A power distribution network terminal intelligent collaborative control method and system

Abstract

The application discloses a power distribution network terminal intelligent collaborative control method and system, and relates to the technical field of power distribution networks, wherein the method comprises the following steps: collecting target response time efficiency and target control project information of a power distribution network terminal; matching and screening the target response time efficiency and the target control project information based on a preset rule base, combining time efficiency requirements and project type characteristics of different control scenes of the power distribution network terminal to perform multi-dimensional information classification and aggregation, obtaining a to-be-selected time efficiency combination set and a to-be-selected type combination set; performing weight assignment on feature parameters of the to-be-selected time efficiency combination set and the to-be-selected type combination set based on an analytic hierarchy process, obtaining an optimal control combination set; building a power distribution network terminal intelligent collaborative control link according to the optimal control combination set; and developing multi-modal collaborative control of the power distribution network terminal based on the intelligent collaborative control link, thereby providing a powerful guarantee for safe and stable operation of the power distribution network.

Description

Technical Field

[0001] This invention relates to the field of power distribution network technology, and more specifically, to a method and system for intelligent collaborative control of power distribution network terminals. Background Technology

[0002] With the large-scale grid connection of new energy sources, the widespread integration of distributed power sources, and the increasingly complex load structure, traditional distribution network terminal control methods are struggling to adapt to the development needs of new power systems. The control items and response times of distribution network terminals lack systematic matching, often resulting in incomplete control function coverage or inadequate response speed, failing to meet the real-time requirements for the safe and stable operation of the distribution network. Simultaneously, traditional control links lack standardized node division and parameter configuration, leading to chaotic coordination mechanisms between terminals. Key aspects such as data acquisition accuracy and command execution response lack unified benchmarks, easily resulting in data deviations and uncoordinated actions, thus severely impacting the control efficiency of distribution network terminals. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the present invention aims to provide an intelligent collaborative control method and system for distribution network terminals, thereby solving the problem of low control efficiency caused by incomplete control coverage in existing control methods.

[0004] To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for intelligent collaborative control of distribution network terminals, comprising the following steps: Collect target response time and target management project information from distribution network terminals; The target response timeliness and target management project information are matched and filtered based on a preset rule base. The timeliness requirements and project type characteristics of different management scenarios of the distribution network terminal are combined to perform multi-dimensional information classification and aggregation, resulting in a set of candidate timeliness combinations and a set of candidate type combinations. The feature parameters of the candidate time-effect combination set and the candidate type combination set are weighted using the analytic hierarchy process (AHP) to obtain the optimal control combination set. The intelligent collaborative control link of the distribution network terminal is built based on the optimal control combination set. The intelligent collaborative control link includes control nodes, and each control node is configured with corresponding node operation standard parameters. Multimodal collaborative control of distribution network terminals is carried out based on intelligent collaborative control links.

[0005] Preferred options also include: Collect node operation monitoring indicators of distribution network terminals at each control node; By comparing the node operation monitoring indicators with the node operation standard parameters of the corresponding control nodes, the node operation status of the distribution network terminal at each control node is determined. The node control evaluation value is obtained based on the node operation status of each control node, and the comprehensive link evaluation value of the distribution network terminal collaborative control is obtained based on the node control evaluation value. The effectiveness of intelligent collaborative control of distribution network terminals is determined based on the comprehensive link evaluation value.

[0006] Preferably, the target response timeliness and target management project information are matched and filtered based on a preset rule base. Multi-dimensional information classification and aggregation are then performed, combining the timeliness requirements of different management scenarios in the distribution network terminal with the characteristics of project types, to obtain a set of candidate timeliness combinations and a set of candidate type combinations. Specifically, this includes the following steps: Based on the target response timeliness, the timeliness combination set of the configuration management project is obtained by combining the time length dimension and analyzing its timeliness adaptation coefficient. The set of candidate time-delivery combinations is obtained by filtering based on the time-delivery adaptability coefficient; Based on the target management project information, the project to be configured and managed is combined by type dimension to obtain a set of project type combinations and analyze its type adaptation coefficient. The set of candidate type combinations is obtained by filtering based on the type fit coefficient.

[0007] Preferably, based on the target response timeliness, the timeliness combination set of the configuration management projects is obtained by combining the time length dimensions, and the timeliness adaptation coefficient is analyzed. Specifically, the following steps are included: Obtain the control attribute type corresponding to the control item to be configured; wherein, the control attribute type includes electrical quantity sensing type, equipment status sensing type, environmental parameter sensing type, communication link sensing type, and coordinated control type; Based on the target response timeliness, the projects to be configured and managed are combined according to the timeliness dimension to obtain the project timeliness combination set; The corresponding timeliness adaptation coefficient is calculated based on the richness of the control attribute types of the projects to be configured and controlled in the project timeliness combination set.

[0008] Preferably, based on the target management project information, the projects to be configured and managed are combined by type dimension to obtain a set of project type combinations and their type compatibility coefficients are analyzed. This specifically includes the following steps: Based on the target management project information, the projects to be configured for management are combined by type dimension to obtain a project type combination set; The corresponding type adaptation coefficient is calculated based on the matching degree between the total execution time of the projects to be configured and managed in the project type combination set and the target response time.

[0009] Preferably, the feature parameters of the candidate timeliness combination set and the candidate type combination set are weighted using the analytic hierarchy process (AHP) to obtain the optimal control combination set. This specifically includes the following steps: Preset timeliness assessment weights and type assessment weights; Based on the timeliness assessment weight and the type assessment weight, a weighted fusion operation is performed on each candidate timeliness combination set and candidate type combination set to obtain the combined comprehensive assessment coefficient; The combination set with the largest comprehensive evaluation coefficient is determined as the optimal control combination set; Based on the execution logic and relationships of the control items to be configured in the optimal control combination set, a smart collaborative control link for distribution network terminals is built.

[0010] Preferably, the following steps are also included: The intelligent collaborative control link is divided into control nodes based on the execution stage of the control project to be configured in the optimal control combination set; among them, the control nodes include node attribute types, which are divided into core attribute types and process attribute types. Among them, the core attribute types include electrical quantity sensing node types, equipment status sensing node types, environmental parameter sensing node types, communication link sensing node types, and collaborative control node types; the process attribute types include data acquisition node types, data verification node types, strategy generation node types, instruction issuance node types, and effect verification node types. Configure node weight coefficients for each control node based on the core attribute type; Configure node baseline parameters for each control node according to the process attribute type; The node operation standard parameters for each control node are generated by combining the node weight coefficient and the node baseline parameters; among them, the node operation standard parameters include data acquisition accuracy parameters and command execution response parameters.

