Report processing method and device, computer equipment and storage medium

Abstract

The invention relates to a report processing method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: generating an associated detail data set according to each business detail data report; inputting each piece of associated detail data in the associated detail data set into a stream filter, obtaining dimension data corresponding to each piece of associated detail data through the stream filter, and generating a dimension label corresponding to each piece of associated detail data by using the dimension data corresponding to each piece of associated detail data; obtaining target associated detail data of which the dimension labels meet a preset condition, and summarizing the access data of the target associated detail data of which the dimension labels are the same to obtain summarized data corresponding to the dimension labels; and according to the dimension data corresponding to each dimension label and the summarized data corresponding to each dimension label, obtaining a summarized data report corresponding to each business detail data report. By adopting the method, the real-time query summarization response speed of the business report can be improved.

Description

Technical Field

[0001] The present application relates to the field of database technology, and in particular to a report processing method, apparatus, computer equipment, storage medium and computer program product. Background Art

[0002] Business organizations have a strong demand for real-time statistics of business reports, and therefore have high requirements for the query response speed of data reports.

[0003] At present, when business organizations perform data query and aggregation, they need to first associate multiple business reports based on the dimension fields to be aggregated, obtain the dimension field data, select the dimension field data with the highest ranking, and then associate multiple business reports based on the dimension field data with the highest ranking to obtain the dimension detail data corresponding to the dimension field data, and then aggregate the dimension detail data through the calculation method of the indicator field and display it to the front end. However, when it involves the association of multiple business reports and the amount of data in each business report is large, this data query and aggregation method requires multiple full-volume association queries, which has poor performance and requires a long response time when querying and aggregating in real time.

[0004] Therefore, there is a problem in traditional technologies that the response speed of real-time query and summary of business reports is not high. Summary of the invention

[0005] Based on this, it is necessary to provide a report processing method, device, computer equipment, computer-readable storage medium and computer program product that can improve the real-time query summary response speed of business reports in response to the above technical problems.

[0006] A report processing method, comprising:

[0007] Generate related detailed data sets based on each business detailed data report;

[0008] Input each associated detail data in the associated detail data set into the stream filter, obtain the dimension data corresponding to each associated detail data through the stream filter, and generate the dimension label corresponding to each associated detail data by using the dimension data corresponding to each associated detail data;

[0009] Obtain target-related detailed data whose dimension labels meet preset conditions, and aggregate the data of target-related detailed data with the same dimension labels to obtain aggregated data corresponding to each dimension label;

[0010] Based on the dimensional data corresponding to each dimensional label and the summary data corresponding to each dimensional label, a summary data report corresponding to each business detail data report is obtained.

[0011] A report processing device, comprising:

[0012] A generation module is used to generate related detailed data sets according to each business detailed data report;

[0013] An input module, used for inputting each associated detail data in the associated detail data set into a stream filter, obtaining dimension data corresponding to each associated detail data through the stream filter, and generating a dimension label corresponding to each associated detail data using the dimension data corresponding to each associated detail data;

[0014] A summary module is used to obtain target-related detailed data whose dimension tags meet preset conditions, and summarize the data of target-related detailed data with the same dimension tags to obtain summary data corresponding to each dimension tag;

[0015] The acquisition module is used to obtain the summary data report corresponding to each business detail data report according to the dimension data corresponding to each dimension label and the summary data corresponding to each dimension label.

[0016] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0017] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0018] A computer program product comprises a computer program, which implements the steps of the above method when executed by a processor.

[0019] The report processing method, device, computer equipment, storage medium and computer program product generate an associated detail data set according to each business detail data report; input each associated detail data in the associated detail data set into a stream filter, obtain the dimension data corresponding to each associated detail data through the stream filter, and generate the dimension label corresponding to each associated detail data using the dimension data corresponding to each associated detail data; obtain the target associated detail data whose dimension label meets the preset conditions, and summarize the data of each target associated detail data with the same dimension label to obtain the summary data corresponding to each dimension label; obtain the summary data report corresponding to each business detail data report according to the dimension data corresponding to each dimension label and the summary data corresponding to each dimension label; in this way, according to the correspondence between the data of the preset data extraction field and the dimension data of the target dimension field, the data involved in each business detail data report can be quickly associated to obtain the associated detail data set, and then each associated detail data in the associated detail data set is processed one by one through the stream filter. , generate dimension labels for each associated detail data according to the dimension data corresponding to each associated detail data, determine the associated detail data that meets the preset conditions as the target associated detail data, and filter out the associated detail data that does not meet the preset conditions from the stream filter, which can accurately retain the required associated detail data, filter out the unnecessary associated detail data, reduce unnecessary storage and calculation, and then summarize the data fetching of each target associated detail data with the same dimension label, accurately summarize each target associated detail data with the same dimension label into a summary data, and merge the dimension data corresponding to each dimension label and the summary data corresponding to each dimension label to generate a summary data report, which can solve the problem that the real-time query summary response speed is not fast enough in the traditional report real-time query summary process, which requires all reports to be associated twice to complete the data fetching summary. When it involves multi-table query and the amount of data in each table is large, the dimension data and the data fetching data can be extracted and summarized at one time through the stream filter, which improves the response speed of the report real-time query summary. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] FIG1 is an application environment diagram of a report processing method in one embodiment;

[0022] Figure 2 A flowchart of a report processing method in an embodiment;

[0023] Figure 3 is a schematic diagram of a data processing process in an embodiment;

[0024] Figure 4 is a schematic diagram of the processing logic of a stream filter in one embodiment;

[0025] Figure 5 A flowchart of a report processing method in another embodiment;

[0026] Figure 6 is a structural block diagram of a report processing device in one embodiment;

[0027] FIG. 7 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0029] The technical solution of the present application can be applied to various information management systems, such as Enterprise Resource Planning (ERP), enterprise management system, financial system, human resource system, supply chain system, etc. For example, in the field of human resource (HR) management, due to the strong demand for real-time statistics of reports, the query response requirements for reports are high. When the existing technology summarizes and displays reports in real time, it is necessary to perform the first association on each report to extract the dimension data of each target dimension field involved in the summary calculation and perform paging to obtain the paginated dimension data, and then perform the second association on each report based on the paginated dimension data to obtain the detailed data associated with the paginated dimension data, and finally perform summary calculation on the detailed data. It can be seen that the entire summary calculation process involves two full associations of each report to complete the report data collection and summary. When the summary calculation process involves multiple table associations and the amount of data in each table is large, it is impossible to quickly respond to the real-time query summary requirements of the business report. The report processing method involved in the present application can solve the problem that when the summary calculation process involves multiple table associations and the amount of data in each table is large, it is impossible to quickly respond to the real-time query summary requirements of the business report.

