Inspection and detection service system

Abstract

The invention discloses an inspection and detection service system, and relates to the technical field of inspection and detection services, and the system comprises the following components: a digital twinborn model construction module, a virtual detection simulation module, a problem analysis and optimization module, and a detection personnel training module. Through integrated application of the digital twinborn model building module, the virtual detection simulation module, the problem analysis and optimization module and the detector training module, the accuracy and efficiency of the detection process are remarkably improved, the system can simulate the whole detection process through a virtual environment before actual detection, potential problems are found and optimized, and the detection efficiency is improved. Therefore, errors and delays possibly occurring in actual detection are avoided, the effectiveness and feasibility of the detection scheme are ensured through the pre-simulation and optimization process, and the detection cost and time consumption are greatly reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of inspection and testing services, and specifically provides an inspection and testing service system. Background Art

[0002] With the rapid development of technology and the acceleration of the industrialization process, inspection and testing services play a crucial role in multiple fields such as quality control, product R & D, and environmental monitoring. As the detection projects become increasingly complex and diverse, higher requirements are put forward for the accuracy, efficiency, and safety of the detection process.

[0003] Traditional inspection and testing service technologies have many deficiencies. On the one hand, due to the lack of effective pre-simulation and optimization means, traditional technologies often have difficulty in discovering and solving potential problems before actual detection, resulting in frequent errors and delays during the actual detection process, increasing the detection cost and time consumption. On the other hand, traditional detection technologies rely heavily on detection personnel, and the experience and skill level of detection personnel directly affect the accuracy and reliability of detection results. However, the training and practical opportunities for detection personnel are limited, making it difficult to quickly improve their professional skills. In addition, traditional technologies lack personalized training platforms and cannot meet the training needs of detection personnel at different levels, restricting the improvement of the overall skill level of detection personnel.

[0004] In summary, the traditional inspection and testing service system has difficulty in meeting the requirements of efficient, accurate, and safe detection when facing increasingly complex and diverse detection needs. Therefore, it is particularly important to develop an inspection and testing service system. Summary of the Invention

[0005] The purpose of the present invention is to make up for the deficiencies of the existing technology and provide an inspection and testing service system. It can simulate the virtual environment before actual detection, discover and optimize potential problems, improve the accuracy and efficiency of the detection process. At the same time, through a personalized training platform, it can improve the professional skills and response capabilities of detection personnel, providing a strong guarantee for the smooth progress of actual detection work.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: An inspection and testing service system, which includes the following components: a digital twin model construction module, a virtual detection simulation module, a problem analysis and optimization module, and a detection personnel training module; The digital twin model construction module: For each detection project, it includes collecting data on detection equipment, sample characteristics, and detection environment, and uses modeling algorithms to construct a high-precision digital twin model, which can accurately reflect the structure, behavior, and performance of the actual detection system; The virtual detection simulation module: Before actual detection, based on the constructed digital twin model, it simulates the entire detection process in a virtual environment. The simulation process covers all aspects of the startup of detection equipment, operation, sample processing, and data acquisition. The simulation environment is highly consistent with the actual detection environment. The problem analysis and optimization module: During the virtual detection simulation process, the system monitors the simulation data in real time, evaluates the detection process using data analysis algorithms. If potential problems are found, it automatically generates a problem report and gives optimization suggestions through intelligent algorithms to adjust and optimize the detection plan. The detection personnel training module: It uses the digital twin model to build a training platform for detection personnel. Detection personnel can practice detection operations in a virtual environment. The system monitors and evaluates the operation process in real time, provides operation guidance and error correction. Through multiple simulation exercises, it can quickly improve the operation proficiency of detection personnel.

