Data verification method and device, electronic equipment and computer readable storage medium

Abstract

The invention relates to the technical field of data processing, and provides a data verification method and device, electronic equipment and a computer readable storage medium, and the method comprises the steps: obtaining to-be-verified data; performing type analysis processing on the to-be-verified data to obtain data type information; selecting a target data label from a preset label set according to the data type information; selecting a target verification rule from a preset verification rule set according to the target data label; and performing verification analysis processing on the to-be-verified data according to the target verification rule to obtain a data verification result. By means of the technical scheme, intelligent data verification processing can be achieved, and the data verification process can be more efficient, flexible and reliable.

Description

Technical Field

[0001] The embodiments of the present application relate to, but are not limited to, the field of data processing technology, and in particular, to a data verification method, device, electronic device, and computer-readable storage medium. Background Art

[0002] At present, in the field of financial investment, accurate analysis of financial data is crucial for measuring and deciding future investment directions; however, traditional verification and analysis of financial data often relies on manual identification, but the data verification process is relatively cumbersome and time-consuming, and the efficiency of data verification is low, which makes it difficult to meet the high timeliness requirements of investment; and manual data verification methods are difficult to guarantee accuracy, making data verification less reliable. Summary of the invention

[0003] The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.

[0004] In order to solve the problems mentioned in the above background technology, the embodiments of the present application provide a data verification method, device, electronic device and computer-readable storage medium, which can realize intelligent data verification processing, so that the data verification process can be more efficient, flexible and reliable.

[0005] In a first aspect, an embodiment of the present application provides a data verification method, the method comprising:

[0006] Get the data to be verified;

[0007] Performing type analysis on the data to be verified to obtain data type information;

[0008] Selecting a target data tag from a preset tag set according to the data type information;

[0009] Selecting a target verification rule from a preset verification rule set according to the target data tag;

[0010] The data to be verified is verified and analyzed according to the target verification rules to obtain a data verification result.

[0011] According to some embodiments of the present application, performing type analysis on the data to be verified to obtain data type information includes:

[0012] Performing data attribution analysis on the data to be verified to obtain data attribution information;

[0013] The data source information corresponding to the data attribution information is used as the data type information.

[0014] According to some embodiments of the present application, the tag set includes a plurality of data tags, each of the data tags carries a data type tag, and selecting a target data tag from a preset tag set according to the data type information includes:

[0015] Matching the data type information with the plurality of data type tags to obtain a target data type tag;

[0016] The data label corresponding to the target data type mark is determined as the target data label.

[0017] According to some embodiments of the present application, the verification rule set includes multiple verification rules, each of the verification rules carries tag attribute information, and the target verification rule is selected from the preset verification rule set according to the target data tag, including:

[0018] Matching the target data tag with the plurality of tag attribute information to obtain target tag attribute information;

[0019] The verification rule corresponding to the target tag attribute information is determined as the target verification rule.

[0020] According to some embodiments of the present application, performing verification analysis on the data to be verified according to the target verification rule to obtain a data verification result includes:

[0021] Parsing the target verification rule to obtain a plurality of verification sub-rules;

[0022] Classifying the plurality of check sub-rules to obtain public check sub-rules and private check sub-rules;

[0023] Performing a first verification process on the data to be verified according to the public verification sub-rule to obtain a first verification analysis result;

[0024] Performing a second verification process on the business data in the data to be verified according to the private verification sub-rule to obtain a second verification analysis result;

[0025] The first verification analysis result and the second verification analysis result are combined into the data verification result.

[0026] According to some embodiments of the present application, after combining the first verification analysis result and the second verification analysis result into the data verification result, the method further includes:

[0027] Get private validation requirement information;

[0028] The private check sub-rule is analyzed and processed according to the private check requirement information.

[0029] According to some embodiments of the present application, after performing verification analysis on the data to be verified according to the target verification rule and obtaining the data verification result, the method further includes:

[0030] Compare the data verification result with a preset result comparison threshold to obtain a comparison result;

[0031] The legitimacy of the data to be verified is determined according to the comparison result.

