Large-screen data statistics method based on multi-dimensional configuration

Abstract

The present invention proposes a large-screen data statistics method based on multi-dimensional configuration, which includes: configuring database query statements and determining the configurations corresponding to the input database and the output database; aligning the source data business fact table using a high-frequency value inheritance conflict resolution algorithm; configuring different field accumulation strategies for local organizational statistical data with policy tags, and configuring an accumulation extraction strategy; sorting the data bound to the extraction strategy and the local organizational statistical data with policy tags; generating a path animation sequence based on a secondary pop-up data set and a statistical optimization path scheme, and integrating it into the display large screen to obtain the final large-screen display result. The present invention automatically standardizes heterogeneous fields of different databases through a database-driven semantic alignment algorithm, solves the problem of multi-source data integration, and uses high-frequency value inheritance conflict resolution technology to significantly improve the accuracy of filling empty fields and eliminate statistical deviations.

Description

Technical Field

[0001] The present invention relates to the field of data statistics, and in particular to a large-screen data statistics method based on multi-dimensional configuration. Background Art

[0002] As the union system improves, various types of union data continue to increase in the system. Customers need to intuitively view statistics for various union organizations on a large screen, and they also need to stack data in multiple dimensions. This is because high-level union branches may not generate specific business data, and data generated by grassroots unions needs to be stacked up to the union branches to present the high-level union hierarchy. Furthermore, because union organizational hierarchies are often quite deep, previous technologies required recursive queries for the next level of data. When the organizational tree is too deep, the interface will be stuck due to repeated recursive queries and a large number of database connections will be occupied.

[0003] As business expands, the dimensions of statistical logic used for stacking up data may become inconsistent. For example, statistics for trade unions may be aggregated based on organizational categories, grassroots union branches, and higher-level unions. Queries for union personnel may also be categorized by age. When data statistics involve multiple dimensions, developers often need to write complex code, and each level requires separate calculations. Furthermore, due to business complexity, data may be stored in multiple databases, requiring data extraction from different databases for statistical purposes. Summary of the Invention

[0004] In view of the above situation, the main purpose of the present invention is to propose a large-screen data statistics method based on multi-dimensional configuration to solve the above technical problems.

[0005] The present invention proposes a large-screen data statistics method based on multi-dimensional configuration, the method comprising the following steps:

[0006] Step 1: Configure the database query statement and determine the corresponding configuration of the input database and output database. Use the custom ETL tool to execute the database query statement to obtain the source data business fact table.

[0007] Step 2: Use the high-frequency value inheritance conflict resolution algorithm to align the source data business fact table to obtain a standardized business fact table;

[0008] Step 3: Determine and obtain a business rule library, and use the business rule library to match the standardized business fact table with an optimization strategy to output a strategy file;

[0009] Step 4: Calculate local organization data based on the standardized business fact table and policy file to obtain local organization statistical data with policy tags;

[0010] Step 5: Configure different field accumulation strategies for the local organization statistical data with policy tags, and configure the accumulation extraction strategy to obtain the data bound to the extraction strategy;

[0011] Step 6: Sort the data bound to the extraction strategy and the local organization statistical data with strategy tags based on the hierarchical mapping and reverse sorting algorithm, and accumulate them into the database using the cumulative extraction strategy to obtain a multi-dimensional statistical data table;

[0012] Step 7: Determine and obtain a MongoDB database, input the component configuration and the multi-dimensional statistical data table, group the multi-dimensional statistical data table according to the component icons in the input component configuration, and store the grouped data in a MongoDB statistical data set. Use the MongoDB statistical data set to query the lower-level data to obtain a secondary pop-up window data set.

[0013] Utilize the secondary pop-up data set and multi-dimensional statistical data table to construct the organizational feature vector, and calculate the influence value based on the organizational feature vector to generate the optimization path and obtain the statistical optimization path solution;

[0014] Generate a path animation sequence based on the secondary pop-up window data set and the statistically optimized path plan, and integrate it into the large display screen to obtain the final large-screen display result.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] 1. This invention adopts a unified code to perform calculations based on the developed configuration and uses a self-developed data superposition algorithm. It can achieve accurate multi-level accumulation of multiple data in one calculation. Compared with the results of self-developed calculations, it ensures the accuracy of the data and greatly improves the efficiency of the large-screen development of the union system. It also reduces the cost of front-end and back-end docking. Since the statistical table is generated by a unified code, a public interface can be used to query data when it is displayed on the large screen, without having to encapsulate other complex interfaces to query data.

