A method, apparatus, device and medium for generating a mesh report

Abstract

The present disclosure provides a grid report generation method, device, equipment and medium, the method comprising: constructing an index knowledge graph for a grid scene; collecting index data of all grids through a timing task scheduling, and updating the index knowledge graph using the index data; obtaining corresponding index analysis dimensions and indexes under each dimension from the updated index knowledge graph according to the role permission of the current user and the grid attribution, and analyzing the obtained indexes based on dynamic prompt words and a preset large model to obtain short-board indexes; obtaining strategy suggestions corresponding to the short-board indexes based on a preset strategy knowledge base using a strategy recommendation agent; and matching and filtering effective ability content from an ability knowledge base based on a time window using an ability training agent, and generating a latest ability text summary. According to the embodiments of the present disclosure, the efficiency of grid daily operation and the scientific nature of decision-making can be significantly improved.

Description

Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to a method, apparatus, device and medium for generating grid reports. Background Technology

[0002] In the grid-based operation and management of telecom operators, the daily "routine management and scheduling meetings" (such as morning meetings and work deployment meetings) are the core scheduling and command links. The main purposes of the meetings include reviewing yesterday's tasks, analyzing current key performance indicators (KPIs), deploying today's work, and disseminating recent capabilities. It is the core mechanism for the grid CEO to achieve efficient management and rapid response of the grid.

[0003] However, existing methods for generating grid reports have the following drawbacks:

[0004] 1) Templated output cannot meet personalized needs: Existing solutions generally rely on fixed report templates, which cannot be dynamically adjusted according to grid characteristics, resulting in missing key information, rigid analysis dimensions, poor relevance of the generated report content, low practical value, and difficulty in meeting the refined needs of personalized grid management.

[0005] 2) Fragmented scenarios and lack of end-to-end closed loop: Data acquisition, indicator analysis, and strategy recommendations operate in isolation, resulting in the analysis results not being accurately applied to marketing decisions and failing to form an end-to-end closed loop.

[0006] 3) Lack of intelligence and insufficient depth of analysis: Relying on human experience or simple rule engines, it lacks the ability to perform in-depth analysis and decision support driven by AI (Artificial Intelligence), making it difficult to achieve accurate and quantifiable business insights. Summary of the Invention

[0007] This disclosure provides a method, apparatus, device, and medium for generating grid reports, which addresses the problems of templated output, scene fragmentation, and lack of intelligence in existing grid report generation methods.

[0008] In a first aspect, this disclosure provides a method for generating a grid report, the method comprising:

[0009] For grid-based scenarios, construct an indicator knowledge graph;

[0010] By scheduling timed tasks, indicator data from all grids is collected, and the indicator knowledge graph is updated using the indicator data.

[0011] The indicator analysis agent obtains the corresponding indicator analysis dimensions and indicators under each dimension from the updated indicator knowledge graph based on the current user's role permissions and grid affiliation. It then analyzes the obtained indicators based on dynamic prompts and a preset large model to obtain the weak indicators.

[0012] A strategy recommendation agent is used to obtain strategy suggestions corresponding to the aforementioned shortcomings based on a pre-set strategy knowledge base.

[0013] The intelligent agent, through capability training, selects effective capability content from the capability knowledge base based on time window matching and generates the latest capability text summary.

[0014] A corresponding grid report is generated based on the aforementioned weakness indicators, strategy recommendations, and the latest capability text summary.

[0015] Furthermore, before constructing the indicator knowledge graph for the grid scenario, the method further includes:

[0016] Construct a four-tiered organizational structure: city / prefecture, district / county, grid, and personnel.

[0017] Define different roles and their corresponding permissions, wherein the roles include at least one of the following: Grid CEO, Integrated Marketing Manager, Smart Home Engineer, and Channel Manager.

[0018] Furthermore, the analysis of the acquired indicators based on dynamic prompts and a pre-set large model yields the weakness indicators, specifically including:

[0019] A dynamic prompting project is constructed, which includes: basic instructions for grid and personnel basic analysis dimensions, personalized enhancement instructions defined by the current user, and a small number of sample Few-shot injection instructions based on historical analysis result examples;

[0020] The dynamic prompting project and the acquired indicators are input into a preset large model, which drives the large model to analyze the acquired indicators from multiple dimensions and generate indicator analysis content in a preset format.

[0021] Based on the analysis of the indicators, and in accordance with the preset shortcoming diagnosis rules, shortcoming indicators are diagnosed to obtain the corresponding shortcoming indicators.

[0022] Furthermore, after performing shortcoming indicator diagnosis based on the indicator analysis content and a preset shortcoming diagnosis rule to obtain the corresponding shortcoming indicator, the method further includes:

[0023] For the aforementioned shortcomings, a root cause analysis was conducted at the personnel level, and a list of specific personnel whose performance did not meet expectations was compiled.

[0024] Furthermore, the strategy recommendation agent, based on a preset strategy knowledge base, obtains strategy suggestions corresponding to the shortcomings indicators, specifically including:

[0025] A strategy recommendation agent is used to retrieve strategy suggestions that match the shortcoming index from the strategy knowledge base using a hybrid retrieval mechanism. The strategy knowledge base stores the mapping relationship between the shortcoming index and the corresponding strategy suggestion in the form of question-and-answer (QA) knowledge pairs.

