Inspection and testing data management system and method based on the Internet of Things

Abstract

The present invention relates to the field of data management technology, and in particular to an inspection and testing data management system and method based on the Internet of Things. The present invention performs an abnormal evolution analysis of the operation of the inspection equipment according to the operation status of the inspection equipment, analyzes the abnormal operation of the inspection equipment, performs an impact analysis of the material on the equipment based on the characteristic data of the inspection sample material of the corresponding inspection equipment, analyzes the impact of the inspection workpiece on the abnormal operation of the equipment during the inspection process, performs an equipment damage analysis based on the analysis results of the abnormal evolution of the operation of the corresponding inspection equipment and the analysis results of the material impact on the equipment, performs an equipment damage analysis during the inspection process based on the comprehensive impact of the material on the abnormal equipment, sets a maintenance cycle based on the equipment damage analysis results, dynamically adjusts the maintenance cycle by matching the inspection material with the equipment damage status, and improves the timeliness and safety of equipment maintenance.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to an inspection and testing data management system and method based on the Internet of Things. Background Art

[0002] Currently, testing equipment maintenance primarily follows a fixed-cycle model. However, in actual operation, the degree of equipment wear is significantly affected by the characteristics of the test materials. For example, when testing materials above HRC60, the life of the indenter on a metal hardness tester is 40% shorter than under normal operating conditions. Traditional methods fail to consider material-equipment interactions, leading to: insufficient maintenance (standard maintenance cycles are still applied even under high-wear conditions) causing equipment failures; and excessive maintenance (premature maintenance of low-load equipment) causing resource waste.

[0003] For example, the application number CN202411293773.3 discloses a metering equipment maintenance management system based on data analysis, which includes an equipment management unit, a maintenance management unit, a maintenance cycle analysis unit, a usage statistics analysis unit, and an environmental statistics analysis unit. In this invention, when maintaining and managing metering equipment, the metering equipment is no longer maintained at fixed maintenance intervals. Instead, the usage of the metering equipment is analyzed based on the workload and working hours of the metering equipment. The usage is used as the basis for maintenance of the metering equipment. At the same time, the external environment of the metering equipment is analyzed, and the environmental interference factors caused by environmental factors are fully combined to make the maintenance of the metering equipment more in line with the actual situation.

[0004] However, the above-mentioned prior art ignores the material-equipment interaction, resulting in low punctuality and safety of equipment maintenance;

[0005] In order to solve these problems, this application designs an inspection and testing data management system and method based on the Internet of Things. Summary of the Invention

[0006] In order to overcome the defects and shortcomings of the existing technology, the present invention provides an inspection and testing data management system and method based on the Internet of Things.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] In a first aspect, the present invention provides an inspection and testing data management method based on the Internet of Things, comprising the following steps:

[0009] S1. Obtaining the operating status of the corresponding testing equipment and the characteristic data of the test sample material of the corresponding testing equipment;

[0010] S2. Analyze the abnormal evolution of the detection equipment operation based on the operation status of the corresponding detection equipment;

[0011] S3. Analyze the impact of materials on equipment based on the characteristic data of the test sample materials of the corresponding test equipment;

[0012] S4. Perform equipment damage analysis based on the corresponding detection equipment operation abnormality evolution analysis results and the material impact analysis results on the equipment;

[0013] S5. Set maintenance cycles based on equipment damage analysis results.

[0014] In one implementation of the present invention, the operating conditions of the corresponding detection equipment include data on changes in the equipment's measurement accuracy for standard objects, as well as damage data and circuit operation data of the detection equipment, wherein the circuit operation data includes circuit operation voltage and current data, and the characteristic data of the detection sample material include shape data and hardness data, magnetic data, and data on the types of parameters that need to be measured for the material, wherein the shape of the material is acquired through the corresponding image acquisition terminal, and the damage to the measuring equipment during detection is analyzed through the corresponding material shape and hardness data, and the damage to the equipment circuit by the material is analyzed through the influence of the material's magnetic field strength on the circuit operation data.