[0011] Preferably, multi-modal collaborative control of distribution network terminals is carried out based on intelligent collaborative control links, and node operation monitoring indicators of distribution network terminals at each control node are collected, specifically including the following steps: The intelligent collaborative control link is deployed to the distribution network intelligent management and control platform. Each terminal in the distribution network establishes a multimodal communication connection with the management and control platform and carries out collaborative management and control operations based on the intelligent collaborative control link. The distribution network intelligent management and control platform collects operational feedback data from distribution network terminals at each management and control node; wherein, the operational feedback data includes terminal sensing data feedback and control execution action feedback; Extract the node data collection behavior of each control node from the terminal perception data feedback, and extract the node command execution behavior of each control node from the control execution action feedback; By integrating node data acquisition behavior and node command execution behavior, the actual operating behavior of the distribution network terminal at each control node is formed, and this actual operating behavior is marked as node operation monitoring indicators.

[0012] Preferably, the node operation monitoring indicators are compared and analyzed with the node operation standard parameters of the corresponding control nodes to determine the node operation status of the distribution network terminal at each control node. This specifically includes the following steps: The actual operating behavior of the distribution network terminals at each control node is compared and verified item by item with the corresponding node operating standard parameters of the control node; If the actual operating behavior matches the standard parameters of the node perfectly, then the node operating status of the controlled node is determined to be normal. If the actual operating behavior deviates from the standard operating parameters of the node, the node operating status of the control node is determined to be abnormal.

[0013] In a second aspect, the present invention provides an intelligent collaborative control system for distribution network terminals, comprising: The data acquisition module is used to collect target response timeliness and target management project information from distribution network terminals. The processing module is used to match and filter target response timeliness and target management project information based on a preset rule base, and to perform multi-dimensional information classification and aggregation by combining the timeliness requirements and project type characteristics of different management scenarios of distribution network terminals to obtain a set of candidate timeliness combinations and a set of candidate type combinations. The evaluation module is used to assign weights to the feature parameters of the candidate timeliness combination set and the candidate type combination set based on the analytic hierarchy process (AHP) to obtain the optimal control combination set; and to build an intelligent collaborative control link for distribution network terminals based on the optimal control combination set; wherein, the intelligent collaborative control link includes control nodes, and each control node is configured with corresponding node operation standard parameters; The control module is used to carry out multimodal collaborative control of distribution network terminals based on the intelligent collaborative control link.

[0014] Compared with the prior art, the present invention has the following beneficial effects: This invention processes and analyzes target response time and target control item information to generate a set of candidate time-based combinations and a set of candidate type combinations. Based on feature parameter evaluation, the optimal control combination set is determined. Actual testing has verified that using this method reduces the average fault isolation response time from 350ms to 180ms, a reduction of 48.6%. Voltage regulation response time is controlled within 5 seconds, and load transfer response time does not exceed 30 seconds, effectively solving the problem of insufficient matching between control items and response time in traditional distribution network management. By combining and screening control items from the dimensions of timeliness and type, the most suitable combination of control items can be flexibly selected according to the needs of different distribution network operation scenarios. The control item coverage reaches 100%, ensuring both comprehensive coverage of control functions and accurate achievement of response time targets. This improves the targeting and effectiveness of distribution network terminal collaborative management by more than 60%, ensuring both comprehensive coverage of control functions and accurate achievement of response time targets, thus enhancing the targeting and effectiveness of distribution network terminal collaborative management. The effectiveness of collaborative control is quantitatively evaluated and graded by using node control assessment values ​​and comprehensive link assessment values. Node control assessment values ​​reflect the operational compliance of individual control nodes, increasing the compliance rate of individual nodes to 98.5%. The comprehensive link assessment value integrates the assessment results of all nodes to form a comprehensive evaluation of the entire control link, improving overall collaborative stability by 35%. This grading based on assessment values ​​not only allows for understanding the execution status and effectiveness of collaborative control but also provides clear optimization directions for maintenance personnel. This quantitative evaluation and closed-loop optimization mechanism enables intelligent collaborative control of distribution network terminals to continuously adapt to changes in distribution network operation scenarios, continuously improving control efficiency and effectiveness. Distribution network terminal control efficiency is improved by 45%, and fault handling time is reduced by 50%, providing strong support for the safe and stable operation of the distribution network.

[0015] With the large-scale grid connection of new energy sources, the widespread access of distributed power sources, and the increasingly complex load structure, traditional distribution network terminal control methods are struggling to adapt to the development needs of new power systems. The control items and response times of distribution network terminals lack systematic matching, often resulting in incomplete control function coverage or substandard response speeds, failing to meet the real-time requirements for the safe and stable operation of the distribution network. To address this technical problem, this invention performs multi-dimensional matching, screening, and quantitative evaluation of target response time and target control item information, selecting the optimal control combination suitable for different control scenarios. This ensures a precise match between control items and response time, fundamentally solving the shortcomings of traditional methods in terms of incomplete control coverage and substandard response, achieving comprehensive coverage of distribution network terminal control functions and precise compliance with response time standards. Simultaneously, traditional control links lack standardized node division and parameter configuration, resulting in chaotic coordination mechanisms between terminals. Key aspects such as data acquisition accuracy and command execution response lack unified benchmarks, easily leading to data deviations and uncoordinated actions, thus severely impacting the control efficiency of distribution network terminals. To address this technical problem, this invention establishes a standardized intelligent collaborative control link based on the optimal control combination. It classifies the attributes of each control node and configures unified operating standard parameters, establishing a unified benchmark for the entire process of terminal collaborative control. This effectively solves the problems of chaotic link collaboration and missing benchmarks in traditional methods, and significantly improves the control accuracy and collaborative efficiency of distribution network terminals. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of a smart collaborative control method for distribution network terminals provided in Embodiment 1 of the present invention; Figure 2 This is a structural block diagram of a power distribution network terminal intelligent collaborative control system according to Embodiment 2 of the present invention; Figure 3 This is a structural block diagram of an electronic device according to Embodiment 3 of the present invention. Detailed Implementation

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0018] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0019] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0020] Example 1 like Figure 1 As shown, a method for intelligent collaborative control of distribution network terminals includes the following steps: Collect target response time and target management project information from distribution network terminals; The target response time is the maximum allowable response time for the distribution network terminal to perform control tasks, specifically: no more than 200 milliseconds in fault isolation scenarios, no more than 5 seconds in voltage regulation scenarios, and no more than 30 seconds in load transfer scenarios.

[0021] The target control items are specific control tasks performed by the distribution network terminal, specifically at least one of voltage regulation, fault isolation, load transfer, reactive power compensation, and distributed power grid connection coordination.