[0026] Furthermore, the step of using a capability training agent to filter effective capability content from the capability knowledge base based on time window matching and generating the latest capability text summary specifically includes:

[0027] The capability training agent filters out capability content from the capability knowledge base whose start time is less than or equal to the current time and whose end time is greater than or equal to the current time or whose end time indicates long-term validity, and then uses this content as valid capability content.

[0028] The effective capability content is processed by natural language processing (NLP) to generate a summary. The core clauses of each effective capability content are extracted using a BERT model based on a Transformer bidirectional encoder, and the generated summary and the core clauses are combined to form a single capability text summary.

[0029] Based on the pre-set priority of each of the effective capabilities, the individual capability text summaries are sequentially concatenated to obtain the latest capability text summary.

[0030] Furthermore, after generating the corresponding grid report based on the bottleneck indicators, strategy recommendations, and the latest capability text summary, the method further includes:

[0031] The generated grid report is returned to the front-end touchpoint for storage, and responds to user operations by providing one-click viewing, editing, downloading, and sending back of the grid report.

[0032] Secondly, this disclosure provides an apparatus for generating a grid report, the apparatus comprising:

[0033] The indicator graph construction module is used to build indicator knowledge graphs for grid scenarios.

[0034] The indicator graph update module, connected to the indicator graph construction module, is used to collect indicator data from all grids through timed task scheduling and update the indicator knowledge graph using the indicator data.

[0035] The shortcoming indicator acquisition module is connected to the indicator graph update module. It is used to obtain the corresponding indicator analysis dimensions and indicators under each dimension from the updated indicator knowledge graph through the indicator analysis agent, based on the current user's role permissions and grid affiliation. The acquired indicators are then analyzed based on dynamic prompt words and a preset large model to obtain the shortcoming indicators.

[0036] The strategy suggestion acquisition module is connected to the shortcoming indicator acquisition module and is used to acquire strategy suggestions corresponding to the shortcoming indicator by using a strategy recommendation agent based on a preset strategy knowledge base.

[0037] The capability summary acquisition module, connected to the strategy suggestion acquisition module, is used to filter effective capability content from the capability knowledge base based on time window matching through the capability training agent and generate the latest capability text summary.

[0038] The grid report generation module, connected to the capability summary acquisition module, is used to generate a corresponding grid report based on the bottleneck indicators, strategy recommendations, and the latest capability text summary.

[0039] Thirdly, this disclosure provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores one or more computer programs executable by the at least one processor, the one or more computer programs being executed by the at least one processor to enable the at least one processor to perform the grid report generation method described in the first aspect above.

[0040] Fourthly, this disclosure provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the grid report generation method described in the first aspect above.

[0041] The grid report generation method, apparatus, equipment, and medium disclosed herein achieve highly personalized report generation by constructing an indicator knowledge graph adapted to the grid morning meeting scenario and acquiring and analyzing data based on the current user's role permissions and grid affiliation, completely eliminating the limitations of fixed templates. Simultaneously, through the collaborative operation of multiple agents, an end-to-end automated closed loop is constructed, from data acquisition and in-depth analysis to strategy recommendations and capability synchronization, bridging previously fragmented business scenarios. Furthermore, by leveraging large-model-driven intelligent analysis, combined with a bottleneck indicator diagnosis and indicator-policy mapping mechanism, it provides in-depth, accurate, and quantifiable attribution of business insights and decision support, ultimately significantly improving the efficiency of daily grid operations and the scientific rigor of decision-making. This addresses the problems of template-based output, scenario fragmentation, and lack of intelligence in existing grid report generation methods. Attached Figure Description

[0042] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the embodiments of the present disclosure to explain the disclosure and do not constitute a limitation thereof. In the drawings:

[0043] Figure 1 A flowchart illustrating a method for generating a grid report according to an embodiment of this disclosure;

[0044] Figure 2 An architecture diagram of the grid report generation system provided in this embodiment of the disclosure;

[0045] Figure 3 A flowchart of the strategy recommendation agent provided in this embodiment of the disclosure;

[0046] Figure 4 A flowchart of a capability training agent provided in an embodiment of this disclosure;

[0047] Figure 5 A block diagram of a grid report generation apparatus provided in an embodiment of this disclosure;

[0048] Figure 6 This is a block diagram of an electronic device provided in an embodiment of the present disclosure. Detailed Implementation

[0049] To enable those skilled in the art to better understand the technical solutions of this disclosure, exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments of this disclosure to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0050] Where there is no conflict, the various embodiments of this disclosure and the features thereof in the embodiments may be combined with each other.

[0051] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0052] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Words such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0053] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and this disclosure, and will not be interpreted as having an idealized or overly formal meaning, unless expressly so defined herein.

[0054] Figure 1 A flowchart illustrating a method for generating a grid report according to an embodiment of this disclosure. (Refer to...) Figure 1 The method includes:

[0055] Step S101: For grid scenarios, construct an indicator knowledge graph.