[0030] The report processing method provided in the embodiment of the present application can be applied to the application environment shown in FIG. 1. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The user can initiate a business report summary request through the terminal 102, and the server 104 responds to the business report summary request and generates an associated detail data set according to each business detail data report; the server 104 inputs each associated detail data in the associated detail data set into the stream filter, obtains the dimension data corresponding to each associated detail data through the stream filter, and generates the dimension label corresponding to each associated detail data using the dimension data corresponding to each associated detail data; the server 104 obtains the target associated detail data whose dimension label meets the preset conditions, and summarizes the data of each target associated detail data with the same dimension label to obtain the summary data corresponding to each dimension label; the server 104 obtains the summary data report corresponding to each business detail data report according to the dimension data corresponding to each dimension label and the summary data corresponding to each dimension label, and displays the summary data report in the terminal 102. The terminal 102 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers.

[0031] In an exemplary embodiment, Figure 2 As shown, a report processing method is provided, which is described by taking the method applied to the server 104 in FIG. 1 as an example, and includes the following steps 202 to 206. Among them:

[0032] Step 202: Generate a related detail data set according to each business detail data report.

[0033] The business detail data report may be a report storing various detailed data in the business information management system. For example, in a human resource management system, the business detail data report may be a data report storing employee information such as basic personal information, employee education information, and employee job transfer information.

[0034] The associated detail data stored in the associated detail data set is used to characterize the correspondence between the dimension data of each target dimension field in the business detail data report and the data of each preset data field in the business detail data report. The associated detail data set may be obtained by associating and storing the detail data of each business detail data report. The associated detail data set may exist in the form of a report. The associated detail data set may include multiple pieces of associated detail data. One piece of associated detail data may include the dimension data of each target dimension field and the data of each preset data field, and may also include field data corresponding to other fields.

[0035] Among them, the target dimension field can refer to the field that needs to be used as the basis for aggregation, and the dimension data of the target dimension field can refer to the field value of the field that needs to be used as the basis for aggregation. In actual applications, the number of target dimension fields can be one or more.

[0036] The preset number-taking field may be a field that needs to be calculated for number-taking, and the number-taking data of the preset number-taking field may refer to the field value of the field that needs to be calculated for number-taking.

[0037] For example, the associated detail data set G includes associated detail data 1, 2, 3, ..., n, where associated detail data 1 is {Zhang San, region A, position: programmer, working time 3 years}, associated detail data 2 in the associated detail data set is {Zhang San, region A, position: manager, working time 2 years}, and associated detail data 3 in the associated detail data set is {Li Si, region B, position: manager, working time 5 years}. When performing data collection and aggregation, the employee name can be used as the target dimension field and the employee's working time can be used as the preset data collection field. For associated detail data 1, the dimension data of the target dimension field of employee name of associated detail data 1 is the field value "Zhang San", and the data of the preset number field of employee tenure of associated detail data 1 is "3 years of tenure"; for associated detail data 2, the dimension data of the target dimension field of employee name of associated detail data 2 is the field value "Zhang San", and the data of the preset number field of employee tenure of associated detail data 1 is "2 years of tenure"; for associated detail data 3, the dimension data of the target dimension field of employee name of associated detail data 3 is the field value "Li Si", and the data of the preset number field of employee tenure of associated detail data 3 is "5 years of tenure".

[0038] Optionally, since in actual applications, the employee information to be extracted is often stored in different business detail data reports, if data needs to be extracted at one time, it is necessary to associate the business detail data reports to obtain an associated detail data set.

[0039] For example, when it is necessary to extract employee information such as employee name, employee id, employee region, employee job title, employee on-the-job hours, etc., since the employee information such as employee name, employee id, employee region, employee job title, employee on-the-job hours, etc. is stored in different business detail data tables, it is necessary to associate the business detail data tables storing the employee information such as employee name, employee id, employee region, employee job title, employee on-the-job hours, etc.

[0040] Step 204: input each associated detail data in the associated detail data set into the stream filter, obtain the dimension data corresponding to each associated detail data through the stream filter, and generate the dimension label corresponding to each associated detail data using the dimension data corresponding to each associated detail data.

[0041] The stream filter may refer to a stream processor for processing stream data, which implements the data filtering function by presetting data filtering conditions. In the process of stream processing, there may be multi-operator calculations. In the technical solution of this application, the concurrency of stream processing needs to be set to 1 to ensure that each associated detail data enters the stream filter in a unidirectional and orderly manner.

[0042] The dimension label corresponding to the associated detail data may refer to a unique key corresponding to the associated detail data, and the dimension label corresponding to the associated detail data may be understood as a data identifier of the associated detail data.

[0043] Optionally, each associated detail data in the associated detail data set is input into the stream filter. For each detail associated data input into the stream filter, the stream filter generates a dimension label for the associated detail data using the field value corresponding to the target dimension field in the associated detail data.

[0044] For example, when the target dimension field only has employee name, the dimension data corresponding to associated detail data 1 only has the field value "Zhang San" corresponding to the target dimension field of employee name, and "Zhang San" is used as the dimension label of associated detail data 1. The dimension data corresponding to associated detail data 2 only has the field value "Zhang San" corresponding to the target dimension field of employee name, and "Zhang San" is used as the dimension label of associated detail data 2. The dimension data corresponding to associated detail data 3 only has the field value "Li Si" corresponding to the target dimension field of employee name, and "Li Si" is used as the dimension label of associated detail data 3. It can be seen that associated detail data 1 and 2 have the same dimension label, and associated detail data 3 has a different dimension label from associated detail data 1 and 2. When grouping associated detail data, associated detail data 1 and 2 will be grouped together, and associated detail data 3 will be grouped together.

