Intelligent planning and optimizing method for power distribution operation and maintenance inspection track

Abstract

The invention discloses a power distribution operation and maintenance inspection track intelligent planning and optimizing method, and relates to the technical field of power distribution network intelligent operation and maintenance, and the method comprises the steps: collecting the topological data and equipment state information of a power distribution network, and constructing a power equipment association relationship model through an intelligent analysis model; on the basis of the incidence relation model, the inspection importance degree of the power equipment is calculated, and an initial inspection sequence is generated according to the inspection importance degree; and in combination with the initial inspection sequence and the real-time road condition information, constructing a dynamic inspection track network, and generating a final inspection path through an intelligent optimization algorithm. The problems of inaccurate equipment state evaluation, unreasonable routing inspection task planning, insufficient routing inspection path optimization and the like in the prior art are solved. The method has the main beneficial effects that the utilization efficiency of inspection resources is improved, the adaptability of an inspection strategy is enhanced, and the scientificity of an inspection path is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent operation and maintenance of power distribution networks, and specifically to an intelligent planning and optimization method for power distribution operation and maintenance inspection trajectories. Background Art

[0002] The distribution network is the infrastructure closest to the user end in the power system, and its operation and maintenance level directly affects the reliability of power supply and service quality. The operation and maintenance of traditional distribution networks mainly rely on fixed-cycle manual inspections and passive fault handling modes. This mode has problems such as low efficiency of inspection resource utilization, suboptimal inspection route planning, and untimely perception of equipment status. In recent years, with the introduction of the Internet of Things and artificial intelligence technologies, intelligent operation and maintenance has gradually become an important development direction for distribution network management. However, due to the wide coverage of the distribution network, the variety of equipment, and the complex operating environment, how to achieve accurate scheduling of inspection resources and intelligent planning of inspection routes still faces many technical challenges.

[0003] The existing power distribution operation and maintenance inspection methods have the following main shortcomings: first, there is a lack of in-depth correlation analysis of equipment status and network topology, making it difficult to accurately evaluate the inspection priority of equipment; second, the inspection task arrangement often adopts a static planning method, which fails to fully consider the dynamic changes in equipment status and real-time road conditions; third, the multi-dimensional constraints are not effectively balanced during the inspection path optimization process, resulting in a significant contradiction between inspection efficiency and resource utilization. These problems have seriously restricted the improvement of the operation and maintenance efficiency of the distribution network. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method for intelligent planning and optimization of distribution operation and maintenance inspection trajectories, which can solve the problems mentioned in the background technology.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a method for intelligent planning and optimization of distribution operation and maintenance inspection trajectories, comprising: collecting topological data and equipment status information of the distribution network, and using an intelligent analysis model to construct a power equipment association model; the association model includes equipment feature analysis results and the degree of association between equipment; based on the association model, the inspection importance of the power equipment is calculated, and an initial inspection sequence is generated according to the inspection importance; wherein the inspection importance is comprehensively determined based on the equipment status and the degree of network impact; the initial inspection sequence includes equipment inspection priority and inspection time requirements; combining the initial inspection sequence and real-time road condition information, constructing a dynamic inspection trajectory network, and generating a final inspection path through an intelligent optimization algorithm; the dynamic inspection trajectory network includes a set of optional inspection paths and path dynamic weights.

[0007] As a preferred solution of the method for intelligent planning and optimization of distribution operation and maintenance inspection trajectories described in the present invention, the construction process of the intelligent analysis model includes: constructing an association graph of the distribution network, taking the power equipment as network nodes, and establishing network edges based on the electrical connection relationship, spatial distribution relationship and operation status correlation between the power equipment to form a multi-level network topology structure; obtaining static attribute data and dynamic operation data of the network nodes, wherein the static attribute data includes equipment type, installation location and rated parameters, and the dynamic operation data includes load level, operation time and fault records; based on the static attribute data and the dynamic operation data, a multi-dimensional feature fusion method is used to calculate the association strength between the network nodes, and the association relationship model is iteratively updated in combination with the network topology until the association relationship model converges to a stable state.

