A data reporting method and device based on intelligent verification data, equipment, medium and program product

Abstract

This application relates to the field of database technology, and in particular to a data reporting method, apparatus, device, medium, and program product based on intelligent data verification. The method includes: in response to acquiring submitted data, loading a pre-configured set of verification rules based on the form identifier corresponding to the submitted data; querying corresponding historical data values ​​according to cross-validation rules, logically comparing the target field value with the historical data value, and generating a cross-validation result; extracting basic field values ​​as input parameters according to complex verification rules, performing multi-level logical judgment operations, and generating a complex rule verification result; performing data type and format matching verification on specified field values ​​in the submitted data according to format verification rules, and generating a format verification result; generating verification result data, and returning the verification result data to the client, wherein the verification result data is used to identify erroneous units in the submitted data. This method can improve the management efficiency and effectiveness of reported data.

Description

Technical Field

[0001] This application relates to the field of database technology, and in particular to a data reporting method, apparatus, device, medium, and program product based on intelligent verification data. Background Technology

[0002] Project management in the construction industry involves planning, organizing, coordinating, and controlling multiple elements such as project schedule, cost, quality, safety, personnel, materials, and equipment. In traditional management models, information from each stage is scattered across paper documents, spreadsheets, or isolated business systems, easily creating data silos and impacting management efficiency and decision-making accuracy.

[0003] In existing technologies, data collection for construction project management primarily relies on the following methods: on-site management personnel input construction logs, information on incoming materials, and equipment usage via mobile terminals or handheld devices; some construction sites deploy IoT devices such as sensors, cameras, and RFID tags to automatically collect data on environmental parameters, entry and exit records, and equipment operating status; simultaneously, enterprise resource planning (ERP) systems, labor real-name systems, and material management systems each generate and store business data. This heterogeneous data is typically uploaded to the project management system or cloud platform according to preset formats for basic data aggregation and storage. Regarding data verification, existing technologies have begun to incorporate intelligent methods. Common practices include: setting field-level verification conditions based on rule engines, such as data type, numerical range, and required fields; using cross-referencing of multi-source data for consistency checks, such as automatically matching reported material usage with purchase receipts to determine for any abnormal deviations; and using image recognition technology to count incoming materials such as steel bars and blocks and verifying the quantities against the documents. Furthermore, some systems use preset thresholds to monitor key indicators such as construction progress and cost expenditures in real time, triggering alerts when collected data exceeds reasonable ranges. For process-related data such as personnel attendance and work process inspection reports, logical verification can be performed in conjunction with the workflow status to ensure that the data status meets the requirements of the current node.

[0004] However, current data verification methods have the following technical problems:

[0005] The current data management system is mainly used for general data reporting. The verification logic and inspection rules configured in the system have significant limitations and need to be optimized. Summary of the Invention

[0006] Therefore, it is necessary to provide a data reporting method, device, computer equipment, computer-readable storage medium, and computer program product based on intelligent verification data, which can set complex verification rules and improve the management efficiency and effectiveness of reported data, in order to address the above-mentioned technical problems.

[0007] Firstly, this application provides a data reporting method based on intelligent verification data. The method includes:

[0008] In response to obtaining the data to be filled in, a pre-configured set of validation rules is loaded according to the form identifier corresponding to the data to be filled in. The set of validation rules includes cross-validation rules, complex validation rules and format validation rules.

[0009] According to the cross-validation rules, the corresponding historical data values ​​are queried from the historical database, and the target field values ​​in the filled data are logically compared with the historical data values ​​to generate cross-validation results;

[0010] Based on the complex verification rules, multiple basic field values ​​from the data entered are extracted as input parameters, multi-level logical judgment operations are performed, and complex rule verification results are generated.

[0011] According to the format validation rules, the data type and format matching validation is performed on the specified field values ​​in the filled data to generate a format validation result.

[0012] The cross-validation results, the complex rule validation results, and the format validation results are merged to generate validation result data, which is then returned to the client. The validation result data is used to identify erroneous units in the data entered.

