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

Abstract

The invention relates to the technical field of data verification, 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 validity analysis processing on the to-be-verified data to obtain a data analysis result; under the condition that the data analysis result represents that all the to-be-verified data are normal data, performing classification verification processing on the to-be-verified data based on a preset verification classification multi-thread processor to obtain multiple pieces of classification verification information; performing index verification processing on the multiple pieces of classification verification information based on a preset index classification multi-thread processor to obtain multiple pieces of index verification information; and performing comprehensive analysis processing on the multiple pieces of index verification information to obtain a data verification result. According to the technical scheme, the data verification efficiency can be improved, and the data verification requirement of big data can be well met.

Description

Technical Field

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

[0002] In the financial industry, various application systems are increasingly used to store and process various business data. For the sake of data security, it is often necessary to verify and process various business data in the application system. At present, the data verification mode is a single-instance operation mode, that is, an instance interface calls an interface according to known operation indicator rules to perform data operations. However, when an instance calls the interface of the used indicators, all data needs to be verified in sequence, but this will be affected by the number of logical operations and the amount of data, resulting in low data verification efficiency and difficulty in meeting the verification requirements of more and more business data. 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 improve the data verification efficiency and well meet the data verification requirements of big data.

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

[0006] Obtain the data to be verified;

[0007] Performing validity analysis on the data to be verified to obtain data analysis results;

[0008] When the data analysis result indicates that the data to be verified are all normal data, the data to be verified are classified and verified based on a preset verification classification multi-thread processor to obtain multiple classification verification information;

[0009] Based on a preset indicator classification multi-thread processor, an indicator verification process is performed on multiple classification verification information to obtain multiple indicator verification information;

[0010] Comprehensive analysis and processing are performed on multiple indicator verification information to obtain data verification results.

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

[0012] Obtain business scenario information;

[0013] The business scope validity analysis is performed on the data to be verified according to the business scenario information to obtain the data analysis result.

[0014] According to some embodiments of the present application, the preset verification classification based multi-threaded processor performs classification verification processing on the data to be verified to obtain multiple classification verification information, including:

[0015] Performing verification classification and distribution processing on the data to be verified to obtain multiple categories to be verified;

[0016] Based on the verification classification multi-thread processor, parallel classification verification processing is performed on the multiple categories to be verified to obtain multiple classification verification information.

[0017] According to some embodiments of the present application, the preset indicator classification based multi-threaded processor performs indicator verification processing on multiple classification verification information to obtain multiple indicator verification information, including:

[0018] Performing indicator classification and distribution processing on the plurality of classified verification information to obtain a plurality of indicators to be verified;

[0019] Based on the indicator classification, the multi-threaded processor performs parallel indicator verification processing on the multiple indicators to be verified to obtain multiple indicator verification information.

[0020] According to some embodiments of the present application, the multi-threaded processor based on the indicator classification performs parallel indicator verification processing on the multiple indicators to be verified to obtain the multiple indicator verification information, including:

[0021] In the case where the indicator to be verified is a recheck indicator, the multi-threaded processor performs recheck indicator calculations on the indicator to be verified respectively based on the indicator classification to obtain recheck calculation results;

[0022] In the case where the indicator to be verified is a single indicator, the multi-threaded processor performs single indicator operations on the indicator to be verified respectively based on the indicator classification to obtain a single operation result;

[0023] In the case where the indicator to be verified is a specific indicator, the multi-threaded processor performs specific indicator operations on the indicator to be verified respectively based on the indicator classification to obtain a specific operation result;

[0024] The review operation result, the single operation result and the specific operation result are determined as the indicator verification information.

[0025] According to some embodiments of the present application, the comprehensive analysis and processing of the plurality of indicator verification information to obtain the data verification result includes:

[0026] Integrate and process the multiple indicator verification information to obtain the indicator verification result;

[0027] Statistical analysis is performed on the indicator verification results to obtain the data verification results.

[0028] According to some embodiments of the present application, after the comprehensive analysis and processing of the plurality of indicator verification information is performed to obtain the data verification result, the method further includes:

[0029] Constructing a data verification portrait based on the data verification results;

[0030] The data verification portrait is sent to a preset data monitoring terminal.