[0015] In one implementation of the present invention, performing abnormal evolution analysis of the detection equipment operation based on the operation status of the corresponding detection equipment in step S2 includes the following specific steps:

[0016] S21. Obtaining data on changes in the measurement accuracy of the device for the standard object and data on damage to the detection device;

[0017] S22. Perform equipment operation abnormality analysis based on the measurement accuracy change data of the equipment on the standard object and the damage data of the detection equipment. The calculation formula for the equipment operation abnormality analysis is: , where V() is the volume of the image in brackets, This is the image of the measuring end when the measuring device is not damaged. is the image of the real-time measuring end of the measuring device, exp() is the power of the natural constant e, mi is the real-time measurement result of the device on the standard object, and mc is the standard measurement result of the standard object. Here, the damage caused by the measurement process to the measuring end is analyzed by analyzing the changes in the image of the measuring end during the measurement process. At the same time, the abnormality of the device measurement is analyzed by the comprehensive measurement accuracy.

[0018] S23. Obtain circuit operation data of the corresponding detection device, and perform circuit abnormality analysis based on the circuit operation data of the corresponding detection device, wherein the circuit abnormality analysis formula is: , where N is the type of circuit operation data, xj is the specific value of the j-th type of operation data during the operation of the corresponding detection equipment, and xjm is the standard value of the j-th type of operation data during the operation of the corresponding detection equipment.

[0019] In one implementation of the present invention, performing material impact analysis on equipment based on characteristic data of the test sample material of the corresponding test equipment in step S3 includes the following specific steps:

[0020] S31. Obtain shape data and hardness data of future batches of materials, and perform equipment collision risk analysis caused by material movement during measurement based on the shape data and hardness data of future batches of materials. The equipment collision risk analysis formula is: ,in, is the safe distance between the measuring end and the workpiece during measurement, Lz is the real-time distance between the measuring end and the workpiece during measurement, kz is the hardness of the measuring end, and kx is the hardness of the object. It is the angle between the workpiece and the nearest corner of the measuring end during measurement. Understanding the impact of the workpiece's shape, position, and corresponding hardness on the risk of collision with the measuring end, corresponding to the sharpness of the workpiece's corners, helps to accurately assess the risk of collision between the workpiece and the measuring end during measurement;

[0021] S32. Obtain the magnetic condition of the corresponding workpiece, and perform electromagnetic interference anomaly assessment based on the magnetic condition of the corresponding workpiece. The electromagnetic interference anomaly assessment formula is: , where Tr is the magnetic field strength generated by the workpiece, and Ts is the magnetic field strength that the measuring device can safely withstand. In this step, the electromagnetic interference anomaly is analyzed by the impact of the workpiece's magnetic field on the device.

[0022] In one implementation of the present invention, in step S4, the equipment damage analysis is performed based on the corresponding detection equipment operation abnormality evolution analysis results and the material impact analysis results, including the following specific contents:

[0023] S41. Obtain equipment operation abnormality analysis results and equipment collision risk analysis results, and perform equipment operation risk analysis based on the equipment operation abnormality analysis results and the equipment collision risk analysis results. The equipment operation risk analysis formula is: ,This step comprehensively analyzes the aging abnormalities of the equipment and the collision risk of ,the workpiece to the equipment, analyzes the future equipment operation risk, ,and analyzes the degree of operation risk of the aging equipment under the ,collision risk;

[0024] S42. Obtain electromagnetic interference anomaly assessment results and circuit anomaly analysis results, and perform circuit operation risk assessment based on the electromagnetic interference anomaly assessment results and circuit anomaly analysis results. The circuit operation risk assessment formula is: ,This allows analyzing the circuit operation danger of equipment with abnormal electrical signals under the influence of electromagnetic interference;

[0025] S43. Obtain the equipment operation hazard analysis results and the circuit operation hazard assessment results, and perform weighted summation to obtain the equipment damage analysis results.

