A data processing method and device

Abstract

The present application provides a data processing method, including: obtaining data from a data source, determining a standard data granularity according to the granularity range of the data, and updating the data to application data according to the standard data granularity, wherein the standard data granularity is the upper limit of the granularity of the data; obtaining a data report associated with a business scenario, setting a data label for the application data according to the data report and the upper limit of the granularity of the application data, and adding the application data with the set data label to a knowledge base; setting a personalized data report, and extracting the application data from the knowledge base for display according to the personalized data report and the data label. By updating the data according to the standard data granularity, the data can be processed in the same data dimension, so that the business changes can be passed from the front end to the business logic, saving manpower and time costs. The present application also provides a data processing device.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a data processing method and device. Background Art

[0002] In the modern business operation process, the content expressed and displayed in the operation data report can clearly and directly show the business situation of the enterprise. Processing data through visual reports and providing a basis for enterprise decision-making has become an indispensable part of enterprise management. However, the data processing rules of visual reports are unsystematic, and various data are randomly combined with the formula of the Excel table. Therefore, the quality of the report depends entirely on the editing ability of the staff who make the report on the formula code, and is also affected by the source of the data.

[0003] In the prior art, through conditional screening on the web page front end, SQL query data is constructed according to the specified conditional parameters transmitted by the front end, and the back-end database pre-processes the data scenarios that may be used in advance, thereby reducing the difficulty of Excel data processing and streamlining the data processing logic.

[0004] However, the existing technology only solves the problem of reducing the complexity of some Excel rules. At the same time, it also leads to a high degree of coupling between report logic rules and business data. Slight changes in the business will cause the structure, data dimensions and indicators of the report logic to be readjusted, which consumes a lot of manpower and time costs.

[0005] Application Contents

[0006] In order to solve the above technical problems, the present application provides a method for processing document anti-counterfeiting data, which can solve the problem in the prior art that reports need to be readjusted due to business changes. At the same time, the present application also provides a data processing device.

[0007] The present application provides a data processing method, comprising:

[0008] Acquire data from a data source, determine a standard data granularity according to a granularity range of the data, and update the data into application data according to the standard data granularity, wherein the standard data granularity is an upper limit of the granularity of the data;

[0009] Acquire a data report associated with the business scenario, set a data tag for the application data according to the data report and the upper limit of the granularity of the application data, and add the application data with the set data tag to the knowledge base;

[0010] A personalized data report is set, and the application data is extracted from the knowledge base for display according to the personalized data report and the data tag.

[0011] Optionally, a new standard data granularity is obtained, and the application data is updated according to the new standard data granularity.

[0012] Optionally, updating the knowledge base according to the updated application data;

[0013] According to the update of the knowledge base, data update messages are sent in real time through personalized data reports.

[0014] Optionally, the data update message is displayed on the report front page.

[0015] Optionally, the new standard data granularity is determined by the granularity range of data newly sent by the data source.

[0016] The present application provides a data processing device, comprising:

[0017] A granularity module is used to obtain data from a data source, determine a standard data granularity according to a granularity range of the data, and update the data into application data according to the standard data granularity, wherein the standard data granularity is an upper limit of the granularity of the data;

[0018] A label module, used to obtain a data report associated with a business scenario, set a data label for the application data according to the data report and the upper limit of the granularity of the application data, and add the application data with the set data label to the knowledge base;

[0019] The display module is used to set a personalized data report, and extract the application data for display from the knowledge base according to the personalized data report and the data tag.

[0020] Optionally, also include:

[0021] The data updating module is used to obtain a new standard data granularity and update the application data according to the new standard data granularity.

[0022] Optionally, also include:

[0023] A knowledge base updating module is used to update the knowledge base according to the updated application data.

[0024] The message module is used to send data update messages in real time through personalized data reports according to the update of the knowledge base.

[0025] Optionally, also include:

[0026] The display module is used to display the data update message on the report front-end page.

