Report data processing method and device, computer equipment and readable storage medium

Abstract

The invention relates to a submitted data processing method and device, computer equipment and a readable storage medium. The method comprises the following steps: receiving submitted data and incoming parameters, and screening data to be verified from the submitted data according to the incoming parameters; determining a verification type according to the incoming parameter, and matching a verification statement template and a verification parameter matched with the verification type from a constructed rule configuration table; generating a verification statement based on the verification statement template and the verification parameter; executing the verification statement, verifying the to-be-verified data, and identifying problem data meeting a preset supplementing and reporting condition in the to-be-verified data; and determining a supplementing and reporting mode of the problem data, and performing supplementing and reporting processing on the problem data according to the supplementing and reporting mode. By adopting the method, the submitted data processing efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the field of information technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium and computer program product for processing reported data. Background Art

[0002] With the maturity of cloud computing and distributed computing technologies, effective data extraction solutions based on cloud platforms have become a research hotspot. Data indicator monitoring and automatic reporting technologies stem from the need to extract effective information in a complex data environment. Such technologies involve regulatory reporting data inspection, data comparison, and data correction technologies. Regulatory reporting data inspection is a key link in ensuring the accuracy of reported data. Through a comprehensive inspection of the data, errors and omissions in the data can be discovered and corrected in a timely manner, avoiding compliance risks caused by data problems.

[0003] At present, there is still a problem of low efficiency in the processing of reported data. Summary of the invention

[0004] Based on this, it is necessary to provide a reporting data processing method, device, computer equipment, computer-readable storage medium and computer program product that can improve the efficiency of reporting data processing in response to the above-mentioned technical problems.

[0005] In a first aspect, the present application provides a method for processing reported data, comprising:

[0006] Receive submitted data and input parameters, and filter out the data to be verified from the submitted data according to the input parameters;

[0007] Determine the verification type based on the input parameters, and match the verification statement template and verification parameters that match the verification type from the constructed rule configuration table;

[0008] Generate a verification statement based on the verification statement template and verification parameters;

[0009] Execute the verification statement to verify the data to be verified, and identify the problematic data that meets the preset supplementary reporting conditions in the data to be verified;

[0010] Determine the method for supplementary reporting of problematic data, and carry out supplementary reporting processing on the problematic data according to the supplementary reporting method.

[0011] In one embodiment, the input parameters include the customer type and the verification method; after identifying the problematic data that meets the preset supplementary reporting conditions in the data to be verified, the method further includes:

[0012] Based on the submitted data, statistics are collected on the total amount of submitted data and the number of problematic data corresponding to different customer types under different verification methods;

[0013] For each verification method, based on the number of problematic data and the total amount of data for each customer type under the verification method, determine the ratio of the number of problematic data to the total amount of data;

[0014] Generate a data authenticity calculation report based on the proportion of problematic data corresponding to different customer types under different verification methods.

[0015] In one embodiment, the input parameters include the provincial institution number and the customer type, and the data to be verified is screened from the submitted data according to the input parameters, including:

[0016] Filter out the data to be verified that matches the verification method and customer type from the submitted data;

[0017] According to the provincial institution number, the data to be verified is stored in the constructed provincial temporary table corresponding to the provincial institution number.

[0018] In one embodiment, after identifying the problematic data that meets the preset supplementary reporting conditions in the data to be verified, the method further includes:

[0019] Write the problem data into the constructed problem zipper table and record the valid time range of the problem data.

[0020] In one embodiment, before determining the supplementary reporting method of the problematic data, the method further includes:

[0021] Filter out the target question data that matches the preset rule number from the question zipper table;

[0022] According to the preset rule recognition conditions, the reason for the supplementary reporting of the target problem data is identified to obtain the supplementary reporting of smoking addiction recognition results;

[0023] According to the supplementary reporting reason identification result, determine the supplementary reporting reason label of the target problem data, and add the supplementary reporting reason label to the target problem data.

[0024] In one embodiment, the supplementary reporting reason tag includes an information delay tag and a non-information delay tag, determining a supplementary reporting method for the problem data, and performing supplementary reporting processing on the problem data according to the supplementary reporting method, including:

[0025] In the case where the supplementary reporting reason label of the problem data is an information delay label, the supplementary reporting method of the problem data corresponding to the information delay label is determined to be incremental supplementary reporting, and the problem data corresponding to the information delay label is added to the incremental reporting sequence for incremental supplementary reporting processing;

[0026] When the re-reporting reason label of the problem data is a non-information delay label, the re-reporting method of the problem data corresponding to the non-information delay label is determined to be periodic re-reporting, and the problem data corresponding to the non-information delay label is added to the weekly re-reporting sequence for weekly re-reporting processing.

