Data processing method and device, electronic equipment and storage medium

Abstract

The data processing method and device, electronic equipment and storage medium provided in the application, the electronic equipment obtains standard statistical data including preset index standard information and preset dimension standard information, calls at least one preset analysis task, processes the standard statistical data respectively, and obtains corresponding processing results, processes all the processing results by using the data screening model corresponding to each preset analysis task to determine the effective degree of each processing result in all the processing results, and obtains a target processing result according to the effective degree, so as to improve the acquisition efficiency of the user on the effective information in the index generated based on the statistical data.

Description

Technical Field

[0001] This application relates to the field of data analysis technology, and in particular to a data processing method, apparatus, electronic device and storage medium. Background Technology

[0002] In recent years, with the gradual development of information and intelligent technologies, and guided by national strategies, domestic enterprises have been actively engaged in digital transformation, achieving workload reduction and efficiency improvement through digitalization, automation, and intelligentization. The analysis and presentation of statistical data has become an important medium for understanding the development process and status of enterprises and obtaining crucial information.

[0003] In existing technologies, statistical data analysis often employs diverse yet fixed analytical methods to process the obtained statistical data and obtain corresponding indicators. However, the statistical data generates an excess of related indicators, and the statistical reports based on these indicators are also quite lengthy, making it difficult for users to capture the effective information within the indicators. Summary of the Invention

[0004] This application provides a data processing method, apparatus, electronic device, and storage medium to solve the technical problem of low efficiency in obtaining effective information from indicators generated based on statistical data.

[0005] Firstly, this application provides a data processing method, the method comprising:

[0006] Obtain standardized statistical data; standardized statistical data includes preset indicator standard information and preset dimension standard information;

[0007] Invoke at least one preset analysis task to process the standardized statistical data and obtain the corresponding processing results;

[0008] All processing results are processed using the data filtering model corresponding to each preset analysis task to obtain the target processing result.

[0009] In the above technical solution, after obtaining the standardized statistical data, the electronic device processes the standardized statistical data using at least one preset analysis task. After obtaining the processing results, it processes all the processing results using the data filtering model corresponding to each preset analysis task to determine whether the processing results corresponding to each preset analysis task are valid among all the processing results. In this way, the target processing result among all the processing results is determined as the valid data of the standardized statistical data, so as to improve the efficiency of obtaining effective information from the indicators generated based on the statistical data.

[0010] Optionally, the data filtering model is a least squares support vector model; all processed data are processed using the data filtering model corresponding to each preset analysis task to obtain the target processing result, specifically including:

[0011] A result vector is generated based on all processing results and a preset order; the preset order includes the position information of the processing results corresponding to each preset analysis task.

[0012] The target processing result is obtained by processing the result vectors using the least squares support vector model corresponding to each preset analysis task.

[0013] Optionally, the target processing result is obtained by processing the result vectors of the least squares support vector model corresponding to each preset analysis task, specifically including:

[0014] Using the least squares support vector model processing result vectors corresponding to each preset analysis task, the display status of the processing result corresponding to each preset analysis task is determined; the display status includes valid status and invalid status.

[0015] Based on the displayed status of the processing results, determine the target processing result that is in a valid state.

[0016] Optionally, obtain standardized statistical data, specifically including:

[0017] Obtain raw statistical data; raw statistical data includes raw indicator information, raw dimension information, and raw data;

[0018] The original indicator information and original dimension information are standardized to obtain preset indicator standard information and preset dimension standard information.

[0019] Based on preset indicator standards, preset dimension standards, and raw data, standardized statistical data is generated.

[0020] Optionally, at least one preset analysis task is invoked, and the corresponding processing results are obtained, specifically including:

[0021] Query the preset indicator standard information and preset dimension standard information in the analysis task mapping table to obtain all preset analysis task identifiers corresponding to the preset indicator standard information and preset dimension standard information.

