Field comparison method and device, equipment, storage medium and program product

Abstract

The invention provides a field comparison method and device, equipment, a storage medium and a program product, and relates to the field of big data. The method comprises the following steps: determining a to-be-compared field and a target comparison rule of the to-be-compared field according to field selection information and rule selection information input by a user; determining a to-be-compared data table selected by the user; and according to the target comparison rule, performing comparison processing on the to-be-compared fields in the to-be-compared data table, and outputting comparison processing result data of the to-be-compared fields. According to the method provided by the invention, the user-defined comparison fields and rules are introduced, so that business personnel can determine the fields needing to be compared and the comparison rules of the fields according to actual business requirements. Meanwhile, according to the method, business requirements and technology implementation are decoupled, and bottom-layer code modification and release do not need to be conducted by technical personnel.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of big data, and in particular to a field comparison method and device, equipment, storage medium and program product. BACKGROUND

[0002] In the financial field, especially in the management of public accounts, it is necessary to ensure that the customer account information recorded by the bank's internal system is highly consistent with the information of the external management department. There are many key fields between the bank's internal account information and the external business information that need to be continuously checked, such as enterprise name, legal representative identity, registered capital, registered address, and scope of business, etc. The accuracy of these fields is directly related to the authenticity of customer identity recognition, the effectiveness of anti-money laundering monitoring, and the reliability of credit risk assessment. The traditional manual comparison method not only consumes time and effort, is prone to errors, but also is difficult to achieve comprehensive coverage and regular checking in the face of millions of account sizes and high-frequency business scenarios.

[0003] At present, the common automatic comparison scheme in the industry is usually based on a pre-set, fixed rule data processing flow. A typical implementation is that the developer pre-encodes (Hardcode) the specific field set and its comparison rules that need to be compared (for example, the enterprise name must be completely consistent, or the registered capital value must be absolutely equal) in the data processing script or program. This kind of technical solution usually relies on a conventional ETL (Extraction, Transformation, Loading) process or a customized database script to execute. When the script is executed, it extracts data from the specified source database table, associates with the external data source, and performs comparison operations between fields based on the pre-set fixed logic, and finally outputs the comparison result. This method solves the problem of pure manual operation to a certain extent and realizes batch processing.

[0004] However, the above-mentioned prior art solution has significant defects. First, the comparison fields and logical rules are fixed in the technical system. Once the business requirements change, for example, a new comparison field (such as "shareholder information") needs to be added, a matching rule of a certain field (such as allowing a certain error threshold for registered capital) needs to be adjusted, or the comparison frequency needs to be changed, all of which must go through a cumbersome code modification, testing and verification, and system online release process. This process completely depends on technical personnel, is time-consuming and labor-intensive, and is inefficient, resulting in a serious lag in the speed of business response to market changes or new regulatory requirements. Secondly, this rigid architecture cannot support different branches to configure individual comparison requirements according to their own business characteristics, making it difficult to meet the diversified and refined business management scenarios, thereby limiting the promotion effectiveness and long-term sustainability of large-scale data management platforms. SUMMARY

[0005] The application provides a field comparison method, device, equipment, storage medium and program product, to solve the technical problems that the fixed comparison field and comparison rule in the prior art cannot adapt to diversified business requirements; when the business requirements change, technical personnel need to modify, test and put online the code, which is time-consuming, labor-consuming and low in efficiency.

[0006] In a first aspect, the application provides a field comparison method, which comprises:

[0007] According to the field selection information and rule selection information input by a user, a to-be-compared field is determined, and a target comparison rule of the to-be-compared field is determined;

[0008] A to-be-compared data table selected by the user is determined.

[0009] According to the target comparison rule, the to-be-compared field in the to-be-compared data table is subjected to comparison processing, and comparison processing result data of the to-be-compared field is output.

[0010] In a second aspect, the application provides a field comparison device, which comprises:

[0011] A determination module is configured to determine a to-be-compared field according to field selection information and rule selection information input by a user, and determine a target comparison rule of the to-be-compared field;

[0012] A to-be-compared data table selected by the user is determined.