[0027] In one embodiment, after the problem data is supplemented, the method further includes:

[0028] Perform data verification on the problematic data after supplementary reporting to obtain verification results;

[0029] Based on the verification results, update the problem zipper table.

[0030] In a second aspect, the present application also provides a reporting data processing device, comprising:

[0031] The data screening module is used to receive the submitted data and the input parameters, and screen the data to be verified from the submitted data according to the input parameters;

[0032] The verification statement generation module is used to determine the verification type according to the input parameters, match the verification statement template and verification parameters that match the verification type from the constructed rule configuration table; and generate the verification statement based on the verification statement template and verification parameters;

[0033] The data verification module is used to execute the verification statement, verify the data to be verified, and identify the problematic data that meets the preset supplementary reporting conditions in the data to be verified;

[0034] The data supplementary reporting module is used to determine the supplementary reporting method for the problematic data and to perform supplementary reporting processing on the problematic data according to the supplementary reporting method.

[0035] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps in any one of the above-mentioned reporting data processing method embodiments are implemented.

[0036] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in any one of the above-mentioned reporting data processing method embodiments are implemented.

[0037] In a fifth aspect, the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps in any one of the above-mentioned reporting data processing method embodiments.

[0038] The above-mentioned reported data processing method, device, computer equipment, computer-readable storage medium and computer program product, by inputting parameters, screen out the data to be verified from the reported data, determine the verification type according to the input parameters, match the verification statement template and verification parameters corresponding to the verification type from the constructed rule configuration table, generate the corresponding verification statement, execute the verification statement to perform data verification on the data to be verified, and identify the problem data that meets the preset supplementary reporting conditions in the data to be verified. In this way, by configuring multiple verification statement templates, the automatic verification rule configuration method of generating verification statements by matching the verification statement templates with the input parameters can, on the one hand, configure the verification statement templates in the rule configuration table according to different verification scenarios and requirements, so as to adapt to different verification scenarios and improve the efficiency and flexibility of data verification; on the other hand, compared with the traditional reporting data verification method in which one rule corresponds to one verification logic, the maintenance cost and repeated development cost of the verification rules are reduced. Therefore, after identifying the problem data, the supplementary reporting method of the problem data is determined, and the corresponding supplementary reporting processing is performed, thereby improving the efficiency of data supplementary reporting. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 An application environment diagram of a reporting data processing method in an embodiment;

[0041] Figure 2 A flowchart of a method for processing reported data in one embodiment is shown;

[0042] Figure 3 A flowchart of a method for processing reported data in another embodiment is shown;

[0043] Figure 4 A flowchart of a method for processing data reported in another embodiment;

[0044] Figure 5 A flowchart of a method for processing reported data in yet another embodiment;

[0045] Figure 6 It is a structural block diagram of a reporting data processing device in one embodiment;

[0046] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0048] The reporting data processing method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the terminal 102 communicates with the server 104 through a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers.

[0049] Specifically, the operator may upload the reported data and the input parameters to the server 104 through the terminal 102. The server 104 obtains the reported data and the input parameters, and selects the data to be verified from the reported data according to the input parameters. Secondly, the verification type is determined according to the input parameters, and the verification statement template and verification parameters that match the verification type are matched from the constructed rule configuration table. Then, based on the verification statement template and the verification parameters, a verification statement is generated, and the verification statement is executed to verify the data to be verified, and the problem data that meets the preset supplementary reporting conditions in the data to be verified is identified. Finally, the supplementary reporting method of the problem data is determined, and the problem data is supplemented according to the supplementary reporting method.

[0050] The terminal 102 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, etc. The server 104 may be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0051] In an exemplary embodiment, Figure 2 As shown, a method for processing reported data is provided, which is applied to Figure 1 The server 104 in the example is used for explanation, and includes the following S100 to S500. Among them:

[0052] S100, receiving submitted data and input parameters, and filtering out data to be verified from the submitted data according to the input parameters.

[0053] Among them, data submission can refer to the behavior of various departments or units submitting relevant data to superiors or relevant institutions in a unified format and manner according to specific requirements and time nodes. The submitted data can include statistical data, financial data, human resources data, market research data, etc. The incoming parameters are used to filter out the data to be verified from the submitted data, and the incoming parameters can include the verification method. Different verification methods correspond to different data screening ranges. If the verification method is duplicate verification, the data screening range is the specified fields to be checked, such as contact phone number and ID number.

[0054] In actual applications, for example, for data reporting by insurance companies, regulatory authorities require insurance companies to regularly self-check the authenticity of customer data, report detailed policy-level data daily, and require customer information to meet integrity, compliance, and consistency. According to the data to be reported, a reporting data model is constructed, which includes a personal policy table model, an insured table model, and a beneficiary table model.