[0008] As a preferred solution of the distribution operation and maintenance inspection trajectory intelligent planning and optimization method described in the present invention, the calculation of the inspection importance includes: establishing a multidimensional evaluation index system for the power equipment, the multidimensional evaluation index system includes a set of equipment status indicators and a set of network impact indicators, wherein the set of equipment status indicators covers operating parameter deviations, equipment degradation levels and fault risk levels, and the set of network impact indicators includes load power supply importance, network node importance and fault impact range; respectively calculating the operating status score and network impact of the power equipment, wherein the operating status score is obtained based on the equipment status indicator set through a hierarchical analysis method, and the network impact is obtained based on the network impact indicator set through a network centrality analysis method; adopting an adaptive weight allocation mechanism, dynamically adjusting the weight coefficients of the operating status score and the network impact according to the historical operating data of the power equipment, and using the weighted result as the inspection importance of the power equipment; determining a dynamic classification threshold based on the distribution characteristics of the inspection importance, and marking the power equipment exceeding the dynamic classification threshold as a priority inspection object.

[0009] As a preferred solution of the method for intelligent planning and optimization of distribution operation and maintenance inspection trajectories described in the present invention, the generation process of the initial inspection sequence includes: dividing the power equipment into first-priority power equipment, second-priority power equipment and third-priority power equipment according to the inspection importance, and constructing an inspection area in combination with the spatial distribution characteristics of the power equipment; obtaining the standard maintenance cycle and historical maintenance records of the power equipment, and analyzing the equipment failure patterns and maintenance time sensitivity based on the historical maintenance records; for the first-priority power equipment, comprehensively considering its failure risk trend and the maintenance time sensitivity, the first-priority power equipment is given priority to be arranged in the first time window that meets the inspection conditions within the standard maintenance cycle; for the second-priority power equipment, combined with the geographical distribution characteristics of the inspection area and the inspection resource constraints, the second-priority power equipment is reasonably arranged in the first half of the time window of the standard maintenance cycle; for the third-priority power equipment, based on the proximity principle and the inspection efficiency optimization principle, the third-priority power equipment is comprehensively arranged in the second half of the time window of the standard maintenance cycle.

[0010] As a preferred solution of the intelligent planning and optimization method of distribution operation and maintenance inspection trajectory described in the present invention, the optimization process of the dynamic inspection trajectory network includes: establishing a multi-level path evaluation system to obtain and analyze the traffic status information of the inspection path in real time; constructing a comprehensive evaluation model of the inspection path, calculating the physical distance factor of the inspection path, the travel time factor of the inspection path and the travel difficulty factor of the inspection path, and performing multi-objective weighing on the physical distance factor of the inspection path, the travel time factor of the inspection path and the travel difficulty factor of the inspection path, wherein the travel difficulty factor of the inspection path takes into account road grade, traffic conditions and environmental influences; calculating the dynamic score of the optional inspection path based on the comprehensive evaluation model, and combining the time constraints and resource limitations of the inspection task, using a heuristic search algorithm to generate the optimal inspection path combination that meets multiple constraints.

[0011] As a preferred solution of the distribution operation and maintenance inspection trajectory intelligent planning and optimization method described in the present invention, it also includes: a data preprocessing step: constructing a standardized processing flow for multi-source heterogeneous data, and performing time alignment and dimensional unification on the status data of the power equipment; designing abnormal data identification rules based on statistical characteristics and expert knowledge, establishing a multi-level data quality assessment mechanism, and classifying and processing abnormal data; establishing a hierarchical status assessment indicator system according to the type characteristics and operating characteristics of the power equipment, and determining the effective value range and warning threshold of each indicator based on historical data.

[0012] As a preferred solution of the distribution operation and maintenance inspection trajectory intelligent planning and optimization method described in the present invention, it also includes effect evaluation and optimization steps: establishing a multi-dimensional evaluation system for inspection effects, collecting and storing the execution process data of inspection tasks in real time; calculating key performance indicators based on the execution process data; the key performance indicators include inspection coverage rate, task timeliness rate and path optimal rate; building a model optimization feedback mechanism, dynamically adjusting the parameter configuration of the intelligent analysis model according to the changing trend of the key performance indicators, and continuously optimizing the inspection strategy through incremental learning.

