Regional current distribution network development diagnosis and weak index analysis method

Abstract

The invention relates to the technical field of intelligent analysis of a power distribution network, and discloses a regional current situation power distribution network development diagnosis and weak index analysis method. The method comprises the following steps: constructing a power distribution network basic cognitive model based on a general large model, and processing original operation data to generate a power distribution network knowledge graph containing an equipment state and a network topology; the knowledge graph is finely adjusted through industry documents and standards, industry rules are injected into the model, and an industry knowledge enhancement model with professional cognition is formed; performing cooperative processing on the structured knowledge by using a multi-agent cooperative system to generate a multi-dimensional diagnosis report; and collecting evaluation deviations through a scene feedback mechanism, driving the model and the system to carry out reinforcement learning training, and forming a closed-loop analysis process of iterative optimization. According to the method, deep combination of professional knowledge and artificial intelligence is realized, the problems of insufficient professional adaptability and stiff static analysis of a traditional method are solved, and the diagnosis accuracy and the self-adaptive capability of the system are improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent analysis technology for power distribution networks, specifically a method for diagnosing the development of a regional power distribution network and analyzing its weak indicators. Background Technology

[0002] In the field of regional power distribution network development diagnosis, existing technologies mainly rely on traditional data analysis and predefined rule systems. These methods typically process equipment operation data based on statistical models or classical machine learning algorithms, use fixed rule bases to match abnormal states, and employ standardized indicator systems for network evaluation. Some solutions attempt to introduce general knowledge graph technology to characterize network topology relationships. The analysis process of existing technologies is mostly linear, executing in the order of data input, feature extraction, indicator calculation, and report output, lacking deep integration with power distribution network expertise. The model construction process does not systematically integrate industry standards such as planning guidelines and operation and maintenance procedures, leading to discrepancies between diagnostic logic and the actual needs of the power industry.

[0003] Existing technical solutions suffer from two main drawbacks: Firstly, general data analysis models struggle to accurately understand the unique operational constraints and technical standards of distribution networks. Secondly, equipment status assessment and network structure analysis are disconnected during the diagnostic process, resulting in weak indicators that often deviate from actual industry application scenarios. Thirdly, the diagnostic system employs a static analysis framework, where model parameters and evaluation thresholds remain fixed once set, lacking a mechanism for self-adjustment based on actual operational feedback. Consequently, the system's diagnostic accuracy gradually declines when the distribution network structure changes or new operating modes emerge, failing to develop continuously evolving analytical capabilities.

[0004] A novel analytical method needs to be developed that deeply integrates industry knowledge, enabling diagnostic models to possess professional distribution network expertise. Simultaneously, a self-optimizing diagnostic system must be constructed, employing a closed-loop feedback mechanism to continuously iterate analytical capabilities and adapt to the dynamic evolving needs of the distribution network. Summary of the Invention

[0005] The purpose of this invention is to provide a method for diagnosing the development of a regional power distribution network and analyzing its weak points, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, this invention provides a method for diagnosing the development of a regional power distribution network and analyzing its weak points, the method comprising:

[0007] A basic cognitive model of the distribution network is constructed based on a general large model. The basic cognitive model of the distribution network is used to process the original operation data of the regional distribution network and generate a distribution network knowledge graph that includes equipment status and network topology.

[0008] Collect industry documents and standards for distribution network planning, operation and maintenance and evaluation, fine-tune the distribution network knowledge graph with industry knowledge, inject industry rules into the basic cognitive model of distribution network, and form an industry knowledge enhancement model with professional knowledge of distribution network.

[0009] Construct a multi-agent collaborative system comprising a data parsing agent, an indicator calculation agent, and a diagnostic reasoning agent. Through the multi-agent collaborative system, the structured knowledge output by the industry knowledge enhancement model is collaboratively processed to generate an initial diagnostic report on the development level of the power distribution network, which includes multiple dimensions.

[0010] A scenario for identifying weak links in the development of the distribution network is set up, and the initial diagnostic report on the development level of the distribution network is put into the scenario for operation. The evaluation deviation of the initial diagnostic report on the development level of the distribution network in the scenario is collected through the scenario feedback mechanism, and the model optimization signal is generated.

[0011] Based on the model optimization signal, the decision logic of the industry knowledge enhancement model and the multi-agent collaborative system is trained through reinforcement learning. The model parameters and collaboration strategies are adjusted to form an iteratively optimized closed-loop process for analyzing weak indicators of the regional power distribution network.

[0012] Preferably, the step of constructing a basic cognitive model of the distribution network based on a general large model, and using the basic cognitive model of the distribution network to process the raw operating data of the regional distribution network to generate a distribution network knowledge graph containing equipment status and network topology, specifically includes:

[0013] It connects to the data interface of the regional power distribution network dispatching system and equipment monitoring system to obtain raw operational data, including real-time load, voltage and current, switch status and protection action information;

[0014] The raw operating data is input into the distribution network basic cognitive model, which identifies data entities and their attributes and extracts the relationships between entities.

[0015] Based on the identified data entities, attributes, and relationships, a distribution network knowledge graph is constructed with substations, lines, distribution transformers, and switching equipment as nodes and electrical connections and operational logic relationships as edges.

[0016] The aforementioned construction of a basic cognitive model for the distribution network based on a general large model specifically includes:

[0017] Obtain the pre-trained weight parameters of a general large model, wherein the pre-trained weight parameters include natural language understanding and basic logical reasoning capabilities;

[0018] Collect basic concepts and entity relationship definitions in the power distribution network field, and construct a dictionary and entity relationship mapping table for the power distribution network field;

[0019] The domain dictionary and entity relationship mapping table of the power distribution network are used as domain adaptation samples to inject domain knowledge into the general model for training, so that the model can master the terminology of power distribution network equipment and the basic operating principles.

[0020] After the domain adaptation training is completed, the model is trained to enhance its graph structure reasoning ability, enabling the model to extract entities from unstructured text data and build relational networks.

[0021] By iteratively training and optimizing the model parameters, the construction of the basic cognitive model of the distribution network is considered complete when the accuracy of the model in the distribution network knowledge extraction task reaches a preset threshold.

[0022] Preferably, the process of collecting industry documents and standards for distribution network planning, operation, maintenance, and evaluation, fine-tuning the distribution network knowledge graph with industry knowledge, and injecting industry rules into the basic distribution network cognitive model to form an industry knowledge enhancement model with professional distribution network knowledge specifically includes:

[0023] Establish an industry knowledge base, which stores the calculation standards and threshold definitions for distribution network reliability, line loss rate, voltage qualification rate, and load transfer capacity;

[0024] Rules and constraints are extracted from the industry knowledge base, and these rules and constraints are used as fine-tuning samples to conduct supervised training on the basic cognitive model of the power distribution network, so that the model can understand industry terminology and evaluation logic.