[0031] In a second aspect, the embodiment of the present application further provides a data verification device, including:

[0032] A first processing module, used for obtaining data to be verified;

[0033] The second processing module is used to perform validity analysis on the data to be verified to obtain data analysis results;

[0034] A third processing module is used for, when the data analysis result indicates that the data to be verified are all normal data, performing classification verification processing on the data to be verified based on a preset verification classification multi-thread processor to obtain multiple classification verification information;

[0035] A fourth processing module is used to perform indicator verification processing on multiple classification verification information based on a preset indicator classification multi-thread processor to obtain multiple indicator verification information;

[0036] The fifth processing module is used to perform comprehensive analysis and processing on the multiple indicator verification information to obtain data verification results.

[0037] 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.

[0038] 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.

[0039] According to the data verification method of the embodiment provided in the present application, there are at least the following beneficial effects: in the process of data verification, first the data to be verified is obtained, then the validity analysis is performed on the data to be verified to obtain the data analysis result, and then when the data analysis result indicates that the data to be verified are all normal data, the data to be verified will be classified and verified based on the preset verification classification multi-thread processor to obtain multiple classification verification information; subsequently, the multiple classification verification information can be subjected to indicator verification based on the preset indicator classification multi-thread processor to obtain multiple indicator verification information; finally, the multiple indicator verification information can be subjected to comprehensive analysis to obtain the data verification result; through the above-mentioned technical scheme, the verification classification multi-thread processor is used for classification verification, and the indicator classification multi-thread processor is used for indicator verification, so that the efficiency of data verification can be greatly accelerated to better meet the data verification requirements of big data. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] 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.

[0041] Figure 1 is a flow chart of a data verification method provided by an embodiment of the present application;

[0042] Figure 2 is a specific flow chart of step S200 provided in one embodiment of the present application;

[0043] Figure 3 is a specific flow chart of step S300 provided in one embodiment of the present application;

[0044] Figure 4 is a specific flow chart of step S400 provided in one embodiment of the present application;

[0045] Figure 5 is a specific flow chart of step S420 provided in one embodiment of the present application;

[0046] Figure 6 is a specific flow chart of step S500 provided in one embodiment of the present application;

[0047] Figure 7 is a flow chart of a data verification method provided by another embodiment of the present application;

[0048] Figure 8 is a schematic diagram of a data verification device provided by an embodiment of the present application;

[0049] Fig. 9 It is a schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0050] 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.

[0051] 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.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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 validity analysis and processing of the data to be verified is performed to obtain the data analysis results. Then, when the data analysis results indicate that the data to be verified are all normal data, the data to be verified will be classified and verified based on a preset verification classification multi-thread processor to obtain multiple classification verification information; subsequently, the multiple classification verification information can be subjected to indicator verification processing based on a preset indicator classification multi-thread processor to obtain multiple indicator verification information; finally, the multiple indicator verification information can be subjected to comprehensive analysis and processing to obtain the data verification result; through the above-mentioned technical scheme, the verification classification multi-thread processor is used for classification verification processing, and the indicator classification multi-thread processor is used for indicator verification processing, so that the efficiency of data verification can be greatly accelerated to better meet the data verification requirements of big data.

[0059] The data verification method provided in the embodiment of the present application relates to the field of data verification 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.

[0060] 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.

[0061] 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.

[0062] The embodiments of the present application are further described below in conjunction with the accompanying drawings.

[0063] 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.

[0064] Step S100, obtaining data to be verified;

[0065] Step S200, performing validity analysis on the data to be verified to obtain data analysis results;

[0066] Step S300, when the data analysis result indicates that the data to be verified are all normal data, the data to be verified are classified and verified based on a preset verification classification multi-thread processor to obtain multiple classification verification information;

[0067] Step S400, performing indicator verification processing on multiple classification verification information based on a preset indicator classification multi-thread processor to obtain multiple indicator verification information;

[0068] Step S500, comprehensively analyzing and processing multiple indicator verification information to obtain data verification results.