[0013] To further solve the above technical problems, the present invention provides the following technical solutions: an intelligent planning and optimization system for distribution operation and maintenance inspection trajectories, comprising: a data processing unit, used to collect topological data and equipment status information of the distribution network, and use an intelligent analysis model to construct a correlation model including equipment feature analysis results and the degree of correlation between equipment; a sequence planning unit, used to calculate the inspection importance of power equipment based on the correlation model, and generate an initial inspection sequence including equipment inspection priority and inspection time requirements according to the inspection importance; a trajectory optimization unit, used to combine the initial inspection sequence and real-time road condition information to construct a dynamic inspection trajectory network including a set of optional inspection paths and dynamic path weights, and generate a final inspection path.

[0014] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the above-mentioned distribution power operation and maintenance inspection trajectory intelligent planning and optimization method are implemented.

[0015] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the above-mentioned method for intelligent planning and optimization of distribution power operation and maintenance inspection trajectories are implemented.

[0016] Beneficial effects of the present invention: The present invention constructs multi-level equipment association relationships through intelligent analysis models, and realizes accurate evaluation of equipment status and network impact; adopts an adaptive weight allocation mechanism to dynamically calculate inspection importance, ensuring that inspection resources are preferentially allocated to key equipment; optimizes inspection paths through a multi-dimensional evaluation system and heuristic algorithms, effectively balancing inspection efficiency and path feasibility. Specifically, the data processing link realizes the standardized integration of multi-source heterogeneous data and improves the accuracy of status assessment; the inspection planning link optimizes task allocation efficiency and reduces resource waste through hierarchical classification; the path optimization link comprehensively considers multiple factors such as physical distance and traffic conditions, and improves the reliability of inspection execution. This data-driven intelligent planning method significantly improves the accuracy and efficiency of distribution network operation and maintenance, and has practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 This is a schematic diagram of the overall process of a method for intelligent planning and optimization of power distribution operation and maintenance inspection trajectories proposed by the present invention; Figure 2 This is a schematic diagram of the overall structure of a power distribution operation and maintenance inspection trajectory intelligent planning and optimization system proposed by the present invention.

[0019] Figure 3 This is a computer equipment diagram in the distribution operation and maintenance inspection trajectory intelligent planning and optimization method proposed by the present invention. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0021] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0022] Example 1, reference Figure 1 , which is an embodiment of the present invention, provides a method for intelligent planning and optimization of distribution operation and maintenance inspection trajectories.

[0023] Figure 1 The overall process diagram of a distribution power operation and maintenance inspection trajectory intelligent planning and optimization method is shown, including the following steps: S1: Collect topological data and equipment status information of the distribution network, and use the intelligent analysis model to build a power equipment association model.

[0024] Specifically, the association relationship model includes the device feature analysis results and the degree of association between devices.

[0025] Specifically, the construction process of the intelligent analysis model includes: Construct a correlation graph of the power distribution network, take power equipment as network nodes, establish network edges based on the electrical connection relationship, spatial distribution relationship and operating status correlation between power equipment, and form a multi-level network topology structure; Obtain static attribute data and dynamic operation data of network nodes, wherein the static attribute data includes device type, installation location and rated parameters, and the dynamic operation data includes load level, operation time and fault records; Based on static attribute data and dynamic operation data, a multidimensional feature fusion method is used to calculate the association strength between network nodes, and the association relationship model is iteratively updated in combination with the network topology until the association relationship model converges to a stable state.

[0026] Furthermore, after collecting the topological data and equipment status information of the power distribution network, data preprocessing is required, including the following steps: Build a standardized processing flow for multi-source heterogeneous data, and perform time-series alignment and dimension unification for the status data of power equipment; Design abnormal data identification rules based on statistical features and expert knowledge, establish a multi-level data quality assessment mechanism, and classify and process abnormal data; According to the type characteristics and operating characteristics of power equipment, a hierarchical status assessment indicator system is established, and the effective value range and warning threshold of each indicator are determined based on historical data.

[0027] It should be noted that in this embodiment, step S1 first needs to collect the topological data and equipment status information of the distribution network. The topological data of the distribution network includes static information such as feeder architecture, equipment connection relationship, and geographic spatial distribution; the equipment status information includes dynamic data such as operating parameters, load characteristics, and environmental conditions. These data are collected through multiple data sources such as distribution automation systems, intelligent sensor networks, and operation and maintenance management platforms.

