A logistics and distribution coordination operation index management and control method and system

Abstract

This invention provides a method and system for managing operational indicators related to logistics and distribution. The method includes: determining the target operational indicator to be managed, along with its corresponding associated indicator management attribute data and business rule data, based on a pre-constructed knowledge graph of operational indicators and a currently specified operational scenario type; performing semantic parsing based on the business rule data to obtain the indicator constraints, anomaly triggering logic, and collaborative handling subject for the corresponding target operational indicator; performing association mapping based on the indicator constraints and anomaly triggering logic combined with the knowledge graph of operational indicators to obtain dynamic anomaly detection rules; analyzing and identifying the dynamic anomaly detection rules and the real-time acquired current indicator data to obtain anomaly operational indicators, and then associating and retrieving these anomaly operational indicators in the knowledge graph of operational indicators to obtain associated indicator groups. This invention ensures the timeliness, effectiveness, and closed-loop nature of operational indicator management related to logistics and distribution.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method and system for controlling operational indicators in a coordinated operation. Background Technology

[0002] In the context of power industry operation and distribution collaborative management, in order to support the effective control of operational performance indicators at all levels of municipal and county bureaus, and to ensure power supply reliability, customer service quality and operating efficiency, it is necessary to dynamically monitor and control key operational indicators such as power outage time for medium-voltage customers, comprehensive line loss rate and customer complaint progress.

[0003] Existing indicator control methods manage indicator information by constructing indicator dictionaries and indicator data modules, collect indicator data through indicator template configuration and reporting approval processes, display indicator status using indicator dashboards, and handle indicator anomalies through an anomaly handling tracking module. However, existing operational indicator control methods lack a unified indicator association knowledge system. The relationships between indicators, between indicators and business rules, and between the handling entities are not effectively explored, requiring manual querying and analysis to identify indicator anomalies and their associated impacts, resulting in low control efficiency. Furthermore, the fixed indicator constraints and anomaly triggering logic cannot be flexibly adapted and dynamically adjusted according to actual business rules, leading to insufficient accuracy in anomaly identification. Moreover, the lack of collaborative handling decision support based on relationships and historical data makes it difficult to quickly match and adapt the collaborative handling entities and corresponding processing tasks for the current anomaly event, failing to guarantee the timeliness and closed-loop nature of control, and consequently, failing to provide comprehensive and accurate support for management decisions at all levels. Summary of the Invention

[0004] This invention provides a method and system for controlling operational indicators of marketing and distribution coordination, which ensures the timeliness, effectiveness and closed-loop nature of operational indicator control, and provides comprehensive and accurate support for management decisions at all levels.

[0005] In a first aspect, the present invention provides a method for controlling operational indicators of logistics and distribution coordination, including: Based on the pre-built knowledge graph of operation and distribution coordination indicators and the currently specified operation scenario type, the target operation indicators to be managed and the corresponding associated indicator management attribute data and business rule data are determined. Based on the business rule data, semantic parsing is performed to obtain the corresponding target operation indicators, indicator constraints, anomaly triggering logic, and collaborative handling entities; Based on the aforementioned indicator constraints and the aforementioned anomaly triggering logic, and combined with the operational coordination indicator knowledge graph, a dynamic anomaly detection rule is obtained through association mapping. Based on the dynamic anomaly detection rules and the real-time acquired current indicator data, the abnormal operation indicators that are abnormal are identified, and the abnormal operation indicators are associated and retrieved in the operation and distribution coordination indicator knowledge graph to obtain the associated indicator group. Based on the collaborative handling entity and the associated indicator group, and combined with the historical anomaly handling data in the operational coordination indicator knowledge graph, a list of handling tasks for the current anomaly event is determined.

[0006] Preferably, the associated indicator group includes associated indicators and corresponding business processes and responsible departments; Based on the collaborative handling entity and the associated indicator group, and combined with historical anomaly handling data in the operational coordination indicator knowledge graph, a list of handling tasks for the current anomaly event is determined, including: Based on the abnormal operation indicators, the related indicator groups, and the collaborative handling entities, event aggregation is performed to obtain the current abnormal event; the current abnormal event also includes an abnormal semantic description and business context constraints; Based on the collaborative handling entity and each related indicator in the related indicator group, the corresponding business process and responsible department, and combined with the historical anomaly handling data in the operation and distribution collaborative indicator knowledge graph, a set of historical handling patterns is constructed. Based on the set of historical handling patterns, structural alignment matching is performed with the collaborative handling entities and related indicator groups in the current abnormal event to obtain a structural matching subset; Based on the action semantic features of each action in the structure matching subset, an action semantic topology graph is constructed, and the action semantic topology structure is determined based on the action semantic topology graph; the action semantic features include action type, action target, and action triggering conditions; Based on the action semantic topology and the business context constraints of the current abnormal event, topology path pruning is performed to obtain a set of actionable paths. Based on the set of possible action paths and the set of associated indicators, task generation and collaborative optimization are performed to obtain a list of processing tasks for the current abnormal event.

[0007] Preferably, the set of historical handling patterns consists of a five-element group consisting of the handling entity, related indicators, business process, responsible department, and handling action corresponding to multiple historical abnormal events.

[0008] Preferably, the business context constraints include the current time window, resource availability status, and business priority.

[0009] Preferably, the step of generating and coordinating tasks based on the set of feasible action paths and the set of associated indicators to obtain a list of processing tasks for the current anomaly event includes: Based on the semantic description of the degree of abnormality of each abnormal operation indicator in the associated indicator group, semantic action path matching and filtering are performed to obtain the preferred action path. Based on each action node in the preferred action path and its topological order in the action semantic topology, an original task sequence carrying execution order constraints is determined; each task item in the original task sequence includes action content, target indicator, responsible department, and preceding task identifier. Based on the original task sequence and the cross-departmental collaboration rules in the knowledge graph of the operation and distribution collaboration indicators, the task items are reconstructed to enhance collaboration, resulting in a collaboratively enhanced task sequence; the cross-departmental collaboration rules include a countersigning mechanism, a joint response threshold, and information synchronization requirements. Based on the collaborative enhancement task sequence and the timeliness requirements of the current abnormal event, the task sequence compliance verification and conflict resolution are performed to obtain the processing task list.

[0010] Preferably, the indicator control attribute data includes indicator definition information, target value configuration information, and decomposition dimension information.

[0011] Preferably, the dynamic anomaly detection rule is obtained by combining the indicator constraints and the anomaly triggering logic with the knowledge graph of the operation and distribution coordination indicators, and performing association mapping. This includes: Based on the semantic alignment mapping between the aforementioned indicator constraints and the indicator ontology structure in the knowledge graph of the operational coordination indicators, a set of structured constraint nodes is obtained. Based on the aforementioned anomaly triggering logic and the set of structured constraint nodes, an event-driven path is expanded to obtain a set of triggering path instances of the anomaly triggering logic in the knowledge graph; Based on the trigger path instance set and the collaborative handling subject ontology in the operation and distribution coordination index knowledge graph, role path binding is performed to obtain the collaborative handling subject allocation mapping table corresponding to each trigger path; Based on the collaborative handling subject allocation mapping table and the trigger path instance set, rule atomic decomposition is performed to obtain a dynamic anomaly detection atomic rule set for collaborative handling subjects; Based on the pattern matching between each atomic rule in the dynamic anomaly detection atomic rule set and the indicator state node in the operational coordination indicator knowledge graph at the current moment, if the detection condition in the atomic rule completely matches the current state, then the rule is activated and a corresponding dynamic anomaly detection rule is generated. Secondly, the present invention also provides a system for controlling operational indicators of supply chain coordination, applied to the method for controlling operational indicators of supply chain coordination as described in the first aspect; the system for controlling operational indicators of supply chain coordination includes: The indicator and associated data determination module is used to determine the target operational indicators to be managed and the corresponding associated indicator management attribute data and business rule data based on the pre-built operational and distribution coordination indicator knowledge graph and the currently specified operational scenario type. The semantic parsing module is used to perform semantic parsing based on the business rule data to obtain the indicator constraints, anomaly triggering logic, and collaborative handling entities for the corresponding target operational indicators. The anomaly detection rule generation module is used to perform association mapping based on the indicator constraints and the anomaly triggering logic, combined with the operation and distribution coordination indicator knowledge graph, to obtain dynamic anomaly detection rules. The indicator identification and retrieval module is used to analyze and identify the abnormal operation indicators based on the dynamic anomaly detection rules and the real-time acquired current indicator data, to obtain the abnormal operation indicators that are abnormal, and to retrieve the abnormal operation indicators in the operation and distribution coordination indicator knowledge graph to obtain the associated indicator group. The task list generation module is used to determine a task list for handling the current abnormal event based on the collaborative handling entity and the associated indicator group, combined with the historical abnormal handling data in the operation and distribution collaborative indicator knowledge graph.

[0012] Thirdly, the present invention also provides an electronic device, comprising: a memory for storing computer software programs; and a processor for reading and executing the computer software programs, thereby realizing the operation and maintenance collaborative management method for operational indicators as described above.

[0013] Fourthly, the present invention also provides a non-transitory computer-readable storage medium storing a computer software program, which, when executed by a processor, implements the above-described method for managing and controlling operational indicators in a coordinated operation.

[0014] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the operation and maintenance collaborative management method described above.

[0015] The operational indicator control method for supply and distribution coordination provided in this invention determines target operational indicators and associated indicator control attribute data and business rule data by combining a pre-constructed supply and distribution coordination indicator knowledge graph with the current operational scenario type. This establishes a unified indicator association knowledge system, improving subsequent control efficiency. Furthermore, semantic parsing based on business rule data yields indicator constraints, anomaly triggering logic, and collaborative handling entities, providing a basis for dynamically adjusting anomaly detection rules and overcoming the limitations of fixed indicator constraints and anomaly triggering logic in existing technologies. Finally, dynamic anomaly detection rules are obtained by combining indicator constraints and anomaly triggering logic with the supply and distribution coordination indicator knowledge graph for association mapping, realizing the implementation of dynamic anomaly detection rules. The dynamic detection rules are flexibly adapted and adjusted according to business rules, improving the accuracy of anomaly identification. In addition, based on the dynamic anomaly detection rules and real-time indicator data analysis, abnormal operation indicators are identified, and related indicator groups are obtained through association retrieval via the operation and distribution coordination indicator knowledge graph. This replaces the manual query and analysis method for identifying anomalies and their related impacts, further improving management efficiency. Finally, based on the collaborative handling entity and related indicator groups, and combined with historical anomaly handling data in the knowledge graph, a list of processing tasks is determined. This enables rapid matching of the collaborative handling entity and processing tasks for the current anomaly event, ultimately ensuring the timeliness, effectiveness, and closed-loop nature of operation and distribution coordination indicator management, providing comprehensive and accurate support for management decisions at all levels. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the method for controlling operational indicators in a coordinated operation of logistics provided in this embodiment of the invention. Figure 2 This is a schematic diagram of the structure of the operation and maintenance collaborative performance indicator control system provided in this embodiment of the invention; Figure 3 An embodiment diagram of the electronic device provided in this invention; Figure 4 An embodiment diagram of a computer-readable storage medium provided in accordance with the present invention. Detailed Implementation

[0017] 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.

