level split grid information to obtain a set of power grid resource spatial grids; determining a grid space-time coding format based on the earth split grid coding system, the grid space-time coding format including grid level, grid location, collection timestamp, and resource attribute parameter; and performing space-time coding assignment on each resource grid in the set of power grid resource spatial grids based on the grid space-time coding format to obtain a set of power grid resource space-time grid codes.

[0010] Preferably, the power grid inspection scheduling method based on multi-level subdivision grid driving further comprises: adopting the multi-level subdivision grid information to sequentially perform level-by-level spatial subdivision on the power grid resource attribute parameter set to obtain a power grid resource multi-level grid set; performing cross-grid resource determination on the power grid resource attribute parameter set based on the power grid resource multi-level grid set to obtain a cross-grid power grid resource attribute parameter set; constructing a power grid resource segmentation strategy, performing segmentation processing on the cross-grid power grid resource attribute parameter set according to the power grid resource segmentation strategy to obtain a multi-segment cross-grid power grid resource parameter set; performing segment-by-segment grid allocation on the multi-segment cross-grid power grid resource parameter set to determine a power grid resource segmented grid set, and combining the power grid resource multi-level grid set and the power grid resource segmented grid set to obtain the power grid resource spatial grid set.

[0011] Preferably, the power grid inspection scheduling method based on multi-level subdivision grid driving further comprises: obtaining a current position grid code set, a flight capability parameter set and a current state parameter set of the unmanned inspection equipment set; associating the current position grid code set with the target grid coding task set in space to determine an equipment spatial position relationship set; performing inspection task scheduling allocation based on the equipment spatial position relationship set to obtain an initial inspection equipment allocation task set; constructing a task scheduling allocation strategy, and performing scheduling optimization on the initial inspection equipment allocation task set based on the task scheduling allocation strategy, the flight capability parameter set and the current state parameter set to determine an inspection equipment branch scheduling task set.

[0012] Preferably, the power grid inspection scheduling method based on multi-level subdivision grid driving further comprises: performing inspection demand analysis on the initial inspection equipment allocation task set to determine an initial grid task inspection demand parameter set; performing matching conflict identification on the initial grid task inspection demand parameter set based on the flight capability parameter set and the current state parameter set to obtain a conflict grid task set; taking the flight capability parameter set and the current state parameter set as constraint parameters, and performing scheduling allocation correction on the conflict grid task set based on the task scheduling allocation strategy to determine the inspection equipment branch scheduling task set.

[0013] Preferably, the power grid inspection scheduling method based on multi-level subdivision grid driving further comprises: performing alarm type extraction and influence degree evaluation on the historical power grid alarm data to construct an alarm influence degree index set; performing inspection execution influence index splitting on the environmental constraint condition to obtain an environmental complexity influence index set, and determining an inspection priority index set according to the alarm influence degree index set and the environmental complexity influence index set; performing priority scoring and task sorting on the inspection equipment branch scheduling task set based on the inspection priority index set respectively to obtain the target branch inspection task sequence set.

[0014] Preferably, the power grid inspection scheduling method based on multi-level split grid driving further comprises: selecting a target branch task path planning algorithm based on the characteristic information of the target branch inspection task sequence set, and setting a power grid inspection path planning cost function according to the power grid inspection target; using the target branch task path planning algorithm to dynamically analyze the target branch inspection task sequence set by using the power grid inspection path planning cost function, and generating the target branch inspection path.

[0015] Preferably, the power grid inspection scheduling method based on multi-level split grid driving further comprises: performing power grid inspection monitoring on the set of unmanned inspection devices through the target branch inspection path to obtain power grid inspection feedback data; performing abnormal situation identification and scheduling optimization analysis on the power grid inspection feedback data to determine a power grid inspection optimization scheduling strategy; and performing inspection scheduling cooperative optimization on the set of unmanned inspection devices based on the power grid inspection optimization scheduling strategy.

[0016] In a second aspect, the present application also provides a power grid inspection scheduling system based on multi-level split grid driving, which is used to execute the power grid inspection scheduling method based on multi-level split grid driving as described in the first aspect, and comprises: a space-time grid code set obtaining module, which is used to perform multi-level spatial splitting and space-time coding on a target power grid resource by using an earth split grid coding system to obtain a set of power grid resource space-time grid codes; a power grid resource split index large table establishing module, which is used to design a table structure and insert resource data based on the set of power grid resource space-time grid codes and the target power grid resource to establish a power grid resource split index large table; an inspection device branch scheduling task set determining module, which is used to query and determine a target grid code task set of a set of devices to be inspected from the power grid resource split index large table, and to determine a set of inspection device branch scheduling tasks by performing task scheduling and distribution on the target grid code task set based on a set of unmanned inspection devices; a target branch inspection task sequence set obtaining module, which is used to obtain a target branch inspection task sequence set by respectively performing priority sorting on the set of inspection device branch scheduling tasks in combination with historical power grid alarm data and environmental constraint conditions; and a power grid inspection cooperative scheduling module, which is used to perform scheduling planning analysis based on the target branch inspection task sequence set, dynamically generate a target branch inspection path, and perform power grid inspection cooperative scheduling on the set of unmanned inspection devices through the target branch inspection path.

[0017] The technical solutions provided in the present application have at least the following technical effects or advantages: by achieving the technical target of unified grid management and intelligent inspection scheduling of power grid resources based on the earth split grid coding system, the technical effects of improving the spatial organization capability of inspection tasks, optimizing the device scheduling strategy, and enhancing the real-time response capability and resource cooperative efficiency of the system are achieved.

[0018] The above description is only a summary of the technical solutions of the present application. In order to enable one skilled in the art to better understand the technical means of the present application, and to implement the same according to the contents of the specification, and in order to enable the above and other purposes, characteristics and advantages of the present application to be more apparent and easy to understand, the following specific embodiments of the present application are described. It should be understood that the contents described in this part are not intended to identify the key or important features of the embodiments of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only exemplary, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of the provided drawings.

[0020] Figure 1 The flowchart of the power grid inspection scheduling method based on multi-level subdivision grid driving of the present application;

[0021] Figure 2 The structural diagram of the power grid inspection scheduling system based on multi-level subdivision grid driving of the present application.

