1229 results about "Test scene" patented technology

The invention relates to the field of chip verification, and provides a chip verification method, device and equipment, a medium and a chip. The method comprises the following steps: establishing a dynamic simulation verification environment of a to-be-verified design, and generating a plurality of test scenes and corresponding test excitation according to a design specification definition; sending the test excitation to a driver through a sequencer, converting the test excitation into a signal conforming to a to-be-verified design interface protocol by the driver, and transmitting the signal to the to-be-verified design; the monitor collects an input signal and an output signal of a to-be-verified design in real time, converts the input signal and the output signal into transactions, and transmits the transactions to the scoreboard; comparing the reference model with an output signal of the to-be-verified design to generate a dynamic simulation verification result; according to the dynamic simulation result, the state space is reduced, state space traversal is carried out on a design part which is not covered by dynamic simulation in the design to be verified, and a formal verification analysis result is generated; and evaluating the to-be-verified design according to the formal verification analysis result.

The invention discloses a software test automation report generation optimization method which comprises the following steps: automatically registering and authenticating a multi-source test data access endpoint, uniformly collecting and normalizing test data with a multi-dimensional attribute tag, and forming a high-quality grouped data set through structured modeling and automatic clustering; further extracting event and result feature nodes, and establishing an interpretable tree rule dependency relationship; according to the method, the minimum redundancy and reusable customized rule path is generated by applying a dependency tree traversal and optimization algorithm and automatic reasoning, and the continuous adaptive evolution of the rule base among multiple projects is realized by combining conflict detection and visual interaction, so that the efficiency and expandability of data processing in a complex test scene are improved, and the test efficiency is improved. And the flexibility and automation level of report customization rule configuration are enhanced.

The invention belongs to the technical field of charging detection, and particularly relates to a charging pile extreme environment test method based on composite stress simulation, a terminal and a storage medium, and the method comprises the steps: inputting an identifier corresponding to a target test scene through a human-computer interaction interface of a test cabin, and executing a query operation based on the input identifier, retrieving a preset parameter combination from a preset scenarized stress library by using the mapping function; in a closed test cabin, environment parameter data are collected in real time through a distributed sensor array and fed back to a central controller, and the central controller drives an execution component to cooperatively regulate and control parameters such as temperature and humidity, so that the environment parameters are stabilized in a preset range; a sequential stress scheme is generated based on a pre-stored dynamic loading algorithm, an execution component is controlled to realize stress superposition or stress alternation, and a charging load is synchronously adjusted to simulate a composite stress effect under an actual working condition; simulation of multi-factor combined stress and dynamic loading is realized, an actual use fault can be found in advance, and the matching degree of a test result and a real environment is remarkably improved.

The invention relates to a test case generation method and system based on an industrial agent. The method comprises the steps that an industrial data set is acquired, and a system dependency model is constructed; generating a path coverage test sequence through the first agent; generating a boundary value test data set through the second agent; generating abnormal test scene information through a third agent; and generating a test case set based on the path coverage test sequence, the boundary value test data set and the abnormal test scene information. In conclusion, by constructing the system dependency model and utilizing the multi-agent collaborative analysis process logic path, boundary condition and dependency relationship, the multi-surface coverage of the industrial control system test scene is realized, the automation of the test case generation is realized, the effects of improving the generation efficiency and enhancing the comprehensiveness of the test coverage are achieved, and the method is suitable for industrial control system test. Especially for the coverage of boundary conditions, abnormal scenes and concurrent paths, the manual dependence is reduced, and the reliability and consistency of the test are improved.

The invention provides a simulation test system simulation degree evaluation method and device, equipment, a storage medium and a product. The method comprises the steps of determining a test scene corresponding to a to-be-tested vehicle according to a design operation range of the to-be-tested vehicle; according to the test scene, determining a corresponding evaluation item, and respectively testing in the simulation test system and the actual environment to obtain simulation test data corresponding to the simulation test system and actual test data of the to-be-tested vehicle in the actual environment; based on the simulation test data, obtaining a simulation evaluation index corresponding to each evaluation item; based on the actual test data, obtaining an actual evaluation index corresponding to each evaluation item; and obtaining a simulation degree evaluation result of the simulation test system based on the simulation evaluation index and the actual evaluation index. According to the method, the simulation data and the actual data are compared according to the scene characteristics and the evaluation indexes related to the driving scene, and targeted evaluation results can be accurately obtained in different driving scenes.