[0027] Optionally, the new standard data granularity is determined by the granularity range of data newly sent by the data source.

[0028] The advantages of this application compared to the prior art are:

[0029] The present application provides a data processing method, including: obtaining data from a data source, determining a standard data granularity according to the granularity range of the data, and updating the data to application data according to the standard data granularity, wherein the standard data granularity is the upper limit of the granularity of the data; obtaining a data report associated with a business scenario, setting a data label for the application data according to the data report and the upper limit of the granularity of the application data, and adding the application data with the set data label to a knowledge base; setting a personalized data report, and extracting the application data from the knowledge base for display according to the personalized data report and the data label. By setting the standard granularity of the data and updating the data according to the standard granularity, the data can be processed in the same data dimension, so that the business changes can be passed from the front end to the business logic, saving manpower and time costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic diagram of the data processing process in this application.

[0031] Figure 2 It is a logical diagram of the data processing process in this application.

[0032] Figure 3 It is a schematic diagram of how changes in granularity rules affect the granularity of generated standard data in this application.

[0033] Figure 4 It is a schematic diagram of the application data combined with the business scenario in this application.

[0034] Figure 5 It is a schematic diagram of the subject matter in this application in combination with application data.

[0035] Figure 6 This is a schematic diagram of the data processing flow of this application.

[0036] Figure 7 is a schematic diagram of the data processing device described in this application. DETAILED DESCRIPTION

[0037] In the following description, many specific details are set forth to facilitate a full understanding of the present application, but the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar generalizations without violating the connotation of the present application. Therefore, the present application is not limited to the specific implementation disclosed below.

[0038] The present application provides a data processing method, including: obtaining data from a data source, determining a standard data granularity according to the granularity range of the data, and updating the data to application data according to the standard data granularity, wherein the standard data granularity is the upper limit of the granularity of the application data; obtaining a data report associated with the business scenario, setting a data label for the application data according to the data report and the upper limit of the granularity of the application data, and adding the application data with the data label to the knowledge base; setting a personalized data report, and extracting data from the knowledge base for display according to the personalized data report and the data label. By standardizing the data granularity, the data can be applied to more business scenarios while ensuring universality. On this basis, by establishing a knowledge base and setting the report according to the business scenario, the data does not need to be adjusted on a large scale when applied in different business scenarios, saving manpower and time costs.

[0039] Figure 1 is a schematic diagram of the data processing process in this application, Figure 2 It is a logical diagram of the data processing process of this application.

[0040] Please refer to Figure 1 and Figure 2 As shown, the data processing described in this application is mainly to simplify the report logic adjustment of different business data by adjusting the data granularity and establishing a common knowledge base.

[0041] like Figure 1 As shown, S101 acquires data from a data source, determines a standard data granularity according to a granularity range of the data, and updates the data to application data according to the standard data granularity, wherein the standard data granularity is an upper limit of the granularity of the application data;

[0042] The data source refers to the source of data. In this application, the source of the data source can be set to different sources according to actual conditions. That is, the data provided by a source of data can be used as the data to be processed by this application, or the data provided by multiple sources of data can be used as the data to be processed by this application. In this application, it mainly refers to multiple subsidiaries in a group company as data sources, that is, the data source includes data provided by multiple subsidiaries.

[0043] This application links to a remote server through the Federated engine of the MYSQL database. The Federated engine can achieve remote access to the server without using a copy operation. Therefore, this application can access and modify data in the server without storing any data locally.

[0044] After acquiring the data from the data source, the data can be further processed. The present application determines the standard data granularity according to the granularity range of the data, which is to determine the maximum granularity of the data granularity subdivision, and the granularity represents the degree of data subdivision.

[0045] The granularity range refers to the specific granularity of each data analyzed and identified after the data is acquired. The specific granularity varies according to the description dimension of the data. Therefore, the granularity of the data is not uniform. The granularity range of this application is composed of the maximum granularity (also called the coarsest granularity) and the minimum granularity (also called the finest granularity) of the specific granularity of the data, and the interval between the maximum granularity and the minimum granularity.