[0017] 2. The present invention adopts unified processing. When unified business changes occur in the later stage, it is only necessary to change the public code data overlay strategy to adapt to the public business changes, which further improves the scalability; since the storage of statistical data is isolated from the source database and stored in different databases, the impact of business change data on the large-screen statistical results is reduced, the system data is more stable, and is not affected by the source database and business. When large traffic accesses the business, the large screen independently accesses the database more stably and there will be no lag.

[0018] 3. The present invention uses a database-driven semantic alignment algorithm to automatically standardize heterogeneous fields in different databases, solve the problem of multi-source data integration, and use high-frequency value inheritance conflict resolution technology to greatly improve the accuracy of filling empty fields and eliminate statistical bias. At the same time, it uses field feature automatic labeling technology to intelligently identify the statistical value of data, and uses business rule library matching to automatically generate initial strategies, so that local statistical data becomes a high-value intermediate product with business semantic tags, pre-grouping dimensions and complete strategy lineage, providing standardized input that does not require parsing or cleaning for subsequent multi-dimensional accumulation, solving the problem of broken statistical chains in traditional solutions.

[0019] 4. The present invention achieves deep insights into cross-organizational data associations by constructing an intelligent statistical influence network; combines dynamic optimization path generation with real-time deduction technology to upgrade static data display to a decision support system; uses 3D visualization technology to intuitively present best practice paths, significantly improving the analytical depth and decision-making value of large-screen data; and effectively improves the operability and business guidance of statistical data through a closed-loop "data storage-insight analysis-deduction and display" process.

[0020] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a flowchart of the steps of the large-screen data statistics method based on multi-dimensional configuration proposed by the present invention.

[0022] Figure 2 This is a framework diagram for generating a source data business fact table for the large-screen data statistics method based on multi-dimensional configuration proposed by the present invention.

[0023] Figure 3 This is a framework diagram for resolving high-frequency value inheritance conflicts in the large-screen data statistics method based on multi-dimensional configuration proposed by the present invention.

[0024] Figure 4 This is a business rule matching framework diagram of the large-screen data statistics method based on multi-dimensional configuration proposed by the present invention.

[0025] Figure 5 This is a strategically labeled statistics generation framework diagram for the large-screen data statistics method based on multi-dimensional configuration proposed by the present invention.

[0026] Figure 6 This is a diagram of the accumulation strategy binding framework of the large-screen data statistics method based on multi-dimensional configuration proposed by the present invention.

[0027] Figure 7This is a framework diagram for generating a multi-dimensional statistical table for the large-screen data statistical method based on multi-dimensional configuration proposed by the present invention.

[0028] Figure 8 This is a large-screen display framework diagram of the large-screen data statistics method based on multi-dimensional configuration proposed by the present invention. DETAILED DESCRIPTION

[0029] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0030] These and other aspects of the embodiments of the present invention will become clear with reference to the following description and accompanying drawings. In these descriptions and accompanying drawings, some specific implementations of the embodiments of the present invention are specifically disclosed to provide some ways to implement the principles of the embodiments of the present invention, but it should be understood that the scope of the embodiments of the present invention is not limited thereto.

[0031] See also Figure 1 , an embodiment of the present invention proposes a large-screen data statistics method based on multi-dimensional configuration, the method comprising the following steps:

[0032] Step 1: Configure the database query statement and determine the corresponding configuration of the input database and output database. Execute the database query statement through the custom ETL tool to obtain the source data business fact table.

[0033] See also Figure 2 In step 1, configure the database query statement and determine the corresponding configuration of the input database and output database. Use the custom ETL tool to execute the database query statement to obtain the source data business fact table. The specific steps include the following:

[0034] Configure database query statements based on the source database and determine the corresponding configurations of the input and output databases;

[0035] According to the input database configuration and output database configuration, use the custom ETL tool to configure the input unit and output unit, and execute the database query statement to extract the source data to form the source data business fact table.

[0036] Furthermore, in the custom ETL tool, the input unit (input) has three fields: database name (dataBaseName), database specific address alias (used to identify the specific database address and link in multiple data sources), and the ID of the query statement to be executed in the execution table.