[0055] The reported data may include basic policy information (such as policy number, batch number, customer number, provincial agency number, transaction serial number and transaction type, etc.), policyholder information (such as name, date of birth, ID number, contact number and address, etc.), insured person information (such as name, date of birth, ID number, contact number, address and relationship with the policyholder, etc.), beneficiary information (such as name, date of birth, ID number, contact number and address, etc.), etc.

[0056] For the verification of a large amount of data, by defining the stored procedure of the database, the data to be verified is filtered from the submitted data according to the input parameters. Specifically, the input parameters include the verification method, the data screening range is determined according to the verification method, and the data is extracted according to the data screening range. If the verification method is "incremental check", the corresponding data screening range is the submitted data of the batch on the same day; if the verification method is "batch check", the corresponding data screening range is the submitted data of the specified batch number; if the verification method is "full amount duplicate check", the corresponding data screening range is the customer number and mobile phone number in the full amount of submitted data; if the verification method is "quarterly duplicate check", the corresponding data screening range is the customer number and mobile phone number in the submitted data within the quarter; if the verification method is "in-batch duplicate check", the corresponding data screening range is the customer number and mobile phone number in the submitted data within the batch. Filter out the data to be verified according to the determined data screening range. Among them, the stored procedure is a set of SQL (Structured Query Language) statements to complete specific functions, which are stored in the database. Extracting data through stored procedures can reduce data transmission between the database and the server, which is conducive to improving the efficiency of data processing.

[0057] S200, determining the verification type according to the input parameters, and matching the verification statement template and verification parameters that match the verification type from the constructed rule configuration table.

[0058] The rule configuration table includes multiple validation conditions and multiple validation statement templates. Each validation rule corresponds to a rule number. The validation types include intra-table validation, inter-table validation, and duplicate validation. The validation parameters include the validation field name, the reporting model name, and the validation condition.

[0059] In actual applications, in order to assist in the processing of reported data, a rule configuration table is constructed, and its data model includes fields such as rule number, reporting model name, check field name, check type, check statement template, and check condition. After receiving the incoming parameters, the rule configuration table is read in through a cursor loop read method, and the check type is determined according to the check method. The correspondence between the check method and the check type can be pre-built. For example, the check type corresponding to the check method "check duplicates within a batch" can be a check duplicate check, and the check method corresponding to the "incremental check" can be an inter-table check. After determining the check type, the corresponding check statement template is triggered through the rule configuration table, and the check field name, reporting model name, and check condition are automatically matched to obtain the check parameters corresponding to the incoming parameters.

[0060] S300, generating a verification statement based on a verification statement template and verification parameters.

[0061] In actual applications, a verification statement corresponding to the input parameters is generated according to the verification statement template and the verification parameters.

[0062] S400, executing the verification statement to verify the data to be verified, and identifying the problematic data in the data to be verified that meets the preset supplementary reporting conditions.

[0063] The preset supplementary reporting conditions may be that the data does not meet the relevant rules for completeness, compliance and consistency. For example, the supplementary reporting conditions may include "non-standard ID number", "missing relationship between the insured and the policyholder", "missing contact address" and "duplicate contact phone number".

[0064] In actual applications, the screened data to be verified is verified by executing the verification statement, so as to identify the problematic data in the data to be verified. For example, if the rule number "A01" corresponds to the rule "the policyholder's address cannot be empty", the "street" field in the reporting model of the "Personal Insurance Policy Form" is matched through the rule configuration table, and the verification condition is "street isnull", a problematic data is identified: "Transaction serial number: '001'; Policy number: '2024ABC001'; Customer number '000000'; Street: 'NULL'".

[0065] S500, determining a supplementary reporting method for the problematic data, and performing supplementary reporting processing on the problematic data according to the supplementary reporting method.

[0066] Among them, the supplementary reporting methods can include incremental reporting and weekly reporting.

[0067] In practical applications, the method of reporting a problem can be determined based on the nature and urgency of the problem data. For example, problem data caused by data loss or data transmission delay due to system failure can be reported through incremental reporting. Problem data of non-critical information can be reported through weekly reporting. The reporting method can be determined by determining the problem field in the problem data and judging whether the problem field is critical information.