[0013] A comparison module is configured to subject the to-be-compared field in the to-be-compared data table to comparison processing according to the target comparison rule, and output comparison processing result data of the to-be-compared field.

[0014] In a third aspect, the application provides an electronic device, which comprises a processor and a memory connected with the processor in communication;

[0015] The memory stores computer execution instructions.

[0016] The processor executes the computer execution instructions stored in the memory, so as to implement the method in the first aspect.

[0017] In a fourth aspect, the application provides a computer readable storage medium, which stores computer execution instructions, and the computer execution instructions are used to implement the method in the first aspect when executed by a processor.

[0018] In a fifth aspect, the application provides a computer program product, which comprises a computer program, and the computer program is used to implement the method in the first aspect when executed by a processor.

[0019] The field comparison method, apparatus, device, storage medium, and program product provided in this application have the following technical effects:

[0020] This method introduces user-defined comparison fields and rules, enabling business personnel to determine the fields to be compared and the comparison rules for these fields based on actual business needs. Furthermore, this method decouples business requirements from technical implementation, eliminating the need for technical personnel to modify and redeploy the underlying code. Attached Figure Description

[0021] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0022] Figure 1 Flowchart of the field comparison method provided in the embodiments of this application Figure One ;

[0023] Figure 2 Flowchart of the field comparison method provided in the embodiments of this application Figure Two ;

[0024] Figure 3 A flowchart illustrating the method for determining the fields to be compared and the target comparison rules provided in this application embodiment. Figure One ;

[0025] Figure 4 A flowchart illustrating the method for determining the fields to be compared and the target comparison rules provided in this application embodiment. Figure Two ;

[0026] Figure 5 This is a schematic diagram of the field comparison device provided in the embodiments of this application;

[0027] Figure 6 This is a schematic diagram of the electronic device structure provided in an embodiment of this application.

[0028] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0029] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0030] It should be noted that the field comparison method, device, equipment, storage medium and program product provided by the present application can be used in the field of big data, and can also be used in any field other than big data. The application field of the field comparison method, device, equipment, storage medium and program product in the present application is not limited.

[0031] In view of the technical problems in the prior art that the fixed comparison field and comparison rule cannot adapt to diversified business requirements, and when the business requirements change, the technical personnel need to modify, test and put into operation the code, which is time-consuming, labor-consuming and low in efficiency, the present inventors consider that the user can customize the fields to be compared and the comparison rules, and then compare the corresponding fields in the data table to be compared, so as to overcome the problems caused by the fixed comparison field and comparison rule in the prior art, and decouple the comparison field, comparison rule and technical implementation. Therefore, the technical concept of the present application is:

[0032] The user-inputted fields to be compared and the comparison rules of the fields inputted by the user are acquired. After determining the data tables selected by the user and requiring field comparison, the fields to be compared in the data tables are compared according to the comparison rules, and the comparison result is outputted.

[0033] The technical solutions of the present application and how the technical solutions of the present application solve the above technical problems will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes can not be described in detail in some embodiments. The embodiments of the present application will be described below with reference to the drawings.

[0034] Embodiment one

[0035] Figure 1 The field comparison method flow provided by the embodiment of the present application Figure One The method can be applied to a server, as shown in Figure 1 The method comprises the following steps.

[0036] S101, according to the field selection information and rule selection information inputted by the user, determining the fields to be compared and the target comparison rules of the fields to be compared;

[0037] In this step, the server can continuously collect and store the historical rule configuration operation records of all users, forming a large operation log library, and each record can include the configured fields, rule types, business department, configuration time, and other information. Further, the server regularly (e.g., daily) runs data analysis jobs and uses machine learning clustering algorithms (such as K-means) to mine and analyze these logs, automatically identifying the most commonly configured rule combination patterns for different business lines and departments. When a user (especially a new user) enters the rule configuration interface, the server can match the most relevant and commonly used rule configuration schemes from the historical pattern library based on the user's identity attributes (such as the branch and business department to which the user belongs) or the user's own input simple business tags (such as "account annual review" and "risk screening"). The server clearly displays these schemes in the form of "recommended templates" on the interactive interface to the user (for example: "Your colleagues most commonly use the following verification rules for 'account annual review': exact match for legal person name, and 5% tolerance for registered capital"). If the user considers the recommended template to meet the requirements, they can directly click the "one-click apply" button (equivalent to inputting field selection information and rule selection information), and the configuration will automatically take effect and be saved to the parameter configuration table (equivalent to determining the fields to be compared and the target comparison rules), without any manual checking or input.

