Report intelligent management system and method

Abstract

This application provides an intelligent report management system and method. The system includes: a multi-source heterogeneous data access and standardization unit, used to acquire raw data from heterogeneous data sources and generate standardized basic data; an indicator model construction unit, which automatically identifies and constructs an enterprise-level unified indicator model tree through semantic parsing technology; a data quality verification and governance unit, which configures a rule engine to perform real-time quality detection and repair of indicator data; an indicator instantiation and storage unit, which generates data extraction and calculation tasks based on the indicator model tree, forms standardized indicator time series data, and stores it in an indicator data warehouse; and an intelligent report generation unit, which supports users to dynamically generate report templates by referencing indicators through functions and automatically updates report content. This application achieves full-link automated management from data access, indicator modeling, quality governance to intelligent reporting, improving data application efficiency and report generation flexibility.

Description

Technical Field

[0001] This document relates to the field of reporting technology, and in particular to an intelligent reporting management system and method. Background Technology

[0002] Real-time production data, equipment ledger data, and operational data are scattered across different systems, lacking a unified and clean data model. This results in a large workload for developing integrated reports, with inconsistent definitions. Measurement point data suffers from issues such as jumps, interruptions, and lack of calibration, directly impacting report accuracy. Furthermore, there is a lack of effective data governance and data quality monitoring mechanisms.

[0003] Many reporting systems were built a decade or more ago, based on client / server or early browser / server architectures, resulting in difficult maintenance, poor browser compatibility, and a subpar user experience. Report formats and logic are hard-coded into the program; any changes to business logic require developers to modify the code and redeploy, leading to slow response times.

[0004] Business departments are constantly generating new reporting requests, resulting in a massive volume of reports. However, most of these reports are either unviewed or merely archived, failing to provide effective support for critical decision-making. Business personnel heavily rely on the IT department to develop reports, making it impossible for them to quickly and flexibly create and adjust analytical views to meet their own needs.

[0005] Therefore, there is an urgent need for an indicator management system that can achieve unified access and standardized governance of multi-source data, support automated construction of indicator models, have a closed-loop data quality control mechanism, and provide flexible and configurable intelligent report generation capabilities, in order to solve the problems of data dispersion, model missing, uncontrollable quality, low report development efficiency, and delayed business response in existing technologies. Summary of the Invention

[0006] According to embodiments of the present invention, an intelligent report management system and method are provided to solve the above-mentioned problems.

[0007] According to an embodiment of the present invention, a report intelligent management system is provided, comprising: The multi-source heterogeneous data access and standardization unit is used to acquire raw data from heterogeneous data sources, standardize the acquired raw data, and generate standardized basic data that conforms to a unified data model. The indicator model construction unit is used to extract the business meaning of fields based on the standardized basic data through semantic parsing technology, and automatically identify and construct an enterprise-level unified indicator model tree by combining predefined indicator naming specifications and domain dictionaries, and define the unique identifier, data source, calculation method, quality rules and lineage of each indicator. The data quality verification and governance unit is used to configure a data quality verification rule engine for each indicator, perform real-time quality detection on indicator data, identify abnormal data, perform repairs according to preset strategies, and generate data quality scores and governance logs. The indicator instantiation and storage unit is used to generate data extraction and calculation tasks for each indicator according to the indicator model tree, extract and calculate indicator values ​​from standardized basic data at regular intervals, form standardized indicator time series data, and store them in the indicator data warehouse according to a unified model. The intelligent report generation unit provides an interactive interface, allowing users to reference indicators in the indicator data warehouse via functions, dynamically generate report templates, and automatically update report content based on changes in indicator data.

