122 results about "Optimal test" patented technology

The invention relates to the technical field of deep learning, in particular to an AI chip test parameter adaptive optimization method based on deep learning, which comprises the following steps: acquiring historical test data of an AI chip, and calculating correlation strength among different failure modes based on the historical test data; identifying a failure coupling matrix according to the edge weight, and converting a preset static detection parameter constraint boundary into a dynamic constraint space changing along with a failure detection state; a multi-level optimization framework is constructed, the upper layer executes failure type correlation analysis and generates constraint propagation information, the middle layer optimizes a parameter cluster based on the constraint propagation information, and the lower layer adjusts a single detection parameter and outputs a parameter optimization result; establishing a neural network mapping model, and obtaining a nonlinear mapping relationship between the detection parameters and the failure types; based on the physical state parameters, the nonlinear mapping relation is adjusted, the dynamic constraint space is updated, parameter optimization is executed again, a parameter optimization result is output, and an optimal test parameter combination is output.

The invention relates to the technical field of power battery health state evaluation and anomaly recognition, in particular to a remote health state analysis and anomaly recognition method for an electric bicycle battery, which comprises the following steps of: under an access working condition, applying small-signal active excitation and synchronously acquiring to form an opportunity diagnosis excitation data window; time-frequency coupling modeling including equivalent impedance modeling, harmonic wave and intermodulation fingerprint modeling, Koopman operator modeling and entropy coefficient difference is carried out, and a spectral measurement geometric distance is constructed through optimal transmission to generate a multi-domain response packet, a physical invariant vector and an uncertainty matrix; according to the geometric distance and consistency score gating and hierarchical standardization, obtaining a health state estimation record and an abnormal event set by adopting a shape-limited health mapping model; and in combination with mapping Jacobian and dual potential, generating an opportunity diagnosis incentive plan of a next period by adopting D-optimal test design, and updating a group health manifold, thereby realizing cross-environment, cross-equipment and traceable remote health assessment and early anomaly recognition.

The invention relates to the technical field of optical communication, and discloses an optical module adaptive test and rapid convergence method based on reinforcement learning, which can dynamically generate and execute an optimal test sequence for optical modules of different types or states through an off-line trained RL intelligent agent module, avoids redundant steps in a fixed process, and improves the test efficiency. On the premise of ensuring the test coverage, the test time is obviously shortened, and meanwhile, the detection probability of potential defects is improved; through the closed-loop test of the integrated RL intelligent agent module, the test action can be adjusted in real time according to the state of a single optical module, so that the test process can quickly adapt to the individual difference and process fluctuation of the device, the online quick convergence of the test strategy is realized, and the accuracy and stability of the test are guaranteed; and through online iterative optimization of the RL agent module, new data and new changes in the production process can be continuously learned, so that a test strategy is continuously evolved, and the optimal performance is kept for a long time.

The invention relates to the technical field of communication testing, and provides a multi-mode communication equipment test case generation method and device, equipment and a medium, and the method comprises the steps: obtaining a radio frequency parameter, network flow data and equipment log data of communication equipment, carrying out feature extraction and vectorization processing on the radio frequency parameters, the network flow data and the equipment log data to obtain a multi-modal vector; constructing a communication equipment calling relation graph according to the calling relation and the interaction protocol; constructing a test path evolution model based on a graph neural network, and transmitting the multi-modal vector to the test path evolution model, so as to search an optimal test path in a communication equipment calling relation graph through an attention mechanism; constructing a multi-target reinforcement learning reward function and dynamically adjusting the optimal test path through the multi-target reinforcement learning reward function; and according to the optimal test path, generating a target test case, and according to a test result of the target test case, adjusting model parameters of the test path evolution model, and carrying out multi-target reinforcement learning on the reward function.

The invention discloses a dynamic simulation test verification method and system for a pumped storage unit monitoring system, and the method comprises the steps: building a high-dimensional system state matrix of a pumped storage unit based on the physical parameters and structural features of a hydraulic, mechanical and electrical system of the unit, carrying out the dimension reduction of the high-dimensional system state matrix, and obtaining a dynamic simulation test result of the pumped storage unit; a dynamic simulation model of unit multi-physics field coupling is obtained; constructing a multi-dimensional parameter space based on the parameter sensitivity coefficient and the working condition boundary condition of the dynamic simulation model, and calculating optimal test point distribution through a high-dimensional point flow diameter algorithm to obtain a test scene library; based on the dynamic simulation model and the test scene library, obtaining a real-time monitoring signal sequence conforming to a monitoring system communication protocol; and based on the real-time monitoring signal sequence, signal acquisition, logic judgment and alarm control functions of the monitoring system are tested through an automatic test script, and a performance evaluation result of the monitoring system is obtained. The test comprehensiveness and effectiveness of the pumped storage unit monitoring system are improved.