[0026] In one implementation of the present invention, setting the maintenance period based on the equipment damage analysis results in step S5 includes the following specific contents:

[0027] Obtain the obtained equipment damage analysis result, divide the equipment damage analysis result by the equipment damage analysis threshold to obtain the equipment damage ratio, and obtain the maintenance period of the corresponding detection equipment by dividing the set maintenance period by the equipment damage ratio.

[0028] In a second aspect, the present invention further provides an inspection and testing data management system based on the Internet of Things, comprising:

[0029] A data acquisition module is used to obtain the operating conditions of the corresponding detection equipment and the characteristic data of the detection sample material of the corresponding detection equipment;

[0030] Operation abnormality evolution analysis module, which analyzes the abnormality evolution of detection equipment based on the operation status of the corresponding detection equipment;

[0031] The equipment impact analysis module analyzes the impact of materials on equipment based on the characteristic data of the test sample materials of the corresponding test equipment;

[0032] The equipment damage analysis module performs equipment damage analysis based on the abnormal evolution analysis results of the corresponding detection equipment and the analysis results of the impact of materials on the equipment;

[0033] The maintenance cycle setting module sets the maintenance cycle based on the equipment damage analysis results.

[0034] In a third aspect, the present invention provides an electronic device comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes an inspection and testing data management method based on the Internet of Things by calling the computer program stored in the memory.

[0035] In a fourth aspect, the present invention provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute an inspection and testing data management method based on the Internet of Things.

[0036] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0037] Based on the operation status of the corresponding testing equipment, an abnormal operation evolution of the testing equipment is analyzed. The operation abnormality of the testing equipment is analyzed. The influence of the material on the equipment is analyzed based on the characteristic data of the test sample material of the corresponding testing equipment. The influence of the test workpiece on the abnormal operation of the equipment during the testing process is analyzed. The equipment damage analysis is performed based on the analysis results of the abnormal operation evolution of the corresponding testing equipment and the analysis results of the material influence on the equipment. The equipment damage analysis during the testing process is performed based on the comprehensive influence of the material on the abnormal equipment. The maintenance cycle is set based on the equipment damage analysis results. The maintenance cycle is dynamically adjusted by matching the test material with the equipment damage situation, thereby improving the timeliness and safety of equipment maintenance. 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 Schematic diagram of the overall process of an embodiment of the method of the present invention;

[0040] Figure 2 This is a workflow diagram of S2 in the embodiment of the method of the present invention;

[0041] Figure 3 This is a workflow diagram of S4 in the embodiment of the method of the present invention;

[0042] Figure 4 Schematic diagram of the structure of the system embodiment of the present invention. DETAILED DESCRIPTION

[0043] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0044] Example 1

[0045] like Figures 1 to 3 As shown, this embodiment provides an inspection and testing data management method based on the Internet of Things, which specifically includes the following steps:

[0046] S1. Obtaining the operating status of the corresponding testing equipment and the characteristic data of the test sample material of the corresponding testing equipment;

[0047] In this embodiment, the operating status of the corresponding detection equipment includes data on changes in the equipment's measurement accuracy for standard objects, as well as data on damage to the detection equipment and circuit operation data. The circuit operation data includes circuit operation voltage and current data, and the characteristic data of the detection sample material includes shape data, hardness data, magnetic data, and data on the types of parameters that need to be measured for the material in future batches. The shape of the material is acquired via a corresponding image acquisition terminal, and the damage to the measuring equipment during the test is analyzed using the corresponding material shape and hardness data. Simultaneously, the damage to the device circuit by the material is analyzed based on the impact of the material's magnetic field strength on the circuit operation data. The acquired data is stored in a corresponding storage module.

[0048] For example, in this embodiment, the corresponding inspection type is the inspection of the size or hardness of the production workpiece. Then, the parameter type data that needs to be measured for the material is the inspection of the size or hardness of the production workpiece. Since the hardness of the workpiece is required in the equipment collision risk analysis in this embodiment, the approximate value of the hardness of the workpiece is used in the equipment collision risk analysis. The preferred approximate value is the hardness of the standard production workpiece.