[0022] Based on the identifier of each preset analysis task, the corresponding preset analysis task is invoked to process the standardized statistical data and obtain the corresponding processing results.

[0023] Optionally, the method further includes:

[0024] Obtain the corresponding script templates for the processing results of each target;

[0025] Fill the placeholders in the script template with the processing results of each target to generate the corresponding analysis statement;

[0026] All analysis statements are processed using a language model to generate analysis messages.

[0027] Optionally, all analysis statements are processed using a language model to generate analysis messages, specifically including:

[0028] To obtain the relationships between the various analysis statements;

[0029] Based on the correlation, obtain at least one set of message statements; the set of message statements contains at least one analysis statement;

[0030] The first association language model is used to process each message statement set to generate the corresponding analysis sub-message;

[0031] The second association language model is used to process all analysis sub-messages and generate analysis messages.

[0032] The language model includes the first associated language model and the second associated language model.

[0033] In the above technical solution, the electronic device generates analysis sub-messages containing related content using a first association language model based on the correlation between analysis statements, so as to ensure the readability of the content of each sub-message and reduce redundant content. Then, it uses a second association language model to process all analysis sub-messages to generate analysis messages, so as to ensure the coherence of analysis messages generated based on analysis statements.

[0034] Secondly, this application provides a data processing apparatus, comprising:

[0035] The acquisition module is used to obtain standardized statistical data; the standardized statistical data includes preset indicator standard information and preset dimension standard information.

[0036] The processing module is used to call at least one preset analysis task to process the standardized statistical data and obtain the corresponding processing results.

[0037] The processing module is also used to process all processing results using the data filtering model corresponding to each preset analysis task to obtain the target processing result.

[0038] Thirdly, this application provides an electronic device, including: a processor and a memory communicatively connected to the processor;

[0039] The memory stores the instructions that the computer executes;

[0040] The processor is used to implement the data processing method involved in the first aspect when executing computer execution instructions.

[0041] Fourthly, this application provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, are used to implement the data processing method involved in the first aspect.

[0042] The data processing method, apparatus, electronic device, and storage medium provided in this application involve an electronic device that obtains standardized statistical data including preset indicator standard information and preset dimension standard information, calls at least one preset analysis task to process the standardized statistical data respectively and obtains the corresponding processing results, uses the data filtering model corresponding to each preset analysis task to process all processing results, determines the effectiveness of each processing result among all processing results, and obtains the target processing result based on the effectiveness, so as to improve the efficiency of users in obtaining effective information from indicators generated based on statistical data. Attached Figure Description

[0043] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0044] Figure 1 This is a schematic flowchart of a data processing method provided in this application according to an exemplary embodiment;

[0045] Figure 2 A flowchart illustrating a data processing method provided in another exemplary embodiment for this application;

[0046] Figure 3 This is a schematic diagram of the structure of a data processing apparatus provided in accordance with an embodiment of this application;

[0047] Figure 4 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application.

[0048] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0049] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0050] This application provides a data processing method, apparatus, electronic device, and storage medium, aiming to solve the technical problem of low efficiency in obtaining effective information from indicators generated based on statistical data. The technical concept of this application is as follows: After obtaining standardized statistical data containing preset indicator standard information and preset dimension standard information, the electronic device calls a preset analysis task to analyze the standardized statistical data, obtains the corresponding processing results, and uses a data filtering model to select the more important processing results from all processing results, thereby reducing the number of displayable processing results and improving the efficiency for users to obtain effective information from indicators generated based on statistical data.

[0051] The data processing method provided in this application can be applied to scenarios involving automatic analysis of statistical data and generation of corresponding messages. In this scenario, the electronic device is the execution entity, and the electronic device includes a processing unit, an input unit, a storage unit, and an output unit. The processing unit is connected to the input unit, the storage unit, and the output unit, respectively.