The invention discloses a flora prediction and petroleum pollution remediation method based on machine learning. The method comprises the following steps: collecting multiple groups of experimental data of a diesel oil pollution sample treated by a microbial agent, extracting environmental factors, microbial community characteristics and target response variables to construct a training data set after missing value processing, abnormal value detection and standardized pretreatment, and importing the data set into a preset machine learning model to obtain a training result; carrying out feature learning, classification training and hyper-parameter optimization by adopting a GridSearchCV method in combination with 10-fold cross validation; evaluating the correlation between a target response variable classification result and the features through a multivariable Pearson's correlation matrix, and constructing an optimal test set; and finally, selecting an optimal prediction model according to a preset index. According to the method, the model training quality is improved through data preprocessing and correlation analysis, efficient flora prediction and algorithm application evaluation are achieved by means of multiple machine learning algorithms, scientific support is provided for petroleum pollution remediation, and remediation accuracy and efficiency are improved.

The invention discloses an intelligent test paper composition and scoring method and system based on a knowledge graph. The method comprises the following steps: S1, constructing a student knowledge graph state sub-graph; s2, constructing a question bank knowledge graph; s3, on the basis of a node embedding modeling method of an improved DeepWalk algorithm, performing joint training and low-dimensional representation learning on knowledge point nodes by utilizing a continuous bag-of-words model of multi-head context aggregation, and constructing a topic adaptation degree scoring index; s4, outputting an optimal test paper scheme set; s5, collecting a current answer record when the student completes the test paper; and S6, extracting a semantic feature vector and a behavior feature vector, constructing a fusion score feature vector, and generating scores of test paper subjective question answers. According to the invention, accurate test paper composition and multi-dimensional subjective question intelligent scoring for individual knowledge states of students are realized, the method is suitable for personalized learning evaluation and teaching feedback scenes in an education evaluation platform, and the method has the advantages of high personalization, fine feedback and automatic scoring.

The invention discloses a radiator multi-objective collaborative optimization design method which comprises the following steps: defining the geometric dimension of a radiator and the air volume degradation coefficient of a fan as optimization objectives, and defining a key temperature point and the volume of the radiator as evaluation indexes; constructing a small number of test parameter combinations covering all value ranges of the optimization target, and performing analogue simulation to obtain simulation data of corresponding evaluation indexes; a mapping model is constructed, training is performed through simulation data, and a large number of test parameter combinations and corresponding evaluation index predictions are output based on the trained mapping model; and performing parameter optimization on an output result of the mapping model through an optimization algorithm to obtain an optimal test parameter combination of an optimization target conforming to the evaluation index constraint. The method has the beneficial effects that uniform coverage of a parameter space is realized under a limited sample, and the number of invalid simulation times is reduced; and a nonlinear coupling relationship among parameters is captured, and the prediction precision of the agent model is improved.

The invention discloses a test paper generation method and system, and belongs to the technical field of intelligent education, and the method comprises the steps: obtaining student answer records, question-knowledge point mapping, a test question bank and knowledge text information, and constructing a knowledge point graph; constructing a student-question-knowledge three-layer heterogeneous graph, and predicting the answering performance of the students on the test paper; taking the answer expression as environment feedback input of reinforcement learning, taking a question set of the current test paper as a state, replacing questions in the test paper or keeping the questions as an action, determining a comprehensive reward value of the test paper in each dimension by constructing a multi-target reward function, and generating an optimal test paper through iterative optimization by taking maximization of the comprehensive reward value as a target; on the basis of the adjacency relation in the knowledge point graph, candidate test questions are selected from a test question bank to form a candidate sub-question set, and multiple sets of parallel test papers equivalent to the optimal test paper are generated through an equivalent question replacement strategy. Through the method, more efficient, reasonable and fair parallel test paper generation can be realized.

