A grid service capability support management method and system based on big data processing technology

Abstract

This invention relates to a grid-based operational service capability support management method and system based on big data processing technology, belonging to the field of enterprise digital operation management technology. The method includes: a data acquisition step, collecting raw data from multiple heterogeneous business systems; a multi-dimensional relationship model construction step, establishing a multi-dimensional relationship data model containing the relationships between personnel, grids, organizations, and revenue entities; an indicator definition and calculation step, dynamically generating indicator calculation logic based on the model and calculating grid-level indicator values ​​using a distributed computing framework; an analysis and early warning step, monitoring and analyzing based on the model and grid-level indicator values, generating early warning information and handling tasks; and an incentive and closed-loop execution step, automatically calculating incentive values ​​based on the model and dispatching handling tasks to responsible entities, tracking execution status to form a closed loop. This invention solves problems such as data silos, difficulty in indicator expansion, low performance, and lack of closed-loop management.

Description

Technical Field

[0001] This invention relates to the field of enterprise digital operation management technology, specifically to a grid-based operational service capability support management method and system based on big data processing technology. Background Technology

[0002] With the large-scale commercial use of 5G networks and the accelerated digital transformation of various industries, the grid-based operation model of telecom operators has undergone a profound transformation from extensive management to refined operation. As the most basic business unit, the degree of refinement in the operation and management of the grid directly determines the overall business performance of the enterprise. The industry currently has an urgent need for grid-based operations to achieve real-time visibility of core indicators, rapid response to operational anomalies, and precise resource allocation. However, existing grid-based operational service systems generally suffer from the following problems: Core basic data is scattered across multiple heterogeneous business systems with different architectures, formats, and interfaces, such as user centers, CRM, billing, and performance systems, forming serious data silos. Furthermore, the lack of a unified data association and mapping mechanism leads to confusion regarding the attribution of revenue data to personnel and grids, resulting in the inaccurate collection of large amounts of revenue data and distorted statistical results. The indicator system is completely rigid, with all indicator calculation logic hard-coded in the program. Adding or adjusting indicators requires IT personnel to go through multiple stages, including requirements communication, code development, testing, and full deployment, resulting in response cycles of weeks or even months, failing to support the rapidly changing business needs of the market. Operational anomaly monitoring relies entirely on manual periodic checks, which is not only inefficient and has a high rate of missed detections but also cannot identify complex abnormal operational scenarios, leading to a significant delay in anomaly discovery. Finally, the lack of a closed-loop management mechanism covering the entire process from anomaly discovery to task assignment, execution, feedback, and acceptance makes the problem handling process uncontrollable and untraceable, with long processing cycles, failing to meet the core requirements of grid-based refined operations. Summary of the Invention

[0003] The technical problem to be solved by this invention is that the business logic of the existing grid operation and service system is deeply coupled with the underlying infrastructure, making it difficult to adapt to the information technology innovation, task scheduling relies on manual processes, data silos are serious, the indicator system is rigid, the centralized architecture has insufficient processing performance, the response to operational anomalies is slow, and there is a lack of a full-process automated management mechanism, which cannot meet the requirements of information technology innovation compliance and grid-based refined operation.

[0004] The technical solution of this invention to solve the above-mentioned technical problems is as follows: A grid operation and service capability support management method based on big data processing technology, comprising the following steps: S1, collecting raw data of grid operation from multiple heterogeneous business systems through data acquisition channels; S2, cleaning and aggregating the collected raw data, establishing and storing a multi-dimensional relational data model containing the relationship between personnel, grids, organizations, and income in the data storage system, wherein the multi-dimensional relational data model maps the relationship between personnel belonging to grids, income belonging to personnel, and grids and channels belonging to personnel in the entire link through the association field; S3, responding to the user's indicator configuration operation, dynamically generating the calculation logic of indicators based on the multi-dimensional relational data model, and calculating at least one grid-level indicator value using a distributed computing framework; S4, monitoring and analyzing according to preset rules based on the multi-dimensional relational data model and the grid-level indicator value, generating early warning information and corresponding disposal tasks when the early warning conditions are met; S5, automatically dispatching the disposal tasks to the responsible entities associated in the multi-dimensional relational data model, tracking the execution status and feedback results of the disposal tasks, and forming closed-loop management.

[0005] The beneficial effects of this invention are as follows: By uniformly collecting raw data from multiple heterogeneous business systems and constructing a multi-dimensional relational data model that includes the full-link mapping relationship of personnel, grids, organizations, and revenue, the problems of data silos and inaccurate revenue attribution are completely solved. Based on the multi-dimensional relational data model and grid-level indicator values, automatic monitoring and analysis are performed to generate early warning information and handling tasks. The tasks are then automatically dispatched to the relevant responsible entities and the execution status is tracked to form a closed-loop management, which comprehensively improves the efficiency and intelligence level of grid operation.

[0006] Based on the above technical solution, the present invention can be further improved as follows.

[0007] Furthermore, S2 specifically includes: S21, storing the raw data in a distributed file system, storing the cleaned raw data in a first domestic database (serving as the main database) by grid dimension, and using a second domestic database as a disaster recovery database, and writing the indicator results into the data storage system; S22, establishing a personnel information table, a grid affiliation table, an income data table, and a channel information table in the first domestic database, linking the grid affiliation field in the personnel information table, linking the grid identifier and personnel identifier in the income data table, and linking the personnel identifier in the channel information table to form a full-link traceability; S23, automatically aggregating the unattributed volume and revenue data to the grid, county, or city level according to preset priority rules, and synchronously updating the aggregation results to the first domestic database main database and cache system; S24, encrypting and storing sensitive fields, and configuring access permissions based on roles or grid scope.

[0008] The beneficial effects of adopting the above-mentioned further solution are as follows: by establishing personnel information tables, grid affiliation tables, revenue data tables, and channel information tables in the first domestic database and forming a full-link traceability relationship through related fields, full-dimensional traceability from detailed data to business entities is realized. By automatically aggregating unattributed volume and revenue data to the corresponding dimensions according to preset priority rules, the accuracy and completeness of volume and revenue statistics results are guaranteed.

[0009] Furthermore, S3 specifically includes: S31, responding to the indicator name, calculation method, and data source input by a non-technical user through a visual interface, automatically generating an executable calculation expression based on the table and field association relationships in the multidimensional relational data model through the metadata management unit; S32, executing the calculation expression at a preset frequency using a distributed computing framework to obtain the latest grid-level indicator value; S33, supporting adjustments to the personnel and grid affiliation relationships in the multidimensional relational data model, and automatically recalculating historical grid-level indicator values ​​based on the adjusted multidimensional relational data model.

[0010] The beneficial effects of adopting the above-mentioned further solutions are as follows: By automatically generating executable calculation expressions based on the multidimensional relational data model through the metadata management unit, non-technical personnel can define indicators through a visual interface with zero coding, which greatly shortens the indicator launch cycle and improves business response speed. By supporting the adjustment of personnel and grid affiliation relationships and automatically recalculating historical grid-level indicator values ​​based on the adjusted multidimensional relational data model, the accuracy and fairness of performance statistics are guaranteed.

[0011] Furthermore, S4 specifically includes: S41, setting a warning threshold for the indicator, triggering a warning when the grid-level indicator value continuously exceeds or falls below the threshold; S42, using an anomaly detection model trained based on an ensemble learning model to identify abnormal business scenarios and automatically generate an audit push notification as a type of warning information; S43, analyzing the association network of channels, personnel, and communities in the multidimensional relationship data model based on relationship network analysis technology, and recommending resource allocation strategies, including personnel deployment or activity suggestions.

