A data detection method, device, terminal and storage medium

Abstract

The present application is applicable to the field of data processing technology, and provides a data detection method, device, terminal and storage medium, wherein the method comprises: converting the data to be detected in a first format to obtain target data in a second format; converting the trusted data in a third format to obtain control data in a fourth format; performing data difference comparison based on the target data and the control data, and marking the difference of the target data based on the data difference comparison result; and outputting the difference detection result of the data to be detected based on the target data after the difference marking. This solution can improve the accuracy and efficiency of data processing.

Description

Technical Field

[0001] The present application belongs to the field of data processing technology, and in particular, relates to a data detection method, device, terminal and storage medium. Background Art

[0002] With the rapid and stable development of the national economy, capital market, etc., the Internet has also developed and popularized, and various information data has shown an explosive increase.

[0003] The capital market generates a large amount of business data every day, including capital market-related regulations, financial data, etc. The data structure and content of various data are quite different. These data may be processed into structured data or heterogeneous data. The quality of the processed data varies, and the content of the data obtained is very likely to be accompanied by data errors, which often have the characteristics of not obvious errors. It is impossible to accurately detect and check the huge amount of processed data manually, and it takes a long time to check such data and may not necessarily detect errors. Summary of the invention

[0004] The embodiments of the present application provide a data detection method, device, terminal and storage medium to solve the problem in the prior art that it is difficult to accurately and effectively check and verify huge amounts of processed data.

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

[0006] Convert the data to be detected in a first format to obtain target data in a second format;

[0007] Convert the trusted data in the third format to obtain comparison data in the fourth format; wherein the trusted data is original data for generating the data to be detected; or the trusted data and the data to be detected are generated based on the same original data;

[0008] Based on the target data and the control data, a data difference comparison is performed, and based on the data difference comparison result, a difference annotation is performed on the target data;

[0009] Based on the target data after the difference marking, the difference detection result of the data to be detected is output.

[0010] A second aspect of an embodiment of the present application provides a data detection device, including:

[0011] A first conversion module, used for converting the data to be detected in a first format to obtain target data in a second format;

[0012] A second conversion module, used for converting the trusted data in the third format to obtain the comparison data in the fourth format; wherein the trusted data is the original data for generating the data to be detected; or the trusted data and the data to be detected are generated based on the same original data;

[0013] A comparison module, used to perform data difference comparison based on the target data and the control data, and to perform difference annotation on the target data based on the data difference comparison result;

[0014] The output module is used to output the difference detection result of the data to be detected based on the target data after the difference annotation.

[0015] A third aspect of an embodiment of the present application provides a terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in the first aspect when executing the computer program.

[0016] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0017] A fifth aspect of the present application provides a computer program product, which, when executed on a terminal, enables the terminal to execute the steps of the method described in the first aspect.

[0018] As can be seen from the above, in the embodiment of the present application, the data to be detected in the first format is format-converted to obtain the target data in the second format, the trusted data in the third format is format-converted to obtain the control data in the fourth format, and based on the target data and the control data, data difference comparison is performed, and based on the data difference comparison result, the target data is difference-marked, and based on the difference-marked target data, the difference detection result of the data to be detected is output. In this process, both the data to be detected and the trusted data are format-converted, so that the trusted data after the format conversion is formed as the control data of the data to be detected, so that the data to be detected after the format conversion is compared with the control data, and then the data of the data to be detected obtained by processing is difference-marked, and the difference detection result is obtained, which effectively realizes the accurate inspection and verification of the huge processing data, and improves the accuracy and efficiency of data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 This is a process of a data detection method provided in an embodiment of the present application. Figure 1 ;

[0021] Figure 2 This is a process of a data detection method provided in an embodiment of the present application. Figure 2 ;

[0022] Figure 3 is a structural diagram of a data detection device provided in an embodiment of the present application;

[0023] Figure 4 It is a structural diagram of a terminal provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0025] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0026] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0027] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0028] As used in this specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0029] In a specific implementation, the terminal described in the embodiments of the present application includes, but is not limited to, other portable devices such as mobile phones, laptop computers, or tablet computers with touch-sensitive surfaces (e.g., touch screen displays and / or touch pads). It should also be understood that in some embodiments, the device is not a portable communication device, but a desktop computer with a touch-sensitive surface (e.g., touch screen displays and / or touch pads).