The invention discloses an increment testing method and device, a storage medium and electronic equipment, and the method comprises the steps: constructing a dependency graph for describing the dependency relationship between software components in a software system according to software system data, and carrying out the continuous integration of the software components on the basis of a code change set corresponding to a current continuous integration task; a minimum candidate influence range is determined on the dependency graph, a candidate test set is delineated, tests in the candidate test set are ranked according to the sequence of risk scores of all the tests from high to low, a target candidate test set is obtained, all the tests are processed through a greedy approximation algorithm, and a target candidate test set is selected under the constraint of resource budget; and the minimum and optimal test set of software system defects is found to the maximum extent. According to the method, the minimum candidate influence range is determined on the dependency graph based on the code change set, the greedy approximation algorithm is combined, intelligent and accurate selection of the test set under the resource budget constraint is achieved, it can be guaranteed that the defect detection rate is not reduced, and the continuous integration process efficiency can be improved to the maximum extent.

The invention discloses a test and output restoration method for an instruction-guided image editing model based on a metamorphic test technology, and the method comprises the steps: collecting an original input image and an editing instruction as an original test case, collecting an output image of the original test case, and comparing the input image with the output image to obtain an editing region of the original test case; converting the input image according to the metamorphic relation to generate a candidate image set, selecting an equivalent derivative test image, and forming a new derivative test case together with an original editing instruction; collecting output images of all the derivative test cases, comparing the input images with the output images to obtain editing areas of the derivative test cases, and judging whether the editing areas of all the derivative test cases are consistent with the editing areas of the original test cases or not; carrying out cross validation on all derivative test cases and the output of the original test case, selecting the output of the optimal test case, and mapping the output of the optimal test case to the original test case to serve as the restored original test output; and summarizing a test result and outputting a repair result.

The present application relates to the technical field of power battery health state evaluation and abnormality identification, and particularly relates to a remote health state analysis and abnormality identification method for an electric bicycle battery, which comprises: applying a small signal active excitation under an admitted working condition and synchronously collecting to form an opportunity diagnosis excitation data window; developing time-frequency coupling modeling, including equivalent impedance, harmonic and intermodulation fingerprint, Koopman operator modeling and entropy coefficient difference, and generating a multi-domain response package, a physical invariant vector and an uncertainty matrix by constructing a spectral measure geometry distance with an optimal transmission; gating and layering standardization according to the geometry distance and consistency score, obtaining a health state estimation record and an abnormal event set by using a shape-constrained health mapping model; combining the mapping Jacobian and the dual potential, generating a next-period opportunity diagnosis excitation plan by using D-optimal test design and updating the population health manifold, so as to realize remote health evaluation and early abnormality identification across environments and devices and with traceability.

The invention belongs to the field of integrated circuit design automation, and discloses a multi-PVT corner establishment retention time characterization method and a related device.The method comprises the steps that firstly, an initial search interval is pre-estimated through a logical effort analysis method, and a key area is quickly locked; secondly, introducing a bias item into a test point generated based on binary interpolation through hypothesis interpolation error distribution to form a bias test point, and solving an optimal bias amount by taking a minimum expected interval length as a target so as to obtain an optimal test point; and finally, executing simulation and outputting a representation result. By adopting the method, the characterization efficiency is remarkably improved, a higher convergence speed is realized in a steep transformation and multi-PVT angular point scene, the problems of interpolation stagnation and low speed of a dichotomy method are effectively solved, the sign-off level precision is ensured, meanwhile, the simulation iteration times and CPU time consumption are greatly reduced, and an efficient and reliable solution is provided for a chip time sequence bottleneck.

The invention discloses a multi-target test selection method and system under a test uncertainty condition based on E domination guidance. The method comprises the following steps of: firstly, explicitly modeling uncertainty in a test process by using a detection probability matrix and a false alarm probability matrix; secondly, establishing a multi-objective optimization model which takes the minimum test cost, the minimum missing report rate and the minimum false alarm rate as objectives and takes the fault detection rate and the fault isolation rate as constraints; introducing an E domination relation fused with the preference of a decision maker to improve a traditional Pareto domination criterion; on the basis, designing an E-GPSO algorithm fusing a genetic algorithm and particle swarm optimization to perform iterative search, and dynamically maintaining a non-inferior solution set by adopting an external file based on an E domination criterion; and finally, outputting an optimal test scheme set meeting the constraint and the preference. According to the method, the test cost and the diagnosis reliability can be effectively coordinated and optimized, the decision space is remarkably reduced, and the efficiency and the quality of testability design are improved.