[0045] Step 206, obtaining target-related detailed data whose dimension tags meet preset conditions, and summarizing the data of each target-related detailed data with the same dimension tag to obtain summary data corresponding to each dimension tag.

[0046] Among them, the preset condition may refer to a data filtering condition preset in the flow filter. When the associated detail data does not meet the preset data filtering condition, the flow filter will filter out the associated detail data. When the associated detail data meets the preset data filtering condition, the flow filter will store the associated detail data as the target associated detail data in the flow filter. Finally, only the required associated detail data is stored in the flow filter. It should be noted that the flow filter will store the dimension label of each associated detail data in order, and the dimension label will be used as the basis for grouping the associated detail data.

[0047] The summary data corresponding to the dimension tag may refer to the final data obtained by summing up the target-related detailed data belonging to the dimension tag by summing up or averaging.

[0048] Optionally, the stream filter stores the required target-related detail data in the stream filter based on a preset data filtering condition, and aggregates the data of each target-related detail data having the same dimension tag to obtain aggregate data corresponding to each dimension tag.

[0049] For example, when the preset number-taking field only has the employee's tenure, the number-taking data corresponding to the associated detail data 1 only has the field value "3 years" corresponding to the preset number-taking field of employee's tenure, the number-taking data corresponding to the associated detail data 2 only has the field value "2 years" corresponding to the preset number-taking field of employee's tenure, and the number-taking data corresponding to the associated detail data 3 only has the field value "5 years" corresponding to the preset number-taking field of employee's tenure. Since the associated detail data 1 and 2 have the same dimension label "Zhang San", the associated detail data 1 and 2 are summarized, and the summary data corresponding to the dimension label "Zhang San" is "5 years". Since the dimension label "Li Si" of the associated detail data 3 has only one associated detail data, the summary data corresponding to the dimension label "Li Si" is "5 years".

[0050] Steps S202 to S206 can be found in Figure 3 The data processing process shown. Figure 3 In step S202, the business detail data reports are associated with each other through the association relationship between entity A and entity B and entity C to obtain an associated detail data set, corresponding to step S202; Figure 3 In the step S204 to S206, the associated detail data set is input into the stream filter in the form of a data stream to obtain a filtered detail data set.

[0051] Step 208, obtaining summary data reports corresponding to each business detail data report based on the dimension data corresponding to each dimension tag and the summary data corresponding to each dimension tag.

[0052] The summary data report may be a final summary information including each dimension tag, and the final summary information corresponding to a dimension tag includes the dimension data and summary data corresponding to the dimension tag.

[0053] For example, the dimension data corresponding to the dimension label "Zhang San" is "Zhang San", and the corresponding summary data is "5 years on the job", so the corresponding final summary information is {Zhang San, 5 years on the job}; the dimension data corresponding to the dimension label "Li Si" is "Li Si", and the corresponding summary data is "5 years on the job", so the corresponding final summary information is {Li Si, 5 years on the job}.

[0054] Optionally, the stream filter generates a summary data report based on the dimension data corresponding to each dimension tag and the summary data corresponding to each dimension tag.

[0055] For example, the stream filter generates a summary data report based on the final summary information {Zhang San, working time for 5 years} and {Li Si, working time for 5 years} corresponding to the dimension label "Zhang San" and the dimension label "Li Si" respectively.

[0056] In the above report processing method, an associated detail data set is generated according to each business detail data report; each associated detail data in the associated detail data set is input into a stream filter, dimension data corresponding to each associated detail data is obtained through the stream filter, and dimension tags corresponding to each associated detail data are generated using the dimension data corresponding to each associated detail data; target associated detail data whose dimension tags meet preset conditions are obtained, and the access data of each target associated detail data with the same dimension tags are summarized to obtain the summary data corresponding to each dimension tag; based on the dimension data corresponding to each dimension tag and the summary data corresponding to each dimension tag, a summary data report corresponding to each business detail data report is obtained; in this way, according to the correspondence between the access data of the preset access field and the dimension data of the target dimension field, the data involved in each business detail data report can be quickly associated to obtain an associated detail data set, and then each associated detail data in the associated detail data set is processed one by one through the stream filter, and the associated detail data is processed according to each associated detail data. Generate dimension labels for each associated detail data according to the corresponding dimension data, determine the associated detail data that meets the preset conditions as target associated detail data, and filter out the associated detail data that does not meet the preset conditions from the stream filter, so as to accurately retain the required associated detail data, filter out the unnecessary associated detail data, reduce unnecessary storage and calculation, and then summarize the data fetched from each target associated detail data with the same dimension label, accurately summarize each target associated detail data with the same dimension label into one summary data, and merge the dimension data corresponding to each dimension label and the summary data corresponding to each dimension label to generate a summary data report, which can solve the problem that the real-time query summary response speed is not fast enough in the traditional report real-time query summary process, because each report needs to be fully associated twice to complete the data fetching summary. When it involves multi-table query and the amount of data in each table is large, the dimension data and the data fetching data can be extracted and summarized at one time through the stream filter, which improves the response speed of the report real-time query summary.

[0057] In an exemplary embodiment, an associated detail data set is generated based on each business detail data report, including: extracting the dimension data of each target dimension field and the data of each preset numbering field from each business detail data report; based on the correspondence between the dimension data of each target dimension field and the data of each preset numbering field, associating the dimension data of each target dimension field with the data of each preset numbering field to obtain an associated detail data set.

[0058] In actual applications, when associating business detail data reports, first, extract the dimension data of each target dimension field and the data of each preset number field from each business detail data report. For example, extract the dimension data "Zhang San" and "Li Si" of the target dimension field of employee name, extract the data of "Zhang San" in the preset number field of employee on-the-job time, including "on-the-job time of 2 years" and "on-the-job time of 3 years", and extract the data of "Li Si" in the preset number field of employee on-the-job time, including "on-the-job time of 5 years"; then, based on the corresponding relationship between the dimension data of each target dimension field and the data of each preset number field, associate the dimension data of each target dimension field with the data of each preset number field. The association process can be referred to. Figure 3 The association mode between entities A, B, and C is obtained to obtain an associated detail data set, for example, an associated detail data set G, including: associated detail data 1 is {Zhang San, region A, position: programmer, working time for 3 years}, associated detail data 2 in the associated detail data set is {Zhang San, region A, position: manager, working time for 2 years}, and associated detail data 3 in the associated detail data set is {Li Si, region B, position: manager, working time for 5 years}.

