Report data processing method and device, electronic equipment and storage medium

Abstract

Embodiments of the present application provide a report data processing method and device, electronic equipment and storage medium, belonging to the technical field of financial technology. The method comprises: obtaining an original report comprising an original data column and original data to be processed located in the original data column; obtaining rule configuration information of the data to be processed according to rule information lookup of column name information of the original data column in a rule mapping relationship; obtaining a target processing rule of the data to be processed according to rule extraction of the rule configuration information in a rule library; obtaining a data processing function by rule analysis of the target processing rule according to a preset rule engine tool; and performing at least one of the following preset operations on the data to be processed according to the data processing function and the column name information: a de-duplication operation, a merging operation, a classification operation, a summary operation, an average value operation, a difference operation and a data comparison operation to obtain a target report. The embodiments of the present application can save manpower for report data processing and improve the processing efficiency of report data.

Description

Technical Field

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

[0002] Currently, an increasing number of technologies (such as fintech, cloud computing, and blockchain) are being applied in the financial sector, and traditional financial enterprises are gradually transforming into fintech companies. In the context of fintech, data reporting has become a significant challenge in the financial field. Typically, financial data is collected and then compiled into reports, which are then manually categorized, merged, and summarized. This process is not only labor-intensive and increases the workload of data collectors, but also reduces the efficiency of data processing. Therefore, improving the efficiency of data processing has become a pressing technical problem that needs to be solved. Summary of the Invention

[0003] The main objective of this application is to provide a report data processing method, apparatus, electronic device, and storage medium, which aims to save manpower in report data processing and improve the efficiency of report data processing.

[0004] To achieve the above objectives, a first aspect of this application provides a report data processing method, the method comprising:

[0005] Obtain the original report; wherein the original report includes original data columns and data to be processed located in the original data columns;

[0006] Based on the column name information of the original data column, the rule information is searched in the preset rule mapping relationship to obtain the rule configuration information of the data to be processed;

[0007] Based on the rule configuration information, rules are extracted from a preset rule base to obtain the target processing rules for the data to be processed;

[0008] The target processing rules are parsed using a preset rule engine tool to obtain the data processing function;

[0009] Based on the data processing function and the column name information, perform at least one of the following preset operations on the data to be processed: deduplication, merging, classification, summarization, averaging, subtraction, and data comparison to obtain the target report.

[0010] In some embodiments, the rule configuration information includes: a rule configuration sequence number and rule association information; the rule base includes at least two candidate processing rules; the step of extracting rules from a preset rule base based on the rule configuration information to obtain the target processing rule for the data to be processed includes:

[0011] The target rule number is obtained by filtering according to the rule configuration number and the preset candidate rule number.

[0012] The candidate processing rules are filtered according to the target rule number to obtain the selected processing rule;

[0013] The preset association condition function is filtered based on the rule association information to obtain the target condition function;

[0014] The selected processing rules are merged according to the target condition function to obtain the target processing rule.

[0015] In some embodiments, before extracting rules from a preset rule base based on the rule configuration information to obtain the target processing rules for the data to be processed, the method further includes:

[0016] The construction of the rule base specifically includes:

[0017] Obtain candidate processing rules;

[0018] The candidate processing rules are assigned serial numbers to obtain the candidate rule serial numbers;

[0019] The candidate processing rules are stored in a preset database according to the candidate rule number to obtain the rule base.

[0020] In some embodiments, the step of performing at least one of the following preset operations on the data to be processed based on the data processing function and the column name information: deduplication, merging, classification, summarization, averaging, subtraction, and data comparison to obtain the target report includes:

[0021] The data to be processed in the original data column is filtered based on the column name information to obtain the target column data;

[0022] The data processing function is filtered and processed based on the column name information to obtain the target processing function for the target column data;

[0023] Based on the target processing function, one or two operations are selected from the preset operations to obtain the target operation;

[0024] The target operation is performed on the target column data to obtain the target report.

[0025] In some embodiments, performing the target operation on the target column data to obtain the target report includes:

[0026] Obtain the data priority information of the target column data;

[0027] The processing order information of the target operation is set according to the data priority information;

[0028] The target operation is performed on the target column data according to the processing order information to obtain the target report.

[0029] In some embodiments, obtaining the original report includes:

[0030] Obtain data collection information; wherein, the data collection information includes: object information, collection time period information, and task description information;

[0031] Based on the object information, the preset candidate objects are filtered to obtain the target object;

[0032] Data is collected from the target object based on the task description information and the collection time period information to obtain the data to be processed.

[0033] The data to be processed is filled into a preset report template to obtain the original report.

[0034] In some embodiments, after filtering preset candidate objects based on the object information to obtain the target object, the method further includes:

[0035] The data acquisition progress is obtained according to a preset time period to obtain data acquisition progress information.

[0036] The data acquisition progress information is visualized to obtain a data acquisition progress view of the data to be processed.