[0018] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0019] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0020] See Figure 1 , Figure 1 This is a flowchart illustrating the operational indicator control method for coordinated operation of logistics and distribution provided by the present invention. In this embodiment of the invention, the executing entity of the operational indicator control method for coordinated operation of logistics and distribution is the operational indicator control system. Therefore, the operational indicator control method for coordinated operation of logistics and distribution includes: Step 10: Based on the pre-built knowledge graph of operation and distribution coordination indicators and the currently specified operation scenario type, determine the target operation indicators to be managed and the corresponding associated indicator management attribute data and business rule data.

[0021] Optionally, the operational indicator control system, based on a pre-built knowledge graph of operational and distribution coordination indicators, upon receiving the user's specified operational scenario type (such as production operation monitoring scenario, marketing service control scenario, etc.), performs a precise matching query within the knowledge graph using that scenario type as the search condition. Through the established mapping relationship between scenarios and indicators in the knowledge graph, it filters out operational indicators highly relevant to the current scenario as target operational indicators to be controlled. Simultaneously, based on the inherent connection links between indicators and indicator control attribute data and business rule data in the knowledge graph, it synchronously extracts the indicator control attribute data (including indicator definition information, target value configuration information, and decomposition dimension information) and business rule data corresponding to each target operational indicator.

[0022] Furthermore, the pre-built knowledge graph of operation and maintenance coordination indicators is a structured knowledge system constructed by pre-integrating data from the entire process of operation and maintenance coordination management in the power industry. Its nodes cover core elements such as operation indicators, business rules, handling entities, and business links, while edges represent the relationships between various elements (such as indicator-business rule association, indicator-handling entity association, indicator-business link association, etc.). The specified operation scenario type is determined by the user according to actual management needs, including but not limited to "daily indicator monitoring scenario", "monthly assessment indicator control scenario", "abnormal emergency handling scenario", etc. Indicator control attribute data is the basic attribute information supporting the control of target operation indicators, including indicator definition information (used to define the connotation, statistical scope and calculation caliber of indicators), target value configuration information (used to clarify the standard values ​​that indicators need to achieve in each level and period), and decomposition dimension information (used to clarify the management dimensions that indicators can be decomposed into, such as regional dimension, time dimension, department dimension, etc.). Business rule data consists of industry norms, management requirements and business process guidelines related to the control of target operation indicators.

[0023] In one embodiment, it is assumed that the pre-constructed knowledge graph of operation and maintenance coordination indicators includes indicators associated with production and operation monitoring scenarios such as power outage time statistics for medium-voltage customers, power outage time statistics for low-voltage customers, distribution network faults, and distribution transformer overload; and indicators associated with marketing service management scenarios such as electricity sales, comprehensive line loss rate, customer complaint progress monitoring, and electricity bill collection rate. Each indicator is associated with and stored with corresponding indicator management attribute data and business rule data.

[0024] When a user specifies the current operational scenario as a production and operation monitoring scenario, the Operation and Distribution Collaborative Operation Indicator Control System retrieves the relevant indicators corresponding to the production and operation monitoring scenario from the Operation and Distribution Collaborative Indicator Knowledge Graph, determining the target operational indicators as medium-voltage customer power outage time statistics, low-voltage customer power outage time statistics, distribution network faults, and distribution transformer overload. Simultaneously, it extracts the indicator control attribute data for medium-voltage customer power outage time statistics: the indicator definition is "average power outage duration for medium-voltage customers within a specified period, reflecting the reliability of medium-voltage power supply"; the target value configuration is "0.25 hours / customer"; and the decomposition dimension information is "decomposed by month and by unit (e.g., Huangpu, Liwan, etc. district / county bureaus)". The corresponding business rule data is also extracted as "data for the previous month must be submitted by the 5th of each month; if the completed value exceeds the target value by 10%, an anomaly is triggered, and the collaborative handling entities include the production and technical departments and maintenance teams." Other target operational indicators (low-voltage customer power outage time statistics, distribution network faults, and distribution transformer overload) also have their corresponding indicator control attribute data and business rule data extracted in the same way.

[0025] Step 20: Perform semantic parsing based on the business rule data to obtain the indicator constraints, anomaly triggering logic, and collaborative handling entities for the corresponding target operational indicators.

[0026] Optionally, after obtaining the business rule data, the operational indicator control system performs semantic analysis on the business rule data corresponding to each target operational indicator. Specifically, it uses natural language processing technology to segment, tag, and semantically analyze the text information in the business rule data to extract constraint elements, anomaly triggering conditions, and responsible entity elements related to indicator control. The constraint elements are integrated to form indicator constraint conditions, clarifying the restrictions on data reporting, statistical periods, and numerical ranges. The triggering condition elements are sorted to form anomaly triggering logic, defining the conditions under which indicator data is considered an anomaly. The responsible entity element identifies the collaborative handling entities, clarifying the relevant departments, teams, or individuals involved in handling anomaly events.

[0027] Continuing with the above example, let's take the business rule data corresponding to the defined target operational indicator "Statistics on power outage time for medium-voltage customers" as an example: "Data for the previous month must be submitted by the 5th of each month; if the completed value exceeds the target value by 10%, an anomaly is triggered, and the collaborative handling entities include the production and technical departments and maintenance teams." The operation and maintenance collaborative indicator management system performs semantic parsing on this business rule data: Extract the constraint elements: "Data entry time constraint is to complete the data entry for the previous month before the 5th of each month" and "Indicator value constraint is not to exceed 10% of the target value of 0.25 hours / household (i.e. 0.275 hours / household)". Combine them to form the indicator constraint condition: "Complete the statistical data entry of the power outage time of medium voltage customers in the previous month before the 5th of each month, and the indicator completion value shall not exceed 0.275 hours / household".

[0028] Extract the trigger condition element: "The completed value exceeds the target value by 10%", and sort it out to form the abnormality trigger logic: "When the completed value of the medium voltage customer power outage time statistics is > 0.25 hours / household × (1+10%) = 0.275 hours / household, it is determined that the indicator has changed abnormally.

[0029] Extract the responsible entities: "Production and Technology Department, Operation and Maintenance Team", and determine that the entities responsible for collaborative handling are the Production and Technology Department and the Operation and Maintenance Team.

[0030] Step 30: Based on the indicator constraints and the anomaly triggering logic, and combined with the operational coordination indicator knowledge graph, a correlation mapping is performed to obtain dynamic anomaly detection rules.

[0031] Optionally, after obtaining the indicator constraints and anomaly triggering logic, the operation indicator control system maps them to related data in the operation and distribution coordination indicator knowledge graph. It then retrieves historical anomaly data, anomaly judgment criteria for similar indicators, and control requirements for related business processes from the knowledge graph to supplement and optimize the indicator constraints and anomaly triggering logic, forming dynamic anomaly detection rules that dynamically adapt to the current business scenario and indicator characteristics, as detailed in steps 301-305.

[0032] Step 40: Based on the dynamic anomaly detection rules and the real-time acquired current indicator data, analyze and identify the abnormal operation indicators that are abnormal, and then search for the abnormal operation indicators in the operation and distribution coordination indicator knowledge graph to obtain the associated indicator group.

[0033] Optionally, the operation indicator control system first collects real-time operational data of the target operation indicator within the current statistical period through the interface of the power industry operation and maintenance collaborative management information system. The data sources include the marketing system (electricity sales data), the operation and maintenance system (line operation data), and the metering system (electricity metering data). Then, the real-time collected indicator data is substituted into the judgment conditions of the dynamic anomaly detection rules, and logical verification is performed one by one. If any rule condition is met, the indicator is determined to be an abnormal operation indicator. Finally, indicators that meet the anomaly judgment criteria are selected as abnormal operation indicators. Finally, using this abnormal operation indicator as the core node, a correlation search is performed in the operation and maintenance collaborative indicator knowledge graph to find other indicators that have a direct or indirect relationship with this abnormal operation indicator (such as causal relationships, data dependencies, business relationships, etc.), and the corresponding business processes and responsible departments of these related indicators are extracted and integrated to form a related indicator group.

[0034] In one embodiment, taking the target operational indicator "Statistics on power outage time for medium-voltage customers" as an example, the judgment threshold for the normal scenario in its dynamic anomaly detection rules is 0.275 hours / customer. The operation and maintenance collaborative operation indicator management system obtains the current indicator data (directly collected by the system) of the statistics on power outage time for medium-voltage customers in Huangpu District in July 2025 in real time, which is 0.35 hours / customer. Substituting this data into the dynamic anomaly detection rules: 0.35 hours / customer > 0.275 hours / customer, and there are no major event days recorded in that month, the "Statistics on power outage time for medium-voltage customers" is determined to be an abnormal operational indicator.

[0035] Using this abnormal operation indicator as the core node, a correlation search was conducted in the operation and maintenance coordination indicator knowledge graph. A causal relationship was found between it and "distribution network fault count," "medium-voltage fault-trip count," and "equipment fault repair timeliness rate" (an increase in the number of distribution network faults and medium-voltage fault-trip count leads to longer power outage times for medium-voltage customers, and a low equipment fault repair timeliness rate exacerbates the exceeding of outage time limits). The corresponding business processes are "distribution network fault monitoring," "medium-voltage fault handling," and "equipment operation and maintenance management," with the responsible departments being the production technology department and the operation and maintenance team, respectively. Based on this, the following related indicator groups were obtained: Related indicator 1: Distribution network fault count, corresponding business process: distribution network fault monitoring, responsible departments: production technology department and operation and maintenance team; Related indicator 2: Medium-voltage fault-trip count, corresponding business process: medium-voltage fault handling, responsible departments: production technology department and operation and maintenance team; Related indicator 3: Equipment fault repair timeliness rate, corresponding business process: equipment operation and maintenance management, responsible departments: production technology department and operation and maintenance team.

[0036] Step 50: Based on the collaborative handling entity and the associated indicator group, and combined with the historical anomaly handling data in the operational coordination indicator knowledge graph, determine the handling task list for the current anomaly event.