[0037] The output unit (Output) has three fields: target table name, database name, and database specific address alias (used to identify the specific database address and link in multiple data sources).

[0038] It should be noted that in the attached Figure 2 In the function, the fact table represents the source data business fact table, and sql represents the query statement. The developer defines the input cell properties and the output cell properties, obtains data in batches according to the query statement in the input cell, obtains the database connection from the target database configured in the input cell, executes the query statement to obtain the source data, obtains the database connection from the statistical database, automatically generates the corresponding insert query statement, inserts the source data, and automatically inserts it into the statistical database business fact table.

[0039] In this embodiment, the present invention dynamically constructs input / output units through a configured ETL tool, automatically routes multi-source connections based on database aliases, utilizes query statement IDs to perform batch data extraction, and automatically generates target table insertion logic. This overcomes the efficiency bottleneck of traditional ETL development, which requires manual connection code writing and table-by-table mapping rule configuration. It enables one-click generation of source data into business fact tables, significantly shortening the data access cycle and supporting plug-and-play access to heterogeneous databases.

[0040] Step 2: Use the high-frequency value inheritance conflict resolution algorithm to align the source data business fact table to obtain a standardized business fact table.

[0041] In step 2, the source data business fact table is aligned using the high-frequency value inheritance conflict resolution algorithm to obtain a standardized business fact table. The specific steps include the following:

[0042] Determine and obtain a knowledge base, perform similarity calculation on the source data business fact table based on the knowledge base, and obtain a similarity calculation result;

[0043] Determine and obtain a similarity threshold, use the similarity threshold to judge the similarity calculation result, obtain a judgment result, perform field mapping on the judgment result, and obtain a field mapping table;

[0044] Use the high-frequency value inheritance conflict resolution algorithm to process null values ​​in the field mapping table and the source data business fact table to obtain a null value filled record table;

[0045] Perform type unification on the null value filling record table and the source data business fact table to obtain the type conversion log;

[0046] Perform path parsing and type inference on the empty value filling record table in turn to obtain a derived field definition table;

[0047] A virtual view is constructed based on the field mapping table, null value filling record table, type conversion log, and derived field definition table to obtain a standardized business fact table.

[0048] In this embodiment, the present invention uses a high-frequency value inheritance conflict resolution algorithm combined with a knowledge base similarity threshold to achieve automatic field mapping. Null values ​​are filled with high-frequency values ​​from the same field, and derived fields are dynamically derived through path resolution. This addresses the accuracy issues inherent in traditional data cleaning, which relies on manual rule configuration and the subjective nature of null value handling. It also achieves efficient and automated alignment of business fact tables, significantly improving data standardization quality and field mapping reliability.

[0049] Step 3: Determine and obtain a business rule library, and use the business rule library to match the standardized business fact table with an optimization strategy to output a strategy file.

[0050] In step 3, the business rule library is used to match the standardized business fact table with the optimization strategy to output a strategy file, which specifically includes the following steps:

[0051] Dynamically annotate the standardized business fact table to obtain the field feature set;

[0052] Based on the business rule library, the field feature set is matched to obtain the initial strategy draft;

[0053] Using the initial policy draft, business constraints are added to the standardized business fact table to obtain an enhanced policy draft;

[0054] Detect the nested level of the enhanced strategy draft and obtain the optimized strategy solution;

[0055] The strategy is compiled using the optimized strategy solution to generate executable code and finally obtain the strategy file.

[0056] In this embodiment, the present invention extracts field features through dynamic annotation, generates initial policies based on matching within a business rule library, and compiles them into executable code after hierarchical detection and optimization of constraint logic. This overcomes the limitations of traditional business rule implementation, which requires repeated development and verification and struggles to dynamically adapt to data features. It enables intelligent generation and self-optimization of policy files, significantly improving the efficiency of business constraint deployment and reducing the risk of execution errors.

[0057] Step 4: Calculate local organization data based on the standardized business fact table and policy file to obtain local organization statistical data with policy tags.

[0058] Step 5: Configure different field accumulation strategies for local organization statistical data with policy tags, and configure the accumulation extraction strategy to obtain data bound to the extraction strategy.