[0037] To achieve the above objectives, a second aspect of this application provides a report data processing apparatus, the apparatus comprising:

[0038] The report acquisition module is used to acquire the original report; wherein the original report includes original data columns and data to be processed located in the original data columns;

[0039] The rule lookup module is used to search for rule information in a preset rule mapping relationship based on the column name information of the original data column to obtain the rule configuration information of the data to be processed;

[0040] The rule extraction module is used to extract rules from a preset rule base according to the rule configuration information to obtain the target processing rules for the data to be processed.

[0041] The rule parsing module is used to parse the target processing rules according to the preset rule engine tool to obtain the data processing function;

[0042] The data content processing module is used to perform at least one of the following preset operations on the data to be processed according to the data processing function and the column name information: deduplication, merging, classification, summarization, averaging, difference, and data comparison to obtain the target report.

[0043] To achieve the above objectives, a third aspect of the present application provides an electronic device, the electronic device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method described in the first aspect.

[0044] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0045] The report data processing method, apparatus, electronic device, and storage medium proposed in this application retrieve rule configuration information from a preset rule mapping relationship based on the column name information of the original data columns. Then, based on the rule configuration information, the target processing rule for the data to be processed is extracted from the rule base. A rule engine tool then parses the target processing rule to obtain a data processing function. The data processing function and column name information are used to perform at least one of the following operations on the data to be processed: deduplication, merging, classification, summarization, averaging, difference, and data comparison, to obtain the target report. This achieves automatic processing of the data to be processed in the original report, eliminating the need for manual processing, saving manpower, and improving the efficiency of data processing in the original report. Attached Figure Description

[0046] Figure 1 This is a flowchart of the report data processing method provided in the embodiments of this application;

[0047] Figure 2 yes Figure 1 The flowchart of step S101 in the text;

[0048] Figure 3 This is a flowchart of a report data processing method provided in another embodiment of this application;

[0049] Figure 4 yes Figure 1 The flowchart of step S103 in the process;

[0050] Figure 5 This is a flowchart of a report data processing method provided in another embodiment of this application;

[0051] Figure 6 yes Figure 1The flowchart of step S105 in the process;

[0052] Figure 7 yes Figure 6 The flowchart of step S604 in the document;

[0053] Figure 8 This is a schematic diagram of the structure of the report data processing device provided in the embodiments of this application;

[0054] Figure 9 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0056] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0058] First, let's analyze some of the terms used in this application:

[0059] Big data, also known as massive data, refers to data volumes so large that mainstream software tools cannot capture, manage, process, and organize it into information that helps businesses make more proactive decisions within a reasonable timeframe. Technically, big data and cloud computing are inextricably linked, like two sides of the same coin. Big data cannot be processed by a single computer and requires a distributed architecture. The defining characteristic of big data is distributed data mining of massive amounts of data. However, big data relies heavily on cloud computing's distributed processing, distributed databases, cloud storage, and virtualization technologies.

[0060] Information gathering refers to acquiring necessary information through various means. It is the first and crucial step in utilizing information. The quality of information gathering directly impacts the overall quality of information management. Information can be broadly categorized into raw information and processed information. Raw information refers to data, concepts, knowledge, experience, and summaries directly generated or acquired in economic activities; it is unprocessed information. Processed information, on the other hand, is information with new forms and content formed by processing, analyzing, adapting, and reorganizing raw information. Both types of information play an irreplaceable role in a company's marketing management activities.

[0061] Task tracking: The process of following up with the operator after a task is generated, recording the entire lifecycle of the event; mainly used for project management, event scheduling, schedule management, and job scheduling.

[0062] Reports: Reports are tables used to report information to superiors. Simply put, reports use tables, charts, and other formats to dynamically display data. They can be expressed by the formula: "Report = Various formats + Dynamic data".

[0063] Drools: Drools is an open-source business rules engine that is easy to access, adjust, and manage, conforms to industry standards, and is fast and efficient. Business analysts or auditors can easily view business rules using Drools to verify whether the coded rules are executing the required business rules. Drools mainly consists of two parts: Drools rules and the interpretation and execution of Drools rules. The compilation and execution of Drools rules are achieved through the relevant APIs provided by Drools. These APIs can be broadly categorized into three upstream types: rule compilation, rule collection, and rule execution.

[0064] In daily work, it is often necessary to collect various data and generate reports. For example, in the insurance industry, finance personnel need to collect various data, forms, questionnaires, satisfaction surveys, agent information, and business forms to construct financial statements. After collection, finance personnel still need to classify, merge, and summarize the data in the financial statements. Therefore, manually processing the data in the reports not only consumes a lot of manpower but also reduces the efficiency of report processing.

[0065] Based on this, embodiments of this application provide a report data processing method, apparatus, electronic device, and storage medium. The method involves acquiring the data to be processed from the original report, determining the rule configuration information of the data to be processed from the rule mapping relationship based on the column name information of the original data columns, extracting target processing rules from the rule base based on the rule configuration information, and parsing the target processing rules to obtain data processing functions. These functions, along with the column name information, are used to perform at least one preset operation among deduplication, merging, classification, summarization, averaging, subtraction, and data comparison on the data to be processed to obtain the target report. Therefore, by automatically determining the target processing rules for the data to be processed in each original data column, and intelligently processing the data to be processed according to the target processing rules to obtain the target report, manual processing of the collected original reports is eliminated, reducing the workload of collection personnel, saving manpower, and improving the processing efficiency of the data to be processed within the report.