[0025] The power distribution network knowledge graph is re-analyzed using the fine-tuned model, and industry rules are mapped to the nodes and edges of the knowledge graph, outputting an industry knowledge enhancement model containing industry quantitative labels.

[0026] Preferably, the construction of a multi-agent collaborative system comprising a data parsing agent, an indicator calculation agent, and a diagnostic reasoning agent, and the collaborative processing of the structured knowledge output by the industry knowledge enhancement model through the multi-agent collaborative system to generate an initial diagnostic report on the power distribution network development level containing multiple dimensions, specifically includes:

[0027] The data parsing agent receives the structured knowledge output by the industry knowledge enhancement model and performs data classification and cleaning according to the device layer, line layer and network layer;

[0028] The indicator calculation agent, based on the cleaned data, calls the calculation standards in the industry knowledge base to calculate multiple development level indicators such as power supply reliability, equipment load rate, network connectivity rate, and voltage fluctuation rate in parallel.

[0029] The diagnostic reasoning agent, based on multiple calculated development level indicators and combined with threshold definitions in the industry knowledge base, performs comprehensive comparison and logical reasoning to generate qualitative problem descriptions and quantitative indicator deviations, and summarizes them into an initial diagnostic report on the development level of the power distribution network.

[0030] Preferably, the data parsing agent receives the structured knowledge output by the industry knowledge enhancement model and performs data classification and cleaning according to the device layer, line layer, and network layer, specifically including:

[0031] The data parsing agent identifies entity types in structured knowledge and divides entities into transformers and switches belonging to the equipment layer, feeder segments belonging to the line layer, and power supply areas belonging to the network layer.

[0032] For each layer of data, check the integrity and consistency of the data, interpolate missing telemetry data, and remove or mark obviously abnormal telemetry data.

[0033] The cleaned, layered data is packaged into a standardized data package, and a data quality label is attached. This package is then sent to the indicator calculation agent.

[0034] Preferably, the indicator calculation agent, based on the cleaned data, invokes the calculation standards in the industry knowledge base to calculate in parallel multiple development level indicators, including power supply reliability, equipment load rate, network connectivity rate, and voltage fluctuation rate, specifically including:

[0035] The indicator calculation agent receives the standardized data packet and selects the corresponding calculation fault tolerance strategy according to the data quality label;

[0036] The definition formulas and required data fields of various development level indicators are synchronously read from the industry knowledge base.

[0037] Multiple computing threads are started in parallel to calculate the corresponding data fields in the standardized data packet according to the defined formula, and generate power supply reliability index value, equipment load rate index value, network connectivity index value and voltage fluctuation rate index value.

[0038] Preferably, the diagnostic reasoning agent, based on multiple calculated development level indicators and combined with threshold definitions in the industry knowledge base, performs comprehensive comparison and logical reasoning to generate qualitative problem descriptions and quantitative indicator deviations, and summarizes them into an initial diagnostic report on the development level of the power distribution network, specifically including:

[0039] The diagnostic reasoning agent acquires all development level indicator values ​​and retrieves the threshold definitions corresponding to each indicator from the industry knowledge base;

[0040] Compare the value of each development level indicator with the corresponding threshold definition to determine whether the indicator exceeds the limit, and record the indicators that exceed the limit and their deviation values.

[0041] For related groups of out-of-limit indicators, logical reasoning is used to identify the fundamental weaknesses, and a qualitative problem description for the weaknesses is generated.

[0042] Integrate the quantitative deviations and qualitative problem descriptions of all out-of-limit indicators, and generate a structured initial diagnostic report on the development level of the power distribution network according to the preset report template.

[0043] Preferably, the step of setting up a scenario for identifying weak links in the development of the distribution network involves running the initial diagnostic report on the development level of the distribution network in the identified scenario, collecting the evaluation deviation of the initial diagnostic report on the development level of the distribution network in the identified scenario through a scenario feedback mechanism, and generating a model optimization signal, specifically including:

[0044] In the simulation platform, we construct a scenario to identify weak links in the development of distribution networks, including typical problems such as high load, low reliability, and weak interconnection.

[0045] The problem descriptions and indicator deviations from the initial diagnostic report on the development level of the power distribution network are input into the identification scenario to simulate the evolution of the problems described in the diagnostic report in the identification scenario, and the simulation results are obtained.

[0046] The simulation results are compared with the preset ideal operating state to calculate the evaluation deviation of the diagnostic report, and the evaluation deviation is quantified into a model optimization signal.

[0047] Preferably, the step of constructing an identification scenario in the simulation platform that includes typical problems such as high load, low reliability, and weak interconnection in the development of a distribution network specifically includes:

[0048] In power system simulation software, a simulation model consistent with the distribution network topology of the target area is established;

[0049] In the simulation model, three scenarios are set up: high load identification scenario, low reliability identification scenario, and weak connection identification scenario. The high load identification scenario simulates equipment overload by gradually increasing the load. The low reliability identification scenario calculates the power outage impact by simulating a sequence of fault events. The weak connection identification scenario simulates insufficient power transfer capacity by disconnecting some connection switches.

[0050] An evaluation benchmark for the ideal operating state is set for each identified scenario. The evaluation benchmark includes the maximum allowable load rate, the minimum power supply reliability, and the minimum connectivity rate.

[0051] Preferably, the step of performing reinforcement learning training on the decision logic of the industry knowledge enhancement model and the multi-agent collaborative system based on the model optimization signal, adjusting the model parameters and collaboration strategy, and forming an iteratively optimized closed-loop process for analyzing weak indicators of the regional power distribution network specifically includes:

[0052] The model optimization signal is decomposed into a perception optimization signal for the industry knowledge enhancement model and a decision optimization signal for the multi-agent collaborative system.

[0053] The weight parameters of knowledge extraction and rule mapping in the industry knowledge enhancement model are adjusted using the perception optimization signal to improve the model's accuracy in identifying weak link features.

[0054] The decision optimization signal is used to adjust the task triggering conditions and collaboration process of each agent in the multi-agent collaborative system, thereby optimizing the collaborative efficiency of index calculation and diagnostic reasoning.

[0055] The adjusted industry knowledge enhancement model and the multi-agent collaborative system are applied to a new round of regional distribution network data to initiate an iteratively optimized closed-loop process for analyzing weak indicators of the regional distribution network.

[0056] Compared with the prior art, the beneficial effects of the present invention are:

[0057] By fine-tuning the knowledge graph using industry documents and standards for distribution network planning, operation, maintenance, and assessment, industry rules are injected into the basic cognitive model. This technology aligns the model's internal representation with professional parameters such as the distribution network's safe operation boundaries, equipment load rate limits, and network reliability criteria, enabling industry-specific semantic parsing of raw operational data. The structured knowledge output process is subject to explicit industry constraints, and indicator calculations can directly relate to the technical requirements in planning guidelines. The standardization of diagnostic criteria is enhanced, overcoming the cognitive biases present in general models within specialized fields.