[0049] At the same time, in this embodiment, the methods of collecting data are all conventional technical means in the field, such as collecting current through a current sensor, collecting shape through an image sensor, etc.;

[0050] S2. Analyze the abnormal evolution of the detection equipment operation based on the operation status of the corresponding detection equipment;

[0051] In this embodiment, the abnormal evolution analysis of the detection equipment operation is performed based on the operation status of the corresponding detection equipment, including the following specific steps:

[0052] S21. Obtaining data on changes in the measurement accuracy of the device for the standard object and data on damage to the detection device;

[0053] S22. Perform equipment operation abnormality analysis based on the measurement accuracy change data of the equipment on the standard object and the damage data of the detection equipment. The calculation formula for the equipment operation abnormality analysis is: , where V() is the volume of the image in brackets, This is the image of the measuring end when the measuring device is not damaged. is the image of the real-time measurement end of the measurement device, exp() is the power of the natural constant e, mi is the real-time measurement result of the device on the standard object, and mc is the standard measurement result of the standard object. Here, the damage caused by the measurement process to the measurement end is analyzed by analyzing the changes in the measurement end image during the measurement process. At the same time, the device measurement anomalies are analyzed by comprehensive measurement accuracy. Not only the deviation of the measurement data is considered, but also the physical state of the device is introduced to more comprehensively reflect the device anomaly. For example, if the device causes measurement distortion due to mechanical wear, even if the current error is not large, physical damage can still affect the analysis of device operation anomalies.

[0054] S23. Obtain circuit operation data of the corresponding detection device, and perform circuit abnormality analysis based on the circuit operation data of the corresponding detection device, wherein the circuit abnormality analysis formula is: , where N is the type of circuit operation data, xj is the specific value of the j-th type of operation data during the operation of the corresponding detection equipment, and xjm is the standard value of the j-th type of operation data during the operation of the corresponding detection equipment;

[0055] For example, the circuit operation data includes the device operating current and voltage data. The quality of the device circuit is evaluated by the difference between the specific operating data and the standard operating data. For example, the standard values ​​of the device operating current and operating voltage are 5A and 220V, respectively. However, due to the influence of the device's own defects, the real-time operating current and voltage are 4.8A and 210V. Therefore, the circuit abnormality analysis value is calculated as: (5-4.8) / 5+(220-210) / 220=0.09;

[0056] S3. Analyze the impact of materials on equipment based on the characteristic data of the test sample materials of the corresponding test equipment;

[0057] In this embodiment, performing material impact analysis on equipment based on characteristic data of the test sample material of the corresponding test equipment includes the following specific steps:

[0058] S31. Obtain shape data and hardness data of future batches of materials, and perform equipment collision risk analysis caused by material movement during measurement based on the shape data and hardness data of future batches of materials. The equipment collision risk analysis formula is: ,in, is the safe distance between the measuring end and the workpiece during measurement, Lz is the real-time distance between the measuring end and the workpiece during measurement, kz is the hardness of the measuring end, and kx is the hardness of the object. It is the angle between the workpiece and the nearest corner of the measuring end during measurement. Understanding the impact of the workpiece's shape, position, and corresponding hardness on the risk of collision with the measuring end, corresponding to the sharpness of the workpiece's corners, helps accurately assess the risk of collision between the workpiece and the measuring end during measurement. By integrating shape sharpness, hardness ratio, and real-time distance, this formula can quantify the collision risk.