[0012] The beneficial effects of adopting the above-mentioned further solutions are: by combining rule-based threshold early warning and anomaly detection based on ensemble learning models, it can simultaneously identify simple indicator anomalies and complex business anomaly scenarios, thereby improving the accuracy and coverage of anomaly detection. By analyzing the relationship network of channels, personnel and communities based on relational network analysis technology, it can automatically generate targeted resource allocation strategy suggestions.

[0013] Furthermore, S5 specifically includes: S51, calling the pre-configured incentive calculation model and reading the multi-dimensional adjustment coefficients, automatically calculating the incentive value of the responsible entity, and synchronizing the incentive value to the performance display interface in real time; S52, pushing the disposal task to the workbench of the corresponding responsible entity recorded in the multi-dimensional relationship data model in real time, the responsible entity provides feedback on the processing progress through the disposal task, and automatically notifies the initiator for acceptance after the task is completed, recording the entire process status of the disposal task from dispatch to execution to feedback.

[0014] The beneficial effects of adopting the above-mentioned further solution are: by pushing the handling tasks to the workbench of the corresponding responsible entity in real time and tracking the status of the entire process, the automatic dispatch, execution, feedback and acceptance of tasks are realized in a closed loop, which greatly improves the efficiency of problem handling.

[0015] Furthermore, the grid service capability support management method also includes: setting up a core scheduling layer, in which the interface differences of the first domestic database, the second domestic database, middleware and operating system are encapsulated through a domestic innovation adaptation engine, so as to decouple the construction of multi-dimensional relational data model and indicator calculation from the underlying basic environment, thereby supporting operation in a full-stack domestic innovation environment.

[0016] The beneficial effects of adopting the above-mentioned further solutions are as follows: by setting up a core scheduling layer and building a domestic IT innovation adaptation engine in the core scheduling layer, the interface differences of the underlying domestic database, middleware and operating system are encapsulated, so that the construction of the upper-layer multi-dimensional relational data model and the calculation of indicators are completely decoupled from the underlying basic environment. It can run smoothly in different full-stack domestic IT innovation environments without modifying the upper-layer business code, which greatly shortens the system migration cycle and reduces migration costs and risks.

[0017] Furthermore, this application also provides a grid operation and service capability support management system based on big data processing technology. The grid operation and service capability support management system includes: a data acquisition module, used to collect raw data of grid operation from multiple heterogeneous business systems through a data acquisition channel; a multi-dimensional relationship model construction module, connected to the output of the data acquisition module, used to clean and aggregate the collected raw data, and establish and store a multi-dimensional relationship data model containing personnel, grids, organizations, and income in a data storage system. The multi-dimensional relationship data model maps the relationships between personnel belonging to grids, income belonging to personnel, and relationships between grids and channel belonging to personnel through association fields; and a dynamic indicator engine module, connected to the output of the multi-dimensional relationship model construction module, used to respond to user indicator configuration operations and dynamically adjust the multi-dimensional relationship data model. The system generates the calculation logic for indicators and uses a distributed computing framework to calculate at least one grid-level indicator value. An intelligent analysis engine module, connected to the outputs of the dynamic indicator engine module and the multidimensional relationship model construction module, monitors and analyzes the data based on the multidimensional relationship data model and the grid-level indicator values ​​according to preset rules or a machine learning-based anomaly detection model. When warning conditions are met, it generates warning information and corresponding handling tasks, and recommends resource allocation strategies. An incentive and closed-loop execution module, connected to the outputs of the dynamic indicator engine module and the intelligent analysis engine module, automatically calculates incentive values ​​based on the multidimensional relationship data model and automatically dispatches the handling tasks to the responsible entities associated in the multidimensional relationship data model. It tracks the execution status and feedback results of the handling tasks, forming a closed-loop management system.

[0018] Furthermore, the multidimensional relationship model construction module includes: a unified data foundation unit, used to store raw data in a distributed file system, store the cleaned raw data in a first domestic database as the main database according to the grid dimension, and use a second domestic database as a disaster recovery database, and write the indicator results into the data storage system; a multidimensional relationship model establishment unit, used to establish personnel information table, grid affiliation table, income data table and channel information table in the first domestic database, and associate grid affiliation field through personnel information table, grid identifier and personnel identifier through income data table, and personnel identifier through channel information table to form full-link traceability, and is also used to automatically collect unattributed volume and revenue data to grid, county or city level according to preset priority rules, and synchronously update the collection results to the first domestic database main database and cache system; a security-level storage and access control unit, which encrypts sensitive fields and configures access permissions based on role or grid scope.

[0019] Furthermore, the dynamic indicator engine module includes: an indicator visualization configuration unit, used to respond to the indicator name, calculation method, and data source input by non-technical users through a visualization interface; an automatic calculation logic generation unit, used to automatically generate executable calculation expressions based on the table and field relationships in the multidimensional relational data model through the metadata management unit, and to support adjustments to the personnel and grid affiliation relationships in the multidimensional relational data model, and to automatically recalculate historical grid-level indicator values ​​based on the adjusted multidimensional relational data model; and a distributed computing unit, used to execute the calculation expressions at a preset frequency using a distributed computing framework to obtain the latest grid-level indicator values.

[0020] Furthermore, the intelligent analysis engine module includes: a rule threshold early warning unit, used to set early warning thresholds for indicators, triggering an early warning when the grid-level indicator value continuously exceeds or falls below the threshold; an anomaly detection model unit, used to identify abnormal business scenarios based on an anomaly detection model trained by an ensemble learning model, and automatically generate audit push notifications as a type of early warning information; and a strategy recommendation unit, used to analyze the relationship network of channels, personnel, and communities in the multidimensional relationship data model based on relationship network analysis technology, and recommend resource allocation strategies, including personnel deployment or activity suggestions.

[0021] Furthermore, the incentive and closed-loop execution module includes: an incentive calculation unit, used to call a pre-configured incentive calculation model and read multi-dimensional adjustment coefficients, automatically calculate the incentive value of the responsible entity, and synchronize the incentive value to the performance display interface in real time; and a disposal task management unit, used to push the disposal task to the workbench of the corresponding responsible entity recorded in the multi-dimensional relationship data model in real time, the responsible entity provides feedback on the processing progress through the disposal task, and automatically notifies the initiator for acceptance after the task is completed, recording the entire process status of the disposal task from dispatch to execution to feedback.

[0022] Furthermore, the grid-based operational service capability support management system also includes a core scheduling module, comprising a domestic IT innovation adaptation engine unit, used to encapsulate the interface differences between the first domestic database, the second domestic database, middleware, and operating system, thereby decoupling the multi-dimensional relational model construction module, the dynamic indicator engine module, the intelligent analysis engine module, and the incentive and closed-loop execution module from the underlying infrastructure environment, thus supporting the grid-based operational service capability support management system to run in a full-stack domestic IT innovation environment; the core scheduling module is connected to the multi-dimensional relational model construction module, the dynamic indicator engine module, the intelligent analysis engine module, and the incentive and closed-loop execution module respectively, for unified scheduling of data flow and task flow.