[0022] Explanation of reference numerals: spatiotemporal grid encoding set obtaining module 11, power grid resource subdivision index large table establishing module 12, inspection equipment branch scheduling task set determining module 13, target branch inspection task sequence set obtaining module 14, power grid inspection collaborative scheduling module 15. DETAILED DESCRIPTION

[0023] The present application provides a power grid inspection scheduling method and system based on multi-level subdivision grid driving, which solves the technical problem in the prior art that due to the lack of a unified spatial subdivision coding system and a dynamic scheduling mechanism, power grid resources are difficult to achieve efficient and accurate spatial organization and task allocation, which further affects the inspection collaborative efficiency of unmanned inspection equipment in complex environments and the intelligent level of the overall operation of the system. The technical goal of unified grid management and intelligent inspection scheduling of power grid resources based on the earth subdivision grid coding system is achieved, and the technical effects of improving the spatial organization ability of inspection tasks, optimizing the equipment scheduling strategy, enhancing the real-time response ability of the system and the resource collaborative efficiency are achieved.

[0024] Below, the technical solutions in the present application will be described clearly and completely with reference to the drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application. In addition, it should be noted that, for the convenience of description, only part of the present application is shown in the drawings.

[0025] Embodiment one, please refer to the attached Figure 1 The present application provides a power grid inspection scheduling method based on multi-level subdivision grid driving, which is applied to a power grid inspection scheduling system based on multi-level subdivision grid driving, and specifically includes the following steps:

[0026] S1: using the earth subdivision grid coding system to perform multi-level spatial subdivision and space-time coding assignment on the target power grid resources to obtain a power grid resource space-time grid coding set.

[0027] Specifically, the earth subdivision grid coding system is a coding method for dividing the earth's surface into a plurality of regular grids, each grid having a unique number, such as GeoHash, Quadtree or H3 system. The target power grid resource refers to the power grid equipment that needs to be managed, monitored or inspected, such as transformers, transmission lines, switch stations, etc. The location and operation time of the target power grid resource have significant space-time characteristics. Using the earth subdivision grid coding system to perform multi-level spatial subdivision and space-time coding assignment on the target power grid resource, the spatial division of various resources in the power grid is performed in layers, and the information of time dimension is added at the same time, so as to build a coding system containing both spatial position and time node, and obtain a power grid resource space-time grid coding set. Multi-level spatial subdivision is to refine the space according to different precision levels, such as from provincial grid subdivision to municipal grid, and then to device level, forming a nested grid structure. Time-space coding assignment is to superimpose time stamp, resource attribute and other information on the basis of spatial grid coding, so that the state of each resource at a certain time can be uniquely corresponded to a certain grid code.

[0028] S2: based on the power grid resource space-time grid coding set and the target power grid resource, designing a table structure and inserting resource data to establish a power grid resource subdivision index large table.

[0029] Specifically, based on the spatiotemporal grid coding set of power grid resources and the target power grid resources, table structure design and resource data insertion are performed. This involves designing a database table to uniformly manage data based on the precise coding information of each power grid resource in the spatial and temporal dimensions, combined with its specific resource attributes such as resource type, installation location, capacity, and voltage level. Table structure design is a detailed plan for field names, data types, primary keys, and indexes within the database system to facilitate storage, querying, and analysis. Fields in the table structure design may include grid codes, timestamps, resource types, location information, and status parameters, ensuring that all power grid resources can be archived and retrieved according to a predetermined format. Resource data insertion involves writing the actually collected or registered power grid resource information into the database line by line according to this table structure, enabling centralized storage of information and supporting subsequent retrieval, analysis, and scheduling tasks. Furthermore, a large power grid resource partitioning index table is established, using a multi-level query mechanism built upon spatial partitioning and hierarchical relationships. This allows for rapid location of resource distribution in a specific area or at a specific time, providing a digital map index library for the entire power grid resources. This not only allows for static management but also provides underlying support for intelligent tasks such as dynamic inspection, equipment scheduling, and early warning prediction.

[0030] S3: Query the target grid coding task set of the set of equipment to be inspected from the power grid resource partitioning index table, perform task scheduling and allocation on the target grid coding task set based on the set of unmanned inspection equipment, and determine the branch scheduling task set of inspection equipment.

[0031] Specifically, by accessing the power grid resource partitioning index table, the equipment requiring inspection and its spatial grid code are selected. The power grid resource partitioning index table records the spatiotemporal grid location information and related attributes of all power grid resources, such as resource number, type, status, and location grid code. By setting filtering conditions, such as the equipment type being a transformer, the most recent inspection being more than 30 days ago, or the historical alarm frequency exceeding 3 times, resources requiring priority inspection can be quickly located, and their corresponding grid code information can be extracted to form a target grid code task set.

[0032] The unmanned inspection equipment set refers to all currently available equipment entities and their capability information, including location information, operating status, and operational capabilities. Inspection tasks are assigned to executable unmanned inspection equipment, such as drones or ground robots, according to reasonable rules. Task scheduling and allocation is a matching process based on equipment capabilities and task requirements. After weighing multiple dimensions such as time, space, and capabilities, a set of correspondences between equipment and tasks is generated. The final branch scheduling task set for inspection equipment represents the specific inspection task assigned to each piece of equipment, indicating the grid code location to be reached, the operation time window, and the task priority.

[0033] S4: Based on the historical power grid alarm data and environmental constraints, the branch scheduling task set of the inspection equipment is prioritized respectively to obtain a target branch inspection task sequence set.

[0034] Specifically, before performing the inspection task, the accumulated fault, abnormality, and other alarm information in the historical power grid system, as well as the current or predicted natural environment and operation constraints, are used to comprehensively evaluate the upcoming inspection task. The historical power grid alarm data includes records of events such as power outages, equipment abnormalities, and line overloads, which can reflect the potential operational risks of certain equipment or areas. Environmental constraints include weather conditions, topography, wind speed and direction, rainfall, temperature and humidity, electromagnetic interference, and ground transportation information, which may affect the normal operation of the inspection equipment.

