Analysis method, system and equipment of AUT detection data and medium
By establishing a mapping relationship between data identification and parsing methods and using an analytical neural network model in the AUT detection system, the problem of inconsistent data formats of different devices is solved, and unified parsing and efficient management of AUT detection data are achieved.
Patent Information
- Application Number
- CN202410254428.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-03-06
- Publication Date
- 2025-09-09
AI Technical Summary
In the existing AUT detection system, the data structure and storage format of each device are different, which makes it inconvenient for the fourth party and the supervision unit to read and analyze the data, and the learning and training process is complicated.
Through the mapping relationship between data identification and parsing method, the target parsing method is determined, the AUT detection data is parsed, standard parsing data is generated, and an analysis report is generated using the pre-established analysis neural network model.
It realizes unified analysis and standardized processing of data from different devices, improves data processing efficiency, and facilitates data management and interpretation at any time.
Smart Images
Figure CN120609916A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of fully automatic ultrasonic testing, and in particular to an analysis method, system, equipment and medium for AUT testing data. Background Art
[0002] Automatic ultrasonic testing (AUT) is a new nondestructive testing technology primarily used for long-distance pipeline weld quality testing. It features high sensitivity, rapid testing speed, and accurate location and quantification of weld defects. AUT testing technology effectively improves inspection quality and efficiency, reduces inspection workload and construction costs, and offers significant social and economic benefits, meeting the needs of developing inspection and construction technologies.
[0003] With the continuous innovation of technology, AUT detection systems are becoming more and more abundant. However, due to the large number of equipment types, although the usage methods of each are similar, the collected data structure and storage format are different, forming certain technical barriers. Therefore, in the actual construction process, especially the fourth party and the supervision unit, they feel various inconveniences in the operation of different equipment and the reading and analysis of data. For users, they often face many inconveniences during the learning and training process. Summary of the Invention
[0004] The technical problem to be solved by the present invention is to address the deficiencies of the existing technology and specifically provide an analysis method, system, device and medium for AUT test data, as follows:
[0005] 1) In a first aspect, the present invention provides a method for analyzing AUT detection data. The specific technical solution is as follows:
[0006] Determining a target parsing method in a mapping relationship between the data identifier and the parsing method according to the data identifier of the AUT detection data to be analyzed, and parsing the AUT detection data to be analyzed based on the target parsing method to obtain standard parsed data;
[0007] According to the pre-established analysis neural network model, the standard parsing data is analyzed and processed to obtain an analysis report corresponding to the AUT detection data to be analyzed.
[0008] The beneficial effects of the AUT detection data analysis method provided by the present invention are as follows:
[0009] This solution improves the efficiency of the data processing process by identifying and comparing the unique data identifiers of the AUT test data to be analyzed and determining the target parsing method corresponding to the AUT test data to be analyzed. In addition, different parsing methods corresponding to different types of AUT test data to be analyzed can achieve unified normalization processing of the data, thereby improving the standardization of the data.
[0010] Based on the above solution, the present invention can also be improved as follows.
[0011] Furthermore, the process of parsing the AUT detection data to be analyzed based on the target parsing method to obtain standard parsed data is as follows:
[0012] Parsing the AUT detection data to be analyzed based on the target parsing method to obtain an analysis result, wherein the analysis result includes a plurality of parameter values, and the parameter value is at least one of a device parameter, a simulation parameter, a process parameter, and a detection configuration parameter;
[0013] The analysis results are filled into a preset data template to generate standard analysis data.
[0014] Furthermore, the process of filling the analysis results into the preset data template to generate standard analysis data is as follows:
[0015] Determine the target parameter value corresponding to the parameter field in the preset data template in the analysis result, and fill the target parameter value into the corresponding position in the preset data template;
[0016] Parse data based on historical standards to determine common values, and fill in the blanks in the preset data template with the common values.