[0008] According to an embodiment of the present invention, a method for intelligent report management is provided, comprising: S1. Obtain raw data from heterogeneous data sources, standardize the collected raw data, and generate standardized basic data that conforms to a unified data model. S2. Based on the standardized basic data, extract the business meaning of the fields through semantic parsing technology, combine the predefined indicator naming specifications and domain dictionary, automatically identify and construct an enterprise-level unified indicator model tree, and define the unique identifier, data source, calculation method, quality rules and lineage of each indicator. S3. Configure a data quality verification rule engine for each indicator to perform real-time quality detection on indicator data, identify abnormal data, perform repair according to preset strategies, and generate data quality scores and governance logs. S4. Generate data extraction and calculation tasks for each indicator based on the indicator model tree, extract and calculate indicator values ​​from standardized basic data at regular intervals, form standardized indicator time series data, and store them in the indicator data warehouse according to a unified model. S5. Provide an interactive interface that allows users to reference indicators in the indicator data warehouse through functions, dynamically generate report templates, and automatically update report content based on changes in indicator data.

[0009] This application addresses the issue of inconsistent data formats by unifying the access of dispersed data through a multi-source heterogeneous data access and standardization unit, and by automatically constructing an enterprise-level unified indicator model tree using semantic parsing technology through an indicator model construction unit, enabling centralized definition and global reuse of indicators. A data quality verification and governance unit configures a multi-dimensional rule engine to perform real-time detection and automatic repair of indicator data, improving data accuracy and reliability. An indicator instantiation and storage unit automatically generates standardized indicator data through data extraction and calculation tasks, and achieves full lifecycle traceability by combining an indicator lineage recording module. An intelligent report generation unit allows users to dynamically generate and automatically update reports by referencing indicators using functions, and business personnel can flexibly customize reports, reducing reliance on the IT department. An indicator service and sharing unit encapsulates indicators as standardized API services for external release, and dynamically adjusts pre-calculation strategies by calling heatmaps, achieving efficient indicator sharing and resource optimization. This application achieves closed-loop management across the entire chain from data access, indicator modeling, quality governance to intelligent reporting, significantly improving enterprise data governance and report application efficiency. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in one or more embodiments of this specification or in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a schematic diagram of the intelligent report management system according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a multi-source heterogeneous data access and standardization unit according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the index model construction unit in an embodiment of the present invention; Figure 4 This is a schematic diagram of the data quality verification and governance unit in an embodiment of the present invention; Figure 5 This is a schematic diagram of the index instantiation and storage unit according to an embodiment of the present invention; Figure 6 This is a flowchart of the intelligent report management method according to an embodiment of the present invention. Detailed Implementation

[0012] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this document.

[0013] System Implementation Examples According to embodiments of the present invention, an intelligent report management system is provided. Figure 1 This is a schematic diagram of the intelligent report management system according to an embodiment of the present invention. Figure 1 As shown, the intelligent report management system of this invention specifically includes: The multi-source heterogeneous data access and standardization unit 10 is used to acquire raw data from heterogeneous data sources, perform standardization processing on the acquired raw data, and generate standardized basic data that conforms to a unified data model. Figure 2 This is a schematic diagram of a multi-source heterogeneous data access and standardization unit according to an embodiment of the present invention. Figure 2 As shown, the multi-source heterogeneous data access and standardization unit specifically includes: The dynamic adapter module is used to automatically load the corresponding parsing driver according to the data source type, and supports multiple access methods; The coding mapping module is used to convert equipment codes, material codes, units of measurement, etc. from different systems into unified enterprise codes according to preset mapping rules; The time alignment module is used to standardize timestamps from different data sources, interpolating or aggregating non-integer or non-equal interval data into a unified time-series granularity.

[0014] Furthermore, the multi-source heterogeneous data access and standardization unit also includes an abnormal data pre-filtering module, which is used to perform preliminary filtering of obviously abnormal raw data based on threshold or pattern matching during the data access stage, reduce the invalid computational load of subsequent processing units, and record the filtering events to the access log for future reference.