[0037] Optionally, the operation indicator control system retrieves historical anomaly handling data from the operation and distribution coordination indicator knowledge graph based on the identified collaborative handling entities and the associated indicator groups. This includes information such as the handling process, task allocation plan, handling measures, and completion time requirements for similar anomaly events. Ultimately, it clarifies the specific handling tasks, task priorities, completion time limits, and assessment standards corresponding to each collaborative handling entity, and integrates them to form a handling task list for the current anomaly event, providing clear guidance for the collaborative handling of anomaly events, as shown in steps 501-506.

[0038] This invention, through a pre-constructed knowledge graph of operational coordination indicators combined with the current operational scenario type, determines target operational indicators and associated indicator control attribute data and business rule data, thereby establishing a unified indicator association knowledge system and improving subsequent control efficiency. Furthermore, semantic parsing based on business rule data yields indicator constraints, anomaly triggering logic, and collaborative handling entities, providing a foundation for dynamically adjusting anomaly detection rules and overcoming the limitations of fixed indicator constraints and anomaly triggering logic in existing technologies. Finally, based on the indicator constraints and anomaly triggering logic, and combined with the operational coordination indicator knowledge graph, dynamic anomaly detection rules are obtained through association mapping, enabling anomaly detection rules to adapt to business changes. The rules are flexibly adapted and adjusted, improving the accuracy of anomaly identification. Furthermore, based on dynamic anomaly detection rules and real-time indicator data analysis, anomaly operation indicators are identified, and related indicator groups are obtained through association retrieval via a knowledge graph of operational and distribution coordination indicators. This replaces manual querying and analysis to identify anomalies and their associated impacts, further improving management efficiency. Finally, based on the collaborative handling entity and related indicator groups, and combined with historical anomaly handling data in the knowledge graph, a list of processing tasks is determined. This enables rapid matching of the collaborative handling entity and processing tasks appropriate for the current anomaly event, ultimately ensuring the timeliness, effectiveness, and closed-loop nature of operational and distribution coordination indicator management, providing comprehensive and accurate support for management decisions at all levels.

[0039] In one embodiment, the process of steps 301-305 includes: Step 301: Based on the indicator constraints and the indicator ontology structure in the operation and distribution coordination indicator knowledge graph, perform semantic alignment mapping to obtain a set of structured constraint nodes.

[0040] Optionally, the operational indicator control system extracts core semantic elements (including data entry time constraints, numerical range constraints, statistical period constraints, and related business scenario constraints) from the determined target operational indicators' constraints. Then, it invokes the operational coordination indicator knowledge graph to parse the indicator ontology structure within the knowledge graph. This ontology structure refers to the structured framework of indicator-related core concepts and their interrelationships defined in the knowledge graph. It includes hierarchical structures such as basic indicator information ontology (e.g., indicator name, statistical period, unit of measurement), constraint condition ontology (e.g., time constraints, numerical constraints, scenario constraints), and business association ontology (e.g., related business processes, related departments). Then, it performs semantic alignment mapping between the core semantic elements in the indicator constraints and the corresponding ontology categories in the indicator ontology structure. This means matching each constraint semantic element to a corresponding ontology node in the knowledge graph. All successfully matched ontology nodes are integrated to form a structured constraint node set, where each node contains information such as constraint type, constraint parameters, and ontology association path.

[0041] Continuing with the above example, taking the target operation indicator "Statistics on power outage time for medium-voltage customers" as an example, the constraint condition for the indicator is "Complete the statistical data of power outage time for medium-voltage customers in the previous month before the 5th of each month, and the completed value of the indicator shall not exceed 0.275 hours / household". The core semantic elements include: time constraint (completing the data of the previous month before the 5th of each month), numerical constraint (completed value ≤ 0.275 hours / household), and statistical period constraint (monthly statistics).

[0042] The operational indicator control system parses the indicator ontology structure in the knowledge graph of operation and maintenance coordination indicators. The constraint ontology includes time constraint nodes (child nodes: reporting deadline, statistical period) and numerical constraint nodes (child nodes: upper limit value, lower limit value, benchmark value). Then, semantic alignment mapping is performed. The time constraint "submit the previous month's data before the 5th of each month" is mapped to "Constraint Conditions Ontology - Time Constraint Node - Submission Deadline Sub-Node" in the indicator ontology structure, forming constraint node 1: {Constraint type: time constraint, constraint parameters: submission deadline is the 5th of each month, submission data period is the previous month, ontology association path: indicator ontology → constraint conditions ontology → time constraint node → submission deadline sub-node}. The numerical constraint “Completion value ≤ 0.275 hours / household” is mapped to “Constraint Condition Ontology - Numerical Constraint Node - Upper Limit Value Sub-Node”, forming Constraint Node 2: {Constraint Type: Numerical Constraint, Constraint Parameters: Upper Limit Value 0.275 hours / household, Comparison Relationship ≤, Ontology Association Path: Indicator Ontology → Constraint Condition Ontology → Numerical Constraint Node → Upper Limit Value Sub-Node}; The statistical period constraint "Monthly Statistics" is mapped to "Constraint Condition Ontology - Time Constraint Node - Statistical Period Sub-Node", forming constraint node 3: {Constraint Type: Statistical Period Constraint, Constraint Parameter: Monthly, Ontology Association Path: Indicator Ontology → Constraint Condition Ontology → Time Constraint Node → Statistical Period Sub-Node}. These three constraint nodes are then integrated to form a structured constraint node set.

[0043] Step 302: Based on the anomaly triggering logic and the set of structured constraint nodes, perform event-driven path expansion to obtain the triggering path instance set of the anomaly triggering logic in the knowledge graph.

[0044] Optionally, the operational indicator control system, based on the obtained set of structured constraint nodes and anomaly triggering logic, first determines the core triggering conditions (such as numerical exceedance triggering, time overdue triggering, scenario association triggering, etc.) and the logical relationships (such as AND or OR relationships) between the triggering conditions. Then, starting from each constraint node in the set of structured constraint nodes, it unfolds an event-driven path in the operational coordination indicator knowledge graph, simulating the entire process of indicator data collection, verification, and anomaly determination, and outlining the complete path of "indicator status input → constraint condition verification → trigger result output" for each constraint node. During the path unfolding process, it associates the indicator data flow ontology and verification rule ontology in the knowledge graph, embedding the core triggering conditions of the anomaly triggering logic into the path, forming a triggering condition satisfaction rule for each path. All instantiated paths containing specific constraint parameters, triggering conditions, and path nodes are integrated to form a triggering path instance set of the anomaly triggering logic in the knowledge graph.

[0045] Continuing with the above embodiment, taking the target operational indicator "Statistics on Power Outage Time for Medium-Voltage Customers" as an example, the abnormality triggering logic is: "When the completed value of the statistics on power outage time for medium-voltage customers is greater than 0.275 hours / household, the indicator is determined to have an abnormality." The core triggering condition is "Value exceeding the limit trigger (completed value > upper limit value 0.275 hours / household)." The generated set of structured constraint nodes includes time constraint node 1, value constraint node 2, and statistical period constraint node 3.

[0046] The operation and maintenance coordination indicator control system unfolds event-driven paths within a knowledge graph: 1. Path starting point: Numerical constraint node 2 (Constraint parameter: upper limit 0.275 hours / household); 2. Path Node 1: Indicator Data Acquisition Node (related to "Indicator Data Flow Ontology - Data Acquisition Sub-Node" in the knowledge graph, to obtain real-time completed value data); 3. Path node 2: Numerical verification node (associated with "Verification rule body - numerical comparison sub-node", performs the comparison operation between the completed value and the upper limit value); 4. Path Node 3: Trigger Condition Judgment Node (related to "Trigger Logic Body - Value Exceeds Limit Trigger Child Node", judges whether the comparison result satisfies "Completion Value > Upper Limit Value"); 5. Path endpoint: Anomaly detection node (output "Anomaly" result if the trigger condition is met, output "Normal" result if the condition is not met).

[0047] Combining statistical cycle constraint node 3 (monthly statistics) and time constraint node 1 (reporting before the 5th of each month), a time verification step is added to the path to form a complete trigger path example 1: {Path node sequence: numerical constraint node 2 → indicator data collection node → numerical verification node → time verification node (verifying whether data reporting and verification are completed before the 5th of each month) → trigger condition judgment node → anomaly judgment node, trigger condition: completion value > 0.275 hours / household and data verification is completed within the statistical period, path associated ontology: indicator ontology → constraint condition ontology → numerical constraint node → indicator data flow ontology → verification rule ontology → trigger logic ontology → anomaly judgment ontology}. Simultaneously, based on the "Major Event Day Scenario-Related Ontology" in the knowledge graph, trigger path instance 2 (Major Event Day Scenario) is generated: {Path node sequence: Numerical constraint node 2 (upper limit adjusted to 0.275×1.2=0.33 hours / household) → Scenario judgment node (verify whether it is a major event day) → Indicator data collection node → Numerical verification node → Time verification node → Trigger condition judgment node → Anomaly judgment node, Trigger condition: The completed value in the major event day scenario is > 0.33 hours / household and the data verification is completed within the statistical period, Path-related ontology: Indicator ontology → Constraint condition ontology → Numerical constraint node → Business scenario ontology → Indicator data flow ontology → Verification rule ontology → Trigger logic ontology → Anomaly judgment ontology}.

[0048] The above two path instances are combined to form a trigger path instance set.

[0049] Step 303: Based on the trigger path instance set and the collaborative handling subject ontology in the operation and distribution coordination index knowledge graph, perform role path binding to obtain the collaborative handling subject allocation mapping table corresponding to each trigger path.

[0050] Optionally, the operational indicator control system first identifies the collaborative handling entities (such as departments, teams, and individuals) for the determined target operational indicators and parses the collaborative handling entity ontology in the operational coordination indicator knowledge graph. This ontology is a structured framework defined in the knowledge graph for various responsible entities involved in indicator change handling and the relationships between them, including hierarchical structures such as department ontology (e.g., production and technology departments, maintenance teams, and marketing departments), position ontology (e.g., indicator administrators, maintenance personnel, and unit leaders), and responsibility association ontology (e.g., direct responsible entities, indirect responsible entities, and reviewing entities). Then, it traverses the trigger path instance set. For each trigger path instance, it analyzes the associated business process ontology nodes and constraint type ontology nodes in the path. Based on the preset association relationships between these nodes and the collaborative handling entity ontology (e.g., numerical constraint-related paths are associated with production and technology departments, and time constraint-related paths are associated with indicator administrators), the collaborative handling entities are assigned to the corresponding trigger path instances, clarifying the primary responsible department, collaborating departments, specific responsible persons, and reviewers for each path. All trigger path instances and their corresponding collaborative handling entities are integrated to form a collaborative handling entity allocation mapping table. The table contains information such as trigger path identifier, collaborative handling entity type, entity name, responsibility and authority, and associated path nodes.