[0017] Further, the process of determining the common value based on historical standard parsing data is as follows:
[0018] Randomly select a target parameter value from the parsing result as a key value, and the parameter field corresponding to the key value is the key field;
[0019] Determining a target key value having the same value as the key value in the historical standard parsing data, and determining all parameter fields corresponding to the target key value as standard parsing data entries;
[0020] A target key field is determined in the standard parsed data entry, and a parameter value corresponding to the target key field is determined as a universal value.
[0021] 2) In a second aspect, the present invention further provides an analysis system for AUT detection data, the specific technical solution of which is as follows:
[0022] The parsing module is used to: determine a target parsing method in a mapping relationship between the data identifier and the parsing method according to the data identifier of the AUT detection data to be analyzed, and parse the AUT detection data to be analyzed based on the target parsing method to obtain standard parsing data;
[0023] The analysis module is used to: analyze and process the standard parsing data according to a pre-established analysis neural network model to obtain an analysis report corresponding to the AUT detection data to be analyzed.
[0024] Based on the above solution, the present invention can also be improved as follows.
[0025] Furthermore, the process of parsing the AUT detection data to be analyzed based on the target parsing method to obtain standard parsed data is as follows:
[0026] Parsing the AUT detection data to be analyzed based on the target parsing method to obtain an analysis result, wherein the analysis result includes a plurality of parameter values, and the parameter value is at least one of a device parameter, a simulation parameter, a process parameter, and a detection configuration parameter;
[0027] The analysis results are filled into a preset data template to generate standard analysis data.
[0028] Furthermore, the process of filling the analysis results into the preset data template to generate standard analysis data is as follows:
[0029] Determine the target parameter value corresponding to the parameter field in the preset data template in the analysis result, and fill the target parameter value into the corresponding position in the preset data template;
[0030] Parse data based on historical standards to determine common values, and fill in the blanks in the preset data template with the common values.
[0031] Further, the process of determining the common value based on historical standard parsing data is as follows:
[0032] Randomly select a target parameter value from the parsing result as a key value, and the parameter field corresponding to the key value is the key field;
[0033] Determining a target key value having the same value as the key value in the historical standard parsing data, and determining all parameter fields corresponding to the target key value as standard parsing data entries;
[0034] A target key field is determined in the standard parsed data entry, and a parameter value corresponding to the target key field is determined as a universal value.
[0035] 3) In a third aspect, the present invention further provides a computer device, comprising a processor coupled to a memory, wherein the memory stores at least one computer program, and the at least one computer program is loaded and executed by the processor so that the computer device implements any of the above methods.
[0036] 4) In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores at least one computer program, and the at least one computer program is loaded and executed by a processor to enable a computer to implement any of the above methods.
[0037] It should be noted that the beneficial effects achieved by the technical solutions of the second to fourth aspects of the present invention and the corresponding possible implementation methods can be found in the above-mentioned technical effects of the first aspect and its corresponding possible implementation methods, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS
[0038] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:
[0039] Figure 1 1 is a flow chart of a method for analyzing AUT detection data according to an embodiment of the present invention;
[0040] Figure 2 The figure is a schematic diagram of a computer structure framework of a method for analyzing AUT detection data according to an embodiment of the present invention. DETAILED DESCRIPTION
[0041] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.
[0042] like Figure 1 As shown, a method for analyzing AUT detection data according to an embodiment of the present invention includes the following steps:
[0043] S1, according to the data identifier of the AUT detection data to be analyzed, determine the target parsing method in the mapping relationship between the data identifier and the parsing method, and parse the AUT detection data to be analyzed based on the target parsing method to obtain standard parsing data.
[0044] The AUT test data to be analyzed refers to the results obtained from any pipe section after non-destructive testing.
[0045] AUT stands for "Automated Ultrasonic Testing." AUT technology is commonly used in industrial inspections, enabling rapid and accurate detection of defects and cracks in metal materials, corrosion in housings and pipelines, and the integrity of other structures. Through automated testing, AUT technology improves efficiency, reduces costs, and mitigates human error, leading to its widespread adoption in the industrial sector.