[0046] It should be noted that before determining the standard data granularity, it is first necessary to verify the granularity rules. The granularity rules, i.e., the influencing conditions that affect the generation of the standard data granularity, include data dimensions that describe the data.

[0047] like Figure 2 As shown, data visualization (BI) is first initialized, that is, data processing is initialized according to the existing granularity rules. Then data testing is performed, that is, data processing is tested according to the existing granularity rules. Then the test results are verified, including: granularity rule verification, logic verification and data verification. When there are no problems with the granularity rules, logic and data, the data processing process can be accepted and the next step can be entered.

[0048] The granularity rule verification is to verify whether the data dimension for generating the standard data granularity is suitable, the verification logic is to verify whether the relationship between the data dimension and the data granularity is correct, and the verification data is to verify whether the data is correct.

[0049] After the above verification steps, the next step is to generate a standard data granularity through the granularity rule.

[0050] In the present application, when no change is found in the granularity rule during verification, the data update process is directly entered, and the standard data granularity at this time is determined when the granularity rule was last changed.

[0051] If the granularity rule is found to have been changed during verification, the standard data granularity under the granularity rule needs to be re-determined.

[0052] When the granularity rule changes, a new standard data granularity is obtained, and the application data is updated according to the new standard data granularity. In the present application, the granularity rule is a rule for determining the standard data granularity and then updating the data according to the standard data granularity.

[0053] Figure 3 It is a schematic diagram of how changes in granularity rules affect the granularity of generated standard data in this application.

[0054] Please refer to Figure 3 As shown, S201 adds a new prompt table, which is a prompt for changes in the data dimension of the standard data granularity generated in the granularity rule. For example, if the data dimension changes because data optimization is required, it will be displayed in the corresponding promotion table.

[0055] S202 determines whether the granularity rule has changed. When the granularity rule has changed, the granularity rule confirmation process is entered.

[0056] S203 adjusts the granularity rule, and adjusts the data dimension of the granularity rule according to the change. In this step, the adjustment includes data dimensions such as: company, account, profit center, business type and other public data dimensions. After the data dimensions are adjusted, a new granularity rule is formed.

[0057] S204: Activate the new granularity rule to update the data. After the new granularity rule is activated, the data obtained from the data source is updated to application data.

[0058] The granularity rules described in this application are preliminary update rules for processing all data, also known as general rules.

[0059] After determining the standard data granularity, the acquired data is updated according to the standard data granularity determined by the granularity rule and the data acquired from the data source, and changed into general business data that conforms to the standard data granularity. Through the above steps, the present application unifies the size of the data granularity within the range limited by the standard data granularity.

[0060] S102: obtaining a data report associated with the business scenario, setting a data tag for the application data according to the data report and the upper limit of the granularity of the application data, and adding the application data with the data tag to the knowledge base;

[0061] In this application, the business scenario refers to the application scenario of each business in the business group composed of different businesses. Each business has a data report corresponding to the business. The data report of the business is obtained according to the business scenario, and then a label is added to each data according to the data report of the business and the standard data granularity. All the tagged data are stored in a storage device to form a knowledge base.

[0062] In this application, to obtain data reports associated with business scenarios, the business scenario rules must first be determined. The business scenario rules may include multiple general rules. The essence is to add a business scenario description dimension to the data of the business scenario on the basis of the general rules, and the business scenario rules are combination rules.

[0063] It should be pointed out that the business scenarios described in this application can be equivalent to data scenarios, and both refer to application scenarios of different businesses.

[0064] Figure 4 It is a schematic diagram of the application data combined with the business scenario in this application.

[0065] Please refer to Figure 4 As shown, S205 confirms the business scenario.

[0066] S206 determines changes in business scenarios, and when business scenarios change, determines increases or decreases in the data dimensions under the business scenarios.