[0030] In the following discussion, a terminal including a display and a touch-sensitive surface is described. However, it should be understood that the terminal may include one or more other physical user interface devices such as a physical keyboard, mouse and / or joystick.

[0031] The terminal supports various applications, such as one or more of the following: a drawing application, a presentation application, a word processing application, a website creation application, a disk burning application, a spreadsheet application, a game application, a telephone application, a video conferencing application, an email application, an instant messaging application, a workout support application, a photo management application, a digital camera application, a digital camcorder application, a web browsing application, a digital music player application, and / or a digital video player application.

[0032] Various applications that can be executed on the terminal can use at least one common physical user interface device such as a touch-sensitive surface. One or more functions of the touch-sensitive surface and corresponding information displayed on the terminal can be adjusted and / or changed between applications and / or within corresponding applications. In this way, the common physical architecture of the terminal (e.g., the touch-sensitive surface) can support various applications with user interfaces that are intuitive and transparent to the user.

[0033] It should be understood that the size of the serial numbers of the steps in this embodiment does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0034] In order to illustrate the technical solution described in this application, a specific embodiment is provided below for illustration.

[0035] See also Figure 1 , Figure 1 This is a process of a data detection method provided in an embodiment of the present application. Figure 1 .like Figure 1 As shown, a data detection method comprises the following steps:

[0036] Step 101 : convert the data to be detected in a first format to obtain target data in a second format.

[0037] The data to be tested is, for example, financial data, laws and regulations, announcements and other data. The first format is the data content format formed by the data to be tested in the process of generating the original data. For example, when the data to be tested is annual report data, the first format is the annual report format; when the data to be tested is regulatory data, the first format is the regulatory text format.

[0038] The second format may be a data format that facilitates display of key information of the data, such as a data work order format, or a data page, data table, etc., or a data arrangement format after the first format is unformatted. Unformatting refers to a data processing method that identifies data from a certain data format to obtain the data content itself. For example, image data is subjected to content recognition to obtain the numbers, symbols, text, etc. in the image and store them. The content recognition process realizes the unformatting of the data in the image.

[0039] The target data may include all or part of the data to be detected. When the target data includes part of the data, the target data may include key information data extracted from the data to be detected.

[0040] As an optional implementation, the step of converting the data to be detected in the first format to obtain target data in the second format includes:

[0041] The data to be detected constructed according to the preset data structure is deconstructed according to the preset data structure to obtain the data content of the data to be detected; based on the data content, a data work order containing the data content is generated; and the data work order is determined as the target data.

[0042] The preset data structure is specifically an array organization structure for arranging data in a structured manner, such as an HTML page-style data arrangement structure, or an Excel-style data arrangement structure, and the like.

[0043] The data work order is specifically obtained by organizing the deconstructed data content in the form of a work order. The data work order may include a data identification code (the data identification code reflects the unique identity of the data, which may be an invoice number, a notice number, a regulation number, etc.), deconstructed fields to be checked, strings to be checked, and operation rules for the data to be checked.

[0044] Step 102: convert the trusted data in the third format to obtain comparison data in the fourth format.

[0045] The trusted data and the data to be detected have the same main data content.

[0046] The credible data is the original data used to generate the data to be detected, that is, the data to be detected is generated based on the original data. The original data is the data source for generating the data to be detected.

[0047] Alternatively, the trusted data and the data to be detected are generated based on the same original data. Here, the trusted data may be data generated by other credible departments or units after processing the original data. The trusted data and the data to be detected are generated from the same data source.