[0051] Continuing with the above embodiment, taking the target operational indicator "Statistics on power outage time for medium-voltage customers" as an example, the identified collaborative handling entities are the production technology department and the maintenance team. The operational indicator control system parses the collaborative handling entity ontology in the knowledge graph, including the department ontology (sub-nodes: production technology department, maintenance team, marketing department) and the responsibility association ontology (sub-nodes: responsible department, collaborating department). It then traverses the trigger path instance set generated in step 302 (including regular scenario path instance 1 and major event day scenario path instance 2): For trigger path instance 1 (a typical scenario, with core related numerical constraint nodes and indicator data flow ontology), based on the preset association relationship in the knowledge graph of "numerical constraint node → production technology department" and "indicator data flow ontology → operation and maintenance team", the primary responsible department is assigned to the production technology department and the collaborating department is the operation and maintenance team. The specific responsible person is the person in charge of the operation and maintenance team and the reviewer is the person in charge of the production technology department. For trigger path instance 2 (major event day scenario, core associated numerical constraint node, business scenario ontology - major event day node), based on the preset association relationship of "major event day scenario node → production technology department (overall coordination)" and "numerical constraint node → operation and maintenance team (specific handling)", the main responsible department is assigned to the production technology department, the collaborating department is the operation and maintenance team, the specific responsible person is the scheduling specialist of the production technology department, and the reviewer is the unit head.

[0052] The final integrated mapping table for the allocation of collaborative handling entities is as follows: ; Step 304: Based on the collaborative handling subject allocation mapping table and the trigger path instance set, perform rule atomization decomposition to obtain a dynamic anomaly detection atomic rule set for collaborative handling subjects.

[0053] Optionally, the operational indicator control system, based on the generated collaborative handling entity allocation mapping table and the generated trigger path instance set, performs rule atomic decomposition for each trigger path instance, combined with its corresponding collaborative handling entity allocation information. Specifically, this rule atomic decomposition breaks down a complete trigger path instance into multiple independent, indivisible atomic rule units. Each atomic rule unit contains a single trigger condition, a single handling entity, and a single judgment result. During the decomposition process, each key node in the trigger path instance (such as a data acquisition node, verification node, or trigger judgment node) is used as a dividing point to extract the constraints, entity responsibilities, and judgment logic corresponding to each node, forming atomic rules. All atomic rules are integrated according to the collaborative handling entity, forming a dynamic anomaly detection atomic rule set oriented towards the collaborative handling entity. Each atomic rule contains information such as rule identifier, belonging entity, trigger condition, judgment logic, and output result.

[0054] Continuing with the above embodiments, taking the trigger path example 1 (a typical scenario) of the target operational indicator "statistics on power outage time for medium-voltage customers" and the corresponding collaborative handling entity allocation information as an example, the operation and maintenance collaborative operation indicator management system performs rule atomization decomposition: 1. Atomic Rule 1 (Subject: Operation and Maintenance Team): Rule ID: Rule-1-1; Triggering Condition: Collect the statistical data of the previous month's medium-voltage customer power outage time before the 5th of each month; Judgment Logic: If the data collection is completed before the 5th of each month, proceed to the next step of verification; if not completed, it is judged as "data entry overdue" anomaly; Output Result: Data collection completed / data entry overdue anomaly.

[0055] 2. Atomic Rule 2 (Subject: Operation and Maintenance Team): Rule Identifier: Rule-1-2; Triggering Condition: Compare the collected completion value with the upper limit of 0.275 hours / household; Judgment Logic: If the completion value ≤ 0.275 hours / household, the indicator is judged as normal; if the completion value > 0.275 hours / household, the value is judged as "numerical deviation"; Output Result: Normal indicator / numerical deviation.

[0056] Atomic Rule 3 (Subject: Production Technology Department): Rule ID: Rule-1-3; Triggering Condition: Receive the anomaly judgment result submitted by the maintenance team; Judgment Logic: Review the accuracy of the anomaly judgment result and confirm whether it is a real anomaly; Output Result: Anomaly Confirmation / Anomaly Rejection.

[0057] Finally, the above three atomic rules are integrated to form a set of dynamic anomaly detection atomic rules for collaborative processing entities.

[0058] Step 305: Perform pattern matching between each atomic rule in the set of atomic rules for dynamic anomaly detection and the indicator state node at the current moment in the knowledge graph of operational coordination indicators. If the detection conditions in the atomic rule completely match the current state, then activate the rule and generate the corresponding dynamic anomaly detection rule.

[0059] Optionally, the operational indicator management system collects the current status data of target operational indicators in real time, including the current statistical period of the indicator, data collection progress, collected completed value data, and the current business scenario (such as whether it is a major event day). This status data is then mapped to the operational coordination indicator knowledge graph to form the indicator status node at the current moment. This node contains the indicator's current status parameters and its real-time association with other ontology nodes in the knowledge graph. Next, each atomic rule in the dynamic anomaly detection atomic rule set generated in step 304 is traversed. The detection conditions in the atomic rules are matched with the corresponding status parameters in the indicator status node, i.e., the triggering conditions of each atomic rule are verified to ensure they completely match the current indicator status (e.g., whether the time condition of the atomic rule matches the current statistical period, whether the scenario condition matches the current business scenario, and whether the numerical condition matches the current completed value data). If the detection conditions of an atomic rule perfectly match the current indicator status, the atomic rule is activated. All activated atomic rules are combined in the logical order of the trigger path (such as data collection → scenario judgment → numerical verification → review and confirmation) to form a complete dynamic anomaly detection rule that adapts to the current business status. The rule clearly defines the complete process of anomaly judgment, the responsible parties for each link, the judgment criteria, and the output results.

[0060] Continuing with the above implementation, taking the target operational indicator "Statistics on power outage time for medium-voltage customers" as an example, the operational indicator management system collects its current status data in real time: the statistical period is August 2025, the data collection time is September 3, 2025 (before the 5th of each month), the current completion value is 0.30 hours / customer, and the current business scenario is a routine scenario (non-major event day). This data is then mapped to a knowledge graph to form indicator status nodes: {Statistical period: August 2025, Collection progress: Completed, Completion value: 0.30 hours / customer, Business scenario: Routine scenario, Collection time: September 3, 2025}.

[0061] Traverse the set of atomic rules for dynamic anomaly detection and perform pattern matching: Atomic rule 1 (Operations and Maintenance Team, collect data before the 5th of each month): The detection condition "collect the previous month's data before the 5th of each month" completely matches the current status "collection time September 3, 2025 (August data), collection progress completed", so this rule is activated; Atomic rule 2 (Operations and Maintenance Team, compare the completed value with 0.275 hours / household): The detection condition "compare the completed value with the original upper limit value" completely matches the current scenario "normal scenario", so this rule is activated; Atomic rule 3 (Production Technology Department, review anomaly results): Depends on the output of atomic rule 2. Since atomic rule 2 is activated and the current completed value 0.30 hours / household > 0.275 hours / household, the detection condition "receive the anomaly judgment result submitted by the Operations and Maintenance Team" is met, so this rule is activated; Combine the activated atomic rules 1-3 in logical order to generate dynamic anomaly detection rules: 1. The maintenance team shall complete the collection of statistical data on power outage time for medium-voltage customers in August 2025 by September 5, 2025. If the collection is not completed within the specified time, it shall be judged as an "overdue data entry" anomaly; 2. The maintenance team shall compare the collected August 2025 completed value (0.30 hours / household) with the upper limit value of 0.275 hours / household. Since 0.30 > 0.275, it shall be judged as a "value exceeding the standard" anomaly; 3. The maintenance team shall submit the "value exceeding the standard" anomaly judgment result to the production technology department. The production technology department shall review and confirm the authenticity of the anomaly. If the anomaly is confirmed, a formal anomaly form shall be generated. If the anomaly is rejected, it shall be returned to the maintenance team for re-verification.

[0062] This invention realizes the transformation of anomaly detection rules from "static fixed" to "dynamic adaptation", which greatly improves the flexibility and accuracy of the rules, ensures that anomaly detection can accurately match the current business status and division of responsibilities, and provides efficient and accurate rule support for subsequent anomaly identification and collaborative handling.

[0063] In one embodiment, steps 501-506 include: Step 501: Based on the abnormal operation indicators, the associated indicator group, and the collaborative handling entity, perform event aggregation to obtain a structured current abnormal event; the current abnormal event also includes an abnormal semantic description and business context constraints; the business context constraints include the current time window, resource availability status, and business priority.

[0064] Optionally, the operational indicator control system aggregates events based on the identified abnormal operational indicators, related indicator groups, and collaborative handling entities, using these three as core elements. During event aggregation, the system first clearly defines the type of abnormality (e.g., exceeding limits, time limits) and severity (e.g., minor, general, major) of the abnormal operational indicators. Then, it integrates the current status of each related indicator in the related indicator group and their relationship with the abnormal operational indicator. Simultaneously, it marks the hierarchical relationship of the collaborative handling entities (responsible department, collaborating departments). Based on this, an anomaly semantic description is generated, clearly describing the core anomaly, its scope, and potential impact using natural language. It also extracts business context constraints, including the current time window (statistical period for the anomaly, processing time limits), resource availability (personnel configuration, equipment resources, and technical support capabilities of the collaborative handling entities), and business priority (handling priority determined based on the scope and severity of the anomaly's impact, such as high, medium, and low). Finally, a structured current anomaly event is formed, containing core elements, anomaly semantic descriptions, and business context constraints.

[0065] In one embodiment, the abnormal operation indicator "Statistics on power outage time for medium-voltage customers" determined in step 40 (the completed value in Huangpu District in July 2025 was 0.35 hours / household, a general abnormality in a normal scenario), the related indicator group (related indicator 1: number of distribution network faults, business link: distribution network fault monitoring, responsible department: production technology department, operation and maintenance team; related indicator 2: number of medium-voltage faults-tripping, business link: medium-voltage fault handling, responsible department: production technology department, operation and maintenance team; related indicator 3: timeliness rate of equipment fault repair, business link: equipment operation and maintenance management, responsible department: production technology department, operation and maintenance team) and the collaborative handling entities (production technology department, operation and maintenance team) determined in step 20 are used as examples.