[0046] Features of AUT detection technology include:
[0047] (1) Using phased array technology and weld zone scanning technology for detection, the detection speed is fast and the efficiency is high;
[0048] (2) The detection accuracy and quality are improved by combining ultrasonic A-scan, B-scan and TOFD scan for simultaneous detection;
[0049] (3) The accuracy of AUT detection setting parameters is ensured by adopting multi-angle ultrasonic sound velocity measurement fitting technology;
[0050] (4) System performance verification technology ensures system detection stability;
[0051] (5) Through system calibration technology, the detection sensitivity and detection quality are guaranteed;
[0052] (6) Comprehensive evaluation was performed through A-scan, B-scan and TOFD scan patterns, which improved the quality of detection and evaluation.
[0053] The data identifier is a unique identifier. Each set of AUT test data to be analyzed corresponds to a data identifier. The data identifier represents the source of the AUT test data to be analyzed. The data identifier can be a file extension or special identification information used to identify the manufacturer (i.e., the source of the data).
[0054] The mapping relationship between data identifiers and parsing methods refers to a mapping table used to represent the different interpretation methods corresponding to different data identifiers. It can provide a good parsing method for each manufacturer, or it can be a human-written specific parsing method.
[0055] The process of parsing the AUT test data to be analyzed according to the target parsing method to obtain standard parsed data is as follows:
[0056] Parsing the AUT test data to be analyzed according to a target parsing method to obtain a plurality of parameter values, the parameter values including at least one of: equipment parameters, simulation parameters, process parameters, and test configuration parameters;
[0057] The device parameters include: at least one of a transmitting voltage, a receiving gain, a pulse repetition frequency, and an operating frequency;
[0058] The simulation parameters include: at least one of a transmitting angle, a receiving angle, and a carrier frequency;
[0059] The process parameters include: at least one of weld size, geometry, and material type;
[0060] The detection configuration parameters include: at least one of a scanning mode, a scanning speed, a probe arrangement, and a data acquisition frequency.
[0061] Equipment parameters: related parameters of ultrasonic testing instruments, such as transmitting voltage, receiving gain, pulse repetition frequency, operating frequency, etc. These parameters are used to control the transmission and reception of ultrasonic signals.
[0062] Analog parameters: Parameters of the ultrasonic sensor (transducer), such as the emission angle, receiving angle, carrier frequency, etc. Analog parameters affect the propagation characteristics of ultrasonic waves in the object being measured.
[0063] Process parameters: Parameters related to the specific test object and process, such as weld size, geometry, material type, etc. Process parameters determine the feasibility of the test and the interpretation of the results.
[0064] Detection configuration parameters: Parameters determined based on the detection task and requirements, such as scanning method, scanning speed, probe layout, data acquisition frequency, etc. These parameters determine how ultrasonic signals are acquired and processed during actual detection.
[0065] It is understandable that the specific parameters will vary depending on different ultrasonic AUT equipment and detection tasks, and the specific types of parameter values in the actual analysis results can be adjusted and optimized according to specific circumstances.
[0066] Construct a preset data template, which contains multiple parameter fields, where each parameter field corresponds to a parameter value. According to the analysis results, the parameter values in the analysis results are filled in the preset data template to obtain standard analysis data.
[0067] Fill in the parameter values in the analysis results into the preset data template to obtain the standard analysis data as follows:
[0068] Fill the parameter values of the parsing results into the parameter fields of the preset data template;
[0069] According to the analysis results, the data is parsed based on historical standards to obtain the preset common value;
[0070] Fill the preset general value into the parameter field of the preset data template that does not have the parameter value of the parsing result filled in to obtain the standard parsing data.
[0071] The preset data template is a table that includes all possible parameter fields. Its content is filled in according to the parsing results. When all data in an entry is completed, the entry can be regarded as a standard parsed data entry. Table 1 shows an example of the content of the preset data template.
[0072] Table 1
[0073]
[0074] It should be noted that the parsing results corresponding to the AUT detection data to be analyzed may not necessarily include all the types (parameter fields) given in Table 1. Therefore, after filling the parsing results into the preset data template, it is necessary to further fill in the unfilled parameter fields, that is, the blank parameter fields.