[0058] A collaborative system is constructed, consisting of intelligent agents for data parsing, indicator calculation, and diagnostic reasoning. This system collects evaluation deviations and generates optimization signals through a scenario feedback mechanism. This technology establishes a dynamic system adjustment mechanism, allowing continuous optimization of model parameters and the collaborative strategies between the agents based on feedback signals. The closed-loop process enables the diagnostic logic to track changes in the distribution network structure and operation. When the system encounters weak scenarios not present in historical data, it can autonomously update its decision-making path through reinforcement learning, reducing reliance on manual rule updates during system maintenance and improving the adaptability for long-term analysis. Attached Figure Description

[0059] Figure 1 This is a schematic diagram illustrating the working principle of the regional current power distribution network development diagnosis and weak index analysis method described in this invention.

[0060] Figure 2 Flowchart for the formation of an industry knowledge enhancement model;

[0061] Figure 3 A flowchart for generating initial diagnostic reports through multi-agent collaboration;

[0062] Figure 4 A trend chart of deviation evaluation for each recognition scenario across 5 rounds of iterations;

[0063] Figure 5 A heatmap summarizing core indicators for distribution network diagnosis and iterative optimization. Detailed Implementation

[0064] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] Please see Figure 1 This invention provides a method for diagnosing the current state of a regional power distribution network and analyzing its weaknesses. The method includes: constructing a basic cognitive model of the power distribution network based on a general large model; generating a power distribution network knowledge graph containing equipment status and network topology after processing the raw operational data of the regional power distribution network; fine-tuning the knowledge graph with industry knowledge by collecting industry documents and standards related to power distribution network planning, operation, maintenance, and evaluation; injecting industry rules into the basic cognitive model to form an industry knowledge enhancement model with professional knowledge of the power distribution network; and constructing a multi-agent collaborative system including a data parsing agent, an indicator calculation agent, and a diagnostic reasoning agent. This multi-agent collaborative system collaboratively processes the structured knowledge output by the industry knowledge enhancement model and generates an initial diagnostic report on the development level of the distribution network, which includes multiple dimensions. It sets up identification scenarios for weak links in the development of the distribution network and puts the initial diagnostic report on the development level of the distribution network into the identification scenarios. Through the scenario feedback mechanism, it collects the evaluation deviation of the initial diagnostic report on the development level of the distribution network in the identification scenarios to generate model optimization signals. Based on the model optimization signals, it performs reinforcement learning training on the decision logic of the industry knowledge enhancement model and the multi-agent collaborative system, and adjusts the model parameters and collaboration strategies to form an iteratively optimized closed-loop process for analyzing weak indicators of the regional distribution network.

[0066] In one embodiment of the present invention, see [reference] Figure 2The specific process of constructing a basic cognitive model for distribution networks based on a general large model involves obtaining pre-trained weight parameters for the general large model, which include natural language understanding and basic logical reasoning capabilities. Basic concepts and entity relationship definitions in the distribution network domain are collected to construct a distribution network domain dictionary and entity relationship mapping table. The distribution network domain dictionary covers equipment terms such as transformers, circuit breakers, and feeders. The entity relationship mapping table defines association types such as electrical connections and membership relationships. The distribution network domain dictionary and entity relationship mapping table are used as domain adaptation samples to inject domain knowledge into the general large model for training, enabling the model to master distribution network equipment terminology and basic operating principles. After domain adaptation training is completed, the model undergoes graph structure reasoning capability enhancement training to enable it to extract entities from unstructured text data and construct relationship networks. Through multiple rounds of iterative training, the model parameters are optimized, and the construction of the basic cognitive model for distribution networks is considered complete when the model's accuracy on the distribution network knowledge extraction task reaches a preset threshold. For example, for a model containing substations, In a regional distribution network consisting of lines and switching equipment, the original operating data includes real-time load curves, voltage and current waveforms, switch opening and closing status records, and protection action event logs. After inputting the original operating data into the distribution network basic cognitive model, the model identifies data entities and their attributes, such as transformer capacity and load, line impedance and length, switch position and number of operations, and extracts the relationships between entities, such as transformers connected to feeders and feeder segments isolated by switches. Based on the identified data entities, attributes, and relationships, a distribution network knowledge graph is constructed with substations, lines, distribution transformers, and switching equipment as nodes and electrical connections and operational logic relationships as edges. In the distribution network knowledge graph, node attributes include equipment nameplate parameters and real-time monitoring values, and edge attributes include connection type and operating status. By comparing the discrete telemetry and teleindication points in the original operating data with the structured node relationships in the distribution network knowledge graph, independent voltage and current measurements in the original data are associated with specific line nodes, and switch status changes are mapped to dynamic updates of the topology edges.

[0067] In some embodiments, the specific process of generating a distribution network knowledge graph containing equipment status and network topology by processing the raw operating data of a regional distribution network using a distribution network basic cognitive model involves accessing the data interfaces of the regional distribution network dispatching system and equipment monitoring system to obtain raw operating data including real-time load, voltage and current, switch status, and protection action information. The raw operating data comes from a data acquisition and monitoring system or fault indicators, and the data format includes time-series measurement points and event records. The raw operating data is then input into the distribution network basic cognitive model, which identifies data entities and their attributes and extracts the relationships between entities. The knowledge model uses natural language processing to parse the device description text and pattern recognition to analyze the entity features in the data stream. Based on the identified data entities, attributes, and relationships, it constructs a distribution network knowledge graph with substations, lines, distribution transformers, and switching equipment as nodes and electrical connections and operational logic relationships as edges. The electrical connection relationships are determined based on the wiring diagram of the power grid geographic information system and real-time topology analysis, while the operational logic relationships are derived based on protection coordination strategies and load transfer paths. It can be understood that the construction of the distribution network knowledge graph follows the graph database storage specifications, and the nodes and edges are persisted in the form of attribute graphs, supporting topology traversal and state retrieval based on graph query language.