[0059] See also Figure 5In step 5, different field accumulation strategies are configured for the local organization statistical data with policy tags, and the accumulation extraction strategy is configured to obtain the data bound by the extraction strategy. The specific steps include the following:

[0060] Determine and obtain defined accumulation rules, perform vertical accumulation rule analysis on local organizational statistical data with policy tags, and generate vertical accumulation logic;

[0061] Configure horizontal splitting rules based on vertical accumulation logic;

[0062] The result table structure is set using vertical accumulation logic and horizontal splitting rules, and the result table structure is applied to local organizational statistical data with policy tags to obtain data bound by the extraction policy.

[0063] In this embodiment, the present invention automatically generates aggregation logic through a vertical accumulation rule parser and dynamically constructs the result table structure in conjunction with horizontal splitting rules. This addresses the drawbacks of manually writing complex window functions for multidimensional statistics, which are prone to errors and have poor reusability in cross-dimensional accumulation logic. It also enables declarative configuration of accumulation strategies, significantly reducing the amount of statistical logic development and supporting dynamic dimensional expansion.

[0064] Step 6: Sort the data bound to the extraction strategy and the local organization statistical data with strategy tags based on hierarchical mapping and reverse sorting algorithm, and accumulate them into the database using the cumulative extraction strategy to obtain a multi-dimensional statistical data table.

[0065] See also Figure 3 、 Figure 4 and Figure 6 In step 6, the data bound to the extraction strategy and the local organization statistical data with strategy tags are sorted based on the hierarchical mapping and reverse sorting algorithm, and accumulated into the database using the cumulative extraction strategy to obtain a multi-dimensional statistical data table, which specifically includes the following steps:

[0066] The data bound by the extraction strategy and the local organization statistical data with the strategy mark are sorted using the reverse output algorithm to obtain the sorted data;

[0067] Perform hierarchical mapping on the sorted data to obtain an ordered organized list and a quick query mapping table;

[0068] Loop through the ordered organized list and quickly query the mapping table in sorted order to obtain the traversal results;

[0069] Apply vertical accumulation logic to the traversal results and extract the current-level statistical value to obtain the current-level statistical value; use the quick query mapping table to find the parent organization to obtain the parent organization; add the current-level statistical value to the parent organization to obtain the complete vertical accumulation value;

[0070] Read the dimension value of the current organization for the vertically accumulated value, split the accumulated value into the corresponding dimension field to obtain the horizontal split value, and then add the horizontal split value to the parent organization to obtain the multi-dimensional accumulated value.

[0071] Create a new table and write the ordered organization list, quick query mapping table, vertical complete accumulated values, current level statistical values, horizontal split values, and multi-dimensional accumulated values ​​into the new table to obtain a multi-dimensional statistical data table.

[0072] In this embodiment, the present invention constructs an ordered list using a reverse sorting algorithm, utilizes hierarchical mapping to quickly locate higher-level data, and combines vertical accumulation with horizontal splitting to atomically update multi-dimensional statistical values. This solves the performance degradation problem of traditional recursive statistics in deep hierarchical scenarios. It enables efficient linear processing of ultra-large-scale hierarchical data, significantly improving the speed of statistical generation and reducing resource consumption.

[0073] Step 7: Determine and obtain a MongoDB database, input the component configuration and the multi-dimensional statistical data table, group the multi-dimensional statistical data table according to the component icons in the input component configuration, and store the grouped data in a MongoDB statistical data set. Use the MongoDB statistical data set to query the lower-level data to obtain a secondary pop-up window data set.

[0074] Utilize the secondary pop-up data set and multi-dimensional statistical data table to construct the organizational feature vector, and calculate the influence value based on the organizational feature vector to generate the optimization path and obtain the statistical optimization path solution;

[0075] Generate a path animation sequence based on the secondary pop-up window data set and the statistically optimized path plan, and integrate it into the large display screen to obtain the final large-screen display result.

[0076] See also Figure 7 In step 7, input the component configuration and multi-dimensional statistical data table, group the multi-dimensional statistical data table according to the component icons in the input component configuration, and store it in the MongoDB statistical data set. Use the MongoDB statistical data set to query the lower-level data to obtain the secondary pop-up window data set. Specifically, the steps include:

[0077] Input component configuration and multi-dimensional statistical data table, parse component icons in the component configuration, group the multi-dimensional statistical data table by component icons, and obtain grouping results;

[0078] Create a MongoDB collection based on the MongoDB database, store the grouping results in the MongoDB collection and create an index to obtain a MongoDB statistical data set;

[0079] Obtain the user click event, obtain the clicked organization icon based on the user click event, and use the clicked organization icon to locate the icon in the MongoDB statistical data set to obtain the positioning result;

[0080] The data of the organization at the current level is queried based on the positioning result to obtain the queried data of the organization at the current level, and the accumulated statistical value is returned using the queried data of the organization at the current level to obtain the statistical value at the current level;

[0081] Perform a query on the lower-level data based on the current-level statistical values ​​combined with the MongoDB statistical data set to obtain the lower-level data query results. Use the lower-level data query results to calculate and verify the percentage of lower-level organizations, and format and display the data based on the percentage of lower-level organizations to obtain the secondary pop-up data set.