[0069] It should be noted that in the process of data verification, the data to be verified is first obtained, and then the validity analysis is performed on the data to be verified to obtain the data analysis results. Then, when the data analysis results indicate that the data to be verified are all normal data, the data to be verified will be classified and verified based on the preset verification classification multi-threaded processor to obtain multiple classification verification information; subsequently, the multiple classification verification information can be subjected to indicator verification based on the preset indicator classification multi-threaded processor to obtain multiple indicator verification information; finally, the multiple indicator verification information can be comprehensively analyzed and processed to obtain the data verification results; through the above-mentioned technical scheme, the verification classification multi-threaded processor is used for classification verification, and the indicator classification multi-threaded processor is used for indicator verification, so that the efficiency of data verification can be greatly accelerated to better meet the data verification requirements of big data.

[0070] It is worth noting that the data to be verified is the data that needs to be verified; before the data to be verified is verified, the validity of the data to be verified needs to be analyzed and processed. On the premise that the data to be verified is normal data, the verification classification multi-thread processor and the indicator classification multi-thread processor will be used to verify the data to be verified. Through the above technical solution, the data verification can be more accurate and reliable. For example, in the financial industry, multiple application systems can be used to store and process relevant data, so these data can be verified and processed later to improve security.

[0071] It is worth noting that the verification classification multi-threaded processor refers to a computing component that can execute multiple threads simultaneously on a processor. This design can improve the resource utilization of the processor, thereby improving performance. Multi-threaded processors are usually very important in the field of servers and high-performance computing because they can handle a large number of concurrent tasks. In the verification classification of multi-threaded processors, the following aspects are usually considered: the number of cores and the number of threads, the number of physical cores of the processor and the number of logical threads implemented through technologies such as hyperthreading. The processor base frequency and maximum frequency, which affect the base and maximum speed of the processor when executing tasks. Cache, the cache size of the processor is crucial to the performance of multi-threaded tasks because it can reduce the number of times the processor accesses the memory. Memory specifications, including the maximum supported memory size, memory type, and maximum memory speed, all affect the performance of multi-threaded applications. Verification method, a multi-threaded data verification method can be used to improve the efficiency and quality of data verification. In addition, when conducting verification, it is necessary to ensure that these characteristics of the multi-threaded processor meet the expected performance standards and can provide the required parallel processing capabilities in actual applications.

[0072] It is worth noting that the index classification multi-threaded processor refers to a series of standards and metrics used to evaluate the performance of multi-threaded processors. These indicators can help users understand the performance of the processor when processing multi-threaded applications. Here are some of the main index classifications: Number of cores and threads, the number of physical cores of the processor and the number of logical threads implemented by multi-threading technology. Memory bandwidth and type, the bandwidth and type of memory, which affects the speed of data exchange between the processor and the memory. Multi-threading technology, which allows each physical core to execute multiple threads and improve multi-tasking capabilities. Comprehensive performance evaluation, evaluates the overall performance of the processor through a series of standardized tests. Actual performance index evaluation, evaluates the performance of the processor in a specific task by simulating real application scenarios. Concurrency and parallel processing capabilities, the ability of the processor to handle multiple tasks at the same time. Thermal design power consumption, the expected heat dissipation of the processor under maximum load, affects the design of the cooling solution. The instruction sets supported by the processor, these instruction sets can accelerate specific types of computing tasks. Extension options, affect the communication capabilities of the processor with other hardware devices. Verification method, using a multi-threaded data verification method to improve the efficiency and quality of data verification. Performance hybrid architecture: some processors use a hybrid architecture of performance cores and efficiency cores to provide different performance and power consumption balances. Energy efficiency: the ratio of performance to power consumption, used to measure the energy efficiency of the processor. Compatibility: the compatibility of the processor with the current and future hardware ecosystem, including motherboards, memory, storage, etc.

[0073] It is worth noting that when the data to be verified are all normal data, the data to be verified can first be classified and verified based on the verification classification multi-threaded processor, and then multiple classification verification information can be obtained; then, the multiple classification verification information can be subsequently subjected to indicator verification based on the indicator classification multi-threaded processor, and multiple indicator verification information can be obtained; finally, the multiple indicator verification information can be comprehensively analyzed and processed to obtain the data verification results corresponding to the data to be verified, so that the entire data verification process can be faster and more convenient.