[0035] According to the alarm risk degree and environmental influence intensity associated with each task, the task list of each unmanned equipment is individually sorted. The branch scheduling task set of the inspection equipment refers to the multi-grid task set initially assigned to each inspection equipment by the system, and branch scheduling means that the tasks have been divided according to regions or functional modules and are ready for optimization in the next stage. Prioritization is usually based on a comprehensive scoring mechanism, for example, alarm intensity can be scored from 1 to 10, and environmental complexity can be scored from 1 to 5. The final comprehensive priority score of each task is calculated by weighting, and the target branch inspection task sequence set is obtained.

[0036] S5: Based on the target branch inspection task sequence set, scheduling planning analysis is performed to dynamically generate a target branch inspection path, and the set of unmanned inspection equipment is cooperatively scheduled for power grid inspection through the target branch inspection path.

[0037] Specifically, based on the target branch inspection task sequence set, scheduling planning analysis is performed to further calculate and optimize the sequence, spatial position, and equipment execution capability between tasks to determine the scheduling order and allocation strategy of each task in actual execution. Scheduling planning analysis includes path shortest analysis, time window limitation analysis, and equipment load balancing analysis to ensure efficient and reasonable task execution. According to the analysis results of the scheduling planning, the optimal and real-time adjusted inspection path is generated for each unmanned inspection equipment. The target branch inspection path is the spatial route planning that should be followed by a specific device during the execution of a certain type of inspection task. Through the target branch inspection path, the set of unmanned inspection equipment is cooperatively scheduled for power grid inspection, and the task allocation and cooperative work between multiple devices are uniformly managed and controlled to avoid inefficient behaviors such as repeated coverage, conflict execution, or idle waiting.

[0038] Further, the application also includes: obtaining a set of power grid resource attribute factors, the set of power grid resource attribute factors including resource type, location longitude and latitude, and specification size; performing attribute classification identification on each resource in the target power grid resource according to the set of power grid resource attribute factors to obtain a set of power grid resource attribute parameters; determining a multi-level subdivision level according to the inspection accuracy requirement of the target power grid resource; and using an earth subdivision grid coding system to sequentially perform multi-level spatial subdivision and space-time coding on the set of power grid resource attribute parameters based on the multi-level subdivision level to obtain a set of power grid resource space-time grid codes.

[0039] Specifically, a set of power grid resource attribute factors is obtained, the set of power grid resource attribute factors being a set of basic information describing the characteristics of power grid resources, and the set of power grid resource attribute factors including resource type, location longitude and latitude, and specification size. The resource type refers to the category of equipment in the power grid, such as substations and distribution lines, the location longitude and latitude are the specific position coordinates of the equipment in the geographical space, and the specification size refers to the physical specifications of the equipment, such as the capacity of a transformer or the length of a line.

[0040] Performing attribute classification identification on each resource in the target power grid resource according to the set of power grid resource attribute factors can clearly determine the specific characteristics of each resource, and a set of power grid resource attribute parameters is obtained. Attribute classification can be represented by an identifier, for example, a transformer can be represented by “T1”, and a distribution line can be represented by “L1”. Through classification, power grid resources can be more efficiently managed, facilitating subsequent data processing and analysis.

[0041] The inspection accuracy requirement of the power grid resource refers to the degree of detail of equipment state monitoring during power grid inspection. For example, for high-voltage electrical equipment, the inspection accuracy requirement can be high, requiring detailed state of each component, while for low-voltage equipment, the inspection accuracy can be low. The multi-level subdivision level is determined according to the inspection accuracy requirement of the target power grid resource. The setting of the subdivision level determines the division level of the resources, and higher-accuracy equipment requires more detailed level division, while low-accuracy requirement equipment can use a more simple layering method.

[0042] The attribute parameter set of the power grid resource is sequentially subjected to multi-level spatial subdivision and space-time coding based on the multi-level subdivision hierarchy using the earth subdivision grid coding system. The earth subdivision grid coding system is a system for dividing the earth's surface into multiple grids, each of which is represented by a coding method. According to different inspection accuracy requirements, the attribute parameter set of the power grid resource is spatially divided and assigned a corresponding space-time code. For example, the power grid resource may be distributed in different geographical locations and different time periods. The coding system combines these information to form a comprehensive coding set, which facilitates the spatial positioning and time scheduling of the power grid resource, and obtains a space-time grid coding set of the power grid resource. Table 1 shows part of the records of the latest space-time grid coding set of the power grid resource.

[0043] Table 1: Part of the records of the latest space-time grid coding set of the power grid resource

[0044]

[0045] Further, the application also includes: based on the earth subdivision grid coding system, the multi-level subdivision hierarchy is analyzed by grid, and the multi-level subdivision grid information is determined; the attribute parameter set of the power grid resource is sequentially subjected to multi-level spatial subdivision using the multi-level subdivision grid information, and a power grid resource spatial grid set is obtained; according to the earth subdivision grid coding system, the grid space-time coding format is determined, which includes grid level, grid location, acquisition timestamp and resource attribute parameter; each resource grid in the power grid resource spatial grid set is subjected to space-time coding based on the grid space-time coding format, and a power grid resource space-time grid coding set is obtained.

[0046] Specifically, the earth subdivision grid coding system is a system for dividing the earth's surface into multiple grid units, each of which has a unique code. Based on the earth subdivision grid coding system, the multi-level subdivision hierarchy is analyzed by grid, and the multi-level subdivision grid information is determined. The multi-level subdivision hierarchy refers to a hierarchy structure with different levels of accuracy. According to different requirements, the space is divided into multiple levels of grids, such as grid levels from coarse to fine, and the specific detail information of each level grid is determined, such as grid size and location, thereby providing a basis for subsequent spatial subdivision.

[0047] The attribute parameter set of the power grid resource is sequentially subjected to multi-level spatial subdivision using the multi-level subdivision grid information, and each resource in the attribute parameter set of the power grid resource is assigned to a corresponding spatial grid. The spatial characteristics of the power grid resource are divided into multiple grids according to different levels and accuracies, and each grid represents a specific area or spatial segment. For example, a substation may be divided into a finer grid, while a distant transmission line may be divided into a larger grid, thereby forming a power grid resource spatial grid set.