[0067] S207 adjusts the business scenario rules, where adjusting the business scenario rules is to adjust the data dimension of the application data.

[0068] S208: updating data according to business scenario rules: after adjusting the data dimension, forming a business scenario rule including the business scenario dimension, and updating the data according to the business scenario rule.

[0069] The business scenario rules are generally implemented after the above-mentioned general rules, that is, the general rules and data scenarios are combined to form combined application data, also known as combined rules. Based on the general rules, the scenario dimension is accessed to determine the combined rules. This step only adds the business scenario dimension of the data on the basis of the general rules, so one combined rule can be associated with multiple general rules.

[0070] It should be pointed out that in the combination rules, the data dimensions include the above-mentioned companies, accounts, profit centers and business types, as well as data dimensions such as the functional scope of the data and the assessment type used to describe the data scenario.

[0071] As mentioned above, the combination rule is to add a description dimension of the business scenario to the data based on the general rule, so the description dimension of a scenario can be associated with multiple general rules. Based on this logic, a preferred solution is to first collect the general rules associated with the business scenario of the data report through a single data report.

[0072] Then, through multiple single reports, after the business scenario description dimensions of the data are determined, multiple general rules corresponding to the business scenario rules can be determined.

[0073] After determining the standard data granularity according to the general rules, the general rules and the data dimensions added according to the business scenarios of the data are combined to form a combination rule, and then labels are set for the application data according to the combination rule, and finally the data with labels are stored to form a knowledge base. It can be achieved that in the knowledge base, each data is set with a standard data granularity according to the general rules, and the size of the data granularity can be applied to all business scenarios, and each data has a label associated with the business scenario, so the knowledge base becomes a unified standard business scenario knowledge base.

[0074] In the present application, the label can be a label composed of the standard data granularity specified in the general rules and the data dimensions added according to the business scenario, or it can be a label established based on the standard data granularity specified in the general rules and the data dimensions added according to the business scenario. Technical personnel in this field can make a choice based on actual conditions and will not go into details here.

[0075] S103 sets a personalized data report, and extracts data for display from the knowledge base according to the personalized data report and the data tags.

[0076] The personalized report described in this application corresponds to a personalized theme, and different themes lead to different reports. Different personalized themes correspond to reports with one or more different data description dimensions.

[0077] Figure 5 It is a schematic diagram of the subject matter in this application in combination with application data.

[0078] Please refer to Figure 5 As shown, S209 actually uses the report. The actually used report is the report determined after the theme is changed.

[0079] S210 report theme changes. Check whether the theme has changed based on the actual report used.

[0080] S211 adjusts the subject business rules, and when the subject changes, determines changes in the data dimension corresponding to the subject.

[0081] S212 updates data according to the business subject rule, adjusts the selection of application data according to the change of the data dimension, forms new subject rules containing subject dimensions after reselecting the application data, and updates data according to the subject rule.

[0082] It should be noted that in the change of the theme rules, the data dimensions include the dimensions in the general rules: company, account, profit center and business type, and the dimensions in the combination rules: data functional scope and assessment type, etc., which are used to describe the data scenario, and also include: dimensions related to the theme display. That is, under the general rules and combination rules, the application data is updated in combination with the display theme dimension, and the theme rules provide filters for data extraction, which can also be called display rules.

[0083] Set up personalized data reports, specifically according to business conditions. For example, a new business requires a new business report.

[0084] Figure 6 This is a schematic diagram of the data processing flow of this application.

[0085] Please refer to Figure 6 As shown, the above steps S101 and S102 process the data at a data granularity and combine the application data with the business scenario. That is, after the above steps, the data becomes application data with a business scenario that meets the standard data granularity limitation.

[0086] Specifically, the present application combines the subject rule with the general rule and the combination rule to process the data as follows.

[0087] Data source 301, obtains data from the data source.

[0088] General rules 302, data obtained by the data source is processed according to the general rules. The general rules are determined, and the data is updated into application data according to the general rules to achieve standardization of the application data.