[0074] It is worth noting that through the above technical solution, the sequential execution is changed to dual multi-threaded parallel processing; the original sequential execution belongs to single-threaded processing, and after the improvement, dual multi-threaded processing is provided, and the execution efficiency has been doubled; it supports dynamic changes. Before the improvement, all logic was limited to the code and could not be adjusted in real time according to the evolution of the business. After the improvement, it supports dynamic changes based on business adjustments without changing the logic such as data structure. The mapping of operation processing classes maps different processing classes according to different indicator classification categories, and there is no need to have a processing class for a single operation indicator, which improves scalability and reusability.

[0075] In some embodiments, Figure 2 As shown, the above step S200 may include but is not limited to steps S210 to S220.

[0076] Step S210, obtaining business scenario information;

[0077] Step S220, performing business scope validity analysis on the data to be verified according to the business scenario information to obtain data analysis results.

[0078] It should be noted that in the process of performing validity analysis on the data to be verified to obtain the data analysis results, the business scenario information is first obtained; then, the business scope validity analysis is performed on the data to be verified based on the business scenario information to obtain the data analysis results; through the above method, the validity analysis is performed on the data to be verified first, and then the verification is continued, which effectively prevents unnecessary data verification operations and saves computing resources.

[0079] It is worth noting that in the process of analyzing the validity of data, it is necessary to base it on business scenario information; different business scenario information may correspond to different valid data. By determining the business scenario information, the corresponding data to be verified can be selected flexibly and conveniently, so that the data verification process can be well adapted to different business scenarios.

[0080] In some embodiments, Figure 3As shown, the above step S300 may include but is not limited to step S310 to step S320.

[0081] Step S310, performing verification classification and distribution processing on the data to be verified to obtain multiple categories to be verified;

[0082] Step S320 , based on the verification classification multi-thread processor, parallel classification verification processing is performed on multiple categories to be verified to obtain multiple classification verification information.

[0083] It should be noted that in the process of classifying and verifying the data to be verified based on the preset verification classification multi-threaded processor to obtain multiple classification verification information, the data to be verified is first subjected to verification classification distribution processing to obtain multiple classifications to be verified; then, based on the verification classification multi-threaded processor, multiple classifications to be verified are subjected to parallel classification verification processing to obtain multiple classification verification information; through the above-mentioned technical scheme, the classification verification of the data to be verified can be made more stable and reliable, and the verification classification multi-threaded processor can be used to greatly improve the data verification efficiency.

[0084] It is worth noting that by performing verification classification and distribution processing on the data to be verified, multiple categories to be verified can be obtained; then, based on the verification classification multi-threaded processor, multiple categories to be verified are classified and verified in parallel. Through parallel processing, the efficiency of classification verification can be greatly improved. There is no need to verify and process the data sequentially as before, which greatly improves efficiency.

[0085] In some embodiments, Figure 4 As shown, the above step S400 may include but is not limited to step S410 to step S420.

[0086] Step S410, performing indicator classification and distribution processing on multiple classified verification information to obtain multiple indicators to be verified;

[0087] Step S420, based on the indicator classification multi-thread processor, parallel indicator verification processing is performed on the multiple indicators to be verified to obtain multiple indicator verification information.

[0088] It should be noted that, in the process of obtaining multiple indicator verification information by performing indicator verification processing on multiple classification verification information based on a preset indicator classification multi-threaded processor, firstly, multiple classification verification information can be subjected to indicator classification distribution processing to obtain multiple indicators to be verified; then, subsequently, multiple indicators to be verified can be subjected to parallel indicator verification processing based on the indicator classification multi-threaded processor, thereby obtaining multiple indicator verification information; through the above technical scheme, the indicator verification of the data to be verified can be more stable and reliable, and the indicator classification multi-threaded processor can be used to further improve the data verification efficiency.

[0089] It is worth noting that based on the above technical solution, the original sequential single-thread execution is adjusted to dual-thread parallel execution, which are respectively a verification classification multi-thread processor and an indicator classification multi-thread processor. The amount of data processed by different classifications is the same, but the difference is that there is no need to proceed in the order of classification, that is, there is no need to wait for the previous classification to be processed before executing the next classification. It can be processed in parallel, and different indicator operation categories are equipped with different core processing classes, which can greatly improve the efficiency of data verification.

[0090] In some embodiments, Figure 5 As shown, the above step S420 may include but is not limited to steps S421 to S424.