[0052] Before processing statistical data, electronic devices need to acquire the statistical data. The processing unit can obtain the statistical data through a connected input unit or retrieve it from a storage unit. The statistical data can be presented in various forms, including statistical graphs, tables, and text information. The statistical data includes at least one dimension and at least one indicator.

[0053] The processing unit performs at least one analysis operation on the obtained statistical data to obtain corresponding analysis result data. The processing unit uses a locally stored, pre-trained data filtering model to process all the obtained analysis result data and determine the target processing result. The processing unit outputs the target processing data through its connected output unit, enabling users to quickly determine the effective information in the statistical data based on the target processing data, thereby improving the efficiency of obtaining effective information from the statistical data.

[0054] Furthermore, the processing unit can generate corresponding messages based on the analysis results data to improve data readability. The size of the target message generated based on the target processing results is smaller than that of the original message based on all analysis results data, which helps to increase the readability of the target message and improve the efficiency of users in obtaining effective information from the message.

[0055] The following will be through Figure 1 and Figure 2 The corresponding embodiments are used to specifically explain the processing procedure of the data processing method proposed in this application.

[0056] Figure 1 This is a schematic flowchart illustrating a data processing method provided in this application according to an exemplary embodiment. Figure 1 As shown, the data processing method includes:

[0057] S101. Electronic equipment obtains standard statistical data.

[0058] Standardized statistical data are statistical data described according to preset standards.

[0059] The statistical data in this specification includes preset indicator standard information and preset dimension standard information.

[0060] Among them, the preset indicator standard information is indicator information described according to preset standards, and the preset dimension standard information is dimension information described according to preset standards.

[0061] Indicator information is used to describe the type of statistical data, such as growth or conversion.

[0062] Dimensional information describes the attributes or characteristics of the above-mentioned indicators. For example: region, channel.

[0063] In one embodiment, the indicator information is the indicator name, and the dimension information is the dimension name. Conversely, the preset indicator standard information is the indicator name named according to preset rules, and the preset dimension standard information is the dimension name named according to preset rules.

[0064] In one embodiment, the naming convention for preset indicator standard information is: product name + time + indicator, for example: mobile service development volume this month. The naming convention for preset dimension standard information is: a description of the classification results of dimension variable information according to preset keywords / keywords, for example: describing the classification results of dimension variable information "Shandong, Shanghai, Guangdong" as the preset keyword "city".

[0065] S102. The electronic device invokes at least one preset analysis task to process the standardized statistical data and obtain the corresponding processing results.

[0066] Preset analysis tasks are tasks that can perform statistical processing on statistically standardized data.

[0067] The electronic device invokes at least one preset analysis task to analyze the standardized statistical data to obtain different processing results, which include the global statistical characteristics of the standardized statistical data and the distribution of specific statistical data.

[0068] S103. The electronic device uses the data filtering model corresponding to each preset analysis task to process all processing results and obtain the target processing result.

[0069] The data filtering model is a pre-trained model stored locally on the electronic device. This model is used to determine, based on all processing results obtained in step S102, whether each processing result effectively reflects the valid information of the standardized statistical data, and thus determines whether the processing result can be displayed. For example, in standardized statistical data with the indicator information "mobile service development volume this month," after analyzing the processed data by "city," the data filtering model analyzes all processing results of the current standardized statistical data. If it determines that the month-on-month development volume data for each city reflects the valid information of the general statistical data better than the average development volume of all cities in the current month, and the average value has not changed significantly compared to historical data, then the data filtering model determines that the month-on-month data can be displayed, while the average value should not be displayed.

[0070] The electronic device will determine the processing result that effectively reflects the standardized statistical data as the target processing result.

[0071] In the above technical solution, after obtaining the standardized statistical data, the electronic device processes the standardized statistical data using at least one preset analysis task. After obtaining the processing results, it processes all the processing results using the data filtering model corresponding to each preset analysis task to determine whether the processing results corresponding to each preset analysis task are valid among all the processing results. In this way, the target processing result among all the processing results is determined as the valid data of the standardized statistical data, so as to improve the efficiency of obtaining effective information from the indicators generated based on the statistical data.