[0056] Specifically, for the grid scenario, we first sort out the indicators including business categories such as development, maintenance, delivery, service, and points. These indicators include key performance indicators (KPIs) and key focus indicators. Then, we classify and grade these indicators to establish a morning meeting indicator system. Subsequently, we conduct indicator analysis from both grid and personnel dimensions. This analysis covers core analytical dimensions such as time progress completion rate, month-on-month changes, and regional ranking. Finally, by deeply mining the correlation between different indicators, we construct a dynamically updatable indicator knowledge graph.

[0057] In some embodiments, before constructing the indicator knowledge graph for a grid scenario, the method further includes:

[0058] Construct a four-tiered organizational structure: city / prefecture, district / county, grid, and personnel.

[0059] Define different roles and their corresponding permissions, wherein the roles include at least one of the following: Grid CEO, Integrated Marketing Manager, Smart Home Engineer, and Channel Manager.

[0060] Specifically, before constructing the indicator knowledge graph, a four-tiered organizational hierarchy is first established: city / prefecture / county, grid, and personnel. Grid types are divided into comprehensive grids and self-operated halls, with the grid being the smallest operational organization, and the next level below the district / county level being the grid. Then, different roles and their corresponding permissions are defined. Preferred roles include grid CEO, comprehensive marketing manager, smart home engineer, and channel manager. Corresponding role permissions restrict each role to accessing only the indicator analysis dimensions and indicator data within its assigned grid and scope of responsibility.

[0061] Step S102: Collect indicator data from all grids through timed task scheduling, and update the indicator knowledge graph using the indicator data.

[0062] Specifically, after the daily data is ready, a scheduled task is triggered through data monitoring. This task performs the following operations: collect indicator data from all grids and update the indicator values ​​in the indicator knowledge graph according to the preset indicator knowledge graph structure.

[0063] Step S103: Through the indicator analysis agent, based on the current user's role permissions and grid affiliation, the corresponding indicator analysis dimensions and indicators under each dimension are obtained from the updated indicator knowledge graph. The obtained indicators are then analyzed based on dynamic prompts and a preset large model to obtain the weakest indicators.

[0064] Specifically, the indicator analysis agent, based on the current user's role permissions and grid affiliation, locates the corresponding indicator analysis dimensions and indicators under each dimension from the updated indicator knowledge graph. It automatically generates SQL (Structured Query Language) queries using indicator names and codes to retrieve the required indicator results. Furthermore, it analyzes the specific values ​​of the indicators based on dynamic prompts and a pre-set large model to identify weak indicators. The indicator analysis agent achieves intelligent indicator analysis capabilities by developing metadata retrieval, data acquisition, and dynamic prompting engineering. Weak indicators refer to those that have not reached the preset assessment threshold, i.e., poorly performed indicators, and are determined according to weak indicator diagnosis rules.

[0065] In some embodiments, the analysis of the acquired indicators based on dynamic prompt words and a preset large model to obtain the weakness indicators specifically includes:

[0066] A dynamic prompting project is constructed, which includes: basic instructions for grid and personnel basic analysis dimensions, personalized enhancement instructions defined by the current user, and Few-shot (small sample) injection instructions based on historical analysis result examples;

[0067] The dynamic prompting project and the acquired indicators are input into a preset large model, which drives the large model to analyze the acquired indicators from multiple dimensions and generate indicator analysis content in a preset format.

[0068] Based on the analysis of the indicators, and in accordance with the preset shortcoming diagnosis rules, shortcoming indicators are diagnosed to obtain the corresponding shortcoming indicators.

[0069] Specifically, the dynamic prompting project consists of three parts: basic instructions, personalized enhanced instructions, and Few-shot injection instructions. Basic instructions define basic analysis dimensions for the grid and personnel. Personalized enhanced instructions allow the current user (e.g., the grid CEO) to define personalized prompt words and output personalized analysis results within the overall framework. Few-shot injection instructions store historical analysis results returned by the grid according to the grid ID and then use them as Few-shots to incorporate prompt words, continuously optimizing the analysis content and generating personalized reports that meet the needs of grid users.

[0070] Specifically, the preferred preset format is Markdown. The dynamic prompting project and the acquired indicators are input into a preset large model (such as the DeepSeek-V3 model). This large model then performs multi-dimensional analysis on the acquired indicators from multiple dimensions, including development volume, month-on-month changes, regional ranking, and time-based progress completion rate, generating Markdown-formatted indicator analysis content. Based on this indicator analysis content, and combined with preset bottleneck diagnosis rules (e.g., indicators ranking low in the region, time-based progress completion rate less than 80%), bottleneck indicator diagnosis is performed, resulting in bottleneck indicators in JSON (JavaScript Object Notation) format.

[0071] In some embodiments, after performing shortness indicator diagnosis based on the indicator analysis content and a preset shortness diagnosis rule to obtain the corresponding shortness indicator, the method further includes:

[0072] For the aforementioned shortcomings, a root cause analysis was conducted at the personnel level, and a list of specific personnel whose performance did not meet expectations was compiled.

[0073] Specifically, for the aforementioned shortcomings indicators, a root cause analysis is conducted at the personnel level. By comparing the performance of each person in the same role for the corresponding shortcomings indicator, a list of specific personnel whose performance did not meet expectations is compiled.