[0068] In the above-mentioned method for processing reported data, by inputting parameters, the data to be verified is screened out from the reported data, the verification type is determined according to the input parameters, the verification statement template and verification parameters corresponding to the verification type are matched from the constructed rule configuration table, the corresponding verification statement is generated, and the verification statement is executed to perform data verification on the data to be verified, and the problem data that meets the preset supplementary reporting conditions in the data to be verified is identified. In this way, by configuring multiple verification statement templates, the automatic verification rule configuration method for generating verification statements by matching the verification statement templates with the input parameters can, on the one hand, configure the verification statement templates in the rule configuration table according to different verification scenarios and requirements, so as to adapt to different verification scenarios and improve the efficiency and flexibility of data verification; on the other hand, compared with the traditional method of verifying reported data by one rule corresponding to one verification logic, the maintenance cost and repeated development cost of the verification rules are reduced. Therefore, after identifying the problem data, the supplementary reporting method of the problem data is determined, and the corresponding supplementary reporting processing is performed, which improves the efficiency of data supplementary reporting.

[0069] In order to evaluate the authenticity of the submitted data, in an exemplary embodiment, Figure 3 As shown, it also includes S620 to S660. Among them:

[0070] S620, based on the submitted data, counting the total amount of submitted data corresponding to different customer types under different verification methods and the amount of problematic data.

[0071] S640, for each verification method, based on the number of problematic data and the total amount of data for each customer type under the verification method, determine the ratio of the number of problematic data to the total amount of data.

[0072] S660, based on the proportion of problematic data corresponding to different customer types under different verification methods, generate a data authenticity calculation report.

[0073] In practical applications, it is possible to set a customer authenticity index measurement task regularly, such as conducting a customer authenticity assessment on the submitted data once a week. Specifically, a multi-indicator measurement method can be adopted to count the amount of problematic data in the submitted data from multiple dimensions of customer type and verification method. In accordance with the above embodiment, the customer types include policyholders, insured persons and beneficiaries, and the verification methods include full-quantity duplicate checking, quarterly duplicate checking and batch duplicate checking. Since there may be a customer's data with multiple fields whose values ​​do not meet the requirements, such as the lack of contact address and repeated contact phone numbers, there are two problematic data for the customer, which will cause repeated statistics. The transaction serial number of the customer is used as the unique identifier of the data, and the verification method is used to perform deduplication statistics on the problematic data, and the total amount of submitted data of different customer types under different verification methods, as well as the amount of problematic data, is obtained. For example, the total amount of data (total number of insurance policies) of the submitted data is 18940. Under the deduplication statistical method of full-quantity duplicate checking, the amount of problematic data about the policyholder is 213, the amount of problematic data about the insured is 20, and the amount of problematic data about the beneficiary is 0.

[0074] After obtaining the total amount of data reported by different customer types under different verification methods and the number of problematic data, the proportion of problematic data of different customer types under different verification methods was determined. For example, under full duplication check, the proportion of problematic data of policyholders is 213 / 18940≈1.1%, the proportion of problematic data of insured persons is 20 / 18940≈0.1%, and the proportion of problematic data of beneficiaries is 0%.

[0075] After obtaining the proportion of problem data corresponding to different customer types under different verification methods, you can also obtain the amount of problem data that hits different rules as detailed data, such as missing name, missing gender, missing date of birth, missing ID type, missing ID number, missing contact number, missing address, missing relationship between the insured and the policyholder, and irregular contact number, etc. Generate a customer authenticity calculation report based on the indicator data (the amount of problem data for different customer types counted under different verification methods, the proportion of problem data, and the total amount of reported data) and detailed data.

[0076] In this embodiment, by counting the proportion of problematic data of different customer types under different verification methods, and considering the relationship between multiple variables, the submitted data is evaluated from multiple angles, which can provide more comprehensive and accurate results of the authenticity calculation of the submitted data. At the same time, this multi-indicator comparison method can reduce the subjective bias caused by a single indicator, and different indicators can verify each other, thereby improving the objectivity and credibility of the calculation results. Furthermore, it is conducive to adjusting the management and processing of submitted data according to the data authenticity calculation report to improve the accuracy of the submitted data.

[0077] In order to improve the processing efficiency of the reported data, in an exemplary embodiment, the input parameters include the provincial institution number and the customer type, and S100 also includes S120 to S140. Among them:

[0078] S120, filtering out the data to be verified that matches the verification method and customer type from the submitted data.

[0079] S140, according to the provincial organization number, the data to be verified is stored in the constructed provincial temporary table corresponding to the provincial organization number.

[0080] Among them, the temporary table is a database object used to temporarily store temporary data and intermediate data. The provincial temporary table corresponds to the provincial agency number one by one and is used to store the reporting data associated with the corresponding provincial agency number.