[0048] The trusted data may include data of different forms and contents; for example, it may include department performance data, employee salary data, operating cost data, tax payment data, etc. The trusted data may include data in the form of Excel spreadsheets, image data, document text data, etc.

[0049] Here, the fourth format may be the same format as the second format, so as to directly realize the conversion of the trusted data and the data to be detected into the same format.

[0050] The comparison data may include all or part of the data in the trusted data. When the comparison data includes part of the data, the comparison data may include key information data extracted from the trusted data.

[0051] As an optional implementation, the format conversion of the trusted data in the third format to obtain the comparison data in the fourth format includes:

[0052] Deformatting the trusted data in the third format;

[0053] The deformatted trusted data is determined as control data.

[0054] Deformatting (i.e., removing formatting) original files of different formats is performed to achieve unified format processing among original files.

[0055] Step 103: perform data difference comparison based on the target data and the control data, and perform difference annotation on the target data based on the data difference comparison result.

[0056] When data difference comparison is performed based on target data and control data, the target data and control data have the same data format. It can be based on the target data and control data of the same format, extract key information for data difference comparison, or compare different data contents in the target data and control data in sequence according to a preset data comparison order.

[0057] Specifically, based on the data difference comparison results, the target data is marked with differences, including:

[0058] Based on the data difference comparison results, the data content in the target data is marked as data content with differences or data content without differences, so as to detect whether there are data processing errors in different data contents in the target data.

[0059] Step 104: output the difference detection result of the data to be detected based on the target data after the difference annotation.

[0060] The difference detection result is used to indicate whether there is any problematic data in the data to be detected and what the problematic data are.

[0061] In the embodiment of the present application, the data to be detected in the first format is format-converted to obtain the target data in the second format, the trusted data in the third format is format-converted to obtain the control data in the fourth format, and a data difference comparison is performed based on the target data and the control data, and the target data is difference-marked based on the data difference comparison result, and the difference detection result of the data to be detected is output based on the target data after the difference marking. In this process, the data to be detected and the trusted data are both format-converted, so that the trusted data after the format conversion is formed as the control data of the data to be detected, so that the data to be detected after the format conversion is compared with the control data, and then the data of the data to be detected obtained by processing is difference-marked to obtain the difference detection result, which effectively realizes the accurate inspection and verification of the huge processing data, improves the accuracy and efficiency of data processing, and reduces the detection cost.

[0062] Further, as an optional implementation, outputting the difference detection result of the data to be detected based on the target data after the difference annotation includes:

[0063] Determine whether there is data content marked as having differences in the target data after the difference marking; if there is data content marked as having differences in the target data after the difference marking, verify the correctness of the data content marked as having differences through the artificial intelligence difference-assisted judgment model; determine the data content with the correctness verification result being correctly marked as the difference data content; and output the difference detection result containing the difference data content.

[0064] The artificial intelligence difference auxiliary judgment model can be a convolutional neural network model capable of feature recognition. The artificial intelligence difference auxiliary judgment model is used to further verify the correctness of the difference annotation result.

[0065] The difference data content is the data content with correct marking that is further determined from the data content marked as having differences in the target data, that is, the data content that does have data differences compared to the control data.

[0066] In this process, the difference annotation content is further screened and determined, which improves the accuracy of data detection and the validity of the data after detection, improves data detection efficiency and reduces detection costs.

[0067] Furthermore, as an optional implementation, if there is data content marked as having differences in the target data after the difference marking, the data content marked as having differences is checked for marking correctness by using an artificial intelligence difference-assisted judgment model, including:

[0068] If there is data content marked as having differences in the target data after the difference marking, the data content marked as having differences is manually verified, and the non-difference data content with the difference marking error is determined from the data content; data features are extracted for the non-difference data content; based on the extracted data features, the artificial intelligence difference-assisted judgment model is trained; and the data content marked as having differences is further verified for the correctness of the marking using the trained artificial intelligence difference-assisted judgment model.