[0068] Optionally, collecting industry documents and standards related to distribution network planning, operation, maintenance, and evaluation to fine-tune the distribution network knowledge graph and injecting industry rules into the basic distribution network cognitive model to form an industry knowledge-enhanced model with professional distribution network knowledge involves establishing an industry knowledge base that stores calculation standards and threshold definitions for distribution network reliability, line loss rate, voltage qualification rate, and load transfer capability. This industry knowledge base originates from power industry standard documents such as power supply reliability management methods or distribution network technical guidelines. Rules and constraints are extracted from the industry knowledge base and used as fine-tuning samples to conduct supervised training on the basic distribution network cognitive model, enabling the model to understand industry terminology and evaluation logic. The fine-tuning samples include... The definitions of average outage time and frequency in distribution network reliability calculations, and the clauses regarding voltage deviation limits in voltage qualification rates, are used to re-analyze the distribution network knowledge graph using a fine-tuned model. Industry rules are mapped to nodes and edges in the knowledge graph to output an industry knowledge enhancement model with industry-quantified labels. In this model, nodes are labeled with reliability levels, and edges are labeled with line loss rate calculation benchmarks. For example, line nodes in the distribution network knowledge graph are marked as normal load, heavy load, or overload states based on load rate standards in the industry knowledge base, and switch nodes are marked as critical connections or ordinary sections based on transfer capacity standards. A formula is used to evaluate the accuracy of the distribution network basic cognitive model in knowledge extraction tasks, expressed as:

[0069]

[0070] in: Indicates accuracy rate. Indicates the number of correctly extracted entities. This represents the total number of entities. Accuracy is calculated based on the entity annotations in the test dataset compared to the model output.

[0071] In one embodiment of the present invention, see [reference] Figure 3 The specific process of constructing a multi-agent collaborative system comprising a data parsing agent, an indicator calculation agent, and a diagnostic reasoning agent, and then using this system to collaboratively process the structured knowledge output by the industry knowledge enhancement model to generate an initial diagnostic report on the development level of the distribution network across multiple dimensions, involves the data parsing agent receiving the structured knowledge output by the industry knowledge enhancement model and classifying and cleaning the data according to the equipment layer, line layer, and network layer. The structured knowledge received by the data parsing agent originates from the industry knowledge enhancement model's parsing output of the distribution network knowledge graph, including equipment nodes and connection edge information with industry-quantified labels. The indicator calculation agent, based on the cleaned data, calls the calculation standards in the industry knowledge base to calculate in parallel multiple development level indicators such as power supply reliability, equipment load rate, network connectivity rate, and voltage fluctuation rate. The diagnostic reasoning agent, based on the calculated multiple development level indicators and combined with threshold definitions in the industry knowledge base, performs comprehensive comparison and logical reasoning to generate qualitative problem descriptions and quantitative indicator deviations, which are then summarized into an initial diagnostic report on the development level of the distribution network. For example, for a 10-channel distribution network containing multiple sectionalizing switches... For a kV feeder, the structured knowledge includes the load rate labels of each distribution transformer on the feeder, the status and position relationship of each section switch, and the reliability history of the power supply area. The data parsing agent classifies the transformer entity to the equipment layer, the feeder segment to the line layer, and the power supply area to the network layer. The index calculation agent calculates the power supply reliability index value of the feeder, the equipment load rate index value of the transformer in the feeder, the network connectivity index value determined by the feeder interconnection relationship, and the voltage fluctuation rate index value of the monitoring point in parallel. The diagnostic reasoning agent compares the calculated equipment load rate index value with the 80% threshold for safe operation of transformers in the industry knowledge base. When the load rate index value exceeds the threshold, a qualitative problem description of "transformer overload" is generated and the percentage value exceeding the threshold is recorded. Finally, the comparison results of all dimensions are integrated to form an initial diagnostic report on the development level of the distribution network, which includes multiple problem dimensions such as equipment overload and insufficient network connectivity. Compared with the scattered index calculation results that have not been processed by the multi-agent collaborative system, the initial diagnostic report on the development level of the distribution network presents the correlation between indicators and the systematic attribution of problems.

[0072] In some embodiments, the specific process by which the data parsing agent receives structured knowledge output from an industry knowledge enhancement model and classifies and cleans the data according to the equipment layer, line layer, and network layer involves the data parsing agent identifying entity types in the structured knowledge and classifying entities into transformers and switches belonging to the equipment layer, feeder segments belonging to the line layer, and power supply areas belonging to the network layer. Based on the predefined type labels carried by the entities in the industry knowledge enhancement model output, the agent checks the integrity and consistency of the data for each layer, imputs missing telemetry data, and removes or marks obviously abnormal telemetry data. The imputation method uses the average data from adjacent time points of the same device to fill in the data. Abnormal data is judged based on monitoring values ​​exceeding the physically possible range or state jumps violating operational logic. The cleaned layered data is packaged into standardized data packets and a data quality label is attached for transmission to the indicator calculation agent. The standardized data packets use a unified data structure to encapsulate the data entities and their attributes at each layer. The data quality label records the integrity score and credibility score of each data field. The formula is used to calculate the integrity score of the data field.

[0073]

[0074] in: Indicates the integrity score. This indicates the number of missing records for this field within the statistical period. This field indicates the expected total number of records within the statistical period. The integrity weight coefficient is represented by the data quality label, which is passed to the indicator calculation agent along with the standardized data package to guide the selection of subsequent fault tolerance strategies.

[0075] It is understandable that the data classification and cleaning process follows a hierarchical management principle. Equipment-level data focuses on the operating parameters and health status of individual devices; line-level data focuses on the power flow and losses of electrical connection sections; and network-level data focuses on the overall reliability level and supply-demand balance of the power supply area. Cleaning rules are pre-set in the knowledge base of the data parsing agent, including data range verification rules, temporal continuity verification rules, and logical consistency verification rules. In specific implementation, the operation of the data parsing agent is triggered by a message queue. When the industry knowledge enhancement model outputs new structured knowledge, it publishes a message event. The data parsing agent subscribes to this event and initiates the data processing flow. The data parsing agent internally includes an entity classifier module, a data validator module, and a data packager module. The entity classifier module classifies transformer entities and switch entities into the equipment-level data set based on rule matching. The data validator module traverses each field in the data set and applies cleaning rules. The data packager module encapsulates the cleaned hierarchical data into standardized data packets with timestamps and regional identifiers. Optionally, for missing telemetry data, the interpolation operation is completed in the data validator module. The interpolation algorithm is selected according to the characteristics of the data sequence. For load data, interpolation based on the historical period is used, and for voltage data, interpolation based on the data of adjacent nodes in the topology is used. Obviously abnormal telemetry data, such as switches repeatedly changing position within a second, will be marked as suspicious and the original value will be recorded. The data structure of the standardized data packet adopts the JavaScript object representation format, which includes a data packet header, device layer data body, line layer data body, network layer data body, and data quality appendix.

[0076] In some embodiments, both the metric calculation agent and the diagnostic reasoning agent operate in an event-driven manner. The metric calculation agent listens for standardized data packet readiness events, while the diagnostic reasoning agent listens for metric calculation completion events. The multi-agent collaborative system coordinates the execution order and data dependencies of each agent through a central event bus. It can be understood that the content of the data quality label affects the behavior of the metric calculation agent. When the data quality label indicates that the integrity score of a device's load data is below a set threshold, the metric calculation agent will use a conservative estimation algorithm or output an unusable data flag for the device's load rate metric calculation. The diagnostic reasoning agent will cite data quality information as supplementary information to support the reliability of its conclusions when generating a diagnostic report.