[0074] Step S104: Using a strategy recommendation agent, based on a preset strategy knowledge base, obtain strategy suggestions corresponding to the shortcomings indicators.

[0075] The strategy knowledge base stores the mapping relationship between the weakness indicators and the corresponding strategy suggestions. It is stored in the form of QA (question and answer) knowledge pairs, where the weakness indicator is Q and the specific strategy suggestion is A.

[0076] In some embodiments, the step of utilizing a strategy recommendation agent to obtain strategy suggestions corresponding to the weakness indicators based on a preset strategy knowledge base specifically includes:

[0077] A strategy recommendation agent is used to retrieve strategy suggestions that match the shortcoming index from the strategy knowledge base using a hybrid retrieval mechanism. The strategy knowledge base stores the mapping relationship between the shortcoming index and the corresponding strategy suggestion in the form of question-and-answer (QA) knowledge pairs.

[0078] Specifically, the strategy recommendation agent is based on an indicator-suggestion mapping model and employs a hybrid retrieval mechanism that combines structured retrieval (using structured query language to accurately query the strategy knowledge base) with semantic retrieval (using embedding models and vector indexes to perform semantic similarity matching on the strategy knowledge base) to recommend corresponding strategy suggestions based on the weakness indicators.

[0079] Step S105: The capability training agent selects effective capability content from the capability knowledge base based on time window matching and generates the latest capability text summary.

[0080] Specifically, the capability knowledge base stores the latest capability information, with each capability including at least a capability ID, capability content, start time, and end time. The capability training agent uses time window matching to filter valid capability content that matches the time window and generates the latest capability text summary.

[0081] In some embodiments, the step of filtering effective capability content from the capability knowledge base based on time window matching by the capability training agent and generating the latest capability text summary specifically includes:

[0082] The capability training agent filters out capability content from the capability knowledge base whose start time is less than or equal to the current time and whose end time is greater than or equal to the current time or whose end time indicates long-term validity, and then uses this content as valid capability content.

[0083] The effective capability content is summarized using NLP (Natural Language Processing). The core clauses of each effective capability content are extracted using the BERT (Bidirectional Encoder Representations from Transformers) model. The generated summary and the core clauses are then combined to form a single capability text summary.

[0084] Based on the pre-set priority of each of the effective capabilities, the individual capability text summaries are sequentially concatenated to obtain the latest capability text summary.

[0085] Specifically, the capability training agent uses time window matching to filter out capability content with "start time ≤ current time and (end time ≥ current time or indication of long-term validity, such as NULL)" as valid capability content. It then performs NLP summary generation on the valid capability content, extracts the core clauses of each valid capability content based on the BERT model, and combines the generated summary with the core clauses to form a single capability text summary. Finally, it labels the valid capability content according to its priority when entering it into the capability knowledge base (including important, ordinary, etc.), and concatenates the single capability text summaries sequentially to form the latest capability text summary.

[0086] Specifically, a scheduled task can be used to periodically (e.g., daily) clean up expired capability content in the capability knowledge base with an end time < current time, thereby avoiding redundancy in the knowledge base and improving filtering and query efficiency.

[0087] Step S106: Generate a corresponding grid report based on the aforementioned bottleneck indicators, strategy recommendations, and the latest capability text summary.

[0088] Specifically, the grid report should include at least the shortcomings indicators, strategic recommendations, and a textual summary of the latest capabilities.

[0089] In some embodiments, after generating the corresponding grid report based on the bottleneck indicator, strategy recommendations, and latest capability text summary, the method further includes:

[0090] The generated grid report is returned to the front-end touchpoint for storage, and responds to user operations by providing one-click viewing, editing, downloading, and sending back of the grid report.

[0091] Specifically, the generated grid report is returned to the front-end touchpoint (Public CEO Workbench) for storage, and users can log in to the Public CEO Workbench to view, edit, download, and send back the grid report with one click.

[0092] In one specific embodiment, the grid report generation method is applied to a grid report generation system, the architecture of which is shown in the figure below. Figure 2 As shown, a four-layer technical architecture—data integration, task triggering, multi-agent collaboration, and personalized report output—is used to create personalized grid reports and improve grid production and operation efficiency. Based on this architecture, the method for generating this grid report may include the following steps:

[0093] Step S1: Data Integration

[0094] 1. Grid organization hierarchy

[0095] The organization is structured into four levels: city / prefecture, district / county, grid, and personnel. Grid types are divided into comprehensive grids and self-operated halls, and roles are divided into grid CEO, comprehensive marketing manager, smart home engineer, and channel manager.

[0096] Among them, role settings can be used to analyze the performance of individuals in the same role.

[0097] 2. Indicator Knowledge Graph

[0098] For grid-based scenarios, the system systematically identified key performance indicators (KPIs) and key focus indicators, including development, maintenance, delivery, service, and points, and established a morning meeting indicator system through classification and grading. Indicator analysis was conducted from both grid and personnel dimensions, covering core analytical dimensions such as time-based progress completion rate, month-on-month changes, and regional rankings. By deeply mining the relationships between indicators, a dynamically updatable indicator knowledge graph was constructed, ultimately forming a high-quality morning meeting dataset to support intelligent decision-making and providing a standardized data foundation for multi-agent collaborative analysis.