[0066] The operational metrics control system aggregates events: Core element integration: The abnormal operation indicator is "statistics on power outage time for medium-voltage customers", the abnormality type is "abnormality exceeding the standard", and the abnormality degree is "general abnormality"; in the related indicator group, the number of distribution network faults increased by 20% compared with the previous month, the number of medium-voltage faults-trips increased by 15% compared with the previous month, and the equipment fault repair timeliness rate decreased by 10% compared with the previous month, all of which are causally related to the abnormal operation indicator; in the collaborative handling entities, the production technology department is the main responsible department, and the operation and maintenance team is the collaborating department.

[0067] Abnormal semantic description: "In July 2025, the completed value of power outage time for medium-voltage customers in Huangpu District was 0.35 hours / household, which exceeded the normal scenario threshold of 0.275 hours / household, and is considered a general abnormality due to excessive values; the number of related distribution network faults and the number of medium-voltage faults-trips are both showing an increasing trend, and the timeliness of equipment fault repair is decreasing, which may further reduce the reliability of medium-voltage power supply and affect the customer's power experience."

[0068] Business context constraints: The current time window is "July 2025 statistical period, and the handling must be completed within 15 working days"; the resource availability status is "the maintenance team currently has 10 maintenance personnel and 5 fault detection devices, and the production technology department can coordinate 2 technical experts"; the business priority is "high". Integrate the above information to form a structured current anomaly event.

[0069] Step 502: Based on the collaborative handling entity and each associated indicator, corresponding business process and responsible department in the associated indicator group, and combined with the historical anomaly handling data in the operation and distribution collaborative indicator knowledge graph, construct a set of historical handling patterns; the set of historical handling patterns consists of a five-tuple of handling entity, associated indicator, business process, responsible department and handling action corresponding to multiple historical anomaly events.

[0070] Optionally, the operational indicator control system retrieves historical anomaly handling data from the operational coordination indicator knowledge graph based on the identified collaborative handling entity, related indicators in the associated indicator group, corresponding business processes, and responsible departments. Historical anomaly handling data stores records of all past closed-loop anomaly events, including operational indicators, related indicators, collaborative handling entity, business processes, responsible departments, handling actions, and handling results. The retrieved historical anomaly handling data is filtered, retaining historical records highly relevant to the current collaborative handling entity, related indicators, business processes, and responsible departments. For each filtered historical record, its corresponding handling entity, related indicators, business processes, responsible departments, and handling actions are extracted and constructed into a quintuple (handling entity, related indicator, business process, responsible department, handling action). All quintuples are integrated to form a set of historical handling patterns.

[0071] Continuing with the above embodiments, the search criteria are: collaborative handling entities (production technology departments, operation and maintenance teams), related indicator groups (number of distribution network faults, number of medium voltage faults-trips, and equipment fault repair timeliness), corresponding business processes (distribution network fault monitoring, medium voltage fault handling, and equipment operation and maintenance management), and responsible departments (production technology departments and operation and maintenance teams).

[0072] The operational indicator control system retrieves historical anomaly handling data from the operation and distribution coordination indicator knowledge graph, filters out three highly correlated historical records, and constructs a set of historical handling patterns: 1. Historical Handling Model 1 (Five Elements): (Production Technology Department + Maintenance Team, Number of Distribution Network Faults + Number of Medium Voltage Faults - Number of Trips, Distribution Network Fault Monitoring + Medium Voltage Fault Handling, Production Technology Department + Maintenance Team, Maintenance Team Investigates Fault Causes and Implements Rectification, Production Technology Department Reviews Rectification Plans and Results). 2. Historical Handling Model 2 (Five-Element Group): (Production Technology Department + Maintenance Team, Timeliness of Equipment Failure Repair, Equipment Maintenance Management, Production Technology Department + Maintenance Team, Maintenance Team Optimizes Equipment Inspection Plan and Strengthens Maintenance Personnel Training, Production Technology Department Tracks Rectification Progress). 3. Historical Handling Model 3 (Five-Element Group): (Production Technology Department + Maintenance Team, Number of Distribution Network Faults + Timeliness of Equipment Fault Repair, Distribution Network Fault Monitoring + Equipment Maintenance Management, Production Technology Department + Maintenance Team, Maintenance Team Replaces Aging Equipment and Establishes a Rapid Fault Response Mechanism, Production Technology Department Coordinates Resource Support). The above three five-element groups are integrated to form a set of historical handling models.

[0073] Step 503: Perform structural alignment matching between the historical handling pattern set and the collaborative handling subject and related indicator group in the current abnormal event to obtain a structural matching subset.

[0074] Optionally, the operational indicator control system extracts the collaborative handling entity and related indicators from each quintuple in the generated historical handling pattern set, and performs structural alignment matching with the collaborative handling entity and related indicator group in the generated current anomaly event. This structural alignment matching refers to verifying one by one whether the collaborative handling entity in the historical quintuple is completely consistent with (or has an inclusion relationship with) the current collaborative handling entity, and whether the related indicators in the historical quintuple have an intersection (or are completely matched) with the related indicators in the current related indicator group. If a historical quintuple meets the matching condition of "consistent (or inclusion) collaborative handling entity and intersection of related indicators", then the quintuple is included in the structural matching subset; otherwise, the quintuple is excluded. All quintuples that meet the matching conditions are integrated to form the structural matching subset.

[0075] Continuing with the above embodiments, the main entities for collaborative handling of the current abnormal events are (production technology department + operation and maintenance team), and the related indicator groups are (number of distribution network faults, number of medium voltage faults-trips, and timeliness of equipment fault repair).

[0076] The operational indicator control system performs structural alignment and matching on three quintuples in the historical handling pattern set: Historical handling mode 1: The collaborative handling entities (production technology department + operation and maintenance team) are the same as the current ones, and the related indicators (number of distribution network faults + number of medium voltage faults - number of trips) have an intersection with the current related indicator group, which meets the matching conditions and is included in the structural matching subset; Historical handling mode 2: The collaborative handling entities (production technology department + operation and maintenance team) are the same as the current ones, and the related indicators (timeliness of equipment failure repair) have an intersection with the current related indicator group, thus meeting the matching conditions and being included in the structural matching subset; Historical handling mode 3: The collaborative handling entities (production technology department + operation and maintenance team) are the same as the current ones, and the related indicators (number of distribution network failures + equipment failure repair timeliness rate) overlap with the current related indicator group, meeting the matching conditions and being included in the structural matching subset. Therefore, the structural matching subset contains the above three historical handling mode quintuples.

[0077] And assume that the five-tuple of a certain historical disposal mode 4 is (marketing department + finance department, electricity sales + transmission and distribution revenue, electricity sales statistics + revenue accounting, marketing department + finance department, optimized sales strategy + revenue data accounting), its collaborative disposal subject is inconsistent with the current (production technology department + operation and maintenance team), the related indicators have no intersection with the current related indicator group, do not meet the matching conditions, and are excluded from the structural matching subset.

[0078] Step 504: Based on the action semantic features of each action in the structure matching subset, construct an action semantic topology graph, and determine the action semantic topology structure based on the action semantic topology graph; the action semantic features include action type, action target, and action triggering conditions.

[0079] Optionally, the operational indicator control system extracts the action of each five-tuple from the generated structure matching subset. Semantic parsing is performed on each action to extract semantic features, including action type (e.g., investigation, rectification, training, coordination, tracking), action target (e.g., cause of failure, equipment, personnel, plan), and action triggering conditions (e.g., after a failure occurs, after a rectification plan is formulated, when personnel lack skills). Then, using each action as a node and the logical relationships between actions (e.g., sequential, causal, parallel) as edges, an action semantic topology graph is constructed. In the action semantic topology graph, each node is labeled with its corresponding action semantic features, and each edge is labeled with the type of logical relationship between actions. Based on the action semantic topology graph, the semantic associations and logical arrangement of all actions are analyzed to extract a common and reusable action combination logic framework, which is the action semantic topology structure.

[0080] Continuing with the above embodiments, the actions for the three quintuples in the structure matching subset are as follows: 1. Action 1: The maintenance team investigates the cause of the fault and implements rectification, and the production technology department reviews the rectification plan and its effectiveness; 2. Action 2: The maintenance team optimizes the equipment inspection plan and strengthens the training of maintenance personnel, and the production technology department tracks the rectification progress; 3. Action 3: The maintenance team replaces aging equipment and establishes a rapid fault response mechanism, and the production technology department coordinates resource support.

[0081] The operational indicator control system performs semantic analysis on the above-mentioned actions and extracts their semantic features: Semantic features of action 1: action type (investigation + rectification + review), action target (cause of failure + rectification plan + rectification effect), action triggering condition (after the anomaly occurs + after the rectification plan is formulated); Semantic features of action 2: action type (optimization + training + tracking), action target (equipment inspection plan + maintenance personnel), action triggering conditions (when maintenance timeliness rate declines + after rectification is implemented); Semantic features of action 3: Action type (replacement + establishment + coordination), Action target (aging equipment + rapid fault response mechanism + resources), Action triggering condition (equipment aging + untimely fault response). Using actions as nodes and logical relationships between actions as edges, construct an action semantic topology graph: Node 1 (Investigate the cause of the fault) → Node 2 (Develop a rectification plan) → Node 3 (Implement rectification) → Node 4 (Review the rectification effect) (sequential order); Node 5 (Optimize equipment inspection plan) → Node 6 (Strengthen maintenance personnel training) (parallel relationship); Node 7 (Replacing aging equipment) → Node 8 (Establishing a rapid fault response mechanism) (Cause and effect relationship); Node 9 (Coordinate Resource Support) → Node 3 (Implement Rectification) (Support Relationship). Based on this topology, the semantic topology of the actions is extracted as follows: "First, investigate the cause of the problem → Develop a targeted solution → Implement specific rectification measures (optimization, training, replacement, etc. can be carried out simultaneously) → Coordinate resources to provide support → Track the progress of rectification → Review the effectiveness of rectification."

[0082] Step 505: Based on the action semantic topology and the business context constraints of the current abnormal event, perform topology path pruning to obtain a set of actionable paths.

[0083] Optionally, the operational indicator control system, based on the business context constraints of the generated current anomaly event, including the current time window, resource availability, and business priority, performs topology path pruning on each action path in the action semantic topology generated in step 504, using these business context constraints as filtering criteria. This topology path pruning involves eliminating action paths that do not meet the business context constraints and retaining those that do. For example, if the current time window is short (tight processing deadline), action paths that take too long are eliminated; if there is a shortage of a certain type of equipment in the resource availability status, action paths that depend on that type of equipment are eliminated; if the business priority is high, action paths with high processing efficiency and significant effects are retained. All pruned action paths are integrated to form a set of actionable paths.

