CN116860754A

CN116860754A - Report data processing method and device, electronic equipment and storage medium

Info

Publication number: CN116860754A
Application number: CN202310840628.1A
Authority: CN
Inventors: 王振兴
Original assignee: Ping An Life Insurance Company of China Ltd
Current assignee: Ping An Life Insurance Company of China Ltd
Priority date: 2023-07-07
Filing date: 2023-07-07
Publication date: 2023-10-10

Abstract

The embodiment of the application provides a report data processing method and device, electronic equipment and a storage medium, and belongs to the technical field of financial science and technology. The method comprises the following steps: acquiring an original report comprising an original data column and data to be processed positioned in the original data column; according to the column name information of the original data column, carrying out rule information searching in a rule mapping relation to obtain rule configuration information of the data to be processed; performing rule extraction on a rule base according to rule configuration information to obtain a target processing rule of the data to be processed; performing rule analysis on the target processing rule according to a preset rule engine tool to obtain a data processing function; according to the data processing function and the column name information, at least one of the following preset operations is executed on the data to be processed: the target report is obtained through the operations of duplicate removal, merging, classification, summarizing, averaging, differencing and data comparison. The embodiment of the application can save the labor for processing the report data and improve the processing efficiency of the report data.

Description

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

Technical Field

The present application relates to the technical field of financial science and technology, and in particular, to a report data processing method and apparatus, an electronic device, and a storage medium.

Background

Currently, more and more technologies (e.g., financial technology, cloud computing, or blockchain) are applied in the financial field, and traditional financial enterprises gradually shift to the financial technology, and in the context of the financial technology, the processing of data report forms is called as an important problem in the financial field. The report data is formed after the data of the financial business are collected, and the collected report data is classified, combined and summarized manually, so that a great amount of manpower is consumed, the workload of report data collection personnel is increased, and the report data processing efficiency is reduced. Therefore, how to improve the processing efficiency of report data becomes a technical problem to be solved.

Disclosure of Invention

The embodiment of the application mainly aims to provide a report data processing method and device, electronic equipment and storage medium, and aims to save the labor for report data processing and improve the processing efficiency of report data.

To achieve the above object, a first aspect of an embodiment of the present application provides a report data processing method, where the method includes:

Acquiring an original report; the original report comprises an original data column and data to be processed, wherein the data to be processed is positioned in the original data column;

searching rule information according to column name information of an original data column in a preset rule mapping relation to obtain rule configuration information of the data to be processed;

performing rule extraction in a preset rule base according to the rule configuration information to obtain a target processing rule of the data to be processed;

performing rule analysis on the target processing rule according to a preset rule engine tool to obtain a data processing function;

and executing 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: the method comprises the steps of duplicate removal operation, merging operation, classification operation, summarizing operation, averaging operation, difference calculation operation and data comparison operation, and a target report is obtained.

In some embodiments, the rule configuration information includes: rule configuration sequence number and rule association information; the rule base comprises at least two candidate processing rules; the rule extraction is carried out in a preset rule base according to the rule configuration information to obtain a target processing rule of the data to be processed, and the method comprises the following steps:

Screening according to the rule configuration sequence number and a preset candidate rule sequence number to obtain a target rule sequence number;

screening the candidate processing rules according to the target rule sequence number to obtain a selected processing rule;

screening the preset association condition function according to the rule association information to obtain a target condition function;

and combining the selected processing rules according to the target condition function to obtain the target processing rules.

In some embodiments, before the rule extraction is performed in a preset rule base according to the rule configuration information to obtain the target processing rule of the data to be processed, the method further includes:

the rule base is constructed, and the rule base specifically comprises the following steps:

acquiring candidate processing rules;

performing sequence number distribution processing on the candidate processing rule to obtain the candidate rule sequence number;

and storing the candidate processing rules into a preset database according to the candidate rule sequence numbers to obtain the rule base.

In some embodiments, the data processing function and the column name information perform at least one of the following preset operations on the data to be processed: the method comprises the steps of performing duplication removal operation, merging operation, classification operation, summarizing operation, averaging operation, difference calculation operation and data comparison operation to obtain a target report, and comprises the following steps:

Screening the data to be processed of the original data column according to the column name information to obtain target column data;

screening the data processing function according to the column name information to obtain a target processing function of the target column data;

screening one or two operations from the preset operations according to the target processing function to obtain a target operation;

and executing the target operation on the target column data to obtain the target report.

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

acquiring data priority information of the target column data;

setting processing order information of the target operation according to the data priority information;

and executing the target operation on the target column data according to the processing order information to obtain the target report.

In some embodiments, the obtaining the original report includes:

acquiring data collection information; wherein the data collection information comprises: object information, collection period information, and task description information;

screening the preset candidate objects according to the object information to obtain target objects;

Acquiring data of the target object according to the task description information and the collection time period information to obtain the data to be processed;

and filling the data to be processed into a preset report template to obtain the original report.

In some embodiments, after the screening process is performed on the preset candidate object according to the object information to obtain the target object, the method further includes:

acquiring the acquisition progress of the data to be processed according to a preset time period, and obtaining data acquisition progress information;

and carrying out visual processing on the data acquisition progress information to obtain a data acquisition progress view of the data to be processed.