The invention belongs to the field of integrated circuit testing, and discloses a Shmoo testing method and device and a storage medium. The method comprises the steps of determining to-be-optimized Shmoo test parameters of an integrated circuit and defining a search range; an optimization objective function is established, an initial test parameter set is generated, and initial test or simulation is executed to obtain initial fitness data; executing an iterative loop of the grey wolf optimization algorithm, calculating a fitness value of a current test parameter set in each iteration, identifying an optimal test parameter, a suboptimal test parameter and a third optimal test parameter, and updating an algorithm control parameter; updating other test parameters according to the position information of the three test parameters, and carrying out boundary processing on the updated test parameters to keep the test parameters in a predefined search range; and when the termination condition is met, outputting the optimal test parameter as an optimal Shmoo test parameter combination, thereby determining the performance boundary of the integrated circuit or generating a Shmoo graph, and realizing more comprehensive and accurate Shmoo test.

A method for generating a test coupon specification for predicting fatigue life of a component includes determining a load condition for the component, providing a component design, and performing a strength analysis of the component design under the load condition determining a critical area of the component and a stress-related parameter of the critical area. The method includes providing a material condition of the component at least for the critical area of the component. To assist an end-user in determining which are optimal tests to be performed in order to obtain most relevant data for fatigue prediction of a specific component, the method also includes providing a material model and providing, as an input to the material model, the stress-related parameter, and the material condition. The material model generates, as an output, a test coupon specification for being tested in a testing machine.

The invention discloses a test result generation method and device, equipment and a storage medium, and relates to the technical field of testing. The method comprises the following steps: acquiring a plurality of alternative test cases, and constructing a Markov decision process model according to the alternative test cases; the Markov decision process model is calculated to determine an optimal test case from the alternative test cases, a reward function value of the Markov decision process model is determined according to the execution cost of the test case, and the execution cost is related to the execution time of the test case; and testing the financial system to be tested by using the optimal test case to obtain a test result. According to the technical scheme, the global optimal test case is obtained, the execution cost of the test case is comprehensively considered, the use of test resources is optimized, the test effect is improved, and the method is suitable for large-scale and complex software test scenes.

The invention provides an embedded space security analysis method and device, electronic equipment and a storage medium, and belongs to the technical field of network security, and the method comprises the steps: obtaining a target vector set based on a knowledge base of a to-be-tested RAG system; clustering the target vector set to obtain a topic cluster set, and for each topic cluster in the topic cluster set, calculating a vector center point of the topic cluster; generating a rejection set based on the vector center points of all topic clusters; determining an initial load document and an initial trigger query, and taking the initial load document and the initial trigger query as an initial test combination; based on the rejection set, performing iterative updating on the initial test combination through a greedy coordinate gradient algorithm to obtain an optimal test combination; and taking the optimal test combination as a test sample of the RAG system to obtain a test result, and obtaining a security analysis result based on the test result. According to the invention, the accuracy of RAG system security analysis can be improved.

This invention belongs to the field of automatic generation of EOL (End-of-Life) test programs, specifically involving a reinforcement learning-based method for generating EOL test programs for vehicle domain controllers. It includes the following steps: S1: Obtaining raw component data from the schematic diagram, processing the raw circuit and component connection attributes to form a minimum circuit combination; S2: Constructing an optimal EOL test case selection model on the two-dimensional vector dataset obtained in step S1; S3: Based on the model in S2, generating the highest-priority EOL test program using the Q-Learning algorithm; S4: Selecting the optimal test case according to evaluation criteria. This invention addresses the shortcomings of reward-based test results methods, such as low failure rate, difficulty in convergence, and long test feedback time.

The invention relates to an optimal test position searching system and method in an OTA camera obscura, and belongs to the technical field of position searching, the system comprises the OTA camera obscura, a real-time monitoring module, a self-adaptive scheduling algorithm module, a test control module, a data analysis and evaluation module and a result output module, the OTA camera obscura is used for providing a non-interference test environment; the real-time monitoring module is used for acquiring MCS parameters of the equipment to be tested; the adaptive scheduling algorithm module, the test control module and the data analysis and evaluation module are used for evaluating the performance of a test position according to the MCS parameters, determining whether an optimal test position is reached or not, if the optimal test position is not reached, determining a next test position according to an adaptive scheduling algorithm, and controlling the test probe or the antenna array to move to the position; whether the optimal test position is found or not is judged, and if the optimal test position is not found, searching continues; and if the optimal test position is found, the result output module outputs the optimal test position and the test result.