[0084] Continuing with the above embodiments, the business context constraints of the current abnormal event are: the current time window (to be handled within 15 working days), resource availability status (the maintenance team currently has 10 maintenance personnel and 5 fault detection devices, and the production technology department can coordinate 2 technical experts), and business priority (high).

[0085] The operational performance indicator control system uses this constraint as a condition to prune the action paths in the action semantic topology: Eliminate action paths that take too long: such as "conducting a long-term personnel skills enhancement project" (estimated to take 30 days, exceeding the 15 working days time limit); Eliminate action paths that rely on scarce resources, such as "introducing external high-end testing equipment" (currently, this resource configuration is unavailable). Retain efficient and effective action paths, such as "quickly identify the cause of the fault → formulate a simple and feasible rectification plan → implement the rectification using existing equipment and personnel → provide support from technical experts → track progress → review the results".

[0086] After final pruning, three action paths were obtained, forming a set of action paths: 1. Action Path 1: Investigate the causes of power distribution network faults and medium-voltage fault trips → Develop targeted rectification plans → Production and technical departments review the plans → Maintenance teams implement rectification using existing equipment → Track rectification progress → Review rectification results; 2. Action Path 2: Optimize equipment inspection plans → Strengthen short-term training for maintenance personnel (1-2 days) → Track inspection and training results → Adjust and optimize measures; 3. Action Path 3: Replace key aging equipment → Establish a rapid fault response mechanism → Production and technical departments coordinate with technical experts to provide support → Implement rectification → Verify results.

[0087] Step 506: Based on the set of actionable paths and the set of associated indicators, perform task generation and collaborative optimization to obtain a list of processing tasks for the current anomaly event.

[0088] Optionally, the operation indicator control system, based on the determined set of actionable action paths and associated indicator groups, generates and optimizes tasks for each actionable action path in conjunction with the associated indicator groups to obtain a list of processing tasks for the current abnormal event, as detailed in steps 5061-5064.

[0089] The embodiments of this invention fully explore the correlation between historical handling experience and indicators, realize the intelligent and precise generation of handling tasks, avoid the subjectivity and inefficiency of manual task allocation, and ensure the timeliness, closed-loop and effectiveness of abnormal event handling.

[0090] In one embodiment, the process of steps 5061-5064 includes: Step 5061: Based on the semantic description of the degree of abnormality of each abnormal operation indicator in the set of possible action paths and the associated indicator group, perform semantic action path matching and filtering to obtain the preferred action path.

[0091] Optionally, the operational indicator control system, based on each path in the determined set of actionable paths, first clarifies the core action objectives, handling intensity, and applicable scenarios for each path. Simultaneously, it extracts semantic descriptions of the degree of abnormality for each abnormal operational indicator in the generated current anomaly event's associated indicator group. These semantic descriptions are qualitative or quantitative descriptions of the severity of the indicator's abnormality (e.g., slight exceedance, moderate exceedance, severe exceedance, or specific exceedance percentages and numerical deviation ranges). Then, it performs semantic action path matching and filtering between the handling intensity and applicable scenarios of each actionable path and the degree of abnormality of each abnormal operational indicator, i.e., determining whether the path's handling capacity is suitable for the degree of abnormality (e.g., severe abnormalities correspond to high-intensity handling paths, and slight abnormalities correspond to lightweight handling paths). The system selects one or more paths with the highest suitability as preferred action paths, based on the matching degree between the path's handling intensity and the degree of abnormality, and the percentage of associated indicators covered by the path.

[0092] Continuing with the above embodiments, the actionable path set generated in step 505 includes three paths: 1. Actionable path 1: Investigate the causes of power distribution network faults and medium-voltage fault tripping → Develop targeted rectification plans → Production and technical departments review the plans → Maintenance teams implement rectification using existing equipment → Track rectification progress → Review rectification effects (Core action objective: Solve root cause problems of faults, handling intensity: medium to high, applicable scenario: moderate to severe anomalies caused by faults); 2. Actionable path 2: Optimize equipment inspection plans → Strengthen short-term training for maintenance personnel → Track the inspection and training effects → Adjust and optimize measures (Core action objective: Preventive optimization, handling intensity: medium, applicable scenario: minor to moderate anomalies caused by insufficient capabilities); 3. Actionable path 3: Replace key aging equipment → Establish a rapid fault response mechanism → Production and technical departments coordinate with technical experts to provide support → Implement rectification → Verify effects (Core action objective: Hardware upgrade + mechanism construction, handling intensity: high, applicable scenario: severe anomalies caused by equipment aging) as an example.

[0093] The semantic description of the degree of abnormality of the related indicator groups in the current abnormal event is as follows: Abnormal operation indicator "Statistics on power outage time for medium-voltage customers": The completed value is 0.35 hours / customer, which exceeds the threshold of 0.275 hours / customer by about 27.27%, which is considered a moderate exceedance; Related indicator 1 "Number of distribution network faults": Increased by 20% compared to the previous month, which is considered a slight to moderate exceedance; Related indicator 2 "Number of medium-voltage faults-trips": Increased by 15% compared to the previous month, which is considered a slight to moderate exceedance; Related indicator 3 "Timely repair rate of equipment failures": Decreased by 10% compared to the previous month, which is considered a slight exceedance.

[0094] The operational indicator management system performs semantic action path matching and filtering: Action path 1 covers core abnormal indicators such as distribution network faults and medium-voltage fault tripping, with a medium-to-high handling intensity that matches the overall medium level of abnormality, achieving a matching rate of 85%. Action path 2 focuses on prevention and optimization, but does not directly target the root cause of the current fault type, with an adaptability of 60%. Action path 3 has a high handling intensity, exceeding the actual needs of the current moderate anomaly, with a suitability of 70%. Action path 1, with the highest suitability, is selected as the preferred action path.

[0095] Step 5062: Based on each action node in the preferred action path and its topological order in the action semantic topology, determine the original task sequence carrying execution order constraints; each task item in the original task sequence includes action content, target indicator, responsible department, and preceding task identifier.

[0096] Optionally, the operational indicator control system extracts each core action from the determined preferred action path as an action node, clarifying the specific action content, target (corresponding indicators in the associated indicator group), and responsible department (based on the responsibility allocation of the collaborative handling entity in step 20 and the structural matching subset in step 503) for each action node. Simultaneously, based on the topological order (i.e., the logical sequence of actions) of each action node in the generated action semantic topology, execution order constraints are assigned to each action node. Based on the action nodes, action content, target indicators, responsible departments, and execution order constraints, an original task sequence is constructed. Each task item includes action content (specific execution operation), target indicator (associated indicator corresponding to the action), responsible department (the entity executing the task), and a preceding task identifier (the number of the preceding task to be completed before this task), ensuring the logical coherence of the task sequence.

[0097] Continuing with the above embodiments, taking the determined preferred action path 1 as an example: investigating the causes of power distribution network faults and medium-voltage fault tripping → formulating targeted rectification plans → production and technical departments reviewing the plans → maintenance teams implementing rectification using existing equipment → tracking rectification progress → reviewing rectification results, the operation indicator control system constructs the following original task sequence based on the topological sequence in the action semantic topology structure: ; Step 5063: Based on the original task sequence and the cross-departmental collaboration rules in the knowledge graph of the operation and distribution collaboration indicators, the task items are reconstructed to enhance collaboration, resulting in a collaboratively enhanced task sequence; the cross-departmental collaboration rules include a countersigning mechanism, a joint response threshold, and information synchronization requirements.

[0098] Optionally, the operational indicator control system, based on the generated original task sequence, clarifies the responsible department, task content, and relationships with other tasks for each task item. Simultaneously, it invokes cross-departmental collaboration rules from the operational coordination indicator knowledge graph. These rules are pre-defined rules ensuring smooth multi-departmental collaboration, including a countersigning mechanism (a process requirement for joint review and confirmation by multiple departments), a joint response threshold (conditions triggering joint participation by multiple departments), and information synchronization requirements (the objects, timing, and content of information to be synchronized during task execution). Based on these cross-departmental collaboration rules, the task items in the original task sequence are restructured to enhance collaboration: for tasks involving multi-departmental collaboration, countersigning process requirements are added; for tasks reaching the joint response threshold, joint participating departments and their responsibilities are added; and for all tasks, the objects, timing, and content of information synchronization are clarified to ensure barrier-free cross-departmental collaboration. The restructured sequence forms a collaboratively enhanced task sequence, with each task item having added collaboration rule requirements based on the original information.

[0099] Continuing with the above embodiments, the cross-departmental collaboration rules in the operational coordination indicator knowledge graph are as follows: Joint approval mechanism: Tasks involving the use of funds or major adjustments to plans require joint approval from the production technology department and the finance department; Joint response threshold: If the rectification plan involves equipment procurement amount ≥ 50,000 yuan, a joint response from the maintenance team, production technology department, and procurement department is required. Information synchronization requirements: Key milestones in task execution (completion of plan formulation, initiation of rectification, completion of rectification) must be synchronized with the unit leader. During task execution, progress must be synchronized with relevant departments in real time. Based on these rules, the original task sequence is restructured with enhanced collaboration, resulting in the following enhanced collaborative task sequence: For Task T1, the action content is: to investigate the specific causes of power distribution network faults and medium-voltage fault trips in Huangpu District in July 2025; the target indicators are: number of power distribution network faults and number of medium-voltage fault trips; the responsible department is: the operation and maintenance team; the preceding task identifier is: none; the collaboration rule requirement is: information synchronization requirement: within 24 hours after the investigation is completed, a synchronous investigation report shall be submitted to the production and technology department. For Task T2, the actions are as follows: Based on the investigation results of T1, formulate targeted rectification plans for distribution network faults and medium-voltage fault trips; the target indicators are: number of distribution network faults, number of medium-voltage fault-trips, and statistics on medium-voltage customer power outage time; the responsible department is: the operation and maintenance team; the preceding task is identified as: T1; the collaboration rules require: information synchronization: after the initial draft of the plan is completed, it should be synchronized with the production and technology department in real time; if the plan involves equipment procurement amount ≥ 50,000 yuan, triggering the joint response threshold, the procurement department needs to participate in the plan discussion; For Task T3, the actions are as follows: review the rectification plan formulated in T2; the target indicators are: number of distribution network faults, number of medium-voltage faults-trips, and statistics on the power outage time of medium-voltage customers; the responsible department is: the production technology department; the preceding task is identified as: T2; the collaboration rules require: a countersigning mechanism: if the plan involves the use of funds, it must be countersigned with the finance department; the information synchronization requirement is: within 12 hours after the review is completed, the review results must be synchronized with the operation and maintenance team and the unit leader. For Task T4, the actions are as follows: Implement specific rectification measures using existing equipment according to the rectification plan approved in T3; the target indicators are: number of distribution network faults, number of medium-voltage faults-trips, and statistics on the power outage time of medium-voltage customers; the responsible department is: the operation and maintenance team; the preceding task is identified as: T3; the collaboration rules require: information synchronization requirements: daily progress synchronization after rectification starts, and results synchronization within 24 hours after rectification is completed; For Task T5, the actions are as follows: track the progress of the rectification of T4 throughout the entire process and coordinate and resolve problems in the process in a timely manner; the target indicators are: number of distribution network faults, number of medium voltage faults-trips, and statistics on the power outage time of medium voltage customers; the responsible department is: the production and technology department; the preceding task is identified as: T4; the collaboration rules require: information synchronization requirement: weekly tracking and reporting to the unit leader, and real-time synchronization in case of major problems; For Task T6, the actions are as follows: review the implementation effect of the rectification in T4, and verify whether the power outage time for medium-voltage customers has been restored to the target range; the target indicators are: statistics on power outage time for medium-voltage customers, number of distribution network faults, and number of medium-voltage faults-trips; the responsible department is: the production and technology department; the preceding task is identified as: T5; the collaboration rules require: information synchronization requirement: within 12 hours after the review is completed, the final effect report shall be synchronized to the maintenance team, the unit leader, and the finance department (if funds are involved); Step 5064: Based on the timeliness requirements of the collaborative enhancement task sequence and the current abnormal event, perform task timing compliance verification and conflict resolution to obtain the processing task list.

