Target dimension driven multi-dimensional data statistical system and method

Abstract

The invention relates to the field of data statistics, and particularly discloses a multi-dimensional data statistics system and method driven by a target dimension, and the system comprises a data collection module which is used for collecting data containing mathematics, statistics and economy from a plurality of data sources; the data preprocessing module is used for cleaning, integrating, formatting and cross-domain standardizing the collected data so as to ensure the quality and consistency of the data, and the data preprocessing module has a data collection and preprocessing comprehensive evaluation function; carrying out quantitative evaluation on the integrity of data acquisition and the validity of preprocessing through a preset evaluation index; according to the invention, through the efficient data acquisition and preprocessing module, high-quality data can be rapidly integrated from a plurality of data sources, and the consistency and accuracy of the data are ensured; through flexible target dimension setting and a cross-domain multi-dimensional data analysis module, accurate response to diversified research requirements of users is realized, and deep data insight and correlation analysis are provided.

Description

Technical Field

[0001] The present invention belongs to the field of data statistics, and more specifically, it is a multi-dimensional data statistics system and method driven by target dimensions. Background Art

[0002] The background art of multi-dimensional data statistics is rooted in the rapid development of information technology and the growing demand for data analysis. The core of this technology lies in the systematic collection, integration, and analysis of massive and diverse data, aiming to uncover the deep information and value behind the data.

[0003] In traditional technologies, the setting of target dimensions and the selection of analysis dimensions are often affected by technical limitations and human factors, resulting in inaccurate and incomplete analysis results. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a multi-dimensional data statistics system driven by target dimensions to solve the problem that in the prior art, the setting of target dimensions and the selection of analysis dimensions are often affected by technical limitations and human factors, resulting in inaccurate and incomplete analysis results.

[0005] A multi-dimensional data statistics system driven by target dimensions includes:

[0006] A data acquisition module for collecting data covering mathematics, statistics, and economics from multiple data sources;

[0007] A data preprocessing module for cleaning, integrating, formatting, and cross-domain standardizing the collected data to ensure the quality and consistency of the data. The data preprocessing module includes a comprehensive evaluation function for data acquisition and preprocessing, and quantitatively evaluates the integrity of data acquisition and the effectiveness of preprocessing through preset evaluation indicators;

[0008] A target dimension setting module that allows users to set specific target dimensions according to research needs, including economic growth, market trends, consumer behavior, and social changes;

[0009] A cross-domain multi-dimensional data analysis module that, based on the set target dimensions, selects and combines analysis dimensions related to mathematics, statistics, and economics, deeply analyzes the data by combining mathematical algorithms, statistical methods, and economic theories, and calculates the multi-dimensional data fusion degree under the set target dimensions to quantitatively evaluate the fusion degree of data in different dimensions on the target dimension;

[0010] A visualization display module that intuitively displays the analysis results in the form of charts, maps, and dashboards, supports user-defined visualization styles and interactive functions, so that users can easily understand the associations and trends between cross-domain data;

[0011] A report generation module that automatically generates an interdisciplinary research report containing data analysis results, conclusions, and recommendations.

[0012] Preferably, the comprehensive evaluation formula for data collection and preprocessing evaluates the overall performance of the data collection and preprocessing module through integration and weighted summation, and its formula is as follows:

[0013]

[0014] Where P 1 is the performance evaluation of the data collection and preprocessing module, where T is the time span of data collection, N is the total number of data sources, and w i is the weight of each data source, and f clean (d i ) is the data cleaning function, representing the quality of data source i after cleaning, and f integrate (d i ) is the data integration function, representing the integrity of data source i after integration.

[0015] Preferably, the multi-dimensional data fusion degree under the target dimension setting evaluates the fusion degree of different-dimensional data under the target dimension setting by using the exponential function and KL divergence, and its calculation formula is as follows:

[0016]

[0017] Where F fusion is the measure of the multi-dimensional data fusion degree under the target dimension setting, where M is the total number of cross-domain analysis dimensions, and D KL (P j ||Q j ) is the KL divergence between the data distribution P j and the standard distribution Q j under dimension j, and σ j is the tolerance parameter for data fusion under dimension j.