[0015] The indicator model construction unit 12 is used to extract the business meaning of fields based on the standardized basic data through semantic parsing technology, and automatically identify and construct an enterprise-level unified indicator model tree by combining predefined indicator naming specifications and domain dictionaries, and define the unique identifier, data source, calculation method, quality rules and lineage of each indicator. Figure 3 This is a schematic diagram of the index model construction unit in an embodiment of the present invention. Figure 3 It can be seen that the index model construction unit in this embodiment of the invention specifically includes: The semantic parsing module is used to perform natural language processing on field names, comments, and historical query logs, identify the physical quantities, equipment objects, and statistical calibers corresponding to the fields, and automatically generate triplet descriptions of the indicators. The indicator relationship reasoning module is used to automatically identify the derivation relationship between indicators by analyzing the field naming rules and the association relationship in the SQL query log, and generate a candidate set of indicator calculation formulas. The model version management module is used to generate new indicator model versions when the data source structure or business definition changes, and supports the parallel operation of old and new versions to ensure that historical reports can still be rendered according to the old version model.

[0016] Furthermore, the indicator model construction unit also includes an indicator popularity statistics module, which is used to count the frequency of use of each indicator in report generation, ad-hoc query and API call, generate an indicator popularity ranking list, and provide data support for indicator model optimization and offline decision-making.

[0017] The data quality verification and governance unit 14 is used to configure a data quality verification rule engine for each indicator, perform real-time quality detection on indicator data, identify abnormal data, perform repair according to preset strategies, and generate data quality scores and governance logs. Figure 4 This is a schematic diagram of the data quality verification and governance unit according to an embodiment of the present invention. Figure 4 As can be seen, the data quality verification and governance unit in this embodiment of the invention specifically includes: The rule configuration module is used to configure multi-dimensional quality verification rules for each indicator, including integrity rules, accuracy rules, consistency rules, timeliness rules, and business logic rules. An anomaly detection engine is used to scan indicator data in real time or at regular intervals, match the quality verification rules, identify abnormal data and generate abnormal events. Anomaly types include: jump, interruption, dead value, exceeding limit, and multi-source inconsistency. The automatic repair module is used to process abnormal data according to a preset repair strategy and add repair tags and confidence scores to the repaired data. The quality closed-loop feedback module is used to feed back abnormal data and repair operations after manual review and confirmation as training samples to the anomaly detection engine, continuously optimizing the accuracy of the detection model.

[0018] Furthermore, the data quality verification and governance unit also includes a quality dashboard module, which is used to display the health score of each indicator, abnormal event trend, repair success rate and governance progress in the form of a visual dashboard, providing data administrators with a global data quality monitoring view.

[0019] The indicator instantiation and storage unit 16 is used to generate data extraction and calculation tasks for each indicator according to the indicator model tree, periodically extract and calculate indicator values ​​from standardized basic data, form standardized indicator time series data, and store them in the indicator data warehouse according to a unified model. Figure 5 This is a schematic diagram of the index instantiation and storage unit according to an embodiment of the present invention. Figure 5 As can be seen, the index instantiation and storage unit in this embodiment of the invention specifically includes: The task orchestration module is used to automatically generate a DAG (Directed Acyclic Graph) of indicator calculation tasks based on the indicator collection frequency and calculation dependencies, and schedule the entire process of data extraction, cleaning, calculation and storage. The indicator lineage recording module is used to record the original data source, cleaning operation, calculation process and timestamp of each indicator value, and generate a complete indicator data lineage map, supporting the tracing from report data to the original collection point.

[0020] Specifically, the indicator lineage record module includes: The lineage metadata collection unit is used to collect source layer metadata, operation layer metadata and flow layer metadata throughout the entire life cycle of indicator data. The source layer metadata records the original data source identifier and original value corresponding to the indicator value. The operation layer metadata records the cleaning operation identifier and calculation formula hash of the indicator value. The flow layer metadata records the migration path of the indicator value between different computing nodes. The time-series graph construction unit is used to organize the source layer metadata, operation layer metadata and flow layer metadata into a time-series graph structure. The time-series graph structure uses original data points, cleaning operation nodes, calculation operation nodes and index value nodes as node types, and uses "from", "cleaned at", and "calculated at" as edge types, and carries an effective time window for each edge. The impact domain analysis unit is used to identify all indicator nodes that depend on the changed node by traversing the time series graph from the changed node when the original data source changes, generate an impact domain set, and trigger corresponding recalculation or review tasks based on the impact coefficient to classify the affected indicators. The operation fingerprint verification unit is used to construct a Merkle hash tree for the complete lineage path of each indicator value to generate an operation fingerprint. When a user initiates a verification request, the Merkle root hash of the current lineage path is recalculated and compared with the stored historical fingerprints to locate the changed node positions.