[0032] In a second aspect, the embodiment of the present application further provides a data verification device, including:

[0033] A first processing module, used for obtaining data to be verified;

[0034] A second processing module is used to perform type analysis on the data to be verified to obtain data type information;

[0035] A third processing module, configured to select a target data tag from a preset tag set according to the data type information;

[0036] A fourth processing module, configured to select a target verification rule from a preset verification rule set according to the target data tag;

[0037] The fifth processing module is used to perform verification and analysis processing on the data to be verified according to the target verification rule to obtain a data verification result.

[0038] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the data verification method as described in the first aspect above is implemented.

[0039] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the data verification method described in the first aspect above.

[0040] The data verification method according to the embodiment provided in the present application has at least the following beneficial effects: in the process of data verification, firstly, the data to be verified is obtained, and then the type analysis is performed on the data to be verified to obtain the data type information; then, the target data label is selected from the preset label set according to the data type information; then, the target verification rule is selected from the preset verification rule set according to the target data label; finally, the verification analysis is performed on the data to be verified according to the target verification rule to obtain the data verification result; through the above method, intelligent data verification processing can be realized, so that the data verification process can be more efficient, flexible and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings are used to provide further understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.

[0042] Figure 1 is a flow chart of a data verification method provided by an embodiment of the present application;

[0043] Figure 2 is a specific flow chart of step S200 provided in one embodiment of the present application;

[0044] Figure 3 is a specific flow chart of step S300 provided in one embodiment of the present application;

[0045] Figure 4 is a specific flow chart of step S400 provided in one embodiment of the present application;

[0046] Figure 5 is a specific flow chart of step S500 provided in one embodiment of the present application;

[0047] Figure 6 is a specific flow chart after step S550 provided in one embodiment of the present application;

[0048] Figure 7 is a flow chart of a data verification method provided by another embodiment of the present application;

[0049] Figure 8 is a schematic diagram of a data verification device provided by an embodiment of the present application;

[0050] Fig. 9 It is a schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0052] It should be noted that although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification and claims and the above drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0053] It should be noted that, unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0054] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0055] AI is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. Artificial intelligence is a branch of computer science. Artificial intelligence attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. Research in this field includes robots, language recognition, image recognition, natural language processing and expert systems. Artificial intelligence can simulate the information process of human consciousness and thinking. Artificial intelligence is also a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0056] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0057] Artificial intelligence is AI. AI is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0058] The servers involved in artificial intelligence technology can be independent servers or cloud servers that provide basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN), as well as big data and artificial intelligence platforms.

[0059] The present application provides a data verification method, device, electronic device and computer-readable storage medium. In the process of data verification, the data to be verified is first obtained, and then the type analysis and processing of the data to be verified is performed to obtain the data type information; then the target data label is selected from a preset label set according to the data type information; then the target verification rule is selected from a preset verification rule set according to the target data label; finally, the verification analysis and processing of the data to be verified can be performed according to the target verification rule to obtain the data verification result; through the above method, intelligent data verification processing can be realized, so that the data verification process can be more efficient, flexible and reliable.

[0060] The data verification method provided in the embodiment of the present application relates to the field of data processing technology. The data verification method provided in the embodiment of the present application can be applied to a terminal, can be applied to a server side, or can be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the data verification method, etc., but is not limited to the above forms.

[0061] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0062] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0063] The embodiments of the present application are further described below in conjunction with the accompanying drawings.

[0064] like Figure 1 As shown, Figure 1 It is a flow chart of a data verification method provided by an embodiment of the present application, and the data verification method includes but is not limited to steps S100 to S500.

[0065] Step S100, obtaining data to be verified;

[0066] Step S200, performing type analysis on the data to be verified to obtain data type information;

[0067] Step S300, selecting a target data tag from a preset tag set according to data type information;

[0068] Step S400, selecting a target verification rule from a preset verification rule set according to the target data tag;

[0069] Step S500, performing verification analysis on the data to be verified according to the target verification rule to obtain a data verification result.

[0070] It should be noted that in the process of data verification, the data to be verified is first obtained, and then the type analysis is performed on the data to be verified to obtain the data type information; then the target data label is selected from the preset label set according to the data type information; then the target verification rule is selected from the preset verification rule set according to the target data label; finally, the data to be verified can be verified and analyzed according to the target verification rule to obtain the data verification result; through the above method, intelligent data verification processing can be realized, so that the data verification process can be more efficient, flexible and reliable.