[0089] The combined rule 303 combines the application data of the general rule with the business scenario processing to form a scenario knowledge base.

[0090] Display rules 304, obtain application data including business scenario dimensions according to the processing of general rules and combined rules, and build a report theme according to the application data.

[0091] Report topic 305, that is, selecting a topic, after which data matching the topic is selected from the knowledge base for display.

[0092] Data display 306 is to fill the application data that matches the report theme into the report for display.

[0093] As mentioned above, the application data is tagged according to the business scenario, so the application data can be applied to personalized topics for different business scenarios, or to personalized group topics set with business scenarios.

[0094] If the general rules, combination rules and display rules are changed, the data processing flow is adjusted according to the above method, and data processing can be performed after the adjustment is completed.

[0095] like Figure 2 As shown, after the rules and data of the above steps are adjusted, a sample check is first performed, that is, the description dimension of the personalized theme needs to be determined. Then, the application data is used to fill in the report, that is, according to the description dimension of the personalized theme, the application data for display is selected by combining the general rules and the combination rules as filters, and the application data is filled in the personalized data report. After data verification, the report is completed.

[0096] Before this, if the application data changes, a new standard data granularity is obtained, and the application data is updated according to the new standard data granularity, which is determined by the granularity range of the data newly sent by the data source.

[0097] According to the updated application data, the knowledge base is updated; according to the update of the knowledge base, a data update message is sent in real time through a personalized data report. The data update message is displayed on the report front page.

[0098] In step S102, the data is labeled. The labels are set according to the standard data granularity and business scenario description dimensions of the general rules. Therefore, in this step, only the data marked with the labels that meet the personalized theme need to be extracted and included in the data report. It should be pointed out here that the labels can be regarded as a form of expression of the above theme.

[0099] Corresponding to the above data processing method, the present application also provides a data processing device, including: a granularity module 101 , a label module 102 and a display module 103 .

[0100] Figure 7 is a schematic diagram of the data processing device described in this application.

[0101] Please refer to Figure 7 As shown, the granularity module 101 is used to obtain data from a data source, determine a standard data granularity according to a granularity range of the data, and update the data to application data according to the standard data granularity, wherein the standard data granularity is an upper limit of the granularity of the data;

[0102] The data source refers to the source of data. In this application, the source of the data source can be set to different sources according to actual conditions. That is, the data provided by a source of data can be used as the data to be processed by this application, or the data provided by multiple sources of data can be used as the data to be processed by this application. In this application, it mainly refers to multiple subsidiaries in a group company as data sources, that is, the data source includes data provided by multiple subsidiaries.

[0103] This application links to a remote server through the Federated engine of the MYSQL database. The Federated engine can achieve remote access to the server without using a copy operation. Therefore, this application can access and modify data in the server without storing any data locally.

[0104] Determining the standard data granularity according to the granularity range of the data is to determine the maximum granularity of the data granularity subdivision, and the granularity represents the degree of data subdivision.

[0105] The granularity range refers to the specific granularity of each data analyzed and identified after the data is acquired. The specific granularity varies according to the description dimension of the data. Therefore, the granularity of the data is not uniform. The granularity range of this application is composed of the maximum granularity (also called the coarsest granularity) and the minimum granularity (also called the finest granularity) of the specific granularity of the data, and the interval between the maximum granularity and the minimum granularity.

[0106] It should be noted that before determining the standard data granularity, it is first necessary to verify the granularity rules. The granularity rules, i.e., the influencing conditions that affect the generation of the standard data granularity, include data dimensions that describe the data.

[0107] In this application, data visualization (BI) is first initialized, that is, data processing is initialized according to the existing granularity rules. Then data testing is performed, that is, data processing is tested according to the existing granularity rules. Then the test results are verified, including: granularity rule verification, logic verification and data verification. When there are no problems with the granularity rules, logic and data, the data processing process can be accepted and the next step of processing can be entered.