[0038] S102, determining the user-selected data table to be compared;

[0039] This step first involves a data table acquisition process.

[0040] Specifically, first, a data replication agent is deployed and run on the server where each source business database (such as the account system and customer information system) is located. The agent continuously monitors and parses the database binary log, capturing all data insertion, deletion, and change events in real time. For each data change record captured (e.g., an update to a certain account information), the agent converts it into a universal, structured data format (such as Avro or JSON format), which includes the changed data itself, the data table name, and operation type (insert / update / delete) metadata.

[0041] The converted data change record is immediately sent to a distributed stream processing platform producer client also deployed on the source server.

[0042] The producer client publishes the data as a message to a specific Topic in the designated distributed stream processing platform cluster. The distributed stream processing platform cluster acts as a high-throughput distributed message queue, effectively decoupling data producers and consumers, and providing persistent buffering of data to ensure data is not lost during transmission and high availability.

[0043] In the data lake platform, a distributed stream processing platform consumer client program is running. The consumer client program subscribes to the above-mentioned distributed stream processing platform Topic and continuously pulls messages from the Topic.

[0044] The consumer client program parses each message obtained and, according to the format and pre-defined table structure mapping rules, loads the data into the corresponding target table of the data lake. This process can be micro-batch processing (write a batch of data to the data lake at a time) or real-time stream processing (write one by one), and finally form a complete data table in the data lake that is synchronized with the source business database.

[0045] Further, the user can determine the data table to be compared by selecting the data time range to be compared, such as "full data up to last weekend" or "incremental data of the last week", so as to determine the data table in the set time range as the data table to be compared. The user can also set the scheduling period of comparison, for example, "execute once every Monday morning".

[0046] S103, according to the target comparison rule, comparing the to-be-compared fields in the to-be-compared data table, and outputting the comparison processing result data of the to-be-compared fields.

[0047] In this step, the server can trigger a batch processing job at a predetermined time.

[0048] After the job is started, it first reads the parameter configuration table generated in S101 from its configuration source (such as a relational database) to obtain all to-be-compared fields and target comparison rules currently in effect.

[0049] Subsequently, the to-be-compared data table (such as the internal account table and the external business table) is directly loaded into the memory through the API of the batch processing job to form a data set available for operation.

[0050] Various general processing functions of comparison rules (for example, a function is specially designed to handle numerical tolerance comparison, and another function is designed to handle string fuzzy matching) can be pre-written in the batch processing job code. The program will iterate through each configured rule and dynamically call the corresponding processing function to perform comparison operations on the to-be-compared fields in the data set.

[0051] After all the rules are traversed and executed, the inconsistent data records output by each function are merged to obtain a complete comparison result dataset. The final comparison result data (i.e., all records that do not pass the rule check) can be written into a dedicated comparison result table in the data lake. The table structure usually includes: account number, comparison inconsistent field name, field value in the source system, field value in the target system, comparison time, used rule ID, etc. This result table is the final output of this step. Subsequently, the task distribution system queries this table and distributes the records to the corresponding downstream system or business personnel for governance.

[0052] The field comparison method provided by the embodiment has the following technical effects:

[0053] The method introduces user-defined comparison fields and rules, so that business personnel can determine the fields to be compared and the comparison rules of the fields according to actual business needs. At the same time, the method decouples business needs and technical implementation, and does not need to rely on technical personnel to modify and release the underlying code.