[0081] In practical applications, a stored procedure may be predefined to construct multiple provincial temporary tables, and the provincial institution number corresponds to the provincial temporary table one by one. Receive the incoming parameters, and extract the corresponding data to be verified according to the customer type and verification method in the incoming parameters. Specifically, the reporting data model to be screened can be determined according to the customer type, and the data to be verified can be screened from the reporting data model with reference to the above method for screening out the data to be verified from the reported data. The specific implementation process refers to the above embodiment to determine the data screening range according to the verification method in the incoming parameters, and extract data according to the data screening range, which will not be repeated here. According to the provincial institution number, the extracted data to be verified is stored in the corresponding provincial temporary table. In this way, by passing in parameters, the splitting and storage of the data to be verified in the temporary table is realized.

[0082] In this embodiment, the data to be verified is filtered out by passing in parameters and split and stored in the corresponding provincial temporary table, which can reduce the possibility of repeated calculation and data processing during each query, and is conducive to improving the efficiency of subsequent data verification.

[0083] In an exemplary embodiment, after identifying the problem data that meets the preset supplementary reporting conditions in the data to be verified, it also includes writing the problem data into a constructed problem zipper table and recording the effective time range of the problem data.

[0084] Among them, the zipper table is a data model used to record all the change information of things from the beginning to the current state.

[0085] In practical applications, a problem zipper table can be pre-built to store problem data, including rule number, transaction serial number (unique identifier of data), problem field, provincial agency number, batch number, verification time, start date and end date, etc. After identifying the problem data, the problem data is written into the problem zipper table, and its start date is determined to be the verification date, and the end date is determined to be a preset date, such as "9999-12-31". This effectively manages historical problem data and the latest problem data, and facilitates querying snapshots of problem data at a specific time point. Exemplarily, through the rule "Insured address cannot be empty" corresponding to the rule number "A01", a problem data "transaction serial number: '001'; policy number: '2024ABC001'; customer number '000000'; street: 'NULL'" is identified and stored in the problem zipper table.

[0086] In this embodiment, the complete verification record of the problem data is stored through the zipper storage method of the constructed problem zipper table, which is conducive to timely processing of the problem data to meet the data reporting requirements and accelerate the data governance process.

[0087] In other embodiments, based on the submitted data, statistics are conducted on the total amount of submitted data corresponding to different customer types under different verification methods, as well as the amount of problematic data. This may also include: pre-building an auxiliary data model of an indicator rule mapping table for data authenticity measurement, including a primary indicator: customer type (policyholder, insured, beneficiary) and a secondary indicator: verification method (full duplicate check, quarterly duplicate check, batch duplicate check). The indicator rule mapping table is associated with the problem zipper table, and the customer's transaction serial number is used as the unique identifier of the data to perform deduplication statistics on the amount of problematic data under multiple levels of indicators, thereby obtaining the total amount of submitted data corresponding to different customer types under different verification methods, as well as the amount of problematic data.

[0088] In order to improve the efficiency of data supplementation processing, in an exemplary embodiment, before determining the supplementation method of the problematic data, Figure 4 As shown, the method further includes S820 to S860. Wherein:

[0089] S820, filter out target question data corresponding to a preset rule number from the question zipper table.

[0090] In practical applications, considering that the reported data may be delayed due to factors such as network delays and bandwidth limitations during the transmission process, resulting in errors in the reported data, it is necessary to identify and mark such data to facilitate data correction and re-reporting. Therefore, a scenario configuration table can be pre-built, including rule number, customer type, field name, and rule identification conditions. After identifying the problem data in the reported data and storing it in the problem zipper table, the problem zipper table can be associated with the rule number to filter out the target problem data that matches the rule number.

[0091] S840, identifying the reason for supplementary reporting of the target problem data according to the preset rule identification conditions, and obtaining a supplementary reporting reason identification result.

[0092] The preset rule identification condition may be identifying a preset field value. The supplementary reporting reason identification result includes the supplementary reporting reason of the target problem data.

[0093] In actual applications, due to data link delay, the reported data is incomplete or inaccurate. For example, if the values ​​of some fields are missing, the missing values ​​are filled with preset field values. During identification, the preset rule identification condition can be to use the preset field value as a tag to match the target problem data. When the target problem data matches the tag, it is determined that the reason for the supplementary report of the target data is the data link delay. If they do not match, the target data does not belong to delayed data, and the reason for the supplementary report of the target problem data can be identified according to the rule number. The identification result of the reason for the supplementary report is obtained.

[0094] S860, determining a label of the reason for supplementary reporting of the target problem data according to the result of the identification of the reason for supplementary reporting, and adding the label of the reason for supplementary reporting to the target problem data.

[0095] Among them, the supplementary reporting reason label may include an information delay label and a non-information delay label.