[0021] Furthermore, the indicator lineage record module also includes a traceability query interface, which is used to receive traceability requests initiated by users from report data points, display the complete path from indicator value to original data point through reverse traversal of time series graph, and mark the timestamp and operation parameters of each node in the graph.

[0022] Furthermore, the indicator instantiation and storage unit also includes a dynamic optimization module for computing tasks; The dynamic optimization module for computing tasks is used to monitor the execution time, resource consumption, and data freshness status of indicator calculation tasks in real time. When an indicator calculation task times out or resource usage is abnormal, a task rescheduling mechanism is automatically triggered. The task rescheduling mechanism includes: splitting the timed-out task into multiple sub-tasks for parallel execution, migrating the calculation tasks of high-frequency indicators to dedicated computing nodes, or adjusting the calculation tasks of periodic indicators to be executed in batches during off-peak hours. The dynamic optimization module for computing tasks is also used to build a performance prediction model for computing tasks based on historical task execution data, and to estimate the resource requirements of new indicator calculation tasks and allocate computing resources in advance based on the performance prediction model.

[0023] Furthermore, the computing task dynamic optimization module also includes a computing task priority management unit, which is used to dynamically adjust the priority of the task queue according to the real-time requirements of the indicators, service level agreements and business importance, so as to ensure that the computing tasks of key indicators get computing resources first.

[0024] Furthermore, the computing task dynamic optimization module also includes an elastic resource scaling unit; The elastic resource scaling unit is used to monitor the overall load status of the cluster. When the backlog of the task queue exceeds the threshold, it automatically requests the resource manager to expand the computing nodes. When the load decreases, it automatically releases idle nodes to save computing resources, thereby realizing the elastic scaling of the computing cluster.

[0025] The intelligent report generation unit 18 provides an interactive interface, allowing users to reference indicators in the indicator data warehouse through functions, dynamically generate report templates, and automatically update report content based on changes in indicator data.

[0026] Furthermore, the intelligent report management system of this embodiment also includes an indicator service and sharing unit; The indicator service and sharing unit is used to encapsulate the indicators in the indicator data warehouse into standardized API services and publish them to the outside world through the indicator routing gateway. The indicator routing gateway has a built-in version negotiation mechanism. When an external business system initiates an indicator call request, it automatically matches the currently valid indicator model version according to the business context carried in the request and routes it to the corresponding indicator calculation instance.

[0027] The following is a more detailed description of an embodiment of the present invention using a specific implementation: Specifically, the intelligent reporting system's user interface mainly consists of four parts: a time display area, a user information area, a function menu area, and a function operation area. The time display area shows today's date and time; the user information area allows users to lock the screen, switch to full-screen mode, view user information, change passwords, change skin animations, and exit the system; the function menu area allows users to collapse and expand function menus and select function items; and the function operation area allows users to design, share, and view reports.

[0028] Specifically, creating a new report uses a wizard-style creation method. Clicking the "New" button on the report homepage takes you to the report creation page, which consists of two main steps: report configuration information and template creation. The report homepage displays the name of the report created by the user and allows for functions such as format design, report viewing, report sharing, and report saving.

[0029] Specifically, the report display provides a centralized view and management of published reports. Users can import, export, modify, and save reports as needed. The reports also have a recalculation function; clicking "recalculate" updates the latest data and style for previously saved reports. The report display section is categorized by report type: Production Management Department Reports, Operations Department Reports, and Energy Conservation Reports. The directory is flexibly configurable, allowing for additions or deletions based on report classification.

[0030] Specifically, the operation evaluation function includes report templates such as real-time indicator data, daily performance reports, monthly performance reports, and load-sharing analysis. The system supports flexible addition and deletion of indicators and supports competition assessments for indicators with lower scores. Load-sharing analysis focuses on analyzing the differences in performance indicators across different load segments. Flue gas temperature analysis focuses on analyzing the impact of flue gas temperature on unit efficiency.