[0077] In one embodiment of the present invention, the specific process of the indicator calculation agent in parallel calculating multiple development level indicators such as power supply reliability, equipment load rate, network connectivity rate, and voltage fluctuation rate based on the cleaned data and calling the calculation standards in the industry knowledge base is as follows: The indicator calculation agent receives standardized data packets and selects the corresponding calculation fault tolerance strategy according to the data quality label. The integrity score and reliability score carried by the data quality label determine the level of the calculation fault tolerance strategy. For example, when the integrity score of the transformer load data is lower than the threshold, a fault tolerance strategy of replacing the data of nearby similar transformers is adopted. The definition formulas and required data fields of various development level indicators are read synchronously from the industry knowledge base. The definition formula of the power supply reliability indicator is the average power supply reliability rate formula, and the required data fields are the total number of users in the statistical period, the average power outage time of users, and the statistical time. The definition formula of the equipment load rate indicator is the percentage formula of the ratio of the real-time apparent power of the equipment to the rated capacity. The required data fields are the real-time current and voltage of the transformer or line and the rated values ​​on the equipment nameplate. Multiple calculation threads are started in parallel and calculate the corresponding data fields in the standardized data packet according to the defined formulas to generate power supply reliability index values, equipment load rate index values, network connectivity index values, and voltage fluctuation rate index values. The calculation threads are managed by the thread pool inside the index calculation intelligent body. Each thread is bound to a specific index calculation task and independently accesses specific data fields in the standardized data packet. For example, the power supply reliability calculation thread accesses the power outage event record and user number fields in the network layer data body, and the equipment load rate calculation thread accesses the transformer power measurement value and rated capacity fields in the equipment layer data body. Finally, each thread outputs independent index values. Compared with the single-threaded sequential calculation mode, the parallel calculation mode completes the calculation of all development level index values ​​within the same statistical period, shortening the overall time delay from data readiness to index generation.

[0078] In some embodiments, the diagnostic reasoning agent performs comprehensive comparison and logical reasoning based on calculated multiple development level indicators combined with threshold definitions in the industry knowledge base to generate a qualitative problem description and quantitative indicator deviation, which are then summarized into an initial diagnostic report on the development level of the distribution network. The specific process involves the diagnostic reasoning agent acquiring all development level indicator values ​​and retrieving the corresponding threshold definitions from the industry knowledge base. The threshold definitions are stored in a data structure, including the indicator name, upper threshold, lower threshold, and severity level of exceeding the limit. Each development level indicator value is compared with its corresponding threshold definition to determine whether the indicator exceeds the limit and records the exceeded indicators and their deviation values. The judgment logic is that if the indicator value is greater than the upper threshold or less than the lower threshold, it is marked as exceeding the limit. The deviation value is the difference between the indicator value and the upper threshold. The absolute difference between the upper and lower limits is used to logically infer the fundamental weaknesses of related over-limit indicator groups and generate qualitative problem descriptions for these weaknesses. For example, if high equipment load rate and low power supply reliability occur simultaneously, logical reasoning based on a causal knowledge graph determines that high load leading to equipment overheating and subsequent power outages is the root cause. This generates a qualitative problem description of "long-term transformer overload operation leading to increased failure rate and affecting power supply reliability." The quantitative deviations and qualitative problem descriptions of all over-limit indicators are integrated and a structured initial diagnostic report on the distribution network development level is generated according to a preset report template. The report template is an XML document containing a report header, a list of over-limit indicator details, a related problem analysis paragraph, and a comprehensive evaluation level field. The formula is used to calculate the voltage fluctuation rate index value at the feeder outlet during a specific observation period.

[0079]

[0080] in: This represents the voltage fluctuation rate index value. This represents the total number of sampling points during the observation period. Indicates the first Voltage measurement values ​​at each sampling point It represents the arithmetic mean of all voltage measurements during the observation period. The voltage fluctuation rate index is calculated by an independent calculation thread based on the time-series voltage field of the line layer data body in the standardized data packet.

[0081] It is understandable that the selection of the fault tolerance strategy is based on the predefined rule mapping table in the data quality label. The rule mapping table associates different integrity score ranges with specific calculation behaviors. For example, when the integrity score is above 90%, the standard formula is used for calculation; when the integrity score is between 70% and 90%, imputed data is used for calculation with an added confidence interval. In specific implementation, the storage of indicator definition formulas in the industry knowledge base is described using Extensible Markup Language. The variables in the formula are associated with the data field names of the standardized data package through a configuration mapping file. After reading the definition formula, the indicator calculation agent parses the formula structure and extracts the required variable names. Then, it finds the corresponding data field address in the standardized data package according to the mapping file to obtain the value. After completing the calculation, the parallel computing thread publishes the indicator value and its metadata to the event bus through a message mechanism. Optionally, the logical reasoning module in the diagnostic reasoning agent adopts a rule-based reasoning engine. The rule base is derived from the experience of industry experts and encoded in the form of "IF<condition>THEN<conclusion>". The condition part is a logical combination of multiple indicators exceeding the limit, and the conclusion part is a qualitative description of the fundamental weakness. The report template is filled in through a template engine. The template engine substitutes the quantitative deviation value and the qualitative problem description into the placeholders in the template file to generate the final structured document.

[0082] In some embodiments, the calculation of the device load rate index may involve the aggregation of multiple data sources. For example, the load of a transformer requires the aggregation of multiple measurement points from the high-voltage side and the low-voltage side. Aggregation rules, such as taking the maximum value or average value, are defined in the industry knowledge base. The index calculation agent performs data aggregation operations according to the rules and then substitutes them into the defined formula for calculation. The calculation of the voltage fluctuation rate index requires processing high-frequency sampled voltage data. The calculation thread first filters the original sampling sequence to remove noise interference, and then applies the formula to calculate the fluctuation rate index.

[0083] In one embodiment of the present invention, the specific process of setting up identification scenarios for weak links in distribution network development and running the initial diagnostic report on the development level of the distribution network in the identification scenarios, and collecting the evaluation deviation of the initial diagnostic report on the development level of the distribution network in the identification scenarios through a scenario feedback mechanism to generate model optimization signals, involves constructing identification scenarios for weak links in distribution network development that include typical problems such as high load, low reliability, and weak interconnection in a simulation platform, establishing a simulation model in the power system simulation software that is consistent with the distribution network topology of the target area, and setting up high load identification scenarios, low reliability identification scenarios, and weak interconnection identification scenarios in the simulation model. The high load identification scenario simulates equipment overload by gradually increasing the load, the low reliability identification scenario calculates the impact of power outages by simulating fault event sequences, and the weak interconnection identification scenario disconnects... Some interconnection switches have insufficient simulated power transfer capacity. An evaluation benchmark for the ideal operating state is set for each identification scenario. This benchmark includes the maximum allowable load rate, minimum power supply reliability, and minimum interconnection rate. Problem descriptions and indicator deviations from the initial diagnostic report of the distribution network development level are input into the identification scenario to simulate the evolution of the problems described in the report and obtain simulation results. These results include the number of households experiencing system outages, equipment overload duration, and the percentage of voltage-over-limit nodes during the simulation period. The simulation results are compared with the preset ideal operating state to calculate the evaluation deviation of the initial diagnostic report and quantify it into a model optimization signal. This model optimization signal is a numerical vector containing deviation components for each scenario and a comprehensive score. Refer to Table 1, the identification scenario configuration table, which shows the key parameters and evaluation benchmarks for the three identification scenarios.