[0059] In this embodiment, by quickly extracting and associating data from each business detail data report according to the fields to be retrieved, the associated detail data set can be quickly and accurately acquired.

[0060] In an exemplary embodiment, the dimensional data corresponding to any associated detail data includes the dimensional data of the associated detail data under each target dimensional field, and the dimensional data corresponding to each associated detail data is used to generate the dimensional label corresponding to each associated detail data, including: for any associated detail data, the dimensional data of the associated detail data under each target dimensional field are spliced ​​to obtain the dimensional data splicing result corresponding to the associated detail data; and the dimensional data splicing result corresponding to any associated detail data is used as the dimensional label of the associated detail data.

[0061] Among them, the dimension data splicing result corresponding to the associated detailed data can be the result obtained by splicing the field values ​​of the associated detailed data under each target dimension field. Using the dimension data splicing result as a dimension label is suitable for scenarios when the number of target dimension fields is greater than or equal to two.

[0062] Optionally, when there is more than one target dimension field, the field values ​​of the associated detailed data under each target dimension field are concatenated to obtain a field value concatenation result, and the field value concatenation result is used as the dimension label of the associated detailed data.

[0063] For example, taking the associated detail data set G as an example, when the target dimension fields include employee name and employee region, the field values ​​of the associated detail data 1 under the two target dimension fields of employee name and employee region are "Zhang San" and "Region A" respectively, then "Zhang San" and "Region A" are spliced ​​to obtain "Zhang San_Region A", and "Zhang San_Region A" is used as the dimension label of the associated detail data 1. According to the same splicing method, the dimension label of the associated detail data 2 can be obtained as "Zhang San_Region A", and the dimension label of the associated detail data 3 can be obtained as "Li Si_Region B".

[0064] In this embodiment, the dimension data of each target dimension field in the associated detail data are spliced ​​to obtain the dimension data splicing result corresponding to the associated detail data, and the dimension data splicing result is used as the dimension label of the associated detail data, so that each associated detail data can be quickly labeled. When filtering each associated detail data subsequently, the dimension label of each associated detail data can be quickly determined based on the dimension label of each associated detail data, which is conducive to quickly aggregating each associated detail data belonging to the same dimension label, thereby improving data aggregation efficiency. In addition, it can be quickly determined whether each associated detail data needs to be filtered out based on the dimension label of each associated detail data, thereby improving data filtering efficiency.

[0065] In an exemplary embodiment, target associated detail data whose dimension tags satisfy preset conditions is obtained, including: for any associated detail data, comparing the dimension tag of the associated detail data with each existing dimension tag stored in the flow filter through a flow filter to obtain a comparison result; when the comparison result indicates that the dimension tag of the associated detail data is the same as any existing dimension tag, and the storage order of the existing dimension tags in the flow filter is within a preset sequence range, determining that the dimension tag of the associated detail data satisfies the preset condition, and determining the associated detail data as the target associated detail data; when the comparison result indicates that the dimension tag of the associated detail data is the same as any existing dimension tag, and the storage order of the existing dimension tags is not within the preset sequence range, determining that the dimension tag of the associated detail data does not satisfy the preset condition, and filtering out the associated detail data.

[0066] Among them, the existing dimension label can refer to the dimension label that has been recorded in the flow filter. In actual applications, the associated detail data flows into the flow filter one by one, and the flow filter records the dimension labels of the associated detail data in the flow filter in the order of the inflow of the associated detail data. Taking the associated detail data set G as an example, since the associated detail data 1 is the first associated detail data flowing into the flow filter, the associated detail data 1 has no existing dimension label, and the associated detail data 2 is the second associated detail data flowing into the flow filter. The dimension label of the associated detail data 1 is the existing dimension label of the associated detail data 2. Similarly, the dimension labels of the associated detail data 1 and the associated detail data 2 are also the existing dimension labels of the associated detail data 3.

[0067] The comparison result may refer to whether the dimension label of the associated detail data is the same as the existing dimension label in the flow filter.

[0068] The storage order may refer to the storage sequence number corresponding to the dimension label of the associated detail data when the associated detail data flows into the flow filter. For example, taking the associated detail data set G as an example, since the dimension labels of the associated detail data 1 and the associated detail data 2 are "Zhang San", and the dimension label of the associated detail data 3 is "Li Si", the storage order of the dimension label "Zhang San" of the associated detail data 1 in the flow filter is 1, the storage order of the dimension label "Zhang San" of the associated detail data 2 in the flow filter is also 1, and the storage order of the dimension label "Li Si" of the associated detail data 3 in the flow filter is 2.

[0069] Among them, the preset sequence range can be a preset sequence range of the dimension labels to be selected, the preset sequence range can be set to 1-10, or can be set to 20-30, and the preset sequence range can be set according to actual needs.

[0070] Optionally, the dimension tag generated by each associated detail data will be stored in the flow filter in sequence, and the dimension tag generated by the next associated detail data will be compared with the existing dimension tags in the flow filter. For each associated detail data, the flow filter compares the dimension tag of the associated detail data with the existing dimension tags stored in the flow filter to obtain a comparison result; if the comparison result indicates that the dimension tag of the associated detail data is the same as one of the existing dimension tags of the flow filter, and the storage order of the existing dimension tags in the flow filter is within a preset sequence range, then it is determined that the dimension tag of the associated detail data meets the preset condition, and the associated detail data is determined as the target associated detail data and stored in the flow filter; if the comparison result indicates that the dimension tag of the associated detail data is the same as one of the existing dimension tags of the flow filter, but the storage order of the existing dimension tags in the flow filter is not within the preset sequence range, then it is determined that the dimension tag of the associated detail data does not meet the preset condition, and the associated detail data is filtered out.