The invention discloses a thermal out-of-focus compensation method and device, computer equipment and a storage medium, and the method comprises the steps: collecting a plurality of temperature values of at least one heating position of a projection device in sampling time, and obtaining target temperature data; according to the target temperature data and a preset focal length compensation model, obtaining an optimal current focusing position currently corresponding to a focusing motor of the projection device; the focal length compensation model is obtained by training based on test temperature data and a corresponding optimal test focusing position; acquiring a focal length compensation value of a focusing motor according to the optimal current focusing position; and adjusting the focusing position of the focusing motor according to the focal length compensation value. According to the method, through the established focal length compensation model, the real-time optimal focusing position of the focusing motor can be accurately predicted, meanwhile, the temperature change is monitored in real time, preventive fine adjustment is carried out before defocusing, the defocusing problem caused by the temperature change is effectively solved, and rapid and accurate self-adaptive focusing control is achieved.

The application discloses a system function test method, device, equipment and medium, and relates to the field of system test. The method comprises the following steps: acquiring a function description interface from a preset automatic script, and sending the function description interface to a matching bus; determining function information of a function to be tested in the function description interface by using the matching bus, and matching an optimal interface from all current tool description interfaces based on the function information and matching information defined in a preset function set; testing the function to be tested by using an automatic test tool corresponding to the optimal interface, and returning a test result to the automatic script. In this way, the method in the application can automatically match the optimal test tool to test the function to be tested of the system to be tested without manual packaging and adaptation of the tool, greatly reduces the time for selecting and packaging and adapting the test tool in the function test, and further improves the system test efficiency.

A system and method for sampling a flight envelope of an air vehicle under test. The method includes obtaining engineering models of the vehicle to identify how the vehicle behaves, obtaining empirical models through test data, and obtaining historical models of similar legacy aircraft. The method further includes ensembling the models into an integrated model and assessing the ensemble uncertainty at any particular test point, for a variety of aircraft conditions, to determine model reliability. The method includes determining the optimal order of test points based on local model uncertainty, testing the air vehicle at a point, revising the integrated model with that data and reassessing the uncertainty, and determining if a stop testing condition has been met, in which case the testing series is closed. The last calibrated integrated model could then be used to generate trusted data for any remaining required test using a certification by analysis method.

This invention belongs to the field of structural static testing technology, and discloses a method, equipment, medium, and terminal for controlling static deformation measurement errors. Under a given load mode, the static deformation of a structure is approximately expressed as a linear combination of a few contributing modes. The influence of structural parameter changes on static deformation is reflected in the changes in the contributing mode combination coefficients. The problem of monitoring structural static deformation is transformed into the problem of measuring the contributing mode combination coefficients. The measurement error of the contributing mode combination coefficients becomes the decisive factor in the static deformation measurement error. Based on the characteristic that the larger the contributing mode combination coefficient, the smaller the upper bound of the estimation error tends to be, the optimal test load corresponding to each contributing mode of the load to be measured is found sequentially. Then, the static test is carried out on the structure using the optimal test load. Finally, the static deformation of the load to be measured is calculated by indirect measurement, thereby realizing the control of the measurement error of the static deformation of the load to be measured.

The application relates to the technical field of chip intelligent testing, in particular to an amplitude modulation signal source scheme intelligent generation system based on vector simulation gating, which comprises a data acquisition module used for acquiring a response characteristic vector of a chip to be tested; a state inference module used for inferring an underlying state vector based on the response characteristic vector and a preset probability agent model; a risk calculation module used for calculating an uncertainty index based on the underlying state vector and a preset risk weight coefficient; a risk judgment module used for judging the risk level of the chip to be tested according to the uncertainty index and a preset risk threshold; a resource allocation module used for allocating a calculation time budget according to the risk level; a scheme generation module used for determining an optimal test scheme from a preset candidate scheme library within the calculation time budget; and a test execution module used for executing the optimal test scheme; and the application avoids wasting unnecessary time on a large number of healthy chips and significantly improves resource utilization efficiency.