[0075] During the filling process, limited confirmation of the preset universal value is required. The process of determining the preset universal value is as follows:
[0076] From the parsing result, a parameter value is selected as a key value, and the parameter field corresponding to the key value is a key field;
[0077] According to the key value, based on the historical standard parsing data, a standard parsing data entry is obtained, wherein the parameter value corresponding to the key field in the standard parsing data entry is the same as the key value;
[0078] The parameter value of the standard parsed data item is used as the preset universal value.
[0079] Historical standard parsed data refers to standard parsed data entries that have been completed and saved in the past. Key fields can be important data such as time and date, workpiece parameters, etc., and can also be manually selected based on specific needs. In this embodiment, standard parsed data entries with operating conditions that are more similar to the actual situation are selected from the historical standard parsed data using key fields and key values. The corresponding parameter fields are then added to the incomplete preset data template, achieving the unification of AUT test data from different sources, different manufacturers, and different formats.
[0080] After the preset universal value is determined, the blank parameter field is filled in. After filling, standard parsing data, namely, standard parsing data entry, is obtained.
[0081] S2, analyzing and processing the standard parsing data according to a pre-established analysis neural network model to obtain an analysis report corresponding to the AUT detection data to be analyzed.
[0082] An analytical neural network model is established, wherein the input of the analytical neural network model is standard analytical data, and the output of the analytical neural network model is an analysis report.
[0083] The process of generating reports based on standard parsed data can adopt any existing report generation method.
[0084] For example, the ConvLSTM model is selected as the analysis neural network model. The ConvLSTM model adds the concept of convolution kernel to the traditional LSTM model, and is also called the convolutional LSTM model.
[0085] In this embodiment, the convolutional LSTM model can process sequence data while also better processing the spatial structural relationship and feature extraction in the input data, can more effectively extract features from sensor data, and also performs well in prediction and generation.
[0086] When processing sensor data, ConvLSTM models are more suitable than traditional LSTM models. This is because sensor data often contains spatial structure, such as the spatial correlation between the values measured by multiple sensors in a sensor array, and these relationships are important for data prediction and report generation.
[0087] The ConvLSTM model builds on the LSTM model by incorporating the concept of convolutional neural network (CNN) layers. By replacing the LSTM unit layer with the ConvLSTM, the model can process both sequential and spatially structured data. The ConvLSTM model can better leverage the multidimensional characteristics of sensor data and better capture spatial and temporal interactions, resulting in excellent prediction and generation performance. In summary, the ConvLSTM model offers advantages when processing spatially structured sensor data, and its network structure can be further optimized through methods such as the addition of an attention mechanism.
[0088] The beneficial effects of the AUT detection data analysis method provided by the present invention are as follows:
[0089] This solution realizes the unified storage format, unified analysis and interpretation of test data of multiple brands, which facilitates data management and the interpretation of test data at any time.
[0090] Furthermore, the process of parsing the AUT detection data to be analyzed based on the target parsing method to obtain standard parsed data is as follows:
[0091] Parsing the AUT detection data to be analyzed based on the target parsing method to obtain an analysis result, wherein the analysis result includes a plurality of parameter values, and the parameter value is at least one of a device parameter, a simulation parameter, a process parameter, and a detection configuration parameter;
[0092] The analysis results are filled into a preset data template to generate standard analysis data.
[0093] Furthermore, the process of filling the analysis results into the preset data template to generate standard analysis data is as follows:
[0094] Determine the target parameter value corresponding to the parameter field in the preset data template in the analysis result, and fill the target parameter value into the corresponding position in the preset data template;
[0095] Parse data based on historical standards to determine common values, and fill in the blanks in the preset data template with the common values.
[0096] Further, the process of determining the common value based on historical standard parsing data is as follows:
[0097] Randomly select a target parameter value from the parsing result as a key value, and the parameter field corresponding to the key value is the key field;
[0098] Determining a target key value having the same value as the key value in the historical standard parsing data, and determining all parameter fields corresponding to the target key value as standard parsing data entries;
[0099] A target key field is determined in the standard parsed data entry, and a parameter value corresponding to the target key field is determined as a universal value.