[0013] In one embodiment, the step of querying the corresponding historical data value from the historical database according to the cross-validation rules, and logically comparing the target field value in the filled data with the historical data value to generate a cross-validation result includes:

[0014] Parse the cross-validation rules to obtain the target field identifier, database query statement, and comparison operators;

[0015] Execute the database query statement to extract the historical data value from the historical database;

[0016] The field value corresponding to the target field identifier in the filled data is compared with the historical data value according to the comparison operator. If the comparison result is false, an error record containing the target field identifier is generated.

[0017] In one embodiment, the step of performing data type and format matching verification on the specified field values ​​in the filled data according to the format verification rules, and generating a format verification result, includes:

[0018] Parse the complex validation rules to obtain a nested expression containing multiple basic field identifiers and logical judgment operators;

[0019] Extract the field values ​​corresponding to each basic field identifier from the data entered, substitute them into the nested expression to evaluate layer by layer, and obtain the evaluation result;

[0020] In response to the evaluation result being false, an error record is generated, the error record including a set of associated field identifiers.

[0021] In one embodiment, the step of performing data type and format matching verification on the specified field values ​​in the filled data according to the format verification rules, and generating a format verification result, includes:

[0022] Parse the format validation rules to obtain the mapping relationship between field identifiers and preset format types;

[0023] According to the mapping relationship, the format matching and verification of each field value in the data is performed in sequence. The preset format types include integer type, amount type, percentage type, date type, text length type or enumeration value type.

[0024] In response to any field value not matching the corresponding preset format type, an error record containing the identifier of that field is generated.

[0025] In one embodiment, after merging the cross-validation result, the complex rule validation result, and the format validation result to generate validation result data, and returning the validation result data to the client, the validation result is used to identify erroneous units in the entered data, and the process further includes:

[0026] If the verification result data does not contain any error records, the entered data is written into the business data table, and a log record indicating that the verification has passed is generated.

[0027] When the verification result data contains error records, the write operation is abandoned, and the field identifiers and corresponding error types in each error record are encapsulated into preset verification result data.

[0028] In one embodiment, in response to obtaining the filled-in data, loading a pre-configured set of validation rules based on the form identifier corresponding to the filled-in data, the set of validation rules including cross-validation rules, complex validation rules, and format validation rules, further includes:

[0029] Based on the priority identifier of each verification rule in the verification rule set, the execution order of cross-validation, complex rule validation, and format validation is determined;

[0030] The corresponding verification engines are called sequentially according to the execution order to perform verification, and the error fields identified in the previous verification results are recorded as cached data.

[0031] During subsequent verification, the error fields already recorded in the cached data are skipped, and only the fields that fail verification are verified.

[0032] Secondly, this application also provides a data reporting device based on intelligent verification data. The device includes:

[0033] The preloading module is used to load a pre-configured set of validation rules in response to the acquisition of data and according to the form identifier corresponding to the data. The set of validation rules includes cross-validation rules, complex validation rules, and format validation rules.

[0034] The cross-validation module is used to query the corresponding historical data value from the historical database according to the cross-validation rules, and to logically compare the target field value in the filled data with the historical data value to generate the cross-validation result.

[0035] The complex rule verification module is used to extract multiple basic field values ​​from the filled data as input parameters according to the complex verification rules, perform multi-level logical judgment operations, and generate complex rule verification results.

[0036] The format verification module is used to perform data type and format matching verification on the specified field values ​​in the filled data according to the format verification rules, and generate format verification results.

[0037] The verification result module is used to merge the cross-validation result, the complex rule validation result, and the format validation result to generate verification result data, and return the verification result data to the client. The verification result data is used to identify erroneous units in the data entered.

[0038] In one embodiment, the cross-validation module includes:

[0039] The first parsing module is used to parse the cross-validation rules and obtain the target field identifier, database query statement and comparison operator;

[0040] The query module is used to execute the database query statement and extract the historical data value from the historical database;

[0041] The field comparison module is used to compare the field value corresponding to the target field identifier in the filled data with the historical data value according to the comparison operator. If the comparison result is false, an error record containing the target field identifier is generated.

[0042] In one embodiment, the format verification module includes:

[0043] The second parsing module is used to parse the complex verification rules and obtain a nested expression containing multiple basic field identifiers and logical judgment operators;

[0044] The layer-by-layer evaluation module is used to extract field values ​​corresponding to the identifiers of each basic field from the data entered, substitute them into the nested expression to perform layer-by-layer evaluation, and obtain the evaluation result.