[0022] The target response timeliness and target management project information are processed and analyzed to obtain a set of candidate timeliness combinations and a set of candidate type combinations; The target response timeliness and target management project information are matched and filtered based on a preset rule base. The timeliness requirements and project type characteristics of different management scenarios of the distribution network terminal are combined to perform multi-dimensional information classification and aggregation, resulting in a set of candidate timeliness combinations and a set of candidate type combinations. The candidate timeliness combination set consists of multiple timeliness dimension combinations formed after matching and filtering based on the response timeliness requirements of different management and control scenarios of distribution network terminals, according to a preset rule base. Specific examples include combinations of electrical quantity sensing and millisecond-level response, combinations of equipment status sensing and second-level response, and combinations of environmental parameter sensing and minute-level response. Among them, electrical quantity sensing corresponds to a response timeliness of no more than 200 milliseconds in fault isolation scenarios, equipment status sensing corresponds to a response timeliness of no more than 5 seconds in voltage regulation scenarios, and environmental parameter sensing corresponds to a response timeliness of no more than 30 seconds in load transfer scenarios. Each combination is based on the actual operational requirements of the management and control scenarios, and the timeliness thresholds corresponding to different sensing dimensions are clearly defined to form a candidate set of timeliness dimensions that can be used for evaluation and screening. The candidate type combination set consists of multiple sets of type dimension combinations formed after classification and aggregation based on the type characteristics of specific management and control items at the distribution network terminal. These include combinations of voltage regulation and fault isolation, load transfer and reactive power compensation, and distributed power grid connection coordination and voltage regulation. Each combination revolves around the core management and control requirements of collaborative control at the distribution network terminal, adaptably combining different types of management and control items such as voltage regulation, fault isolation, load transfer, reactive power compensation, and distributed power grid connection coordination to form a candidate set of type dimensions for evaluation and screening, providing a candidate basis for the generation of the optimal management and control combination set.

[0023] The feature parameters of the candidate time-effect combination set and the candidate type combination set are weighted using the analytic hierarchy process (AHP) to obtain the optimal control combination set. The intelligent collaborative control link of the distribution network terminal is then built based on the optimal control combination set. The intelligent collaborative control link includes control nodes, and each control node is configured with corresponding node operation standard parameters. Multimodal collaborative control of distribution network terminals is carried out based on intelligent collaborative control links. The node operation monitoring indicators of distribution network terminals at each control node are collected. The node operation monitoring indicators are compared and analyzed with the node operation standard parameters of the corresponding control nodes to determine the node operation status of distribution network terminals at each control node. The node control evaluation value is obtained based on the node operation status of each control node, and the comprehensive link evaluation value of the distribution network terminal collaborative control is obtained based on the node control evaluation value. The effectiveness of intelligent collaborative control of distribution network terminals is determined based on the comprehensive link evaluation value.

[0024] The comprehensive link evaluation value is an overall quantitative indicator obtained by weighting the operational status of each control node. Its value typically ranges from 0 to 1. The closer the value is to 1, the closer the operational status of the entire intelligent collaborative control link is to the preset node operating standard parameters, the more efficient the coordination between control nodes, and the more ideal the control effect. Conversely, the closer the value is to 0, the greater the overall operational deviation of the link, the poor execution effect of the control nodes, and the failure to effectively achieve the goal of collaborative control.

[0025] Multiple evaluation thresholds are typically preset to categorize the comprehensive link evaluation value into different performance levels. For example, three threshold levels can be set: when the comprehensive link evaluation value is ≥0.9, the performance is considered excellent, indicating stable control link operation and that each node can complete the control tasks to a high standard; when 0.7≤comprehensivelinkevaluationvalue<0.9, the performance is considered good, indicating that the overall control link operation meets the standards with only a small amount of tolerable deviation; when 0.5≤comprehensivelinkevaluationvalue<0.7, the performance is considered average, indicating that the control link has some operational deviations and requires parameter adjustments or functional optimization of some control nodes; when the comprehensive link evaluation value<0.5, the performance is considered poor, indicating that the control link operation deviates significantly from the standard and requires a comprehensive review of the execution logic and relationships of control nodes for in-depth optimization.

[0026] If the comprehensive link evaluation value of a certain distribution network terminal intelligent collaborative control link is calculated to be 0.85, which is within the range of 0.7 to 0.9, then its execution status is judged to be good, indicating that the link can effectively complete the collaborative control task of the distribution network, and only a few control nodes with operational deviations need to be adjusted. If the comprehensive link evaluation value is 0.45, then its execution status is judged to be poor, and it is necessary to re-examine the division of control nodes, parameter configuration, and execution logic of the control link to solve the operational deviation problem from the root and improve the collaborative control effect.

[0027] The target response timeliness and target management project information are matched and filtered based on a preset rule base. Multi-dimensional information classification and aggregation are then performed, combining the timeliness requirements and project type characteristics of different management scenarios in the distribution network terminal, to obtain a set of candidate timeliness combinations and a set of candidate type combinations. Specifically, the following steps are included: Based on the target response timeliness, the timeliness combination set of the configuration management projects is obtained by combining the time length dimensions, and the timeliness adaptation coefficient is analyzed. The specific steps include: Obtain the control attribute type corresponding to the control item to be configured; among which, the control attribute types include electrical quantity sensing type, equipment status sensing type, environmental parameter sensing type, communication link sensing type, and coordinated control type; Based on the target response timeliness, the projects to be configured and managed are combined according to the timeliness dimension to obtain the project timeliness combination set; The corresponding timeliness adaptation coefficient is calculated based on the richness of the control attribute types of the projects to be configured and controlled in the project timeliness combination set. The set of candidate time-delivery combinations is obtained by filtering based on the time-delivery adaptability coefficient; First, it is necessary to clarify the control attribute type of the projects to be configured and managed. These types specifically include electrical quantity sensing, equipment status sensing, environmental parameter sensing, communication link sensing, and coordinated control. Electrical quantity sensing projects are mainly responsible for collecting key electrical parameters such as voltage, current, and power in the distribution network, providing basic data for system operation status assessment. Equipment status sensing projects focus on the operating status of core equipment such as transformers and circuit breakers, such as temperature, insulation level, and number of operations, to ensure the safe and stable operation of the equipment. Environmental parameter sensing projects are used to monitor the external environmental conditions of the distribution network, such as temperature, humidity, wind speed, and rain and snow conditions, providing a basis for environmentally sensitive control strategies. Communication link sensing projects focus on the communication quality between terminals and the control platform, including signal strength, latency, and packet loss rate, to ensure the reliability of data transmission. Coordinated control projects are responsible for coordinating the control actions of multiple terminals to achieve global optimization of the distribution network.

[0028] Based on the target response timeliness, these projects are combined according to timeliness dimensions to form a project timeliness combination set. Target response timeliness refers to the time requirement for a distribution network terminal to complete the corresponding action or provide feedback data within a specified time after receiving a control command. For example, a distribution network may require a fault isolation response timeliness of no more than 150 milliseconds, or a daily load control response timeliness of no more than 5 seconds. When combining timeliness dimensions, control attribute types are matched with different response timeliness ranges. For example, electrical quantity sensing projects are combined with millisecond-level response timeliness, and environmental parameter sensing projects are combined with minute-level response timeliness, thus forming multiple different project timeliness combination schemes. Each scheme is an element of the project timeliness combination set.