[0023] Furthermore, this application also provides an electronic device, including: a processor; a memory for storing executable instructions of the processor; wherein the processor is configured to execute the instructions to implement the grid service capability support management method based on big data processing technology.

[0024] Furthermore, this application also provides a computer-readable storage medium storing at least one computer program, which is loaded and executed by a processor to enable the computer to implement the grid service capability support management method based on big data processing technology. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating a grid-based operational service capability support management method based on big data processing technology according to the present invention. Figure 2 This is a schematic diagram of the structure of a grid-based operational service capability support management system based on big data processing technology according to the present invention. Figure 3 This is a schematic diagram of the structure of an electronic device according to the present invention. Detailed Implementation

[0026] The principles and features of the present invention are described below. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0027] Example 1: Reference Figure 1 This embodiment provides a grid operation and service capability support management method based on big data processing technology, including the following steps: S1, collecting raw data of grid operation from multiple heterogeneous business systems through data acquisition channels; S2, cleaning and aggregating the collected raw data, establishing and storing a multi-dimensional relationship data model containing the correlation between personnel, grids, organizations, and income in the data storage system, wherein the multi-dimensional relationship data model maps the relationships between personnel belonging to grids, income belonging to personnel, and grids and channels belonging to personnel in the entire link through the association fields; S3, responding to the user's indicator configuration operation, dynamically generating the calculation logic of indicators based on the multi-dimensional relationship data model, and calculating at least one grid-level indicator value using a distributed computing framework; S4, monitoring and analyzing according to preset rules based on the multi-dimensional relationship data model and the grid-level indicator value, generating early warning information and corresponding disposal tasks when the early warning conditions are met; S5, automatically dispatching the disposal tasks to the responsible entities associated in the multi-dimensional relationship data model, tracking the execution status and feedback results of the disposal tasks, and forming closed-loop management.

[0028] In another possible embodiment, raw data for grid operation is first collected from multiple heterogeneous business systems via data acquisition channels. These data acquisition channels specifically include a real-time data acquisition channel based on a Kafka message queue and a batch data acquisition channel based on a Flume log collector. The heterogeneous business systems specifically include core business systems such as a user center, line system, resource management system, CRM system, billing system, reward system, and performance system. The collected data covers all dimensions of grid operation data, including basic user information, network resource information, business processing information, revenue details, and performance points. Next, the collected raw data is cleaned and aggregated. Cleaning refers to removing duplicate data, completing missing data, correcting erroneous data, and organizing the data according to a unified... The process of standardizing business rules involves the aggregation of similar data scattered across different systems according to business dimensions. A multi-dimensional relational data model is established and stored in the data storage system, containing relationships between personnel, grids, organizations, and revenue. Through association fields, the relationships between personnel belonging to grids, revenue belonging to personnel and grids, and channel belonging to personnel are mapped across the entire chain, ensuring that any revenue data can be traced back to the corresponding handler, grid, and superior organization. Then, in response to user indicator configuration operations, the calculation logic for indicators is dynamically generated based on the multi-dimensional relational data model, and at least one grid-level indicator value is calculated using a distributed computing framework. This distributed computing framework specifically includes Apache for processing batch historical data. The Spark offline distributed computing framework and the Apache Flink real-time distributed computing framework for processing real-time streaming data are then used. Based on the multidimensional relational data model and the grid-level indicator values, monitoring and analysis are performed according to preset rules. The preset rules specifically include indicator threshold early warning rules and business anomaly detection rules based on machine learning. When the early warning conditions are met, early warning information and corresponding handling tasks are generated. The early warning information refers to prompts containing anomaly type, anomaly location, and anomaly details. The handling task refers to work instructions containing processing requirements, completion deadlines, and responsible entities. Finally, the handling tasks are automatically dispatched to the responsible entities associated in the multidimensional relational data model. The responsible entities refer to personnel or teams that undertake specific work responsibilities in the telecommunications operation system, specifically including grid managers, team leaders, community managers, installation and maintenance personnel, marketing personnel, etc. The execution status and feedback results of the handling tasks are tracked, forming a complete closed-loop management from data collection, indicator calculation, anomaly early warning to task handling.

[0029] By uniformly collecting raw data from multiple heterogeneous business systems and constructing a multi-dimensional relational data model that includes full-link mapping relationships of personnel, grids, organizations, and revenue, the problems of data silos and inaccurate revenue attribution have been completely solved. By responding to users' visual indicator configuration operations, the indicator calculation logic is automatically generated and the indicator calculation is completed using a distributed computing framework. This enables non-technical personnel to define and adjust indicators with zero coding, significantly improving business response speed. By automatically monitoring and analyzing based on the multi-dimensional relational data model and grid-level indicator values, early warning information and handling tasks are generated. The tasks are then automatically dispatched to the relevant responsible entities and the execution status is tracked to form a closed-loop management, comprehensively improving the efficiency and intelligence level of grid operations.

[0030] Preferably, in this embodiment, S2 specifically includes: S21, storing the original data in a distributed file system, storing the cleaned original data in a first domestic database (as the main database) according to grid dimensions, and using a second domestic database as a disaster recovery database, and writing the indicator results into the data storage system; S22, establishing a personnel information table, a grid affiliation table, an income data table, and a channel information table in the first domestic database, linking the grid affiliation field in the personnel information table, linking the grid identifier and personnel identifier in the income data table, and linking the personnel identifier in the channel information table to form a full-link traceability; S23, automatically aggregating the unattributed volume and revenue data to the grid, county, or city level dimensions according to preset priority rules, and synchronously updating the aggregation results to the first domestic database main database and cache system; S24, encrypting and storing sensitive fields, and configuring access permissions based on roles or grid scope.

[0031] In another possible embodiment, the collected raw data is first stored in a distributed file system, specifically HDFS. The cleaned raw data is then partitioned and stored in a first domestic database (specifically, DM V8 database) as the primary database, and a second domestic database (specifically OceanBase database) as the disaster recovery database. Data consistency between the primary and disaster recovery databases is ensured through a real-time data synchronization mechanism. High-frequency access metrics are written to the data storage system's cache system, which uses a high-performance Redis key-value pair cache to accelerate front-end query response. Next, a personnel information table, a grid affiliation table, an income data table, and a channel information table are created in the first domestic database. The personnel information table stores fields such as employee ID, name, position, grid ID, and start date. The grid affiliation table stores fields such as grid ID, grid name, grid address, district / county ID, and grid manager employee ID. The income data table stores income ID, detail number, income amount, income type, payment period, grid ID, and personnel ID. The channel information table stores fields such as channel ID, channel name, channel type, and responsible person's employee ID. The affiliation relationship between personnel and grids is mapped by associating the grid ID field of the personnel information table with the grid ID field of the grid affiliation table. Similarly, the affiliation relationship between income, grids, and personnel is mapped by associating the grid ID field of the income data table with the grid affiliation table, and the affiliation relationship between income, grids, and personnel is mapped by associating the responsible person's employee ID field of the channel information table with the employee ID field of the personnel information table. Finally, the binding relationship between channels and personnel is mapped by associating the responsible person's employee ID field of the channel information table with the employee ID field of the personnel information table. This system establishes a complete traceability chain from channels to personnel to grids to organizations and then to revenue. For revenue data with unclear attribution, it automatically aggregates data to grids, counties, or cities according to preset priority rules. These preset priority rules prioritize aggregating data to the finest-grained grid level; if data cannot be aggregated to a grid, it is aggregated to a county level; and if it cannot be aggregated to a county level, it is aggregated to a city level. The aggregation results are then synchronously updated to the first domestically produced database master database and cache system. Finally, sensitive fields involving user privacy and corporate secrets are encrypted and stored. These sensitive fields specifically include CB employee ID, salary, user ID card number, and user mobile phone number, encrypted using the AES-256 symmetric encryption algorithm. Access permissions are configured based on roles or grid scope. Users with different roles can only access data within their authorized scope; grid-level users can only access data within their own grid; and city-level users can access data from all subordinate districts, counties, and grids, thus achieving fine-grained data security control.