To achieve the above object, a second aspect of an embodiment of the present application provides a report data processing apparatus, including:

the report acquisition module is used for acquiring an original report; the original report comprises an original data column and data to be processed, wherein the data to be processed is positioned in the original data column;

the rule searching module is used for searching rule information in a preset rule mapping relation according to column name information of an original data column to obtain rule configuration information of the data to be processed;

The rule extraction module is used for extracting rules from a preset rule base according to the rule configuration information to obtain target processing rules of the data to be processed;

the rule analysis module is used for carrying out rule analysis on the target processing rule according to a preset rule engine tool to obtain a data processing function;

the data content processing module is used for executing 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: the method comprises the steps of duplicate removal operation, merging operation, classification operation, summarizing operation, averaging operation, difference calculation operation and data comparison operation, and a target report is obtained.

To achieve the above object, a third aspect of the embodiments of the present application proposes an electronic device, including a memory storing a computer program and a processor implementing the method according to the first aspect when the processor executes the computer program.

To achieve the above object, a fourth aspect of the embodiments of the present application proposes a computer-readable storage medium storing a computer program which, when executed by a processor, implements the method of the first aspect.

The report data processing method and device, the electronic equipment and the storage medium provided by the application have the advantages that the rule configuration information is searched in the preset rule mapping relation according to the column name information of the original data column, the target processing rule of the data to be processed is extracted from the rule base according to the rule configuration information, then the rule engine tool is used for carrying out rule analysis on the target processing rule to obtain the data processing function, and at least one operation of the deduplication operation, the merging operation, the classification operation, the summarization operation, the averaging operation, the difference solving operation and the data comparison operation is carried out on the data processing function to obtain the target report according to the column name information, so that the automatic processing of the data to be processed of the original report is realized, the manual processing is not needed, the labor is saved, and the data processing efficiency of the original report is improved.

Drawings

FIG. 1 is a flowchart of a report data processing method according to an embodiment of the present application;

fig. 2 is a flowchart of step S101 in fig. 1;

FIG. 3 is a flowchart of a report data processing method according to another embodiment of the present application;

fig. 4 is a flowchart of step S103 in fig. 1;

FIG. 5 is a flowchart of a report data processing method according to another embodiment of the present application;

Fig. 6 is a flowchart of step S105 in fig. 1;

fig. 7 is a flowchart of step S604 in fig. 6;

FIG. 8 is a schematic diagram of a report data processing apparatus according to an embodiment of the present application;

fig. 9 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present application.

Detailed Description

The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.

It should be noted that although functional block division is performed in a device diagram and a logic sequence is shown in a flowchart, in some cases, the steps shown or described may be performed in a different order than the block division in the device, or in the flowchart. The terms first, second and the like in the description and in the claims and in the above-described figures, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.

Unless defined otherwise, 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 the application only and is not intended to be limiting of the application.

First, several nouns involved in the present application are parsed:

big data (big data): big data or huge amount of data refers to information which is huge in size and cannot be retrieved, managed, processed and tidied through a mainstream software tool in a reasonable time, and becomes a positive purpose for helping business operation decision. Technically, the relation between big data and cloud computing is just as dense as the front side and the back side of a coin. Big data must not be processed by a single computer, and a distributed architecture must be adopted. Big data is characterized by performing distributed data mining on mass data. But big data must rely on distributed processing of cloud computing, distributed databases and cloud storage, virtualization technologies.

Information collection (Information Gathering): information collection refers to obtaining the required information in various ways. Information collection is the first step in which information is utilized and is also the key step. The quality of the information collection work is directly related to the quality of the whole information management work. Information can be divided into two main categories, original information and processed information. Raw information refers to data, concepts, knowledge, experience, and summaries thereof that are directly generated or obtained in an economic campaign, and is raw information. The processing information is information with new form and new content formed by processing, analyzing, adapting and reorganizing the original information. Both types of information play an irreplaceable role in the marketing management activities of the enterprise.

Task tracking: the process of following the task with an operator after the task is generated, and recording the life cycle of the whole event; the method mainly applies project management, event arrangement, schedule arrangement, scheduling management, job scheduling and the like.

Report forms: the report reports the status to an upper level. Briefly, the method comprises the following steps: the report forms dynamically display data in the formats of tables, charts and the like, and can be expressed as follows: "report = multiple formats + dynamic data".

Drools: drools has an open source business rule engine which is easy to access enterprise policies, easy to adjust and easy to manage, accords with the industry standard, and has high speed and high efficiency. Business analysts or auditors can easily review business rules using Drools to verify whether the encoded rules implement the desired business rules. Drools is mainly divided into two parts: firstly, the Drools rule and secondly, the interpretation and execution of the Drools rule. The compiling and running of the Drools rules are implemented through relevant APIs provided by Drools. While these APIs can be generally divided into three categories upstream: rule compilation, rule collection, and rule execution.