The invention provides a multi-step automatic testing method based on machine vision, and the method comprises the steps: capturing an original image sequence of a test scene through a camera, extracting the contour features of a test object in an image through an edge detection algorithm, and obtaining a preliminary positioning coordinate; if the dynamic position change trend exceeds a preset threshold value, adjusting an image enhancement parameter to suppress background noise, and obtaining an enhanced target image; feature matching is carried out through the enhanced target image, a robust identifier such as a texture mode is extracted, and an accurate three-dimensional position coordinate is obtained; according to the accurate three-dimensional position coordinates, calculating an execution deviation value of the current step, and judging whether the deviation value is within an allowable range or not; if the deviation value is within the allowable range, a control instruction sequence is generated and transmitted to the mechanical arm, and a step execution confirmation signal is obtained; comparing a confirmation signal with a next image sequence through the steps, updating positioning model parameters, and determining a continuous adjustment scheme of the whole test process.

The invention provides a test case library construction method and system based on a large model, and relates to the technical field of intelligent automobile tests.The method comprises the steps that semantic analysis of a tested function document is separated from test scene recognition, and an initial test case is generated in combination with a rule-guided large language model; systematization, intellectualization and standardization of a test case library construction process are realized; formats and content specifications output by the large model are effectively restrained through preset professional rules, and the professionality and structural consistency of the generated use cases are guaranteed; meanwhile, problems are found and fed back in time through a multi-dimensional verification and quality evaluation mechanism, self-adaptive optimization of a construction strategy is driven, and the quality and generation efficiency of the test cases are improved; finally, qualified cases are integrated into standardized records, a high-quality test case library with both manageability and technicality is formed, and efficient and automatic conversion from unstructured documents to reusable and maintainable test assets is achieved on the whole.

The invention discloses a test method, device and equipment and a computer readable storage medium, which are applied to the technical field of computers, and comprise the following steps: obtaining multi-source data of a distributed storage cluster, the multi-source data comprising hardware state data, service associated operation data and business dependence data; constructing a relation dependency graph among the hardware nodes, the service nodes and the service nodes based on the multi-source data; adjusting the chaos test script according to the real-time state of the distributed storage cluster and the dynamic adjustment strategy to obtain a target chaos test script; and executing the target chaos test script, determining a fault range and a recovery mechanism based on the relation dependency graph, and outputting a test report. According to the method, comprehensive and accurate presentation of the dependency relationship of each node is realized, and the chaos test script can adapt to the dynamic change of the cluster, so that the test scene is more practical, the invalid test is avoided, the effectiveness, reliability and efficiency of the cluster chaos test are remarkably improved, and the high availability and service continuity of the cluster are powerfully guaranteed.

The invention provides a performance testing method and device for a large language model and related equipment. The performance testing method comprises the steps that multiple testing platforms for testing, testing scene configuration parameters and a structured testing data set for testing are determined; on the basis of the test scene configuration parameters and the structured test data set, synchronous constraint parameters are generated and distributed to the multiple test platforms, and then a preset load generator is used for controlling the multiple test platforms to concurrently execute reasoning test tasks about the large language model; and when the test is executed, collecting multiple dimension performance indexes of each test platform in real time for data analysis, and generating a reasoning performance comparison evaluation report of the large language model in the multiple test platforms according to a data analysis result. According to the method, the cross-platform performance evaluation period is greatly shortened, and the waste of computing resources is reduced. Manual operation errors are avoided, comparability of multi-platform performance indexes is guaranteed, and more accurate and reliable multi-platform performance test results can be obtained.

The invention discloses an automatic test case generation method and system based on a large model, and the method comprises the steps: multi-modal data fusion: receiving a natural language demand, a UML design drawing, an API document, a historical defect log and system monitoring data, aligning text and image features through a CLIP model, analyzing the interface constraint through CodeBERT, and carrying out the multi-modal data fusion; feature fusion is realized by adopting a cross-modal attention mechanism; a dynamic test scene modeling step: constructing an environment simulator based on reinforcement learning, and simulating high-concurrency, distributed abnormal and other complex scenes; and a layered test case generation step: generating a function test case, a boundary test case and an increment test case through GPT-4, GAN and LSTM respectively, and introducing an entropy evaluation model to quantify the effectiveness of the cases. According to the method, the multi-modal data is brought into the test case generation process for the first time, the test case generation time is shortened by 80%, the complex scene coverage rate is increased to 95%, the semantic matching degree reaches 92%, and the intelligent level and efficiency of software testing are remarkably improved.