[0071] For example, taking the associated detail data set G as an example, if it is necessary to obtain associated detail data with a preset sequence range of 1-10, the stream processor will store the first 10 dimension labels in order. When the associated detail data flows through, the stream filter calculates whether the dimension label of the associated detail data is in the top 10. If it is, the associated detail data is the target associated detail data. If not, the associated detail data needs to be filtered out. The storage order of the dimension label "Zhang San" of associated detail data 1 in the stream filter is 1, the storage order of the dimension label "Zhang San" of associated detail data 2 in the stream filter is also 1, and the storage order of the dimension label "Li Si" of associated detail data 3 in the stream filter is 2. Since the storage order of "Zhang San" and "Li Si" in the stream filter is between 0-10, "Zhang San" and "Li Si" are both in the preset sequence range. Associated detail data 1, associated detail data 2 and associated detail data 3 meet the preset conditions and should all be stored as target associated detail data in the stream filter.

[0072] Assume that the dimension label of the subsequently flowing in associated detail data k (k>3) is "Li Si". Since "Li Si" is an existing dimension label and the storage order of the existing dimension label "Li Si" in the flow filter is 2, which is between 1-10, "Li Si" is within the preset order range, and the associated detail data k meets the preset conditions and should be stored in the flow filter as the target associated detail data.

[0073] Assume that the dimension label of the subsequently flowing in associated detail data j (j>3) is "Wang Wu", "Wang Wu" is the existing dimension label in the flow filter, and the storage order is 12. Although "Wang Wu" matches the existing dimension label successfully, since the storage order of "Wang Wu" is not between 1-10 and is not within the preset order range, the associated detail data j does not meet the preset conditions and should be filtered out from the flow filter.

[0074] For example, taking the associated detail data set G as an example, if it is necessary to obtain associated detail data with a preset sequence range of 20-30, the stream processor will store the first 30 dimension labels in order. When the associated detail data flows through, the stream filter calculates whether the dimension label of the associated detail data is in the top 30. If not, and the number of stored dimension labels is less than 20, it means that the associated detail data belongs to the sequence range of 0-19 and does not meet the requirements. The associated detail data is filtered out, but the stream filter still records the dimension label of the associated detail data in order until the dimension label of the associated detail data flowing through the stream filter is between 20-30, then the required associated detail data is filtered out. For the subsequent dimension labels whose storage order exceeds 30, there is no need to record and they are directly removed. Since the storage order of the dimension labels corresponding to the associated detail data 1, 2, and 3 is not between 20-30, the associated detail data 1, 2, and 3 need to be removed.

[0075] Assume that the dimension label of the subsequently flowing in associated detail data i (i>3) is "Zhang Qi", "Zhang Qi" is an existing dimension label, and the storage order of the existing dimension label "Zhang Qi" in the flow filter is 21, which is between 20-30 and within the preset order range. The associated detail data i meets the preset conditions and should be stored in the flow filter as the target associated detail data.

[0076] In this embodiment, the dimension tag of the associated detail data is compared with each existing dimension tag stored in the flow filter through the flow filter to obtain a comparison result; when the comparison result indicates that the dimension tag of the associated detail data is the same as any existing dimension tag, and the storage order of the existing dimension tags in the flow filter is within a preset sequence range, it is determined that the dimension tag of the associated detail data meets the preset condition, and the associated detail data is determined as the target associated detail data; when the comparison result indicates that the dimension tag of the associated detail data is the same as any existing dimension tag, and the storage order of the existing dimension tags is not within the preset sequence range, In the case where the dimension label of the associated detail data does not meet the preset conditions, the associated detail data is determined to be not in compliance with the preset conditions, and the associated detail data is filtered out; in this way, the dimension label of each piece of associated detail data can be compared with the existing dimension label in the stream filter. When the dimension label of the associated detail data is the same as one of the existing dimension labels, and the storage order of the existing dimension labels belongs to the preset sequence range, it is determined that the associated detail data needs to be retained in the stream filter, thereby realizing fast and accurate screening of the associated detail data, and quickly filtering out the unnecessary associated detail data, thereby improving the data filtering efficiency and data filtering accuracy.

[0077] In an exemplary embodiment, the method further includes: when the comparison result indicates that the dimension tags of the associated detail data are different from the existing dimension tags, determining the storage order of the dimension tags of the associated detail data in the stream filter; when the storage order of the dimension tags of the associated detail data in the filter is within a preset sequence range, determining that the dimension tags of the associated detail data meet preset conditions, and determining the associated detail data as target associated detail data; when the storage order of the dimension tags of the associated detail data in the filter is not within the preset sequence range, determining that the dimension tags of the associated detail data do not meet the preset conditions, and filtering out the associated detail data.

[0078] Optionally, for each piece of associated detail data, the flow filter compares the dimension label of the associated detail data with each existing dimension label stored in the flow filter to obtain a comparison result; if the comparison result indicates that the dimension label of the associated detail data is different from each existing dimension label of the flow filter, it means that the dimension label of the associated detail data is a new dimension label for each existing dimension label. In this case, the dimension label of the associated detail data is stored in the flow filter, and the storage order of the dimension label of the associated detail data in the flow filter is determined. If the storage order of the existing dimension label in the flow filter is within a preset order range, then The dimension label of the associated detail data meets the preset conditions, and the associated detail data is determined as the target associated detail data; if the comparison result indicates that the dimension label of the associated detail data is different from the existing dimension labels of the flow filter, it means that the dimension label of the associated detail data is a new dimension label for the existing dimension labels. At this time, the dimension label of the associated detail data is stored in the flow filter, and the storage order of the dimension label of the associated detail data in the flow filter is determined. If the storage order of the existing dimension label in the flow filter is not within the preset order range, the dimension label of the associated detail data does not meet the preset conditions, and the associated detail data needs to be filtered out.