[0100] Optionally, the operational indicator control system, based on the timeliness requirements of the generated current anomaly events, including the overall task completion time and the deadlines for key task nodes, performs a task sequence compliance check on the generated collaborative enhancement task sequence. This involves checking whether the planned execution time of each task item is within the overall completion time, whether the transitions between tasks are smooth, and whether there are any conflicts in the deadlines of key nodes. If a sequence conflict exists (e.g., the total task execution time exceeds the overall time limit, or a subsequent task's predecessor task is not completed but needs to be started early), conflict resolution is performed. Conflict resolution methods include optimizing the task execution order, compressing the duration of non-critical tasks, and adding parallel execution tasks (provided there are no dependencies). After time sequence compliance verification and conflict resolution, the completion time and assessment criteria for each task item are added, ultimately forming a complete, time-sequential, and clearly defined task list.

[0101] Continuing with the above embodiments, the timeliness requirement for the current anomaly event is "the overall completion time is 15 working days, with key nodes: the plan review is completed no later than the 6th working day, and the rectification implementation is completed no later than the 12th working day."

[0102] The operational performance indicator control system performs time-series compliance checks on the collaborative enhancement task sequences: Initial timeline: T1 (3 days) → T2 (2 days) → T3 (1 day) → T4 (6 days) → T5 (full synchronization) → T6 (2 days), total duration 14 days. The key node T3 is completed on the 6th day (3+2+1), and T4 is completed on the 12th day (6+6), which meets the timeliness requirements. Potential conflict: If T2 involves a joint procurement department discussion, it may be extended by 1 day, causing the completion time of T3 to be postponed to the 7th day, which violates the critical milestone requirements.

[0103] Conflict resolution method: When T1 is executed to the second day, the maintenance team will synchronize the preliminary investigation results with the procurement department in advance. After T2 starts, the team will conduct discussions in parallel with the procurement department without adding extra time to T2.

[0104] After adding the completion deadline and assessment criteria, a task list will be generated: For Task 1, the responsible department is the Operations and Maintenance Team; the action is to investigate the specific causes of power distribution network faults and medium-voltage fault trips in Huangpu District in July 2025; the target indicators are: number of power distribution network faults and number of medium-voltage fault trips; the prerequisite task is: none; the collaboration rule requires that a report be submitted to the production and technology department within 24 hours after the investigation is completed; the completion time is 3 working days; the assessment standard is that the investigation of fault causes is comprehensive and accurate, a detailed investigation report is generated, and no key information is omitted.

[0105] For Task 2, the responsible department is the Operations and Maintenance Team; the actions are: based on the investigation results of T1, formulate targeted rectification plans for distribution network faults and medium-voltage fault trips; the target indicators are: the number of distribution network faults, the number of medium-voltage fault-trips, and the statistics of medium-voltage customer power outage time; the prerequisite task is marked as: 1; the collaboration rules require that the initial draft of the plan be synchronized with the production and technology department in real time after completion; if the equipment procurement amount is ≥50,000 yuan, the procurement department should participate in the discussion; the completion time limit is: 2 working days (the 4th-5th working day); the assessment criteria are: the rectification plan is scientific and feasible, clearly defines the rectification measures, resource requirements and time nodes, and covers all core fault causes.

[0106] For Task 3, the responsible department is the Production Technology Department; the action content is to review the rectification plan formulated by T2; the target indicators are: number of distribution network faults, number of medium-voltage faults-trips, and statistics on the power outage time of medium-voltage customers; the preceding task identifier is: 2; the collaboration rules require that the use of funds must be countersigned by the Finance Department; the results must be synchronized to the maintenance team and the unit leader within 12 hours after the review is completed; the completion deadline is: 1 working day (the 6th working day); the assessment criteria are: timely review, reasonable optimization suggestions, and a clear conclusion on whether the plan is approved or rejected, with specific reasons attached for rejection.

[0107] For Task 4, the responsible department is the Operations and Maintenance Team; the action content is to implement specific rectification measures using existing equipment according to the rectification plan approved by T3; the target indicators are: the number of distribution network faults, the number of medium-voltage faults-trips, and the statistics of medium-voltage customer power outage time; the preceding task identifier is: 3; the coordination rule requirements are: daily progress synchronization after rectification starts, and results synchronization within 24 hours after completion; the completion deadline is: 6 working days (7th-12th working days); the assessment standard is: the rectification measures are implemented effectively, and the number of distribution network faults and the number of medium-voltage faults-trips decrease by ≥30% compared with before rectification.

[0108] For Task 5, the responsible department is the Production Technology Department; the actions include: tracking the implementation progress of the T4 rectification throughout the entire process and coordinating and resolving issues encountered during the process; the target indicators are: the number of distribution network faults, the number of medium-voltage faults / trips, and statistics on the power outage time for medium-voltage customers; the preceding task is identified as: 4; the coordination rules require: weekly tracking reports to the unit leader, with real-time updates on major issues; the completion deadline is: the entire process (working days 7-12); the assessment criteria are: adequate progress tracking, problem response time ≤ 2 hours, and a coordination and resolution success rate ≥ 90%.

[0109] For Task 6, the responsible department is the Production Technology Department; the actions are: reviewing the implementation effect of T4 rectification and verifying whether the power outage time of medium-voltage customers has returned to the target range; the target indicators are: statistics of power outage time of medium-voltage customers, number of distribution network faults, and number of medium-voltage faults-trips; the preceding task identifier is: 5; the collaboration rule requires that the results be synchronized to relevant parties within 12 hours after the review is completed; the completion time limit is: 2 working days (13th-14th working days); the assessment standard is: the rectification effect meets the standard, the power outage time of medium-voltage customers is ≤0.275 hours / customer, and the related indicators have returned to the normal range.

[0110] This invention enables the refined generation of processing tasks from path selection to sequence construction, collaboration enhancement, and timing optimization. The resulting processing task list is characterized by clear responsibilities, orderly collaboration, reasonable timing, and clear assessment, which can directly guide the closed-loop handling of abnormal events and improve the efficiency of cross-departmental collaborative handling and the accuracy of task execution.

[0111] Furthermore, the operational coordination indicator control system provided by the present invention will be described below. The operational coordination indicator control system described below can be referred to in correspondence with the operational coordination indicator control method described above.

[0112] Optional, refer to Figure 2 , Figure 2 This is a schematic diagram of the operational performance indicator control system for coordinated operation provided by the present invention. The operational performance indicator control system for coordinated operation includes: The indicator and associated data determination module 210 is used to determine the target operational indicators to be controlled and the corresponding associated indicator control attribute data and business rule data based on the pre-built operational coordination indicator knowledge graph and the currently specified operational scenario type. The semantic parsing module 220 is used to perform semantic parsing based on the business rule data to obtain the indicator constraints, anomaly triggering logic, and collaborative handling subject of the corresponding target operation indicator; The anomaly detection rule generation module 230 is used to perform association mapping based on the indicator constraints and the anomaly triggering logic, combined with the operation and distribution coordination indicator knowledge graph, to obtain dynamic anomaly detection rules. The indicator identification and retrieval module 240 is used to analyze and identify the abnormal operation indicators based on the dynamic anomaly detection rules and the real-time acquired current indicator data, obtain the abnormal operation indicators that have anomalies, and retrieve the abnormal operation indicators in the operation and distribution coordination indicator knowledge graph to obtain the associated indicator group. The task list generation module 250 is used to determine a task list for handling the current abnormal event based on the collaborative handling subject and the associated indicator group, combined with the historical abnormal handling data in the operation and distribution collaborative indicator knowledge graph.

[0113] This invention, through a pre-constructed knowledge graph of operational coordination indicators combined with the current operational scenario type, determines target operational indicators and associated indicator control attribute data and business rule data, thereby establishing a unified indicator association knowledge system and improving subsequent control efficiency. Furthermore, semantic parsing based on business rule data yields indicator constraints, anomaly triggering logic, and collaborative handling entities, providing a foundation for dynamically adjusting anomaly detection rules and overcoming the limitations of fixed indicator constraints and anomaly triggering logic in existing technologies. Finally, based on the indicator constraints and anomaly triggering logic, and combined with the operational coordination indicator knowledge graph, dynamic anomaly detection rules are obtained through association mapping, enabling anomaly detection rules to adapt to business changes. The rules are flexibly adapted and adjusted, improving the accuracy of anomaly identification. Furthermore, based on dynamic anomaly detection rules and real-time indicator data analysis, anomaly operation indicators are identified, and related indicator groups are obtained through association retrieval via a knowledge graph of operational and distribution coordination indicators. This replaces manual querying and analysis to identify anomalies and their associated impacts, further improving management efficiency. Finally, based on the collaborative handling entity and related indicator groups, and combined with historical anomaly handling data in the knowledge graph, a list of processing tasks is determined. This enables rapid matching of the collaborative handling entity and processing tasks appropriate for the current anomaly event, ultimately ensuring the timeliness, effectiveness, and closed-loop nature of operational and distribution coordination indicator management, providing comprehensive and accurate support for management decisions at all levels.