[0059] S32. Obtain the magnetic condition of the corresponding workpiece, and perform electromagnetic interference anomaly assessment based on the magnetic condition of the corresponding workpiece. The electromagnetic interference anomaly assessment formula is: , where Tr is the magnetic field strength generated by the workpiece, and Ts is the magnetic field strength that the measuring device can safely withstand. In this step, the electromagnetic interference anomaly is analyzed by the influence of the magnetic field of the corresponding workpiece on the device;

[0060] S4. Perform equipment damage analysis based on the corresponding detection equipment operation abnormality evolution analysis results and the material impact analysis results on the equipment;

[0061] In this embodiment, the equipment damage analysis is performed based on the corresponding detection equipment operation abnormality evolution analysis results and the material impact analysis results, including the following specific contents:

[0062] S41. Obtain equipment operation abnormality analysis results and equipment collision risk analysis results, and perform equipment operation risk analysis based on the equipment operation abnormality analysis results and the equipment collision risk analysis results. The equipment operation risk analysis formula is: ,This step comprehensively analyzes the aging abnormalities of the equipment and the collision risk of ,the workpiece to the equipment, analyzes the future equipment operation risk, ,and analyzes the degree of operation risk of the aging equipment under the ,collision risk;

[0063] S42. Obtain electromagnetic interference anomaly assessment results and circuit anomaly analysis results, and perform circuit operation risk assessment based on the electromagnetic interference anomaly assessment results and circuit anomaly analysis results. The circuit operation risk assessment formula is: ,This allows analyzing the circuit operation danger of equipment with abnormal electrical signals under the influence of electromagnetic interference;

[0064] S43, obtaining the obtained equipment operation hazard analysis results and circuit operation hazard assessment results, performing weighted summation to obtain an equipment damage analysis result;

[0065] S5. Setting maintenance cycles based on equipment damage analysis results;

[0066] In this embodiment, the maintenance cycle is set based on the equipment damage analysis results, including the following specific contents:

[0067] Obtain the obtained equipment damage analysis result, divide the equipment damage analysis result by the equipment damage analysis threshold to obtain the equipment damage ratio, and obtain the maintenance period of the corresponding detection equipment by dividing the set maintenance period by the equipment damage ratio;

[0068] For example, the maintenance period set here is the maintenance period of the equipment during original production.

[0069] It should be noted in this embodiment that the setting parameters in this embodiment (such as various weighted weights and equipment damage analysis thresholds, etc.) are obtained by technical personnel in this embodiment through experiments based on historical data. A specific experimental method is exemplified as follows: obtaining the historical operating conditions of the corresponding detection equipment and the characteristic data of the detection sample materials of the corresponding detection equipment, as well as the equipment maintenance cycle, analyzing the ranking results of the equipment with the best operating conditions under the influence of the equipment maintenance cycle, substituting the operating conditions of the corresponding detection equipment and the characteristic data of the detection sample materials of the corresponding detection equipment into each step of this embodiment to obtain the maintenance cycle, iteratively fitting the data based on the acquisition results of the maintenance cycle and the equipment maintenance cycle results fitting software, and outputting the setting parameter values ​​of this embodiment that meet the maximum judgment accuracy. Acquiring and optimizing the setting parameters of this embodiment through historical data and experiments can significantly improve the judgment accuracy and early warning effect of the system. The specific benefits include improving the judgment accuracy. The specific process includes data collection, data preprocessing, parameter initialization, model training and testing, data fitting and parameter optimization, and parameter verification and iteration, which can provide a scientific basis for inspection and testing data management.

[0070] It should be noted that in this embodiment, this embodiment has the following benefits and advantages: an analysis of the evolution of abnormal operation of the detection equipment is performed based on the operating conditions of the corresponding detection equipment, the operation abnormalities of the detection equipment are analyzed, the influence of the material on the equipment is analyzed based on the characteristic data of the detection sample material of the corresponding detection equipment, the influence of the detection workpiece on the abnormal operation of the equipment during the detection process is analyzed, equipment damage analysis is performed based on the analysis results of the evolution of abnormal operation of the corresponding detection equipment and the analysis results of the influence of the material on the equipment, equipment damage analysis is performed during the detection process based on the comprehensive influence of the material on the abnormal equipment, maintenance cycles are set based on the equipment damage analysis results, and the maintenance cycles are dynamically adjusted by matching the detection materials with the equipment damage conditions, thereby improving the punctuality and safety of equipment maintenance.