[0028] To ensure data quality, the system performs data preprocessing operations. By building a standardized processing flow, heterogeneous data is processed uniformly, including data format conversion, timestamp alignment, unit conversion and other operations. At the same time, abnormal data identification rules are designed based on statistical characteristics and expert knowledge, and a multi-level data quality assessment mechanism is established. In addition, a hierarchical status assessment indicator system is established for different types of power equipment, and the effective value range and warning threshold of each indicator are determined based on historical data.

[0029] The intelligent analysis model is a computational framework for analyzing and processing complex associations in distribution networks. The model first constructs an association graph of the distribution network, takes power equipment as network nodes, and establishes network edges based on the electrical connection relationship, spatial distribution relationship, and operating status correlation between equipment to form a multi-level network topology. Then, the static attribute data (such as equipment type, installation location, and rated parameters) and dynamic operation data (such as load level, operating time, and fault records) of the nodes are obtained. Finally, a multidimensional feature fusion method is used to calculate the association strength between nodes, and the model parameters are optimized through iterative updates.

[0030] Based on the intelligent analysis model, the system constructs a power equipment association model. The association model contains two core components: equipment feature analysis results and the degree of association between equipment. Among them, the equipment feature analysis results reflect the status characteristics and importance evaluation of a single device; the degree of association between equipment quantifies the mutual influence and dependency between equipment, providing data support for subsequent inspection importance evaluation.

[0031] During the iterative optimization of the model, the system sets a model convergence judgment threshold. This threshold is determined based on historical operation data and expert experience, and mainly considers the two dimensions of the rate of change of model parameters and prediction accuracy. When the parameter change rate of multiple consecutive iterations is less than 0.1% and the prediction accuracy reaches more than 95%, the model is considered to have reached a stable state. This convergence judgment mechanism based on dual indicators ensures both the stability of the model and the reliability of the prediction results.

[0032] S2: Based on the association model, the inspection importance of the power equipment is calculated, and an initial inspection sequence is generated according to the inspection importance; wherein the inspection importance is comprehensively determined based on the equipment status and the degree of network impact.

[0033] Specifically, the calculation of inspection importance includes: Establish a multi-dimensional evaluation index system for power equipment, which includes a set of equipment status indicators and a set of network impact indicators. The equipment status indicator set covers the deviation of operating parameters, the degree of equipment degradation and the level of fault risk. The network impact indicator set includes the importance of load power supply, the importance of network nodes and the scope of fault impact. The operating status score and network impact degree of the power equipment are calculated respectively, wherein the operating status score is obtained based on the equipment status index set through the hierarchical analysis method, and the network impact degree is obtained based on the network impact index set through the network centrality analysis method; Adopting an adaptive weight allocation mechanism, the weight coefficients of the operating status score and the network impact degree are dynamically adjusted according to the historical operating data of the power equipment, and the weighted result is used as the inspection importance of the power equipment; The dynamic classification threshold is determined based on the distribution characteristics of the inspection importance, and the power equipment exceeding the dynamic classification threshold is marked as a priority inspection object.

[0034] Specifically, the initial inspection sequence includes the equipment inspection priority and inspection time requirements. The generation process of the initial inspection sequence includes: According to the inspection importance, the power equipment is divided into first-priority power equipment, second-priority power equipment and third-priority power equipment, and the inspection area is constructed in combination with the spatial distribution characteristics of the power equipment; Obtain standard maintenance cycles and historical maintenance records of power equipment, and analyze equipment failure patterns and maintenance time sensitivity based on historical maintenance records; For the first-priority power equipment, taking into account its failure risk trend and maintenance time sensitivity, the first-priority power equipment will be scheduled in the first time window that meets the inspection conditions within the standard maintenance cycle; For the second priority power equipment, the second priority power equipment is reasonably arranged in the first half of the standard maintenance cycle, taking into account the geographical distribution characteristics of the inspection area and the inspection resource constraints; For the third priority power equipment, based on the proximity principle and the principle of optimizing inspection efficiency, the third priority power equipment will be comprehensively arranged in the second half of the standard maintenance cycle.