[0048] According to the earth section grid coding system, the grid space-time coding format is determined, which is a coding mode combining the space information of each grid unit with the time information, and then a complete space-time information record is established for each power grid resource. The grid space-time coding format includes grid level, grid location, acquisition timestamp and resource attribute parameters. The grid level represents the accuracy or level of the grid, the grid location is the specific location of the grid in the geographical space, the acquisition timestamp is the data acquisition time at a certain time, and the resource attribute parameters are specific information related to the power grid resource, such as device type, state, etc.

[0049] Based on the grid space-time coding format, each resource grid in the grid space-time coding format is coded and assigned. Each power grid resource space grid will obtain a unique space-time code, and the grid space-time coding set of the power grid resource is obtained, which contains the spatial position, accuracy level, timestamp and resource attribute of the grid, facilitating subsequent management, analysis and scheduling.

[0050] Further, the application also includes: using the multi-level section grid information to perform hierarchical spatial sectioning on the power grid resource attribute parameter set in turn to obtain a multi-level grid set of power grid resources; based on the multi-level grid set of power grid resources, cross-grid resource determination is performed on the power grid resource attribute parameter set to obtain a cross-grid power grid resource attribute parameter set; a power grid resource segmentation strategy is constructed, and the cross-grid power grid resource attribute parameter set is segmented according to the power grid resource segmentation strategy to obtain a multi-segment cross-grid power grid resource parameter set; the multi-segment cross-grid power grid resource parameter set is allocated grid by segment to determine a segmented grid set of power grid resources, and according to the multi-level grid set of power grid resources and the segmented grid set of power grid resources, the power grid resource space grid set is obtained.

[0051] Specifically, the multi-level section grid information is to divide the geographical space into multiple levels or grades of grids according to the location, type and other information of the power grid resource. The multi-level section grid information is used to perform hierarchical spatial sectioning on the power grid resource attribute parameter set in turn to obtain a multi-level grid set of power grid resources. Hierarchical spatial sectioning is to allocate power grid resources according to multi-level section grid information. Each power grid resource attribute parameter set contains basic information of the power grid resource, such as the type, location and specification of the resource, which will be divided into corresponding grids according to the set grid level, thereby forming a grid set containing multiple grid levels. The management of power grid resources can be carried out at different accuracies and levels, making the management more detailed and flexible.

[0052] The grid resource attribute parameter set of the power grid resource is determined based on the multi-level grid set of the power grid resource. The cross-grid resource determination refers to determining the power grid resource crossing multiple grid units in the multi-level grid set, and then obtaining the cross-grid power grid resource attribute parameter set. Some resources in the power grid resource attribute parameter set may be located at the boundary of multiple grids. By determining, it is confirmed whether it belongs to the cross-grid resource, which helps to accurately identify the power grid resources that need to be processed and scheduled in multiple grids under the multi-level grid division, ensuring the integrity of the resources and the accuracy of the management.

[0053] The power grid resource segmentation strategy is constructed. The power grid resource segmentation strategy refers to a strategy for dividing the resource into multiple parts according to the spatial distribution and attribute characteristics of the power grid resource. The cross-grid power grid resource attribute parameter set is segmented and processed according to the power grid resource segmentation strategy. For the cross-grid power grid resource, it may need to be reasonably segmented according to its distribution and function, and then a multi-segment cross-grid power grid resource parameter set is obtained. Through segmentation processing, each part can be independently managed and adapted to different grid accuracy requirements. The cross-grid resource can be more effectively managed and scheduled.

[0054] The multi-segment cross-grid power grid resource parameter set is allocated a grid segment by segment. The grid segment allocation refers to allocating a corresponding grid to each segment after segmenting each power grid resource according to its location and accuracy requirements, and then determining the power grid resource segmentation grid set to ensure that each power grid resource segment can be matched with a suitable grid and form a complete power grid resource spatial grid set. According to the multi-level grid set of the power grid resource and the power grid resource segmentation grid set, the power grid resource spatial grid set is obtained, and the spatial distribution and management requirements of the power grid resource are fully covered.

[0055] Further, the application also includes: obtaining a current position grid code set, a flight capability parameter set and a current state parameter set of the unmanned inspection equipment set; associating the current position grid code set with the target grid encoding task set in space position to determine an equipment space position relationship set; performing inspection task scheduling and distribution based on the equipment space position relationship set to obtain an initial inspection equipment distribution task set; constructing a task scheduling and distribution strategy, and performing scheduling optimization on the initial inspection equipment distribution task set based on the task scheduling and distribution strategy, the flight capability parameter set and the current state parameter set to determine an inspection equipment branch scheduling task set.

[0056] Specifically, the unmanned inspection equipment set includes devices such as unmanned aerial vehicles and inspection robots that have automatic movement and sensing capabilities. Key operating information is extracted from all unmanned devices participating in the inspection to obtain a current position grid code set, a flight capability parameter set, and a current state parameter set of the unmanned inspection equipment set. The current position grid code set represents the real-time position code of the unmanned inspection equipment set under the earth subdivision grid coding system, and the space area where each device is currently located can be described by a unique grid code. The flight capability parameter set refers to the flight performance indicators of the device, such as maximum endurance time, maximum flight height, maximum load capacity, etc., which are used to assess the operating range and capacity of the device. The current state parameter set is the operating state of the device at the current time, such as power, load, speed, sensor state, etc., reflecting its availability and operating health.

[0057] The target grid coding task set is a set of target region grids generated according to the power grid resource inspection plan, and each grid corresponds to an inspection task. The location grid of the device is compared with the grid code of the target region that needs to be inspected in the plan to determine the spatial proximity or association between the device and the task, determine the device spatial position relationship set, and form a spatial adjacency mapping between the device and the task, which is used for subsequent task allocation and scheduling.

[0058] Based on the spatial matching result, each inspection task is initially allocated to the nearest or most suitable device, and the spatial distance between the current location and the target task is used to ensure that the device can reach the inspection area in the shortest time to form an initial task allocation scheme, i.e., an initial inspection equipment allocation task set, where each record represents an initial binding relationship between a device and one or more corresponding tasks.