[0066] The report data processing method, apparatus, electronic device, and storage medium provided in this application are specifically described through the following embodiments. First, the report data processing method in this application is described.

[0067] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0068] Foundational artificial intelligence technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, financial technology processing technologies, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0069] The report data processing method provided in this application relates to the field of artificial intelligence technology. This report data processing method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and fintech and artificial intelligence platforms; the software can be an application implementing the report data processing method, but is not limited to the above forms.

[0070] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0071] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0072] Figure 1 This is an optional flowchart of the report data processing method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S105.

[0073] Step S101: Obtain the original report; wherein the original report includes the original data columns and the data to be processed located in the original data columns;

[0074] Step S102: Based on the column name information of the original data column, search for rule information in the preset rule mapping relationship to obtain the rule configuration information of the data to be processed;

[0075] Step S103: Extract rules from the preset rule base according to the rule configuration information to obtain the target processing rules for the data to be processed;

[0076] Step S104: Parse the target processing rules according to the preset rule engine tool to obtain the data processing function;

[0077] Step S105: Based on the data processing function and column name information, perform at least one of the following preset operations on the data to be processed: deduplication, merging, classification, summarization, averaging, difference, and data comparison to obtain the target report.

[0078] Steps S101 to S105 of this embodiment involve acquiring the data to be processed from the original data column, searching for the rule configuration information of the data to be processed in a preset rule mapping relationship based on the column name information of the original data column, and the rule configuration information recording which processing rules are required for the data to be processed. Then, the target processing rule of the data to be processed is extracted from the preset rule library according to the rule configuration information, and the target processing rule is parsed by the rule engine tool to obtain the data processing function. The corresponding operation is performed on the corresponding data to be processed using the data processing function and the column name information to obtain the target report. The operation includes at least one of the following preset operations: deduplication, merging, classification, summarization, averaging, difference, and data comparison. This automatically processes the data to be processed in the original report to complete at least one of the preset operations of deduplication, merging, classification, summarization, averaging, difference, and data comparison in the data to be processed. There is no need for manual processing of the data to be processed in the original report, which not only saves the workload of the collection personnel but also improves the efficiency of processing the data to be processed in the original report.

[0079] Please see Figure 2 In some embodiments, step S101 may include, but is not limited to, steps S201 to S204:

[0080] Step S201: Obtain data collection information; wherein, the data collection information includes: object information, collection time period information, and task description information;

[0081] Step S202: Based on the object information, the preset candidate objects are filtered to obtain the target object;

[0082] Step S203: Collect data from the target object based on the task description information and the collection time period information to obtain the data to be processed;

[0083] Step S204: Fill the data to be processed into the preset report template to obtain the original report.

[0084] In step S201 of some embodiments, a task collection system is set up to facilitate the collection of data to be processed. This system performs task assignment and data collection. The user selects the "publish" function on the task collection system to enter a task publishing rule setting page. Collection rules are set by the user on this page to form data collection information. Specifically, the user selects a collection target on the task publishing rule setting page to obtain target information. The target can be an individual or an organization. The target information includes the target's address and name, which determines which targets will be collected from. The user selects a collection start time and a collection end time on the task publishing rule setting page to determine the collection period, which in turn determines the start time for collecting the data to be processed. Simultaneously, the user fills in a task description on the task publishing rule setting page to obtain task description information, which informs the target objects of the target information which information to upload. Therefore, the data collection information is determined based on the content filled in and selected by the user on the task publishing rule setting page, and the objects to be collected and the data to be collected are determined accordingly.

[0085] For example, if the application scenario is in the insurance field and the data collection task is a satisfaction survey, the user can select the target audience for the satisfaction survey task on the task publishing rules settings page to determine the target audience information. Then, the user can set the start and end times of the data collection to obtain the collection period information, and also set the task description information. Therefore, by understanding the target audience, collection period, and task description of the current task through the target audience information, collection period information, and task description information, accurate data collection can be achieved.

[0086] In step S202 of some embodiments, preset candidate objects are filtered based on object information to obtain the candidate objects corresponding to the object information as target objects. The object information includes object address information and object name information. Selected objects are filtered from the candidate objects based on the object address information, and then the target objects are filtered from the selected objects based on the object name information. For example, a task collection system pre-sets multiple candidate objects. The target object is selected from the candidate objects based on the user's task publishing rule settings page, i.e., the object name information and object address information of the target object are determined. A TCP connection is established between the object address information and the corresponding candidate objects to obtain the selected objects. Then, the selected objects with established TCP connections are determined based on the object name information to identify which are the correct target objects.

[0087] In step S203 of some embodiments, data is collected from the target objects based on the task description information and the collection time period information. Specifically, the task description information is sent to the target objects with established TCP connections. The target objects then receive the data to be processed by responding to the task description information. For example, if the current collection task is a satisfaction survey, the task description information is sent to the target objects participating in the survey. The target objects provide feedback on their satisfaction with each item, and the satisfaction levels of all target objects for each item are aggregated into the data to be processed. Simultaneously, data collected within the collection start time period corresponding to the collection time period information is collected. If the target object's feedback time is outside the collection start time period, no data collection is performed.