[0035] It should be noted that, first of all, the system constructs a multi-dimensional evaluation index system for power equipment based on the aforementioned association model. This index system includes two dimensions: equipment status and network impact: the equipment status index set mainly reflects the operation of the equipment itself, including the deviation of operating parameters (such as the degree of deviation of key parameters such as voltage, current, temperature and standard values), the degree of equipment degradation (assessed by indicators such as insulation performance and mechanical properties), and the level of fault risk (assessed based on fault feature quantities and early warning models); the network impact index set reflects the importance of the equipment in the entire distribution network, including the importance of load power supply (considering factors such as user type and power consumption capacity), the importance of network nodes (based on the criticality assessment of the topology structure), and the scope of fault impact (the scope of power supply interruption that may be caused by potential faults).

[0036] On this basis, the system calculates the operating status score and network impact of each device. The operating status score is calculated using the analytic hierarchy process (AHP). First, a judgment matrix is ​​established to compare the various indicators of the device status in pairs, and the weight of each indicator is determined by eigenvalue calculation, and finally a comprehensive score is obtained. The network impact is calculated using the network centrality analysis method, which comprehensively considers the three dimensions of degree centrality, betweenness centrality, and closeness centrality to comprehensively evaluate the importance of the device in the network.

[0037] In order to adapt to the dynamic changes in the equipment's operating status, the system uses an adaptive weight allocation mechanism to dynamically adjust the weight coefficients of the operating status score and the degree of network impact. This mechanism is based on the equipment's historical operating data and extracts state change characteristics through time series analysis. When the equipment's status is detected to have significantly deteriorated, the weight of the operating status score is increased accordingly; when the network load distribution changes significantly, the weight of the network impact is adjusted accordingly. The weighted result is finally used as the inspection importance of the equipment.

[0038] The system analyzes the distribution characteristics of inspection importance and uses a clustering algorithm to determine the dynamic classification threshold. Specifically, based on the density estimation of the importance distribution, the natural separation points of the data are identified and used as the classification threshold. When the inspection importance of a device exceeds the advanced threshold (for example, in the top 20% quantile), it is marked as a priority inspection object.

[0039] After calculating the inspection importance of each device, the system starts to generate the initial inspection sequence. First, the power equipment is divided into three priorities according to the inspection importance classification threshold: the equipment with importance higher than the first threshold (such as the first 33% quantile) is divided into the first priority, the equipment between the first threshold and the second threshold (such as the 33%-66% quantile) is divided into the second priority, and the rest is divided into the third priority. At the same time, combined with the geographical location information of the equipment, a clustering algorithm is used to construct the inspection area so that the equipment inspection path in the same area is optimized as much as possible.

[0040] For equipment of different priorities, the system adopts differentiated sorting strategies: first-priority power equipment is scheduled in the first time window that meets the inspection conditions in the standard maintenance cycle (usually one month), and the specific time selection is based on the fault risk prediction results and maintenance time sensitivity analysis; second-priority power equipment is scheduled in the first half of the maintenance cycle (such as 1-15 days), and the inspection time is coordinated on the basis of meeting the principle of geographical proximity; third-priority power equipment is scheduled in the second half of the maintenance cycle (such as 16-30 days), mainly considering the principle of nearby inspection to minimize the total length of the inspection path.

[0041] Through the above implementation steps, the system finally generates an initial inspection sequence that includes equipment inspection priorities and specific time requirements, providing a basic basis for subsequent inspection path optimization. This inspection sequence generation method based on multi-dimensional evaluation and hierarchical classification not only ensures timely inspection of important equipment, but also realizes the reasonable allocation of inspection resources.

[0042] S3: Combine the initial inspection sequence and real-time traffic information to build a dynamic inspection trajectory network, and generate the final inspection path through an intelligent optimization algorithm.

[0043] Specifically, the dynamic inspection trajectory network includes a set of optional inspection paths and dynamic path weights. The optimization process of the dynamic inspection trajectory network includes: Establish a multi-level path assessment system to obtain and analyze the traffic status information of the inspection path in real time, including dynamic factors such as traffic flow, road construction, weather conditions and special events; Construct a comprehensive evaluation model for the inspection path, calculate the physical distance factor, the time consumption factor and the difficulty factor of the inspection path, and make a multi-objective trade-off among the physical distance factor, the time consumption factor and the difficulty factor of the inspection path. The difficulty factor of the inspection path takes into account the road grade, traffic conditions and environmental impact. The dynamic scores of optional inspection paths are calculated based on the comprehensive evaluation model. Combined with the time constraints and resource limitations of the inspection tasks, a heuristic search algorithm is used to generate the optimal inspection path combination that meets multiple constraints.