[0091] Step S421, when the indicator to be verified is a review indicator, the multi-threaded processor performs review indicator calculations on the indicators to be verified respectively based on the indicator classification to obtain review calculation results;

[0092] Step S422, when the indicator to be verified is a single indicator, the multi-threaded processor performs single indicator calculations on the indicator to be verified respectively based on the indicator classification to obtain a single calculation result;

[0093] Step S423, when the indicator to be verified is a specific indicator, the multi-threaded processor performs specific indicator operations on the indicator to be verified respectively based on the indicator classification to obtain a specific operation result;

[0094] Step S424, determining the review operation result, the single operation result and the specific operation result as the indicator verification information.

[0095] It should be noted that in the process of performing parallel indicator verification processing on multiple indicators to be verified based on the indicator classification multi-thread processor to obtain multiple indicator verification information, when the indicator to be verified is a review indicator, the indicator classification multi-thread processor can be used to perform review indicator calculation processing on the indicator to be verified to obtain a review calculation result; when the indicator to be verified is a single indicator, the indicator classification multi-thread processor can be used to perform single indicator calculation processing on the indicator to be verified to obtain a single calculation result; when the indicator to be verified is a specific indicator, the indicator classification multi-thread processor can be used to perform specific indicator calculation processing on the indicator to be verified to obtain a specific calculation result; finally, the review calculation result, the single calculation result and the specific calculation result can be summarized and analyzed to obtain the corresponding indicator verification information. Through the above method, the indicator verification process can be more stable and reliable.

[0096] It is worth noting that in the process of indicator verification processing for multiple indicators to be verified based on indicator classification multi-threaded processor, the type of the indicator to be verified is first determined, and then the corresponding indicator operation is performed on the indicator to be verified, so as to obtain multiple operation results, and finally the multiple operation results are comprehensively analyzed and processed to obtain the data verification result. The whole process is simple, fast and reliable.

[0097] It should be noted that single indicators refer to those that can only reflect a certain feature of the analysis object. This type of indicator is single and accurate, and can directly and clearly reflect certain aspects of the problem, but its disadvantage is that it is not comprehensive enough. Review indicators usually refer to indicators that re-examine data or results to ensure the accuracy and reliability of the analysis. Review indicators can be used for a variety of purposes, including but not limited to: confirming the correctness of data by recalculating or using different methods; re-evaluating the analysis results to ensure the rationality of the analysis methods and processes; and making decisions based on data with greater confidence by reviewing key indicators.

[0098] In some embodiments, Figure 6 As shown, the above step S500 may include but is not limited to step S510 to step S520.

[0099] Step S510, integrating and processing multiple indicator verification information to obtain indicator verification results;

[0100] Step S520, statistically analyzing and processing the indicator verification results to obtain data verification results.

[0101] It should be noted that in the process of comprehensively analyzing and processing multiple indicator verification information to obtain data verification results, the indicator verification results can be obtained by first integrating the multiple indicator verification information; then, the indicator verification results can be statistically analyzed to obtain the corresponding data verification results; through the above method, the process of determining the data verification results can be simpler and faster.

[0102] It is worth noting that after obtaining multiple indicator verification information, the multiple indicator verification information can be integrated to obtain the indicator verification results; then the obtained indicator verification results can be statistically analyzed to obtain the corresponding data verification results. The whole process is simple and fast.

[0103] In some embodiments, Figure 7 As shown, after executing the above step S500, steps S610 to S620 may also be included but not limited to.

[0104] Step S610, constructing a data verification portrait according to the data verification result;

[0105] Step S620, sending the data verification portrait to a preset data monitoring terminal.

[0106] It should be noted that after comprehensive analysis and processing of multiple indicator verification information to obtain data verification results, a data verification portrait can be constructed based on the data verification results, and then the data verification portrait can be sent to a pre-set data monitoring terminal, so that the operating monitoring personnel can intuitively and quickly understand the data verification status, which brings great convenience to the operating monitoring personnel.