[0088] In step S204 of some embodiments, after collecting the data to be processed, the data to be processed is classified to obtain data categories, and the data to be processed is filled into the report template according to the data categories. That is, the original data column of the report template is determined according to the data categories to obtain the original report, so as to complete the automatic collection of the data to be processed and form a report style, so that financial personnel can directly view the original report to fully and clearly understand the data status of the current collection task.

[0089] For example, to collect satisfaction data for each insurance item in a satisfaction survey, the satisfaction is categorized to determine the item category. Based on the item category, each satisfaction level is located in the original data column of the report template. Then, the satisfaction levels are filled into the report template according to the item category to obtain the original report.

[0090] In steps S201 to S204 of the embodiments of this application, object information, collection time period information, and task description information are obtained. Target objects are selected from candidate objects using the object information. Data is collected from the target objects based on the collection time period information and task description information to obtain data to be processed. The data to be processed is then classified to obtain data categories. Based on the data categories, the data to be processed is filled into a preset report template to obtain the original report. Therefore, the collection of data to be processed is automatically completed, and the data to be processed is constructed into an original report, realizing automatic organization of the data to be processed. This allows collection personnel to directly view the original report and clearly understand the data to be processed.

[0091] Please see Figure 3 In some embodiments, after step S202, the report data processing method further includes:

[0092] The progress of data collection is tracked.

[0093] It should be noted that, in order to allow the data collectors to keep track of the progress of the data feedback from the target objects, and to track the data collection progress of the target objects so as to know the progress of the data collection, the data collectors can follow up with the target objects according to the progress of the data collection to collect complete data to be processed.

[0094] The progress of data collection may include, but is not limited to, steps S301 to S302:

[0095] Step S301: Obtain the data collection progress of the data to be processed according to the preset time period to obtain data collection progress information;

[0096] Step S302: Visualize the data acquisition progress information to obtain a data acquisition progress view of the data to be processed.

[0097] In step S301 of some embodiments, data acquisition progress information of the data to be processed is obtained through a preset time period. The data acquisition progress information includes data acquisition in progress, data acquired, and data not acquired. If the data acquisition progress is "data acquisition in progress," it indicates that the target object is providing feedback on the data to be processed; if the data acquisition progress is "data acquired," it indicates that the target object has completed providing feedback on the data to be processed; and if the data acquisition progress is "data not acquired," it indicates that the target object has not provided feedback on the data to be processed.

[0098] It should be noted that the data acquisition progress information includes the data acquisition progress of the data to be processed. Based on the data acquisition information, it is possible to know which target objects' data to be processed has been collected and which target objects' data to be processed has not been collected, so as to clearly understand the collection progress of the data to be processed of the entire target object.

[0099] In step S302 of some embodiments, the data acquisition progress information is visualized, that is, the data acquisition progress information is processed using antG6 graphical technology to generate a data acquisition progress view, and the data acquisition progress view is displayed as a data acquisition progress tracking dashboard. The antG6 graphical technology mainly uses the antG6 graphical engine tool to visualize the data acquisition information. The antG6 graphical engine tool is an open-source engine tool for graphical visualization and analysis, focusing on relational data. Furthermore, the antG6 graphical engine tool accelerates the layout of the data acquisition progress view, improving layout calculation performance by tens or even hundreds of times, and the entire layout is more compact, allowing for a clear and intuitive understanding of the acquisition progress of the data to be processed for each target object.

[0100] It should be noted that the data collection view will be updated once according to the preset time period so that the data collection personnel can keep track of the data collection progress of each target object in real time.

[0101] It should be noted that after the data collection view is constructed, if the data collection progress information of the target object is "data not collected" and the current time is close to the collection deadline of the collection time period information, the target object will be urged to complete the feedback of the pending data in a timely manner via email or SMS.

[0102] In steps S301 to S302 of this embodiment, data acquisition progress information of the data to be processed is collected through a preset time period. The data acquisition progress information is then visualized using antG6 graphical technology to obtain a data acquisition progress view for each piece of data to be processed, and this view is created as a visual progress tracking dashboard. Therefore, personnel can intuitively view the data acquisition progress of each piece of data to be processed through the data acquisition progress view, facilitating the follow-up actions taken based on the data acquisition progress.

[0103] In step S102 of some embodiments, after obtaining the original report, the original report records the data to be processed for each target object. After collecting the original report, the data to be processed needs to be intelligently processed. Before intelligent processing of the data to be processed, the column name information of each original data column is obtained. A preset rule mapping relationship includes a mapping relationship between column name information and rule configuration information. Then, the rule configuration information corresponding to the column name information is determined in the rule mapping relationship based on the column name information. The rule configuration information includes: rule configuration sequence number and rule association information, and the number of rule configuration sequence numbers is set to at least one. For example, if the column name information is a user name, the rule configuration sequence number is found in the preset rule mapping relationship based on the user name, which is the first rule and the second rule. If the column name information is a favorability rating, the rule configuration information is found in the preset rule mapping relationship based on the favorability rating, which is the third rule. Therefore, by using the column name information and the rule mapping relationship, the rule configuration information of the data to be processed for each original data column in the original report can be known, thus revealing the target processing rule corresponding to the data to be processed for each original data column.