[0032] By collecting production and support relationship data through special projects, we have achieved precise binding of business data with personnel and grids, established a standardized data verification and preprocessing mechanism, unified the data format, and improved data quality.

[0033] Preferably, in this embodiment, S3 specifically includes: S31, responding to the indicator name, calculation method, and data source input by a non-technical user through a visual interface, automatically generating an executable calculation expression based on the table and field association relationships in the multidimensional relational data model through the metadata management unit; S32, executing the calculation expression at a preset frequency using a distributed computing framework to obtain the latest grid-level indicator value; S33, supporting adjustments to the personnel and grid affiliation relationships in the multidimensional relational data model, and automatically recalculating historical grid-level indicator values ​​based on the adjusted multidimensional relational data model.

[0034] In another possible embodiment, the system first responds to a non-technical user's input of the indicator name, calculation method, and data source via a visual interface. This visual interface refers to a web interface that provides drag-and-drop operation and formula editing functions, allowing users to define indicators without writing code. Then, a metadata management unit automatically parses the user-input indicator definition based on the table and field relationships in the multidimensional relational data model, generating an executable calculation expression. This metadata management unit is a functional module that stores and manages metadata information for all data tables, fields, and relationships, including table names, field names, field types, field meanings, and inter-table relationships. It can automatically identify the data involved in the indicator definition. The system first obtains the source and computational logic, then uses a distributed computing framework to execute the computational expression at a preset frequency to obtain the latest grid-level indicator values. The preset frequency can be set to hourly, daily, weekly, or monthly levels according to business needs. The distributed computing framework can distribute computational tasks to multiple nodes for parallel execution, significantly improving computational efficiency. Finally, it supports adjustments to the personnel and grid affiliation relationships in the multidimensional relational data model. When a user modifies the grid to which a person belongs or the superior organization of a grid in the system, the system automatically triggers a historical indicator recalculation task. Based on the adjusted multidimensional relational data model, it recalculates all historical grid-level indicator values ​​of the person and related grids within a specified time period and updates the historical data in the database to ensure the accuracy of performance statistics.

[0035] By automatically generating executable calculation expressions based on a multidimensional relational data model through the metadata management unit, non-technical personnel can define indicators through a visual interface without coding, which significantly shortens the indicator launch cycle and improves business response speed. By supporting the adjustment of personnel and grid affiliation relationships and automatically recalculating historical grid-level indicator values ​​based on the adjusted multidimensional relational data model, the accuracy and fairness of performance statistics are ensured, and the work enthusiasm of front-line personnel is effectively stimulated.

[0036] Preferably, in this embodiment, S4 specifically includes: S41, setting a warning threshold for the indicator, and triggering a warning when the grid-level indicator value continuously exceeds or falls below the threshold; S42, using an anomaly detection model trained based on an ensemble learning model to identify abnormal business scenarios and automatically generate an audit push notification as one type of warning information; S43, analyzing the association network of channels, personnel, and communities in the multidimensional relationship data model based on relationship network analysis technology, and recommending resource allocation strategies, including personnel deployment or activity suggestions.

[0037] In another possible embodiment, firstly, warning thresholds for indicators are set. When the grid-level indicator value continuously exceeds or falls below the threshold, an warning is triggered. Managers can flexibly adjust the warning thresholds for each indicator according to the characteristics of different businesses. Then, based on an anomaly detection model trained by an ensemble learning model, complex abnormal business scenarios such as inflated revenue, abnormal user churn, and points fraud are identified. The ensemble learning model is specifically an XGBoost gradient boosting tree model, which can automatically identify abnormal patterns in the data by learning the features of historical normal and abnormal samples. There is no need to manually write complex detection rules, and audit push notifications are automatically generated as a type of warning information. Then, the relationship network analysis technology is used to analyze the relationship network of channels, personnel, and communities in the multidimensional relationship data model. The relationship network analysis technology is specifically a graph computing technology based on the Neo4j graph database. Channels, personnel, and communities are treated as nodes of the graph, and the relationships between them are treated as edges of the graph. The relationship strength and business load between nodes are calculated to identify problems such as unreasonable support relationships, unbalanced personnel load, and insufficient resource allocation. Resource allocation strategies, including personnel increase, support relationship adjustment, and on-site promotional activities, are recommended to assist managers in optimizing grid resource allocation.

[0038] By combining rule-based threshold alerts with anomaly detection based on ensemble learning models, it can simultaneously identify simple indicator anomalies and complex business anomaly scenarios, improving the accuracy and coverage of anomaly detection. By analyzing the relationship network of channels, personnel, and communities based on relational network analysis technology, it automatically generates targeted resource allocation strategy suggestions to assist managers in making scientific decisions and improve resource utilization efficiency. By automatically generating audit push notifications and handling tasks, it ensures that abnormal issues can be discovered and handled in a timely manner.

[0039] Preferably, in this embodiment, S5 specifically includes: S51, calling the pre-configured incentive calculation model and reading the multi-dimensional adjustment coefficients, automatically calculating the incentive value of the responsible entity, and synchronizing the incentive value to the performance display interface in real time; S52, pushing the disposal task to the workbench of the corresponding responsible entity recorded in the multi-dimensional relationship data model in real time, the responsible entity provides feedback on the processing progress through the disposal task, and automatically notifies the initiator to accept the task after it is completed, recording the entire process status of the disposal task from dispatch to execution to feedback.

[0040] In another possible embodiment, a pre-configured incentive calculation model is first invoked and multi-dimensional adjustment coefficients are read to automatically calculate the incentive value for the responsible entity. The incentive calculation model defines incentive standards for different business types, and the multi-dimensional adjustment coefficients include business difficulty coefficients, regional coefficients, and key business adjustment coefficients. These coefficients can dynamically adjust the incentive value according to different business scenarios and regional characteristics. The specific calculation formula is: Reward Points = Business Sales Volume × Unit Price Coefficient × Regional Coefficient × Key Business Adjustment Coefficient. The unit price coefficient is set according to the value of different businesses, the regional coefficient is set according to the market difficulty of different regions, and the key business adjustment coefficient is set according to the company's current key business direction. The calculated incentive value is synchronized to the performance display interface in real time, allowing frontline staff to view it at any time. The system provides real-time incentives and details, and then pushes the assigned tasks to the workbench of the corresponding responsible entity recorded in the multi-dimensional relational data model. The responsible entities include grid managers, team leaders, community managers, installation and maintenance personnel, etc. The system automatically matches the corresponding responsible entity according to the task type and realizes real-time task push through the WebSocket protocol. The responsible entity provides feedback on the processing progress through the task and uploads supporting materials during the processing, such as on-site photos, user signed confirmation forms, rectification reports, etc. After the task is completed, the system automatically notifies the initiator for acceptance. After the initiator approves the acceptance, the task is closed. The system records the entire process status of the task from dispatch to execution to feedback, forming a complete closed-loop management. All task records can be traced and queried for subsequent assessment and review.