[0072] Figure 2 This is a schematic flowchart illustrating a data processing method provided in this application according to another exemplary embodiment. Figure 2 As shown, the data processing method includes:

[0073] S201. Electronic devices obtain standard statistical data.

[0074] The standardized statistical data has been explained in detail in step S101 and will not be repeated here.

[0075] In one embodiment, the electronic device obtains standardized statistical data directly through its input unit.

[0076] In another embodiment, the electronic device obtains raw statistical data through its input unit, standardizes the raw indicator information and raw dimension information in the raw statistical data to obtain preset indicator standard information and preset dimension standard information, and generates standardized statistical data based on the preset indicator standard information, preset dimension standard information, and raw data. The raw statistical data includes raw indicator information, raw dimension information, and raw data.

[0077] More specifically, when the electronic device standardizes the original dimensional information, it performs keyword matching to determine whether the original dimensional information is named according to preset rules. If so, the original dimensional information is identified as preset dimensional standard information; otherwise, semantic analysis is performed on the original dimensional information to obtain semantic results, and the corresponding preset dimensional standard information is determined in the normative information mapping table based on the semantic results. The normative information mapping table represents the mapping relationship between semantic information and standard information, where the standard information is either preset dimensional standard information or preset indicator standard information.

[0078] When electronic devices standardize the original indicator information, they perform word segmentation on the original indicator information and determine whether the grammatical structure of the original indicator information is correct based on the word segmentation results.

[0079] If the grammatical structure is incorrect, semantic analysis is performed on the original indicator information. Based on the analysis results, it is determined whether the original indicator information contains all the naming elements in the naming convention of the preset indicator standard information. For example, if the naming convention of the preset indicator standard information is: Product Name + Time + Indicator, then the naming elements include: Product Name, Time, and Indicator. If all naming elements are included, the electronic device generates the preset indicator standard information according to the naming convention and the semantic analysis results; if not all naming elements are included, a request is made to re-acquire the original statistical data. The request to re-acquire the original statistical data includes the missing naming elements.

[0080] If the grammatical structure is correct, keyword matching is used to determine whether all segmentation results are named according to the preset rules. If so, the original indicator information is determined as the preset indicator standard information; if not, semantic analysis is performed on the segmentation results, and the corresponding preset indicator standard sub-information is determined in the normative information mapping table according to the semantic results. Then, the electronic device generates the preset indicator standard information according to the naming convention of the preset indicator standard information and the preset indicator standard sub-information.

[0081] S202. The electronic device invokes at least one preset analysis task to process the standardized statistical data and obtain the corresponding processing results.

[0082] The preset analysis task is a data analysis task that can perform data analysis on standardized statistical data related to dimensional and indicator information. The processing results obtained by the preset analysis task include, but are not limited to: the measures of central tendency and deviations determined among variables in a single dimension; the measures of central tendency and deviations determined by cross-analysis of corresponding variables in multiple dimensions; and outliers in standardized statistical data. For example, analyzing the statistical data corresponding to the indicator "mobile business development volume in the current year" using the "city" dimension can yield the corresponding mean, variance, frequency, month-on-month, and year-on-year results. Alternatively, the above statistical data can be analyzed by cross-traversing the "city" and "month" dimensions to obtain the corresponding processing results.

[0083] In one embodiment, the electronic device queries the preset indicator standard information and preset dimension standard information in the analysis task mapping table to obtain all preset analysis task identifiers corresponding to the preset indicator standard information and preset dimension standard information; according to each preset analysis task identifier, it calls the corresponding preset analysis task to process the standardized statistical data and obtain the corresponding processing result.

[0084] S203. The electronic device generates a result vector based on all processing results and a preset order.

[0085] The preset order includes the position information of the processing results corresponding to each preset analysis task.