[0099] The morning meeting indicator system includes a grid information table, a personnel information table, an indicator details table, and a table showing the correspondence between grids and indicators.

[0100] Step S2, Task Trigger

[0101] Once the data is ready, a scheduled task will be activated to push the collected indicator data to the indicator analysis agent.

[0102] 1. Data monitoring

[0103] Monitor the data processing flow and initiate scheduled tasks once the data is available.

[0104] Specifically, the data processing chain is monitored in real time, and the data processing task is started every day at dawn. The data is ready at around 5 a.m., triggering the scheduled task: collecting all the full index data of all grids, classifying and associating the full index data according to the preset index knowledge graph structure, and completing the dynamic update of the index knowledge graph.

[0105] 2. Scheduled task scheduling

[0106] Once the data is available, information such as grid organization hierarchy, personnel, and roles is pushed out, and daily morning meeting indicator data is obtained from the graph database based on the grid personnel affiliation.

[0107] Step S3, Multi-agent Collaboration

[0108] Establish an intelligent agent for indicator analysis, a smart agent for strategy recommendation, and a smart agent for capability training. These three agents work together to intelligently generate grid reports.

[0109] 1. The indicator analysis intelligent agent realizes the intelligent analysis capability of indicators by developing metadata retrieval, data acquisition and dynamic prompting projects, and encapsulates the intelligent analysis capability into an intelligent agent for use in report generation scenarios.

[0110] (1) Metadata retrieval

[0111] The graph query enables rapid retrieval of indicator analysis dimensions and indicator data under each dimension.

[0112] (2) Data acquisition

[0113] The system retrieves the dimensions of indicator analysis and the indicators under each dimension from the knowledge graph, and automatically generates SQL query statements based on the indicator name and indicator code to retrieve the required indicator results.

[0114] (3) Dynamic prompting project

[0115] The dynamic prompting project consists of three parts: basic commands, personalized enhancement commands, and Few-shot injection commands.

[0116] (a) Basic instructions are oriented towards the grid, and personnel define the basic analysis dimensions;

[0117] (b) Personalized and highly instructive: The grid CEO defines personalized prompts and outputs personalized analysis results within the overall framework;

[0118] (c) The Few-shot injection command stores the historical analysis results returned by the grid according to the grid ID and uses it as the Few-shot concatenation prompt word to continuously optimize the analysis content and form a personalized report that meets the needs of the grid CEO.

[0119] It should be noted that the basic instructions are the prompts for the large model, while the personalized enhancement instructions are the personalized prompts set by the mesh CEO under the basic instructions. Few-shot injection refers to editing the content of historically generated reports and using it as an example to insert prompts. The second and third parts will be applied if there is content; otherwise, only the first basic instruction will be used.

[0120] (4) Analysis of key indicators

[0121] The key indicators selected for the grid were analyzed from multiple dimensions, including development volume, year-on-year change, regional ranking, and time-bound progress completion rate. The DeepSeek-V3 model was used to generate the indicator analysis content in Markdown format. The analysis of these indicators was achieved by combining the model with pre-defined bottleneck diagnosis rules (rules used to determine when an indicator is a bottleneck, such as a low regional ranking or a time-bound progress completion rate of less than 80%). Further root cause analysis of personnel performance was conducted, and a list of poorly performing personnel was compiled.

[0122] Among them, development volume refers to the actual incremental scale of the indicator, month-on-month change is the increase or decrease of the current indicator relative to the same period of the previous month, regional ranking refers to the ranking among the same grid type within the same city, and the progress completion rate refers to the ratio of the current indicator value to the progress target value, which is used to measure whether the indicator is completed on schedule.

[0123] 2. The strategy recommendation agent establishes an indicator-suggestion mapping model (for example, if mobile network development is a weakness, it is recommended to log in to the cross-network topic to obtain target users for outbound call invitations). It receives the analysis results from the indicator analysis agent, and uses a hybrid retrieval mechanism to retrieve the weakness indicator strategy based on the weakness indicator, and calls the large model to realize the strategy recommendation.

[0124] (1) Structured retrieval: Standardized solutions in the strategy knowledge base (i.e., QA knowledge base) are retrieved through SQL queries;

[0125] (2) Semantic retrieval: Based on the QA knowledge base, vectorized matching (BGE model + Faiss index) is used to obtain the corresponding recommendation strategy.

[0126] It should be noted that the strategy knowledge base stores indicator strategy suggestions, which are stored in the form of QA knowledge pairs. The weakest indicator is Q, and the specific strategy suggestion is A.

[0127] Specifically, the process of the strategy recommendation agent can be as follows: Figure 3 As shown, the strategy recommendation agent first receives the bottleneck indicators in JSON format output by the index analysis agent. Then, the strategy recommendation agent retrieves content from the structured policy library and the QA knowledge base through a hybrid retrieval process, forming a policy candidate set (i.e., policy suggestions). After processing by the large model policy generation, the automatic integration of the grid report is finally completed. The structured policy library includes a data query library for the policy suggestions; for example, if broadband development is insufficient, it is recommended to target low-penetration communities, and the structured policy library stores information about these low-penetration communities.