[0045] The record generation module is used to generate an error record in response to the evaluation result being false. The error record includes a set of associated field identifiers.

[0046] In one embodiment, the format verification module includes:

[0047] The third parsing module is used to parse the format validation rules and obtain the mapping relationship between field identifiers and preset format types;

[0048] The format matching module is used to perform format matching and verification on the field values ​​in the data to be filled in according to the mapping relationship. The preset format types include integer type, amount type, percentage type, date type, text length type or enumeration value type.

[0049] The field identifier module is used to generate an error record containing the field identifier in response to any field value not matching the corresponding preset format type.

[0050] In one embodiment, after the verification result module, the system further includes:

[0051] The verification pass module is used to write the filled data into the business data table and generate a verification pass log record when the verification result data does not contain error records.

[0052] The encapsulation module is used to abandon the write operation when the verification result data contains error records, and encapsulate the field identifiers and corresponding error types in each error record into preset verification result data.

[0053] In one embodiment, after the preloading module, the system further includes:

[0054] The priority module is used to determine the execution order of cross-validation, complex rule validation, and format validation based on the priority identifier of each validation rule in the validation rule set.

[0055] The caching module is used to sequentially call the corresponding verification engines to perform verification according to the execution order, and record the identified error fields in the previous verification results as cached data;

[0056] The post-verification module is used to skip the error fields already recorded in the cached data during the post-verification process and only verify the fields that fail the verification.

[0057] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of a data reporting method based on intelligent verification data as described in any embodiment of the first aspect.

[0058] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of a data reporting method based on intelligent verification data as described in any embodiment of the first aspect.

[0059] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of a data reporting method based on intelligent verification data as described in any embodiment of the first aspect.

[0060] The aforementioned data reporting method, apparatus, computer equipment, storage medium, and computer program product based on intelligent verification data, derived from the technical features in the embodiments, can achieve the following beneficial effects to address the technical problems in the background art:

[0061] This application provides a data reporting method based on intelligent data verification, comprising: in response to obtaining data to be filled in, loading a pre-configured set of verification rules according to the form identifier corresponding to the data to be filled in, the set of verification rules including cross-validation rules, complex validation rules, and format validation rules; according to the cross-validation rules, querying the corresponding historical data values ​​from the historical database, and logically comparing the target field values ​​in the data to be filled in with the historical data values ​​to generate a cross-validation result; according to the complex validation rules, extracting multiple basic field values ​​in the data to be filled in as input parameters, performing multi-level logical judgment operations to generate a complex rule validation result; according to the format validation rules, performing data type and format matching validation on the specified field values ​​in the data to be filled in, generating a format validation result; merging the cross-validation result, the complex rule validation result, and the format validation result to generate validation result data, and returning the validation result data to the client, wherein the validation result data is used to identify erroneous units in the data to be filled in. In implementation, by loading the three types of pre-configured rule sets of cross-validation, complex validation, and format validation, multi-dimensional automated validation of the data to be filled in is achieved. Cross-validation logically compares the entered data with the actual data in the historical database, helping to improve data consistency and business continuity. Complex validation extracts multiple basic field values ​​to perform multi-layered logical judgments, handling complex business scenarios such as conditional branches and nested judgments, helping to compensate for the shortcomings of simple size comparison rules. Format validation matches and verifies the data type and format of fields, helping to improve the standardization of data entry. The results of the three types of validation are merged and returned to the client in a unified manner, which can accurately locate erroneous units in the entered data. This method decouples business rules from code logic into configurable validation rules, realizing flexible arrangement and dynamic loading of validation logic. It can adapt to the validation needs of different business scenarios without modifying the code, significantly reducing the maintenance cost and error rate of the data reporting system, and improving the quality of data entry and validation efficiency. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0063] Figure 1 This is a schematic diagram of the first process of a data reporting method based on intelligent verification data in one embodiment;

[0064] Figure 2This is a schematic diagram of the second process of a data reporting method based on intelligent verification data in another embodiment;

[0065] Figure 3 This is a schematic diagram of the third process of a data reporting method based on intelligent verification data in another embodiment;

[0066] Figure 4 This is a schematic diagram of the fourth process of a data reporting method based on intelligent verification data in another embodiment;

[0067] Figure 5 This is a schematic diagram of the fifth process of a data reporting method based on intelligent verification data in another embodiment;

[0068] Figure 6 This is a schematic diagram of the sixth process of a data reporting method based on intelligent verification data in another embodiment;

[0069] Figure 7 This is a structural block diagram of a data reporting device based on intelligent verification data in one embodiment;

[0070] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0071] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0072] In one embodiment, such as Figure 1 As shown, a data reporting method based on intelligent verification data is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0073] Step 102: In response to obtaining the data to be filled in, load a pre-configured set of validation rules according to the form identifier corresponding to the data to be filled in. The set of validation rules includes cross-validation rules, complex validation rules and format validation rules.