[0108] The granularity rule verification is to verify whether the data dimension for generating the standard data granularity is suitable, the verification logic is to verify whether the relationship between the data dimension and the data granularity is correct, and the verification data is to verify whether the data is correct.

[0109] In the present application, when no change is found in the granularity rule during verification, the data update process is directly entered, and the standard data granularity at this time is determined when the granularity rule was last changed.

[0110] If the granularity rule is found to have been changed during verification, the standard data granularity under the granularity rule needs to be re-determined.

[0111] When the granularity rule changes, a new standard data granularity is obtained, and the application data is updated according to the new standard data granularity. In this application, the granularity rule is a rule for determining the standard data granularity and then updating the data according to the standard data granularity. The specific process is as follows:

[0112] The first step is to add a new prompt table, which is a prompt for changes in the data dimension of the standard data granularity generated in the granularity rule. For example, if the data dimension changes because data optimization is required, it will be displayed in the corresponding promotion table.

[0113] The second step is to enter the granularity rule confirmation process when the granularity rule changes.

[0114] The third step is to adjust the data dimensions of the granularity rules according to the changes. In this step, the data dimensions included in the adjustment are public data dimensions such as company, account, profit center, and business type. After the data dimensions are adjusted, a new granularity rule is formed.

[0115] The fourth step is to enable the new granularity rule to update the data. After the new granularity rule is enabled, the data obtained from the data source is updated to the application data.

[0116] The granularity rules described in this application are preliminary update rules for processing all data, also known as general rules.

[0117] After determining the standard data granularity, the acquired data is updated according to the standard data granularity determined by the granularity rule and the data acquired from the data source, and changed into general business data that conforms to the standard data granularity. Through the above steps, the present application unifies the size of the data granularity within the range limited by the standard data granularity.

[0118] The label module 102 is used to obtain a data report associated with the business scenario, set a data label for the application data according to the data report and the upper limit of the granularity of the application data, and add the application data with the set data label to the knowledge base;

[0119] In this application, the business scenario refers to the application scenario of each business in the business group composed of different businesses. Each business has a data report corresponding to the business. The data report of the business is obtained according to the business scenario, and then a label is added to each data according to the data report of the business and the standard data granularity. All the tagged data are stored in a storage device to form a knowledge base.

[0120] In this application, to obtain data reports associated with business scenarios, the business scenario rules must first be determined. The business scenario rules may include multiple general rules. The essence is to add a business scenario description dimension to the data of the business scenario on the basis of the general rules, and the business scenario rules are combination rules.

[0121] It should be noted that the business scenarios described in this application can be equivalent to data scenarios, both of which refer to application scenarios of different businesses. The process of determining the business scenario is as follows:

[0122] The first step is to confirm the business scenario.

[0123] The second step is to determine changes in business scenarios. When business scenarios change, determine the increase or decrease of the data dimensions under the business scenarios.

[0124] The third step is to adjust the business scenario rules, wherein adjusting the business scenario rules is to adjust the data dimension of the application data.

[0125] Step 4: Update data according to business scenario rules. After adjusting the data dimensions, business scenario rules including business scenario dimensions are formed, and data is updated according to the business scenario rules.

[0126] The business scenario rules are generally implemented after the above-mentioned general rules, that is, the general rules and data scenarios are combined to form combined application data, also known as combined rules. Based on the general rules, the scenario dimension is accessed to determine the combined rules. This step only adds the business scenario dimension of the data on the basis of the general rules, so one combined rule can be associated with multiple general rules.

[0127] It should be pointed out that in the combination rules, the data dimensions include the above-mentioned companies, accounts, profit centers and business types, as well as data dimensions such as the functional scope of the data and the assessment type used to describe the data scenario.

[0128] As mentioned above, the combination rule is to add a description dimension of the business scenario to the data based on the general rule, so the description dimension of a scenario can be associated with multiple general rules. Based on this logic, a preferred solution is to first collect the general rules associated with the business scenario of the data report through a single data report.