[0128] Example:

[0129] Input: Slowest-end performance index for grid G_1024 {"index":"Grid relocation schedule completion rate", "value":82%}

[0130] Processing flow: Search for and match QA_20 (association strategy: log in to the external network topic to obtain the target user).

[0131] Output strategy suggestion: It is recommended to log in to the cross-network special topic to obtain target users for outbound marketing (refer to QA_20).

[0132] 3. The capability training agent, through precise time window extraction, semantic-level deduplication, and scenario-based summary generation, ensures that grid reports include the latest capabilities while avoiding information overload. The process of the capability training agent is as follows:Figure 4 As shown, the new capability text is first obtained, and then preprocessed. Next, semantic segmentation is performed, and then the corresponding summary is generated through the large model. Finally, the generated summary is used to populate the grid report.

[0133] Specifically, the following steps can be performed when generating a grid report:

[0134] (1) Time window matching

[0135] Timestamp: Each piece of capability content stores two time fields in the capability knowledge base: start time and end time.

[0136] Table structure example:

[0137] Storage structure example (database table):

[0138] ability_idability_textability_start_time ability_end_time

[0139] 001 "Subsidy Capacity for the Construction of Gigabit Communities..." 2025-01-01 00:00:00 2025-06-30 23:59:59

[0140] 002 "5G Package Promotion Incentive Program..." 2025-03-15 00:00:00NULL (Valid indefinitely)

[0141] Perform time window matching and filter content that matches the time window (i.e., the current time is within the capability's validity period).

[0142] (2) Generation of dynamic summaries for large models

[0143] The selected effective capability content is summarized using NLP, and the core capability clauses are extracted based on the BERT model.

[0144] Arrange them according to priority to the "Recent Capability Training" module of the grid report.

[0145] When entering ability texts into the ability knowledge base, different priority tags will be applied (e.g., important and ordinary tags).

[0146] (3) Handling of failure capabilities

[0147] A daily scheduled task cleans up expired capabilities (ability_end_time < CURRENT_TIMESTAMP) to avoid knowledge base redundancy and improve query efficiency.

[0148] Step S4: Output report

[0149] The report feedback interface is encapsulated, and the generated report is returned to the front-end touchpoint (Public CEO Workbench) for storage. The Grid CEO logs in to the workbench, clicks on the AI ​​Grid Report, selects the report date, and can view the report content with one click. It also supports operations such as editing, downloading, and feedback.

[0150] It should be noted that the grid report generation method provided in this disclosure has the following characteristics:

[0151] a) Compared to a single static prompt, this disclosure adopts a dynamic prompt engineering, which includes a three-layer structure (basic instructions / personalized enhancement instructions / Few-shot injection instructions). It combines the experience of the grid CEO and historical feedback to iteratively achieve dynamic prompts and continuously optimize the report content.

[0152] b) Multi-agent collaboration: The indicator analysis agent, the strategy recommendation agent, and the capability training agent collaborate to intelligently generate grid reports.

[0153] c) Supports user-selectable grid indicators and performs abnormal indicator diagnosis, automatically generating personalized grid reports.

[0154] The grid report generation method provided in this disclosure constructs an indicator knowledge graph adapted to the grid morning meeting scenario and acquires and analyzes data based on the current user's role permissions and grid affiliation, achieving highly personalized report content generation and completely eliminating the limitations of fixed templates. Simultaneously, through the collaborative operation of multiple agents, an end-to-end automated closed loop is constructed, from data acquisition and in-depth analysis to strategy recommendations and capability synchronization, bridging previously fragmented business scenarios. Furthermore, by leveraging large-model-driven intelligent analysis, combined with a weakness indicator diagnosis and indicator-policy mapping mechanism, it provides in-depth, accurate, and quantifiable attribution of business insights and decision support, ultimately significantly improving the efficiency of daily grid operations and the scientific nature of decision-making. This solves the problems of templated output, scenario fragmentation, and lack of intelligence in existing grid report generation methods.

[0155] It is understood that the various method embodiments mentioned above in this disclosure can be combined with each other to form combined embodiments without violating the principle and logic. Due to space limitations, this disclosure will not elaborate further. Those skilled in the art will understand that in the above methods of specific implementation, the specific execution order of each step should be determined by its function and possible internal logic.

[0156] Figure 5 A block diagram of a grid report generation apparatus provided in an embodiment of this disclosure.

[0157] Reference Figure 5 This disclosure provides a mesh report generation apparatus for performing the above-described mesh report generation method. The apparatus includes:

[0158] Indicator Graph Construction Module 11 is used to construct indicator knowledge graphs for grid scenarios;

[0159] The indicator graph update module 12 is connected to the indicator graph construction module 11 and is used to collect indicator data from all grids through timed task scheduling and update the indicator knowledge graph using the indicator data.

[0160] The shortcoming indicator acquisition module 13 is connected to the indicator graph update module 12. It is used to obtain the corresponding indicator analysis dimension and the indicator under each dimension from the updated indicator knowledge graph through the indicator analysis agent according to the current user's role and grid affiliation. It then analyzes the acquired indicators based on dynamic prompt words and a preset large model to obtain the shortcoming indicator.