[0071] It is worth noting that the embodiments of the present application will obtain the user's permission or consent before obtaining the data to be verified, and the collection, use and processing of such data will comply with relevant laws, regulations and standards. In addition, when the embodiments of the present application need to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After the user's separate permission or consent is clearly obtained, the necessary user-related data for the normal operation of the embodiments of the present application will be obtained.

[0072] It is worth noting that in the process of data verification, the data to be verified can be obtained first; then the data to be verified can be analyzed and processed to obtain data type information; then the target data label is selected from the preset label set according to the data type information; then the target verification rule is selected from the preset verification rule set according to the target data label; finally, the data to be verified is analyzed and processed according to the target verification rule to obtain the data verification result. Through the above technical solution, intelligent data verification processing is realized, making the data verification process more simple and reliable.

[0073] It is worth noting that the tag set includes multiple data tags. According to the data type information, the corresponding target data tag can be selected from the tag set to prepare for the subsequent data verification processing. Later, the corresponding target verification rule can be selected from the pre-set verification rule set according to the target data tag. Later, the verification analysis and processing of the verification data can be carried out according to the target verification rule. The whole data verification process is simple, fast and reliable.

[0074] It is worth noting that a validation rule set usually refers to a set of rules used to verify whether data conforms to a specific standard or format. These rules can be applied to various scenarios, such as form validation, data import, etc. A validation rule set can include but is not limited to the following types: Data type validation: Ensure that the data is of the correct type, such as string, integer, floating point number, Boolean value, etc. Length validation: Verify that the length of a string or array is within a specified range. Format validation: Check whether the data conforms to a specific format, such as an email address, phone number, date, etc. Range validation: Ensure that the value is within a specified range, such as age between 18 and 65. Regular expression validation: Use regular expressions to match complex string patterns. Required field validation: Ensure that certain fields are not empty. Uniqueness validation: Ensure that the data is unique in the data set, such as a username or email address. Dependency validation: The value of some fields depends on the value of other fields, such as when an option is selected, other fields become required. Custom validation: Validation rules written according to specific business logic.

[0075] It is worth noting that a tag set generally refers to a set of tags used to classify, organize, or mark content. These tags can be used for a variety of purposes, such as document management, content recommendation, information retrieval, etc. In different application scenarios, the implementation and use of tag sets may vary. The following are some common tag set usage scenarios: Content management systems, tags are used to classify content such as articles, blog posts, product descriptions, etc., so that users can search and browse. Social media platforms, users can add tags to posts so that other users can find related content, or to track discussions on specific topics. Libraries and archives, used to classify and index books, articles, and other resources for easy retrieval. E-commerce websites, products can be tagged with multiple tags so that users can filter according to their interests or needs. Personal knowledge management, individuals can use tags to organize notes, documents, and projects to improve work efficiency. Machine learning and data mining, in these fields, tags are used to mark data for the purpose of training models or performing data analysis. Image and video recognition, in image and video analysis, tags are used to describe content for retrieval and content understanding.

[0076] In some embodiments, Figure 2 As shown, the above step S200 may include but is not limited to steps S210 to S220.

[0077] Step S210, performing data attribution analysis on the data to be verified to obtain data attribution information;

[0078] Step S220: Use the data source information corresponding to the data attribution information as data type information.

[0079] It should be noted that in the process of performing type analysis on the verification data to obtain data type information, firstly, data attribution analysis is performed on the verification data to obtain data attribution information; then, the data source information corresponding to the data attribution information is used as the data type information. Through the above data type analysis, the determination of the data type information can be more accurate.

[0080] It is worth noting that in the process of performing type analysis on the data to be verified to obtain data type information, you can first perform data attribution analysis on the data to be verified to obtain data attribution information, and then use the data source information corresponding to the data attribution information as the data type information to prepare for subsequent data verification processing.

[0081] In some embodiments, Figure 3 As shown, the tag set includes multiple data tags, each data tag carries a data type tag, and the above step S300 may include but is not limited to steps S310 to S320.

[0082] Step S310, matching the data type information with multiple data type tags to obtain a target data type tag;

[0083] Step S320: determine the data tag corresponding to the target data type mark as the target data tag.