[0129] Then, through multiple single reports, after the business scenario description dimensions of the data are determined, multiple general rules corresponding to the business scenario rules can be determined.

[0130] After determining the standard data granularity according to the general rules, the general rules and the data dimensions added according to the business scenarios of the data are combined to form a combination rule, and then labels are set for the application data according to the combination rule, and finally the data with labels are stored to form a knowledge base. It can be achieved that in the knowledge base, each data is set with a standard data granularity according to the general rules, and the size of the data granularity can be applied to all business scenarios, and each data has a label associated with the business scenario, so the knowledge base becomes a unified standard business scenario knowledge base.

[0131] In the present application, the label can be a label composed of the standard data granularity specified in the general rules and the data dimensions added according to the business scenario, or it can be a label established based on the standard data granularity specified in the general rules and the data dimensions added according to the business scenario. Technical personnel in this field can make a choice based on actual conditions and will not go into details here.

[0132] The display module 103 is used to set a personalized data report, and extract the application data for display from the knowledge base according to the personalized data report and the data tag.

[0133] The personalized report described in this application corresponds to a personalized theme, and different themes lead to different reports. Different personalized themes correspond to reports with one or more different data description dimensions.

[0134] First, the topic needs to be determined based on the data updated according to the general rules and combined rules. The process is as follows:

[0135] The first step is to determine the actual usage report, which is the report determined after the theme is changed.

[0136] The second step is to determine whether the report theme has changed and confirm whether the theme has changed based on the actual report used.

[0137] The third step is to adjust the subject business rules and determine the changes in the data dimensions corresponding to the subject when the subject changes.

[0138] The fourth step is to update the data according to the business subject rules, adjust the selection of application data according to the changes in the data dimensions, form new subject rules containing subject dimensions after reselecting the application data, and update the data according to the subject rules.

[0139] It should be noted that in the change of the theme rules, the data dimensions include the dimensions in the general rules: company, account, profit center and business type, and the dimensions in the combination rules: data functional scope and assessment type, etc., which are used to describe the data scenario, and also include: dimensions related to the theme display. That is, under the general rules and combination rules, the application data is updated in combination with the display theme dimension, and the theme rules provide filters for data extraction, which can also be called display rules.

[0140] After the above processing, set up personalized data reports, specifically according to the business situation. For example, a new business needs a new business report.

[0141] Each of the above modules processes the data at a data granularity, combines the application data with the business scenario, and the data becomes application data with a business scenario that complies with the standard data granularity limitation.

[0142] Specifically, the present application combines the subject rule with the general rule and the combination rule to process the data as follows.

[0143] Get data from the data source, and process the data obtained from the data source according to the general rules. Determine the general rules, and update the data to application data according to the general rules to achieve standardization of application data. Combine the application data that passes the general rules with the business scenario processing to form a scenario knowledge base, obtain application data containing business scenario dimensions according to the processing of general rules and combined rules, and build a report theme based on the application data. Select a theme, and after selecting the theme, select data that matches the theme from the knowledge base for display. Fill the application data that matches the report theme into the report for display.

[0144] As mentioned above, the application data is tagged according to the business scenario, so the application data can be applied to personalized topics for different business scenarios, or to personalized group topics set with business scenarios.

[0145] If the general rules, combination rules and display rules are changed, the data processing flow is adjusted according to the above method, and data processing can be performed after the adjustment is completed.

[0146] After the above rules and data are adjusted, a sample check is first performed, that is, the description dimension of the personalized theme needs to be determined. Then, the application data is used to fill in the report, that is, according to the description dimension of the personalized theme, the application data for display is selected by combining the general rules and the combination rules as filters, and the application data is filled in the personalized data report. After data verification, the report is completed.

[0147] Before this, if the application data changes, a new standard data granularity is obtained, and the application data is updated according to the new standard data granularity, which is determined by the granularity range of the data newly sent by the data source.