[0082] The organizational feature vector is constructed using the secondary pop-up data set and the multi-dimensional statistical data table. The influence value is calculated based on the organizational feature vector to generate an optimization path and obtain a statistical optimization path solution. The specific steps include the following:

[0083] Extract key statistical dimensions from the secondary pop-up data set and multi-dimensional statistical data table, and generate organizational feature vectors based on the key statistical dimensions;

[0084] Constructing an organizational feature matrix based on the organizational feature vector;

[0085] The cosine similarity between organizations is calculated using the organizational feature matrix to obtain the calculated cosine similarity;

[0086] An organizational association graph is constructed based on the calculated cosine similarity, and the influence value is calculated by applying the webpage ranking algorithm to the organizational association graph to obtain the organizational influence network;

[0087] The multi-dimensional statistical data table is sorted by influence value using the organizational influence network to obtain a sorted multi-dimensional statistical data table;

[0088] The sorted multi-dimensional statistical data table is sequentially subjected to high-impact screening and best practice marking to obtain a best practice set;

[0089] For the best practice set, the organizational influence network and the local statistical values ​​are combined to select relevant best practice examples, and the shortest path in the network is found based on the best practice examples to obtain the shortest path found;

[0090] An expected improvement value is calculated for the shortest path found to obtain a calculated expected improvement value, and optimization steps are generated according to the calculated expected improvement value to obtain a statistically optimized path solution.

[0091] Generate a path animation sequence based on the secondary pop-up data set and the statistically optimized path plan, and integrate it into the large display screen to obtain the final large-screen display result. The specific steps include the following:

[0092] The statistically optimized path plan is extracted using the organizational influence network, and the node spatial coordinates are calculated to generate a 3D flight animation.

[0093] Add information annotation points to the 3D flight animation to obtain optimized path animation;

[0094] Perform adjustable parameter analysis on the statistical optimization path solution to obtain the analysis results, and use the analysis results to create a parameter slider control;

[0095] The parameter slider control is simulated by the algorithm to construct the result display area and obtain the real-time deduction panel;

[0096] The display screen is divided into display areas to obtain the divided display screen. The optimized path animation, real-time deduction panel, current level statistical values ​​and secondary pop-up data sets are input into the divided display screen. The basic statistical view is integrated, and the large-screen component communication is established based on the basic statistical view to obtain the final large-screen display result.

[0097] In this embodiment, the present invention implements on-demand MongoDB storage by grouping component icons, constructs an influence network based on organizational feature vectors, applies graph algorithms to generate optimization paths, and dynamically displays decision chains through 3D animation. This addresses the pain points of traditional large-screen data display, which is isolated and lacks decision guidance. It enables penetrating analysis of statistical data and visual decision support, effectively improving management response speed and reducing implementation risks.

[0098] It should be understood that various components of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0099] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0100] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A large-screen data statistics method based on multi-dimensional configuration, characterized in that: The method comprises the following steps: Step 1: Configure the database query statement and determine the corresponding configuration of the input database and output database. Use the custom ETL tool to execute the database query statement to obtain the source data business fact table. Step 2: Use the high-frequency value inheritance conflict resolution algorithm to align the source data business fact table to obtain a standardized business fact table; Step 3: Determine and obtain a business rule library, and use the business rule library to match the standardized business fact table with an optimization strategy to output a strategy file; Step 4: Calculate local organization data based on the standardized business fact table and policy file to obtain local organization statistical data with policy tags; Step 5: Configure different field accumulation strategies for the local organization statistical data with policy tags, and configure the accumulation extraction strategy to obtain the data bound to the extraction strategy; Step 6: Sort the data bound to the extraction strategy and the local organization statistical data with strategy tags based on the hierarchical mapping and reverse sorting algorithm, and accumulate them into the database using the cumulative extraction strategy to obtain a multi-dimensional statistical data table; Step 7: Determine and obtain a MongoDB database, input the component configuration and the multi-dimensional statistical data table, group the multi-dimensional statistical data table according to the component icons in the input component configuration, and store the grouped data in a MongoDB statistical data set. Use the MongoDB statistical data set to query the lower-level data to obtain a secondary pop-up window data set. Utilize the secondary pop-up data set and multi-dimensional statistical data table to construct the organizational feature vector, and calculate the influence value based on the organizational feature vector to generate the optimization path and obtain the statistical optimization path solution; Generate a path animation sequence based on the secondary pop-up window data set and the statistical optimization path plan, and integrate it into the large display screen to obtain the final large-screen display result.