[0059] A task scheduling and allocation strategy is constructed, which may include shortest path priority, energy minimization, task priority sorting, time window constraints, etc. Based on the task scheduling and allocation strategy, the flight capability parameter set, and the current state parameter set, the initial inspection equipment allocation task set is optimized to determine the optimal allocation relationship between the device and the task, and form an inspection equipment branch scheduling task set, in which each device will obtain a group of executable, reasonable, and efficient task allocations.

[0060] Further, the application also includes: performing inspection demand analysis on the initial inspection equipment allocation task set to determine an initial grid task inspection demand parameter set; performing matching conflict identification between the flight capability parameter set and the current state parameter set and the initial grid task inspection demand parameter set to obtain a conflict grid task set; using the flight capability parameter set and the current state parameter set as constraint parameters, and based on the task scheduling and allocation strategy, performing scheduling and allocation correction on the conflict grid task set to determine the inspection equipment branch scheduling task set.

[0061] Specifically, after completing the initial device and task allocation, a refined analysis is performed on each allocated task to determine the specific requirements of each task in terms of space, time, operation capability, etc. The initial grid task inspection demand parameter set is a task list generated based on the relationship between the current location and the target location of the device, and the inspection demand analysis is to extract the resource conditions required by the task, such as the required flight height, estimated time consumption, required sensor type, minimum stable flight speed, etc. The final initial grid task inspection demand parameter set is a set of parameters bound to the grid task, which is used for subsequent adaptation analysis with device capability.

[0062] Based on the flight capability parameter set and the current state parameter set, the initial grid task inspection demand parameter set is matched and conflict is identified, so that the situation that the device cannot meet the task demand is identified, and the conflict grid task set is obtained. The flight capability parameter set includes, for example, maximum flight time, maximum operation radius, maximum load capacity, etc., and the current state parameter set includes dynamic information such as device remaining power, current load, device state, etc. When there is a contradiction between the parameters and the demand of the inspection task, for example, a UAV currently has only 10 minutes of remaining power, but is assigned a task that needs at least 15 minutes to complete, it will be judged as a conflict. Collect such cases to form a conflict grid task set, waiting for further scheduling modification.

[0063] After identifying the conflict, the allocation relationship between the device and the task is adjusted according to the preset scheduling strategy. The flight capability parameter set and the current state parameter set are used as constraint parameters, and the conflict grid task set is scheduled and allocated based on the task scheduling allocation strategy to determine the inspection device branch scheduling task set. The scheduling allocation strategy can include task priority-based reordering, task transfer based on device load balancing, or joint inspection mode based on multi-device cooperation. In the modification process, flight capability and current state are used as rigid constraint conditions to ensure that the newly allocated task does not exceed the physical and operating limits of the device, and finally an optimized inspection device branch scheduling task set is formed, making the task allocation more reasonable, safe and efficient.

[0064] Further, the application also includes: performing alarm type extraction and impact degree evaluation on the historical power grid alarm data to construct an alarm impact degree index set; performing inspection execution impact index splitting on the environmental constraint conditions to obtain an environmental complexity impact index set; determining a grid task inspection priority index set according to the alarm impact degree index set and the environmental complexity impact index set; performing priority scoring and task sorting on the grid task inspection priority index set to obtain the target branch inspection task sequence set.

[0065] Specifically, various alarm types such as overload, trip, short circuit, ground fault, etc. are extracted from the accumulated power grid alarm records in the past, and the actual impact of each type of alarm is quantitatively evaluated to construct an alarm impact index set. The alarm type extraction is to identify the type of event from the log or monitoring data, and the impact degree evaluation gives a numerical evaluation result, such as a score of 1 to 10, according to whether the alarm causes a large-scale power outage, equipment damage or safety hazards, etc. The alarm impact index set finally formed is an important data set for measuring the severity of various alarms in the power grid, supporting the subsequent decision model to focus on high-risk areas.

[0066] Various external environmental factors affecting the execution effect of unmanned equipment inspection are decomposed and quantified to obtain an environment complexity impact index set. For example, wind speed, rainfall, temperature change in meteorological conditions, mountainous terrain, building shielding, water density in topographic factors, etc. will affect the safety and efficiency of unmanned aerial vehicles or robots working in a certain grid. By converting environmental factors into a set of quantitative indicators, the environment complexity impact index set can clearly reflect the difficulty of a task area inspection.

[0067] According to the alarm impact index set and the environment complexity impact index set, different weights are given to the alarm severity and environment complexity, for example, alarm proportion 70%, environment proportion 30%, and weighted determination of the inspection priority index set is adopted for importance evaluation of all inspection tasks. The higher the score, the more urgent or challenging the task, which should be arranged for execution in priority.

[0068] Based on the inspection priority index set, the priority score and task sorting of the inspection equipment branch scheduling task set are performed, and the branch task of each inspection equipment is scored and sorted, so as to adjust the original task execution order and obtain a target branch inspection task sequence set. The priority scoring process will consider the urgency and environmental complexity of each task, and the target branch inspection task sequence set obtained after sorting is the inspection task list to be completed in sequence by each device next, and the task arrangement is more scientific and reasonable.

[0069] Further, the application also includes: selecting a target branch task path planning algorithm based on the characteristic information of the target branch inspection task sequence set, and setting a power grid inspection path planning cost function according to the power grid inspection target; using the target branch task path planning algorithm to adopt the power grid inspection path planning cost function to dynamically analyze the scheduling planning of the target branch inspection task sequence set, and generate the target branch inspection path.

[0070] Specifically, based on the characteristic information of the target branch inspection task sequence set, i.e. the spatial distribution of each task in the inspection task list, task urgency, complexity of the environment, etc., the target branch task path planning algorithm is selected to arrange the device action route. The target branch inspection task sequence set refers to the execution task list obtained by each device according to the priority score; its characteristic information includes the number of tasks, the distance between tasks, the task execution time, and possible dynamic change factors. The path planning algorithm includes, for example, the A-star algorithm suitable for static scenarios, the Dijkstra algorithm suitable for weight graph analysis, the ant colony algorithm or genetic algorithm suitable for complex or dynamic environments. According to the characteristics of the task, different algorithms are selected flexibly, which can greatly improve the execution efficiency of the device.