[0007] Further, the data collected by the digital twin model construction module includes the technical parameters of detection equipment, sample characteristic data, and detection environment parameters. Among them, the technical parameters of detection equipment are collected in real time through the built-in sensors and interfaces of the equipment, covering the key performance indicators of equipment operation. The sample characteristic data is determined by professional analysis instruments, involving the physical, chemical, and biological characteristics of the samples. The detection environment parameters are obtained by environmental monitoring equipment, including temperature, humidity, air pressure, and electromagnetic radiation intensity. The collected data is cleaned and denoised through a preprocessing algorithm. The formula of this preprocessing algorithm is: ; Where is the original collected data, is the original collected data, is the processed data, is the adaptive weight, and its value range is , which is dynamically adjusted according to the data fluctuation situation. n is the size of the sliding window, which is determined according to the data change frequency and generally takes values from 5 to 15 to ensure the accuracy and stability of the data input into the digital twin model.

[0008] Even further, the detection process simulated by the virtual detection simulation module covers the startup of detection equipment, operation, sample processing, and data acquisition links. During the simulation process, a time synchronization algorithm is used to ensure that the simulation time of each link is consistent with the actual detection time. The formula of this time synchronization algorithm is: ; Where is the simulation time of simulation link k, is the theoretical time of link k in actual detection, is the time compensation coefficient, which is determined through statistical analysis of the time deviation of each link in historical detection data, and its value range is in , To simulate the real - time deviation value from the actual time, through this algorithm, the virtual detection simulation highly restores the actual detection process in the time dimension, providing reliable time - series data for subsequent problem analysis.

[0009] Furthermore, the problem analysis and optimization module uses data - analysis algorithms to analyze the simulation data and generates optimization solutions through intelligent algorithms. The data - analysis algorithm adopts an anomaly - detection algorithm based on feature entropy, and the formula is: ; where \(X\) is the set of simulation data, \(x_i\) is the data point in the set, \(P(x_i)\) is the probability of \(x_i\) occurring, \(m\) is the total number of data points. When the calculated feature entropy exceeds the preset normal entropy value range, it is determined that there are potential problems. When the intelligent algorithm generates an optimization solution, it considers the priority weights of each detection link. The priority weights are determined by the analytic hierarchy process. A judgment matrix is constructed based on the importance, cost impact, and time - sensitivity factors of the detection items, and the priority weights of each link are calculated after passing the consistency test, and a more targeted optimization solution is generated based on this.

[0010] Furthermore, the detection personnel training module monitors, evaluates, and guides the operation process of the detection personnel in real - time to improve the operation proficiency. During the evaluation process, an operation behavior scoring algorithm is used, and the formula is: ; where \(S\) is the comprehensive score of the detection personnel's operation behavior, \(n\) is the total number of operation behavior categories, \(w_j\) is the weight of the \(j\) - th type of operation behavior, which is determined by the expert scoring method according to the influence degree of each type of operation behavior on the accuracy of the detection result, and the value range is in , \(r_{ij}\) is the score of the detection personnel on the \(j\) - th type of operation behavior, and the score is given according to whether the operation conforms to the standard process and the closeness of the operation time to the standard time. The system gives operation guidance and error correction in real - time according to the comprehensive score to help the detection personnel quickly improve the operation proficiency.

[0011] Furthermore, when the digital twin model construction module constructs the model, it uses a structure - adaptive modeling algorithm, and the formula of this algorithm is: ; where \(\hat{M}\) is the updated digital twin model, \(M\) is the initial model, \(\alpha\) is the model update weight, and the value range is , dynamically adjusted according to the change frequency of the detection items, where q is the number of data feature categories, is the weight corresponding to the i-th type of data feature, determined by the principal component analysis method, reflecting the importance of each feature to the model construction, is a function for feature extraction and transformation of the i-th type of data feature D. Through this algorithm, the digital twin model can adaptively adjust its structure according to the newly collected data, improving the accuracy and adaptability of the model to the actual detection system.