The invention relates to a power line carrier high-frequency channel test system, which belongs to the technical field of power system test, and comprises a delay characteristic detection module, a signal stability evaluation module, a self-adaptive time interval calculation module, a frequency stepping control module and a weight coefficient adjustment module, the possibility of wrong sampling in the signal transition stage is avoided, the measurement accuracy is improved compared with a traditional method, the high reliability of a test result is guaranteed, dynamic optimization of the test efficiency is achieved through cooperative work of the two core modules, and on the premise that the accuracy is guaranteed, the test efficiency is greatly improved without manual intervention. According to the method, the optimal test efficiency is continuously sought, the extremely high intelligent level is shown, and due to the traceability of the parameters, debugging, maintenance and optimization of the system are facilitated, and a crucial confidence guarantee is provided in the application fields of power system protection and the like with extremely high reliability requirements.

The invention provides an automatic connection detection method compatible with multi-category metering equipment, and the method comprises the following steps: S1, forming a multi-dimensional feature vector of the metering equipment based on a multi-modal sensor; s2, constructing a multivariate interaction state tensor based on the multi-dimensional feature vector, obtaining the relative performance of the metering equipment in different feature spaces from multiple dimensions, generating a comprehensive response measure, minimizing the state error of the metering equipment, and obtaining an optimal strategy vector; and S3, converting the optimal strategy vector into a feature control code through feature mapping, matching the feature control code with a preset test process library to obtain an optimal test scheme of the current metering equipment, generating a nested structure vector of control logic, and controlling joint selection and test process execution. According to the invention, through fusion of the multi-modal information, the multi-dimensional features of the metering equipment can be fully extracted, and accurate distinguishing of different types of metering equipment is realized, so that the accuracy of identification and connection of the metering equipment is effectively improved.

The application provides a limit test method, system, equipment and medium of an artificial intelligence model, and the method specifically comprises the following steps: constructing a plurality of scene templates for different types of games, generating a test case set by combining and transforming each scene template according to a boundary condition input by a user; based on the test case set, determining an optimal test parameter combination satisfying the artificial intelligence model by using a heuristic search algorithm according to a preset optimization objective function; automatically testing the artificial intelligence model according to the optimal test parameter combination, generating a model test result; determining the performance of the artificial intelligence model under the boundary condition by analyzing the model test result, generating a model test report, and optimizing and retesting the artificial intelligence model through the model test report. The application realizes full-process automation and intelligentization of game AI model testing, and significantly improves the test efficiency and model quality.

The invention relates to an RISC-V and TPU heterogeneous code optimization use method based on machine learning, and belongs to the technical field of RISC-V architecture embedded heterogeneous computing. A deep learning model comprising an embedded layer and an RNN layer is used for learning representation of codes, low-dimensional feature vectors are extracted, then an optimal classic machine learning algorithm is searched, and the optimal classic machine learning algorithm is obtained. The method comprises the following steps of: mapping characteristics, namely optimal test performance obtained in a plurality of machine learning methods, to specific output so as to choose to distribute tasks to the TPU and the CPU to execute the required tasks; according to the method, the RISC-V architecture characteristics are accurately matched, the heterogeneous collaborative bottleneck is broken through, the classification accuracy is guaranteed, the error allocation risk is reduced, the development cost is reduced, the computing resources are allocated according to needs, and the heterogeneous system performance is maximized.

The invention discloses a metal plate folding seam constant-speed welding system, and relates to the technical field of constant-speed welding, a to-be-welded metal plate is scanned, folding seam three-dimensional data of the metal plate is constructed, and hot spot candidate areas of a preliminary welding test are analyzed and extracted; and screening out an optimal test section from the candidate region as a preliminary test section. And welding the test section at a constant speed, and judging whether the length of the test section needs to be prolonged or not according to a welding feedback result. If the test section needs to be lengthened, the test section is adjusted, constant-speed welding is continued, and whether the remaining folding seams are suitable for continuous constant-speed welding or not is evaluated according to feedback. According to the method, the test section is selected in an auxiliary mode through three-dimensional data, feedback dynamic adjustment is combined, accurate prediction and cost control of the welding quality are considered, it is avoided that judgment is affected due to the fact that the test section is too short or material and time waste is caused due to the fact that the test section is too long, and balance between welding quality guarantee and economic benefits is achieved.