[0084] It should be noted that in the process of selecting a target data label from a preset label set based on data type information, the data type information is first matched with multiple data type labels to obtain the target data type label; then, the data label corresponding to the target data type label can be determined as the target data label.

[0085] It is worth noting that in the process of determining the target data label, the target data type label can be obtained by first matching the data type information with multiple data type labels; then the data label corresponding to the target data type label can be determined as the corresponding target data label, making preparations for subsequent data verification.

[0086] In some embodiments, Figure 4 As shown, the verification rule set includes multiple verification rules, each verification rule carries tag attribute information, and the above step S400 may include but is not limited to steps S410 to S420.

[0087] Step S410, matching the target data tag with multiple tag attribute information to obtain the target tag attribute information;

[0088] Step S420: determine the verification rule corresponding to the target tag attribute information as the target verification rule.

[0089] It should be noted that in the process of selecting the target verification rule from the preset verification rule set according to the target data tag, the target data tag is first matched with multiple tag attribute information to obtain the target tag attribute information; then the verification rule corresponding to the target tag attribute information can be determined as the corresponding target verification rule, so as to make the prerequisite preparation for the subsequent data verification processing.

[0090] It is worth noting that by matching the target data label with multiple label attribute information, the target label attribute information can be quickly and accurately selected from multiple data labels, and the target verification rules can be subsequently determined based on the selected target label attribute information.

[0091] It is worth noting that in data validation, the validation rules corresponding to the target tag attribute information are determined as the target validation rules, which usually involves the following steps: Identify the target tag attributes: First, it is necessary to clarify which tags or fields need to be validated. These tags may be form inputs, database fields, API parameters, etc. Define validation rules: For each tag attribute that needs to be validated, define specific validation rules. These rules may include: Data type: such as string, integer, floating point number, date, etc. Length restriction: such as the minimum and maximum length of the string. Format requirements: such as specific formats for email addresses, phone numbers, zip codes, etc. Range restrictions: such as the minimum and maximum values ​​of values. Mandatory: Determine whether a field must be filled in. Custom rules: According to the needs of business logic, some specific validation rules may be required. Implement validation logic: Implement the validation rules defined above in the code. This usually involves writing conditional statements to check whether the data meets the rules. Error handling: When the data does not meet the validation rules, there needs to be a corresponding error handling mechanism, such as returning error messages, recording logs, etc. Testing: Test the validation rules to ensure that they can correctly identify data that does not meet the requirements. Maintenance and Updates: As business needs change, validation rules may need to be maintained and updated.

[0092] In some embodiments, Figure 5 As shown, the above step S500 may include but is not limited to step S510 to step S550.

[0093] Step S510, parsing the target verification rule to obtain multiple verification sub-rules;

[0094] Step S520, classifying the multiple check sub-rules to obtain public check sub-rules and private check sub-rules;

[0095] Step S530, performing a first verification process on the data to be verified according to the public verification sub-rule to obtain a first verification analysis result;

[0096] Step S540, performing a second verification process on the business data in the data to be verified according to the private verification sub-rule to obtain a second verification analysis result;

[0097] Step S550: Combining the first verification analysis result and the second verification analysis result into a data verification result.

[0098] It should be noted that, in the process of performing verification analysis on the verification data according to the target verification rules to obtain the data verification results, first, multiple verification sub-rules are parsed from the target verification rules; then, the multiple verification sub-rules are classified and processed to obtain public verification sub-rules and private verification sub-rules; then, a first verification process is performed on the verification data according to the public verification sub-rules to obtain a first verification analysis result, and then a second verification process is performed on the business data in the verification data according to the private verification sub-rules to obtain a second verification analysis result; finally, the first verification analysis result and the second verification analysis result are combined to obtain the corresponding data verification result, so that the data verification process can be more simple, quick and reliable.

[0099] It is worth noting that in the process of verifying and analyzing the data, you can first parse the target verification rules to obtain multiple verification sub-rules; then, classify the multiple verification sub-rules to obtain public verification sub-rules and private verification sub-rules; subsequently, perform a first verification process on the verification data according to the public verification sub-rules to obtain a first verification analysis result; then, perform a second verification process on the business data in the verification data according to the private verification sub-rules to obtain a second verification analysis result; finally, the first verification analysis result and the second verification analysis result can be combined to obtain the corresponding data verification result, and the entire data verification process is simple, fast and reliable.