[0071] In combination with the actual needs of the inspection task, a power grid inspection path planning cost function is constructed to measure the pros and cons of each selectable path. The power grid inspection goals may include the shortest completion time, the lowest energy consumption, the most comprehensive coverage area, and the avoidance of risk areas, etc. The cost function is a function model that quantitatively integrates multiple factors such as flight distance, flight time, power consumption, obstacle bypass path, etc., and its goal is to minimize or maximize a certain cost value. For example, the following cost function can be constructed: path cost = flight distance x 1.2 + wind zone risk coefficient x 10 + energy consumption x 0.8. The function is set to ensure that various influencing factors are considered in the path planning process.

[0072] Dynamic path calculation is performed on each task in the target task list to finally generate an executable and optimized inspection path. Dynamic analysis of scheduling planning means that not only static geographical location relationships are considered in the planning process, but also real-time changes in the environment, fluctuations in device state, sudden task insertion, etc. are dynamically included to adjust and optimize the path planning in real time. The target branch inspection path obtained finally is the most reasonable inspection route formed by each device according to the current actual situation and the optimal cost path calculation result.

[0073] Further, the present application further comprises: performing power grid inspection monitoring on the set of unmanned inspection devices through the target branch inspection path to obtain power grid inspection feedback data; performing abnormal situation identification and scheduling optimization analysis on the power grid inspection feedback data to determine a power grid inspection optimization scheduling strategy; and performing inspection scheduling cooperative optimization on the set of unmanned inspection devices based on the power grid inspection optimization scheduling strategy.

[0074] Specifically, based on the generated optimal inspection path for each device, all unmanned inspection devices participating in the task are monitored and guided in real time to ensure that they complete the task according to the predetermined path. The target branch inspection path refers to the specific action route generated under the constraints of task priority, environmental factors, and device performance. The set of unmanned inspection devices refers to the group of robots or drones participating in the execution of this round of inspection tasks, and the power grid inspection monitoring includes continuous perception and recording of device location, speed, execution state, energy remaining, and path deviation. During the inspection process, the devices perform task point inspection and data collection according to the path, while the background synchronously records their behavior information and environmental response to form complete power grid inspection feedback data.

[0075] The collected device execution data and field information are analyzed to identify problems affecting task execution, such as path deviation, image blur, insufficient power, sensor failure, task interruption, and other abnormal situations. Power grid inspection feedback data refers to the operation log, sensor sampling value, image or video data, task completion status, and other system indicators uploaded by the device. Abnormal situation identification refers to timely detection of abnormalities through feature matching, threshold judgment, or intelligent algorithm means. Dispatching optimization analysis refers to re-evaluating whether the current dispatching strategy is reasonable after evaluating the impact of these abnormalities to determine whether the inspection plan or path of the device needs to be adjusted.

[0076] Based on the power grid inspection optimization dispatching strategy, the inspection tasks, paths, and execution order of all current unmanned inspection devices are reconfigured, achieving more efficient and stable multi-device collaborative work. The power grid inspection optimization dispatching strategy refers to a dynamically generated dispatching adjustment scheme based on device state, environmental factors, task completion, and other variables; inspection dispatching collaborative optimization emphasizes intelligent cooperation between multiple devices in terms of task crossing, substitution, concurrency, and obstacle avoidance. For example, when a drone cannot complete the task due to insufficient energy, the unfinished part will be assigned to another nearby device in good condition to complete, and its path will be updated simultaneously to adapt to the new task.

[0077] In summary, the power grid inspection dispatching method based on multi-level subdivision grid driving provided by the present application has the following technical effects: by achieving the technical goal of unified grid management and intelligent inspection dispatching of power grid resources based on the earth subdivision grid coding system, the technical effects of improving inspection task space organization capability, optimizing device dispatching strategy, enhancing system real-time response capability, and enhancing resource collaboration efficiency are achieved.

[0078] Embodiment two, based on the same inventive concept as the power grid inspection dispatching method based on multi-level subdivision grid driving in the preceding embodiments, the present application also provides a power grid inspection dispatching system based on multi-level subdivision grid driving, please refer to the attached Figure 2, comprising: a spatiotemporal grid code set obtaining module 11 configured to perform multi-level spatial subdivision and spatiotemporal coding assignment on target power grid resources by using an earth subdivision grid coding system to obtain a power grid resource spatiotemporal grid code set; a power grid resource subdivision index large table establishing module 12 configured to perform table structure design and resource data insertion based on the power grid resource spatiotemporal grid code set and the target power grid resources to establish a power grid resource subdivision index large table; a patrol device branch scheduling task set determining module 13 configured to query and determine a target grid code task set of a set of devices to be patrolled from the power grid resource subdivision index large table, perform task scheduling and allocation on the target grid code task set based on a set of unmanned patrol devices, and determine a patrol device branch scheduling task set; a target branch patrol task sequence set obtaining module 14 configured to perform priority sorting on the patrol device branch scheduling task set respectively in combination with historical power grid alarm data and environmental constraint conditions to obtain a target branch patrol task sequence set; and a power grid patrol cooperative scheduling module 15 configured to perform scheduling planning analysis based on the target branch patrol task sequence set, dynamically generate a target branch patrol path, and perform power grid patrol cooperative scheduling on the set of unmanned patrol devices through the target branch patrol path.

[0079] Further, the power grid patrol scheduling system driven by the multi-level subdivision grid is further configured to: obtain a set of power grid resource attribute factors, the set of power grid resource attribute factors including resource type, location longitude and latitude, and size specification; perform attribute classification and identification on each resource in the target power grid resources according to the set of power grid resource attribute factors to obtain a set of power grid resource attribute parameters; determine a multi-level subdivision level according to a patrol accuracy requirement of the target power grid resources; and perform multi-level spatial subdivision and spatiotemporal coding assignment on the set of power grid resource attribute parameters based on the multi-level subdivision level by using the earth subdivision grid coding system to obtain a power grid resource spatiotemporal grid code set.