[0031] Specifically, the system management functions mainly involve configuring basic data for front-end display modules, configuring the basic data required for the entire product framework, and assigning corresponding roles to different users. Based on these roles, users are given appropriate display menu functions, with the aim of assigning appropriate permissions to system users and enabling different functions for different personnel. The system management menu management page manages and maintains the sub-menus and subordinate functional modules of the entire system. Administrators can create new functional modules and pages; this page manages the hierarchical structure of the entire system's functional menu. Specifically, the report management area primarily manages functions such as report publishing, shift information, indicator configuration, report statistics, and indicator appeals. Indicator configuration management includes adding indicators, adding sub-indicators, editing indicators, deleting indicators, and refreshing indicators. Shift information corresponds to the company's actual shift schedule, providing a basis for performance calculations for each shift. Report statistics provide overall system data. Indicator appeals allow for the removal of abnormal data for a specific indicator. The report management statistics page displays information such as visitor count, number of system reports, and number of users.

[0032] Specifically, the report configuration management allows users to design, view, publish, and export report templates. For searching, users can enter the report name or the user to perform a fuzzy search. For viewing reports, selecting the corresponding report will open a new interface for data viewing. For publishing reports, selecting the corresponding report template and clicking "Publish Report" will publish the report to the menu management module; this function allows reports to be attached to the system's left-hand menu.

[0033] Specifically, publishing a report externally involves displaying and publishing the report template via a link. Published templates can be linked to the current system or other business systems via a menu. Display permissions can also be modified when publishing a report. Clicking "Publish Externally" creates an external publishing link. This link can be copied to a browser within the factory network for direct viewing. Clicking "Modify Permissions" allows modification of external publishing operation permissions (save, recalculate, import); query and export are enabled by default. The report must be published before modifying permissions.

[0034] Specifically, the report recalculation function is used to recalculate and save historical data for automatically generated reports. This function allows you to specify a time period, during which the system automatically recalculates and saves the report data according to the report's cycle. The recalculation period includes the start time but excludes the end time. Recalculation sources include: reports (recalculated and saved based on generated reports) and templates (deleted existing reports and recalculated using formulas from templates).

[0035] The embodiments of the present invention have the following beneficial effects: This application addresses the issue of inconsistent data formats by unifying the access of dispersed data through a multi-source heterogeneous data access and standardization unit, and by automatically constructing an enterprise-level unified indicator model tree using semantic parsing technology through an indicator model construction unit, enabling centralized definition and global reuse of indicators. A data quality verification and governance unit configures a multi-dimensional rule engine to perform real-time detection and automatic repair of indicator data, improving data accuracy and reliability. An indicator instantiation and storage unit automatically generates standardized indicator data through data extraction and calculation tasks, and achieves full lifecycle traceability by combining an indicator lineage recording module. An intelligent report generation unit allows users to dynamically generate and automatically update reports by referencing indicators using functions, and business personnel can flexibly customize reports, reducing reliance on the IT department. An indicator service and sharing unit encapsulates indicators as standardized API services for external release, and dynamically adjusts pre-calculation strategies by calling heatmaps, achieving efficient indicator sharing and resource optimization. This application achieves closed-loop management across the entire chain from data access, indicator modeling, quality governance to intelligent reporting, significantly improving enterprise data governance and report application efficiency.

[0036] Method Implementation Examples According to embodiments of the present invention, an intelligent report management method is provided. Figure 6 This is a flowchart of the intelligent report management method according to an embodiment of the present invention. Figure 6 As shown, the intelligent report management method of this invention includes: S1. Obtain raw data from heterogeneous data sources, standardize the collected raw data, and generate standardized basic data that conforms to a unified data model. S2. Based on the standardized basic data, extract the business meaning of the fields through semantic parsing technology, combine the predefined indicator naming specifications and domain dictionary, automatically identify and construct an enterprise-level unified indicator model tree, and define the unique identifier, data source, calculation method, quality rules and lineage of each indicator. S3. Configure a data quality verification rule engine for each indicator to perform real-time quality detection on indicator data, identify abnormal data, perform repair according to preset strategies, and generate data quality scores and governance logs. S4. Generate data extraction and calculation tasks for each indicator based on the indicator model tree, extract and calculate indicator values ​​from standardized basic data at regular intervals, form standardized indicator time series data, and store them in the indicator data warehouse according to a unified model. S5. Provide an interactive interface that allows users to reference indicators in the indicator data warehouse through functions, dynamically generate report templates, and automatically update report content based on changes in indicator data.