[0071] Example 2

[0072] like Figure 4 As shown, this embodiment provides an inspection and testing data management system based on the Internet of Things, including: a data acquisition module, the data acquisition module is used to obtain the operating status of the corresponding detection equipment and the characteristic status data of the detection sample material of the corresponding detection equipment;

[0073] Operation abnormality evolution analysis module, which analyzes the abnormality evolution of detection equipment based on the operation status of the corresponding detection equipment;

[0074] The equipment impact analysis module analyzes the impact of materials on equipment based on the characteristic data of the test sample materials of the corresponding test equipment;

[0075] The equipment damage analysis module performs equipment damage analysis based on the abnormal evolution analysis results of the corresponding detection equipment and the analysis results of the impact of materials on the equipment;

[0076] The maintenance cycle setting module sets the maintenance cycle based on the equipment damage analysis results.

[0077] Example 3

[0078] An electronic device according to an embodiment of the present invention includes a processor and a memory, wherein the memory stores a computer program that can be called by the processor. The processor executes an Internet of Things-based inspection and testing data management method by calling the computer program stored in the memory. It should be noted that all computer programs of the Internet of Things-based inspection and testing data management method are implemented using the C language.

[0079] Example 4

[0080] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0081] When the computer program runs on a computer device, the computer device executes the above-mentioned inspection and testing data management method based on the Internet of Things.

[0082] The various embodiments of the present invention are described in a progressive manner. Similar portions between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the IoT device and medium embodiments are described briefly because they are generally similar to the method embodiments. For relevant portions, refer to the description of the method embodiments.

[0083] The system and medium provided in the embodiments of the present invention correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to their corresponding methods. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0084] Those skilled in the art will appreciate that embodiments of the present invention may provide methods, systems, or computer program products. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0085] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, 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 produce 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 flowcharts and / or block diagrams. 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.

[0086] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The function specified in one or more boxes.

[0087] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0088] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0089] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can be implemented using any method or technology for information storage. 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 RAM (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 technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0090] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0091] The above are merely embodiments of the present invention and are not intended to limit the present invention. It will be apparent to those skilled in the art that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are intended to be included within the scope of the claims of the present invention.

Claims

1. The inspection and testing data management method based on the Internet of Things is characterized by: The steps include: S1. Obtaining the operating status of the corresponding testing equipment and the characteristic data of the test sample material of the corresponding testing equipment; S2. Analyze the abnormal evolution of the detection equipment operation based on the operation status of the corresponding detection equipment; S3. Analyze the impact of materials on equipment based on the characteristic data of the test sample materials of the corresponding test equipment; S4. Perform equipment damage analysis based on the corresponding detection equipment operation abnormality evolution analysis results and the material impact analysis results on the equipment; S5. Setting maintenance cycles based on equipment damage analysis results. The analysis of abnormal operation evolution of the detection equipment based on the operation status of the corresponding detection equipment includes the following specific steps: S21. Obtain data on changes in the measurement accuracy of the device on the standard object, as well as data on damage to the detection device; S22. Analyze equipment operation abnormalities based on the data on changes in the equipment's measurement accuracy for standard objects and the data on damage to the testing equipment; S23. Obtain circuit operation data of the corresponding detection device, and perform circuit abnormality analysis based on the circuit operation data of the corresponding detection device, wherein the circuit abnormality analysis formula is: , where N is the type of circuit operation data, xj is the specific value of the j-th type of operation data during the operation of the corresponding detection device, and xjm is the standard value of the j-th type of operation data during the operation of the corresponding detection device; the material impact analysis based on the characteristic data of the detection sample material of the corresponding detection device includes the following specific steps: S31. Obtain shape data and hardness data of future batches of materials, and perform equipment collision risk analysis caused by material movement during measurement based on the shape data and hardness data of future batches of materials. The equipment collision risk analysis formula is: ,in, is the safe distance between the measuring end and the workpiece during measurement, Lz is the real-time distance between the measuring end and the workpiece during measurement, kz is the hardness of the measuring end, and kx is the hardness of the object. It is the angle between the workpiece and the nearest corner of the measuring end during measurement; S32. Obtain the magnetic condition of the corresponding workpiece, and perform electromagnetic interference anomaly assessment based on the magnetic condition of the corresponding workpiece. The electromagnetic interference anomaly assessment formula is: , where Tr is the magnetic field strength generated by the workpiece, and Ts is the magnetic field strength that the measuring equipment can safely withstand; the equipment damage analysis based on the corresponding detection equipment operation abnormality evolution analysis results and the material impact analysis results on the equipment includes the following specific contents: S41. Obtain equipment operation abnormality analysis results and equipment collision risk analysis results, and perform equipment operation risk analysis based on the equipment operation abnormality analysis results and the equipment collision risk analysis results. The equipment operation risk analysis formula is: ,This step comprehensively analyzes the aging abnormalities of the equipment and the collision risk of ,the workpiece to the equipment, analyzes the future equipment operation risk, ,and analyzes the degree of operation risk of the aging equipment under the ,collision risk; S42. Obtain electromagnetic interference anomaly assessment results and circuit anomaly analysis results, and perform circuit operation risk assessment based on the electromagnetic interference anomaly assessment results and circuit anomaly analysis results. The circuit operation risk assessment formula is: ,This allows analyzing the circuit operation danger of equipment with abnormal electrical signals under the influence of electromagnetic interference; S43. Obtain the equipment operation hazard analysis results and the circuit operation hazard assessment results, and perform weighted summation to obtain the equipment damage analysis results.