[0069] The non-difference data content is data content that is marked as having errors in the data content marked as having differences.

[0070] Here, a manual verification process is introduced to further check the data after machine annotation. The purpose of this manual verification is to find the data content that is marked incorrectly from the data content marked as having differences as more accurate and effective training samples, to achieve accurate training of the artificial intelligence difference-assisted judgment model, so that the trained artificial intelligence difference-assisted judgment model has better auxiliary judgment performance, accurately finds the wrong data product content, further improves the accuracy of data detection, and the effectiveness of the detected data, to ensure data quality.

[0071] Different implementation methods of the data detection method are also provided in the examples of the present application.

[0072] See also Figure 2 , Figure 2 This is a process of a data detection method provided in an embodiment of the present application. Figure 2 .like Figure 2As shown, a data detection method comprises the following steps:

[0073] Step 201 : convert the data to be detected in a first format to obtain target data in a second format.

[0074] The implementation process of this step is the same as the implementation method of step 101 in the aforementioned embodiment, and will not be repeated here.

[0075] Step 202: convert the trusted data in the third format to obtain comparison data in the fourth format.

[0076] The trusted data is original data used to generate the data to be detected; or, the trusted data and the data to be detected are generated based on the same original data.

[0077] The implementation process of this step is the same as the implementation method of step 102 in the aforementioned embodiment, and will not be repeated here.

[0078] Step 203, extracting the first key information from the target data and extracting the second key information from the comparison data.

[0079] The key information is, for example, a data identification code (invoice number, announcement number, regulation number, etc.).

[0080] Step 204: Based on the first key information and the second key information, the comparison order of the data content in the comparison data is converted.

[0081] The key information of each data content in the comparison data after the comparison order is converted corresponds to the key information of the data content in the target data one by one.

[0082] Here, the data content of the control data is mainly replaced so that the arrangement order of the data content in the control data can be consistent with that in the target data, so as to facilitate the detection of the target data.

[0083] Among them, each data content in the comparison data has corresponding key information. For example, the key information of the invoice data is the invoice number, and the invoice data may include the invoice issuing entity, amount, date, etc. The key information is used to correspond the corresponding data content in the comparison data with the data content in the target data, so as to adjust the arrangement order of different data contents in the comparison data during data comparison, so that the data content in the comparison data can be consistent with the arrangement order of the corresponding content in the target data, thereby realizing correct and effective comparison of the data between the target data and the comparison data.

[0084] Step 205 , performing data difference comparison between the target data and the control data after the comparison order is converted, and marking the difference of the target data based on the data difference comparison result.

[0085] Step 206: output the difference detection result of the data to be detected based on the target data after the difference annotation.

[0086] The implementation process of this step is the same as the implementation method of step 104 in the aforementioned embodiment, and will not be repeated here.

[0087] In an embodiment of the present application, the data to be detected in a first format is format-converted to obtain target data in a second format, the trusted data in a third format is format-converted to obtain control data in a fourth format, key information is extracted from the target data and the control data, the comparison order of the data content in the control data is converted based on the key information, the target data is compared with the control data after the comparison order is converted, and the target data is differentially annotated based on the data difference comparison result, and the difference detection result of the data to be detected is output based on the target data after the difference annotated. In this process, both the data to be detected and the trusted data are format-converted, so that the trusted data after the format conversion is formed as the control data of the data to be detected, so that the data to be detected after the format conversion is compared with the control data, and then the data of the data to be detected obtained by processing is differentially annotated to obtain the difference detection result, which effectively realizes the accurate inspection and verification of the huge processing data, improves the accuracy and efficiency of data processing, and reduces the detection cost.

[0088] See also Figure 3 , Figure 3 This is a structural diagram of a data detection device provided in an embodiment of the present application. For the sake of convenience of explanation, only the parts related to the embodiment of the present application are shown.