[0080] Further, the power grid patrol scheduling system driven by the multi-level subdivision grid is further configured to: perform grid analysis on the multi-level subdivision level based on the earth subdivision grid coding system to determine multi-level subdivision grid information; perform multi-level spatial subdivision on the set of power grid resource attribute parameters based on the multi-level subdivision grid information to obtain a power grid resource spatial grid set; determine a grid spatiotemporal coding format based on the earth subdivision grid coding system, the grid spatiotemporal coding format including grid level, grid location, collection timestamp, and resource attribute parameter; and perform spatiotemporal coding assignment on each resource grid in the power grid resource spatial grid set based on the grid spatiotemporal coding format to obtain a power grid resource spatiotemporal grid code set.

[0081] Further, the power grid inspection scheduling system based on the multi-level split grid driving is also used for: adopting the multi-level split grid information to sequentially perform level-by-level spatial splitting on the power grid resource attribute parameter set, to obtain a power grid resource multi-level grid set; performing cross-grid resource determination on the power grid resource attribute parameter set based on the power grid resource multi-level grid set, to obtain a cross-grid power grid resource attribute parameter set; constructing a power grid resource segmentation strategy, performing segmentation processing on the cross-grid power grid resource attribute parameter set according to the power grid resource segmentation strategy, to obtain a multi-segment cross-grid power grid resource parameter set; performing segment-by-segment grid allocation on the multi-segment cross-grid power grid resource parameter set, to determine a power grid resource segmented grid set, and according to the power grid resource multi-level grid set and the power grid resource segmented grid set, to obtain the power grid resource spatial grid set.

[0082] Further, the power grid inspection scheduling system based on the multi-level split grid driving is also used for: obtaining a current position grid code set, a flight capability parameter set and a current state parameter set of the unmanned inspection equipment set; performing spatial position association on the current position grid code set and the target grid coding task set, to determine an equipment spatial position relationship set; performing inspection task scheduling allocation based on the equipment spatial position relationship set, to obtain an initial inspection equipment allocation task set; constructing a task scheduling allocation strategy, performing scheduling optimization on the initial inspection equipment allocation task set based on the task scheduling allocation strategy, the flight capability parameter set and the current state parameter set, to determine an inspection equipment branch scheduling task set.

[0083] Further, the power grid inspection scheduling system based on the multi-level split grid driving is also used for: performing inspection demand analysis on the initial inspection equipment allocation task set, to determine an initial grid task inspection demand parameter set; performing matching conflict identification on the initial grid task inspection demand parameter set based on the flight capability parameter set and the current state parameter set, to obtain a conflict grid task set; taking the flight capability parameter set and the current state parameter set as constraint parameters, performing scheduling allocation correction on the conflict grid task set based on the task scheduling allocation strategy, to determine the inspection equipment branch scheduling task set.

[0084] Further, the power grid inspection scheduling system based on the multi-level split grid driving is also used for: performing alarm type extraction and influence degree evaluation on the historical power grid alarm data, to construct an alarm influence degree index set; performing inspection execution influence index splitting on the environmental constraint condition, to obtain an environmental complexity influence index set, and according to the alarm influence degree index set and the environmental complexity influence index set, to determine an inspection priority index set; performing priority scoring and task sorting on the inspection equipment branch scheduling task set based on the inspection priority index set, to obtain the target branch inspection task sequence set.

[0085] Further, the power grid inspection scheduling system based on the multi-level split grid driving is also used for: selecting a target branch task path planning algorithm based on the characteristic information of the target branch inspection task sequence set, and setting a power grid inspection path planning cost function according to the power grid inspection target; using the target branch task path planning algorithm to adopt the power grid inspection path planning cost function to dynamically analyze the target branch inspection task sequence set for scheduling planning, and generating the target branch inspection path.

[0086] Further, the power grid inspection scheduling system based on the multi-level split grid driving is also used for: performing power grid inspection monitoring on the set of unmanned inspection devices through the target branch inspection path, obtaining power grid inspection feedback data; performing abnormal situation identification and scheduling optimization analysis on the power grid inspection feedback data, and determining a power grid inspection optimization scheduling strategy; and performing inspection scheduling cooperative optimization on the set of unmanned inspection devices based on the power grid inspection optimization scheduling strategy.

[0087] The embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The power grid inspection scheduling method and specific example based on the multi-level split grid driving in the first embodiment are also applicable to the power grid inspection scheduling system based on the multi-level split grid driving in the present embodiment. Based on the detailed description of the power grid inspection scheduling method based on the multi-level split grid driving, those skilled in the art can clearly know the power grid inspection scheduling system based on the multi-level split grid driving in the present embodiment. Therefore, for the sake of brevity of the specification, it will not be described in detail here.

[0088] The above description of disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0089] Obviously, those skilled in the art can make various modifications and variations 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 present application and its equivalent technologies, the present application also intends to include these modifications and variations.