[0100] It is worth noting that public validation sub-rules refer to general validation rules that apply to multiple scenarios or multiple data fields. These rules usually do not involve sensitive data and can be shared and reused by multiple systems or modules. Examples of public validation sub-rules include: Data type validation: Ensure that the input data conforms to the expected data type, such as string, integer, Boolean value, etc. Length validation: Check whether the length of the string is within the specified range. Format validation: Verify whether the data conforms to a specific format, such as email address, phone number, date, etc. Range validation: Ensure that the value is within a specific range, such as age, price, etc. Required field validation: Confirm whether certain fields have been filled in. Enumeration validation: Check whether the input is in a predefined list of options. Private validation sub-rules refer to validation rules that are customized for specific scenarios or specific data fields. These rules may involve sensitive data or are designed to meet specific business logic requirements. Examples of private validation sub-rules include: Business logic validation: Validate data according to specific business rules, such as the order amount must be greater than zero. Permission validation: Ensure that the user has permission to perform specific operations or access specific data. Data consistency validation: Check whether the data is consistent with existing data in the database, such as the uniqueness of the user name. Security verification: Ensure that the data does not contain malicious code or potential security threats. Compliance verification: Ensure that the data complies with specific laws or industry standards, such as the protection of personally identifiable information. Dependency verification: Check whether the data depends on the validity of other data, such as the value of one field must be valid only if the value of another field is valid. In practical applications, public and private verification sub-rules can be used in combination to ensure the integrity, consistency, and security of the data. Developers need to design and implement these verification rules based on specific business needs and data protection requirements. At the same time, the performance impact of the verification rules must also be considered to ensure that they do not have a negative impact on the response time of the system.

[0101] In some embodiments, Figure 6 As shown, after executing the above step S550, it may include but not be limited to steps S610 to S620.

[0102] Step S610, obtaining private verification requirement information;

[0103] Step S620: Analyze and process the private verification sub-rule according to the private verification requirement information.

[0104] It should be noted that after combining the first verification analysis result and the second verification analysis result into a data verification result, the private verification requirement information can be obtained first; then the private verification sub-rules can be analyzed and processed according to the private verification requirement information to verify the data more comprehensively.

[0105] It is worth noting that after obtaining the private verification requirement information, the private verification sub-rules can be analyzed and processed according to the private verification requirement information. The whole process is simple, fast and reliable.

[0106] In some embodiments, Figure 7 As shown, after executing the above step S500, it may include but not be limited to steps S630 to S640.

[0107] Step S630, comparing the data verification result with a preset result comparison threshold to obtain a comparison result;

[0108] Step S640: determine the validity of the data to be verified based on the comparison result.

[0109] It should be noted that after the data to be verified is verified and analyzed according to the target verification rules to obtain the data verification results, the data verification results can also be compared with a pre-set result comparison threshold to obtain the comparison results; then the legitimacy of the data to be verified can be determined based on the comparison results, so that the entire data verification process can be more accurate, stable and reliable.

[0110] It is worth noting that in the process of judging the legitimacy of the data, the data verification results can be compared with the pre-set result comparison threshold to obtain the comparison result; the subsequent analysis and processing of the comparison results can determine the legitimacy of the data, making the judgment of the legitimacy of the entire data more simple, stable and reliable.

[0111] In addition, if Figure 8 As shown, an embodiment of the present application further provides a data verification device 10, comprising:

[0112] The first processing module 100 is used to obtain data to be verified;

[0113] The second processing module 200 is used to perform type analysis on the data to be verified to obtain data type information;

[0114] The third processing module 300 is used to select a target data tag from a preset tag set according to the data type information;

[0115] The fourth processing module 400 is used to select a target verification rule from a preset verification rule set according to the target data tag;

[0116] The fifth processing module 500 is used to perform verification analysis on the data to be verified according to the target verification rule to obtain a data verification result.