[0044] Furthermore, the final inspection path is evaluated and optimized, including the following steps: Establish a multi-dimensional evaluation system for inspection results, and collect and store inspection task execution process data in real time; Based on the execution process data, several key performance indicators, including inspection coverage rate, task timeliness rate, and path optimality rate, are calculated respectively; Build a model optimization feedback mechanism to dynamically adjust the parameter configuration of the intelligent analysis model according to the changing trends of key performance indicators, and continuously optimize the inspection strategy through incremental learning.

[0045] First, the system combines the initial inspection sequence and real-time traffic information to build a dynamic inspection trajectory network. The network contains two core elements: a set of optional inspection paths and dynamic path weights. The system has established a multi-level path evaluation system, which obtains and analyzes the traffic status information of the inspection path in real time through multiple data sources such as traffic monitoring systems, meteorological departments, and urban management platforms. This information includes: real-time traffic flow data (such as road traffic volume, driving speed, etc.), road construction information (construction location, scope and duration), weather conditions (precipitation, visibility, etc.), and emergencies (traffic accidents, temporary control, etc.) and other dynamic factors.

[0046] On this basis, the system constructs a comprehensive evaluation model for inspection routes, which comprehensively considers three key factors: the physical distance factor reflects the actual length of the route, which is calculated through an electronic map; the travel time factor reflects the time consumption of the route, which is based on historical traffic data and real-time traffic forecasts; the travel difficulty factor considers the road grade (such as main roads, secondary roads, branch roads, etc.), traffic conditions (road width, turning radius, etc.) and environmental impacts (slope, road conditions, etc.). The system adopts a multi-objective trade-off method, determines the weight coefficient of each factor through fuzzy comprehensive evaluation, and realizes a unified evaluation of different route plans.

[0047] For each optional inspection path, the system calculates its dynamic score based on the comprehensive evaluation model. In the calculation process, the time window constraints of the inspection task (such as the inspection of a specific device must be completed within the specified time) and resource constraints (such as the number of inspection personnel, vehicle availability, etc.) are fully considered. The system uses an improved ant colony algorithm as a heuristic search method, and through multiple rounds of iterative optimization, it generates an optimal inspection path combination that meets multiple constraints. Based on the standard ant colony algorithm, the algorithm introduces an adaptive pheromone update strategy and a local search mechanism to improve the solution efficiency and solution quality.

[0048] To ensure the actual execution effect of the inspection path, the system has established a complete effect evaluation and optimization mechanism. First, a multi-dimensional evaluation system for inspection effect is established, and the execution process data of the inspection task is collected in real time through the mobile terminal, including the arrival time of the inspection point, the length of stay, the path deviation and other information. Based on these data, the system calculates three key performance indicators: inspection coverage rate (the proportion of the number of completed inspection points to the total number of planned inspection points), task timeliness rate (the proportion of inspection tasks completed within the specified time window) and path optimality rate (the degree of consistency between the actual execution path and the theoretical optimal path).

[0049] The system has built a model optimization feedback mechanism to identify optimization directions by analyzing the changing trends of key performance indicators. When the indicators fluctuate significantly or continue to decline, the system automatically triggers the parameter adjustment process and updates the model parameters through incremental learning. Specifically, for areas with low inspection coverage, the inspection priority of the equipment in the area is appropriately increased; for periods of time when the task timeliness is insufficient, the time window allocation strategy is optimized; for sections with low path optimality, the weight configuration of the path evaluation model is adjusted. This continuous optimization mechanism based on actual execution results ensures that the inspection strategy can continuously adapt to changes in the actual operating environment.

[0050] Through the above implementation steps, the system realizes dynamic optimization and continuous improvement of the inspection path, which not only ensures the efficient completion of the inspection task, but also ensures the rational use of inspection resources. This path planning method based on multi-source data fusion and intelligent algorithm provides scientific decision-making support for the inspection of distribution equipment.