Claims

1. A power grid inspection scheduling method based on multi-level partitioning grid driving, characterized in that, The method includes: The target power grid resources are spatially partitioned and spatiotemporally encoded using the Earth subdivision grid coding system to obtain a set of spatiotemporal grid codes for power grid resources. Based on the spatiotemporal grid coding set of the power grid resources and the target power grid resources, a table structure design and resource data insertion are performed to establish a large index table for power grid resource partitioning. The target grid coding task set of the set of equipment to be inspected is determined by querying the large table of power grid resource partitioning index. Based on the set of unmanned inspection equipment, the target grid coding task set is scheduled and allocated to determine the branch scheduling task set of inspection equipment. By combining historical power grid alarm data and environmental constraints, the branch scheduling task sets of the inspection equipment are prioritized to obtain the target branch inspection task sequence set. Based on the target branch inspection task sequence set, a scheduling plan is analyzed to dynamically generate the target branch inspection path, and the unmanned inspection equipment set is coordinated and scheduled for power grid inspection through the target branch inspection path. The obtained spatiotemporal grid coding set of power grid resources includes: Obtain a set of power grid resource attribute factors, which includes resource type, location latitude and longitude, and specifications. Each resource in the target power grid resource is classified and identified according to the power grid resource attribute factor set to obtain the power grid resource attribute parameter set. Based on the inspection accuracy requirements of the target power grid resources, a multi-level subdivision hierarchy is determined; Using the Earth subdivision grid coding system, the set of power grid resource attribute parameters is sequentially subjected to multi-level spatial subdivision and spatiotemporal coding based on the multi-level subdivision hierarchy to obtain a set of spatiotemporal grid codes for power grid resources. The obtained spatiotemporal grid coding set of power grid resources includes: Based on the Earth subdivision grid coding system, the multi-level subdivision hierarchy is analyzed to determine the multi-level subdivision grid information; The power grid resource attribute parameter set is sequentially spatially partitioned into multiple levels using the multi-level partitioned grid information to obtain a power grid resource spatial grid set. Based on the Earth subdivision grid coding system, the grid spatiotemporal coding format is determined, which includes grid level, grid location, acquisition timestamp, and resource attribute parameters. Based on the aforementioned spatiotemporal coding format, each resource grid in the power grid resource spatial grid set is assigned a spatiotemporal code to obtain a power grid resource spatiotemporal grid coding set. The obtained power grid resource spatial grid set includes: The power grid resource attribute parameter set is spatially partitioned level by level using the multi-level mesh information to obtain a multi-level mesh set of power grid resources. Based on the multi-level grid set of power grid resources, cross-grid resource determination is performed on the set of power grid resource attribute parameters to obtain a cross-grid power grid resource attribute parameter set. A grid resource segmentation strategy is constructed, and the cross-grid grid resource attribute parameter set is segmented according to the grid resource segmentation strategy to obtain a multi-segment cross-grid grid resource parameter set. The multi-section cross-grid power grid resource parameter set is allocated section by section to determine a power grid resource segmented grid set, and the power grid resource multi-level grid set and the power grid resource segmented grid set are combined to obtain the power grid resource spatial grid set.

2. The power grid inspection scheduling method based on multi-level partitioning mesh drive according to claim 1, wherein, The determining of the inspection equipment branch scheduling task set comprises: acquiring a current position grid code set, a flight capability parameter set and a current state parameter set of the unmanned inspection equipment set; associating the current position grid code set with the target grid coding task set in space position to determine an equipment space position relationship set; performing inspection task scheduling distribution based on the equipment space position relationship set to obtain an initial inspection equipment distribution task set; constructing a task scheduling distribution strategy, and performing scheduling optimization on the initial inspection equipment distribution task set based on the task scheduling distribution strategy, the flight capability parameter set and the current state parameter set to determine the inspection equipment branch scheduling task set. 3.The power grid inspection scheduling method based on multi-level partition grid driving according to claim 2, wherein, The determining of the inspection equipment branch scheduling task set comprises: performing inspection demand analysis on the initial inspection equipment distribution task set to determine an initial grid task inspection demand parameter set; performing matching conflict identification on the initial grid task inspection demand parameter set based on the flight capability parameter set and the current state parameter set to obtain a conflict grid task set; taking the flight capability parameter set and the current state parameter set as constraint parameters, and performing scheduling distribution correction on the conflict grid task set based on the task scheduling distribution strategy to determine the inspection equipment branch scheduling task set.

4. The power grid inspection scheduling method based on multi-level partitioning mesh drive according to claim 1, wherein, The obtaining of the target branch inspection task sequence set comprises: performing alarm type extraction and influence degree evaluation on the historical power grid alarm data to construct an alarm influence degree index set; performing inspection execution influence index splitting on the environmental constraint condition to obtain an environmental complexity influence index set, and determining an inspection priority index set based on the alarm influence degree index set and the environmental complexity influence index set; performing priority scoring and task sorting on the inspection equipment branch scheduling task set based on the inspection priority index set to obtain the target branch inspection task sequence set.

5. The multi-level hierarchical partitioning grid driven power grid inspection scheduling method of claim 1, wherein, The dynamic generation of the target branch inspection path comprises: selecting a target branch task path planning algorithm based on characteristic information of the target branch inspection task sequence set, and setting a power grid inspection path planning cost function according to a power grid inspection target; performing scheduling planning dynamic analysis on the target branch inspection task sequence set by using the target branch task path planning algorithm and the power grid inspection path planning cost function to generate the target branch inspection path.

6. The multi-level partitioned grid driven power grid inspection scheduling method of claim 1, wherein, The power grid inspection cooperative scheduling of the unmanned inspection equipment set through the target branch inspection path comprises: performing power grid inspection monitoring on the unmanned inspection equipment set through the target branch inspection path to obtain power grid inspection feedback data; performing abnormal situation identification and scheduling optimization analysis on the power grid inspection feedback data to determine a power grid inspection optimization scheduling strategy; performing inspection scheduling cooperative optimization on the unmanned inspection equipment set based on the power grid inspection optimization scheduling strategy.

7. A power grid inspection scheduling system based on multi-level partitioning grid driving, characterized in that, The step for implementing the power grid inspection scheduling method based on the multi-level partition grid driving according to any one of claims 1 to 6 comprises: A space-time grid code set obtaining module is configured to perform multi-level spatial partitioning and space-time coding on target power grid resources by using an earth partition grid coding system to obtain a power grid resource space-time grid code set; A power grid resource partition index large table establishing module is configured to perform table structure design and resource data insertion based on the power grid resource space-time grid code set and the target power grid resources to establish a power grid resource partition index large table; An inspection device branch scheduling task set determining module is configured to query and determine a target grid code task set of a to-be-inspected device set from the power grid resource partition index large table, perform task scheduling and allocation on the target grid code task set based on a set of unmanned inspection devices, and determine an inspection device branch scheduling task set; A target branch inspection task sequence set obtaining module is configured to perform priority sorting on the inspection device branch scheduling task set respectively in combination with historical power grid alarm data and environmental constraint conditions to obtain a target branch inspection task sequence set; A power grid inspection collaborative scheduling module is configured to perform scheduling planning analysis based on the target branch inspection task sequence set, dynamically generate a target branch inspection path, and perform power grid inspection collaborative scheduling on the set of unmanned inspection devices through the target branch inspection path.

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