[0012] Furthermore, in the simulated data acquisition link of the virtual detection simulation module, a noise suppression and data enhancement algorithm is adopted, and the algorithm formula is: ; where is the enhanced data, is the original data collected by simulation, is the noise intensity adjustment coefficient, and its value range is , estimated according to the actual detection environment noise level, is Gaussian noise with a mean of 0 and a variance of , determined according to the noise statistics of historical detection data, where r is the number of data enhancement operation types, is the weight of the k-th data enhancement operation, determined by the cross-validation method, is the k-th data enhancement operation function. This algorithm adds appropriate noise to the simulated data and performs data enhancement, making the simulated data closer to the complex situation in actual detection and improving the reliability of subsequent problem analysis.

[0013] Furthermore, when analyzing potential problems, the problem analysis and optimization module introduces a causal relationship analysis algorithm. The algorithm determines the causal relationship between each detection data variable by constructing a Bayesian network. The conditional probability table of the Bayesian network is determined by learning and statistics of a large amount of historical detection data. After discovering a problem, resource constraints are considered in the process of generating an optimization plan. The resource constraint weights are determined by a linear programming algorithm. A linear programming model is constructed according to the actual limitation conditions of detection equipment resources, human resources, and time resources, and the weights of each resource constraint are obtained by solving. Through causal relationship analysis and consideration of resource constraints, the generated optimization plan can not only solve the problem but also ensure feasibility and optimality under actual resource conditions.

[0014] Furthermore, during the training process, the tester training module adopts a personalized learning path planning algorithm. First, based on the evaluation results of the initial operation level of the testers, they are divided into different learning levels, and different levels correspond to different learning goals and knowledge and skill requirements. For each learning level, according to the feedback of the operation behavior scoring algorithm, an operation skill association matrix is constructed. The matrix elements represent the degree of association between different operation skills, which is determined by the association rule mining algorithm. Based on the operation skill association matrix, the shortest path algorithm is used to plan the personalized learning path. The formula is: ; where P is the optimal learning path planned, is the set of all possible learning paths, is the learning cost from operation skill i to j, which is determined according to factors such as operation difficulty and learning time. Through personalized learning path planning, testers can improve their operation proficiency more efficiently and meet the training needs of different personnel.

[0015] Compared with the prior art, this inspection and testing service system has the following beneficial effects: First, through the integrated application of the digital twin model construction module, virtual inspection simulation module, problem analysis and optimization module, and tester training module, the system significantly improves the accuracy and efficiency of the inspection process. Before actual inspection, the system can simulate the entire inspection process through a virtual environment, discover and optimize potential problems, thus avoiding errors and delays that may occur in actual inspection. This pre-simulation and optimization process ensures the effectiveness and feasibility of the inspection plan, greatly reducing inspection costs and time consumption.

[0016] Second, by using the digital twin model, testers can perform repeated operation exercises in a virtual environment. The system monitors and evaluates their operation process in real time, providing targeted operation guidance and error correction. This training method not only improves the operation proficiency and accuracy of testers, but also enhances their safety awareness and ability to handle emergencies. Through continuous learning and practice, testers can better adapt to complex and changing inspection tasks, providing strong guarantee for the smooth progress of actual inspection work.

[0017] Other advantages, objectives, and features of the present invention will be described to some extent in the subsequent specification, and to some extent, will be obvious to those skilled in the art based on the study of the following text, or can be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 A flow chart for realizing the functions of an inspection and testing service system; Figure 2 This is a flow chart of the overall architecture of an inspection and testing service system. DETAILED DESCRIPTION

[0020] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation mode, structure, characteristics and effects of the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0021] Example 1: In the water quality testing project, the technical parameters of the testing equipment (such as pH meter, dissolved oxygen meter) are collected in real time using the equipment's own sensors and interfaces, such as the instrument's measurement accuracy, response time and other key performance indicators. The sample characteristic data, such as the pH value, dissolved oxygen content, and chemical oxygen demand of the water sample, are measured by professional analytical instruments. The physical and chemical properties of the test environment are obtained with the help of environmental monitoring equipment, including the temperature, humidity, air pressure and electromagnetic radiation intensity of the testing laboratory. The collected data is cleaned and denoised using a preprocessing algorithm, assuming an adaptive weight , sliding window size n=3, original collected data A series of water sample pH value measurement data that changes over time. The processed data By formula The data is calculated to construct a digital twin model that can accurately reflect the actual water quality detection system.