In daily work, various data are usually required to be collected and formed into a report, for example, in the field of insurance, financial staff is required to collect various data, form files, questionnaires, satisfaction studies, agents and business forms to construct the financial report, and the financial staff is still required to classify, combine and summarize the data content in the financial report after collection. Therefore, the data content processing of the report forms by manpower not only consumes a great deal of manpower, but also reduces the efficiency of the report form processing.

Based on the above, the embodiment of the application provides a report data processing method and device, an electronic device and a storage medium, which are used for obtaining data to be processed of an original report, determining rule configuration information of the data to be processed from a rule mapping relation according to column name information of an original data column, extracting target processing rules from a rule base according to the rule configuration information, and carrying out rule analysis on the target processing rules to obtain a data processing function, so as to execute at least one preset operation of a deduplication operation, a merging operation, a classification operation, a summarizing operation, an averaging operation, a difference solving operation and a data comparison operation on the data to be processed through the data processing function and the column name information to obtain a target report. Therefore, the target processing rule of the data to be processed of each original data column is automatically determined, so that the data to be processed is intelligently processed according to the target processing rule to obtain the target report, the collected original report is not required to be manually processed, the workload of collection personnel is reduced, the manpower is saved, and the processing efficiency of the data to be processed in the report is improved.

The report data processing method and device, the electronic equipment and the storage medium provided by the embodiment of the application are specifically described through the following embodiments, and the report data processing method in the embodiment of the application is described first.

The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.

Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, financial technology processing technologies, operation/interaction systems, electromechanical integration, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.

The embodiment of the application provides a report data processing method, which relates to the technical field of artificial intelligence. The report data processing method provided by the embodiment of the application can be applied to a terminal, a server and software running in the terminal or the server. In some embodiments, the terminal may be a smart phone, tablet, notebook, desktop, etc.; the server side can be configured as an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and a cloud server for providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, basic cloud computing services such as financial science and technology and artificial intelligence platforms and the like; the software may be an application or the like that implements the report data processing method, but is not limited to the above form.

The application is operational with numerous general purpose or special purpose computer system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

It should be noted that, in each specific embodiment of the present application, when related processing is required according to user information, user behavior data, user history data, user location information, and other data related to user identity or characteristics, permission or consent of the user is obtained first, and the collection, use, processing, and the like of the data comply with related laws and regulations and standards. In addition, when the embodiment of the application needs to acquire the sensitive personal information of the user, the independent permission or independent consent of the user is acquired through popup or jump to a confirmation page and the like, and after the independent permission or independent consent of the user is definitely acquired, the necessary relevant data of the user for enabling the embodiment of the application to normally operate is acquired.

Fig. 1 is an optional flowchart of a report data processing method according to an embodiment of the present application, where the method in fig. 1 may include, but is not limited to, steps S101 to S105.

Step S101, an original report is obtained; the original report comprises an original data column and data to be processed, wherein the data to be processed is positioned in the original data column;

step S102, searching rule information according to column name information of an original data column in a preset rule mapping relation to obtain rule configuration information of data to be processed;

step S103, rule extraction is carried out on a preset rule base according to rule configuration information, and target processing rules of data to be processed are obtained;

step S104, carrying out rule analysis on the target processing rule according to a preset rule engine tool to obtain a data processing function;

step S105, 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: the method comprises the steps of duplicate removal operation, merging operation, classification operation, summarizing operation, averaging operation, difference calculation operation and data comparison operation, and a target report is obtained.

Step S101 to step S105 shown in the embodiment of the present application, by obtaining data to be processed located in an original data column, searching rule configuration information of the data to be processed in a preset rule mapping relation according to column name information of the original data column, where the rule configuration information records which processing rules are required for the data to be processed, extracting a target processing rule of the data to be processed from a preset rule base according to the rule configuration information, and analyzing the target processing rule by a rule engine tool to obtain a data processing function, so as to execute a corresponding operation on the corresponding data to be processed to obtain a target report through the data processing function and the column name information, where the operation includes at least one of the following preset operations: the method comprises the steps of carrying out duplicate removal operation, merging operation, classifying operation, summarizing operation, averaging operation, differencing operation and data comparison operation on the data to be processed in the original report, so as to finish at least one preset operation in duplicate removal, merging, classifying, summarizing, averaging, differencing and data comparison of the data to be processed, and the data to be processed in the original report does not need to be manually processed, so that the workload of collecting personnel is saved, and the efficiency of processing the data to be processed in the original report is improved.

Referring to fig. 2, in some embodiments, step S101 may include, but is not limited to, steps S201 to S204:

step S201, acquiring data collection information; wherein the data collection information comprises: object information, collection period information, and task description information;

step S202, screening a preset candidate object according to object information to obtain a target object;

step S203, data acquisition is carried out on the target object according to the task description information and the collection time period information, and data to be processed are obtained;

and S204, filling the data to be processed into a preset report template to obtain an original report.