[0018] Preferably, it further includes a data quality monitoring module for continuously monitoring the data quality during the processes of data collection, preprocessing, analysis, and report generation, evaluating it in real time through preset quality indicators, and triggering an alarm or automatically performing data correction operations when the data quality does not meet the standards.

[0019] Preferably, it further includes a user permission management module for managing the access permissions and operation permissions of different users, ensuring the security and compliance of data, supporting role-based access control models, and allowing administrators to assign different system access and operation permissions according to user roles.

[0020] Preferably, the cross - domain multi - dimensional data analysis module further includes a custom analysis dimension and index library, allowing users to customize analysis dimensions and metrics according to specific research needs. These custom dimensions and metrics can be combined, calculated, and transformed based on existing data fields, thereby expanding the system's analysis capabilities and flexibility.

[0021] Preferably, the system has good scalability and integration capabilities, supports seamless docking with other information systems, and realizes data sharing and business process collaboration through API interfaces and data exchange platforms, improving the overall data management and analysis efficiency.

[0022] A multi - dimensional data statistics method driven by a target dimension includes the multi - dimensional data statistics system driven by a target dimension as described above, and further includes the following steps:

[0023] Step 1: Start the system and initialize each module to ensure that the data collection module, data pre - processing module, target dimension setting module, cross - domain multi - dimensional data analysis module, visualization display module, and report generation module are all in an available state;

[0024] Step 2: According to the user's research needs, clearly set the target dimension in the target dimension setting module, including economic growth, market trends, consumer behavior, and social changes, and optionally adjust the analysis parameters and settings;

[0025] Step 3: Use the data collection module to automatically collect data covering the fields of mathematics, statistics, and economics from multiple specified data sources. At the same time, the data pre - processing module performs real - time cleaning, integration, formatting, and cross - domain standardization processing on the data, and quantitatively evaluates the processing process according to the data collection and pre - processing comprehensive evaluation formula;

[0026] Step 4: In the cross - domain multi - dimensional data analysis module, based on the set target dimension, intelligently select and combine relevant analysis dimensions, and conduct in - depth analysis of the data by combining mathematical algorithms, statistical methods, and economic theories. At the same time, use the multi - dimensional data fusion degree calculation formula to evaluate the fusion degree of data in different dimensions on the target dimension;

[0027] Step 5: Present the analysis results to the user intuitively in the form of charts, maps, and dashboards through the visualization display module, and support the user to customize the visualization style and interaction functions according to needs to better understand the associations and trends between cross - domain data;

[0028] Step 6: According to the analysis results, the report generation module automatically generates an interdisciplinary research report containing detailed data analysis results, conclusions, and suggestions. The user can further edit and adjust the report content to meet specific needs.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] Through an efficient data collection and preprocessing module, high-quality data from multiple data sources is quickly integrated, ensuring data consistency and accuracy;

[0031] Through a flexible target dimension setting and cross-domain multi-dimensional data analysis module, accurate responses to diverse user research needs are achieved, providing in-depth data insights and correlation analysis;

[0032] Through intuitive visualization displays and automated report generation functions, quick understanding and application of data analysis results are realized, improving work efficiency and the scientific nature of decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0035] As Figure 1 shown:

[0036] Embodiment 1: The present invention provides a multi-dimensional data statistics system driven by target dimensions, including:

[0037] A data collection module for collecting data covering mathematics, statistics, and economics from multiple data sources;

[0038] A data preprocessing module for cleaning, integrating, formatting, and cross-domain standardizing the collected data to ensure data quality and consistency. The data preprocessing module includes a comprehensive evaluation function for data collection and preprocessing, and quantitatively evaluates the integrity of data collection and the effectiveness of preprocessing through preset evaluation indicators;