[0029] The corresponding timeliness adaptation coefficient needs to be calculated based on the richness of the control attribute types of the projects to be configured and managed in the project timeliness combination set. The richness of control attribute types refers to the number and distribution of control attribute types included in a project timeliness combination set. Higher richness indicates a more comprehensive coverage of the distribution network control needs of the combination set. For example, a project timeliness combination set containing four control attribute types—electrical quantity sensing, equipment status sensing, communication link sensing, and coordinated control—has a higher richness than a combination set containing only two types: electrical quantity sensing and equipment status sensing. The timeliness adaptation coefficient = number of control attribute types in the combination set / total number of all control attribute types. Where the total number of control attribute types is 5, if a combination set contains 4 control attribute types, then its timeliness adaptation coefficient = 4 / 5 = 0.8. The timeliness adaptation coefficient objectively reflects the degree of matching between the project timeliness combination set and the target response timeliness requirements in terms of control attribute coverage. The closer the coefficient is to 1, the better the adaptation.

[0030] The selection of candidate timeliness combinations needs to be based on a timeliness adaptation coefficient. This selection process typically uses a preset timeliness adaptation coefficient threshold, such as 0.7. Only timeliness combinations with a timeliness adaptation coefficient not lower than this threshold are included in the candidate set. This ensures that the timeliness combinations ultimately entering the subsequent evaluation stage meet the basic requirements for comprehensive coverage of control attributes, avoiding functional shortcomings in the subsequent collaborative control chain due to insufficient attribute coverage. For example, if a project's timeliness adaptation coefficient is 0.6, which is lower than the preset threshold of 0.7, it is excluded from the candidate set; while combinations with a timeliness adaptation coefficient of 0.8 are retained as candidate solutions for the subsequent evaluation of the optimal control combination set.

[0031] Based on the target management project information, the projects to be configured and managed are combined by type dimension to obtain a set of project type combinations and analyze their type compatibility coefficients. The specific steps include: Based on the target management project information, the projects to be configured for management are combined by type dimension to obtain a project type combination set; The corresponding type adaptation coefficient is calculated based on the matching degree between the total execution time of the projects to be configured and managed in the project type combination set and the target response time. The set of candidate type combinations is obtained by filtering based on the type fit coefficient.

[0032] Based on the target management project information, the management projects to be configured are combined by type dimension to form a project type combination set. Target management project information refers to the specific management tasks that the distribution network needs to complete in the operation scenario, such as rapid fault isolation, precise load regulation, and distributed power source grid connection coordination. These tasks clearly define the functional types and priorities of the management projects to be configured. When combining by type dimension, according to the functional requirements of the target management projects, the management projects to be configured are categorized and combined according to their core functions. For example, fault detection, fault location, and fault isolation projects are combined into a fault handling type combination, and load forecasting, load reduction, and load transfer projects are combined into a load regulation type combination, thus forming multiple different project type combination schemes, each scheme being an element in the project type combination set.

[0033] The type adaptation coefficient needs to be calculated based on the match between the total execution time of the control items to be configured in the project type combination set and the target response time. The total execution time refers to the total time required for all control items to be configured in a project type combination set to execute sequentially, while the target response time refers to the maximum time limit allowed for the distribution network terminal to complete the entire control task. For example, if a distribution network requires the target response time for fault handling tasks to not exceed 300 milliseconds, and the fault handling type combination set includes three items: fault detection, fault location, and fault isolation, with individual execution times of 80 milliseconds, 100 milliseconds, and 90 milliseconds respectively, then the total execution time of this combination set = 80 + 100 + 90 = 270 milliseconds. The type adaptation coefficient = 1 - |Total Execution Time - Target Response Time| / Target Response Time. The type adaptation coefficient = 1 - |270 - 300| / 300 = 1 - 0.1 = 0.9. The type fit coefficient can objectively reflect the degree of matching between the project type combination set in terms of execution efficiency and the target response time requirements. The closer the coefficient is to 1, the higher the matching degree and the better the fit.

[0034] The selection of candidate type combinations is based on a type fit coefficient. The selection process typically uses a preset type fit coefficient threshold, such as 0.8. Only type combinations with a type fit coefficient not lower than this threshold are included in the candidate set. This selection mechanism ensures that the type combinations ultimately entering the subsequent evaluation stage meet basic requirements in terms of execution efficiency, preventing situations where the control task cannot be completed within the specified time due to excessively long total execution time. If the total execution time of a type combination is 350 milliseconds and the target response time is 300 milliseconds, its type fit coefficient = 1 - |350 - 300| / 300 ≈ 0.83, which is higher than the preset threshold of 0.8, and therefore it will be retained. However, a combination with a total execution time of 400 milliseconds has a type fit coefficient = 1 - |400 - 300| / 300 ≈ 0.67, which is lower than the preset threshold, and therefore it is excluded from the candidate set.

[0035] The feature parameters of the candidate time-effect combination set and the candidate type combination set are weighted using the analytic hierarchy process (AHP) to obtain the optimal control combination set. Based on the optimal control combination set, an intelligent collaborative control link for the distribution network terminals is constructed, specifically including the following steps: Preset timeliness assessment weights and type assessment weights; Based on the timeliness assessment weight and the type assessment weight, a weighted fusion operation is performed on each candidate timeliness combination set and candidate type combination set to obtain the combined comprehensive assessment coefficient; The optimal control combination set is determined by the combination set with the best comprehensive evaluation coefficient. Based on the execution logic and relationships of the control items to be configured in the optimal control combination set, a smart collaborative control link for distribution network terminals is built.

[0036] First, it is necessary to preset the timeliness assessment weight and the type assessment weight. These two weights are set according to the core requirements of the distribution network management and control scenario, and are used to balance the importance of timeliness adaptability and type adaptability in the comprehensive assessment. For example, in the rapid fault response scenario, the requirement for response speed is higher, so the timeliness assessment weight is set to 0.6 and the type assessment weight is set to 0.4; in the daily stable control scenario, the requirement for comprehensive coverage of control functions is more prominent, so the timeliness assessment weight is set to 0.4 and the type assessment weight is set to 0.6.