[0107] It is worth noting that data verification portrait is a detailed description constructed by integrating and analyzing the multi-dimensional data of users or objects, which can help enterprises better understand user needs, behaviors and goals. The core of data portrait is to label the massive data of users to form a full picture of user information, so as to provide support for big data applications such as personalized recommendation, precision marketing, and intelligent marketing. The construction process of data verification portrait includes data collection, data cleaning, feature extraction, user classification, in-depth analysis and strategy formulation. Data portrait can be applied to multiple scenarios such as precision marketing, personalized services, user experience optimization and market forecasting. When constructing data verification portrait, a single indicator usually refers to a numerical value that reflects a specific characteristic; while a verification indicator may refer to a set of indicators used to verify or review a result or decision. Specific indicators refer to those indicators that are specially designed to measure specific attributes or characteristics. These indicators may have unique calculation methods or data sources to meet specific analysis needs.

[0108] It is worth noting that the data monitoring terminal can be a mobile phone, a computer or other control tablet.

[0109] In addition, if Figure 8 As shown, an embodiment of the present application further provides a data verification device 10, comprising:

[0110] The first processing module 100 is used to obtain data to be verified;

[0111] The second processing module 200 is used to perform validity analysis on the data to be verified to obtain data analysis results;

[0112] The third processing module 300 is used to perform classification verification processing on the data to be verified based on a preset verification classification multi-thread processor to obtain multiple classification verification information when the data analysis result indicates that the data to be verified are all normal data;

[0113] The fourth processing module 400 is used to perform indicator verification processing on multiple classification verification information based on a preset indicator classification multi-thread processor to obtain multiple indicator verification information;

[0114] The fifth processing module 500 is used to perform comprehensive analysis and processing on multiple indicator verification information to obtain data verification results.

[0115] It should be noted that in the process of data verification, the data to be verified is first obtained, and then the validity analysis is performed on the data to be verified to obtain the data analysis results. Then, when the data analysis results indicate that the data to be verified are all normal data, the data to be verified will be classified and verified based on the preset verification classification multi-threaded processor to obtain multiple classification verification information; subsequently, the multiple classification verification information can be subjected to indicator verification based on the preset indicator classification multi-threaded processor to obtain multiple indicator verification information; finally, the multiple indicator verification information can be comprehensively analyzed and processed to obtain the data verification results; through the above-mentioned technical scheme, the verification classification multi-threaded processor is used for classification verification, and the indicator classification multi-threaded processor is used for indicator verification, so that the efficiency of data verification can be greatly accelerated to better meet the data verification requirements of big data.

[0116] It is worth noting that the data to be verified is the data that needs to be verified; before the data to be verified is verified, the validity of the data to be verified needs to be analyzed and processed. On the premise that the data to be verified is normal data, the verification classification multi-thread processor and the indicator classification multi-thread processor will be used to verify the data to be verified. Through the above technical solution, the data verification can be more accurate and reliable. For example, in the financial industry, multiple application systems can be used to store and process relevant data, so these data can be verified and processed later to improve security.

[0117] It is worth noting that the verification classification of multi-threaded processors refers to computing components that can execute multiple threads simultaneously on a single processor. This design can improve the processor's resource utilization, thereby improving performance. Multi-threaded processors are often very important in the fields of servers and high-performance computing because they can handle a large number of concurrent tasks. In the verification classification of multi-threaded processors, the following aspects are usually considered: the number of cores and threads, the number of physical cores of the processor and the number of logical threads implemented through technologies such as hyperthreading. The processor base frequency and maximum frequency, which affects the base and maximum speed of the processor when performing tasks. Cache, the processor's cache size is crucial to the performance of multi-threaded tasks because it reduces the number of times the processor accesses memory.

[0118] It is worth noting that the indicator classification multi-threaded processor refers to a series of standards and metrics used to evaluate the performance of multi-threaded processors. Actual performance indicator evaluation, evaluates the performance of the processor in a specific task by simulating real application scenarios. Concurrency and parallel processing capability, the ability of the processor to handle multiple tasks at the same time. Thermal design power consumption, the expected heat dissipation of the processor under maximum load, affects the design of the cooling solution. The instruction sets supported by the processor, which can accelerate specific types of computing tasks. Expansion options, affect the communication capabilities of the processor with other hardware devices. Performance hybrid architecture, some processors use a hybrid architecture of performance cores and efficiency cores to provide different performance and power consumption balances. Energy efficiency ratio, the ratio of performance to power consumption, is used to measure the energy efficiency of the processor. Compatibility, the compatibility of the processor with the current and future hardware ecosystem, including motherboards, memory, storage, etc.