[0096] In practical applications, according to the supplementary reporting reason of the target problem data in the supplementary reporting reason identification result, a supplementary reporting reason label is added to the target problem data. Specifically, the supplementary reporting reason label corresponding to the target problem data whose supplementary reporting reason is data link delay is an information delay label, and other target problem data are non-information delay labels, and the corresponding supplementary reporting reason labels are added to the problem zipper table.

[0097] In this embodiment, the problem data is classified by rule number, and the reason for supplementary reporting of the problem data is identified according to the rule identification condition, and a supplementary reporting reason label is added to the problem data to facilitate corresponding supplementary reporting processing for different supplementary reporting reasons, which is conducive to improving data supplementary reporting efficiency.

[0098] In an exemplary embodiment, the supplementary reporting reason tag includes an information delay tag and a non-information delay tag, such as Figure 5As shown, S500 includes S520 to S540. Among them:

[0099] S520, when the supplementary reporting reason label of the problem data is an information delay label, determine that the supplementary reporting method of the problem data corresponding to the information delay label is incremental supplementary reporting, add the problem data corresponding to the information delay label to the incremental reporting sequence, and perform incremental supplementary reporting processing.

[0100] In practical applications, the problem data corresponding to the information delay label can be extracted from the problem zipper table, and a data transaction can be generated and added to the daily incremental queue for data synchronization. The data can then be reported as a new business transaction trigger to implement incremental reporting.

[0101] In other embodiments, a scheduled data verification task may be set to perform the above-mentioned receiving and submitting data and input parameters daily, screen out the data to be verified that matches the verification method and customer type from the submitted data, store the data to be verified into a constructed provincial temporary table corresponding to the provincial agency number according to the provincial agency number, determine the verification type according to the input parameters, match the verification statement template and verification parameters that match the verification type from the constructed rule configuration table, generate a verification statement based on the verification statement template and the verification parameters, execute the verification statement, verify the data to be verified, identify the problem data in the data to be verified that meets the preset supplementary reporting conditions, write the problem data into the constructed problem zipper table, and record the effective time range of the problem data, the steps in the embodiment, thereby realizing the T-day inspection of the submitted data and the T+1 day correction and supplementary reporting, thereby improving the real-time nature of the correction and supplementary reporting of the problem data.

[0102] S540, when the supplementary reporting reason label of the problem data is a non-information delay label, determine that the supplementary reporting method of the problem data corresponding to the non-information delay label is periodic supplementary reporting, add the problem data corresponding to the non-information delay label to the weekly supplementary reporting sequence, and perform weekly supplementary reporting processing.

[0103] The weekly supplementary reporting sequence can be the incremental data reported on a weekly basis, which is used to update and supplement the initial reporting data.

[0104] In practical applications, the problem data corresponding to the information delay label, such as missing field values, can be extracted from the problem zipper table, and the problem data can be automatically corrected through the source data correction system to generate data transactions and add them to the weekly supplementary reporting sequence for unified supplementary reporting processing.

[0105] In this embodiment, the supplementary reporting method corresponding to the problem data is determined according to the supplementary reporting reason label of the problem data, and the corresponding supplementary reporting processing is performed, thereby improving the efficiency and accuracy of data supplementary reporting.

[0106] In an exemplary embodiment, after the problem data is supplemented, the process further includes S920 to S940.

[0107] S920, performing data verification on the problematic data after supplementary reporting to obtain verification results.

[0108] S940: Update the problem zipper table based on the verification result.

[0109] In practical applications, data verification is performed on the problematic data after supplementary reporting.

[0110] The problem data of incremental supplementary reports are verified by daily inspection, and the problem data of weekly supplementary reports are verified according to the supplementary reporting batches. The data verification method refers to the above method of identifying the problem data that meets the preset supplementary reporting conditions in the data to be verified. The specific implementation process refers to receiving the incoming parameters, determining the verification type according to the incoming parameters, matching the verification statement template and verification parameters that match the verification type from the constructed rule configuration table, generating the verification statement based on the verification statement template and verification parameters, executing the verification statement, verifying the data to be verified, and identifying the implementation process of the problem data, which will not be repeated here. The verification result is obtained through data verification.

[0111] If the verification result indicates that the problem data after the supplementary report does not hit the rules, the end date of the problem data in the problem zipper table is updated to the verification date; if the verification result indicates that the problem data after the supplementary report still hits the rules, the problem data in the problem zipper table is deleted, the problem data after the supplementary report is added to the problem zipper table, and its end date is set to the preset date.

[0112] In this embodiment, the problem data after the supplementary reporting process is verified, and the problem zipper table is updated based on the verification result, so that the problem data is automatically cleared and the management efficiency of the problem data is improved.