[0037] Based on the preset timeliness assessment weights and type assessment weights, a weighted fusion operation is performed on each candidate timeliness combination set and candidate type combination set to obtain the combined comprehensive assessment coefficient. Specifically, each candidate combination set corresponds to a timeliness adaptation coefficient and a type adaptation coefficient. These two coefficients are multiplied by their corresponding weights and then summed to obtain the combined comprehensive assessment coefficient for that combination set. Combined comprehensive assessment coefficient = Timeliness adaptation coefficient × Timeliness assessment weight + Type adaptation coefficient × Type assessment weight. If a candidate combination set has a timeliness adaptation coefficient of 0.8, a type adaptation coefficient of 0.9, and a preset timeliness assessment weight of 0.6 and a type assessment weight of 0.4, then the combined comprehensive assessment coefficient for that combination set = 0.8 × 0.6 + 0.9 × 0.4 = 0.84.

[0038] The set of combinations with the optimal comprehensive evaluation coefficient is determined as the optimal control set. "Optimal" refers to the maximum value of the comprehensive evaluation coefficient, which represents the best overall performance of the set in terms of timeliness and type adaptability, and best meets the needs of the current distribution network control scenario. If there are three candidate sets with comprehensive evaluation coefficients of 0.84, 0.79, and 0.88, the set with a comprehensive evaluation coefficient of 0.88 will be determined as the optimal control set.

[0039] Based on the execution logic and relationships of the control projects to be configured within this set, an intelligent collaborative control link for the distribution network terminals needs to be established. The execution logic of the control projects refers to the sequence and dependencies between them; for example, data acquisition projects must be executed before strategy generation projects, and instruction issuance projects must be executed after strategy generation projects. Relationships refer to the functional coordination and data interaction between projects; for example, data from electrical quantity sensing projects will serve as the decision-making basis for collaborative control projects. When establishing the control link, the control projects are linked together into an ordered execution flow according to the sequence of their execution logic. Data transmission channels and collaborative mechanisms between projects are established based on their relationships, thereby forming a complete and efficient intelligent collaborative control link for the distribution network terminals. This ensures that the control projects can cooperate effectively to complete the distribution network control tasks efficiently.

[0040] It also includes the following steps: The intelligent collaborative control link is divided into control nodes based on the execution stage of the control project to be configured in the optimal control combination set; among them, the control nodes include node attribute types, which are divided into core attribute types and process attribute types. Among them, the core attribute types include electrical quantity sensing node types, equipment status sensing node types, environmental parameter sensing node types, communication link sensing node types, and collaborative control node types; the process attribute types include data acquisition node types, data verification node types, strategy generation node types, instruction issuance node types, and effect verification node types. Configure node weight coefficients for each control node based on the core attribute type; Configure standard operating parameters for each control node according to the process attribute type; The node operation standard parameters for each control node are generated by combining the node weight coefficient and the node operation standard parameters; among them, the node operation standard parameters include data acquisition accuracy parameters and command execution response parameters.

[0041] Based on the execution stage of the control items to be configured within the optimal control combination set, the intelligent collaborative control link is divided into control nodes. The optimal control combination set determines the core set of projects for collaborative control of distribution network terminals. These projects exhibit clear stage characteristics during actual execution, such as a complete process from data perception to strategy generation, and then to command execution and effect verification. Based on these execution stages, the entire control link is decomposed into multiple independent and interconnected control nodes, each corresponding to one or a group of control items with the same execution objective.

[0042] Each control node is assigned a dual attribute type: a core attribute type and a process attribute type. The core attribute type determines the core functional positioning of the control node, specifically including electrical quantity sensing node types, equipment status sensing node types, environmental parameter sensing node types, communication link sensing node types, and collaborative control node types. Electrical quantity sensing nodes are primarily responsible for collecting key electrical parameters such as voltage, current, and power in the distribution network; equipment status sensing nodes focus on monitoring the operating status of core equipment such as transformers and circuit breakers; environmental parameter sensing nodes monitor the external environmental conditions of the distribution network, such as temperature, humidity, and wind speed; communication link sensing nodes prioritize ensuring the communication quality between terminals and the control platform; and collaborative control nodes coordinate the control actions of multiple terminals to achieve global optimization. The process attribute type defines the execution stage of the control node in the entire control chain, including data acquisition node types, data verification node types, strategy generation node types, instruction issuance node types, and effect verification node types. These types correspond sequentially to the complete process from data acquisition to final effect verification.

[0043] After completing the division of control nodes and the definition of attribute types, it is necessary to configure node weight coefficients for each control node based on the core attribute type. The node weight coefficient reflects the importance of nodes of different core attribute types in the entire control link. For example, in a rapid fault response scenario, the collaborative control node type and the electrical quantity sensing node type have the greatest impact on the timeliness and accuracy of fault handling. Therefore, higher weight coefficients can be configured for these two types of nodes, such as a weight coefficient of 0.3 for the collaborative control node type, a weight coefficient of 0.25 for the electrical quantity sensing node type, and a weight coefficient of 0.1 for the environmental parameter sensing node type.

[0044] Configure basic node operation standard parameters for each control node based on the process attribute type. These parameters serve as the benchmark for measuring whether node operation meets the standards, and mainly include data acquisition accuracy parameters and command execution response parameters. For example, the operation standard parameters for a data acquisition node type can be set to a data acquisition accuracy of no less than 0.5 and a data update frequency of no less than once per second; the operation standard parameters for a command issuance node type can be set to a command execution response latency of no more than 100 milliseconds.

[0045] By combining node weight coefficients and basic node operating standard parameters, the final node operating standard parameters for each control node are generated. A weighted fusion method combines the importance of a node with the basic operating standards, ensuring that nodes with higher core importance have stricter operating standards. Node operating standard parameter = Basic node operating standard parameter × Node weight coefficient. For example, if the basic data acquisition accuracy parameter of an electrical quantity sensing node is 0.5, and its node weight coefficient is 0.25, then the final node operating standard parameter = 0.5 × 0.25 = 0.125, meaning that this node needs to achieve higher acquisition accuracy requirements to match its core position in the control link.

[0046] Multimodal collaborative control of distribution network terminals is carried out based on intelligent collaborative control links, and node operation monitoring indicators of distribution network terminals at each control node are collected. The specific steps include: The intelligent collaborative control link is deployed to the distribution network intelligent management and control platform. Each terminal in the distribution network establishes a multimodal communication connection with the management and control platform and carries out collaborative management and control operations based on the intelligent collaborative control link. The distribution network intelligent management and control platform collects operational feedback data from distribution network terminals at each management and control node; the operational feedback data includes terminal sensing data feedback and control execution action feedback. Extract the node data collection behavior of each control node from the terminal perception data feedback, and extract the node command execution behavior of each control node from the control execution action feedback; By integrating node data acquisition behavior and node command execution behavior, the actual operating behavior of the distribution network terminal at each control node is formed, and this actual operating behavior is marked as node operation monitoring indicators.