[0041] By calling the pre-configured incentive calculation model and reading multi-dimensional adjustment coefficients to automatically calculate the incentive value of the responsible entity, the incentive calculation is automated and real-time, ensuring the fairness and timeliness of the incentive, effectively stimulating the work enthusiasm of front-line personnel. By pushing the handling tasks to the work platform of the corresponding responsible entity in real time and tracking the status of the entire process, the automatic assignment, execution, feedback and acceptance of tasks are realized in a closed loop, which greatly improves the efficiency of problem handling.

[0042] Preferably, in this embodiment, the grid service capability support management method further includes: setting up a core scheduling layer, in which the interface differences of the first domestic database, the second domestic database, middleware and operating system are encapsulated through a domestic innovation adaptation engine, so that the construction of multi-dimensional relational data model and indicator calculation are decoupled from the underlying basic environment, thereby supporting operation in a full-stack domestic innovation environment.

[0043] In another possible embodiment, the grid service capability support management method further includes setting up a core scheduling layer. In this core scheduling layer, an IT innovation adaptation engine encapsulates the interface differences between the first domestic database, the second domestic database, middleware, and the operating system. The IT innovation adaptation engine refers to middleware that provides a unified standard calling interface, capable of shielding interface differences between different domestic software and hardware. Specifically, the middleware is TongWeb middleware, and the operating system is either the Galaxy Kylin operating system or the Tongxin UOS operating system. The IT innovation adaptation engine provides a unified standard calling interface, allowing upper-layer business modules to access underlying resources by calling the standard interface without needing to concern themselves with the specific underlying software and hardware implementation. This decouples the construction of the multi-dimensional relational data model and the calculation of indicators from the underlying infrastructure environment, thereby supporting the grid service capability support management method to run in a full-stack IT innovation environment. When it is necessary to replace the underlying domestic database, middleware, or operating system, only the corresponding interface encapsulation in the IT innovation adaptation engine needs to be updated, without modifying any upper-layer business code, achieving transparent replacement of the underlying environment.

[0044] By setting up a core scheduling layer and building a domestic IT innovation adaptation engine within it, and encapsulating the interface differences of the underlying domestic database, middleware, and operating system, the construction of the upper-layer multidimensional relational data model and the calculation of indicators are completely decoupled from the underlying basic environment. This allows for smooth operation in different full-stack domestic IT innovation environments without modifying the upper-layer business code, significantly shortening the system migration cycle, reducing migration costs and risks, and fully meeting national domestic IT innovation compliance requirements.

[0045] Example 2: Reference Figure 2This embodiment also provides a grid operation and service capability support management system based on big data processing technology. The grid operation and service capability support management system includes: a data acquisition module, used to collect raw data of grid operation from multiple heterogeneous business systems through a data acquisition channel; a multi-dimensional relationship model construction module, connected to the output of the data acquisition module, used to clean and aggregate the collected raw data, and establish and store a multi-dimensional relationship data model containing personnel, grids, organizations, and income in a data storage system. The multi-dimensional relationship data model maps the relationships between personnel belonging to grids, income belonging to personnel, and the relationships between grids and channel belonging to personnel through association fields; and a dynamic indicator engine module, connected to the output of the multi-dimensional relationship model construction module, used to dynamically generate indicators based on the multi-dimensional relationship data model in response to user indicator configuration operations. The system implements the calculation logic for indicators and uses a distributed computing framework to calculate at least one grid-level indicator value. An intelligent analysis engine module, connected to the outputs of the dynamic indicator engine module and the multidimensional relationship model construction module, monitors and analyzes the multidimensional relationship data model and the grid-level indicator values ​​according to preset rules or a machine learning-based anomaly detection model. When warning conditions are met, it generates warning information and corresponding handling tasks, and recommends resource allocation strategies. An incentive and closed-loop execution module, connected to the outputs of the dynamic indicator engine module and the intelligent analysis engine module, automatically calculates incentive values ​​based on the multidimensional relationship data model, automatically dispatches the handling tasks to the responsible entities associated in the multidimensional relationship data model, tracks the execution status and feedback results of the handling tasks, and forms closed-loop management.

[0046] Preferably, in this embodiment, the multidimensional relationship model construction module includes: a unified data foundation unit, used to store the raw data in a distributed file system, store the cleaned raw data in a first domestic database as the main database according to the grid dimension, and use a second domestic database as a disaster recovery database, and write the indicator results into the data storage system; a multidimensional relationship model establishment unit, used to establish a personnel information table, a grid affiliation table, an income data table, and a channel information table in the first domestic database, and form a full-link traceability by associating the grid affiliation field in the personnel information table, associating the grid identifier and personnel identifier in the income data table, and associating the personnel identifier in the channel information table, and also used to automatically collect the unattributed volume and revenue data to the grid, county, or city level according to a preset priority rule, and synchronously update the collection results to the first domestic database main database and cache system; and a security-level storage and access control unit, which encrypts and stores sensitive fields and configures access permissions based on roles or grid scope.

[0047] Preferably, in this embodiment, the dynamic indicator engine module includes: an indicator visualization configuration unit, used to respond to the indicator name, calculation method, and data source input by a non-technical user through a visualization interface; an automatic calculation logic generation unit, used to automatically generate executable calculation expressions based on the table and field association relationships in the multidimensional relational data model through the metadata management unit, and to support adjustments to the personnel and grid affiliation relationships in the multidimensional relational data model, and to automatically recalculate historical grid-level indicator values ​​based on the adjusted multidimensional relational data model; and a distributed computing unit, used to execute the calculation expressions at a preset frequency using a distributed computing framework to obtain the latest grid-level indicator values.

[0048] Preferably, in this embodiment, the intelligent analysis engine module includes: a rule threshold early warning unit, used to set early warning thresholds for indicators, and trigger an early warning when the grid-level indicator value continuously exceeds or falls below the threshold; an anomaly detection model unit, used to identify abnormal business scenarios based on an anomaly detection model trained by an ensemble learning model, and automatically generate an audit push notification as a type of early warning information; and a strategy recommendation unit, used to analyze the association network of channels, personnel, and communities in the multidimensional relationship data model based on relationship network analysis technology, and recommend resource allocation strategies, including personnel deployment or activity suggestions.

[0049] Preferably, in this embodiment, the incentive and closed-loop execution module includes: an incentive calculation unit, used to call a pre-configured incentive calculation model and read multi-dimensional adjustment coefficients, automatically calculate the incentive value of the responsible entity, and synchronize the incentive value to the performance display interface in real time; and a disposal task management unit, used to push the disposal task to the workbench of the corresponding responsible entity recorded in the multi-dimensional relationship data model in real time, the responsible entity provides feedback on the processing progress through the disposal task, and automatically notifies the initiator for acceptance after the task is completed, recording the entire process status of the disposal task from dispatch to execution to feedback.