[0089] The data detection device 300 comprises:

[0090] The first conversion module 301 is used to convert the data to be detected in a first format to obtain target data in a second format;

[0091] The second conversion module 302 is used to convert the trusted data in the third format to obtain the comparison data in the fourth format; wherein the trusted data is the original data for generating the data to be detected; or the trusted data and the data to be detected are generated based on the same original data;

[0092] A comparison module 303 is used to perform data difference comparison based on the target data and the control data, and to perform difference annotation on the target data based on the data difference comparison result;

[0093] The output module 304 is used to output the difference detection result of the data to be detected based on the target data after the difference annotation.

[0094] The first conversion module 301 is specifically used for:

[0095] Deconstructing the data to be detected constructed according to a preset data structure according to the preset data structure to obtain data content of the data to be detected;

[0096] Based on the data content, generating a data work order containing the data content;

[0097] The data work order is determined as the target data.

[0098] The second conversion module 302 is specifically used for:

[0099] Deformatting the trusted data in the third format;

[0100] The deformatted trusted data is determined as the comparison data.

[0101] Wherein, the comparison module 303 is specifically used for:

[0102] Extracting first key information from the target data and extracting second key information from the control data;

[0103] Based on the first key information and the second key information, a comparison order conversion is performed on the data content in the comparison data; wherein the key information of each data content in the comparison data after the comparison order conversion corresponds one-to-one with the key information of the data content in the target data;

[0104] The target data is compared with the control data after the comparison order is converted.

[0105] The output module 304 is specifically used for:

[0106] Determining whether there is data content marked as having differences in the target data after the difference marking;

[0107] If there is data content marked as having differences in the target data after the difference marking, the data content marked as having differences is checked for marking correctness by using an artificial intelligence difference-assisted judgment model;

[0108] Determine the data content with the correct annotation as a result of the annotation correctness check as the difference data content;

[0109] Output the difference detection result including the difference data content.

[0110] The output module 304 is further specifically used for:

[0111] If there is data content marked as having differences in the target data after the difference marking, manually verify the data content marked as having differences, and determine non-difference data content with difference marking errors from the data content;

[0112] Extracting data features from the non-difference data content;

[0113] Based on the extracted data features, model training is performed on the artificial intelligence difference auxiliary judgment model;

[0114] The trained artificial intelligence difference-assisted judgment model is used to further verify the correctness of the marking of the data content marked as having differences.

[0115] The data detection device provided in the embodiment of the present application can implement each process of the embodiment of the above-mentioned data detection method and can achieve the same technical effect. To avoid repetition, it will not be described here.

[0116] Figure 4 4 is a structural diagram of a terminal provided in an embodiment of the present application. As shown in the figure, the terminal 4 of this embodiment includes: at least one processor 40 ( Figure 4 Only one is shown in the figure), a memory 41, and a computer program 42 stored in the memory 41 and executable on the at least one processor 40, wherein the processor 40 implements the steps of any of the above-mentioned method embodiments when executing the computer program 42.

[0117] The terminal 4 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal 4 may include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art will appreciate that Figure 4 It is only an example of terminal 4 and does not constitute a limitation on terminal 4. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal may also include input and output devices, network access devices, buses, etc.

[0118] The processor 40 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0119] The memory 41 may be an internal storage unit of the terminal 4, such as a hard disk or memory of the terminal 4. The memory 41 may also be an external storage device of the terminal 4, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the terminal 4. The memory 41 is used to store the computer program and other programs and data required by the terminal. The memory 41 may also be used to temporarily store data that has been output or is to be output.