[0022] Before the actual water quality test, the entire test process is simulated in a virtual environment based on the constructed digital twin model. The simulation process covers the start-up, operation, sample processing and data collection of the test equipment. The time synchronization algorithm is used to ensure that the simulation time of each link is consistent with the actual test time. The time compensation coefficient is assumed to be , for the detection equipment startup link k=1, the theoretical time of this link in actual detection is Minutes, real-time deviation between simulation and actual time Minutes, then the simulation time of simulation session 1 is According to the formula Calculated as Minutes, in the simulation data collection phase, the noise suppression and data enhancement algorithm is used, assuming that the noise intensity adjustment coefficient , the number of data enhancement operation types r=2, the original data collected by simulation For a series of dissolved oxygen measurements, the formula Make the simulation data closer to the actual complex situation.

[0023] In the virtual detection simulation process, the system uses an anomaly detection algorithm based on characteristic entropy to analyze the simulated data. Assuming that the simulated data set X contains simulated data of multiple indicators in water samples, when the calculated characteristic entropy When the entropy value exceeds the preset normal range, it is determined that there is a potential problem. When the intelligent algorithm generates an optimization plan, the importance, cost impact, and time sensitivity of the detection project are considered to construct a judgment matrix. After the consistency test, the priority weight of each link is calculated. For example, if a certain detection link is found to have a significant impact on the overall water quality detection accuracy and is costly, its priority weight will be increased accordingly. When analyzing potential problems, a causal analysis algorithm is introduced to construct a Bayesian network to determine the causal relationship between the detection data variables. At the same time, a linear programming model is constructed based on the actual constraints of detection equipment resources, human resources, and time resources. The weight of each resource constraint is solved to adjust and optimize the detection plan.

[0024] The digital twin model is used to build a training platform for testers. Testers practice water quality testing operations in a virtual environment. The system uses an operation behavior scoring algorithm to monitor, evaluate and guide the operation process in real time. It is assumed that the total number of operation behavior categories , weight of the j-th type of operation behavior According to the operation importance setting, the score of the inspector on the jth type of operation behavior The operation is scored based on whether it complies with the standard process and the closeness of the operation time to the standard time. The comprehensive score S is calculated by the formula It is calculated that the system provides real-time operation guidance and error correction based on the comprehensive score. At the same time, it adopts a personalized learning path planning algorithm, divides the learning level according to the initial operation level evaluation results of the test personnel, constructs an operation skill association matrix, and uses the shortest path algorithm to plan a personalized learning path. The formula is: , helping testers quickly improve their operational proficiency.

[0025] Example 2: For electronic product performance testing projects, the technical parameters of the testing equipment (such as oscilloscopes, signal generators), such as frequency accuracy, voltage measurement range and other key performance indicators, are collected through the equipment's own sensors and interfaces. Professional analytical instruments are used to measure sample characteristic data, including the electrical parameters of electronic products, signal transmission characteristics, physical and electrical characteristics. Environmental monitoring equipment is used to obtain testing environment parameters, such as the temperature and humidity of the testing workshop, and the intensity of electromagnetic radiation. The collected data is cleaned and denoised by a preprocessing algorithm, and adaptive weights are set. , the sliding window size n = 2, and according to the formula After processing, a high-precision digital twin model is constructed.

[0026] Before actual detection, based on the constructed digital twin model, simulate the detection process in a virtual environment, covering the detection equipment startup, operation, sample processing, and data acquisition links. Use the time synchronization algorithm to ensure that the simulation time is consistent with the actual detection time, and assume the time compensation coefficient. , for the data acquisition link k = 3, the theoretical time of this link in actual detection Minutes, the real-time deviation value between simulation and actual time Minutes, the simulation time of simulation link 3 Calculated by the formula Is Minutes. In the simulated data acquisition link, use noise suppression and data augmentation algorithms, and assume the noise intensity measurement parameter , the number of data augmentation operation types r = 3, and the original data Collected by simulation is the measured value of the signal strength of electronic products. The data is enhanced through the formula .