[0104] Please see Figure 4 In some embodiments, the rule configuration information includes: rule configuration sequence number and rule association information; the rule base includes at least two candidate processing rules; step S103 may include, but is not limited to, steps S401 to S404:

[0105] Step S401: Filter the rules according to the rule configuration number and the preset candidate rule number to obtain the target rule number;

[0106] Step S402: Filter the candidate processing rules according to the target rule number to obtain the selected processing rule;

[0107] Step S403: Based on the rule association information, the preset association condition function is filtered to obtain the target condition function;

[0108] Step S404: The selected processing rules are merged according to the target condition function to obtain the target processing rule.

[0109] In step S401 of some embodiments, since the rule configuration information includes a rule configuration sequence number and rule association information, and the rule configuration information characterizes which rules need to be configured for each original data column, the corresponding candidate rule sequence number is selected from the candidate rule sequence numbers according to the rule configuration sequence number as the target rule sequence number. Because the candidate processing rules in the rule base are stored in advance, and each candidate processing rule carries a candidate rule sequence number, the target rule sequence number of the data to be processed for each original data column can be known through the rule configuration information and the candidate rule sequence number.

[0110] For example, if the rule configuration sequence number is Rule 1 and Rule 2, the corresponding candidate rule sequence numbers are selected as Rule 1 and Rule 2, which are also the target rule sequence numbers. If the rule configuration information is Rule 3, then the candidate rule sequence number is selected as Rule 3, which is also the target rule sequence number. Therefore, determining the corresponding target rule sequence number through the rule configuration sequence number determines which candidate processing rules are available in the rule base.

[0111] In step S402 of some embodiments, candidate processing rules are filtered according to the target rule number to select the candidate processing rule corresponding to the target rule number from multiple candidate processing rules as the selected processing rule, making the selection of the selected processing rule simple.

[0112] For example, if the candidate processing rule corresponding to rule 1 is a deduplication rule, the candidate processing rule corresponding to rule 2 is a merging rule, and the candidate processing rule corresponding to rule 3 is an averaging rule, then the selection of the chosen processing rule becomes simple.

[0113] In step S403 of some embodiments, since there may be multiple processing rules corresponding to the data to be processed in each original data column, the processing relationship between each processing rule can be determined based on the rule association information to determine the processing relationship between each processing rule. Therefore, the rule association information is used to filter from preset association condition functions to select the association condition function corresponding to the rule association information as the target condition function. For example, if the rule association information is an AND relationship, the association condition function is determined to be "and"; if the rule association information is an OR relationship, the association condition function is determined to be "or". Therefore, the rule condition function corresponding to the rule association information is selected from the association condition functions as the target condition function to determine the association relationship between each selected processing rule.

[0114] In step S404 of some embodiments, the selected processing rules are merged according to the target condition function, that is, the selected processing rules are associated with the target condition function to obtain the target processing rule, which makes the construction of the target processing rule simple.

[0115] For example, if the target condition function is "and", and the selected processing rules are deduplication processing rule and merging processing rule, then the deduplication processing rule and merging processing rule will be merged according to the target condition function to construct a target processing rule with the style "deduplication processing rule and merging processing rule".

[0116] It should be noted that steps S401 to S404 are executed by the Drools rule engine tool to select the target processing rule for each raw data column.

[0117] In steps S401 to S404 of the embodiments of this application, the corresponding candidate rule number is selected from the candidate rule number according to the rule configuration number as the target rule number, the corresponding candidate processing rule is selected from the candidate processing rules according to the target rule information as the selected processing rule, and the target condition function is selected from the association condition function according to the rule association information. The selected processing rules are then merged according to the target condition function to obtain the target processing rule. This makes the construction of the target processing rule accurate and simple, and the target processing rule of the data to be processed for each original data column can be known through the target processing rule.

[0118] Please see Figure 5 In some embodiments, before step S103, the report data processing method further includes: building a rule base.

[0119] It should be noted that before extracting the target processing rules from the rule base, the rule base needs to be built first so that data content processing and data content calculation operations can be performed directly from the rule base on the data to be processed.

[0120] Building a rule base may include, but is not limited to, steps S501 to S503:

[0121] Step S501: Obtain candidate processing rules;

[0122] Step S502: Perform sequence number assignment on the candidate processing rules to obtain the candidate rule sequence number;

[0123] Step S503: Store the candidate processing rules into a preset database according to the candidate rule number to obtain the rule base.