[0039] A target dimension setting module that allows users to set specific target dimensions according to research needs, including economic growth, market trends, consumer behavior, and social changes;

[0040] A cross-domain multi-dimensional data analysis module that, based on the set target dimensions, selects and combines analysis dimensions related to mathematics, statistics, and economics, deeply analyzes the data by combining mathematical algorithms, statistical methods, and economic theories, and calculates the multi-dimensional data fusion degree under the set target dimensions to quantitatively evaluate the fusion degree of data in different dimensions on the target dimension;

[0041] The visualization display module visually presents the analysis results in the form of charts, maps, and dashboards, supporting users to customize the visualization styles and interactive functions so that users can easily understand the correlations and trends among cross-domain data;

[0042] The report generation module automatically generates an interdisciplinary research report containing data analysis results, conclusions, and suggestions.

[0043] Specifically, the comprehensive evaluation formula for data collection and preprocessing evaluates the overall performance of the data collection and preprocessing module through integration and weighted summation. The formula is as follows:

[0044]

[0045] Among them, P 1 is the performance evaluation of the data collection and preprocessing module, where T is the time span of data collection, N is the total number of data sources, w i is the weight of each data source, and f clean (d i ) is the data cleaning function, representing the quality of data source i after cleaning, and f integrate (d i ) is the data integration function, representing the integrity of data source i after integration;

[0046] The specific application process of the above formula is as follows:

[0047] Determine the time range T:

[0048] First, clarify the time span T of data collection according to the actual application scenario. For example, it can be one day, one week, one month, or a longer time period.

[0049] Determine the number of data sources N:

[0050] Next, determine the total number of data sources N. The data sources come from different channels or systems, such as databases, sensors, web crawlers, etc.

[0051] Allocate the data source weight w i :

[0052] For each data source i, assign a weight w i . The weight assignment is determined according to factors such as the importance, reliability, and data quality of the data source.

[0053] The data cleaning function f clean (d i ):

[0054] Data cleaning is an important step in the preprocessing process, used to remove noise, outliers, and duplicate data from the data.

[0055] Derive f clean (d i ): The specific form of the function depends on the data cleaning method and strategy. For example, if a missing value filling strategy is adopted, then f clean (d i ) may be a filling function; if a duplicate removal strategy is adopted, then f clean (d i ) may be a duplicate removal function.

[0056] In practical applications, it may be necessary to combine multiple cleaning strategies. Therefore, f clean (d i ) may be a composite function.

[0057] Data integration function f integrate (d i ):

[0058] Data integration is the process of combining and unifying data from different data sources.

[0059] Derive f integrate (d i ): The specific form of this function depends on the data integration method and strategy. For example, if a data mapping strategy is adopted, then f integrate (d i ) may be a mapping function; if a data aggregation strategy is adopted, then f integrate (d i ) may be an aggregation function.

[0060] Similarly, f integrate (d i ) may also be a composite function that combines multiple integration strategies.

[0061] Calculate the weighted sum:

[0062] For each data source i, calculate its quality assessment value w after cleaning and integration i ·f clean (d i )·f integrate (d i ).

[0063] Sum up the quality assessment values of all data sources with weights to obtain the total quality assessment value within the time range T.

[0064] Calculate the total weight:

[0065] At the same time, calculate the sum of the weights w of all data sources i to obtain the total weight within the time range T.

[0066] Calculate the comprehensive evaluation value P 1 :

[0067] Finally, divide the total quality evaluation value by the total weight to obtain the comprehensive evaluation value P 1 . P 1 The closer the value of P is to 1, the better the performance of the data acquisition and preprocessing module.