[0074] The data submitted can refer to the set of business data submitted by users through client-side form pages, such as the project number, contract amount, and labor cost values ​​in a project cost reporting scenario. Form identifiers can refer to unique identification information used to distinguish different business forms, such as form IDs or business type codes. Each form identifier corresponds to an independent set of validation rule configurations. Pre-configured validation rule sets refer to a set of validation logic pre-configured and persistently stored in the backend by system administrators or business personnel, which takes effect dynamically without code modification. Validation rule sets can specifically include three types of rules: cross-validation rules for comparing submitted data with existing data in the historical database; complex validation rules for nested logical judgments on multiple fields; and format validation rules for verifying the data type and format specifications of the fields.

[0075] Step 104: According to the cross-validation rules, query the corresponding historical data values ​​from the historical database, and logically compare the target field values ​​in the filled data with the historical data values ​​to generate cross-validation results.

[0076] Cross-validation rules refer to the validation logic that compares a field value in the currently submitted data with existing data in the historical database. Its core purpose is to ensure logical consistency and business continuity between the submitted data and existing historical data. The historical database can refer to persistent storage space that stores previously submitted and validated business data, such as contract information tables, historical monthly output value tables, and project basic information tables. The target field value can refer to the numerical value of the specific field in the submitted data that needs to participate in cross-validation, such as the cumulative completed output value submitted for the current month.

[0077] Step 106: Based on the complex verification rules, extract multiple basic field values ​​from the data being filled in as input parameters, perform multi-level logical judgment operations, and generate complex rule verification results.

[0078] Step 108: According to the format verification rules, perform data type and format matching verification on the specified field values ​​in the filled data, and generate format verification results.

[0079] Step 1010: Merge the cross-validation results, the complex rule validation results, and the format validation results to generate validation result data, and return the validation result data to the client. The validation result data is used to identify erroneous units in the data entered.

[0080] In the above-mentioned data reporting method based on intelligent verification data, by reasonably deducing the technical features in the embodiments, the following beneficial effects are achieved, which can solve the technical problems raised in the background art:

[0081] This application provides a data reporting method based on intelligent data verification, comprising: in response to obtaining data to be filled in, loading a pre-configured set of verification rules according to the form identifier corresponding to the data to be filled in, the set of verification rules including cross-validation rules, complex validation rules, and format validation rules; according to the cross-validation rules, querying the corresponding historical data values ​​from the historical database, and logically comparing the target field values ​​in the data to be filled in with the historical data values ​​to generate a cross-validation result; according to the complex validation rules, extracting multiple basic field values ​​in the data to be filled in as input parameters, performing multi-level logical judgment operations to generate a complex rule validation result; according to the format validation rules, performing data type and format matching validation on the specified field values ​​in the data to be filled in, generating a format validation result; merging the cross-validation result, the complex rule validation result, and the format validation result to generate validation result data, and returning the validation result data to the client, wherein the validation result data is used to identify erroneous units in the data to be filled in. In implementation, by loading the three types of pre-configured rule sets of cross-validation, complex validation, and format validation, multi-dimensional automated validation of the data to be filled in is achieved. Cross-validation logically compares the entered data with the actual data in the historical database, helping to improve data consistency and business continuity. Complex validation extracts multiple basic field values ​​to perform multi-layered logical judgments, handling complex business scenarios such as conditional branches and nested judgments, helping to compensate for the shortcomings of simple size comparison rules. Format validation matches and verifies the data type and format of fields, helping to improve the standardization of data entry. The results of the three types of validation are merged and returned to the client in a unified manner, which can accurately locate erroneous units in the entered data. This method decouples business rules from code logic into configurable validation rules, realizing flexible arrangement and dynamic loading of validation logic. It can adapt to the validation needs of different business scenarios without modifying the code, significantly reducing the maintenance cost and error rate of the data reporting system, and improving the quality of data entry and validation efficiency.