[0086] The result vector represents a vector generated from the processing results of each analysis task according to its position information. More specifically, the number of dimensions in the result vector is the same as the number of processing results, and the order of the data corresponding to each dimension in the result vector is the same as the preset order.

[0087] In one embodiment, the data filtering model is the Least Squares Support Vector Machine (LSSVM) model, and we proceed to step S204.

[0088] S204. The electronic device uses the least squares support vector model corresponding to each preset analysis task to process the result vector and obtain the target processing result.

[0089] The target processing result is the processed data information that, according to the least squares support vector model, can describe the effective information of the standardized statistical data among all processing results. For example, for the indicator "mobile business development volume in the current year", the analysis from the dimension of "city" is more effective than the hierarchical results of the mean. Therefore, the month-on-month result is effective information.

[0090] More specifically, the process by which the electronic device generates result vectors using the LSSVM model includes: the electronic device processes the result vectors using the least squares support vector model corresponding to each preset analysis task, determines the display status of the processing results corresponding to each preset analysis task, and then determines the target processing result in a valid state based on the display status of the processing results. The display status includes valid and invalid states. A valid state indicates that the processing result can be used to describe valid information of the standardized statistical data and can be displayed; an invalid state indicates that the processing result cannot be used to describe valid information of the standardized statistical data and cannot be displayed.

[0091] Here, the LSSVM model is a pre-trained model, and the filter based on the LSSVM model can be represented as:

[0092] y(x1, x2, ..., x n )=(w1(x1,x2,...,x n w2(x1, x2, ..., x) n ), ..., w n (x1, x2, ..., x n ))

[0093] Where x1 represents the first processing result obtained after the first preset analysis task processes the standardized statistical data, and x2 represents the second processing result obtained after the second preset analysis task processes the standardized statistical data. n This represents the nth processing result obtained after the nth preset analysis task processes the standardized statistical data, (x1, x2, ..., x...). n Let w1(·) represent the result vector generated from n processing results. w2(·) is a filter in the result vector used to determine the display status of the first processing result, and w2(·) is a filter in the result vector used to determine the display status of the second processing result. n (·) represents the filter in the result vector that determines the display status of the nth processing result, y(x1, x2, ..., x...). n ) represents the vector composed of the discrimination results of the above filters.

[0094] S205. The electronic device obtains the script templates corresponding to the processing results of each target, fills the placeholders in the script templates with the processing results of each target, and generates the corresponding analysis statements.

[0095] Let's take ranking analysis tasks as an example to explain how to fill in the dialogue template:

[0096] The sales script template is: "Leading city in development: Number one"<FIRST_BRANCH_NAME> (<FIRST_BRANCH_VALUE> ), second place<SECOND_BRANCH_NAME> (<SECOND_BRANCH_VALUE> ).

[0097] In contrast, the analysis statement generated based on the script template is: "The leading cities in development: first place Guangdong (100), second place Hunan (80)".

[0098] S206. The electronic device uses a language model to process all analysis statements and generate analysis messages.

[0099] The language model is a model that generates analytical messages based on analytical statements. This language model is a pre-trained model stored locally. The language model includes a first relevance language model and a second relevance language model.

[0100] The electronic device obtains the correlation between various analysis statements, and based on the correlation, obtains at least one set of message statements, wherein the set of message statements contains at least one analysis statement. The electronic device processes each set of message statements using a first correlation language model to generate corresponding analysis sub-messages, and processes all analysis sub-messages using a second correlation language model to generate an analysis message.

[0101] More specifically, the electronic device determines the correlation between each analysis statement based on the target processing data in each analysis statement, and obtains the corresponding message statement set based on the correlation. The electronic device uses a first correlation language model to generate corresponding sub-messages from the highly correlated analysis statements, and a second correlation language model is used to generate analysis messages based on the aforementioned sub-messages. The first correlation language model is used to add parallel conjunctions between analysis statements, delete repetitive words, and adjust the descriptive word order to make the description logic of the sub-messages coherent. The second correlation language model is used to add conjunctions between sub-messages based on the semantics of each sub-message. In one embodiment, the first and second correlation language models adopt an N-Gram model.