[0050] Preferably, in this embodiment, the grid service capability support management system further includes: a core scheduling module, including a domestic IT innovation adaptation engine unit, used to encapsulate the interface differences between the first domestic database, the second domestic database, middleware, and operating system, so that the multi-dimensional relationship model construction module, the dynamic indicator engine module, the intelligent analysis engine module, and the incentive and closed-loop execution module are decoupled from the underlying basic environment, thereby supporting the grid service capability support management system to run in a full-stack domestic IT innovation environment; the core scheduling module is connected to the multi-dimensional relationship model construction module, the dynamic indicator engine module, the intelligent analysis engine module, and the incentive and closed-loop execution module respectively, for unified scheduling of data flow and task flow.

[0051] Example 3 First, the system administrator logs into the provincial management platform to complete the system initialization configuration. Through the core scheduling module's domestic IT adaptation engine unit, interface encapsulation and adaptation verification are completed with the DM V8 database, OceanBase database, TongWeb middleware, and the Galaxy Kylin operating system. This includes steps such as testing database connections, middleware deployment, and operating system compatibility testing, ensuring that all upper-layer business modules can access underlying resources through a unified standard interface without needing to concern themselves with specific hardware and software implementations. Next, the data acquisition module connects to the user center, line system, resource management system, and CRM system through a real-time data acquisition channel based on a Kafka message queue. Real-time data such as user account opening, cancellation, package changes, work order status changes, and resource usage are written in standard JSON format to corresponding topics in Kafka. Each business system corresponds to an independent topic, and data is collected via Flume. The log collector's batch data collection channel connects to the billing system, reward system, and performance system, periodically pulling grid-level revenue data, personnel points data, and salary data. It also specifically collects production relationship data and support relationship data. Production relationship data includes channel codes, developer codes, CB employee numbers, level 7 addresses, project codes, ICT projects, and detailed numbers. Support relationship data includes the binding relationship between community manager employee numbers and neighborhood codes. Then, the unified data foundation unit of the multi-dimensional relationship model construction module stores the raw data in fixed-size file blocks on the HDFS distributed file system. Each file block is stored in multiple copies to ensure data reliability. The cleaned data is then partitioned and stored in the DM V8 main database according to grid dimensions, with each grid corresponding to an independent data table. Simultaneously, data is synchronized to the OceanBase disaster recovery database through a real-time synchronization mechanism. When the main database fails, the disaster recovery database can quickly switch to the main database, ensuring uninterrupted business operations. High-frequency access metrics results are written to the Redis cache, and reasonable time limits are set. To ensure data consistency, the multidimensional relationship model building unit creates personnel information tables, grid affiliation tables, revenue data tables, and channel information tables in the Dameng V8 main database. A full-link mapping relationship between personnel, grids, organizations, and revenue is established through foreign key associations. Data with unclear affiliation is automatically aggregated according to the priority order of community → grid → district / county → city. Specifically, it first matches the specific community based on the seven-level address corresponding to the detailed number, then assigns it to the corresponding grid. If no community can be matched, it is assigned to the corresponding district / county. If still no match can be found, it is assigned to the city-level branch. The security-level storage and access control unit uses the AES-256 algorithm to encrypt sensitive fields such as CB employee ID, salary, and user ID number. The CB employee ID is a unique code used internally by the telecom operator's billing system to uniquely identify business personnel. The encryption key is uniformly managed by a dedicated key management system and rotated periodically. Role-based access control is also configured, with predefined roles such as provincial administrator, city administrator, grid administrator, ordinary employee, and auditor.Grid administrators can only view data for their own grid, while city-level administrators can view data for all subordinate districts and grids. Auditors can only view audit logs and abnormal data. The dynamic indicator engine module's indicator visualization configuration unit then receives the "Grid Broadband Penetration Rate" indicator definition from district / county-level operations managers via a drag-and-drop interface. The operations manager drags the "Broadband Users" and "Total Households in the Community" fields from the left-hand field list to the right-hand formula editing area, enters the calculation formula "Broadband Users / Total Households in the Community × 100%", sets the statistical period to daily, and the automatic calculation logic generation unit parses the indicator definition through the metadata management unit, automatically identifies the data source as the revenue data table and the grid affiliation table, and generates the corresponding Spark data. The SQL calculation expression is processed by a distributed computing unit, which executes calculation tasks daily at midnight to obtain the broadband penetration rate grid-level indicator value for each grid. When the operations manager adjusts the grid to which a community manager belongs in the system, the system automatically triggers a historical indicator recalculation task. Based on the updated multidimensional relational data model, the system recalculates the historical grid-level indicator values ​​for the community manager and related grids over the past three months and updates the database. Subsequently, the rule threshold early warning unit of the intelligent analysis engine module detects that the broadband penetration rate of a certain grid is lower than the preset threshold for three consecutive days, triggering a level one early warning. The anomaly detection model unit, based on the XGBoost ensemble learning model, extracts features such as the grid's revenue growth rate, user churn rate, business processing volume, and complaint volume, identifying abnormal business scenarios where the grid has abnormal user churn. It automatically generates an audit push notification and sends it to the grid manager's workbench and mobile phone SMS. The strategy recommendation unit, based on the relational network analysis technology of the Neo4j graph database, constructs a channel-personnel-community relationship network graph for the grid, calculates the business load and coverage scope of each person, and if it is found that two installation and maintenance personnel in the grid are responsible for 12 The installation and maintenance workload in this community is significantly higher than average, and there is overlap in the coverage relationships of three communities. A resource allocation suggestion is generated to dispatch one additional installation and maintenance personnel and adjust the coverage relationships of two communities. Finally, the task management unit of the incentive and closed-loop execution module automatically generates a task containing anomaly details, processing requirements, and a 72-hour completion deadline. This task is pushed in real-time to the grid leader's mobile workbench and PC to-do list via WebSocket protocol. After receiving the task, the grid leader requests additional installation and maintenance personnel from their superiors and organizes team members to conduct on-site promotional activities in communities with high user churn. Team members upload marketing results data in real-time via mobile applications, including business transaction numbers, customer identifiers, and processing times. The system automatically updates the grid-level broadband penetration rate indicator value for this grid. After the task is completed, the grid leader uploads on-site promotional photos and user follow-up records as supporting materials. The system automatically notifies the district / county-level operations manager for acceptance. After reviewing the processing results and supporting materials, the operations manager confirms acceptance, closes the task, and records the entire process status. The incentive calculation unit then calls the pre-configured incentive calculation model.The system reads the business difficulty coefficient, rural area coefficient, and broadband business priority adjustment coefficient for the region. It then automatically calculates the team members' reward points according to the formula "Reward Points = Business Sales Volume × Unit Price Coefficient × Regional Coefficient × Priority Business Adjustment Coefficient" and synchronizes them in real-time to the performance display interface. This serves as the basis for month-end salary distribution and performance evaluation.