[0124] In step S501 of some embodiments, candidate processing rules are obtained, that is, custom processing rules are defined in advance based on the data of each data category as candidate processing rules. For example, traditional report data requires merging, deduplication, summarizing, averaging, difference calculation, and data comparison operations. Merging processing rules are defined based on merging operations, deduplication processing rules are defined based on deduplication operations, summarizing processing rules are defined based on summarizing operations, averaging processing rules are defined based on averaging, difference calculation rules are defined based on difference calculation, and data comparison processing rules are defined based on data comparison operations. Therefore, defining processing rules in advance as candidate processing rules allows subsequent execution of preset operations on the data to be processed to directly call the candidate processing rules, eliminating the need for manual definition of processing rules for each original data column. This saves manpower and improves the data processing efficiency of the original report.

[0125] In step S502 of some embodiments, in order to facilitate the extraction of each candidate processing rule, it is necessary to set a sequence number for each candidate processing rule, that is, to assign a predefined rule sequence number to each candidate processing rule in order to determine the candidate rule sequence number of each candidate processing rule.

[0126] In step S503 of some embodiments, candidate processing rules are stored in a preset database according to the candidate rule number to construct candidate processing rules carrying the candidate rule number and form a rule base.

[0127] In steps S501 to S503 of this application embodiment, a custom processing rule is obtained as a candidate processing rule, and a candidate rule number is obtained by performing a sequence number assignment process on each candidate processing rule. The candidate processing rule is then stored in a preset database according to the candidate rule number to construct a rule library including candidate processing rules carrying candidate rule numbers. This allows for direct calling of the candidate processing rules in the rule library when processing the data to be processed in the original data column, without the need for manual resetting of the processing rules for each original data column. This not only saves manpower but also improves the data processing efficiency of the original report.

[0128] In step S104 of some embodiments, the preset rule engine tool is the Drools rule engine tool. The Drools rule engine tool is used to parse the target processing rules, which means that the complex and ever-changing target processing rules are freed from hard coding and stored in the storage medium as data processing functions. This allows changes to the target processing rules to be completed immediately in the online environment without modifying the code or restarting the server.

[0129] Please see Figure 6 In some embodiments, step S105 includes, but is not limited to, steps S601 to S604:

[0130] Step S601: Filter the data to be processed in the original data column according to the column name information to obtain the target column data;

[0131] Step S602: Filter the preset candidate objects according to the object information to obtain the target object;

[0132] Step S603: Select one or two operations from the preset operations according to the target processing function to obtain the target operation;

[0133] Step S604: Perform the target operation on the target column data to obtain the target report.

[0134] In step S601 of some embodiments, since the data processing function corresponding to the data to be processed in each original data column is different, it is necessary to fill the data processing function corresponding to each original data column into the corresponding position in the original report to perform the corresponding preset operation on the data to be processed, so as to realize the intelligent processing of the data to be processed in the original report. Therefore, the data to be processed in the original data column is first filtered according to the column name information to select the data to be processed corresponding to each column name information as the target column data. For example, if the column name information is user name, then all user names are selected as the target column data; if the column name information is favorability, then all favorability scores are selected as the target column data.

[0135] In step S602 of some embodiments, after determining the target column data, the target processing function for each target column data is selected from the data processing functions based on the column name information, so that different target column data are processed with different target processing functions. For example, if the column name information is "username", then the target processing functions selected based on the user name are the deduplication function and the merging function; if the column name information is "likability", then the target processing function selected based on the likability is the averaging function, thus determining the target processing functions corresponding to different target column data.

[0136] In step S603 of some embodiments, in order to determine the preset operation to be performed on each column of data, a target operation is selected from the preset operations according to the target processing function, so as to clarify the target operation to be performed on each target column of data. For example, if the target processing function is a deduplication function and a merging function, the target operations are selected as deduplication operation and merging operation; if the target processing function is an averaging function, the target operation is selected as averaging operation, so as to determine the target operation for each target column of data.

[0137] In step S604 of some embodiments, after determining the target processing function and target operation for each target column data, the target operation is performed on the target column data to automatically complete the data content processing and data content calculation for each target column data to obtain the target report.

[0138] For example, if the first column is user names, and the target processing function for each user name is a deduplication function and a merging function, then the user names are read one by one through the I / O file, deduplication is performed on each user name, and merging is performed after deduplication. If the second column is favorability, and the target processing function for favorability is an averaging function (AVG function), then all collected favorability scores are averaged using the AVG function to obtain the average favorability score, which is then filled into the corresponding position in the original data column of the original report to construct the target report. Therefore, by automatically collecting the data to be processed, the corresponding target processing rules are automatically invoked to parse the target processing rules and obtain the target processing function. The target operation is then performed on each target column of data according to the target processing function, so that different target columns of data are processed by different target processing functions to construct the target report. This achieves intelligent processing of the original report, eliminating the need for manual processing of the data to be processed in the original report, saving manpower and improving the data processing efficiency of the original report.

[0139] In steps S601 to S604 of the embodiments of this application, the target column data corresponding to each column name is selected from the data to be processed in the original data column according to the column name information. Then, the data processing function is selected according to the column name information to select the target processing function for each target column data. The target operation for each target column data is known according to the target processing function. Finally, the target operation is executed on the target column data to realize that different target column data are processed by different target processing functions to perform different target operations. This realizes the intelligent processing of the original report, eliminating the need for manual processing of the data to be processed in the original report, which not only saves manpower but also improves the data processing efficiency of the original report.