[0054] Embodiment two

[0055] Figure 2 The field comparison method flow provided by the embodiment of the application is shown in the figure Figure Two As shown in the figure Figure 2 The method comprises the following steps:

[0056] S201, acquire a plurality of candidate fields and a plurality of candidate comparison rules;

[0057] Specifically, in this step, for the candidate fields, the server can read the field names and descriptions that are arranged and business-readable from the pre-defined business metadata management library. For example, "name of legal representative of enterprise", "registered capital", etc. The acquired field information is arranged and de-duplicated to form a structured candidate field list.

[0058] For the candidate comparison rules, the server can acquire the pre-defined comparison rules from the built-in rule engine library. The rule library can be stored in a configurable form, including rule ID, rule name, rule type (such as "exact match", "numerical tolerance"), parameter template (such as tolerance ratio), etc. Common rules can be preset, for example: string equality, numerical equality, numerical range comparison, date format consistency, etc.

[0059] The server calls the rule engine service through an internal interface to acquire a list of all available candidate comparison rules.

[0060] S202, display the candidate fields and the candidate comparison rules on a target interactive interface;

[0061] In this step, the server can encapsulate the candidate field list and the candidate matching rule list obtained in step S201 into JSON format data through a corresponding interface. The front end can obtain the two list data by calling the API.

[0062] Further, the front end can use UI components (such as drop-down selection boxes, multiple selection boxes, tab pages, etc.) to visually render the list data to the target interactive interface. For example, the candidate fields are usually displayed in groups according to business tables, and the candidate rules are presented in the form of a selectable list. The target interactive interface can provide friendly interactive elements to allow users to perform selection, combination, sorting, and other operations.

[0063] S203, in response to the user's selection instruction of the candidate fields and the candidate matching rules, determining the to-be-matched fields and the target matching rules;

[0064] After the user completes the selection operation on the target interactive interface, the front end assembles the user's final selection results (including the selected field ID / name, the selected rule ID, and the corresponding parameter value) into a data model. Then, the data model can be submitted to a server specified interface through an HTTP POST request. The server verifies and parses the data, thereby generating a complete matching task configuration record.

[0065] Further, the server stores the configuration record in a dedicated business rule configuration table, which can be located in a relational database, for example. The business rule configuration table fields include: configuration ID, user information, to-be-matched field list, target matching rule list, state, creation time, etc.

[0066] The server returns a successful response, and the front end prompts the user that the configuration has been completed.

[0067] S201-S203 enable business personnel to easily develop complex matching strategies without technical background through intuitive visual selection operations. Business personnel can quickly adjust the matching fields and rules according to business changes, thereby achieving agile response of the business. Meanwhile, the rule configuration and execution are separated, and rule changes do not require code modification and service restart. The rules can take effect in real time through configuration.

[0068] S204, determining the to-be-matched data table selected by the user;

[0069] According to the user's identity and permissions, a list of selectable data tables is provided, for example, "public account information table", "customer information table", and "business information mirror table". The user can check the to-be-matched data table in the data table list.

[0070] S205, splicing the target matching rules and the to-be-matched fields to generate a target query statement;

[0071] In this step, the server first queries the latest effective configuration record from the business rule configuration table, and then dynamically splices a complete and executable target query statement according to the pre-determined template according to the to-be-compared field list, the target comparison rule list and the rule parameters recorded therein. The query statement may be, for example, an SQL statement.

[0072] It should be noted that the core of the splicing process is to convert the business rule into a SQL WHERE condition. For example:

[0073] Rule ["string equality"] - generate condition AND (table_a.field_x = table_b.field_y);

[0074] Rule ["numeric tolerance", 0.05] - generate condition OR (ABS ((table_a.field_m-table_b.field_n) / table_b.field_n) > 0.05).

[0075] Finally, all these dynamically generated condition fragments are combined by logical operators (AND / OR) and inserted into a pre-set SQL main template to form the final target query statement.

[0076] S206, execute the target query statement to query the comparison processing result data from the to-be-compared data table.

[0077] After generating the target query statement, the server submits the SQL to the computing engine of the data lake for execution.

[0078] The computing engine parses and executes the SQL to perform full scanning, association and condition filtering on the to-be-compared data table determined in S204.