[0068] Specifically, the multi-dimensional data fusion degree under the set target dimension evaluates the fusion degree of different-dimensional data under the set target dimension by using the exponential function and KL divergence. The calculation formula is as follows:

[0069]

[0070] where F fusion is the measure of the multi-dimensional data fusion degree under the set target dimension, where M is the total number of cross-domain analysis dimensions, and D KL (P j ||Q j ) is the KL divergence between the data distribution P j and the standard distribution Q j in dimension j, and σ j is the tolerance parameter for data fusion in dimension j;

[0071] The specific application process of the above formula is as follows:

[0072] Determine the target dimension and the number of dimensions:

[0073] First, clarify the target dimension of the analysis, which is related to the business problem or research goal.

[0074] Determine the total number M of cross-domain analysis dimensions, which include different data types, sources, or characteristics.

[0075] Collect data and calculate the distribution:

[0076] For each dimension j, collect the corresponding data samples.

[0077] Calculate the data distribution P j in dimension j, which involves statistical analysis or fitting a distribution model to the data.

[0078] Set the standard distribution Q j , which is a distribution based on theory, experience, or prior knowledge.

[0079] Calculate the KL divergence:

[0080] For each dimension j, use the KL divergence formula to calculate D KL (P j ||Q j)。The KL divergence is an asymmetric measure for quantifying the difference between two probability distributions, and its formula is:

[0081] where x is the value of the data sample.

[0082] Set the tolerance parameter:

[0083] For each dimension j, set the tolerance parameter σ for data fusion j 。This parameter reflects the acceptable degree of the difference between the data distribution and the standard distribution under dimension j.

[0084] Calculate the fusion degree:

[0085] Use the formula to calculate the data fusion degree F fusion 。This value reflects the overall degree of multi-dimensional data fusion under the target dimension setting.

[0086] Function derivation process

[0087] Derivation of the KL divergence:

[0088] The KL divergence is a measure based on information theory for quantifying the difference between two probability distributions. Its derivation is based on the concepts of entropy and cross-entropy.

[0089] The entropy H(P) is a measure for quantifying the uncertainty or information content of a probability distribution P, and its formula is: H(P) = -∑ x P(x) log P(x);

[0090] The cross-entropy H(P, Q) is a measure for quantifying the average number of bits required to encode information from distribution P using distribution Q, and its formula is: H(P, Q) = -∑ x P(x) log Q(x);

[0091] The KL divergence is the difference between the cross-entropy and the entropy, i.e.: D KL (P||Q) = H(P, Q) - H(P);

[0092] Substituting the formulas for entropy and cross-entropy, we get:

[0093]

[0094] Derivation of the formula:

[0095] The formula combines the exponential function and the KL divergence to evaluate the fusion degree of multi-dimensional data under the target dimension setting.

[0096] The numerator part reflects the weighted sum of the data fusion degree for each dimension, where the weight is given by exp(-(σ k ·DKL (P k ||Q k )) 2 ) is given.

[0097] The denominator part is a normalization factor used to ensure that the value of the fusion degree F fusion is within a reasonable range.

[0098] Through the ratio of the numerator to the denominator, the formula can comprehensively evaluate the fusion degree of multi-dimensional data under the setting of the target dimension.

[0099] As can be seen from the above, this system integrates multiple modules such as data collection, preprocessing, target dimension setting, cross-domain multi-dimensional data analysis, visual display, and report generation; among them, the data collection module is responsible for collecting data covering the fields of mathematics, statistics, and economy from multiple data sources; the data preprocessing module then cleans, integrates, formats, and standardizes the data to ensure data quality and consistency, and quantitatively evaluates the performance of data collection and preprocessing through a comprehensive evaluation formula; the target dimension setting module allows users to set specific target dimensions according to research needs, such as economic growth, market trends, etc.; the cross-domain multi-dimensional data analysis module then conducts in-depth analysis based on the target dimension and calculates the multi-dimensional data fusion degree to quantitatively evaluate the data fusion degree; finally, the visual display module and the report generation module respectively display the analysis results in an intuitive form and a written report; the entire system provides users with comprehensive, accurate, and intuitive data statistics and analysis services through an efficient data processing and analysis process, helping users deeply explore the data value and grasp the associations and trends between cross-domain data.