[0051] In summary, the present invention constructs a multi-level equipment association relationship through an intelligent analysis model, and realizes the accurate evaluation of equipment status and network impact; adopts an adaptive weight allocation mechanism to dynamically calculate the importance of inspections, ensuring that inspection resources are preferentially allocated to key equipment; optimizes inspection paths through a multi-dimensional evaluation system and heuristic algorithms, effectively balancing inspection efficiency and path feasibility. Specifically, the data processing link realizes the standardized integration of multi-source heterogeneous data and improves the accuracy of status assessment; the inspection planning link optimizes the efficiency of task allocation and reduces resource waste through hierarchical classification; the path optimization link comprehensively considers multiple factors such as physical distance and traffic conditions, and improves the reliability of inspection execution. This data-driven intelligent planning method significantly improves the accuracy and efficiency of distribution network operation and maintenance, and has practical application value.

[0052] Example 2, reference Figure 2 , as an embodiment of the present invention, provides an intelligent planning and optimization system for distribution operation and maintenance inspection trajectories.

[0053] like Figure 2 As shown in FIG. 1 , it is a schematic diagram of the overall structure of the system, including: A data processing unit, used to collect topological data and equipment status information of the power distribution network, and to construct a correlation relationship model including equipment feature analysis results and correlation degrees between equipment using an intelligent analysis model; A sequence planning unit, used to calculate the inspection importance of power equipment based on the association relationship model, and generate an initial inspection sequence including equipment inspection priority and inspection time requirements according to the inspection importance; The trajectory optimization unit is used to combine the initial inspection sequence and real-time traffic information to build a dynamic inspection trajectory network including a set of optional inspection paths and dynamic path weights, and generate a final inspection path.

[0054] Example 3, reference Figure 3, is an embodiment of the present invention, which is different from the previous embodiment in that: if the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0055] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0056] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0057] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0058] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for intelligent planning and optimization of power distribution operation and maintenance inspection trajectories, characterized in that: include: S1: Collect topological data and equipment status information of the power distribution network, and use the intelligent analysis model to build a power equipment association model; The association relationship model includes the equipment feature analysis results and the degree of association between equipment; S2: Based on the association relationship model, calculate the inspection importance of the power equipment, and generate an initial inspection sequence according to the inspection importance; wherein the inspection importance is comprehensively determined based on the equipment status and the network impact degree; the initial inspection sequence includes the equipment inspection priority and inspection time requirements; S3: Combining the initial inspection sequence and real-time road condition information, constructing a dynamic inspection trajectory network, and generating a final inspection path through an intelligent optimization algorithm; The dynamic inspection track network includes a set of optional inspection paths and dynamic path weights.

2. The method for intelligent planning and optimization of power distribution operation and maintenance inspection trajectories according to claim 1, characterized in that: The construction process of the intelligent analysis model includes: Constructing an association graph of the power distribution network, taking the power equipment as network nodes, establishing network edges based on the electrical connection relationship, spatial distribution relationship and operation status correlation between the power equipment, and forming a multi-level network topology structure; Acquiring static attribute data and dynamic operation data of the network node, wherein the static attribute data includes device type, installation location and rated parameters, and the dynamic operation data includes load level, operation time and fault record; Based on the static attribute data and the dynamic operation data, a multi-dimensional feature fusion method is used to calculate the association strength between the network nodes, and the association relationship model is iteratively updated in combination with the network topology until the association relationship model converges to a stable state.

3. The method for intelligent planning and optimization of power distribution operation and maintenance inspection trajectories according to claim 2, characterized in that: The calculation of the inspection importance includes: Establishing a multidimensional evaluation index system for the power equipment, the multidimensional evaluation index system includes a set of equipment status indicators and a set of network impact indicators, wherein the set of equipment status indicators covers operating parameter deviation, equipment degradation degree and fault risk level, and the set of network impact indicators includes load power supply importance, network node importance and fault impact range; Respectively calculating the operating status score and network impact degree of the power equipment, wherein the operating status score is obtained based on the equipment status indicator set by a hierarchical analysis method, and the network impact degree is obtained based on the network impact indicator set by a network centrality analysis method; Adopting an adaptive weight allocation mechanism, dynamically adjusting the weight coefficients of the operating status score and the network impact degree according to the historical operating data of the power equipment, and using the weighted result as the inspection importance of the power equipment; A dynamic classification threshold is determined based on the distribution characteristics of the inspection importance, and the power equipment exceeding the dynamic classification threshold is marked as a priority inspection object.