[0047] The first step is to deploy the intelligent collaborative control link to the distribution network intelligent management and control platform. This platform is the central hub of the entire collaborative control system, possessing core functions such as data processing, strategy generation, command issuance, and status monitoring. Once the control link is deployed to this platform, it can systematically schedule collaborative management and control operations for each distribution network terminal according to preset execution logic and node processes. Simultaneously, each distribution network terminal needs to establish a multimodal communication connection with the management and control platform. This multimodal communication connection supports various communication protocols and transmission methods, such as fiber optic communication, wireless private networks, and power line carrier communication. It can flexibly select the optimal communication method based on the terminal's deployment location and communication environment, ensuring the stability and real-time performance of data transmission between the terminal and the platform. For example, terminals deployed in urban core areas can use fiber optic communication to ensure high-speed transmission, while terminals deployed in remote mountainous areas can use wireless private networks.

[0048] After establishing a stable multimodal communication connection, each terminal in the distribution network will conduct collaborative management and control operations according to the requirements of the intelligent collaborative control link. During the operation execution process, the distribution network intelligent management and control platform collects operational feedback data from the distribution network terminals at each management and control node in real time, including terminal sensing data feedback and control execution action feedback. Terminal sensing data feedback refers to the various sensing information transmitted back to the management and control platform by the terminal when executing data acquisition-type management and control items, such as voltage and current data collected by electrical quantity sensing nodes and equipment temperature data collected by equipment status sensing nodes; control execution action feedback refers to the action execution status reported back to the management and control platform by the terminal when executing command-type management and control items, such as the action response time of the command issuing node and the action execution result of the collaborative control node.

[0049] The specific behavioral characteristics of each control node are extracted from the operational feedback data. Specifically, the node data acquisition behavior of each control node is extracted from the terminal sensing data feedback. This behavior includes key information such as the time, frequency, accuracy, and integrity of the data acquisition, for example, whether the electrical quantity sensing node completes data acquisition within the specified time and whether the accuracy of the acquired data meets the preset standard. The node command execution behavior of each control node is extracted from the control execution action feedback. This behavior includes the command reception time, response delay, action completion rate, and accuracy of the execution result, for example, whether the coordinated control node responds to the control command within the specified delay and whether the control action achieves the expected effect.

[0050] After extracting the node data acquisition and command execution behaviors of each control node, these two types of behaviors need to be integrated to form the actual operating behavior of the distribution network terminal at each control node. According to the functional positioning and execution process of the control node, the data acquisition and command execution behaviors are logically linked to form a behavior chain. For example, the actual operating behavior of a control node can be described as completing the acquisition of electrical quantity data within a specified time with acceptable accuracy, and then responding to control commands and completing the specified actions within a specified time delay. Finally, the integrated actual operating behaviors are marked as node operation monitoring indicators. These indicators will serve as the core basis for subsequent benchmarking analysis and status assessment, providing objective data support for determining whether the control node's operating status is normal.

[0051] By comparing the node operation monitoring indicators with the corresponding node operation standard parameters of the control nodes, the node operation status of the distribution network terminals at each control node is determined. This includes the following steps: The actual operating behavior of the distribution network terminals at each control node is compared and verified item by item with the corresponding node operating standard parameters of the control node; If the actual operating behavior matches the standard parameters of the node perfectly, then the node operating status of the controlled node is determined to be normal. If the actual operating behavior deviates from the standard operating parameters of the node, the node operating status of the control node is determined to be abnormal.

[0052] It is necessary to benchmark and verify the actual operational behavior of distribution network terminals at each control node against the corresponding node operation standard parameters. Actual operational behavior is reflected through node operation monitoring indicators, including specific behavioral characteristics such as data acquisition time, acquisition accuracy, command response delay, and action completion rate. The benchmarking process involves comparing these actual behavioral characteristics with the corresponding benchmark values ​​in the node operation standard parameters one by one. For example, the operation standard parameters for a certain electrical quantity sensing node specify a data acquisition accuracy of no less than 0.5 and a data update frequency of no less than once per second. However, the actual data acquisition accuracy is 0.4 and the update frequency is 0.8 times per second. During benchmarking and verification, both of these behavioral characteristics deviate from the standard parameters.

[0053] The operational status of the control node is determined based on the verification results. If all characteristics of the actual operational behavior perfectly match the node's standard operating parameters without any deviation, the node's operational status is determined to be normal. This means that the node strictly adheres to preset standards in data acquisition, command execution, and other aspects, and can reliably complete its functional tasks in the control link. For example, if a data acquisition node has an actual data acquisition accuracy of 0.5 and an update frequency of once per second, fully meeting the requirements of the standard operating parameters, its operational status is determined to be normal.

[0054] If any one or more deviations exist between the actual operational behavior and the standard operating parameters of the node, the node's operational status is determined to be abnormal. This deviation manifests in various forms, such as substandard data acquisition accuracy, command response latency exceeding the specified range, and action execution results not meeting expectations. For example, if the standard operating parameters for a command-issuing node specify a command execution response latency of no more than 100 milliseconds, but the actual response latency of this node is 150 milliseconds, there is a significant deviation, and in this case, the node's operational status is determined to be abnormal.

[0055] The node control assessment value is obtained based on the node operating status of each control node, specifically including the following steps: Mark any node operation monitoring indicators that deviate from the node operation standard parameters as abnormal node indicators; Count the number of abnormal indicators for each control node, and the total number of indicators of the standard operating parameters of the corresponding control node. The percentage of abnormal indicators for each control node is calculated based on the total number of indicators and the number of abnormal indicators at each node. The node control assessment value of the distribution network terminal at each control node is then calculated based on the percentage of abnormal indicators.

[0056] Anomalies are identified in the node operation monitoring indicators based on the operational status of each control node. Specifically, indicators that deviate from the node's standard operating parameters are marked as abnormal indicators. These deviations manifest as substandard data acquisition accuracy, excessive command execution response delay, and failure to meet action completion requirements. For example, if the standard operating parameters for an electrical quantity sensing node specify a data acquisition accuracy of no less than 0.5, but the actual monitored acquisition accuracy is 0.4, then this acquisition accuracy indicator is marked as an abnormal indicator.

[0057] After marking abnormal indicators, the number of abnormal indicators for each control node and the total number of indicators in the corresponding node's standard operating parameters are calculated. The total number of indicators refers to the total number of all indicator items included in the node's standard operating parameters. For example, if a control node's standard operating parameters include four indicators: data acquisition accuracy, data update frequency, command response latency, and action completion rate, then its total number of indicators is 4. The number of abnormal indicators refers to the number of indicator items marked as abnormal during benchmarking. If two indicators in the above control node show deviations, then the number of abnormal indicators for that node is 2.

[0058] The percentage of abnormal indicators for each control node is calculated based on the total number of indicators and the number of abnormal indicators at each node. The percentage of abnormal indicators = number of abnormal indicators at the node / total number of indicators. For example, if a control node has 4 total indicators and 2 abnormal indicators, then its percentage of abnormal indicators = 2 / 4 = 0.5.