[0037] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A report intelligent management system, characterized in that... include: The multi-source heterogeneous data access and standardization unit is used to acquire raw data from heterogeneous data sources, standardize the acquired raw data, and generate standardized basic data that conforms to a unified data model. The indicator model construction unit is used to extract the business meaning of fields based on the standardized basic data through semantic parsing technology, and automatically identify and construct an enterprise-level unified indicator model tree by combining predefined indicator naming specifications and domain dictionaries, and define the unique identifier, data source, calculation method, quality rules and lineage of each indicator. The data quality verification and governance unit is used to configure a data quality verification rule engine for each indicator, perform real-time quality detection on indicator data, identify abnormal data, perform repairs according to preset strategies, and generate data quality scores and governance logs. The indicator instantiation and storage unit is used to generate data extraction and calculation tasks for each indicator according to the indicator model tree, extract and calculate indicator values ​​from standardized basic data at regular intervals, form standardized indicator time series data, and store them in the indicator data warehouse according to a unified model. The intelligent report generation unit provides an interactive interface, allowing users to reference indicators in the indicator data warehouse via functions, dynamically generate report templates, and automatically update report content based on changes in indicator data.

2. The system according to claim 1, characterized in that, The multi-source heterogeneous data access and standardization unit specifically includes: The dynamic adapter module is used to automatically load the corresponding parsing driver according to the data source type, and supports multiple access methods; The coding mapping module is used to convert equipment codes, material codes, units of measurement, etc. from different systems into unified enterprise codes according to preset mapping rules; The time alignment module is used to standardize timestamps from different data sources, interpolating or aggregating non-integer or non-equal interval data into a unified time-series granularity.

3. The system according to claim 1, characterized in that, The indicator model construction unit specifically includes: The semantic parsing module is used to perform natural language processing on field names, comments, and historical query logs, identify the physical quantities, equipment objects, and statistical calibers corresponding to the fields, and automatically generate triplet descriptions of the indicators. The indicator relationship reasoning module is used to automatically identify the derivation relationship between indicators by analyzing the field naming rules and the association relationship in the SQL query log, and generate a candidate set of indicator calculation formulas. The model version management module is used to generate new indicator model versions when the data source structure or business definition changes, and supports the parallel operation of old and new versions to ensure that historical reports can still be rendered according to the old version model.

4. The system according to claim 1, characterized in that, The data quality verification and governance unit includes: The rule configuration module is used to configure multi-dimensional quality verification rules for each indicator, including integrity rules, accuracy rules, consistency rules, timeliness rules, and business logic rules. An anomaly detection engine is used to scan indicator data in real time or at regular intervals, match the quality verification rules, identify abnormal data and generate abnormal events. Anomaly types include: jump, interruption, dead value, exceeding limit, and multi-source inconsistency. The automatic repair module is used to process abnormal data according to a preset repair strategy and add repair tags and confidence scores to the repaired data. The quality closed-loop feedback module is used to feed back abnormal data and repair operations after manual review and confirmation as training samples to the anomaly detection engine, continuously optimizing the accuracy of the detection model.

5. The system according to claim 1, characterized in that, The indicator instantiation and storage unit includes: The task orchestration module is used to automatically generate a DAG (Directed Acyclic Graph) of indicator calculation tasks based on the indicator collection frequency and calculation dependencies, and schedule the entire process of data extraction, cleaning, calculation and storage. The indicator lineage recording module is used to record the original data source, cleaning operation, calculation process and timestamp of each indicator value, and generate a complete indicator data lineage map, supporting the tracing from report data to the original collection point.