[0084] Table 1: Scene Recognition Configuration Table

[0085] Scene type Simulation method Evaluation benchmark indicators benchmark value High-load recognition scenarios Gradually increase the load until the equipment is overloaded. Maximum allowable load rate 80% Low-reliability identification scenarios Simulate random failure event sequences Minimum power supply reliability 99.9% Weak contact identification scenario Disconnect the designated contact switch Minimum contact rate 30%

[0086] In some embodiments, the specific process of constructing identification scenarios for weak links in distribution network development, including typical problems such as high load, low reliability, and weak interconnection, in a simulation platform involves establishing a simulation model in power system simulation software that is consistent with the distribution network topology of the target area. The simulation model is constructed based on a single-line map of the actual power grid using a geographic information system. Component parameters are derived from an equipment ledger database, including transformer impedance, line resistance and reactance, and load power factor. High load identification scenarios, low reliability identification scenarios, and weak interconnection identification scenarios are set in the simulation model. The high load identification scenario simulates equipment overload by gradually increasing the load, with the load increase following a preset annual growth rate curve or a sudden impact mode. The low reliability identification scenario calculates the impact of power outages by simulating fault event sequences, including permanent line faults, sudden transformer faults, and their corresponding protection actions and switching operations. The weak interconnection identification scenario simulates insufficient power transfer capacity by disconnecting some interconnection switches, with the disconnection operation based on network topology analysis to select key interconnection nodes. An evaluation benchmark for the ideal operating state is set for each identification scenario. The evaluation benchmark includes the maximum allowable load rate, the minimum power supply reliability, and the minimum interconnection rate. The maximum allowable load rate is set based on the equipment thermal stability limit, the minimum power supply reliability is set based on industry power supply service standards, and the minimum interconnection rate is set based on network structure planning requirements.

[0087] See Figure 4 In the iterative optimization process of regional power distribution network weakness index analysis, the changing trend of evaluation deviation over 5 iterations (optimization target: deviation = 0) is demonstrated under three identification scenarios: high load, low reliability, and weak interconnection. Specifically: the initial deviation (actual - baseline) for the high load scenario is about 9%, gradually decreasing to around -1% with iterations; the initial deviation (baseline - actual) for the weak interconnection scenario is 8%, showing a continuous decreasing trend, reaching -2% in the 5th iteration; the deviation (baseline - actual) for the low reliability scenario remains within a low fluctuation range of 0-1%, close to the optimization target. The deviation changes in the three scenarios intuitively reflect the improvement in the identification accuracy of different weak links after reinforcement learning training of the industry knowledge enhancement model and the multi-agent collaborative system. The deviation convergence process of the high load and weak interconnection scenarios conforms to the parameter adjustment logic under the scenario feedback mechanism, while the stable performance of the low reliability scenario reflects that its initial identification capability has approached the ideal baseline.

[0088] In one embodiment of the present invention, the specific process of performing reinforcement learning training on the decision logic of the industry knowledge enhancement model and the multi-agent collaborative system based on the model optimization signal, and adjusting the model parameters and collaborative strategies to form an iteratively optimized closed-loop process for analyzing weak indicators in the regional power distribution network, involves decomposing the model optimization signal into a perception optimization signal for the industry knowledge enhancement model and a decision optimization signal for the multi-agent collaborative system. The model optimization signal is a numerical vector, and the decomposition operation is completed based on the physical meaning of different dimensions in the vector and a preset mapping relationship. For example, the model optimization vector contains three scenario deviation components: the high-load scenario deviation component is mapped to the perception optimization signal, and the low-reliability scenario and weak-connection scenario deviation components are jointly mapped to the decision optimization signal. The perception optimization signal is used to adjust the weight parameters of knowledge extraction and rule mapping in the industry knowledge enhancement model to improve the model's accuracy in identifying weak link features. The weight parameters of the industry knowledge enhancement model include those used in the neural network for implementation... The convolutional kernel parameters for body recognition and the attention mechanism parameters for relation extraction are adjusted based on a policy gradient algorithm. This algorithm uses the perceptual optimization signal as the reward signal to calculate the gradient and backpropagates to update the weight parameters. The decision optimization signal is used to adjust the task triggering conditions and collaborative processes of each agent in the multi-agent collaborative system to optimize the collaborative efficiency of indicator calculation and diagnostic inference. The adjustment of task triggering conditions is reflected in modifying the data integrity threshold for the data parsing agent to initiate the cleaning operation. The adjustment of the collaborative process is reflected in reconstructing the message passing priority between the indicator calculation agent and the diagnostic inference agent. The adjusted industry knowledge enhancement model and multi-agent collaborative system are applied to a new round of regional distribution network data to initiate an iterative optimization closed-loop process for analyzing weak indicators in the regional distribution network. The new round of regional distribution network data comes from the next diagnostic cycle. The system will use the updated model and policy to re-execute the entire process from building the distribution network knowledge graph to generating model optimization signals. The formula describes the weight parameters of the industry knowledge enhancement model. Update rules:

[0089]

[0090] in: This represents the updated weight parameters. This represents the weight parameters before the update. Indicates the learning rate. This represents the policy gradient calculated using the sensing optimization signal.

[0091] In some embodiments, the operation of decomposing the model optimization signal into a perceptual optimization signal for the industry knowledge enhancement model and a decision optimization signal for the multi-agent collaborative system is performed by a signal parser. The signal parser loads a preset signal dimension mapping configuration file, which defines the signal type and target module corresponding to each index position in the model optimization signal vector. The perceptual optimization signal corresponds to the part of the evaluation deviation caused by inaccurate identification of original data features, and the decision optimization signal corresponds to the part of the evaluation deviation caused by unreasonable index calculation logic or diagnostic reasoning logic. The process of adjusting the weight parameters of knowledge extraction and rule mapping in the industry knowledge enhancement model using the perceptual optimization signal to improve the model's accuracy in identifying weak link features is completed in a reinforcement learning training environment. The training environment simulates the transformation process from the distribution network operating state to structured knowledge. The perceptual optimization signal serves as environmental feedback reward, and the model updates its policy network parameters based on the reward signal through Monte Carlo search or temporal difference learning. It can be understood that the adjustment of weight parameters is iterative; each time a new model optimization signal is received, a parameter update is performed, and the accuracy of the industry knowledge enhancement model in identifying weak link features gradually approaches the optimum through multiple iterations.