2. The large-screen data statistics method based on multi-dimensional configuration according to claim 1 is characterized in that: In step 1, configure the database query statement, determine the configuration of the input database and the output database, execute the database query statement through the custom ETL tool, and obtain the source data business fact table. The specific steps include: Configure database query statements based on the source database and determine the corresponding configurations of the input and output databases; According to the input database configuration and output database configuration, use the custom ETL tool to configure the input unit and output unit, and execute the database query statement to extract the source data to form the source data business fact table.

3. The large-screen data statistics method based on multi-dimensional configuration according to claim 2 is characterized in that: In step 2, the source data business fact table is aligned using a high-frequency value inheritance conflict resolution algorithm to obtain a standardized business fact table, which specifically includes the following steps: Determine and obtain a knowledge base, perform similarity calculation on the source data business fact table based on the knowledge base, and obtain a similarity calculation result; Determine and obtain a similarity threshold, use the similarity threshold to judge the similarity calculation result, obtain a judgment result, perform field mapping on the judgment result, and obtain a field mapping table; Use the high-frequency value inheritance conflict resolution algorithm to process null values ​​in the field mapping table and the source data business fact table to obtain a null value filled record table; Perform type unification on the null value filling record table and the source data business fact table to obtain the type conversion log; Perform path parsing and type inference on the empty value filling record table in turn to obtain a derived field definition table; A virtual view is constructed based on the field mapping table, null value filling record table, type conversion log, and derived field definition table to obtain a standardized business fact table.

4. The large-screen data statistics method based on multi-dimensional configuration according to claim 3 is characterized in that: In step 3, the business rule library is used to match the standardized business fact table with an optimization strategy to output a strategy file, which specifically includes the following steps: Dynamically annotate the standardized business fact table to obtain the field feature set; Based on the business rule library, the field feature set is matched to obtain the initial strategy draft; Using the initial policy draft, business constraints are added to the standardized business fact table to obtain an enhanced policy draft; Detect the nested level of the enhanced strategy draft and obtain the optimized strategy solution; The strategy is compiled using the optimized strategy solution to generate executable code and finally obtain the strategy file.

5. The large-screen data statistics method based on multi-dimensional configuration according to claim 4 is characterized in that: In step 5, different field accumulation strategies are configured for the local organization statistical data with the strategy mark, and an accumulation extraction strategy is configured to obtain data bound by the extraction strategy, which specifically includes the following steps: Determine and obtain defined accumulation rules, perform vertical accumulation rule analysis on local organizational statistical data with policy tags, and generate vertical accumulation logic; Configure horizontal splitting rules based on vertical accumulation logic; The result table structure is set using vertical accumulation logic and horizontal splitting rules, and the result table structure is applied to local organizational statistical data with policy tags to obtain data bound by the extraction policy.

6. The large-screen data statistics method based on multi-dimensional configuration according to claim 5 is characterized in that: In step 6, the data bound to the extraction strategy and the local organization statistical data with the strategy mark are sorted based on the hierarchical mapping and reverse sorting algorithm, and accumulated into the database using the cumulative extraction strategy to obtain a multi-dimensional statistical data table, which specifically includes the following steps: The data bound by the extraction strategy and the local organization statistical data with the strategy mark are sorted using the reverse output algorithm to obtain the sorted data; Perform hierarchical mapping on the sorted data to obtain an ordered organized list and a quick query mapping table; Loop through the ordered organized list and quickly query the mapping table in sorted order to obtain the traversal results; Apply vertical accumulation logic to the traversal results and extract the statistical value of this level to obtain the statistical value of this level; Use the quick query mapping table to search for the parent organization and obtain the parent organization you are looking for. Add the current level's statistical value to the parent organization to obtain a vertically complete cumulative value; Read the dimension value of the current organization for the vertically accumulated value, split the accumulated value into the corresponding dimension field to obtain the horizontal split value, and then add the horizontal split value to the parent organization to obtain the multi-dimensional accumulated value. Create a new table and write the ordered organization list, quick query mapping table, vertical complete accumulated values, current level statistical values, horizontal split values, and multi-dimensional accumulated values ​​into the new table to obtain a multi-dimensional statistical data table.