[0140] Please see Figure 7 In some embodiments, step S604 may include, but is not limited to, steps S701 to S703:

[0141] Step S701: Obtain the data priority information of the target column data;

[0142] Step S702: Set the processing order information of the target operation according to the data priority information;

[0143] Step S703: Perform target operations on the target column data according to the processing order information to obtain the target report.

[0144] In step S701 of some embodiments, data priority information of the target column data is obtained so as to know which target column data needs to be processed first and which target column data can be processed later.

[0145] In step S702 of some embodiments, the processing order information of the target operation is set according to the data priority information, that is, the target operation is sorted for processing, so that the target operation is executed one by one according to the processing order information to process the corresponding target column data.

[0146] In step S703 of some embodiments, target operations are performed on the target column data according to the processing order information. That is, target operations are performed one by one according to the processing order information, so as to process the target column data one by one through the target processing function to obtain the target report. Therefore, by processing the target column data in an orderly manner, the chaotic situation of performing target operations on the target column data is reduced.

[0147] In steps S701 to S703 of the embodiments of this application, by obtaining the data priority information of each target column data, the processing order information of each target operation is set according to the data priority information, and the target operation is performed on the target column data according to the processing order information, so as to realize the orderly processing of the target column data and reduce the chaotic processing of the target column data.

[0148] This application embodiment acquires object information, collection time period information, and task description information. The target object is selected from candidate objects using the object information. Data is collected from the target object based on the collection time period information and task description information to obtain data to be processed. The data to be processed is then classified to obtain data categories. Based on the data categories, the data to be processed is filled into a preset report template to obtain the original report. Data collection progress information of the target object is collected at preset time periods, and the data collection progress information is visualized using antG6 graphical technology to obtain a data collection progress view, which is then used as a visual progress tracking dashboard. Based on the rule configuration sequence number, the corresponding candidate rule sequence number is selected from the candidate rule sequence number as the target rule sequence number. Based on the target rule requirements, the corresponding candidate processing rule is selected from the candidate processing rules as the selected processing rule. Based on the rule association information, the target condition function is selected from the association condition functions. The selected processing rules are then merged according to the target condition function to obtain the target processing rule. The Drools rule engine tool parses the target processing rules to obtain the target processing functions. Based on column name information, it filters the raw data columns to identify the target columns corresponding to each column name. Then, it filters the data processing functions based on column name information to determine the target processing function for each target column. Based on the target processing function, it determines the target operation for each target column. It then obtains the data priority information for each target column to set the processing order for each target operation. Finally, it executes the target operation on the target column data according to the processing order, allowing different target columns to execute different target operations using different target processing functions. Therefore, it automatically performs corresponding preset operations on the raw report's data to be processed, eliminating the need for manual calculations and data processing, thus saving manpower and improving the efficiency of raw report data processing.

[0149] Please see Figure 8 This application also provides a report data processing apparatus that can implement the above-described report data processing method. The apparatus includes:

[0150] The report acquisition module 801 is used to acquire the original report; wherein the original report includes the original data columns and the data to be processed located in the original data columns;

[0151] The rule lookup module 802 is used to look up rule information in a preset rule mapping relationship based on the column name information of the original data column to obtain the rule configuration information of the data to be processed.

[0152] The rule extraction module 803 is used to extract rules from a preset rule base according to the rule configuration information to obtain the target processing rules for the data to be processed.

[0153] The rule parsing module 804 is used to parse the target processing rules according to the preset rule engine tool to obtain the data processing function;

[0154] The data content processing module 805 is used to perform at least one of the following preset operations on the data to be processed based on the data processing function and column name information: deduplication, merging, classification, summarization, averaging, difference, and data comparison to obtain the target report.

[0155] The specific implementation of this report data processing device is basically the same as the specific implementation of the report data processing method described above, and will not be repeated here.

[0156] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described report data processing method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0157] Please see Figure 9 , Figure 9 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0158] The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0159] The memory 902 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called and executed by the processor 901 using the report data processing method of the embodiments of this application.

[0160] The input / output interface 903 is used to implement information input and output;

[0161] The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0162] Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904);

[0163] The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0164] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described report data processing method.

[0165] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0166] The report data processing method, apparatus, electronic device, and storage medium provided in this application embodiment acquire the data to be processed located in the original data columns, and retrieve the corresponding rule configuration information from a preset rule mapping relationship based on the column name information of the original data columns. This allows the processing rules for each original data column to be known through the rule configuration information. Then, the target processing rule is extracted from the rule library based on the rule configuration information, and a rule engine tool is used to parse the target processing rule to obtain a data processing function. Finally, based on the data processing function and column name information, at least one preset operation is performed on the data to be processed, including deduplication, merging, classification, summarization, averaging, difference, and data comparison, to obtain the target report. Therefore, by automatically performing corresponding preset operations on the data to be processed within the original reports after collecting them, manual calculation and data processing of the data in the original reports are eliminated, saving manpower and improving the data processing efficiency of the original reports.