[0082] In one embodiment, it can be as follows Figure 2 As shown, step 104 includes:

[0083] Step 202: Parse the cross-validation rules to obtain the target field identifier, database query statement, and comparison operators.

[0084] Step 204: Execute the database query statement to extract the historical data value from the historical database.

[0085] Step 206: Compare the field value corresponding to the target field identifier in the filled data with the historical data value according to the comparison operator. If the comparison result is false, generate an error record containing the target field identifier.

[0086] In this embodiment, by parsing the database query statements and comparison operators in the cross-validation rules, data is automatically extracted from the historical database and logically compared with the fields to be filled in, eliminating the need for manual verification. When the comparison result is false, an error record containing the target field identifier is generated, which helps to achieve automatic consistency verification between the current data and historical data, and effectively avoids data conflicts and logical anomalies.

[0087] In one embodiment, it can be as follows Figure 3 As shown, step 108 includes:

[0088] Step 302: Parse the complex validation rules to obtain a nested expression containing multiple basic field identifiers and logical judgment operators.

[0089] Step 304: Extract the field values ​​corresponding to each basic field identifier from the data entered, substitute them into the nested expression to evaluate layer by layer, and obtain the evaluation result.

[0090] Step 306: In response to the evaluation result being false, an error record is generated, the error record including a set of associated field identifiers.

[0091] In this embodiment, by parsing nested expressions and extracting multiple basic field values ​​for layer-by-layer evaluation, automated validation of complex business logic is achieved. When the evaluation result is false, an error record containing a set of associated field identifiers is generated. This can handle complex judgment scenarios involving multiple fields and conditions, solving complex validation needs that cannot be covered by simple size comparisons.

[0092] In one embodiment, it can be as follows Figure 4 As shown, step 108 includes:

[0093] Step 402: Parse the format validation rules to obtain the mapping relationship between field identifiers and preset format types.

[0094] Step 404: According to the mapping relationship, perform format matching and verification on the field values ​​in the data to be filled in. The preset format types include integer type, amount type, percentage type, date type, text length type or enumeration value type.

[0095] Step 406: In response to any field value not matching the corresponding preset format type, generate an error record containing the identifier of that field.

[0096] In this embodiment, the mapping relationship between field identifiers and preset format types is parsed to automatically perform format matching and verification on each field value in sequence. When a field value does not match the preset type, an error record containing the field identifier is generated immediately, covering a variety of commonly used data formats such as integers, amounts, and dates, ensuring the standardization and accuracy of the data entered from the source.

[0097] In one embodiment, it can be as follows Figure 5 As shown, after step 1010, the method further includes:

[0098] Step 502: If the verification result data does not contain any error records, write the data to the business data table and generate a log record indicating that the verification has passed.

[0099] Step 504: When the verification result data contains error records, abandon the write operation and encapsulate the field identifiers and corresponding error types in each error record into preset verification result data.

[0100] In this embodiment, the system automatically decides whether to write data to the database or abandon the write operation based on whether the verification result contains erroneous records. If the verification passes, the data is persistently stored and logged to ensure data traceability; if the verification fails, the data is rejected from being written to the database, and the error field identifier and type are encapsulated and returned, effectively preventing dirty data from being written to the database and ensuring the integrity and consistency of business data.

[0101] In one embodiment, it can be as follows Figure 6 As shown, after step 102, the method further includes:

[0102] Step 602: Determine the execution order of cross-validation, complex rule validation, and format validation based on the priority identifier of each validation rule in the validation rule set.

[0103] Step 604: Call the corresponding verification engine sequentially according to the execution order to perform verification, and record the identified error fields in the previous verification results as cached data.

[0104] Step 606: During the subsequent verification process, skip the error fields already recorded in the cached data and only verify the fields that failed the verification.

[0105] In this embodiment, the execution order is determined based on the priority identifier of the verification rules, and the error fields identified by the preceding verification are cached so that subsequent verifications skip these fields. This mechanism avoids duplicate verification of the same field, reduces the unnecessary computational overhead of the verification engine, and improves the overall execution efficiency in multi-rule verification scenarios.