[0117] It should be noted that in the process of data verification, the data to be verified is first obtained, and then the type analysis is performed on the data to be verified to obtain the data type information; then the target data label is selected from the preset label set according to the data type information; then the target verification rule is selected from the preset verification rule set according to the target data label; finally, the data to be verified can be verified and analyzed according to the target verification rule to obtain the data verification result; through the above method, intelligent data verification processing can be realized, so that the data verification process can be more efficient, flexible and reliable.

[0118] It is worth noting that the embodiments of the present application will obtain the user's permission or consent before obtaining the data to be verified, and the collection, use and processing of such data will comply with relevant laws, regulations and standards. In addition, when the embodiments of the present application need to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After the user's separate permission or consent is clearly obtained, the necessary user-related data for the normal operation of the embodiments of the present application will be obtained.

[0119] It is worth noting that in the process of data verification, the data to be verified can be obtained first; then the data to be verified can be analyzed and processed to obtain data type information; then the target data label is selected from the preset label set according to the data type information; then the target verification rule is selected from the preset verification rule set according to the target data label; finally, the data to be verified is analyzed and processed according to the target verification rule to obtain the data verification result. Through the above technical solution, intelligent data verification processing is realized, making the data verification process more simple and reliable.

[0120] It is worth noting that the tag set includes multiple data tags. According to the data type information, the corresponding target data tag can be selected from the tag set to prepare for the subsequent data verification processing. Later, the corresponding target verification rule can be selected from the pre-set verification rule set according to the target data tag. Later, the verification analysis and processing of the verification data can be carried out according to the target verification rule. The whole data verification process is simple, fast and reliable.

[0121] It is worth noting that a validation rule set usually refers to a set of rules used to verify whether data conforms to a specific standard or format. These rules can be applied to various scenarios, such as form validation, data import, etc. A validation rule set can include but is not limited to the following types: Data type validation: Ensure that the data is of the correct type, such as string, integer, floating point number, Boolean value, etc. Length validation: Verify that the length of a string or array is within a specified range. Format validation: Check whether the data conforms to a specific format, such as an email address, phone number, date, etc. Range validation: Ensure that the value is within a specified range, such as age between 18 and 65. Regular expression validation: Use regular expressions to match complex string patterns. Required field validation: Ensure that certain fields are not empty. Uniqueness validation: Ensure that the data is unique in the data set, such as a username or email address. Dependency validation: The value of some fields depends on the value of other fields, such as when an option is selected, other fields become required. Custom validation: Validation rules written according to specific business logic.

[0122] It is worth noting that a tag set generally refers to a set of tags used to classify, organize, or mark content. These tags can be used for a variety of purposes, such as document management, content recommendation, information retrieval, etc. In different application scenarios, the implementation and use of tag sets may vary. The following are some common tag set usage scenarios: Content management systems, tags are used to classify content such as articles, blog posts, product descriptions, etc., so that users can search and browse. Social media platforms, users can add tags to posts so that other users can find related content, or to track discussions on specific topics. Libraries and archives, used to classify and index books, articles, and other resources for easy retrieval. E-commerce websites, products can be tagged with multiple tags so that users can filter according to their interests or needs. Personal knowledge management, individuals can use tags to organize notes, documents, and projects to improve work efficiency. Machine learning and data mining, in these fields, tags are used to mark data for the purpose of training models or performing data analysis. Image and video recognition, in image and video analysis, tags are used to describe content for retrieval and content understanding.

[0123] The specific implementation of the data verification device 10 is substantially the same as the specific implementation of the above-mentioned data verification method, and will not be described in detail here.

[0124] In addition, if Fig. 9 As shown, an embodiment of the present application further provides an electronic device 700 , which includes: a memory 720 , a processor 710 , and a computer program stored in the memory 720 and executable on the processor 710 .

[0125] The processor 710 and the memory 720 may be connected via a bus or in other ways.

[0126] The non-transitory software programs and instructions required to implement the data verification methods of the above embodiments are stored in the memory 720 , and when executed by the processor 710 , the data verification methods of the above embodiments are executed.

[0127] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0128] In addition, an embodiment of the present application also provides a computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are executed by a processor 710 or a controller, for example, by a processor 710 in the above-mentioned device embodiment, so that the above-mentioned processor 710 can execute the data verification method in the above-mentioned embodiment.

[0129] The above embodiments may be used in combination, and modules with the same name in different embodiments may be the same or different.