[0079] For example, taking the associated detail data set G as an example, if you need to obtain the associated detail data in the preset sequence range of 1-10:

[0080] Assume that the dimension label of the associated detail data m (m>3) flowing into the flow filter is "Liu Jiu", "Liu Jiu" is not an existing dimension label, and the existing dimension labels in the current flow filter are only "Zhang San" and "Li Si", then the storage order of the dimension label "Liu Jiu" in the flow filter is recorded as 3. Since the storage order 3 is between 1-10 and is within the preset order range, the associated detail data m meets the preset conditions and should be stored in the flow filter as the target associated detail data. It can be determined that the dimension labels of the associated detail data 1 to m-1 are either "Zhang San" or "Li Si";

[0081] Assume that the dimension label of the associated detail data o (o>11) flowing into the flow filter is "Li Liu", "Li Liu" is not an existing dimension label, and there are 10 existing dimension labels in the current flow filter. The storage order of the dimension label "Li Liu" in the flow filter is recorded as 11, which is not between 1-10 and is not within the preset sequence range. Therefore, the associated detail data o does not meet the preset conditions and should be filtered out from the flow filter. It can be determined that there are 10 dimension labels for the associated detail data 1 to o-1, and the associated detail data 1 to o-1 can be divided into 10 groups according to the 10 dimension labels;

[0082] If you need to obtain the associated detailed data between the preset sequence range 20-30:

[0083] Assume that the dimension label of the associated detail data n (n>21) flowing into the flow filter is "Liu Shi", "Liu Shi" is not an existing dimension label, and there are 20 existing dimension labels in the current flow filter. Then the storage order of the dimension label "Liu Shi" in the flow filter is recorded as 21, which is between 20-30 and within the preset sequence range. Then the associated detail data n meets the preset conditions and should be stored in the flow filter as the target associated detail data. It can be determined that there are 20 dimension labels for the associated detail data 1 to n-1, and the associated detail data 1 to n-1 can be divided into 20 groups according to the 20 dimension labels.

[0084] Assume that the dimension label of the associated detail data p (p>31) flowing into the flow filter is "Li Qi", "Li Qi" is not an existing dimension label, and there are 30 existing dimension labels in the current flow filter. The storage order of the dimension label "Li Qi" in the flow filter is recorded as 31, which is not between 20-30 and is not within the preset sequence range. Therefore, the associated detail data p does not meet the preset conditions and should be filtered out from the flow filter. It can be determined that there are 30 dimension labels for the associated detail data 1 to p-1, and the associated detail data 1 to p-1 can be divided into 30 groups according to the 30 dimension labels.

[0085] In this embodiment, when the comparison result indicates that the dimension label of the associated detail data is different from the existing dimension labels, the storage order of the dimension labels of the associated detail data in the flow filter is determined; when the storage order of the dimension labels of the associated detail data in the filter is within the preset order range, it is determined that the dimension labels of the associated detail data meet the preset conditions, and the associated detail data is determined as the target associated detail data; when the storage order of the dimension labels of the associated detail data in the filter is not within the preset order range, it is determined that the dimension labels of the associated detail data do not meet the preset conditions, and the associated detail data is filtered out; in this way, the dimension label of each associated detail data can be compared with the existing dimension labels in the flow filter. When the dimension label of the associated detail data does not match the existing dimension label in the flow filter, by judging whether the storage order of the dimension labels of the associated detail data is within the preset order range, it is quickly and accurately judged whether the associated detail data needs to be stored in the flow filter, thereby improving the data filtering efficiency and data filtering accuracy.

[0086] In order to facilitate the understanding of those skilled in the art, Figure 4A processing logic of a stream filter is exemplarily provided, wherein a dimension tag fetching range, including a starting row and a fetching row number, needs to be pre-set in the stream filter, which corresponds to the preset sequence range in each embodiment of the present application. When an associated detail data flows into the stream filter, the stream filter performs dimension field index mapping on the associated detail data, thereby obtaining the dimension tag of the associated detail data, and determining whether the dimension tag of the associated detail data is in a dimension tag set. If not, the dimension tag of the associated detail data is added to the dimension tag set, and then determining whether the position of the dimension tag of the associated detail data in the dimension tag set is greater than or equal to the "starting row" and less than or equal to the "starting row" + the "fetching row number". If so, it indicates that the current associated detail data meets the fetching requirements and true is returned, otherwise false is returned, thereby filtering the associated detail data and obtaining a filtered detail data set that can be used for summary calculations.

[0087] In an exemplary embodiment, the data obtained for any target-associated detailed data includes the data obtained for the target-associated detailed data under each number-extracting field, and the data obtained for each target-associated detailed data with the same dimension label are summarized to obtain the summary data corresponding to each dimension label, including: obtaining the indicator summary rules corresponding to each indicator field; summarizing the data obtained for each target-associated detailed data belonging to the same dimension label under each number-extracting field according to the indicator summary rules corresponding to each indicator field to obtain the indicator data under each indicator field of the dimension label; and using the indicator data under each indicator field of any dimension label as the summary data of the dimension label.

[0088] The indicator field may be a field corresponding to an indicator to be counted, for example, the employee's on-the-job time.

[0089] Among them, the indicator aggregation rule can be the indicator statistical method corresponding to the indicator to be counted. When the indicator field is the employee's on-the-job time, the indicator aggregation rule corresponding to the indicator field of employee's on-the-job time is to add the on-the-job time of the same employee in various positions to obtain the total on-the-job time of the employee.

[0090] Optionally, obtain the indicator aggregation rule corresponding to the indicator field, and then summarize the field values ​​of each target-related detailed data belonging to the same dimension label under each numerical field according to the indicator aggregation rule corresponding to the indicator field to obtain the indicator value of the dimension label under the indicator field, and then use the indicator value of the dimension label under the indicator field as the summary data of the dimension label.

[0091] For example, taking the associated detail data G as an example, assuming that the target dimension fields include the employee name and the region to which the employee belongs, the dimension labels of the associated detail data 1 and the associated detail data 2 are "Zhang San_Region A", and the dimension label of the associated detail data 3 is "Li Si_Region B". Assuming that the number field is the length of time employees work in different positions, the indicator field is the total length of time employees work, and the dimension label "Zhang San_Region A" has only two target associated detail data, namely, associated detail data 1 and associated detail data 2, then it will belong to " The "3 years on the job" of programmers in the associated detail data 1 of the dimension label "Zhang San_Region A" and the "2 years on the job" of managers in the associated detail data 2 are added together to obtain the indicator value "5 years on the job" of the dimension label "Zhang San_Region A" under the indicator field. Then, the indicator data of the dimension label "Zhang San_Region A" under the indicator field "Total hours on the job of employees" is "5 years on the job". The indicator data "5 years on the job" is used as the summary result of the dimension label "Zhang San_Region A".