In step S201 of some embodiments, in order to collect data to be processed, a task collection system is provided, by which task delegation and data collection are performed. And selecting a release function on the task collection system according to the user to enter a task release rule setting page, and setting a collection rule on the task release rule setting page according to the user to form data collection information. The collection objects are selected on the task release rule setting page according to the user to obtain object information, the collection objects can be individuals or departments, and the object information comprises object address information and object name information so as to determine which objects are subjected to data collection through the object information. And selecting the collection starting time and the collection deadline on the task release rule setting page according to the user so as to determine the collection time period information, and determining the starting time of the data collection to be processed through the collection time period information. Meanwhile, task description filled in a page is set according to the task release rule of the user to obtain task description information, so that a target object corresponding to the object information knows which information to upload through the task description information. Accordingly, data collection information is determined according to the contents of the user's filling and selection at the task issuance rule setting page to determine the objects to be collected and the collected data according to the data collection information.

For example, if the application scenario is the insurance field and the collection task is a satisfaction investigation task, the user selects an object associated with the satisfaction investigation task to determine object information on the task release rule setting page, then sets the collection start time and the collection end time of the satisfaction investigation task to obtain collection time period information, and sets task description information of the satisfaction investigation task. Therefore, the collection object, the collection time period and the task description of the current task are known through the object information, the collection time period information and the task description information, so that the data to be processed can be accurately collected.

In step S202 of some embodiments, a screening process is performed on a preset candidate object according to object information, so as to obtain a candidate object corresponding to the object information as a target object. The object information comprises object address information and object name information, a selected object is selected from candidate objects according to the object address information, and a target object is selected from the selected objects according to the object name information. For example, the task collection system presets a plurality of candidate objects, and selects a target object from the candidate objects according to a task release rule setting page by a user, namely, determines object name information and object address information of the target object, establishes TCP connection according to the object address information and the corresponding candidate object to obtain a selected object, and determines which of the selected objects connected by TCP are correct target objects according to the object name information.

In step S203 of some embodiments, data acquisition is performed on the target object according to the task description information and the collection time period information, that is, the task description information is sent to the target object connected to the TCP, and the target object obtains the data to be processed by feeding back the data of the object of the task description information according to the task description information. For example, if the current collection task is a satisfaction investigation task, task description information of the satisfaction investigation task is sent to target objects participating in the satisfaction investigation, and the target objects feed back the satisfaction of each item, the satisfaction of all the target objects on each item is collected into to-be-processed data. Meanwhile, collecting data in a collection starting time period corresponding to the collection time period information, and if the time for feeding back the data by the target object is not in the collection starting time period corresponding to the collection time period information, not collecting the data.

In step S204 of some embodiments, after the data to be processed is collected, the data to be processed is classified to obtain a data category, and the data to be processed is filled into the report template according to the data category, that is, according to the data category, which original data column of the report template the data to be processed is located in to obtain an original report is determined, so that the data to be processed is automatically collected to form a report style, and thus, a financial staff can know the data condition of the current collection task comprehensively and clearly by directly checking the original report.

For example, the satisfaction degree of each security item of the satisfaction degree investigation task is collected, the satisfaction degree is classified to determine item types, the original data column of each satisfaction degree in the report template is determined according to the item types, and then the satisfaction degree is filled into the report template according to the item types to obtain an original report.

In steps S201 to S204 shown in the embodiment of the present application, object information, collection period information and task description information are acquired, and a target object is screened from candidate objects through the object information, so that data acquisition is performed on the target object according to the collection period information and the task description information to obtain data to be processed, and classification processing is performed on the data to be processed to obtain data types, so that the data to be processed is filled into a preset report template according to the data types to obtain an original report. Therefore, the collection of the data to be processed is automatically completed, the data to be processed is built into the original report, the automatic arrangement of the data to be processed is realized, and the data to be processed can be clearly known by collecting personnel by directly referring to the original report.

Referring to fig. 3, in some embodiments, after step S202, the report data processing method further includes:

And tracking the acquisition progress of the data to be processed.

It should be noted that, in order to let the collector master the progress of the feedback data of the target object at any time, task tracking is performed on the data acquisition progress of the target object to know the progress of the data, so that the collector can prompt the target object according to the data acquisition progress to collect the complete data to be processed.

The collection progress of the data to be processed may include, but is not limited to, steps S301 to S302:

step S301, acquiring the acquisition progress of data to be processed according to a preset time period, and acquiring data acquisition progress information;

step S302, the data acquisition progress information is subjected to visual processing, and a data acquisition progress view of the data to be processed is obtained.

In step S301 of some embodiments, data acquisition progress information of data to be processed is acquired through a preset time period. The data acquisition progress corresponding to the data acquisition progress information comprises data acquisition, data acquisition and data non-acquisition. And if the data acquisition progress is that the data are not acquired, the target object is characterized as not feeding back the data to be processed.

It should be noted that the data collection progress information includes a data collection progress of the data to be processed, so as to know which target objects of the current collection task have the data to be processed collected, and which target objects have the data to be processed not collected, so as to clearly know the collection progress of the data to be processed of the whole target object.

In step S302 of some embodiments, the data acquisition progress information is visualized, that is, the data acquisition progress information is processed by an anti 6 patterning technology to generate a data acquisition progress view, and the data acquisition progress view is displayed in a manner of tracking a billboard on the data acquisition progress. The anti-G6 graphic technology mainly performs visual processing on data acquisition information through an anti-G6 graphic engine tool, the anti-G6 graphic engine tool is an open source engine tool for graphic visualization and analysis and is focused on relational data, the anti-G6 graphic engine tool accelerates the layout of a data acquisition progress view, the layout calculation performance is improved by tens of times or hundreds of times, the whole layout is more compact, and the acquisition progress of data to be processed of each target object can be clearly and intuitively known.