[0130] The above describes specific embodiments of the present application, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0131] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and computer-readable storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0132] The apparatus, device, computer-readable storage medium and method provided in the embodiments of the present application correspond to each other. Therefore, the apparatus, device and non-volatile computer storage medium also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the corresponding apparatus, device and computer storage medium will not be repeated here.

[0133] In the 1990s, improvements to a technology could be clearly distinguished as hardware improvements (for example, improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the method flow). However, with the development of technology, many improvements to the method flow today can be regarded as direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement in a method flow cannot be implemented using a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to ask a chip manufacturer to design and produce a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0134] The controller can be implemented in any appropriate manner, for example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a purely computer-readable program code manner, the controller can be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, this controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be regarded as structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0135] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0136] For the convenience of description, the above devices are described in terms of functions and are divided into various units. Of course, when implementing the embodiments of the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0137] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the embodiments of the present application may adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0138] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0139] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0140] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0141] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0142] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash memory (Flash RAM). The memory is an example of a computer-readable medium.

[0143] Computer readable media include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (Transitory Med ia), such as modulated data signals and carrier waves.

[0144] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0145] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0146] Embodiments of the present application may be described in the general context of computer executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. Embodiments of the present application may also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0147] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0148] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A data verification method, characterized in that: The method comprises: Get the data to be verified; Performing type analysis on the data to be verified to obtain data type information; Selecting a target data tag from a preset tag set according to the data type information; Selecting a target verification rule from a preset verification rule set according to the target data tag; The data to be verified is verified and analyzed according to the target verification rules to obtain a data verification result.

2. The data verification method according to claim 1, characterized in that: The performing type analysis on the data to be verified to obtain data type information includes: Performing data attribution analysis on the data to be verified to obtain data attribution information; The data source information corresponding to the data attribution information is used as the data type information.

3. The data verification method according to claim 1, characterized in that: The tag set includes a plurality of data tags, each of which carries a data type tag, and the step of selecting a target data tag from a preset tag set according to the data type information includes: Matching the data type information with the plurality of data type tags to obtain a target data type tag; The data label corresponding to the target data type mark is determined as the target data label.

4. The data verification method according to claim 1, characterized in that: The verification rule set includes a plurality of verification rules, each of which carries tag attribute information, and the target verification rule is selected from the preset verification rule set according to the target data tag, including: Matching the target data tag with the plurality of tag attribute information to obtain target tag attribute information; The verification rule corresponding to the target tag attribute information is determined as the target verification rule.

5. The data verification method according to claim 1, characterized in that: The step of performing verification analysis on the data to be verified according to the target verification rule to obtain a data verification result includes: Parsing the target verification rule to obtain a plurality of verification sub-rules; Classifying the plurality of check sub-rules to obtain public check sub-rules and private check sub-rules; Performing a first verification process on the data to be verified according to the public verification sub-rule to obtain a first verification analysis result; Performing a second verification process on the business data in the data to be verified according to the private verification sub-rule to obtain a second verification analysis result; The first verification analysis result and the second verification analysis result are combined into the data verification result.

6. The data verification method according to claim 5, characterized in that: After combining the first verification analysis result and the second verification analysis result into the data verification result, the method further includes: Get private validation requirement information; The private check sub-rule is analyzed and processed according to the private check requirement information.

7. The data verification method according to claim 1, characterized in that: After performing verification analysis on the data to be verified according to the target verification rule to obtain the data verification result, the method further includes: Compare the data verification result with a preset result comparison threshold to obtain a comparison result; The legitimacy of the data to be verified is determined according to the comparison result.

8. A data verification device, characterized in that: include: A first processing module, used for obtaining data to be verified; A second processing module is used to perform type analysis on the data to be verified to obtain data type information; A third processing module, configured to select a target data tag from a preset tag set according to the data type information; A fourth processing module, configured to select a target verification rule from a preset verification rule set according to the target data tag; The fifth processing module is used to perform verification and analysis processing on the data to be verified according to the target verification rule to obtain a data verification result.

9. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the data verification method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing computer-executable instructions, characterized in that: The computer executable instructions are used to execute the data verification method described in any one of claims 1 to 7.

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