[0092] In this embodiment, the indicator aggregation rules corresponding to each indicator field are obtained; according to the indicator aggregation rules corresponding to each indicator field, the data of each target-related detailed data belonging to the same dimension label under each data-acquisition field are aggregated to obtain the indicator data of the dimension label under each indicator field; the indicator data of any dimension label under each indicator field is used as the summary data of the dimension label; in this way, the target-related detailed data corresponding to each dimension label after filtering can be grouped and aggregated, so as to quickly and accurately obtain the summary data corresponding to each dimension label, thereby improving the data aggregation efficiency and data aggregation accuracy.

[0093] In actual applications, the summary data report may be displayed according to the preset sorting rules when it is displayed. At this time, it is necessary to sort the associated detail data in the associated detail data set according to the preset sorting rules. Since the existing sorting functions are relatively rich, the fields based on which the sorting is performed may not be the summary fields displayed in the summary data report. A simple sorting operation cannot ensure that the associated detail data corresponding to each summary field can be accurately obtained. Therefore, this application generates a dimension label for each associated detail data according to the summary fields to be displayed (for example, the summary fields are "employee name + employee region") (for example, the dimension label of associated detail data 1 is "Zhang San_Region A") as a unique key, and determines the dimension label range to be 1-10 according to the sorting of the summary fields displayed in the summary data report (for example, the sorting of the summary fields from front to back is: 1, Zhang San_Region A; 2, Li Si_Region B; ...; 10, Wang Yi_Region A), which is conducive to ensuring the accuracy of the subsequent stream filter when filtering each associated detail data.

[0094] In another embodiment, Figure 5As shown, a report processing method is provided, which is described by taking the method applied to the server 104 in FIG. 1 as an example, and includes the following steps:

[0095] Step S502: Generate a related detail data set according to each business detail data report.

[0096] Step S504: input each associated detail data in the associated detail data set into the stream filter, obtain the dimension data corresponding to each associated detail data through the stream filter, and generate the dimension label corresponding to each associated detail data using the dimension data corresponding to each associated detail data.

[0097] Step S506: for any associated detail data, the dimension tag of the associated detail data is compared with each existing dimension tag stored in the flow filter through the flow filter to obtain a comparison result.

[0098] Step S508, when the comparison result indicates that the dimension tag of the associated detail data is the same as any existing dimension tag, and the storage order of the existing dimension tags in the flow rate controller is within a preset order range, it is determined that the dimension tag of the associated detail data meets the preset conditions, and the associated detail data is determined as the target associated detail data.

[0099] Step S510, when the comparison result indicates that the dimension tag of the associated detail data is the same as any existing dimension tag and the storage order of the existing dimension tags is not within a preset order range, it is determined that the dimension tag of the associated detail data does not meet the preset conditions, and the associated detail data is filtered out.

[0100] Step S512, summarizing the data of the target-related detailed data with the same dimension label to obtain the summary data corresponding to each dimension label.

[0101] Step S514, obtaining summary data reports corresponding to each business detail data report based on the dimension data corresponding to each dimension tag and the summary data corresponding to each dimension tag.

[0102] It should be noted that the specific limitations of the above steps can refer to the specific limitations of a report processing method above.

[0103] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0104] Based on the same inventive concept, the embodiment of the present application also provides a report processing device for implementing the report processing method involved above. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above method, so the specific limitations in one or more report processing device embodiments provided below can refer to the limitations on the report processing method above, and will not be repeated here.

[0105] In an exemplary embodiment, Figure 6 As shown, a report processing device is provided, including: a generating module 602, an input module 604, a summarizing module 606 and an acquiring module 608, wherein:

[0106] A generating module 602 is used to generate a related detailed data set according to each business detailed data report;

[0107] An input module 604 is used to input each associated detail data in the associated detail data set into a stream filter, obtain dimension data corresponding to each associated detail data through the stream filter, and generate a dimension tag corresponding to each associated detail data using the dimension data corresponding to each associated detail data;

[0108] A summary module 606 is used to obtain target-related detailed data whose dimension tags meet preset conditions, and summarize the data of target-related detailed data with the same dimension tags to obtain summary data corresponding to each dimension tag;

[0109] The acquisition module 608 is used to obtain the summary data report corresponding to each of the business detail data reports according to the dimension data corresponding to each of the dimension tags and the summary data corresponding to each of the dimension tags.

[0110] In one of the embodiments, the generation module 602 is specifically used to extract the dimension data of each target dimension field and the data of each preset numbering field from each business detail data report; based on the correspondence between the dimension data of each target dimension field and the data of each preset numbering field, the dimension data of each target dimension field is associated with the data of each preset numbering field to obtain an associated detail data set.

[0111] In one of the embodiments, the dimensional data corresponding to any associated detail data includes the dimensional data of the associated detail data under each target dimensional field, and the input module 604 is specifically used to splice the dimensional data of the associated detail data under each target dimensional field for any associated detail data to obtain the dimensional data splicing result corresponding to the associated detail data; and use the dimensional data splicing result corresponding to any associated detail data as the dimension label of the associated detail data.

[0112] In one of the embodiments, the summary module 606 is specifically used to compare the dimension label of the associated detail data with each existing dimension label stored in the flow filter for any associated detail data through the flow filter to obtain a comparison result; when the comparison result indicates that the dimension label of the associated detail data is the same as any existing dimension label, and the storage order of the existing dimension labels in the flow filter is within a preset sequence range, it is determined that the dimension label of the associated detail data meets the preset condition, and the associated detail data is determined as the target associated detail data; when the comparison result indicates that the dimension label of the associated detail data is the same as any existing dimension label, and the storage order of the existing dimension labels is not within the preset sequence range, it is determined that the dimension label of the associated detail data does not meet the preset condition, and the associated detail data is filtered out.