[0079] After execution is completed, all data records that meet the conditions (i.e., comparison inconsistency) are queried out.

[0080] The executor obtains the query result set and writes it into a designated comparison result table. The structure of the table clearly records key information such as account number, inconsistent field, source value, target value, comparison time, etc.

[0081] S205 and S206 can cope with diversified business rule requirements with the same code logic through dynamic SQL generation technology, have good scalability and reusability. Moreover, the process of generating comparison results does not require manual intervention at all, and from reading configuration, generating query to execution output, everything is automatically completed by the server, efficiently processing massive data.

[0082] Embodiment Three

[0083] The embodiment provides a method for determining a field to be compared and a target comparison rule, Figure 3 The embodiment provides a method for determining a field to be compared and a target comparison rule, Figure One As shown in the method, Figure 3 The method comprises the following steps.

[0084] S301, acquiring a business rule description text input by a user.

[0085] Specifically, a text input box can be arranged on a front-end interactive interface, and the interactive interface can be provided with clear guidance prompts, for example, "Please input the comparison rule you want to execute. For example, the business rule description text input by the user can be: compare whether the legal person's name and the business information are consistent, but the registered capital is allowed to have a 5% error.

[0086] After the user completes the input, a "submit" or "parse" button is clicked. The front-end application sends the text input by the user as a payload to a special interface of a server back end through one HTTP POST request. After the back-end server receives the request, the input data is first subjected to security check (such as SQL injection and XSS attack prevention) and validity check (such as checking whether the text is empty and the length is within a reasonable range). After the check passes, the text content is passed to a downstream service layer for processing.

[0087] S302, extracting business field information and business rule information contained in the business rule description text based on the business rule description text by using a pre-trained language model.

[0088] In this step, after the service layer component of the server receives the business rule description text, an independent natural language processing (NLP) microservice can be called. The NLP microservice deploys a pre-trained language model.

[0089] After the NLP microservice receives the text, the language model inside the NLP microservice is called for inference. The language model performs a named entity recognition (NER) and a relationship extraction task to identify key business entities (such as "legal person's name" and "registered capital") and operations or relationships (such as "whether consistent" is mapped to "exact match" and "allowed to have a 5% error" is mapped to "numerical tolerance" rule and extracts the parameter "0.05") corresponding to the entities from the text.

[0090] The NLP microservice arranges the output of the model into a structured data format, such as a JSON format, as the business field information and the business rule information.

[0091] S303, determining a field to be compared and a target comparison rule according to the business field information and the business rule information.

[0092] In this step, the server can traverse all identified initial fields and initial rules to check their confidence. A pre-set confidence threshold (e.g. 0.8) can be set, and fields and rules with confidence higher than the threshold are determined as the fields to be matched and the target matching rules.

[0093] Figure 3 The provided scheme has the following technical effects:

[0094] 1. The data governance configuration is changed from professional "form filling" to natural "language description", greatly reducing the use threshold. Business personnel do not need to understand the background database structure or rule logic, and only need to describe the requirements in the most familiar business language to complete complex configuration, freeing business personnel from the heavy work of finding, selecting, and configuring.

[0095] 2. Traditional form configuration is difficult to cover all scenarios, while natural language input can flexibly express various exceptional situations, complex logic, and combined conditions (such as "except for XXX case, YYY field"), and can meet more variable and refined data governance requirements.

[0096] For the initial field, the confidence of the initial field output by the language model can be obtained, and it is determined whether the confidence is less than a pre-set confidence threshold. If it is less than the threshold, a prompt information is generated, and the initial field and the prompt information are displayed on the target display interface. The prompt information indicates to the user that the confidence of the initial field is less than the pre-set threshold. Further, the correction information input by the user is obtained, and the initial field is corrected according to the correction information to generate the field to be matched.

[0097] Specifically, the server receives the confidence data of each initial field output by the language model, and compares each confidence with a pre-set threshold (e.g. 0.8).