[0027] When performing virtual detection simulation, use the anomaly detection algorithm based on feature entropy to analyze the simulation data. Assume that the simulation data set X contains the simulation data of various performance of electronic products. When the feature entropy Exceeds the preset range, it is determined that there are potential problems. When the intelligent algorithm generates an optimization plan, consider the importance of detection items, cost impact, and time sensitivity factors to construct a judgment matrix to determine the priority weights of each link. Introduce the causal relationship analysis algorithm to construct a Bayesian network to determine the causal relationship of detection data variables. According to the actual limitations of detection equipment resources, human resources, and time resources, construct a linear programming model to solve the resource constraint weights of each item to optimize the detection plan.

[0028] Use the digital twin model to build a training platform. Detection personnel practice the detection operation of electronic product performance in a virtual environment. The system uses an operation behavior scoring algorithm to monitor, evaluate, and guide in real time. Assume that the total number of operation behavior categories , the weight of the jth type of operation behavior Is determined according to the operation importance. The score Of the detection personnel on the jth type of operation behavior is scored according to whether the operation conforms to the standard process and the proximity of the operation time to the standard time. The comprehensive score S is calculated through the formula . The system gives operation guidance and error correction in real time according to the comprehensive score. Use the personalized learning path planning algorithm to divide the learning levels according to the initial operation level evaluation, construct an operation skill association matrix, and use the shortest path algorithm to plan the personalized learning path. The formula is , improving the operation proficiency of detection personnel.

[0029] As described above, it is only the preferred embodiment of the present invention, and there is no limitation to the present invention in any form. Although the present invention has been disclosed above with the preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments by using the disclosed technical content within the scope of the technical solution of the present invention. However, as long as it does not depart from the content of the technical solution of the present invention, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A inspection and testing service system, characterized in that, The system includes the following components: a digital twin model construction module, a virtual detection simulation module, a problem analysis and optimization module, and a detector training module; The digital twin model construction module: For each detection project, it includes collecting data on various aspects such as detection equipment, sample characteristics, and detection environment, and using modeling algorithms to construct a high-precision digital twin model; The virtual detection simulation module: Before actual detection, based on the constructed digital twin model, it simulates the entire detection process in a virtual environment. The simulation process covers various links such as the startup, operation, sample processing, and data collection of the detection equipment; The problem analysis and optimization module: During the virtual detection simulation process, the system monitors the simulation data in real time, uses data analysis algorithms to evaluate the detection process, discovers potential problems, automatically generates problem reports, and gives optimization suggestions through intelligent algorithms to adjust and optimize the detection plan; The detector training module: Using the digital twin model to build a detector training platform, detectors can practice detection operations in a virtual environment. The system monitors and evaluates the operation process in real time, provides operation guidance and error correction, and quickly improves the operation proficiency of detectors through multiple simulation exercises.

2. The inspection and testing service system according to claim 1, characterized in that, The data collected by the digital twin model construction module includes the technical parameters of detection equipment, sample characteristic data, and detection environment parameters. Among them, the technical parameters of detection equipment are collected in real time through the built-in sensors and interfaces of the equipment, covering the key performance indicators of equipment operation. The sample characteristic data is determined by professional analysis instruments and involves the physical, chemical, and biological characteristics of the samples. The detection environment parameters are obtained by environmental monitoring equipment, including temperature, humidity, air pressure, and electromagnetic radiation intensity. The collected data is cleaned and denoised through a preprocessing algorithm, and the formula of this preprocessing algorithm is: ； Among them is the original acquisition data, is the original acquisition data, is the processed data, is the adaptive weight, which is dynamically adjusted according to the data fluctuation situation, and n is the sliding window size.