It should be noted that, the data acquisition view is updated once according to a preset time period, so that the collector can grasp the data acquisition progress of each target object in real time.

After the data collection view is constructed, if the data collection progress corresponding to the data collection progress information of the target object is that the data is not collected and the current time is close to the collection interception time corresponding to the collection time period information, the target object is prompted to timely complete feedback of the data to be processed through mails or short messages.

In the steps S301 to S302 shown in the embodiment of the present application, data acquisition progress information of data to be processed is acquired through a preset time period, the data acquisition progress information is visualized through an antG6 patterning technology to obtain a data acquisition progress view of each data to be processed, and the data acquisition progress view is made into a visualized progress tracking billboard. Therefore, the collector can intuitively check the data acquisition progress of each piece of data to be processed through the data acquisition progress view, so as to urge the target object according to the data acquisition progress.

In step S102 of some embodiments, after the original report is acquired, the original report describes the data to be processed of each target object. After the collection of the original report is completed, the data to be processed needs to be intelligently processed. Before the data to be processed is intelligently processed, column name information of each original data column is acquired, and a preset rule mapping relation comprises a mapping relation between the column name information and rule configuration information, and rule configuration information corresponding to the column name information is determined in the rule mapping relation according to the column name information. The rule configuration information includes: the rule configuration sequence number and the rule association information, and the number of the rule configuration sequence number is set to at least one. For example, if the column name information is a user name, a rule configuration sequence number is found in a preset rule mapping relation according to the user name to be a first rule and a second rule respectively, and if the column name information is a goodness, a rule configuration information is found in the preset rule mapping relation according to the goodness to be a third rule. Therefore, the rule configuration information of the data to be processed of each original data column in the original report can be known through the column name information and the rule mapping relation so as to know the target processing rule corresponding to the data to be processed of each original data column.

Referring to fig. 4, in some embodiments, the rule configuration information includes: rule configuration sequence number and rule association information; the rule base comprises at least two candidate processing rules; step S103 may include, but is not limited to, steps S401 to S404:

step S401, screening processing is carried out according to the rule configuration sequence number and a preset candidate rule sequence number, and a target rule sequence number is obtained;

step S402, screening the candidate processing rules according to the target rule sequence numbers to obtain selected processing rules;

step S403, screening the preset association condition function according to the rule association information to obtain a target condition function;

and step S404, combining the selected processing rules according to the target condition function to obtain target processing rules.

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 of each original data column can be known through the rule configuration information and the candidate rule sequence number.

For example, if the rule configuration sequence numbers are the first rule and the second rule, selecting the corresponding candidate rule sequence numbers as rule 1 and rule 2 according to the rule configuration sequence numbers, that is, the target rule sequence numbers as rule 1 and rule 2; if the rule configuration information is the third rule, selecting the candidate rule sequence number as rule 3 according to the rule configuration sequence number, namely, the target rule sequence number as rule 3. Therefore, the corresponding target rule sequence number, i.e. which candidate processing rules of the rule base are determined by the rule configuration sequence number.

In step S402 of some embodiments, a filtering process is performed on the candidate processing rules according to the target rule sequence number, so that the candidate processing rule corresponding to the target rule sequence number is selected from the plurality of candidate processing rules as a selected processing rule, so that the selected processing rule is easy to select.

For example, if the candidate processing rule corresponding to the rule 1 is a deduplication processing rule, the candidate processing rule corresponding to the rule 2 is a merging processing rule, and the candidate processing rule corresponding to the rule 3 is an averaging processing rule, so that the selection of the selected processing rule is easy.

In step S403 of some embodiments, since there may be a plurality of processing rules corresponding to the data to be processed of each raw data column, in order to determine the processing relationship of each processing rule, the processing relationship between each processing rule may be determined according to rule association information. Therefore, screening processing is carried out from preset association condition functions according to the rule association information, so that the association condition function corresponding to the rule association information is screened out as a target condition function. For example, if the rule association information is the sum relationship, the association condition function is determined to be "and", and if the rule association information is the 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 relation between each selected processing rule.

In step S404 of some embodiments, the selected processing rules are combined according to the target condition function, that is, the selected processing rules are associated according to the target condition function to obtain the target processing rules, so that the target processing rules are simply constructed.

For example, if the target condition function is "and the selected processing rule is a deduplication processing rule and a merge processing rule, the deduplication processing rule and the merge processing rule are merged according to the target condition function, so as to construct the target processing rule in the style of" deduplication processing rule and merge processing rule ".

It should be noted that, steps S401 to S404 are performed by a Drools rule engine tool to select the target processing rule of each raw data column by the Drools rule engine tool.