[0113] In order to make a clearer description of the reporting data processing method provided by the present application, a specific embodiment is described below, and the specific embodiment includes the following steps:

[0114] S1, receiving the reported data and input parameters, the input parameters include verification method, provincial agency number and customer type, filtering out the data to be verified that matches the verification method and customer type from the reported data; according to the provincial agency number, storing the data to be verified into the constructed provincial temporary table corresponding to the provincial agency number.

[0115] S2, based on the input parameters, determines the verification type, matches the verification statement template and verification parameters that match the verification type from the constructed rule configuration table, generates a verification statement based on the verification statement template and verification parameters, executes the verification statement, verifies the data to be verified, and identifies the problematic data in the data to be verified that meets the preset supplementary reporting conditions.

[0116] S3, write the problem data into the constructed problem zipper table and record the valid time range of the problem data.

[0117] S4, based on the reported data, count the total amount of reported data corresponding to different customer types under different verification methods, and the number of problematic data; for each verification method, determine the proportion of the number of problematic data relative to the total amount of problematic data based on the number of problematic data and the total amount of data for each customer type under the verification method; generate a data authenticity calculation report based on the proportion of problematic data corresponding to different customer types under different verification methods.

[0118] S5, filter out the target problem data corresponding to the preset rule number from the problem zipper table, identify the reason for supplementary reporting of the target problem data according to the preset rule recognition conditions, obtain the supplementary reporting of smoking addiction recognition result, determine the supplementary reporting reason label of the target problem data according to the supplementary reporting reason recognition result, and add the supplementary reporting reason label to the target problem data.

[0119] S6. When the reporting reason label of the problem data is an information delay label, determine that the reporting method of the problem data corresponding to the information delay label is incremental reporting, add the problem data corresponding to the information delay label to the incremental reporting sequence, and perform incremental reporting. When the reporting reason label of the problem data is a non-information delay label, determine that the reporting method of the problem data corresponding to the non-information delay label is periodic reporting, add the problem data corresponding to the non-information delay label to the weekly reporting sequence, and perform weekly reporting.

[0120] S7, perform data verification on the problem data after the supplementary report processing, obtain the verification result, and update the problem zipper table based on the verification result.

[0121] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0122] In an exemplary embodiment, Figure 6 As shown, a reporting data processing device 600 is provided, comprising: a data screening module 610, a verification statement generating module 620, a data verification module 630 and a data supplementary reporting module 640, wherein:

[0123] The data screening module 610 is used to receive the submitted data and the input parameters, and screen the data to be verified from the submitted data according to the input parameters.

[0124] The verification statement generation module 620 is used to determine the verification type according to the input parameters, match the verification statement template and verification parameters that match the verification type from the constructed rule configuration table; and generate a verification statement based on the verification statement template and verification parameters.

[0125] The data verification module 630 is used to execute verification statements, verify the data to be verified, and identify problematic data in the data to be verified that meets preset supplementary reporting conditions.

[0126] The data supplementary reporting module 640 is used to determine a supplementary reporting method for the problematic data and perform supplementary reporting processing on the problematic data according to the supplementary reporting method.

[0127] In an exemplary embodiment, the reporting data processing device also includes an authenticity measurement module 650, which is used to count the total amount of reported data and the number of problem data corresponding to different customer types under different verification methods based on the reported data; for each verification method, based on the number of problem data and the total amount of data for each customer type under the verification method, determine the proportion of the number of problem data relative to the total amount of problem data; based on the proportion of problem data corresponding to different customer types under different verification methods, generate a data authenticity measurement report.

[0128] In an exemplary embodiment, the data screening module 610 is also used to screen out the data to be verified that matches the verification method and customer type from the reported data; and store the data to be verified in a constructed provincial temporary table corresponding to the provincial agency number according to the provincial agency number.

[0129] In an exemplary embodiment, the reporting data processing device 600 further includes a problem data storage module 660 for writing the problem data into a constructed problem zipper table and recording the effective time range of the problem data.

[0130] In an exemplary embodiment, the reporting data processing device 600 also includes a label adding module 670, which is used to filter out target problem data corresponding to a preset rule number from the problem zipper table; identify the reason for supplementary reporting of the target problem data according to the preset rule recognition conditions, and obtain a supplementary reporting of smoking addiction identification result; determine the supplementary reporting reason label of the target problem data according to the supplementary reporting reason identification result, and add the supplementary reporting reason label to the target problem data.

[0131] In an exemplary embodiment, the data supplementary reporting module 640 is also used to determine that the supplementary reporting method of the problem data corresponding to the information delay tag is incremental reporting when the supplementary reporting reason tag of the problem data is an information delay tag, and add the problem data corresponding to the information delay tag to the incremental reporting sequence for incremental reporting processing; when the supplementary reporting reason tag of the problem data is a non-information delay tag, determine that the supplementary reporting method of the problem data corresponding to the non-information delay tag is periodic reporting, and add the problem data corresponding to the non-information delay tag to the weekly reporting sequence for weekly reporting processing.