6. The system according to claim 5, characterized in that, The indicator lineage record module includes: The lineage metadata collection unit is used to collect source layer metadata, operation layer metadata and flow layer metadata throughout the entire life cycle of indicator data. The source layer metadata records the original data source identifier and original value corresponding to the indicator value. The operation layer metadata records the cleaning operation identifier and calculation formula hash of the indicator value. The flow layer metadata records the migration path of the indicator value between different computing nodes. The time-series graph construction unit is used to organize the source layer metadata, operation layer metadata and flow layer metadata into a time-series graph structure. The time-series graph structure uses original data points, cleaning operation nodes, calculation operation nodes and index value nodes as node types, and uses "from", "cleaned at", and "calculated at" as edge types, and carries an effective time window for each edge. The impact domain analysis unit is used to identify all indicator nodes that depend on the changed node by traversing the time series graph from the changed node when the original data source changes, generate an impact domain set, and trigger corresponding recalculation or review tasks based on the impact coefficient to classify the affected indicators. The operation fingerprint verification unit is used to construct a Merkle hash tree for the complete lineage path of each indicator value to generate an operation fingerprint. When a user initiates a verification request, the Merkle root hash of the current lineage path is recalculated and compared with the stored historical fingerprints to locate the changed node positions.

7. The system according to claim 1, characterized in that, It also includes an indicator service and sharing unit; The indicator service and sharing unit is used to encapsulate the indicators in the indicator data warehouse into standardized API services and publish them to the outside world through the indicator routing gateway. The indicator routing gateway has a built-in version negotiation mechanism. When an external business system initiates an indicator call request, it automatically matches the currently valid indicator model version according to the business context carried in the request and routes it to the corresponding indicator calculation instance.

8. The system according to claim 1, characterized in that, The indicator instantiation and storage unit also includes a dynamic optimization module for computing tasks; The dynamic optimization module for computing tasks is used to monitor the execution time, resource consumption, and data freshness status of indicator calculation tasks in real time. When an indicator calculation task times out or resource usage is abnormal, a task rescheduling mechanism is automatically triggered. The task rescheduling mechanism includes: splitting the timed-out task into multiple sub-tasks for parallel execution, migrating the calculation tasks of high-frequency indicators to dedicated computing nodes, or adjusting the calculation tasks of periodic indicators to be executed in batches during off-peak hours. The dynamic optimization module for computing tasks is also used to build a performance prediction model for computing tasks based on historical task execution data, and to estimate the resource requirements of new indicator calculation tasks and allocate computing resources in advance based on the performance prediction model.

9. The system according to claim 8, characterized in that, The computing task dynamic optimization module also includes an elastic resource scaling unit; The elastic resource scaling unit is used to monitor the overall load status of the cluster. When the backlog of the task queue exceeds the threshold, it automatically requests the resource manager to expand the computing nodes. When the load decreases, it automatically releases idle nodes to save computing resources, thereby realizing the elastic scaling of the computing cluster.

10. A management method based on the intelligent report management system according to any one of claims 1-9, characterized in that... include: S1. Obtain raw data from heterogeneous data sources, standardize the collected raw data, and generate standardized basic data that conforms to a unified data model. S2. Based on the standardized basic data, extract the business meaning of the fields through semantic parsing technology, combine the predefined indicator naming specifications and domain dictionary, automatically identify and construct an enterprise-level unified indicator model tree, and define the unique identifier, data source, calculation method, quality rules and lineage of each indicator. S3. Configure a data quality verification rule engine for each indicator to perform real-time quality detection on indicator data, identify abnormal data, perform repair according to preset strategies, and generate data quality scores and governance logs. S4. Generate data extraction and calculation tasks for each indicator based on the indicator model tree, extract and calculate indicator values ​​from standardized basic data at regular intervals, form standardized indicator time series data, and store them in the indicator data warehouse according to a unified model. S5. Provide an interactive interface that allows users to reference indicators in the indicator data warehouse through functions, dynamically generate report templates, and automatically update report content based on changes in indicator data.

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