[0114] Please see Figure 3 , Figure 3 An embodiment diagram of an electronic device provided in accordance with the present invention. For example... Figure 3 As shown, this embodiment of the invention provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor 320. When the processor 320 executes the computer program 311, it performs the following steps: Based on the pre-built knowledge graph of operation and distribution coordination indicators and the currently specified operation scenario type, the target operation indicators to be managed and the corresponding associated indicator management attribute data and business rule data are determined. Based on the business rule data, semantic parsing is performed to obtain the corresponding target operation indicators, indicator constraints, anomaly triggering logic, and collaborative handling entities; Based on the aforementioned indicator constraints and the aforementioned anomaly triggering logic, and combined with the operational coordination indicator knowledge graph, a dynamic anomaly detection rule is obtained through association mapping. Based on the dynamic anomaly detection rules and the real-time acquired current indicator data, the abnormal operation indicators that are abnormal are identified, and the abnormal operation indicators are associated and retrieved in the operation and distribution coordination indicator knowledge graph to obtain the associated indicator group. Based on the collaborative handling entity and the associated indicator group, and combined with the historical anomaly handling data in the operational coordination indicator knowledge graph, a list of handling tasks for the current anomaly event is determined.

[0115] Please see Figure 4 , Figure 4 An embodiment diagram of a computer-readable storage medium provided in accordance with an embodiment of the present invention is shown. Figure 4 As shown, this embodiment provides a computer-readable storage medium 400 on which a computer program 311 is stored. When the computer program 311 is executed by a processor, it performs the following steps: Based on the pre-built knowledge graph of operation and distribution coordination indicators and the currently specified operation scenario type, the target operation indicators to be managed and the corresponding associated indicator management attribute data and business rule data are determined. Based on the business rule data, semantic parsing is performed to obtain the corresponding target operation indicators, indicator constraints, anomaly triggering logic, and collaborative handling entities; Based on the aforementioned indicator constraints and the aforementioned anomaly triggering logic, and combined with the operational coordination indicator knowledge graph, a dynamic anomaly detection rule is obtained through association mapping. Based on the dynamic anomaly detection rules and the real-time acquired current indicator data, the abnormal operation indicators that are abnormal are identified, and the abnormal operation indicators are associated and retrieved in the operation and distribution coordination indicator knowledge graph to obtain the associated indicator group. Based on the collaborative handling entity and the associated indicator group, and combined with the historical anomaly handling data in the operational coordination indicator knowledge graph, a list of handling tasks for the current anomaly event is determined.

[0116] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the operation and maintenance coordination indicator control method provided by the above methods, the method including: Based on the pre-built knowledge graph of operation and distribution coordination indicators and the currently specified operation scenario type, the target operation indicators to be managed and the corresponding associated indicator management attribute data and business rule data are determined. Based on the business rule data, semantic parsing is performed to obtain the corresponding target operation indicators, indicator constraints, anomaly triggering logic, and collaborative handling entities; Based on the aforementioned indicator constraints and the aforementioned anomaly triggering logic, and combined with the operational coordination indicator knowledge graph, a dynamic anomaly detection rule is obtained through association mapping. Based on the dynamic anomaly detection rules and the real-time acquired current indicator data, the abnormal operation indicators that are abnormal are identified, and the abnormal operation indicators are associated and retrieved in the operation and distribution coordination indicator knowledge graph to obtain the associated indicator group. Based on the collaborative handling entity and the associated indicator group, and combined with the historical anomaly handling data in the operational coordination indicator knowledge graph, a list of handling tasks for the current anomaly event is determined.

[0117] The system 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.

[0118] 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.

[0119] 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 method for controlling operational indicators in a coordinated supply and distribution system, characterized in that, include: Based on the pre-built knowledge graph of operation and distribution coordination indicators and the currently specified operation scenario type, the target operation indicators to be managed and the corresponding associated indicator management attribute data and business rule data are determined. Based on the business rule data, semantic parsing is performed to obtain the corresponding target operation indicators, indicator constraints, anomaly triggering logic, and collaborative handling entities; Based on the aforementioned indicator constraints and the aforementioned anomaly triggering logic, and combined with the operational coordination indicator knowledge graph, a dynamic anomaly detection rule is obtained through association mapping. Based on the dynamic anomaly detection rules and the real-time acquired current indicator data, the abnormal operation indicators that are abnormal are identified, and the abnormal operation indicators are associated and retrieved in the operation and distribution coordination indicator knowledge graph to obtain the associated indicator group. Based on the collaborative handling entity and the associated indicator group, and combined with the historical anomaly handling data in the operational coordination indicator knowledge graph, a list of handling tasks for the current anomaly event is determined.

2. The method for controlling operational indicators of coordinated operation according to claim 1, characterized in that, The associated indicator group includes the associated indicators and their corresponding business processes and responsible departments; Based on the collaborative handling entity and the associated indicator group, and combined with historical anomaly handling data in the operational coordination indicator knowledge graph, a list of handling tasks for the current anomaly event is determined, including: Based on the abnormal operation indicators, the related indicator groups, and the collaborative handling entities, event aggregation is performed to obtain the current abnormal event; the current abnormal event also includes an abnormal semantic description and business context constraints; Based on the collaborative handling entity and each related indicator in the related indicator group, the corresponding business process and responsible department, and combined with the historical anomaly handling data in the operation and distribution collaborative indicator knowledge graph, a set of historical handling patterns is constructed. Based on the set of historical handling patterns, structural alignment matching is performed with the collaborative handling entities and related indicator groups in the current abnormal event to obtain a structural matching subset; Based on the action semantic features of each action in the structure matching subset, an action semantic topology graph is constructed, and the action semantic topology structure is determined based on the action semantic topology graph; the action semantic features include action type, action target, and action triggering conditions; Based on the action semantic topology and the business context constraints of the current abnormal event, topology path pruning is performed to obtain a set of actionable paths. Based on the set of possible action paths and the set of associated indicators, task generation and collaborative optimization are performed to obtain a list of processing tasks for the current abnormal event.

3. The method for controlling operational indicators of coordinated operation according to claim 2, characterized in that, The set of historical handling patterns consists of a five-element group consisting of the handling entity, related indicators, business process, responsible department, and handling action corresponding to multiple historical abnormal events.

4. The method for controlling operational indicators of coordinated operation according to claim 2, characterized in that, The business context constraints include the current time window, resource availability status, and business priority.

5. The method for controlling operational indicators of coordinated operation according to claim 2, characterized in that, The process of generating and coordinating tasks based on the set of feasible action paths and the set of associated indicators yields a list of processing tasks for the current anomaly event, including: Based on the semantic description of the degree of abnormality of each abnormal operation indicator in the associated indicator group, semantic action path matching and filtering are performed to obtain the preferred action path. Based on each action node in the preferred action path and its topological order in the action semantic topology, an original task sequence carrying execution order constraints is determined; each task item in the original task sequence includes action content, target indicator, responsible department, and preceding task identifier. Based on the original task sequence and the cross-departmental collaboration rules in the knowledge graph of the operation and distribution collaboration indicators, the task items are reconstructed to enhance collaboration, resulting in a collaboratively enhanced task sequence; the cross-departmental collaboration rules include a countersigning mechanism, a joint response threshold, and information synchronization requirements. Based on the collaborative enhancement task sequence and the timeliness requirements of the current abnormal event, the task sequence compliance verification and conflict resolution are performed to obtain the processing task list.

6. The method for controlling operational indicators of coordinated operation according to claim 1, characterized in that, The indicator control attribute data includes indicator definition information, target value configuration information, and decomposition dimension information.

7. The method for controlling operational indicators of coordinated operation according to claim 1, characterized in that, The dynamic anomaly detection rules are obtained by combining the indicator constraints and the anomaly triggering logic with the knowledge graph of the operation and distribution coordination indicators, and by performing association mapping. These rules include: Based on the semantic alignment mapping between the aforementioned indicator constraints and the indicator ontology structure in the knowledge graph of the operational coordination indicators, a set of structured constraint nodes is obtained. Based on the aforementioned anomaly triggering logic and the set of structured constraint nodes, an event-driven path is expanded to obtain a set of triggering path instances of the anomaly triggering logic in the knowledge graph; Based on the trigger path instance set and the collaborative handling subject ontology in the operation and distribution coordination index knowledge graph, role path binding is performed to obtain the collaborative handling subject allocation mapping table corresponding to each trigger path; Based on the collaborative handling subject allocation mapping table and the trigger path instance set, rule atomic decomposition is performed to obtain a dynamic anomaly detection atomic rule set for collaborative handling subjects; Based on the pattern matching between each atomic rule in the dynamic anomaly detection atomic rule set and the indicator state node in the operational coordination indicator knowledge graph at the current moment, if the detection condition in the atomic rule completely matches the current state, then the rule is activated and the corresponding dynamic anomaly detection rule is generated.

8. A system for controlling operational indicators in a collaborative distribution system, characterized in that, Applied to the operational indicator control method for collaborative operation as described in any one of claims 1 to 7; The operational coordination and management system includes: The indicator and associated data determination module is used to determine the target operational indicators to be managed and the corresponding associated indicator management attribute data and business rule data based on the pre-built operational and distribution coordination indicator knowledge graph and the currently specified operational scenario type. The semantic parsing module is used to perform semantic parsing based on the business rule data to obtain the indicator constraints, anomaly triggering logic, and collaborative handling entities for the corresponding target operational indicators. The anomaly detection rule generation module is used to perform association mapping based on the indicator constraints and the anomaly triggering logic, combined with the operation and distribution coordination indicator knowledge graph, to obtain dynamic anomaly detection rules. The indicator identification and retrieval module is used to analyze and identify the abnormal operation indicators based on the dynamic anomaly detection rules and the real-time acquired current indicator data, to obtain the abnormal operation indicators that are abnormal, and to retrieve the abnormal operation indicators in the operation and distribution coordination indicator knowledge graph to obtain the associated indicator group. The task list generation module is used to determine a task list for handling the current abnormal event based on the collaborative handling entity and the associated indicator group, combined with the historical abnormal handling data in the operation and distribution collaborative indicator knowledge graph.

9. An electronic device, characterized in that, include: Memory, used to store computer software programs; A processor is configured to read and execute the computer software program, wherein when the processor executes the computer software program, it implements the operation and maintenance collaborative management method for operational indicators as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that, The storage medium stores a computer software program, which, when executed by a processor, implements the operation and maintenance collaborative indicator control method as described in any one of claims 1 to 7.

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