[0100] In the above embodiments, although the steps are numbered S1, S2, etc., these are only specific embodiments given by the present invention. Those skilled in the art may adjust the execution order of S1, S2, etc. according to actual conditions, which is also within the scope of protection of the present invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.
[0101] The present invention also provides an analysis system for AUT detection data, and the specific technical solution is as follows:
[0102] The parsing module is used to: determine a target parsing method in a mapping relationship between the data identifier and the parsing method according to the data identifier of the AUT detection data to be analyzed, and parse the AUT detection data to be analyzed based on the target parsing method to obtain standard parsing data;
[0103] The analysis module is used to: analyze and process the standard parsing data according to a pre-established analysis neural network model to obtain an analysis report corresponding to the AUT detection data to be analyzed.
[0104] Based on the above solution, the present invention can also be improved as follows.
[0105] Furthermore, the process of parsing the AUT detection data to be analyzed based on the target parsing method to obtain standard parsed data is as follows:
[0106] Parsing the AUT detection data to be analyzed based on the target parsing method to obtain an analysis result, wherein the analysis result includes a plurality of parameter values, and the parameter value is at least one of a device parameter, a simulation parameter, a process parameter, and a detection configuration parameter;
[0107] The analysis results are filled into a preset data template to generate standard analysis data.
[0108] Furthermore, the process of filling the analysis results into the preset data template to generate standard analysis data is as follows:
[0109] Determine the target parameter value corresponding to the parameter field in the preset data template in the analysis result, and fill the target parameter value into the corresponding position in the preset data template;
[0110] Parse data based on historical standards to determine common values, and fill in the blanks in the preset data template with the common values.
[0111] Further, the process of determining the common value based on historical standard parsing data is as follows:
[0112] Randomly select a target parameter value from the parsing result as a key value, and the parameter field corresponding to the key value is the key field;
[0113] Determining a target key value having the same value as the key value in the historical standard parsing data, and determining all parameter fields corresponding to the target key value as standard parsing data entries;
[0114] A target key field is determined in the standard parsed data entry, and a parameter value corresponding to the target key field is determined as a universal value.
[0115] It should be noted that the beneficial effects of the AUT test data analysis system provided in the above embodiment are the same as the beneficial effects of the AUT test data analysis method provided in the above embodiment, and will not be repeated here. In addition, when implementing its functions, the system provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to actual conditions to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.
[0116] like Figure 2 As shown, a computer device 300 according to an embodiment of the present invention includes a processor 320, which is coupled to a memory 310. The memory 310 stores at least one computer program 330. The at least one computer program 330 is loaded and executed by the processor 320, so that the computer device 300 implements any of the above methods. Specifically:
[0117] The computer device 300 may vary significantly due to different configurations or performance, and may include one or more processors 320 (Central Processing Units, CPUs) and one or more memories 310. The one or more memories 310 store at least one computer program 330, which is loaded and executed by the one or more processors 320 to enable the computer device 300 to implement the AUT test data analysis method provided in the above-mentioned embodiment. Of course, the computer device 300 may also include components such as a wired or wireless network interface, a keyboard, and input / output interfaces for input and output. The computer device 300 may also include other components for implementing device functions, which are not detailed here.
[0118] A computer-readable storage medium according to an embodiment of the present invention stores at least one computer program, and the at least one computer program is loaded and executed by a processor to enable a computer to implement any of the above methods.
[0119] Alternatively, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, or the like.
[0120] In an exemplary embodiment, a computer program product or computer program is also provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform any of the above methods.
[0121] It should be noted that the terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects and to define a specific order or precedence. Where appropriate, the order used for similar objects may be interchanged, such that the embodiments of the present application described herein can be implemented in an order other than the order shown or described.
[0122] Those skilled in the art will appreciate that the present invention may be implemented as a system, method, or computer program product. Therefore, the present disclosure may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, the present invention may be implemented in the form of a computer program product embodied in one or more computer-readable media containing computer-readable program code.