[0052] Example 4 This grid-based operational service capability support management system also supports the construction of a five-level linkage architecture: provincial, municipal, district / county, grid, and team. First, the provincial platform receives the annual operational targets issued by the group, formulates unified business rules, indicator systems, and data standards for the entire province, and builds a province-wide dynamic indicator engine framework. Through a provincial overview screen, it displays summary data and trend charts of the province's indicator completion status, supports multi-dimensional filtering and analysis by city, business type, and grid type, coordinates and allocates resources across the province, and handles major cross-city resource collaboration requests. Then, the municipal platform automatically inherits the rules and indicators from the provincial platform. The system expands personalized indicators based on the city's specific circumstances. For example, a new indicator, "smart home product penetration rate," has been added to reflect the city's unique business characteristics. Annual operating targets issued by the provincial government are broken down into administrative divisions for each district and county, specifying target values ​​and deadlines for each. The city-level monitoring dashboard displays the indicator status of each district, allowing for supervision of lagging districts and counties and coordinating cross-district resource collaboration. District and county-level platforms then inherit the city-level indicator templates and expand with localized personalized indicators. For instance, a new indicator, "enterprise broadband penetration rate," has been added for industrial parks within the district, with trigger conditions set for indicator anomalies. When the district / county-level engine detects anomalies... When an anomaly is detected in a grid's metrics, a resource coordination task order is automatically generated, and the warning work order is distributed to the corresponding grid. The district-level resource management interface is then invoked to coordinate the allocation of personnel, finances, and materials within the jurisdiction, providing necessary resource support to the grid. Regular district / county operational daily reports are generated and pushed to district / county managers and all grid leaders. Subsequently, the grid-level platform receives metric tasks and warning work orders from higher levels, executes specific data collection and event handling, and allows grid leaders to customize micro-metrics based on the actual situation of their grid, such as "success rate of door-to-door marketing in the community." The calculation results of these customized metrics are fed back to the higher-level platform in real time. To provide more granular data support for superior decision-making, timely report problems and resource needs encountered in operations, and finally, the team-level platform undertakes marketing tasks assigned by the grid. The tasks include detailed information such as target communities, marketing activity types, performance indicators, completion deadlines, and reward rules. Team members receive tasks through mobile applications, perform front-line marketing and customer service work, and report marketing data and performance progress in real time. Team leaders can view the real-time performance rankings and points of team members through mobile devices and adjust work arrangements in a timely manner, forming a two-way flow mechanism of real-time aggregation of upward data and rapid penetration of downward instructions.This system also provides standardized open interface capabilities, offering JDBC and RESTful interfaces through the core scheduling module. The JDBC interface is used by external systems to securely query and write data to the DM V8 and OceanBase databases, supporting fine-grained access control that can restrict external systems to access only specified tables and fields. The RESTful interface is used to interface with external business systems such as the user center, data collection system, and billing system. All interfaces use HTTPS for encrypted transmission, and requests and responses use standard JSON format. Interface documentation is automatically generated using Swagger, and dynamic indicator configuration is available to district / county and team-level platforms. The system utilizes a programmable interface (API) that allows users to customize reports and dashboards through a drag-and-drop interface. The backend automatically parses metadata and generates query services. An open API for registering alert rules allows users to submit rule definitions that include the rule name, dynamic indicator identifier, threshold comparison operator, threshold value, notification method, and a list of notify recipients. The core scheduling engine loads these rules into the in-memory rule library, monitors changes in corresponding indicators in real time, and automatically calls the to-do push API to generate to-do tasks when the indicator changes meet the rule conditions. It then notifies the relevant responsible parties according to the user-specified notification method. This further enhances the system's flexibility and scalability, enabling it to meet the differentiated operational needs of different cities and grids.

[0053] In some embodiments, the grid service capability support management system based on big data processing technology of the present invention can be implemented in a combination of hardware and software. As an example, the grid service capability support management system based on big data processing technology of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the grid service capability support management method based on big data processing technology of the present invention. For example, the processor in the form of a hardware decoding processor can be one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0054] The modules described in the embodiments of this invention can be implemented in software or hardware. The names of the modules are not, in some cases, limiting the scope of the module itself.

[0055] An electronic device according to an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-mentioned grid service capability support management methods based on big data processing technology. That is, an electronic device according to an embodiment of the present invention may include, but is not limited to: a processor and a memory; the memory is used to store the computer program; the processor is used to execute the grid service capability support management method based on big data processing technology shown in any embodiment of the present invention by calling the computer program.

[0056] In one alternative embodiment, an electronic device is provided, such as Figure 3 As shown, Figure 3 The illustrated electronic device 4000 includes a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0057] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), a FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in connection with this disclosure. Processor 4001 may also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0058] Bus 4002 may include a path for transmitting information between the aforementioned components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 4002 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3The bus 4002 is represented by only one thick line, but this does not mean that there is only one bus or one type of bus.

[0059] The memory 4003 may be a ROM (Read-Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or it may be an EEPROM (Electrically Erasable Programmable Read-Only Memory), a CD-ROM (Compact Disc Read-Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0060] The memory 4003 stores application code (computer program) for executing the present invention, and its execution is controlled by the processor 4001. The processor 4001 executes the application code stored in the memory 4003 to implement the content shown in the foregoing method embodiments.

[0061] Among them, electronic devices can also be terminal devices, which can be any device that can install applications, including at least one of smartphones, tablets, laptops, desktop computers, smart speakers, smartwatches, smart TVs, and smart in-vehicle devices.

[0062] It should be noted that, Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0063] An embodiment of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the above-mentioned grid service capability support management methods based on big data processing technology.

[0064] Alternatively, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device, etc.

[0065] In an exemplary embodiment, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. The processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the aforementioned grid service capability support management method based on big data processing technology.

[0066] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0067] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0068] The computer-readable storage medium provided in this invention can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EEPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0069] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the method shown in the above embodiments.

[0070] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

[0071] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and do not imply a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that shown or described.

[0072] Those skilled in the art will recognize that this invention can be implemented as a system, method, or computer program product. Therefore, this invention can be specifically implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, this invention can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0073] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A grid-based operational service capability support management method based on big data processing technology, characterized in that, Includes the following steps: S1. Collect raw data of grid operation from multiple heterogeneous business systems through the data acquisition channel; S2. Clean and aggregate the collected raw data, and establish and store a multi-dimensional relational data model containing the relationships between personnel, grids, organizations, and income in the data storage system. The multi-dimensional relational data model maps the relationships between personnel belonging to grids, income belonging to personnel, and grids and channels belonging to personnel through the association fields. S3. In response to the user's indicator configuration operation, dynamically generate the indicator calculation logic based on the multidimensional relational data model, and use the distributed computing framework to calculate at least one grid-level indicator value. S4. Based on the multidimensional relationship data model and the grid-level indicator values, perform monitoring and analysis according to preset rules, and generate early warning information and corresponding handling tasks when the early warning conditions are met. S5. Automatically dispatch the disposal tasks to the responsible entities associated in the multidimensional relational data model, track the execution status and feedback results of the disposal tasks, and form a closed-loop management.

2. The grid-based operational service capability support management method based on big data processing technology according to claim 1, characterized in that, S2 specifically includes: S21. Store the raw data in a distributed file system, divide the cleaned raw data into databases and tables according to the grid dimension and store it in the first domestic database as the main database, and use the second domestic database as the disaster recovery database. Write the indicator results into the cache system of the data storage system. S22. Establish a personnel information table, a grid affiliation table, an income data table, and a channel information table in the first domestic database. Link the grid affiliation field in the personnel information table, link the grid identifier and personnel identifier in the income data table, and link the personnel identifier in the channel information table to form a full-chain traceability. S23. For unattributed data, automatically collect it to the grid, county or city level according to the preset priority rules, and update the collection results to the first domestic database master and cache system. S24. Encrypt sensitive fields and configure access permissions based on roles or grid scopes.

3. The grid-based operational service capability support management method based on big data processing technology according to claim 1, characterized in that, S3 specifically includes: S31. In response to the indicator name, calculation method and data source input by non-technical users through the visual interface, the metadata management unit automatically generates an executable calculation expression based on the table and field association relationship in the multidimensional relational data model. S32. Execute the calculation expression at a preset frequency using a distributed computing framework to obtain the latest grid-level index value; S33. Supports adjusting the personnel and grid affiliation relationships in the multidimensional relational data model, and automatically recalculates historical grid-level index values ​​based on the adjusted multidimensional relational data model.