In steps S401 to S404 shown in the embodiment of the present application, the corresponding candidate rule sequence number is selected from the candidate rule sequence numbers according to the rule configuration sequence number to be used as the target rule sequence number, the corresponding candidate processing rule is selected from the candidate processing rules according to the target rule information to be used as the selected processing rule, and the target condition function is selected from the association condition function according to the rule association information, so that the selected processing rule is combined according to the target condition function to obtain the target processing rule, so that the target processing rule is constructed accurately and simply, and the target processing rule of the data to be processed of each original data column can be known through the target processing rule.

Referring to fig. 5, in some embodiments, before step S103, the report data processing method further includes: and constructing a rule base.

It should be noted that, before extracting the target processing rule from the rule base, the rule base needs to be constructed so as to extract the data content processing and the data content computing operation of the data to be processed directly from the rule base.

Constructing a rule base may include, but is not limited to including, step S501 to step S503:

step S501, obtaining candidate processing rules;

step S502, carrying out sequence number distribution processing on the candidate processing rules to obtain candidate rule sequence numbers;

step S503, storing the candidate processing rules into a preset database according to the candidate rule sequence numbers to obtain a rule base.

In step S501 of some embodiments, candidate processing rules are acquired, that is, processing rules are customized as candidate processing rules according to data of each data category in advance. For example, if the conventional report data needs to perform a merging operation, a deduplication operation, a summarizing operation, an averaging operation, a difference calculating operation, and a data comparing operation, merging processing rules are customized according to the merging operation, deduplication processing rules are customized according to the deduplication operation, summarizing processing rules are customized according to the summarizing operation, averaging processing rules are customized according to the averaging operation, difference calculating processing rules are customized according to the difference calculating operation, and data comparing processing rules are customized according to the data comparing operation. Therefore, the user-defined processing rule is used as the candidate processing rule in advance, so that the candidate processing rule can be directly called after the preset operation is carried out on the data to be processed, the user-defined processing rule does not need to be manually selected according to each original data column, the labor can be saved, and the data processing efficiency of the original report can be improved.

In step S502 of some embodiments, in order to facilitate extraction of each candidate processing rule, a sequence number needs to be set to each candidate processing rule, that is, a rule sequence number defined in advance is assigned to each candidate processing rule to determine each candidate processing rule candidate rule sequence number.

In step S503 of some embodiments, the candidate processing rules are stored into a preset database according to the candidate rule sequence numbers, so as to construct candidate processing rules carrying the candidate rule sequence numbers, and form a rule base.

In steps S501 to S503 of the embodiment of the present application, a user-defined processing rule is obtained as a candidate processing rule, and sequence number allocation processing is performed on each candidate processing rule to obtain a candidate rule sequence number of each candidate processing rule, so that the candidate processing rule is stored into a preset database according to the candidate rule sequence number, so as to construct a rule base including the candidate processing rule carrying the candidate rule sequence number, so that the candidate processing rule in the rule base is directly invoked for processing the to-be-processed data of the original data column, without manually resetting the processing rule of each original data column, thereby saving manpower and improving the data processing efficiency of the original report.

In step S104 of some embodiments, the preset rule engine tool is a Drools rule engine tool, and the rule analysis is performed on the target processing rule by the Drools rule engine tool, that is, the complex and variable target processing rule is released from the hard code and is processed in a data processing function manner, so that the change of the target processing rule can be immediately completed in an online environment without modifying the code and restarting the server.

Referring to fig. 6, in some embodiments, step S105 includes, but is not limited to, steps S601 to S604:

step S601, screening the data to be processed of the original data column according to the column name information to obtain target column data;

step S602, screening the preset candidate objects according to the object information to obtain target objects;

step S603, screening one or two operations from the preset operations according to the target processing function to obtain a target operation;

step S604, executing target operation on the target column data to obtain a target report.

In step S601 of some embodiments, since the data processing functions corresponding to the data to be processed of each original data column are different, the data processing functions corresponding to each original data column need to be filled into the corresponding position in the original report to execute the corresponding preset operation on the data to be processed, so as to implement the intelligent processing of the data to be processed of the original report. Therefore, the to-be-processed data of the original data column is screened according to the column name information, so that the to-be-processed data corresponding to each column name information is screened out as target column data. For example, if the column name information is a user name, all user names are screened out as target column data; if the column name information is the goodness, screening all the goodness as the target column data.

In step S602 of some embodiments, after determining the target column data, the target processing function of each target column data is screened from the data processing functions according to the column name information, so as to implement that different target column data are processed with different target processing functions. For example, if the column name information is a user name, filtering out the target processing function as a duplicate removal function and a merging function according to the user name; and if the column name information is the goodness, screening out the target processing function according to the goodness to be an average function so as to determine the target processing functions corresponding to different target column data.

In step S603 of some embodiments, in order to determine the preset operation performed by each column of data, the target operation is selected from the preset operations according to the target processing function, so as to determine the target operation to be performed by each target column of data. For example, if the target processing function is a deduplication function and a merge function, the target operation is screened out as a deduplication operation and a merge operation; if the target processing function is an averaging function, the target operation is screened out as an averaging operation, so as to determine the target operation of each target column data.