[0113] In one of the embodiments, the summary module 606 is further specifically used to determine the storage order of the dimension tags of the associated detail data in the flow filter when the comparison result indicates that the dimension tags of the associated detail data are different from the existing dimension tags; when the storage order of the dimension tags of the associated detail data in the filter is within a preset sequence range, determine that the dimension tags of the associated detail data meet the preset conditions, and determine the associated detail data as the target associated detail data; when the storage order of the dimension tags of the associated detail data in the filter is not within the preset sequence range, determine that the dimension tags of the associated detail data do not meet the preset conditions, and filter out the associated detail data.

[0114] In one of the embodiments, the data obtained for any target-associated detail data includes the data obtained for the target-associated detail data under each obtained field, and the acquisition module 608 is specifically used to obtain the indicator summary rules corresponding to each indicator field; according to the indicator summary rules corresponding to each indicator field, the data obtained for each target-associated detail data belonging to the same dimension tag under each obtained field are summarized to obtain the indicator data of the dimension tag under each indicator field; the indicator data of any dimension tag under each indicator field is used as the summary data of the dimension tag.

[0115] Each module in the above report processing device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module.

[0116] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in FIG7. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store report processing data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a report processing method is implemented.

[0117] Those skilled in the art will understand that the structure shown in FIG. 7 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0118] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above-mentioned report processing method. The steps of the report processing method here may be the steps of the report processing method in each of the above-mentioned embodiments.

[0119] In one embodiment, a computer readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the processor executes the steps of the above-mentioned report processing method. The steps of the report processing method here can be the steps of the report processing method in each of the above-mentioned embodiments.

[0120] In one embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, causes the processor to execute the steps of the above-mentioned report processing method. The steps of the report processing method here may be the steps of the report processing method in each of the above-mentioned embodiments.

[0121] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0122] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0123] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A report processing method, characterized in that: The method comprises: Generate related detailed data sets based on each business detailed data report; Input each associated detail data in the associated detail data set into a stream filter, obtain dimension data corresponding to each associated detail data through the stream filter, and generate a dimension label corresponding to each associated detail data using the dimension data corresponding to each associated detail data; Obtain target-related detailed data whose dimension tags meet preset conditions, and aggregate the data of target-related detailed data with the same dimension tags to obtain aggregated data corresponding to each dimension tag; According to the dimension data corresponding to each dimension tag and the summary data corresponding to each dimension tag, a summary data report corresponding to each business detail data report is obtained.

2. The method according to claim 1, characterized in that The generating of the associated detailed data set according to each business detailed data report includes: Extracting the dimension data of each target dimension field and the data of each preset data field from each business detail data report; Based on the correspondence between the dimension data of each target dimension field and the data of each preset data field, the dimension data of each target dimension field is associated with the data of each preset data field to obtain the associated detailed data set.

3. The method according to claim 1, characterized in that The dimension data corresponding to any of the associated detail data includes the dimension data of the associated detail data under each of the target dimension fields, and the dimension data corresponding to each of the associated detail data is used to generate the dimension label corresponding to each of the associated detail data, including: For any of the associated detailed data, concatenate the dimension data of the associated detailed data under each of the target dimension fields to obtain a concatenation result of the dimension data corresponding to the associated detailed data; The concatenation result of the dimension data corresponding to any of the associated detail data is used as the dimension label of the associated detail data.

4. The method according to claim 1, characterized in that The step of obtaining target-related detailed data where the dimension tag meets a preset condition includes: For any of the associated detail data, the dimension tag of the associated detail data is compared with each existing dimension tag stored in the flow filter through the flow filter to obtain a comparison result; If the comparison result indicates that the dimension tag of the associated detailed data is the same as any of the existing dimension tags, and the storage order of the existing dimension tags in the flow rate controller is within a preset order range, it is determined that the dimension tag of the associated detailed data meets the preset condition, and the associated detailed data is determined as the target associated detailed data; When the comparison result indicates that the dimension label of the associated detail data is the same as any of the existing dimension labels and the storage order of the existing dimension labels is not within the preset order range, it is determined that the dimension label of the associated detail data does not meet the preset condition, and the associated detail data is filtered out.

5. The method according to claim 4, characterized in that The method further comprises: In a case where the comparison result indicates that the dimension tag of the associated detail data is different from each of the existing dimension tags, determining a storage order of the dimension tags of the associated detail data in the stream filter; In the case where the storage order of the dimension tags of the associated detail data in the filter is within the preset order range, determining that the dimension tags of the associated detail data meet the preset condition, and determining the associated detail data as the target associated detail data; When the storage order of the dimension tags of the associated detail data in the filter is not within the preset order range, it is determined that the dimension tags of the associated detail data do not meet the preset condition, and the associated detail data is filtered out.

6. The method according to claim 1, characterized in that The data obtained from any of the target-associated detailed data includes the data obtained from the target-associated detailed data under each of the obtained data fields, and the data obtained from each of the target-associated detailed data with the same dimension label are summarized to obtain the summary data corresponding to each of the dimension labels, including: Get the indicator summary rules corresponding to each indicator field; According to the indicator aggregation rules corresponding to each indicator field, the number-taking data of each target-related detailed data belonging to the same dimension tag under each number-taking field are aggregated to obtain the indicator data of the dimension tag under each indicator field; The indicator data of any dimension tag under each indicator field is used as the summary data of the dimension tag.

7. A report processing device, characterized in that: The device comprises: A generation module is used to generate related detailed data sets according to each business detailed data report; An input module, used for inputting each associated detail data in the associated detail data set into a stream filter, obtaining dimension data corresponding to each associated detail data through the stream filter, and generating a dimension tag corresponding to each associated detail data by using the dimension data corresponding to each associated detail data; A summary module is used to obtain target-related detailed data whose dimension tags meet preset conditions, and summarize the data of target-related detailed data with the same dimension tags to obtain summary data corresponding to each dimension tag; The acquisition module is used to obtain the summary data report corresponding to each of the business detail data reports according to the dimension data corresponding to each of the dimension tags and the summary data corresponding to each of the dimension tags.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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