[0123] Any combination of one or more computer-readable media can be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or device.
[0124] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.
Claims
1. A method for analyzing AUT detection data, characterized in that: include: Determining a target parsing method in a mapping relationship between the data identifier and the parsing method according to the data identifier of the AUT test data to be analyzed, and parsing the AUT test data to be analyzed based on the target parsing method to obtain standard parsed data; The standard parsing data is analyzed and processed according to a pre-established analysis neural network model to obtain an analysis report corresponding to the AUT detection data to be analyzed.
2. The method for analyzing AUT detection data according to claim 1, characterized in that: The process of parsing the AUT detection data to be analyzed based on the target parsing method to obtain standard parsed data is as follows: Parsing the AUT detection data to be analyzed based on the target parsing method to obtain an analysis result, wherein the analysis result includes a plurality of parameter values, and the parameter value is at least one of a device parameter, a simulation parameter, a process parameter, and a detection configuration parameter; The analysis results are filled into a preset data template to generate standard analysis data.
3. The method for analyzing AUT detection data according to claim 2, characterized in that: The process of filling the analysis results into the preset data template to generate standard analysis data is as follows: Determine the target parameter value corresponding to the parameter field in the preset data template in the analysis result, and fill the target parameter value into the corresponding position in the preset data template; Parse data based on historical standards to determine common values, and fill in the blanks in the preset data template with the common values.
4. The method for analyzing AUT detection data according to claim 3, characterized in that: The process of parsing data to determine common values based on historical standards is: Randomly select a target parameter value from the parsing result as a key value, and the parameter field corresponding to the key value is the key field; Determining a target key value having the same value as the key value in the historical standard parsing data, and determining all parameter fields corresponding to the target key value as standard parsing data entries; A target key field is determined in the standard parsed data entry, and a parameter value corresponding to the target key field is determined as a universal value.
5. An analysis system for AUT test data, characterized in that: include: The parsing module is used to: determine a target parsing method in a mapping relationship between the data identifier and the parsing method according to the data identifier of the AUT detection data to be analyzed, and parse the AUT detection data to be analyzed based on the target parsing method to obtain standard parsing data; The analysis module is used to: analyze and process the standard parsing data according to a pre-established analysis neural network model to obtain an analysis report corresponding to the AUT detection data to be analyzed.
6. The AUT detection data analysis system according to claim 5, characterized in that: The process of parsing the AUT detection data to be analyzed based on the target parsing method to obtain standard parsed data is as follows: Parsing the AUT detection data to be analyzed based on the target parsing method to obtain an analysis result, wherein the analysis result includes a plurality of parameter values, and the parameter value is at least one of a device parameter, a simulation parameter, a process parameter, and a detection configuration parameter; The analysis results are filled into a preset data template to generate standard analysis data.
7. The AUT detection data analysis system according to claim 6, characterized in that: The process of filling the analysis results into the preset data template to generate standard analysis data is as follows: Determine the target parameter value corresponding to the parameter field in the preset data template in the analysis result, and fill the target parameter value into the corresponding position in the preset data template; Parse data based on historical standards to determine common values, and fill in the blanks in the preset data template with the common values.
8. The AUT detection data analysis system according to claim 7, characterized in that: The process of parsing data to determine common values based on historical standards is: Randomly select a target parameter value from the parsing result as a key value, and the parameter field corresponding to the key value is the key field; Determining a target key value having the same value as the key value in the historical standard parsing data, and determining all parameter fields corresponding to the target key value as standard parsing data entries; A target key field is determined in the standard parsed data entry, and a parameter value corresponding to the target key field is determined as a universal value.
9. A computer device, characterized in that: The computer device includes a processor coupled to a memory, wherein the memory stores at least one computer program, and the at least one computer program is loaded and executed by the processor, so that the computer device implements the method according to any one of claims 1 to 4.
10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores at least one computer program, which is loaded and executed by a processor to enable a computer to implement the method according to any one of claims 1 to 4.