[0148] According to the updated application data, the knowledge base is updated; according to the update of the knowledge base, a data update message is sent in real time through a personalized data report. The data update message is displayed on the report front page.

[0149] In module 102, the data is labeled, and the label is set according to the standard data granularity and business scenario description dimension of the general rule. Therefore, in this step, only the data marked with the label that meets the personalized theme needs to be extracted and included in the data report. It should be pointed out here that the label can be regarded as a form of expression of the above theme.

[0150] The above is all the contents of this application. Those skilled in the art can combine the technical means in the above contents in different orders, or replace the technical means therein with well-known means to achieve the technical effects of this application. These settings are all within the scope of the technical solution of this application.

Claims

1. A data processing method, characterized in that: include: Acquire data from a data source, determine a standard data granularity according to a granularity range of the data, and update the data into application data according to the standard data granularity, wherein the standard data granularity is an upper limit of the granularity of the data; Acquire a data report associated with the business scenario, set a data tag for the application data according to the data report and the upper limit of the granularity of the application data, and add the application data with the set data tag to the knowledge base; Setting a personalized data report, and extracting the application data for display from the knowledge base according to the personalized data report and the data tag; To obtain data reports associated with business scenarios, firstly, business scenario rules must be determined. The business scenario rules include multiple general rules, which are combination rules. In the combination rule, data dimensions include company, account, profit center and business type, as well as data dimensions such as data functional scope and assessment type used to describe data scenarios; After determining the business scenario description dimensions of the data through multiple single reports, determine multiple general rules corresponding to the business scenario rules; The label is a label composed of the standard data granularity specified by the general rule and the data dimension added according to the business scenario, or an established label is added based on the standard data granularity specified by the general rule and the data dimension added according to the business scenario.

2. The data processing method according to claim 1, characterized in that: A new standard data granularity is obtained, and application data is updated according to the new standard data granularity.

3. The data processing method according to claim 2, characterized in that: updating the knowledge base according to the updated application data; According to the update of the knowledge base, data update messages are sent in real time through personalized data reports.

4. The data processing method according to claim 3, characterized in that: The data update message is displayed on the report front page.

5. The data processing method according to claim 3, characterized in that: The new standard data granularity is determined by the granularity range of the data newly sent by the data source.

6. A data processing device, characterized in that: include: A granularity module is used to obtain data from a data source, determine a standard data granularity according to a granularity range of the data, and update the data into application data according to the standard data granularity, wherein the standard data granularity is an upper limit of the granularity of the data; A label module, used to obtain a data report associated with a business scenario, set a data label for the application data according to the data report and the upper limit of the granularity of the application data, and add the application data with the set data label to the knowledge base; A display module, used to set a personalized data report, and extract the application data for display from the knowledge base according to the personalized data report and the data tag; To obtain data reports associated with business scenarios, firstly, business scenario rules must be determined. The business scenario rules include multiple general rules, which are combination rules. In the combination rule, data dimensions include company, account, profit center and business type, as well as data dimensions such as data functional scope and assessment type used to describe data scenarios; After determining the business scenario description dimensions of the data through multiple single reports, determine multiple general rules corresponding to the business scenario rules; The label is a label composed of the standard data granularity specified by the general rule and the data dimension added according to the business scenario, or an established label is added based on the standard data granularity specified by the general rule and the data dimension added according to the business scenario.

7. The data processing device according to claim 6, characterized in that: Also includes: The data updating module is used to obtain a new standard data granularity and update the application data according to the new standard data granularity.

8. The data processing device according to claim 7, characterized in that: Also includes: A knowledge base updating module, used for updating the knowledge base according to the updated application data; The message module is used to send data update messages in real time through personalized data reports according to the update of the knowledge base.

9. The data processing device according to claim 8, characterized in that: Also includes: The display module is used to display the data update message on the report front-end page.

10. The data processing device according to claim 8, characterized in that: The new standard data granularity is determined by the granularity range of the data newly sent by the data source.

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