[0106] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0107] Based on the same inventive concept, this application also provides a data reporting device based on intelligent verification data for implementing the data reporting method based on intelligent verification data described above. The solution provided by this device is similar to the implementation described in the above method. Therefore, the specific limitations of one or more embodiments of a data reporting device based on intelligent verification data provided below can be found in the limitations of the data reporting method based on intelligent verification data described above, and will not be repeated here.

[0108] In one embodiment, such as Figure 7 As shown, a data reporting device based on intelligent data verification is provided, including: a preloading module, a cross-validation module, a complex rule verification module, a format verification module, and a verification result module, wherein:

[0109] The preloading module is used to load a pre-configured set of validation rules in response to the acquisition of data and according to the form identifier corresponding to the data. The set of validation rules includes cross-validation rules, complex validation rules, and format validation rules.

[0110] The cross-validation module is used to query the corresponding historical data value from the historical database according to the cross-validation rules, and to logically compare the target field value in the filled data with the historical data value to generate the cross-validation result.

[0111] The complex rule verification module is used to extract multiple basic field values ​​from the filled data as input parameters according to the complex verification rules, perform multi-level logical judgment operations, and generate complex rule verification results.

[0112] The format verification module is used to perform data type and format matching verification on the specified field values ​​in the filled data according to the format verification rules, and generate format verification results.

[0113] The verification result module is used to merge the cross-validation result, the complex rule validation result, and the format validation result to generate verification result data, and return the verification result data to the client. The verification result data is used to identify erroneous units in the data entered.

[0114] In one embodiment, the cross-validation module includes:

[0115] The first parsing module is used to parse the cross-validation rules and obtain the target field identifier, database query statement and comparison operator;

[0116] The query module is used to execute the database query statement and extract the historical data value from the historical database;

[0117] The field comparison module is used to compare the field value corresponding to the target field identifier in the filled data with the historical data value according to the comparison operator. If the comparison result is false, an error record containing the target field identifier is generated.

[0118] In one embodiment, the format verification module includes:

[0119] The second parsing module is used to parse the complex verification rules and obtain a nested expression containing multiple basic field identifiers and logical judgment operators;

[0120] The layer-by-layer evaluation module is used to extract field values ​​corresponding to the identifiers of each basic field from the data entered, substitute them into the nested expression to perform layer-by-layer evaluation, and obtain the evaluation result.

[0121] The record generation module is used to generate an error record in response to the evaluation result being false. The error record includes a set of associated field identifiers.

[0122] In one embodiment, the format verification module includes:

[0123] The third parsing module is used to parse the format validation rules and obtain the mapping relationship between field identifiers and preset format types;

[0124] The format matching module is used to perform format matching and verification on the field values ​​in the data to be filled in according to the mapping relationship. The preset format types include integer type, amount type, percentage type, date type, text length type or enumeration value type.

[0125] The field identifier module is used to generate an error record containing the field identifier in response to any field value not matching the corresponding preset format type.

[0126] In one embodiment, after the verification result module, the system further includes:

[0127] The verification pass module is used to write the filled data into the business data table and generate a verification pass log record when the verification result data does not contain error records.

[0128] The encapsulation module is used to abandon the write operation when the verification result data contains error records, and encapsulate the field identifiers and corresponding error types in each error record into preset verification result data.

[0129] In one embodiment, after the preloading module, the system further includes:

[0130] The priority module is used to determine the execution order of cross-validation, complex rule validation, and format validation based on the priority identifier of each validation rule in the validation rule set.

[0131] The caching module is used to sequentially call the corresponding verification engines to perform verification according to the execution order, and record the identified error fields in the previous verification results as cached data;

[0132] The post-verification module is used to skip the error fields already recorded in the cached data during the post-verification process and only verify the fields that fail the verification.

[0133] The modules in the aforementioned data reporting device based on intelligent verification data can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0134] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8As shown, this computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network. When executed by the processor, the computer program implements a data reporting method based on intelligent verification data.