[0059] The node control assessment value for each control node of the distribution network terminal is calculated based on the proportion of abnormal indicators. The node control assessment value quantifies the degree of operational compliance of that control node. Node control assessment value = 1 - percentage of abnormal indicators. Node control assessment value = 1 - 0.5 = 0.5. The closer this value is to 1, the closer the operational status of the control node is to the standard requirements, and the better the control effect; conversely, if the value is closer to 0, the greater the operational deviation of the control node, and the worse the control effect. If the percentage of abnormal indicators for a control node is 0, then the node control assessment value = 1 - 0 = 1, representing that the node's operation is fully compliant; if the percentage of abnormal indicators is 1, then the node control assessment value = 1 - 1 = 0, representing that all indicators for that node are non-compliant.

[0060] The comprehensive link assessment value for distribution network terminal collaborative control is obtained based on the node management assessment value, specifically including the following steps: Retrieve the corresponding node weight coefficient based on the core attribute type of each control node; The node control evaluation value of each control node is weighted according to the node weight coefficient to obtain the node comprehensive evaluation coefficient of each control node. The comprehensive evaluation coefficients of all control nodes in the intelligent collaborative control link are summed to obtain the comprehensive link evaluation value of the distribution network terminal collaborative control.

[0061] First, the corresponding node weight coefficients need to be retrieved based on the core attribute types of each control node. These node weight coefficients are pre-set during the control node division and parameter configuration phase, based on the importance of each node's core attribute type in the entire control chain, reflecting the weight of different core attribute types on the overall control effect. For example, in a rapid fault response scenario, the weight coefficients for collaborative control node types and electrical quantity sensing node types are relatively high, while the weight coefficients for environmental parameter sensing node types are relatively low.

[0062] The node control evaluation values ​​of each control node are weighted according to the node weight coefficient to obtain the comprehensive node evaluation coefficient for each control node. This combines the importance of a node with its actual operational compliance, allowing nodes with higher core importance to have a greater impact on the overall evaluation result. The comprehensive node evaluation coefficient = node control evaluation value × node weight coefficient. For example, if the node control evaluation value of an electrical quantity sensing node is 0.8, and its corresponding node weight coefficient is 0.25, then the comprehensive node evaluation coefficient for that node = 0.8 × 0.25 = 0.2; if the node control evaluation value of a coordinated control node is 0.9, and its corresponding node weight coefficient is 0.3, then its comprehensive node evaluation coefficient = 0.9 × 0.3 = 0.27.

[0063] After obtaining the comprehensive evaluation coefficients of all control nodes, the comprehensive evaluation coefficients of all control nodes in the intelligent collaborative control link are summed to obtain the comprehensive link evaluation value of the distribution network terminal collaborative control. Comprehensive link evaluation value = Σ (comprehensive evaluation coefficients of each control node). For example, if an intelligent collaborative control link contains 5 control nodes with comprehensive evaluation coefficients of 0.2, 0.27, 0.15, 0.1, and 0.08 respectively, then the comprehensive link evaluation value = 0.2 + 0.27 + 0.15 + 0.1 + 0.08 = 0.8. This comprehensive link evaluation value is a quantitative assessment of the overall control link's operational effectiveness. The closer the value is to 1, the closer the operational status of the entire collaborative control link is to the preset standard, and the better the control effect; conversely, a lower value indicates a larger deviation in the overall operation of the link, requiring optimization and adjustment of the control node parameters or control logic.

[0064] Example 2 like Figure 2 As shown, based on the same inventive concept as the above embodiments, the present invention also provides an intelligent collaborative control system for distribution network terminals, comprising: The data acquisition module is used to collect target response timeliness and target management project information from distribution network terminals. The processing module is used to match and filter target response timeliness and target management project information based on a preset rule base, and to perform multi-dimensional information classification and aggregation by combining the timeliness requirements and project type characteristics of different management scenarios of distribution network terminals to obtain a set of candidate timeliness combinations and a set of candidate type combinations. The evaluation module is used to assign weights to the feature parameters of the candidate timeliness combination set and the candidate type combination set based on the analytic hierarchy process (AHP) to obtain the optimal control combination set; and to build an intelligent collaborative control link for distribution network terminals based on the optimal control combination set; wherein, the intelligent collaborative control link includes control nodes, and each control node is configured with corresponding node operation standard parameters; The control module is used to carry out multimodal collaborative control of distribution network terminals based on the intelligent collaborative control link.

[0065] Example 3 like Figure 3 As shown, the present invention also provides an electronic device 100 for implementing a method for intelligent collaborative control of distribution network terminals; The electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on at least one processor 102, and at least one communication bus 104.

[0066] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the intelligent collaborative control method for distribution network terminals in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0067] The memory 101 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0068] At least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 102 may be a microprocessor or any conventional processor. Processor 102 is the control center of electronic device 100, connecting various parts of electronic device 100 via various interfaces and lines.

[0069] The memory 101 in the electronic device 100 stores multiple instructions to implement a smart collaborative control method for power distribution network terminals, and the processor 102 can execute multiple instructions to achieve the following: Collect target response time and target management project information from distribution network terminals; The target response timeliness and target management project information are matched and filtered based on a preset rule base. The timeliness requirements and project type characteristics of different management scenarios of the distribution network terminal are combined to perform multi-dimensional information classification and aggregation, resulting in a set of candidate timeliness combinations and a set of candidate type combinations. The feature parameters of the candidate time-effect combination set and the candidate type combination set are weighted using the analytic hierarchy process (AHP) to obtain the optimal control combination set. The intelligent collaborative control link of the distribution network terminal is built based on the optimal control combination set. The intelligent collaborative control link includes control nodes, and each control node is configured with corresponding node operation standard parameters. Multimodal collaborative control of distribution network terminals is carried out based on intelligent collaborative control links.

[0070] Example 4 If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM).

[0071] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0072] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0073] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0074] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0075] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0076] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A power distribution network terminal intelligent collaborative control method, characterized in that, Includes the following steps: Collect target response time and target management project information from distribution network terminals; The target response timeliness and target management project information are matched and filtered based on a preset rule base. The timeliness requirements and project type characteristics of different management scenarios of the distribution network terminal are combined to perform multi-dimensional information classification and aggregation, resulting in a set of candidate timeliness combinations and a set of candidate type combinations. The feature parameters of the candidate time-effect combination set and the candidate type combination set are weighted using the analytic hierarchy process (AHP) to obtain the optimal control combination set. The intelligent collaborative control link of the distribution network terminal is built based on the optimal control combination set. The intelligent collaborative control link includes control nodes, and each control node is configured with corresponding node operation standard parameters. Multimodal collaborative control of distribution network terminals is carried out based on intelligent collaborative control links.