[0167] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0168] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0169] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0170] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0171] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0172] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0173] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0174] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0175] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0176] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0177] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A report data processing method characterized by comprising: The method comprises: acquiring an original report; wherein the original report comprises an original data column and to-be-processed data located in the original data column; performing rule information lookup on a preset rule mapping relationship according to column name information of the original data column to obtain rule configuration information of the to-be-processed data; performing rule extraction on a preset rule library according to the rule configuration information to obtain a target processing rule of the to-be-processed data; performing rule analysis on the target processing rule according to a preset rule engine tool to obtain a data processing function; performing at least one of the following preset operations on the to-be-processed data according to the data processing function and the column name information: a de-duplication operation, a merging operation, a classification operation, an aggregation operation, an average value operation, a difference operation, and a data comparison operation, to obtain a target report; the rule configuration information comprises a rule configuration serial number and rule association information; the rule library comprises at least two candidate processing rules; the rule extraction on the preset rule library according to the rule configuration information to obtain the target processing rule of the to-be-processed data comprises: performing screening processing according to the rule configuration serial number and a preset candidate rule serial number to obtain a target rule serial number; performing screening processing on the candidate processing rules according to the target rule serial number to obtain a selected processing rule; performing screening processing on a preset association condition function according to the rule association information to obtain a target condition function; performing merging processing on the selected processing rule according to the target condition function to obtain the target processing rule; before the rule extraction on the preset rule library according to the rule configuration information to obtain the target processing rule of the to-be-processed data, the method further comprises: constructing the rule library, specifically comprising: acquiring a candidate processing rule; performing serial number allocation processing on the candidate processing rule to obtain the candidate rule serial number; storing the candidate processing rule in a preset database according to the candidate rule serial number to obtain the rule library.

2. The method of claim 1, wherein, performing at least one of the following preset operations on the to-be-processed data according to the data processing function and the column name information: a de-duplication operation, a merging operation, a classification operation, an aggregation operation, an average value operation, a difference operation, and a data comparison operation, to obtain a target report, comprising: performing screening processing on the to-be-processed data of the original data column according to the column name information to obtain target column data; performing screening processing on the data processing function according to the column name information to obtain a target processing function of the target column data; performing screening on one or two operations in the preset operations according to the target processing function to obtain a target operation; performing the target operation on the target column data to obtain the target report.

3. The method of claim 2, wherein, performing the target operation on the target column data to obtain the target report, comprising: acquiring data priority information of the target column data; setting processing order information of the target operation according to the data priority information; performing the target operation on the target column data according to the processing order information to obtain the target report.

4. The method according to any one of claims 1 to 2, characterized in that, the acquiring of the original report comprises: Obtaining data collection information; wherein the data collection information comprises: object information, collection time period information and task description information; Filtering a preset candidate object according to the object information to obtain a target object; Collecting data of the target object according to the task description information and the collection time period information to obtain the to-be-processed data; Filling the to-be-processed data into a preset report template to obtain the original report.

5. The method of claim 4, wherein, After the filtering of the preset candidate object according to the object information to obtain the target object, the method further comprises: Obtaining a data collection progress of the to-be-processed data according to a preset time period to obtain data collection progress information; Visualizing the data collection progress information to obtain a data collection progress view of the to-be-processed data.

6. A report data processing apparatus characterized by comprising: The device comprises: A report obtaining module configured to obtain an original report; wherein the original report comprises an original data column and to-be-processed data located in the original data column; A rule searching module configured to search for rule information of the to-be-processed data according to column name information of the original data column in a preset rule mapping relationship to obtain rule configuration information of the to-be-processed data; A rule extracting module configured to extract a target processing rule of the to-be-processed data according to the rule configuration information in a preset rule library; A rule analyzing module configured to analyze the target processing rule according to a preset rule engine tool to obtain a data processing function; A data content processing module configured to execute at least one of the following preset operations on the to-be-processed data according to the data processing function and the column name information: a de-duplication operation, a merging operation, a classification operation, a summarization operation, an average value operation, a difference operation and a data comparison operation, to obtain a target report; The rule configuration information comprises: a rule configuration serial number and rule association information; the rule library comprises at least two candidate processing rules; the extracting of the target processing rule of the to-be-processed data according to the rule configuration information in the preset rule library comprises: Filtering according to the rule configuration serial number and a preset candidate rule serial number to obtain a target rule serial number; Filtering the candidate processing rules according to the target rule serial number to obtain a selected processing rule; Filtering a preset association condition function according to the rule association information to obtain a target condition function; Merging the selected processing rule according to the target condition function to obtain the target processing rule; Before the extracting of the target processing rule of the to-be-processed data according to the rule configuration information in the preset rule library, the method further comprises: Constructing the rule library, specifically comprising: Obtaining a candidate processing rule; Assigning a serial number to the candidate processing rule to obtain the candidate rule serial number; Storing the candidate processing rule in a preset database according to the candidate rule serial number to obtain the rule library.

7. An electronic device, comprising: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the report data processing method in any one of claims 1 to 5 when executing the computer program.

8. A computer-readable storage medium storing a computer program, the computer-readable storage medium comprising: The computer program is executed by the processor to implement the report data processing method in any one of claims 1 to 5.

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