[0135] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0136] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0137] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0138] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0139] 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, data stored, data displayed, 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 related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0140] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), 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. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0141] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0142] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A data reporting method based on intelligent verification data, characterized in that, The method includes: In response to obtaining the data to be filled in, a pre-configured set of validation rules is loaded according to the form identifier corresponding to the data to be filled in. The set of validation rules includes cross-validation rules, complex validation rules and format validation rules. According to the cross-validation rules, the corresponding historical data values ​​are queried from the historical database, and the target field values ​​in the filled data are logically compared with the historical data values ​​to generate cross-validation results; Based on the complex verification rules, multiple basic field values ​​from the data entered are extracted as input parameters, multi-level logical judgment operations are performed, and complex rule verification results are generated. According to the format validation rules, the data type and format matching validation is performed on the specified field values ​​in the filled data to generate a format validation result. The cross-validation results, the complex rule validation results, and the format validation results are merged to generate validation result data, which is then returned to the client. The validation result data is used to identify erroneous units in the data entered.

2. The method according to claim 1, characterized in that, The step of querying the corresponding historical data value from the historical database according to the cross-validation rules, and logically comparing the target field value in the filled data with the historical data value to generate the cross-validation result includes: Parse the cross-validation rules to obtain the target field identifier, database query statement, and comparison operators; Execute the database query statement to extract the historical data value from the historical database; The field value corresponding to the target field identifier in the filled data is compared with the historical data value according to the comparison operator. If the comparison result is false, an error record containing the target field identifier is generated.

3. The method according to claim 1, characterized in that, The step of performing data type and format matching verification on the specified field values ​​in the filled data according to the format verification rules, and generating format verification results, includes: Parse the complex validation rules to obtain a nested expression containing multiple basic field identifiers and logical judgment operators; Extract the field values ​​corresponding to each basic field identifier from the data entered, substitute them into the nested expression to evaluate layer by layer, and obtain the evaluation result; In response to the evaluation result being false, an error record is generated, the error record including a set of associated field identifiers.

4. The method according to claim 1, characterized in that, The step of performing data type and format matching verification on the specified field values ​​in the filled data according to the format verification rules, and generating format verification results, includes: Parse the format validation rules to obtain the mapping relationship between field identifiers and preset format types; According to the mapping relationship, the format matching and verification of each field value in the data is performed in sequence. The preset format types include integer type, amount type, percentage type, date type, text length type or enumeration value type. In response to any field value not matching the corresponding preset format type, an error record containing the identifier of that field is generated.

5. The method according to claim 1, characterized in that, The process of merging the cross-validation results, the complex rule validation results, and the format validation results to generate validation result data, and returning the validation result data to the client, after the validation results are used to identify erroneous units in the entered data, further includes: If the verification result data does not contain any error records, the entered data is written into the business data table, and a log record indicating that the verification has passed is generated. When the verification result data contains error records, the write operation is abandoned, and the field identifiers and corresponding error types in each error record are encapsulated into preset verification result data.

6. The method according to claim 1, characterized in that, In response to obtaining the entered data, the pre-configured validation rule set is loaded according to the form identifier corresponding to the entered data. The validation rule set includes cross-validation rules, complex validation rules, and format validation rules, and further includes: Based on the priority identifier of each verification rule in the verification rule set, the execution order of cross-validation, complex rule validation, and format validation is determined; The corresponding verification engines are called sequentially according to the execution order to perform verification, and the error fields identified in the previous verification results are recorded as cached data. During subsequent verification, the error fields already recorded in the cached data are skipped, and only the fields that fail verification are verified.

7. A data reporting device based on intelligent verification data, characterized in that, The device includes: The preloading module is used to load a pre-configured set of validation rules in response to the acquisition of data and according to the form identifier corresponding to the data. The set of validation rules includes cross-validation rules, complex validation rules, and format validation rules. The cross-validation module is used to query the corresponding historical data value from the historical database according to the cross-validation rules, and to logically compare the target field value in the filled data with the historical data value to generate the cross-validation result. The complex rule verification module is used to extract multiple basic field values ​​from the filled data as input parameters according to the complex verification rules, perform multi-level logical judgment operations, and generate complex rule verification results. The format verification module is used to perform data type and format matching verification on the specified field values ​​in the filled data according to the format verification rules, and generate format verification results. The verification result module is used to merge the cross-validation result, the complex rule validation result, and the format validation result to generate verification result data, and return the verification result data to the client. The verification result data is used to identify erroneous units in the data entered.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

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

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

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