[0092] In practical implementation, the process of adjusting the task triggering conditions and collaborative processes of each agent in a multi-agent collaborative system using decision optimization signals to optimize the collaborative efficiency of indicator calculation and diagnostic reasoning involves rewriting the internal rules of the multi-agent collaborative system. Task triggering conditions are stored in the form of production rules, and decision optimization signals indicate the need to lower this threshold to tolerate more incomplete data and trigger calculations earlier. The collaborative process is defined using a finite state machine or workflow engine, and decision optimization signals indicate the need to adjust the conditions for state transitions or the execution order of workflow nodes. When the adjusted industry knowledge enhancement model and multi-agent collaborative system are applied to a new round of regional distribution network data to initiate an iterative optimization closed-loop process for analyzing weak indicators in the regional distribution network, the system version manager records snapshots of model parameters and collaboration rule versions for each adjustment, and the input data for the new round of analysis is automatically associated with the latest version.

[0093] Optionally, the decomposition of the model optimization signal can be based on contribution analysis. Contribution analysis calculates the degree of influence of the output changes of each agent or model component on the final evaluation deviation, and assigns the deviation component corresponding to the component with the largest influence as its optimization signal. It can be understood that the iterative optimization of the closed-loop process of regional power distribution network weakness index analysis enables the system to adapt to changes in power distribution network structure and operational modes. Each iteration self-corrects based on the evaluation feedback from the previous cycle's diagnostic report in the identified scenario.

[0094] See Figure 5The heatmap uses the iteration cycle (stages 1-5) as the vertical dimension and the core evaluation indicators as the horizontal dimension. The color depth (corresponding to the normalized values ​​of the indicators ranging from 0.60 to 1.00) visually presents the dynamic changes of key indicators during the distribution network diagnosis and optimization process. Specifically, the graph covers six core indicators: perception accuracy, decision efficiency, power supply reliability, load optimization rate, network connectivity rate, and voltage stability rate. As the iteration cycle progresses, the normalized values ​​of perception accuracy, decision efficiency, power supply reliability, network connectivity rate, and voltage stability rate all show a gradual upward trend (e.g., perception accuracy increased from 0.780 in stage 1 to 0.970 in stage 5, and power supply reliability increased from 0.920 to 0.988), reflecting the positive impact of the iterative optimization of the industry knowledge enhancement model and the multi-agent collaborative system on the core performance of the distribution network. While the load optimization rate continues to improve, the overall value remains relatively low (only 0.350 in stage 5), indicating that this indicator is a weak link in the current distribution network diagnosis and optimization process, requiring strengthening the model's identification and optimization strategies for load characteristics in subsequent iterations. The color gradient of the heatmap (from light yellow to dark blue) clearly distinguishes the performance differences of various indicators under different periods. The high value area (dark blue) is concentrated in the later iteration stage of indicators such as power supply reliability and voltage stability, which reflects the improvement effect of the closed-loop optimization process on the key performance of the distribution network.

[0095] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0096] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for diagnosing the development status of a regional power distribution network and analyzing its weak points, characterized in that, The method includes: A basic cognitive model of the distribution network is constructed based on a general large model. The basic cognitive model of the distribution network is used to process the original operation data of the regional distribution network and generate a distribution network knowledge graph that includes equipment status and network topology. Collect industry documents and standards for distribution network planning, operation and maintenance and evaluation, fine-tune the distribution network knowledge graph with industry knowledge, inject industry rules into the basic cognitive model of distribution network, and form an industry knowledge enhancement model with professional knowledge of distribution network. A multi-agent collaborative system is constructed, comprising a data parsing agent, an indicator calculation agent, and a diagnostic reasoning agent. The structured knowledge output by the industry knowledge enhancement model is collaboratively processed through the multi-agent collaborative system to generate an initial diagnostic report on the development level of the power distribution network, which includes multiple dimensions. A scenario for identifying weak links in the development of the distribution network is set up, and the initial diagnostic report on the development level of the distribution network is put into the scenario for operation. The evaluation deviation of the initial diagnostic report on the development level of the distribution network in the scenario is collected through the scenario feedback mechanism, and the model optimization signal is generated. Based on the model optimization signal, the decision logic of the industry knowledge enhancement model and the multi-agent collaborative system is trained through reinforcement learning. The model parameters and collaboration strategies are adjusted to form an iteratively optimized closed-loop process for analyzing weak indicators of the regional power distribution network.

2. The method for diagnosing the development status of a regional power distribution network and analyzing its weak indicators according to claim 1, characterized in that, The aforementioned method involves constructing a basic cognitive model for the distribution network based on a general large model. This model is then used to process the raw operational data of the regional distribution network, generating a distribution network knowledge graph that includes equipment status and network topology. Specifically, this includes: Access the data interface of the regional power distribution network dispatching system and equipment monitoring system to obtain raw operational data including real-time load, voltage and current, switch status and protection action information; The raw operating data is input into the distribution network basic cognitive model, which identifies data entities and their attributes and extracts the relationships between entities. Based on the identified data entities, attributes, and relationships, a distribution network knowledge graph is constructed with substations, lines, distribution transformers, and switching equipment as nodes and electrical connections and operational logic relationships as edges. The aforementioned construction of a basic cognitive model for the distribution network based on a general large model specifically includes: Obtain the pre-trained weight parameters of a general large model, wherein the pre-trained weight parameters include natural language understanding and basic logical reasoning capabilities; Collect basic concepts and entity relationship definitions in the power distribution network field, and construct a dictionary and entity relationship mapping table for the power distribution network field; The domain dictionary and entity relationship mapping table of the power distribution network are used as domain adaptation samples to inject domain knowledge into the general model for training, so that the model can master the terminology of power distribution network equipment and the basic operating principles. After the domain adaptation training is completed, the model is trained to enhance its graph structure reasoning ability, enabling the model to extract entities from unstructured text data and build relational networks. By iteratively training and optimizing the model parameters, the construction of the basic cognitive model of the distribution network is considered complete when the accuracy of the model in the distribution network knowledge extraction task reaches a preset threshold.

3. The method for diagnosing the development of a regional power distribution network and analyzing its weak points according to claim 2, characterized in that, The process involves collecting industry documents and standards related to distribution network planning, operation, maintenance, and evaluation; fine-tuning the distribution network knowledge graph with industry knowledge; and injecting industry rules into the basic distribution network cognitive model to form an industry knowledge enhancement model with professional distribution network knowledge. Specifically, this includes: Establish an industry knowledge base, which stores the calculation standards and threshold definitions for distribution network reliability, line loss rate, voltage qualification rate, and load transfer capacity; Rules and constraints are extracted from the industry knowledge base, and these rules and constraints are used as fine-tuning samples to conduct supervised training on the basic cognitive model of the power distribution network, so that the model can understand industry terminology and evaluation logic. The power distribution network knowledge graph is re-analyzed using the fine-tuned model, and industry rules are mapped to the nodes and edges of the knowledge graph, outputting an industry knowledge enhancement model containing industry quantitative labels.