4. The method for intelligent planning and optimization of power distribution operation and maintenance inspection trajectories according to claim 3, characterized in that: The generation process of the initial inspection sequence includes: According to the inspection importance, the power equipment is divided into first priority power equipment, second priority power equipment and third priority power equipment, and an inspection area is constructed in combination with the spatial distribution characteristics of the power equipment; Obtaining the standard maintenance cycle and historical maintenance records of the power equipment, and analyzing equipment failure patterns and maintenance time sensitivity based on the historical maintenance records; For the first-priority power equipment, taking into account its failure risk trend and the maintenance time sensitivity, the first-priority power equipment is preferentially arranged in the first time window that meets the inspection conditions within the standard maintenance cycle; For the second-priority power equipment, the second-priority power equipment is reasonably arranged in the first half time window of the standard maintenance cycle in combination with the geographical distribution characteristics of the inspection area and the inspection resource constraints; For the third priority power equipment, based on the proximity principle and the inspection efficiency optimization principle, the third priority power equipment is comprehensively arranged in the second half time window of the standard maintenance cycle.

5. The method for intelligent planning and optimization of power distribution operation and maintenance inspection trajectories according to claim 4, characterized in that: The optimization process of the dynamic inspection trajectory network includes: Establish a multi-level path evaluation system to obtain and analyze the traffic status information of the inspection path in real time; Constructing a comprehensive evaluation model for the inspection path, calculating the physical distance factor, the travel time factor and the travel difficulty factor of the inspection path, and performing multi-objective weighing on the physical distance factor, the travel time factor and the travel difficulty factor of the inspection path, wherein the travel difficulty factor of the inspection path takes into account the road grade, travel conditions and environmental impact; The dynamic scores of the optional inspection paths are calculated based on the comprehensive evaluation model, and combined with the time constraints and resource limitations of the inspection tasks, a heuristic search algorithm is used to generate an optimal inspection path combination that meets multiple constraints.

6. The method for intelligent planning and optimization of power distribution operation and maintenance inspection trajectories according to claim 5, characterized in that: It also includes data preprocessing steps: Establish a standardized processing flow for multi-source heterogeneous data, and perform time sequence alignment and dimension unification on the status data of the power equipment; Design abnormal data identification rules based on statistical features and expert knowledge, establish a multi-level data quality assessment mechanism, and classify and process abnormal data; According to the type characteristics and operating characteristics of the power equipment, a hierarchical status evaluation indicator system is established, and the effective value range and warning threshold of each indicator are determined based on historical data.

7. The method for intelligent planning and optimization of power distribution operation and maintenance inspection trajectories according to claim 6, characterized in that: It also includes effect evaluation and optimization steps: Establish a multi-dimensional evaluation system for inspection results, and collect and store inspection task execution process data in real time; Based on the execution process data, key performance indicators are calculated respectively; the key performance indicators include inspection coverage rate, task timeliness rate and path optimality rate; A model optimization feedback mechanism is constructed to dynamically adjust the parameter configuration of the intelligent analysis model according to the changing trend of the key performance indicators, and continuously optimize the inspection strategy through incremental learning.

8. A distribution power operation and maintenance inspection trajectory intelligent planning and optimization system, based on the distribution power operation and maintenance inspection trajectory intelligent planning and optimization method according to any one of claims 1 to 7, characterized in that: include, A data processing unit, used to collect topological data and equipment status information of the power distribution network, and to construct a correlation relationship model including equipment feature analysis results and correlation degrees between equipment using an intelligent analysis model; A sequence planning unit, used to calculate the inspection importance of the power equipment based on the association relationship model, and generate an initial inspection sequence including equipment inspection priority and inspection time requirements according to the inspection importance; The trajectory optimization unit is used to construct a dynamic inspection trajectory network including a set of optional inspection paths and dynamic path weights in combination with the initial inspection sequence and real-time road condition information, and generate a final inspection path.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the distribution operation and maintenance inspection trajectory intelligent planning and optimization method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for intelligent planning and optimization of distribution operation and maintenance inspection trajectories described in any one of claims 1 to 7 are implemented.

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