[0132] In an exemplary embodiment, the data verification module 630 is also used to perform data verification on the problematic data after the supplementary report processing to obtain a verification result.

[0133] The problem data storage module 660 is also used to update the problem zipper table based on the verification result.

[0134] Each module in the above-mentioned reporting data processing device 600 can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module above.

[0135] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 7As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for reporting data processing is implemented.

[0136] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0137] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps in any one of the above-mentioned reporting data processing method embodiments are implemented.

[0138] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in any one of the above-mentioned reporting data processing method embodiments are implemented.

[0139] In one embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the steps in any one of the above-mentioned reporting data processing method embodiments.

[0140] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0141] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0142] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0143] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for processing reported data, characterized in that: The method comprises: Receiving submitted data and input parameters, and filtering out data to be verified from the submitted data according to the input parameters; Determine the verification type according to the input parameters, and match the verification statement template and verification parameters that match the verification type from the constructed rule configuration table; Generate a verification statement based on the verification statement template and the verification parameters; Execute the verification statement to verify the data to be verified, and identify problematic data in the data to be verified that meets the preset supplementary reporting conditions; Determine a supplementary reporting method for the problematic data, and perform supplementary reporting processing on the problematic data according to the supplementary reporting method.

2. The method according to claim 1, characterized in that The input parameters include the customer type and the verification method; after identifying the problematic data that meets the preset supplementary reporting conditions in the data to be verified, the method further includes: Based on the submitted data, the total amount of submitted data corresponding to different customer types under different verification methods and the amount of problematic data are counted; For each verification method, based on the number of problematic data and the total amount of data for each customer type under the verification method, determine the ratio of the number of problematic data to the total amount of data; Generate a data authenticity calculation report based on the proportion of problematic data corresponding to different customer types under different verification methods.

3. The method according to claim 2, characterized in that The input parameters include verification method, provincial institution number and customer type, and the filtering of the data to be verified from the submitted data according to the input parameters includes: Filtering the data to be verified that matches the verification method and the customer type from the submitted data; According to the provincial organization number, the data to be verified is stored in the constructed provincial temporary table corresponding to the provincial organization number.

4. The method according to claim 3, characterized in that After identifying the problematic data that meets the preset supplementary reporting conditions in the data to be verified, the method further includes: The question data is written into the constructed question zipper table, and the valid time range of the question data is recorded.

5. The method according to claim 4, characterized in that Before determining the supplementary reporting method of the problem data, the method further includes: Filtering out target question data corresponding to a preset rule number from the question zipper table; According to the preset rule recognition conditions, the supplementary report problem of the target problem data is identified to obtain the supplementary report problem recognition result; According to the supplementary problem identification result, a supplementary reporting reason label of the target problem data is determined, and the supplementary reporting reason label is added to the target problem data.

6. The method according to claim 5, characterized in that The supplementary reporting reason tag includes an information delay tag and a non-information delay tag, and the determining of the supplementary reporting method of the problem data and performing supplementary reporting processing on the problem data according to the supplementary reporting method include: In the case where the supplementary reporting reason tag of the problem data is an information delay tag, determining that the supplementary reporting method of the problem data corresponding to the information delay tag is incremental supplementary reporting, adding the problem data corresponding to the information delay tag to the incremental reporting sequence, and performing incremental supplementary reporting processing; When the reporting reason label of the problem data is a non-information delay label, the reporting method of the problem data corresponding to the non-information delay label is determined to be periodic reporting, and the problem data corresponding to the non-information delay label is added to the weekly reporting sequence for weekly reporting processing.

7. The method according to any one of claims 1 to 6, characterized in that: After the problem data is supplemented, the method further includes: Perform data verification on the problematic data after supplementary reporting to obtain verification results; Based on the verification result, the problem zipper table is updated.

8. A reporting data processing device, characterized in that: The device comprises: A data screening module, used for receiving submitted data and input parameters, and screening out data to be verified from the submitted data according to the input parameters; A verification statement generation module is used to determine the verification type according to the input parameters, match the verification statement template and verification parameters that match the verification type from the constructed rule configuration table; and generate a verification statement based on the verification statement template and the verification parameters; A data verification module, used to execute the verification statement, verify the data to be verified, and identify problematic data in the data to be verified that meets preset supplementary reporting conditions; The data supplementary reporting module is used to determine a supplementary reporting method for the problematic data and perform supplementary reporting processing on the problematic data according to the supplementary reporting method.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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