4. The method for diagnosing the development of a regional power distribution network and analyzing its weak points according to claim 3, characterized in that, The construction of a multi-agent collaborative system comprising a data parsing agent, an indicator calculation agent, and a diagnostic reasoning agent, enables collaborative processing of the structured knowledge output by the industry knowledge enhancement model to generate an initial diagnostic report on the distribution network development level containing multiple dimensions. Specifically, this includes: The data parsing agent receives the structured knowledge output by the industry knowledge enhancement model and performs data classification and cleaning according to the device layer, line layer and network layer; The indicator calculation agent, based on the cleaned data, calls the calculation standards in the industry knowledge base to calculate multiple development level indicators such as power supply reliability, equipment load rate, network connectivity rate, and voltage fluctuation rate in parallel. The diagnostic reasoning agent, based on multiple calculated development level indicators and combined with threshold definitions in the industry knowledge base, performs comprehensive comparison and logical reasoning to generate qualitative problem descriptions and quantitative indicator deviations, and summarizes them into an initial diagnostic report on the development level of the power distribution network.

5. The method for diagnosing the development of a regional power distribution network and analyzing its weak points according to claim 4, characterized in that, The data parsing agent receives the structured knowledge output by the industry knowledge enhancement model and performs data classification and cleaning according to the device layer, line layer, and network layer, specifically including: The data parsing agent identifies entity types in structured knowledge and divides entities into transformers and switches belonging to the equipment layer, feeder segments belonging to the line layer, and power supply areas belonging to the network layer. For each layer of data, check the integrity and consistency of the data, interpolate missing telemetry data, and remove or mark obviously abnormal telemetry data. The cleaned, layered data is packaged into a standardized data package, and a data quality label is attached. This package is then sent to the indicator calculation agent.

6. The method for diagnosing the development of a regional power distribution network and analyzing its weak points according to claim 4, characterized in that, The indicator calculation agent, based on the cleaned data, invokes calculation standards from the industry knowledge base to calculate in parallel multiple development level indicators, including power supply reliability, equipment load rate, network connectivity rate, and voltage fluctuation rate. Specifically, these include: The indicator calculation agent receives the standardized data packet and selects the corresponding calculation fault tolerance strategy according to the data quality label; The definition formulas and required data fields of various development level indicators are synchronously read from the industry knowledge base. Multiple computing threads are started in parallel to calculate the corresponding data fields in the standardized data packet according to the defined formula, and generate power supply reliability index value, equipment load rate index value, network connectivity index value and voltage fluctuation rate index value.

7. The method for diagnosing the development of a regional power distribution network and analyzing its weak points according to claim 4, characterized in that, The diagnostic reasoning agent, based on calculated multiple development level indicators and combined with threshold definitions in the industry knowledge base, performs comprehensive comparison and logical reasoning to generate qualitative problem descriptions and quantitative indicator deviations, which are then summarized into an initial diagnostic report on the development level of the power distribution network. Specifically, this report includes: The diagnostic reasoning agent acquires all development level indicator values ​​and retrieves the threshold definitions corresponding to each indicator from the industry knowledge base; Compare the value of each development level indicator with the corresponding threshold definition to determine whether the indicator exceeds the limit, and record the indicators that exceed the limit and their deviation values. For related groups of out-of-limit indicators, logical reasoning is used to identify the fundamental weaknesses, and a qualitative problem description for the weaknesses is generated. Integrate the quantitative deviations and qualitative problem descriptions of all out-of-limit indicators, and generate a structured initial diagnostic report on the development level of the power distribution network according to the preset report template.

8. The method for diagnosing the development of a regional power distribution network and analyzing its weak points according to claim 4, characterized in that, The process involves identifying weak links in the distribution network development scenario, running the initial diagnostic report on the distribution network development level within the identified scenario, collecting the evaluation deviation of the initial diagnostic report on the distribution network development level in the identified scenario through a scenario feedback mechanism, and generating model optimization signals. Specifically, this includes: In the simulation platform, we construct identification scenarios for weak links in the development of distribution networks, including typical problems such as high load, low reliability, and weak interconnection. The problem descriptions and indicator deviations from the initial diagnostic report on the development level of the power distribution network are input into the identification scenario to simulate the evolution of the problems described in the diagnostic report in the identification scenario, and the simulation results are obtained. The simulation results are compared with the preset ideal operating state to calculate the evaluation deviation of the diagnostic report, and the evaluation deviation is quantified into a model optimization signal.

9. The method for diagnosing the development of a regional power distribution network and analyzing its weak points according to claim 8, characterized in that, The aforementioned construction of a scenario in the simulation platform to identify weak links in the development of distribution networks, including typical problems such as high load, low reliability, and weak interconnection, specifically includes: In power system simulation software, a simulation model consistent with the distribution network topology of the target area is established; In the simulation model, three scenarios are set up: high load identification scenario, low reliability identification scenario, and weak connection identification scenario. The high load identification scenario simulates equipment overload by gradually increasing the load. The low reliability identification scenario calculates the power outage impact by simulating a sequence of fault events. The weak connection identification scenario simulates insufficient power transfer capacity by disconnecting some connection switches. An evaluation benchmark for the ideal operating state is set for each identified scenario. The evaluation benchmark includes the maximum allowable load rate, the minimum power supply reliability, and the minimum connectivity rate.

10. The method for diagnosing the development of a regional power distribution network and analyzing its weak points according to claim 9, characterized in that, The step of performing reinforcement learning training on the decision logic of the industry knowledge enhancement model and the multi-agent collaborative system based on the model optimization signal, adjusting the model parameters and collaboration strategy, and forming an iteratively optimized closed-loop process for analyzing weak indicators of the regional power distribution network specifically includes: The model optimization signal is decomposed into a perception optimization signal for the industry knowledge enhancement model and a decision optimization signal for the multi-agent collaborative system. The weight parameters of knowledge extraction and rule mapping in the industry knowledge enhancement model are adjusted using the perception optimization signal to improve the model's accuracy in identifying weak link features. The decision optimization signal is used to adjust the task triggering conditions and collaboration process of each agent in the multi-agent collaborative system, thereby optimizing the collaborative efficiency of index calculation and diagnostic reasoning. The adjusted industry knowledge enhancement model and the multi-agent collaborative system are applied to a new round of regional distribution network data to initiate an iteratively optimized closed-loop process for analyzing weak indicators of the regional distribution network.

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