[0102] Based on the correlation between analysis statements, the electronic device uses a first correlation language model to generate analysis sub-messages containing related content to ensure the readability of each sub-message and reduce redundant content. Then, it uses a second correlation language model to process all analysis sub-messages to generate analysis messages, ensuring the coherence of analysis messages generated based on analysis statements.

[0103] In the above technical solution, the electronic device uses the least squares support vector model to filter all processing results based on the analysis of standardized statistical data, determines the importance of each processing result relative to other processing results, and thus determines the target processing result that can be displayed. On this basis, it uses the first association language model and the second association language model to generate a logically clear and coherent message, which not only helps to improve the efficiency of users in obtaining effective information, but also improves their reading experience of the message.

[0104] Figure 3 This is a schematic diagram of the structure of a data processing apparatus according to an embodiment of the present application. The data processing apparatus 300 includes:

[0105] The acquisition module 301 is used to obtain standardized statistical data, which includes preset indicator standard information and preset dimension standard information.

[0106] The processing module 302 is used to call at least one preset analysis task to process the standardized statistical data and obtain the corresponding processing results.

[0107] The processing module 302 is also used to process all processing results using the data filtering model corresponding to each preset analysis task to obtain the target processing result.

[0108] In one embodiment, the processing module 302 is specifically used for:

[0109] A result vector is generated based on all processing results and a preset order; the preset order includes the position information of the processing results corresponding to each preset analysis task.

[0110] The target processing result is obtained by processing the result vectors using the least squares support vector model corresponding to each preset analysis task; the data filtering model is the least squares support vector model.

[0111] In one embodiment, the processing module 302 is specifically used for:

[0112] Using the least squares support vector model processing result vectors corresponding to each preset analysis task, the display status of the processing result corresponding to each preset analysis task is determined; the display status includes valid status and invalid status.

[0113] Based on the displayed status of the processing results, determine the target processing result that is in a valid state.

[0114] In one embodiment, the acquisition module 301 is specifically used for:

[0115] Obtain raw statistical data; raw statistical data includes raw indicator information, raw dimension information, and raw data;

[0116] The original indicator information and original dimension information are standardized to obtain preset indicator standard information and preset dimension standard information.

[0117] Based on preset indicator standards, preset dimension standards, and raw data, standardized statistical data is generated.

[0118] In one embodiment, the processing module 302 is specifically used for:

[0119] Query the preset indicator standard information and preset dimension standard information in the analysis task mapping table to obtain all preset analysis task identifiers corresponding to the preset indicator standard information and preset dimension standard information.

[0120] Based on the identifier of each preset analysis task, the corresponding preset analysis task is invoked to process the standardized statistical data and obtain the corresponding processing results.

[0121] In one embodiment, the processing module 302 is specifically used for:

[0122] Obtain the corresponding script templates for the processing results of each target;

[0123] Fill the placeholders in the script template with the processing results of each target to generate the corresponding analysis statement;

[0124] All analysis statements are processed using a language model to generate analysis messages.

[0125] In one embodiment, the processing module 302 is specifically used for:

[0126] To obtain the relationships between the various analysis statements;

[0127] Based on the correlation, obtain at least one set of message statements; the set of message statements contains at least one analysis statement;

[0128] The first association language model is used to process each message statement set to generate the corresponding analysis sub-message;

[0129] The second association language model is used to process all analysis sub-messages and generate analysis messages.

[0130] The language model includes the first associated language model and the second associated language model.

[0131] Figure 4 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. The electronic device 400 includes a memory 401 and a processor 402.

[0132] The memory 401 is used to store computer instructions that can be executed by the processor.

[0133] When executing computer instructions, processor 402 implements the various steps of the data processing method with an electronic device as the execution subject in the above embodiments. For details, please refer to the relevant descriptions in the foregoing method embodiments.