[0100] Embodiment 2: This embodiment is basically the same as the previous embodiment, except that it further includes a data quality monitoring module for continuously monitoring the data quality in the processes of data collection, preprocessing, analysis, and report generation, performing real-time evaluation through preset quality indicators, and triggering an alarm or automatically performing data correction operations when the data quality does not meet the standards.

[0101] Specifically, it further includes a user permission management module for managing the access permissions and operation permissions of different users to ensure data security and compliance, supporting role-based access control models, and allowing administrators to assign different system access and operation permissions according to user roles.

[0102] Specifically, the cross-domain multi-dimensional data analysis module further includes a custom analysis dimension and index library, allowing users to customize analysis dimensions and indexes according to specific research needs, and these custom dimensions and indexes can be combined, calculated, and transformed based on existing data fields, thereby expanding the analysis ability and flexibility of the system.

[0103] Specifically, the system has good scalability and integration capabilities, supports seamless docking with other information systems, and realizes data sharing and business process collaboration through API interfaces and data exchange platforms, improving the overall data management and analysis efficiency.

[0104] As can be seen from the above, this embodiment further enhances the functions of the multi-dimensional data statistics system driven by the target dimension. The system newly adds a data quality monitoring module, which can monitor the data quality throughout the whole process of data collection, preprocessing, analysis, and report generation in real time. Once the data quality does not meet the standard, it will trigger an alarm or automatically correct the data to ensure the accuracy and reliability of the data. At the same time, the system also introduces a user permission management module to realize role-based access control. The administrator can flexibly allocate system access and operation permissions according to the user roles, effectively ensuring the security and compliance of the data. In addition, the cross-domain multi-dimensional data analysis module newly adds a custom analysis dimension and index library, and users can freely define analysis dimensions and indexes according to research needs, greatly improving the analysis ability and flexibility of the system. Finally, the system has good scalability and integration capabilities, can be seamlessly docked with other information systems, realizes data sharing and business process collaboration, and further improves the efficiency of data management and analysis.

[0105] Embodiment 3: This embodiment is basically the same as the previous embodiment, except that it further includes a multi-dimensional data statistics method driven by the target dimension, including the multi-dimensional data statistics system driven by the target dimension as described above, and further includes the following steps:

[0106] Step 1: Start the system and initialize each module to ensure that the data collection module, data preprocessing module, target dimension setting module, cross-domain multi-dimensional data analysis module, visualization display module, and report generation module are all in an available state;

[0107] Step 2: According to the user's research needs, clearly set the target dimension in the target dimension setting module, including economic growth, market trends, consumer behavior, and social changes, and optionally adjust the analysis parameters and settings;

[0108] Step 3: Use the data collection module to automatically collect data covering the fields of mathematics, statistics, and economics from multiple specified data sources. At the same time, the data preprocessing module performs real-time cleaning, integration, formatting, and cross-domain standardization processing on the data, and quantitatively evaluates the processing process according to the comprehensive evaluation formula of data collection and preprocessing;

[0109] Step 4: In the cross-domain multi-dimensional data analysis module, based on the set target dimension, intelligently select and combine relevant analysis dimensions, and conduct in-depth analysis of the data by combining mathematical algorithms, statistical methods, and economic theories. At the same time, use the multi-dimensional data fusion degree calculation formula to evaluate the fusion degree of data in different dimensions on the target dimension;

[0110] Step Five: Present the analysis results to the user intuitively in the form of charts, maps, and dashboards through the visualization module, and support the user to customize the visualization style and interaction functions according to needs, so as to understand the associations and trends among cross-domain data more deeply;

[0111] Step Six: According to the analysis results, the report generation module automatically generates an interdisciplinary research report containing detailed data analysis results, conclusions, and suggestions, and the user can further edit and adjust the report content to meet specific needs.