2. The method for managing inspection and testing data based on the Internet of Things according to claim 1, characterized in that: The setting of maintenance cycles based on equipment damage analysis results includes the following specific contents: Obtain the obtained equipment damage analysis result, divide the equipment damage analysis result by the equipment damage analysis threshold to obtain the equipment damage ratio, and obtain the maintenance period of the corresponding detection equipment by dividing the set maintenance period by the equipment damage ratio.

3. The method for managing inspection and testing data based on the Internet of Things according to claim 2, characterized in that: The calculation formula for equipment operation abnormality analysis is: , where V() is the volume of the image in brackets, This is the image of the measuring end when the measuring device is not damaged. is the image of the real-time measurement end of the measuring device, exp() is the power of the natural constant e, mi is the real-time measurement result of the device on the standard object, and mc is the standard measurement result of the standard object.

4. The inspection and testing data management method based on the Internet of Things according to claim 1 is characterized in that: The operating conditions of the corresponding detection equipment include data on changes in the equipment's measurement accuracy for standard objects, as well as damage data and circuit operation data of the detection equipment, wherein the circuit operation data includes circuit operation voltage and current data, and the characteristic data of the detection sample material include shape data and hardness data, magnetic data, and data on the types of parameters that need to be measured for the material in future batches, wherein the shape of the material is obtained through the corresponding image acquisition terminal.

5. An inspection and testing data management system based on the Internet of Things, which is implemented based on the inspection and testing data management method based on the Internet of Things according to any one of claims 1 to 4, characterized in that: The system comprises: A data acquisition module is used to obtain the operating conditions of the corresponding detection equipment and the characteristic data of the detection sample material of the corresponding detection equipment; Operation abnormality evolution analysis module, which analyzes the abnormality evolution of detection equipment based on the operation status of the corresponding detection equipment; The equipment impact analysis module analyzes the impact of materials on equipment based on the characteristic data of the test sample materials of the corresponding test equipment; The equipment damage analysis module performs equipment damage analysis based on the abnormal evolution analysis results of the corresponding detection equipment and the analysis results of the impact of materials on the equipment; The maintenance cycle setting module sets the maintenance cycle based on the equipment damage analysis results.

6. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the inspection and testing data management method based on the Internet of Things as described in any one of claims 1 to 4 by calling the computer program stored in the memory.

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