[0120] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0121] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0122] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0123] In the embodiments provided in the present application, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0124] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0125] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0126] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0127] The present application implements all or part of the processes in the above-mentioned embodiment method, and may also be implemented through a computer program product. When the computer program product runs on a terminal, the terminal can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0128] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A data detection method, characterized in that: include: Convert the data to be detected in a first format to obtain target data in a second format; The data to be tested includes: financial data, regulations or announcement data; the first format is the data content format formed by the data to be tested in the process of generating the original data; the second format is a data format that is convenient for displaying key information of the data; Converting the trusted data in the third format to obtain comparison data in a fourth format; the fourth format is the same as the second format; Wherein, the trusted data is the original data for generating the data to be detected; or, the trusted data and the data to be detected are generated based on the same original data; Based on the target data and the control data, a data difference comparison is performed, and based on the data difference comparison result, a difference annotation is performed on the target data; Based on the target data after difference annotation, outputting a difference detection result of the data to be detected; The data difference comparison based on the target data and the control data includes: extracting the first key information from the target data and extracting the second key information from the control data; based on the first key information and the second key information, performing a comparison order conversion on the data content in the control data; wherein the key information of each data content in the control data after the comparison order conversion corresponds one-to-one with the key information of the data content in the target data; performing a data difference comparison between the target data and the control data after the comparison order conversion; the comparison order conversion on the data content in the control data based on the first key information and the second key information includes: swapping the data content of the control data so that the arrangement order of the data content in the control data is consistent with the arrangement order of the data content in the target data.

2. The data detection method according to claim 1, characterized in that: The step of converting the data to be detected in the first format to obtain target data in the second format includes: Deconstructing the data to be detected constructed according to a preset data structure according to the preset data structure to obtain data content of the data to be detected; Based on the data content, generating a data work order containing the data content; The data work order is determined as the target data.

3. The data detection method according to claim 1, characterized in that: The step of converting the trusted data in the third format to obtain the comparison data in the fourth format includes: Deformatting the trusted data in the third format; The deformatted trusted data is determined as the comparison data.

4. The data detection method according to claim 1, characterized in that: The step of outputting a difference detection result of the to-be-detected data based on the target data after the difference annotation includes: Determining whether there is data content marked as having differences in the target data after the difference marking; If there is data content marked as having differences in the target data after the difference marking, the data content marked as having differences is checked for marking correctness by using an artificial intelligence difference-assisted judgment model; Determine the data content with the correct annotation as a result of the annotation correctness check as the difference data content; Output the difference detection result including the difference data content.

5. The data detection method according to claim 4, characterized in that: If there is data content marked as having differences in the target data after the difference marking, the data content marked as having differences is checked for marking correctness through the artificial intelligence difference-assisted judgment model, including: The model is broken, and the data content marked as having differences continues to be checked for correctness of the marking.

6. A data detection device, characterized in that: include: A first conversion module, used for converting the data to be detected in a first format to obtain target data in a second format; The data to be tested includes: financial data, regulations or announcement data; the first format is the data content format formed by the data to be tested in the process of generating the original data; the second format is a data format that is convenient for displaying key information of the data; A second conversion module is used to convert the trusted data in the third format to obtain the comparison data in the fourth format; wherein the trusted data is the original data for generating the data to be detected; or the trusted data and the data to be detected are generated based on the same original data; and the fourth format is the same format as the second format; A comparison module, used for performing data difference comparison based on the target data and the control data, and marking the difference of the target data based on the data difference comparison result; the comparison module is specifically used for: extracting the first key information from the target data and extracting the second key information from the control data; based on the first key information and the second key information, performing comparison order conversion on the data content in the control data; wherein the key information of each data content in the control data after the comparison order conversion corresponds one-to-one with the key information of the data content in the target data; performing data difference comparison between the target data and the control data after the comparison order conversion; wherein, the comparison order conversion of the data content in the control data based on the first key information and the second key information includes: exchanging the data content of the control data so that the arrangement order of the data content in the control data is consistent with the arrangement order of the data content in the target data; The output module is used to output the difference detection result of the data to be detected based on the target data after the difference annotation.

7. The data detection device according to claim 6, characterized in that: The first conversion module is specifically used for: Deconstructing the data to be detected constructed according to a preset data structure according to the preset data structure to obtain data content of the data to be detected; Based on the data content, generating a data work order containing the data content; The data work order is determined as the target data.

8. A terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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