[0112] As can be seen from the above, this method relies on a multi-dimensional data statistics system; first, start and initialize each module of the system to ensure that all functions are in a ready state; then, according to the specific research needs of the user, set clear target dimensions in the system and adjust the corresponding analysis parameters; next, the system automatically collects data from multiple data sources and performs real-time cleaning, integration, formatting, and standardization processing, and at the same time evaluates the effects of data collection and preprocessing; in the data analysis stage, the system intelligently selects and combines relevant analysis dimensions, conducts in-depth discussions in combination with mathematical, statistical, and economic theories, and evaluates the degree of data fusion; finally, the system presents the analysis results in an intuitive visualization form and automatically generates a detailed interdisciplinary research report, and the user can edit and adjust the report according to needs; providing users with efficient and accurate data statistics and analysis services.

[0113] All standard parts used in the present invention can be purchased from the market. The special-shaped parts can be customized according to the description in the specification and the drawings. The specific connection methods of each part all adopt conventional means such as bolts, rivets, and welding that are mature in the prior art. The machines, parts, and equipment all adopt conventional models in the prior art. Coupled with the circuit connection adopting the conventional connection method in the prior art, details are not described herein. The content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0114] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The meaning of "plurality" is two or more unless otherwise specifically defined.

[0115] In the present invention, unless otherwise clearly defined or limited, terms such as "installed", "connected", "coupled", "fixed", etc. shall be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or integrated; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the internal communication of two components or the interaction relationship between two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0116] In the present invention, unless otherwise clearly defined or limited, the first feature being "on" or "under" the second feature may mean that the first and second features are in direct contact, or the first and second features are indirectly in contact through an intermediate medium. Moreover, the first feature being "above", "over" and "on top of" the second feature may mean that the first feature is directly above or obliquely above the second feature, or merely indicates that the first feature has a higher horizontal height than the second feature. The first feature being "under", "beneath" and "underneath" the second feature may mean that the first feature is directly below or obliquely below the second feature, or merely indicates that the first feature has a lower horizontal height than the second feature.

[0117] In the description of this specification, the descriptions with reference to terms such as "one embodiment", "some embodiments", "example", "specific example" or "some examples", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without conflict, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0118] In the drawings of the disclosed embodiments of the present invention, only the structures related to the disclosed embodiments are involved, and other structures can refer to the general design. Without conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0119] Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A target dimension driven multi-dimensional data statistics system, characterized in that: include: Data collection module, used to collect mathematical, statistical and economic data from multiple data sources; A data preprocessing module is used to clean, integrate, format and standardize the collected data across fields to ensure the quality and consistency of the data. The data preprocessing module includes a comprehensive evaluation function for data collection and preprocessing, and quantitatively evaluates the integrity of data collection and the effectiveness of preprocessing through preset evaluation indicators; The target dimension setting module allows users to set specific target dimensions based on research needs, including economic growth, market trends, consumer behavior, and social change; The cross-domain multi-dimensional data analysis module selects and combines mathematical, statistical and economic analysis dimensions based on the set target dimension, and conducts in-depth analysis of the data by combining mathematical algorithms, statistical methods and economic theories. It also calculates the fusion degree of multi-dimensional data under the target dimension setting to quantitatively evaluate the degree of fusion of data of different dimensions on the target dimension. The visualization module displays the analysis results in the form of charts, maps, and dashboards. It supports user-defined visualization styles and interactive functions so that users can easily understand the connections and trends between cross-domain data. The report generation module automatically generates interdisciplinary research reports containing data analysis results, conclusions and recommendations.