[0098] For fields with confidence lower than the threshold, the server can automatically generate prompt information. The prompt information may, for example, include: field name, confidence value, and suggested correction instructions. The server encapsulates the prompt information and the initial field data into JSON format through an API interface and sends them to the target display interface on the front end. The target display interface receives the data and dynamically renders the display content on the target display interface. For example, low-confidence fields can be highlighted (e.g. yellow background, exclamation mark icon).

[0099] The target display interface can provide multiple correction methods: drop-down selection, text input, and field mapping suggestions. The front end can listen to user operation events to capture correction information in real time, and asynchronously submit the correction results to the server. The server verifies the legality and integrity of the correction information to generate the final version of the field to be matched list and store it.

[0100] The above scheme effectively identifies and intercepts erroneous outputs that the model may produce through a confidence threshold mechanism, and manually corrects them to ensure that the final comparison field is accurate.

[0101] This embodiment also provides another method for determining the fields to be compared and the target comparison rules. Figure 4 A flowchart illustrating the method for determining the fields to be compared and the target comparison rules provided in this application embodiment. Figure Two ,like Figure 4 As shown, the method includes:

[0102] S401. Obtain the first field from the business field information and the second field from the data table to be compared.

[0103] In this step, the server receives business field information from the upstream process (i.e. Figure 3 The service layer parses the business field information in the solution, extracts the key semantic identifiers of the fields as the first field, and usually retains their original text form for subsequent semantic analysis. At the same time, the server queries the metadata database to obtain the complete schema definition of the specified comparison table, and extracts the names, data types, and annotation information of all fields from the schema definition to form the candidate set of the second field.

[0104] S402. Calculate the similarity between the first field and each of the second fields;

[0105] In this step, the server can invoke a natural language processing microservice to use a pre-trained word embedding model to convert the text description of the first field and the name / annotation of the second field into high-dimensional vector representations, respectively. To ensure accuracy, a domain-specific fine-tuned model can be used to handle financial terminology.

[0106] Furthermore, the server calculates the cosine similarity between the first field vector and each second field vector, obtaining a numerical score between 0 and 1.

[0107] S403. Determine the field to be compared in the second field. The similarity between the field to be compared and the first field is greater than or equal to the preset similarity threshold.

[0108] In this step, a preset similarity threshold can be set (e.g., 0.8). All database fields with similarity scores exceeding this threshold will be filtered out and recommended to the user as candidate fields for comparison.

[0109] Since the business field information is extracted from the language model, the fields in this information may not match the actual fields in the data table to be compared. Directly using fields from the business field information for comparison may result in omissions. Therefore... Figure 4The solution uses similarity retrieval to identify the second field in the comparison data table that has a high similarity to the field extracted by the language model. This ensures that the final comparison field exists in the comparison data table and avoids missing fields that need to be compared.

[0110] Figure 5 This is a schematic diagram of the field comparison device provided in the embodiments of this application, as shown below. Figure 5 As shown, the device 50 includes:

[0111] The determination module 501 is used to determine the field to be compared and the target comparison rule for the field to be compared based on the field selection information and rule selection information input by the user.

[0112] Determine the data table to be compared selected by the user;

[0113] The comparison module 502 is used to perform comparison processing on the fields to be compared in the comparison data table according to the target comparison rules, and output the comparison processing result data of the fields to be compared.

[0114] Figure 6 This is a schematic diagram of the electronic device structure provided in the embodiments of this application, such as... Figure 6 As shown, the device 60 includes at least one processor 601 and a memory 602. Optionally, the device 60 also includes a communication component 603. The processor 601, memory 602, and communication component 603 are connected via a bus 604.

[0115] In a specific implementation, at least one processor 601 executes computer execution instructions stored in memory 602, causing at least one processor 601 to perform the above-described method.

[0116] The specific implementation process of processor 601 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0117] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0118] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0119] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all described as a combination of a series of actions, but those skilled in the art should know that the present application is not limited to the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0120] Further, it should be noted that although each step in the flowchart is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, at least part of the steps in the flowchart can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily sequential, but can be executed in rotation or alternation with other steps or sub-steps or stages of other steps.