[0161] The strategy suggestion acquisition module 14 is connected to the shortcoming indicator acquisition module 13 and is used to acquire strategy suggestions corresponding to the shortcoming indicator by using a strategy recommendation agent based on a preset strategy knowledge base.

[0162] The capability summary acquisition module 15, connected to the strategy suggestion acquisition module 14, is used to filter effective capability content from the capability knowledge base based on time window matching through the capability training agent and generate the latest capability text summary.

[0163] The grid report generation module 16 is connected to the capability summary acquisition module 15 and is used to generate a corresponding grid report based on the shortcoming indicators, strategy suggestions and the latest capability text summary.

[0164] Optionally, the device further includes:

[0165] The organizational hierarchy construction module is used to build a four-level organizational hierarchy: city / prefecture / county, grid, and personnel.

[0166] The role definition module is used to define different roles and their corresponding permissions. The roles include at least one of the following: Grid CEO, Integrated Marketing Manager, Smart Home Engineer, and Channel Manager.

[0167] Optionally, the shortcoming indicator acquisition module 13 includes:

[0168] The prompt engineering construction unit is used to construct dynamic prompt engineering, which includes: basic instructions for grid and personnel basic analysis dimensions, personalized enhancement instructions defined by the current user, and a small number of sample Few-shot injection instructions based on historical analysis result examples;

[0169] The indicator analysis unit is used to input the dynamic prompting project and the acquired indicators into a preset large model, drive the large model to analyze the acquired indicators from multiple dimensions, and generate indicator analysis content in a preset format.

[0170] The shortcoming indicator diagnosis unit is used to diagnose the shortcoming indicators based on the indicator analysis content and the preset shortcoming diagnosis rules, and obtain the corresponding shortcoming indicators.

[0171] Optionally, the shortcoming indicator acquisition module 13 further includes:

[0172] The root cause analysis unit is used to perform root cause analysis on the personnel dimension for the aforementioned shortcomings indicators, and to list the specific personnel whose performance did not meet expectations.

[0173] Optionally, the strategy suggestion acquisition module 14 is specifically used for:

[0174] A strategy recommendation agent is used to retrieve strategy suggestions that match the shortcoming index from the strategy knowledge base using a hybrid retrieval mechanism. The strategy knowledge base stores the mapping relationship between the shortcoming index and the corresponding strategy suggestion in the form of question-and-answer (QA) knowledge pairs.

[0175] Optionally, the capability summary acquisition module 15 includes:

[0176] The effective capability content filtering unit is used to filter capability content from the capability knowledge base through the capability training agent, which has a start time less than or equal to the current time and an end time greater than or equal to the current time or whose end time indicates long-term validity, and to use it as effective capability content.

[0177] The single-item summary generation unit is used to generate a natural language processing (NLP) summary of the effective capability content. It uses a Transformer-based bidirectional encoder to represent the BERT model to extract the core clauses of each effective capability content, and combines the generated summary with the core clauses to form a single capability text summary.

[0178] The summary splicing unit is used to sequentially splice the single capability text summaries according to the preset priority of each of the effective capability contents to obtain the latest capability text summary.

[0179] Optionally, the device further includes:

[0180] The report output module is used to return the generated grid report to the front-end touchpoint for storage, and respond to user operations, providing one-click viewing, editing, downloading and sending back of the grid report.

[0181] Figure 6 This is a block diagram of an electronic device provided in an embodiment of the present disclosure.

[0182] Reference Figure 6 This disclosure provides an electronic device comprising: at least one processor 701; at least one memory 702; and one or more I / O interfaces 703 connected between the processor 701 and the memory 702; wherein the memory 702 stores one or more computer programs executable by the at least one processor 701, the one or more computer programs being executed by the at least one processor 701 to enable the at least one processor 701 to perform the above-described method for generating a grid report.

[0183] This disclosure also provides a computer-readable storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the above-described method for generating a grid report. The computer-readable storage medium may be volatile or non-volatile.

[0184] In summary, the grid report generation method, apparatus, device, and medium provided in this disclosure, by constructing an indicator knowledge graph adapted to the grid morning meeting scenario and acquiring and analyzing data based on the current user's role permissions and grid affiliation, achieves highly personalized report content generation, completely eliminating the limitations of fixed templates. Simultaneously, through the collaborative operation of multiple agents, an end-to-end automated closed loop is constructed, from data acquisition and in-depth analysis to strategy recommendations and capability synchronization, bridging previously fragmented business scenarios. Furthermore, by leveraging intelligent analysis driven by large models, combined with a bottleneck indicator diagnosis and indicator-policy mapping mechanism, it provides in-depth, accurate, and quantifiable attribution of business insights and decision support, ultimately significantly improving the efficiency of daily grid operations and the scientific nature of decision-making. This solves the problems of templated output, scenario fragmentation, and lack of intelligence in existing grid report generation methods.

[0185] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).

[0186] As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable program instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0187] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0188] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute 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 a remote computer, the remote computer may 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 may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0189] This disclosure has disclosed exemplary embodiments, and although specific terminology has been used, it is for general illustrative purposes only and should not be construed as limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of this disclosure as set forth by the appended claims.