7. The large-screen data statistics method based on multi-dimensional configuration according to claim 6 is characterized in that: In step 7, the component configuration and the multi-dimensional statistical data table are input, the multi-dimensional statistical data table is grouped according to the component icons in the input component configuration, and stored in a mongoDB statistical data set. The mongoDB statistical data set is used to query the lower-level data to obtain a secondary pop-up window data set, specifically including the following steps: Input component configuration and multi-dimensional statistical data table, parse component icons in the component configuration, group the multi-dimensional statistical data table by component icons, and obtain grouping results; Create a MongoDB collection based on the MongoDB database, store the grouping results in the MongoDB collection and create an index to obtain a MongoDB statistical data set; Obtain the user click event, obtain the clicked organization icon based on the user click event, and use the clicked organization icon to locate the icon in the MongoDB statistical data set to obtain the positioning result; The data of the organization at the current level is queried based on the positioning result to obtain the queried data of the organization at the current level, and the accumulated statistical value is returned using the queried data of the organization at the current level to obtain the statistical value at the current level; Perform a query on the lower-level data based on the current-level statistical values ​​combined with the MongoDB statistical data set to obtain the lower-level data query results. Use the lower-level data query results to calculate and verify the percentage of lower-level organizations, and format and display the data based on the percentage of lower-level organizations to obtain the secondary pop-up data set.

8. The large-screen data statistics method based on multi-dimensional configuration according to claim 7 is characterized in that: The organizational feature vector is constructed using the secondary pop-up data set and the multi-dimensional statistical data table. The influence value is calculated based on the organizational feature vector to generate an optimization path and obtain a statistical optimization path solution. The specific steps include the following: Extract key statistical dimensions from the secondary pop-up data set and multi-dimensional statistical data table, and generate organizational feature vectors based on the key statistical dimensions; Constructing an organizational feature matrix based on the organizational feature vector; The cosine similarity between organizations is calculated using the organizational feature matrix to obtain the calculated cosine similarity; An organizational association graph is constructed based on the calculated cosine similarity, and the influence value is calculated by applying the webpage ranking algorithm to the organizational association graph to obtain the organizational influence network; The multi-dimensional statistical data table is sorted by influence value using the organizational influence network to obtain a sorted multi-dimensional statistical data table; The sorted multi-dimensional statistical data table is sequentially subjected to high-impact screening and best practice marking to obtain a best practice set; For the best practice set, the organizational influence network and the local statistical values ​​are combined to select relevant best practice examples, and the shortest path in the network is found based on the best practice examples to obtain the shortest path found; An expected improvement value is calculated for the shortest path found to obtain a calculated expected improvement value, and optimization steps are generated according to the calculated expected improvement value to obtain a statistically optimized path solution.

9. The large-screen data statistics method based on multi-dimensional configuration according to claim 8, characterized in that: Generate a path animation sequence based on the secondary pop-up data set and the statistically optimized path plan, and integrate it into the large display screen to obtain the final large-screen display result. The specific steps include the following: The statistically optimized path plan is extracted using the organizational influence network, and the node spatial coordinates are calculated to generate a 3D flight animation. Add information annotation points to the 3D flight animation to obtain optimized path animation; Perform adjustable parameter analysis on the statistical optimization path solution to obtain the analysis results, and use the analysis results to create a parameter slider control; The parameter slider control is simulated by the algorithm to construct the result display area and obtain the real-time deduction panel; The display screen is divided into display areas to obtain the divided display screen. The optimized path animation, real-time deduction panel, current level statistical values ​​and secondary pop-up data sets are input into the divided display screen. The basic statistical view is integrated, and the large-screen component communication is established based on the basic statistical view to obtain the final large-screen display result.

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