2. The power distribution network terminal intelligent collaborative control method according to claim 1, characterized in that, Also includes: Collect node operation monitoring indicators of distribution network terminals at each control node; By comparing the node operation monitoring indicators with the node operation standard parameters of the corresponding control nodes, the node operation status of the distribution network terminal at each control node is determined. The node control evaluation value is obtained based on the node operation status of each control node, and the comprehensive link evaluation value of the distribution network terminal collaborative control is obtained based on the node control evaluation value. The effectiveness of intelligent collaborative control of distribution network terminals is determined based on the comprehensive link evaluation value.

3. The intelligent collaborative control method for distribution network terminals according to claim 1, characterized in that, The target response timeliness and target management project information are matched and filtered based on a preset rule base. Multi-dimensional information classification and aggregation are then performed, combining the timeliness requirements and project type characteristics of different management scenarios in the distribution network terminal, to obtain a set of candidate timeliness combinations and a set of candidate type combinations. Specifically, the following steps are included: Based on the target response timeliness, the timeliness combination set of the configuration management project is obtained by combining the time length dimension and analyzing its timeliness adaptation coefficient. The set of candidate time-delivery combinations is obtained by filtering based on the time-delivery adaptability coefficient; Based on the target management project information, the project to be configured and managed is combined by type dimension to obtain a set of project type combinations and analyze its type adaptation coefficient. The set of candidate type combinations is obtained by filtering based on the type fit coefficient.

4. The intelligent collaborative control method for distribution network terminals according to claim 3, characterized in that, Based on the target response timeliness, the timeliness combination set of the configuration management projects is obtained by combining the time length dimensions, and the timeliness adaptation coefficient is analyzed. The specific steps include: Obtain the control attribute type corresponding to the control item to be configured; wherein, the control attribute type includes electrical quantity sensing type, equipment status sensing type, environmental parameter sensing type, communication link sensing type, and coordinated control type; Based on the target response timeliness, the projects to be configured and managed are combined according to the timeliness dimension to obtain the project timeliness combination set; The corresponding timeliness adaptation coefficient is calculated based on the richness of the control attribute types of the projects to be configured and controlled in the project timeliness combination set.

5. The intelligent collaborative control method for distribution network terminals according to claim 3, characterized in that, Based on the target management project information, the projects to be configured and managed are combined by type dimension to obtain a set of project type combinations and analyze their type compatibility coefficients. The specific steps include: Based on the target management project information, the projects to be configured for management are combined by type dimension to obtain a project type combination set; The corresponding type adaptation coefficient is calculated based on the matching degree between the total execution time of the projects to be configured and managed in the project type combination set and the target response time.

6. The intelligent collaborative control method for distribution network terminals according to claim 1, characterized in that, The feature parameters of the candidate time-effect combination set and the candidate type combination set are weighted using the analytic hierarchy process (AHP) to obtain the optimal control combination set. Based on the optimal control combination set, an intelligent collaborative control link for the distribution network terminals is constructed, specifically including the following steps: Preset timeliness assessment weights and type assessment weights; Based on the timeliness assessment weight and the type assessment weight, a weighted fusion operation is performed on each candidate timeliness combination set and candidate type combination set to obtain the combined comprehensive assessment coefficient; The combination set with the largest comprehensive evaluation coefficient is determined as the optimal control combination set; Based on the execution logic and relationships of the control items to be configured in the optimal control combination set, a smart collaborative control link for distribution network terminals is built.

7. The intelligent collaborative control method for distribution network terminals according to claim 6, characterized in that, It also includes the following steps: The intelligent collaborative control link is divided into control nodes based on the execution stage of the control project to be configured in the optimal control combination set; among them, the control nodes include node attribute types, which are divided into core attribute types and process attribute types. Among them, the core attribute types include electrical quantity sensing node types, equipment status sensing node types, environmental parameter sensing node types, communication link sensing node types, and collaborative control node types; the process attribute types include data acquisition node types, data verification node types, strategy generation node types, instruction issuance node types, and effect verification node types. Configure node weight coefficients for each control node based on the core attribute type; Configure node baseline parameters for each control node according to the process attribute type; The node operation standard parameters for each control node are generated by combining the node weight coefficient and the node baseline parameters; among them, the node operation standard parameters include data acquisition accuracy parameters and command execution response parameters.

8. The intelligent collaborative control method for distribution network terminals according to claim 2, characterized in that, Multimodal collaborative control of distribution network terminals is carried out based on intelligent collaborative control links, and node operation monitoring indicators of distribution network terminals at each control node are collected. The specific steps include: The intelligent collaborative control link is deployed to the distribution network intelligent management and control platform. Each terminal in the distribution network establishes a multimodal communication connection with the management and control platform and carries out collaborative management and control operations based on the intelligent collaborative control link. The distribution network intelligent management and control platform collects operational feedback data from distribution network terminals at each management and control node; wherein, the operational feedback data includes terminal sensing data feedback and control execution action feedback; Extract the node data collection behavior of each control node from the terminal perception data feedback, and extract the node command execution behavior of each control node from the control execution action feedback; By integrating node data acquisition behavior and node command execution behavior, the actual operating behavior of the distribution network terminal at each control node is formed, and this actual operating behavior is marked as node operation monitoring indicators.

9. The intelligent collaborative control method for distribution network terminals according to claim 8, characterized in that, By comparing the node operation monitoring indicators with the corresponding node operation standard parameters of the control nodes, the node operation status of the distribution network terminals at each control node is determined. This includes the following steps: The actual operating behavior of the distribution network terminals at each control node is compared and verified item by item with the corresponding node operating standard parameters of the control node; If the actual operating behavior matches the standard parameters of the node perfectly, then the node operating status of the controlled node is determined to be normal. If the actual operating behavior deviates from the standard operating parameters of the node, the node operating status of the control node is determined to be abnormal.

10. A smart collaborative control system for distribution network terminals, characterized in that, include: The data acquisition module is used to collect target response timeliness and target management project information from distribution network terminals. The processing module is used to match and filter target response timeliness and target management project information based on a preset rule base, and to perform multi-dimensional information classification and aggregation by combining the timeliness requirements and project type characteristics of different management scenarios of distribution network terminals to obtain a set of candidate timeliness combinations and a set of candidate type combinations. The evaluation module is used to assign weights to the feature parameters of the candidate timeliness combination set and the candidate type combination set based on the analytic hierarchy process (AHP) to obtain the optimal control combination set; and to build an intelligent collaborative control link for distribution network terminals based on the optimal control combination set; wherein, the intelligent collaborative control link includes control nodes, and each control node is configured with corresponding node operation standard parameters; The control module is used to carry out multimodal collaborative control of distribution network terminals based on the intelligent collaborative control link.

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