[0119] The specific implementation of the data verification device 10 is basically the same as the specific implementation of the above-mentioned data verification method, and will not be repeated here.

[0120] 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 .

[0121] The processor 710 and the memory 720 may be connected via a bus or in other ways.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The above embodiments may be used in combination, and modules with the same name in different embodiments may be the same or different.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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 AT91 SAM, 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 The steps for the functions specified in one or more boxes.

[0137] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0138] 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 RAM. The memory is an example of a computer-readable medium.

[0139] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. 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 disk read-only memory (CD-ROM), digital versatile disk (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 media), such as modulated data signals and carrier waves.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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: Obtain the data to be verified; Performing validity analysis on the data to be verified to obtain data analysis results; When the data analysis result indicates that the data to be verified are all normal data, the data to be verified are classified and verified based on a preset verification classification multi-thread processor to obtain multiple classification verification information; Based on a preset indicator classification multi-thread processor, an indicator verification process is performed on multiple classification verification information to obtain multiple indicator verification information; Comprehensive analysis and processing are performed on multiple indicator verification information to obtain data verification results.

2. The data verification method according to claim 1, characterized in that: The validity analysis of the data to be verified is performed to obtain data analysis results, including: Obtain business scenario information; The business scope validity analysis is performed on the data to be verified according to the business scenario information to obtain the data analysis result.

3. The data verification method according to claim 1, characterized in that: The preset verification classification based multi-threaded processor performs classification verification processing on the data to be verified to obtain multiple classification verification information, including: Performing verification classification and distribution processing on the data to be verified to obtain multiple categories to be verified; Based on the verification classification multi-thread processor, parallel classification verification processing is performed on the multiple categories to be verified to obtain multiple classification verification information.

4. The data verification method according to claim 1, characterized in that: The preset indicator classification based multi-thread processor performs indicator verification processing on multiple classification verification information to obtain multiple indicator verification information, including: Performing indicator classification and distribution processing on the plurality of classified verification information to obtain a plurality of indicators to be verified; Based on the indicator classification, the multi-threaded processor performs parallel indicator verification processing on the multiple indicators to be verified to obtain multiple indicator verification information.

5. The data verification method according to claim 4, characterized in that: The multi-threaded processor based on the indicator classification performs parallel indicator verification processing on the multiple indicators to be verified to obtain multiple indicator verification information, including: In the case where the indicator to be verified is a recheck indicator, the multi-threaded processor performs recheck indicator calculations on the indicator to be verified respectively based on the indicator classification to obtain recheck calculation results; In the case where the indicator to be verified is a single indicator, the multi-threaded processor performs single indicator operations on the indicator to be verified respectively based on the indicator classification to obtain a single operation result; In the case where the indicator to be verified is a specific indicator, the multi-threaded processor performs specific indicator operations on the indicator to be verified respectively based on the indicator classification to obtain a specific operation result; The review operation result, the single operation result and the specific operation result are determined as the indicator verification information.

6. The data verification method according to claim 1, characterized in that: The comprehensive analysis and processing of the plurality of indicator verification information to obtain the data verification results includes: Integrate and process the multiple indicator verification information to obtain the indicator verification result; Statistical analysis is performed on the indicator verification results to obtain the data verification results.

7. The data verification method according to claim 1, characterized in that: After the comprehensive analysis and processing of the plurality of indicator verification information is performed to obtain the data verification result, the method further includes: Constructing a data verification portrait based on the data verification results; The data verification portrait is sent to a preset data monitoring terminal.

8. A data verification device, characterized in that: include: A first processing module, used for obtaining data to be verified; The second processing module is used to perform validity analysis on the data to be verified to obtain data analysis results; A third processing module is used for, when the data analysis result indicates that the data to be verified are all normal data, performing classification verification processing on the data to be verified based on a preset verification classification multi-thread processor to obtain multiple classification verification information; A fourth processing module is used to perform indicator verification processing on multiple classification verification information based on a preset indicator classification multi-thread processor to obtain multiple indicator verification information; The fifth processing module is used to perform comprehensive analysis and processing on the multiple indicator verification information to obtain data verification results.

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 as described in 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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