Claims

1. A method for generating a grid report, characterized in that, The method includes: For grid-based scenarios, construct an indicator knowledge graph; By scheduling timed tasks, indicator data from all grids is collected, and the indicator knowledge graph is updated using the indicator data. The indicator analysis agent obtains the corresponding indicator analysis dimensions and indicators under each dimension from the updated indicator knowledge graph based on the current user's role permissions and grid affiliation. It then analyzes the obtained indicators based on dynamic prompts and a preset large model to obtain the weak indicators. A strategy recommendation agent is used to obtain strategy suggestions corresponding to the aforementioned shortcomings based on a pre-set strategy knowledge base. The intelligent agent, through capability training, selects effective capability content from the capability knowledge base based on time window matching and generates the latest capability text summary. A corresponding grid report is generated based on the aforementioned weakness indicators, strategy recommendations, and the latest capability text summary.

2. The method according to claim 1, characterized in that, Before constructing the indicator knowledge graph for the grid scenario, the method further includes: Construct a four-tiered organizational structure: city / prefecture, district / county, grid, and personnel. Define different roles and their corresponding permissions, wherein the roles include at least one of the following: Grid CEO, Integrated Marketing Manager, Smart Home Engineer, and Channel Manager.

3. The method according to claim 1, characterized in that, The analysis of the acquired indicators based on dynamic prompts and a pre-set large model yields the weakness indicators, which specifically include: A dynamic prompting project is constructed, which includes: basic instructions for grid and personnel basic analysis dimensions, personalized enhancement instructions defined by the current user, and a small number of sample Few-shot injection instructions based on historical analysis result examples; The dynamic prompting project and the acquired indicators are input into a preset large model, which drives the large model to analyze the acquired indicators from multiple dimensions and generate indicator analysis content in a preset format. Based on the analysis of the indicators, and in accordance with the preset shortcoming diagnosis rules, shortcoming indicators are diagnosed to obtain the corresponding shortcoming indicators.

4. The method according to claim 3, characterized in that, After performing shortcoming indicator diagnosis based on the indicator analysis content and a preset shortcoming diagnosis rule to obtain the corresponding shortcoming indicator, the method further includes: For the aforementioned shortcomings, a root cause analysis was conducted at the personnel level, and a list of specific personnel whose performance did not meet expectations was compiled.

5. The method according to claim 1, characterized in that, The strategy-based recommendation agent, based on a pre-set strategy knowledge base, obtains strategy suggestions corresponding to the shortcomings indicators, specifically including: A strategy recommendation agent is used to retrieve strategy suggestions that match the shortcoming index from the strategy knowledge base using a hybrid retrieval mechanism. The strategy knowledge base stores the mapping relationship between the shortcoming index and the corresponding strategy suggestion in the form of question-and-answer (QA) knowledge pairs.

6. The method according to claim 1, characterized in that, The process of using a capability-training agent to filter effective capability content from a capability knowledge base based on time window matching and generating the latest capability text summary specifically includes: The capability training agent filters out capability content from the capability knowledge base whose start time is less than or equal to the current time and whose end time is greater than or equal to the current time or whose end time indicates long-term validity, and then uses this content as valid capability content. The effective capability content is processed by natural language processing (NLP) to generate a summary. The core clauses of each effective capability content are extracted using a BERT model based on a Transformer bidirectional encoder, and the generated summary and the core clauses are combined to form a single capability text summary. Based on the pre-set priority of each of the effective capabilities, the individual capability text summaries are sequentially concatenated to obtain the latest capability text summary.

7. The method according to claim 1, characterized in that, After generating the corresponding grid report based on the bottleneck indicators, strategy recommendations, and the latest capability text summary, the method further includes: The generated grid report is returned to the front-end touchpoint for storage, and responds to user operations by providing one-click viewing, editing, downloading, and sending back of the grid report.

8. A grid report generation apparatus, characterized in that, The device includes: The indicator graph construction module is used to build indicator knowledge graphs for grid scenarios. The indicator graph update module, connected to the indicator graph construction module, is used to collect indicator data from all grids through timed task scheduling and update the indicator knowledge graph using the indicator data. The shortcoming indicator acquisition module is connected to the indicator graph update module. It is used to obtain the corresponding indicator analysis dimensions and indicators under each dimension from the updated indicator knowledge graph through the indicator analysis agent, based on the current user's role permissions and grid affiliation. The acquired indicators are then analyzed based on dynamic prompt words and a preset large model to obtain the shortcoming indicators. The strategy suggestion acquisition module is connected to the shortcoming indicator acquisition module and is used to acquire strategy suggestions corresponding to the shortcoming indicator by using a strategy recommendation agent based on a preset strategy knowledge base. The capability summary acquisition module, connected to the strategy suggestion acquisition module, is used to filter effective capability content from the capability knowledge base based on time window matching through the capability training agent and generate the latest capability text summary. The grid report generation module, connected to the capability summary acquisition module, is used to generate a corresponding grid report based on the bottleneck indicators, strategy recommendations, and the latest capability text summary.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores one or more computer programs that can be executed by the at least one processor, the one or more of the computer programs being executed by the at least one processor to enable the at least one processor to perform the grid report generation method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method for generating a grid report as described in any one of claims 1-7.

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