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

For example, if the first column is a user name, and the target processing functions corresponding to the user name are a deduplication function and a merge function, the user names are read one by one through the IO file, then the user names are subjected to deduplication operation, and after the deduplication operation is completed, the user names are subjected to merge operation; if the second column is the goodness, the target processing function corresponding to the goodness is an average function, and the average function is an AVG function, then all collected goodness is subjected to average calculation through the AVG function to obtain average goodness, and the average goodness is filled in the position corresponding to the original data column of the goodness in the original report, so that the target report is constructed. Therefore, after the data to be processed is automatically collected, the corresponding target processing rule is automatically called to analyze the target processing rule to obtain the target processing function, so that target operations are executed on each target column of data according to the target processing function, different target operations are executed on different target columns of data by different target processing functions to construct a target report, intelligent processing of the original report is realized, and the data to be processed in the original report is not required to be processed manually, so that labor is saved and the data processing efficiency of the original report is improved.

In steps S601 to S604 shown in the embodiment of the present application, target column data corresponding to each column name is screened out from to-be-processed data in an original data column according to column name information, then a data processing function is screened out according to column name information, so as to screen out a target processing function of each target column data, a target operation of each target column data is known according to the target processing function, and finally a target operation is performed on the target column data, so that different target column data and different target processing functions execute different target operations, intelligent processing of an original report is realized, and to-be-processed data in the original report is not required to be manually processed, thereby saving manpower and improving data processing efficiency of the original report.

Referring to fig. 7, in some embodiments, step S604 may include, but is not limited to, steps S701 to S703:

step S701, obtaining data priority information of target column data;

step S702, setting processing order information of a target operation according to the data priority information;

step S703, executing target operation on the target column data according to the processing order information to obtain a target report.

In step S701 of some embodiments, data priority information of the target column data is acquired to know which target column data needs to be processed first and which target column data can be processed later through the data priority information.

In step S702 of some embodiments, processing order information of the target operations is set according to the data priority information, that is, the target operations are processed and ordered, that is, the target operations are executed one by one according to the processing order information to process the corresponding target column data.

In step S703 of some embodiments, the target operations are performed on the target column data according to the processing order information, that is, the target operations are performed one by one according to the processing order information, so that the target column data is processed one by the target processing function to obtain the target report. Thus, the target column data is processed in an orderly manner, and the confusion of the target column data for executing the target operation is reduced.

In the steps S701 to S703 illustrated in the embodiment of the present application, by acquiring the data priority information of each target column data to set the processing order information of each target operation according to the data priority information, the target operation is performed on the target column data according to the processing order information, so as to implement ordered processing of the target column data, and reduce the confusion of processing of the target column data.

According to the embodiment of the application, the object information, the collection time period information and the task description information are acquired, the object information is used for screening the target object from the candidate objects, the target object is subjected to data acquisition according to the collection time period information and the task description information to obtain the data to be processed, the data to be processed is subjected to classification processing to obtain the data category, and the data to be processed is filled into a preset report template according to the data category to obtain the original report. And acquiring data acquisition progress information of the target object through a preset time period, performing visual processing on the data acquisition progress information through an antG6 graphical technology to obtain a data acquisition progress view, and making the data acquisition progress view into a visual progress tracking billboard. And screening out the corresponding candidate rule sequence number from the candidate rule sequence numbers according to the rule configuration sequence number to serve as a target rule sequence number, screening out the corresponding candidate processing rule from the candidate processing rules according to the target rule to serve as a selected processing rule, screening out the target condition function from the association condition function according to the rule association information, and carrying out merging processing on the selected processing rule according to the target condition function to obtain the target processing rule. The target processing rules are subjected to rule analysis through a Drools rule engine tool to obtain target processing functions, target column data corresponding to each column name are screened out from data to be processed of an original data column according to column name information, then the data processing functions are screened out according to the column name information, the target processing functions of each target column data are screened out, the target operation of each target column data is known according to the target processing functions, then the data priority information of each target column data is obtained, the processing order information of each target operation is set according to the data priority information, then the target operation is executed on the target column data according to the processing order information, and different target operations are executed on different target column data according to different target processing functions. Therefore, corresponding preset operation is automatically executed on the data to be processed in the original report, and operation calculation and data processing are not needed to be manually executed on the data to be processed in the original report, so that labor is saved, and the data processing efficiency of the original report is improved.

Referring to fig. 8, an embodiment of the present application further provides a report data processing apparatus, which may implement the report data processing method, where the apparatus includes:

a report acquisition module 801, configured to acquire an original report; the original report comprises an original data column and data to be processed, wherein the data to be processed is positioned in the original data column;

the rule searching module 802 is configured to search rule information according to column name information of an original data column in a preset rule mapping relationship to obtain rule configuration information of data to be processed;

the rule extraction module 803 is configured to perform rule extraction in a preset rule base according to rule configuration information to obtain a target processing rule of the data to be processed;

the rule parsing module 804 is configured to parse the rule of the target processing rule according to a preset rule engine tool, so as to obtain a data processing function;

the data content processing module 805 is configured 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: the method comprises the steps of duplicate removal operation, merging operation, classification operation, summarizing operation, averaging operation, difference calculation operation and data comparison operation, and a target report is obtained.

The specific implementation of the report data processing device is basically the same as the specific embodiment of the report data processing method, and is not described herein.