[0134] Optionally, the memory 401 can be either independent or integrated with the processor 402. When the memory 401 is configured independently, the electronic device 400 also includes a bus for connecting the memory 401 and the processor 402.

[0135] This application also provides a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the various steps of the data processing method in the above embodiments.

[0136] This application also provides a computer program product, including computer instructions that, when executed by a processor, implement the various steps in the data processing method described above.

[0137] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0138] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A data processing method, characterized in that, The method includes: Obtain standardized statistical data; the standardized statistical data includes preset indicator standard information and preset dimension standard information; Invoke at least one preset analysis task, query the preset indicator standard information and the preset dimension standard information in the analysis task mapping table to obtain all preset analysis task identifiers corresponding to the preset indicator standard information and the preset dimension standard information; according to each preset analysis task identifier, invoke the corresponding preset analysis task to process the standardized statistical data and obtain the corresponding processing results; Based on all processing results and a preset order, a result vector is generated. The preset order includes the position information of the processing results corresponding to each preset analysis task. The result vector is processed using the least squares support vector model corresponding to each preset analysis task to obtain the target processing result. The target processing result is the processed data information that can describe the valid information of the standardized statistical data among all processing results as determined by the least squares support vector model.

2. The method according to claim 1, characterized in that, The result vector is processed using the least squares support vector model corresponding to each preset analysis task to obtain the target processing result, specifically including: The result vector is processed using the least squares support vector model corresponding to each preset analysis task, and the display status of the processing result corresponding to each preset analysis task is determined; the display status includes a valid status and an invalid status. Based on the display status of the processing result, the target processing result that is in the valid state is determined.

3. The method according to claim 1, characterized in that, Obtain standardized statistical data, specifically including: Obtain raw statistical data; the raw statistical data includes raw indicator information, raw dimension information, and raw data; The original indicator information and the original dimension information are standardized to obtain the preset indicator standard information and the preset dimension standard information. Based on the preset indicator standard information, the preset dimension standard information, and the original data, standardized statistical data is generated.

4. The method according to claim 1, characterized in that, The method further includes: Obtain the corresponding script templates for each of the target processing results; The processing results of each target are filled into the placeholders of the script template to generate the corresponding analysis statements; All the aforementioned analysis statements are processed using a language model to generate an analysis message.

5. The method according to claim 4, characterized in that, All the aforementioned analysis statements are processed using a language model to generate an analysis message, specifically including: To obtain the relationships between the various analysis statements; Based on the correlation, at least one set of message statements is obtained; the set of message statements contains at least one analysis statement. The first association language model is used to process the message statement sets and generate corresponding analysis sub-messages; The second association language model is used to process all analysis sub-messages to generate the analysis message; The language model includes the first associated language model and the second associated language model.

6. A data processing apparatus, characterized in that, include: The acquisition module is used to obtain standardized statistical data; the standardized statistical data includes preset indicator standard information and preset dimension standard information. The processing module is used to invoke at least one preset analysis task, query the preset indicator standard information and the preset dimension standard information in the analysis task mapping table, obtain all preset analysis task identifiers corresponding to the preset indicator standard information and the preset dimension standard information; and, according to each preset analysis task identifier, invoke the corresponding preset analysis task to process the standardized statistical data and obtain the corresponding processing result. The processing module is further configured to generate a result vector based on all processing results and a preset order, wherein the preset order includes the position information of the processing results corresponding to each preset analysis task, and process the result vector using the least squares support vector model corresponding to each preset analysis task to obtain a target processing result, wherein the target processing result is the processing data information that can describe the valid information of the standardized statistical data among all processing results as determined by the least squares support vector model.

7. An electronic device, characterized in that, include: A processor and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor, when executing the computer execution instructions, is used to implement the data processing method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, are used to implement the data processing method as described in any one of claims 1 to 5.

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