3. The inspection and testing service system according to claim 1, wherein The detection process simulated by the virtual detection simulation module covers the links of detection equipment startup, operation, sample processing, and data acquisition. During the simulation, a time synchronization algorithm is used to ensure that the simulation time of each link is consistent with the actual detection time. The formula of this time synchronization algorithm is: ; Among them is the simulation time of simulation link k, is the theoretical time of link k in actual detection, is the time compensation coefficient, is the real-time deviation value between the simulation and actual times.

4. The inspection and testing service system according to claim 1, characterized in that, The problem analysis and optimization module uses data analysis algorithms to analyze the simulation data and generates an optimization plan through intelligent algorithms. The data analysis algorithm uses an anomaly detection algorithm based on feature entropy, and the formula is: ； Where X is a set of simulation data, is a data point in the set, is the probability of occurrence, m is the total number of data points. When the calculated feature entropy exceeds the preset normal entropy value range, it is determined that there are potential problems. When the intelligent algorithm generates an optimization plan, it considers the priority weights of each detection link, constructs a judgment matrix according to the importance, cost impact, and time sensitivity factors of the detection items, and calculates the priority weights of each link after passing the consistency test.

5. The inspection and testing service system according to claim 1, wherein The detector training module monitors, evaluates, and guides the operation process of detectors in real time to improve the operation proficiency. During the evaluation process, an operation behavior scoring algorithm is used, and the formula is: ； Among them, S is the comprehensive score of the inspector's operation behavior, is the total number of operation behavior categories, is the weight of the j-th type of operation behavior, is the score of the inspector on the j-th type of operation behavior, which is scored according to whether the operation conforms to the standard process and the proximity of the operation time to the standard time. The system gives operation guidance and error correction in real time according to the comprehensive score.

6. The inspection and testing service system according to claim 1, characterized in that When constructing the model, the digital twin model construction module uses a structure adaptive modeling algorithm, and the formula of this algorithm is: ； Among them is the updated digital twin model, is the initial model, is the model update weight, which is dynamically adjusted according to the change frequency of the detection items. q is the number of data feature categories, is the weight corresponding to the i-th type of data feature, reflecting the importance of each feature to the model construction, is a function for feature extraction and transformation of the i-th type of data feature D. Through this algorithm, the digital twin model can adaptively adjust its structure according to the newly collected data.

7. The inspection and testing service system according to claim 1, wherein During the simulation data collection link, the virtual detection simulation module uses a noise suppression and data enhancement algorithm, and the formula of the algorithm is: ； Among them is the enhanced data, is the original data collected by simulation, is the noise intensity adjustment coefficient, estimated according to the actual detected environmental noise level, is Gaussian noise with a mean of 0 and a variance of , r is the number of data augmentation operation types, is the weight of the k-th data augmentation operation, is the k-th data augmentation operation function. This algorithm adds an appropriate amount of noise to the simulated data and performs data augmentation to make the simulated data closer to the complex situations in actual detection.

8. The inspection and testing service system according to claim 1, characterized in that When analyzing potential problems, the problem analysis and optimization module introduces a causal relationship analysis algorithm. The algorithm determines the causal relationship between each detection data variable by constructing a Bayesian network. After discovering problems, resource constraints are considered in the process of generating the optimization plan. A linear programming model is constructed according to the actual constraints of detection equipment resources, human resources, and time resources, and the weights of each resource constraint are obtained by solving.

9. The inspection and testing service system according to claim 1, characterized in that During the training process of the detection personnel training module, a personalized learning path planning algorithm is adopted. First, according to the initial operation level assessment results of the detection personnel, they are divided into different learning levels, and different levels correspond to different learning objectives and knowledge and skill requirements. For each learning level, based on the feedback of the operation behavior scoring algorithm, an operation skill association matrix is constructed. The matrix elements represent the degree of association between different operation skills, which is determined by the association rule mining algorithm. Based on the operation skill association matrix, the shortest path algorithm is used to plan the personalized learning path. The formula is as follows: ； where P is the optimal learning path planned, is the set of all possible learning paths, is the learning cost from operation skill i to j.

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