The embodiment of the application also provides electronic equipment, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the report data processing method when executing the computer program. The electronic equipment can be any intelligent terminal including a tablet personal computer, a vehicle-mounted computer and the like.

Referring to fig. 9, fig. 9 illustrates a hardware structure of an electronic device according to another embodiment, the electronic device includes:

the processor 901 may be implemented by a general purpose CPU (central processing unit), a microprocessor, an application specific integrated circuit (ApplicationSpecificIntegratedCircuit, ASIC), or one or more integrated circuits, etc. for executing related programs to implement the technical solution provided by the embodiments of the present application;

the memory 902 may be implemented in the form of read-only memory (ReadOnlyMemory, ROM), static storage, dynamic storage, or random access memory (RandomAccessMemory, RAM). The memory 902 may store an operating system and other application programs, and when the technical solution provided in the embodiments of the present disclosure is implemented by software or firmware, relevant program codes are stored in the memory 902, and the processor 901 invokes the report data processing method for executing the embodiments of the present disclosure;

An input/output interface 903 for inputting and outputting information;

the communication interface 904 is configured to implement communication interaction between the device and other devices, and may implement communication in a wired manner (e.g. USB, network cable, etc.), or may implement communication in a wireless manner (e.g. mobile network, WIFI, bluetooth, etc.);

a bus 905 that transfers information between the various components of the device (e.g., the processor 901, the memory 902, the input/output interface 903, and the communication interface 904);

wherein the processor 901, the memory 902, the input/output interface 903 and the communication interface 904 are communicatively coupled to each other within the device via a bus 905.

The embodiment of the application also provides a computer readable storage medium which stores a computer program, and the computer program realizes the report data processing method when being executed by a processor.

The memory, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory remotely located relative to the processor, the remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

According to the report data processing method, the report data processing device, the electronic equipment and the storage medium, corresponding rule configuration information is searched from a preset rule mapping relation according to column name information of an original data column, so that processing rules of each original data column are known through the rule configuration information, then target processing rules are extracted from a rule base according to the rule configuration information, rule analysis is conducted on the target processing rules through a rule engine tool to obtain a data processing function, and at least one preset operation of deduplication operation, merging operation, classifying operation, summarizing operation, averaging operation, difference solving operation and data comparison operation is executed on the data to be processed according to the data processing function and the column name information, so that a target report is obtained. Therefore, after the original report is collected, corresponding preset operation is automatically executed on the data to be processed in the original report, and manual operation calculation and data processing on the data to be processed in the original report are not needed, so that labor is saved, and the data processing efficiency of the original report is improved.

The embodiments described in the embodiments of the present application are for more clearly describing the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application, and those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

It will be appreciated by persons skilled in the art that the embodiments of the application are not limited by the illustrations, and that more or fewer steps than those shown may be included, or certain steps may be combined, or different steps may be included.

The above described apparatus embodiments are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

Those of ordinary skill in the art will appreciate that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.

The terms "first," "second," "third," "fourth," and the like in the description of the application and in the above figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

It should be understood that in the present application, "at least one (item)" means one or more, and "a plurality" means two or more. "and/or" for describing the association relationship of the association object, the representation may have three relationships, for example, "a and/or B" may represent: only a, only B and both a and B are present, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b or c may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.

In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the above-described division of units is merely a logical function division, and there may be another division manner in actual implementation, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including multiple instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method of the various embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing a program.

The preferred embodiments of the present application have been described above with reference to the accompanying drawings, and are not thereby limiting the scope of the claims of the embodiments of the present application. Any modifications, equivalent substitutions and improvements made by those skilled in the art without departing from the scope and spirit of the embodiments of the present application shall fall within the scope of the claims of the embodiments of the present application.

Claims

1. A method for processing report data, the method comprising:

2. The method of claim 1, wherein the rule configuration information comprises: rule configuration sequence number and rule association information; the rule base comprises at least two candidate processing rules; the rule extraction is carried out in a preset rule base according to the rule configuration information to obtain a target processing rule of the data to be processed, and the method comprises the following steps:

3. The method according to claim 2, wherein before the rule extraction is performed in a preset rule base according to the rule configuration information to obtain the target processing rule of the data to be processed, the method further comprises:

acquiring candidate processing rules;

4. A method according to any one of claims 1 to 3, wherein said performing at least one of the following preset operations on said data to be processed according to said data processing function and said column name information: the method comprises the steps of performing duplication removal operation, merging operation, classification operation, summarizing operation, averaging operation, difference calculation operation and data comparison operation to obtain a target report, and comprises the following steps:

5. The method of claim 4, wherein performing the target operation on the target column data results in the target report, comprising:

Acquiring data priority information of the target column data;

6. A method according to any one of claims 1 to 3, wherein the obtaining an original report comprises:

7. The method according to claim 6, wherein after the screening process is performed on the preset candidate object according to the object information, the method further comprises:

8. A report data processing apparatus, the apparatus comprising:

9. An electronic device comprising a memory storing a computer program and a processor implementing the report data processing method of any of claims 1 to 7 when the computer program is executed by the processor.

10. A computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the report data processing method of any one of claims 1 to 7.