2. A target dimension driven multi-dimensional data statistics system as claimed in claim 1, characterized in that: The data acquisition and preprocessing comprehensive evaluation formula evaluates the overall performance of the data acquisition and preprocessing module by means of integration and weighted summation. The formula is as follows: Among them, P1 is the performance evaluation of the data collection and preprocessing module, T is the time span of data collection, N is the total number of data sources, and w i is the weight of each data source, f clean (d i ) is the data cleaning function, which indicates the quality of data source i after cleaning, f integrate (d i ) is a function of data integration, which indicates the integrity of data source i after integration.

3. A target dimension driven multi-dimensional data statistics system as claimed in claim 2, characterized in that: The multi-dimensional data fusion degree under the target dimension setting uses the exponential function and KL divergence to evaluate the fusion degree of different dimensional data under the target dimension setting. The calculation formula is as follows: Among them, F fusion It is a measure of the degree of multi-dimensional data fusion under the target dimension setting, where M is the total number of cross-domain analysis dimensions, and D KL (P j ||Q j ) is the data distribution P under dimension j j With the standard distribution Q j The KL divergence between j is the tolerance parameter of data fusion under dimension j.

4. A target dimension driven multi-dimensional data statistics system as claimed in claim 1, characterized in that: It also includes a data quality monitoring module, which is used to continuously monitor the data quality during data collection, preprocessing, analysis and report generation, conduct real-time evaluation through preset quality indicators, and trigger alarms or automatically perform data correction operations when data quality does not meet the standards.

5. The target dimension driven multi-dimensional data statistics system according to claim 1, characterized in that: It also includes a user rights management module for managing the access rights and operation permissions of different users to ensure data security and compliance. It supports a role-based access control model, allowing administrators to assign different system access and operation permissions based on user roles.

6. A target dimension driven multi-dimensional data statistics system as claimed in claim 1, characterized in that: The cross-domain multi-dimensional data analysis module also includes a custom analysis dimension and indicator library, allowing users to customize analysis dimensions and indicators according to specific research needs. These custom dimensions and indicators can be combined, calculated, and converted based on existing data fields, thereby expanding the system's analysis capabilities and flexibility.

7. A target dimension driven multi-dimensional data statistics system as claimed in claim 1, characterized in that: The system has good scalability and integration capabilities, supports seamless connection with other information systems, realizes data sharing and business process collaboration through API interfaces and data exchange platforms, and improves overall data management and analysis efficiency.

8. A target dimension driven multi-dimensional data statistics method, characterized in that: The target dimension driven multi-dimensional data statistics system according to any one of claims 1 to 7 further comprises the following steps: Step 1: Start the system and initialize each module to ensure that the data acquisition module, data preprocessing module, target dimension setting module, cross-domain multi-dimensional data analysis module, visualization module, and report generation module are all in an available state; Step 2: According to the user's research needs, clearly set the target dimensions in the target dimension setting module, including economic growth, market trends, consumer behavior, and social changes, and optionally adjust the analysis parameters and settings; Step 3: Use the data acquisition module to automatically collect data covering mathematics, statistics, and economics from multiple specified data sources. At the same time, the data preprocessing module performs real-time cleaning, integration, formatting, and cross-domain standardization of the data, and quantitatively evaluates the processing process based on the comprehensive evaluation formula for data acquisition and preprocessing; Step 4: In the cross-domain multi-dimensional data analysis module, based on the set target dimension, intelligently select and combine relevant analysis dimensions, combine mathematical algorithms, statistical methods and economic theories to conduct in-depth analysis of the data, and use the multi-dimensional data fusion degree calculation formula to evaluate the degree of fusion of different dimensional data in the target dimension; Step 5: The analysis results are presented to users in the form of charts, maps, and dashboards through the visualization module, which supports users to customize visualization styles and interactive functions according to their needs, so as to gain a deeper understanding of the correlation and trends between cross-domain data; Step 6: Based on the analysis results, the report generation module automatically generates an interdisciplinary research report containing detailed data analysis results, conclusions and recommendations. Users can further edit and adjust the report content to meet specific needs.

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