[0121] It should be understood that the above-described device embodiments are only illustrative, and the device of the present application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and actual implementation can have another division method. For example, multiple units, modules or components can be combined, or can be integrated into another system, or some features can be ignored or not executed.

[0122] In addition, unless otherwise specified, each functional unit / module in each embodiment of the present application can be integrated in one unit / module, or each unit / module can exist physically, or two or more units / modules can be integrated together. The integrated unit / module can be realized in the form of hardware or in the form of a software program module.

[0123] If the integrated units / modules are implemented in the form of hardware, the hardware can be a digital circuit, an analog circuit, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor can be any appropriate hardware processor, such as a CPU, a GPU, an FPGA, a DSP, an ASIC, etc. Unless otherwise specified, the storage unit can be any appropriate magnetic storage medium or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc.

[0124] If the integrated units / modules are implemented in the form of software program modules and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the essential part or all or part of the technical solutions that make contributions to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned storage medium includes a U disk, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0125] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments. The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0126] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the specification and examples be considered as exemplary only, with the true scope and spirit of the application being indicated by the following claims.

[0127] It is to be understood that the application is not limited to the precise construction herein disclosed and shown in the drawings, and that various modifications and changes can be made by those skilled in the art without departing from the scope of the application. The scope of the application is limited only by the claims that follow.

Claims

1. A field comparison method, characterized in that, The method includes: Based on the field selection information and rule selection information input by the user, the field to be compared and the target comparison rule for the field to be compared are determined; Determine the comparison data table selected by the user; According to the target comparison rules, the comparison fields in the comparison data table are compared, and the comparison results of the comparison fields are output.

2. The method according to claim 1, characterized in that, The step of determining the field to be compared based on the field selection information and rule selection information input by the user, and the target comparison rule for the field to be compared, includes: Retrieve multiple candidate fields and multiple candidate comparison rules; Display the candidate fields and the candidate comparison rules on the target interactive interface; In response to the user's selection instruction for the candidate field and the candidate comparison rule, the field to be compared and the target comparison rule are determined.

3. The method according to claim 1, characterized in that, The step of determining the field to be compared based on the field selection information and rule selection information input by the user, and the target comparison rule for the field to be compared, includes: Obtain the business rule description text input by the user; Based on the business rule description text, the pre-trained language model extracts the business field information and business rule information contained in the business rule description text; Based on the business field information and the business rule information, the field to be compared and the target comparison rule are determined.

4. The method according to claim 3, characterized in that, The step of determining the field to be compared and the target comparison rule based on the business field information and the business rule information includes: Obtain the initial field from the business field information, and the confidence level of the initial field output by the language model; Determine whether the confidence level is less than a preset confidence threshold; If so, a prompt message is generated, and the initial field and the prompt message are displayed on the target display interface. The prompt message is used to indicate that the confidence level of the initial field is less than the preset threshold. Obtain the correction information input by the user; Based on the correction information, the initial field is corrected to generate the field to be compared.

5. The method according to claim 3, characterized in that, The step of determining the field to be compared and the target comparison rule based on the business field information and the business rule information includes: Obtain the first field from the business field information, and the second field from the data table to be compared; Calculate the similarity between the first field and each of the second fields; The field to be compared is determined in the second field, and the similarity between the field to be compared and the first field is greater than or equal to a preset similarity threshold.

6. The method according to claim 1, characterized in that, The step of performing comparison processing on the fields to be compared in the data table to be compared according to the target comparison rules, and outputting the comparison processing result data of the fields to be compared, includes: The target comparison rules and the fields to be compared are concatenated to generate a target query statement; The target query statement is executed to retrieve the comparison processing result data from the comparison data table.

7. A field comparison device, characterized in that, The device includes: The determination module is used to determine the field to be compared and the target comparison rule for the field to be compared, based on the field selection information and rule selection information input by the user. Determine the comparison data table selected by the user; The comparison module is used to perform comparison processing on the fields to be compared in the data table to be compared according to the target comparison rules, and output the comparison processing result data of the fields to be compared.

8. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 6.

Images