4. The grid-based operational service capability support management method based on big data processing technology according to claim 1, characterized in that, S4 specifically includes: S41. Set the warning threshold for the indicator. When the value of the grid-level indicator exceeds or falls below the threshold for a consecutive period, a warning will be triggered. S42. An anomaly detection model trained based on an ensemble learning model identifies abnormal business scenarios and automatically generates an audit push notification as a type of early warning information. S43. Analyze the relationship network of channels, personnel and communities in the multidimensional relationship data model based on relationship network analysis technology, and recommend resource allocation strategies, including personnel increase or activity suggestions.

5. The grid-based operational service capability support management method based on big data processing technology according to claim 1, characterized in that, S5 specifically includes: S51. Call the pre-configured incentive calculation model and read the multi-dimensional adjustment coefficients, automatically calculate the incentive value of the responsible entity, and synchronize the incentive value to the performance display interface in real time. S52. The disposal task is pushed to the workbench of the corresponding responsible entity recorded in the multidimensional relationship data model in real time. The responsible entity provides feedback on the processing progress through the disposal task. After the task is completed, the initiator is automatically notified for acceptance. The entire process status of the disposal task from dispatch to execution to feedback is recorded.

6. The grid-based operational service capability support management method based on big data processing technology according to claim 2, characterized in that, The grid-based operational service capability support management method also includes: A core scheduling layer is set up, and the interface differences of the first domestic database, the second domestic database, middleware and operating system are encapsulated through the domestic innovation adaptation engine in the core scheduling layer. This decouples the construction of multi-dimensional relational data models and index calculation from the underlying basic environment, thereby supporting operation in a full-stack domestic innovation environment.

7. A grid-based operational service capability support management system based on big data processing technology, characterized in that, A grid service capability support management method based on big data processing technology as described in any one of claims 1 to 6, wherein the grid service capability support management system comprises: The data acquisition module is used to collect raw data on grid operations from multiple heterogeneous business systems through the data acquisition channel; A multidimensional relationship model construction module is connected to the output end of the data acquisition module. It is used to clean and collect the raw data collected, and to establish and store a multidimensional relationship data model containing personnel, grids, organizations, and income in the data storage system. The multidimensional relationship data model maps the relationships between personnel belonging to grids, income belonging to personnel, grids, and channel belonging to personnel in the entire chain through association fields. The dynamic indicator engine module is connected to the output of the multidimensional relationship model construction module. It is used to respond to the user's indicator configuration operation, dynamically generate the indicator calculation logic based on the multidimensional relationship data model, and use a distributed computing framework to calculate at least one grid-level indicator value. The intelligent analysis engine module is connected to the output of the dynamic indicator engine module and the output of the multidimensional relationship model construction module, respectively. It is used to monitor and analyze the data based on the multidimensional relationship data model and the grid-level indicator values ​​according to preset rules. When the early warning conditions are met, it generates early warning information and corresponding handling tasks, and recommends resource allocation strategies. The incentive and closed-loop execution module is connected to the output ends of the dynamic indicator engine module and the intelligent analysis engine module, respectively. It is used to automatically dispatch the disposal task to the responsible entity associated in the multidimensional relational data model, track the execution status and feedback results of the disposal task, and form a closed-loop management.

8. The grid-based operational service capability support management system based on big data processing technology according to claim 7, characterized in that, The multidimensional relationship model construction module includes: The unified data foundation unit is used to store the raw data in a distributed file system, and to store the cleaned raw data in a grid-based database as the main database and a second domestic database as the disaster recovery database. The indicator results are written into the cache system of the data storage system. The multidimensional relationship model establishment unit is used to establish a personnel information table, a grid affiliation table, an income data table, and a channel information table in the first domestic database. It associates the grid affiliation field with the personnel information table, the grid identifier with the personnel identifier with the income data table, and the personnel identifier with the channel information table to form a full-link traceability. It is also used to automatically collect the volume and income data that is not clearly affiliated to the grid, county, or city level according to the preset priority rules, and synchronize the collection results to the main database and cache system of the first domestic database. The security-tiered storage and access control unit encrypts and stores sensitive fields, and configures access permissions based on roles or grid scopes.

9. A grid-based operational service capability support management system based on big data processing technology according to claim 7, characterized in that, The dynamic indicator engine module includes: The indicator visualization configuration unit is used to respond to non-technical users by inputting the indicator name, calculation method, and data source through the visual interface; The automatic calculation logic generation unit is used to automatically generate executable calculation expressions based on the table and field association relationships in the multidimensional relational data model through the metadata management unit, and supports the adjustment of personnel and grid affiliation relationships in the multidimensional relational data model, and automatically recalculates historical grid-level indicator values ​​based on the adjusted multidimensional relational data model. A distributed computing unit is used to execute the computation expression at a preset frequency using a distributed computing framework to obtain the latest grid-level index value.

10. A grid-based operational service capability support management system based on big data processing technology according to claim 7, characterized in that, The intelligent analysis engine module includes: The rule threshold early warning unit is used to set the early warning threshold for indicators. An early warning is triggered when the grid-level indicator value continuously exceeds or falls below the threshold. An anomaly detection model unit is used to identify anomaly detection models trained based on ensemble learning models, identify abnormal business scenarios, and automatically generate audit push notifications as a type of early warning information. The strategy recommendation unit is used to analyze the relationship network of channels, personnel and communities in the multidimensional relationship data model based on relationship network analysis technology, and recommend resource allocation strategies, including personnel increase or activity suggestions.

11. A grid-based operational service capability support management system based on big data processing technology according to claim 7, characterized in that, The incentive and closed-loop execution module includes: The incentive calculation unit is used to call the pre-configured incentive calculation model and read the multi-dimensional adjustment coefficients, automatically calculate the incentive value of the responsible entity, and synchronize the incentive value to the performance display interface in real time. The task management unit is used to push the task in real time to the workbench of the corresponding responsible entity recorded in the multidimensional relational data model. The responsible entity provides feedback on the processing progress through the task. After the task is completed, the initiator is automatically notified for acceptance. The unit records the entire process status of the task from dispatch to execution to feedback.

12. The grid-based operational service capability support management system based on big data processing technology according to claim 8, characterized in that, The grid-based operational service capability support management system also includes: The core scheduling module includes a domestic IT innovation adaptation engine unit, which is used to encapsulate the interface differences between the first domestic database, the second domestic database, middleware and operating system, so that the multi-dimensional relationship model construction module, the dynamic indicator engine module, the intelligent analysis engine module and the incentive and closed-loop execution module are decoupled from the underlying basic environment, thereby supporting the grid operation and service capability support management system to run in a full-stack domestic IT innovation environment; The core scheduling module is connected to the multidimensional relationship model construction module, the dynamic indicator engine module, the intelligent analysis engine module, and the incentive and closed-loop execution module, respectively, for unified scheduling of data flow and task flow.

13. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the grid service capability support management method based on big data processing technology as described in any one of claims 1-6.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one computer program, which is loaded and executed by a processor to enable the computer to implement the grid service capability support management method based on big data processing technology as described in any one of claims 1-6.

Images