The invention discloses a test case recommendation method and device, electronic equipment, a medium and a product. The method comprises the following steps: when it is monitored that a function demand is changed, obtaining change content; constructing a dynamic knowledge graph according to the change content; wherein the dynamic knowledge graph stores an association relationship among a function requirement, a test scene and a test case; collecting test environment state data; and outputting a to-be-recommended test case set according to the test environment state data and the dynamic knowledge graph. According to the method, the problems of low test case generation efficiency, incomplete coverage and inconsistency with requirements caused by frequent change of function requirements can be solved.

The invention discloses an aging performance evaluation method and platform for a super junction MOS device, and relates to the technical field of performance evaluation, and the method comprises the steps: carrying out the test scene matching, and obtaining a target acceleration test scene group; calling a plurality of aging trend curves according to the target acceleration test scene group; locally calling a historical operation data set of the to-be-tested super junction MOS device; performing degradation feature extraction on the historical operation data set to obtain multiple groups of historical operation time sequence fragments; performing degradation increment correlation prediction on multiple groups of historical operation time sequence fragments, and outputting a device aging time sequence trend curve; comparing the aging time sequence trend curve of the device based on a preset failure threshold value, and outputting the remaining service life of the device; and interactively obtaining an equipment life design index of the target power electronic system, and quantitatively outputting an aging performance evaluation result in combination with the residual service life of the device. The technical problem that the aging performance evaluation accuracy is insufficient in the prior art is solved, and the technical effect of improving the aging performance evaluation precision is achieved.

The invention provides an automatic simulation test system and method for a semiconductor EAP system, and belongs to the field of semiconductor manufacturing, and the method specifically comprises the steps: defining a test scene through test case coding; constructing a test environment by using the equipment simulation module and the external system simulation module; driving a test process of the tested EAP program through an automatic execution engine and generating real-time output data corresponding to the tested EAP program; and comparing the real-time output data generated by the automatic execution engine with an expected result, and generating a test report. According to the processing scheme, behaviors of equipment and an external system are truly simulated in a software simulation mode, an actual production scene is accurately restored in a test link, dependence on a physical environment is reduced, and test coverage is improved; and moreover, the test scene is solidified through codes and configuration files, so that the versioning management and one-key automatic regression test of the test cases are realized, the test efficiency is remarkably improved, and the quality of secondary development is guaranteed.

The invention discloses an automatic compiling method and device for a test case, electronic equipment and a storage medium, and relates to the technical field of computers, and the method comprises the steps: carrying out the structural analysis of a demand document for creative and creative transformation, and extracting the demand information of the demand document; the code warehouse is connected to obtain the source code and performs static analysis to generate code logic; establishing a semantic association mapping table of the demand information and the code logic, generating a test scene library according to the semantic association mapping table, and automatically generating a test case according to the parameter type and the constraint rule; and performing multi-dimensional verification on the generated test case to generate a verification result, and adjusting the priority of the test case according to the verification result. The method has the technical effects of improving marginal scene coverage depth and breadth, improving test case writing efficiency and accuracy, reducing manual dependence, and optimizing test case quality and execution pertinence.

The invention provides a test case generation method and device, equipment, a storage medium and a program product, and relates to the technical field of software testing. The method comprises the following steps: constructing a dependency graph comprising nodes represented by API functions and edges representing a dependency relationship between two connected API functions according to an API specification document; determining a target scene rule matched with the received test task, and performing random walk on the dependency graph according to a scene preference weight provided by the target scene rule and used for representing the degree of correlation between the API function and the target scene rule to obtain an API call sequence; and generating a test case for testing the GPU driver according to the API call sequence. According to the method, the target scene rule matched with the test task is determined, and the scene preference weight which is provided by the target scene rule and represents the correlation degree of the API function and the target scene is utilized to guide random walk on the dependency graph constructed based on the API specification document, so that the generated API calling sequence better meets the requirements of the test scene.

The invention provides a test data diversification generation method and system based on a data factory, and the method comprises the steps: carrying out the multi-dimensional semantic deconstruction of a basic test data set stored in the data factory, and obtaining a basic concept component set through the recognition of a semantic association relationship and a concept hierarchical structure between data entities; constructing a dynamic semantic association network on the basis, and generating an association weight matrix by calculating semantic association strength among different concept components so as to recombine the basic concept component set and generate a structured semantic template set; the method comprises the following steps: acquiring a target test data format, inputting the target test data format into a pre-trained feature evolution generation network, optimizing a feature evolution direction, obtaining an evolved feature set, performing data form conversion and relevance verification, converting the evolved feature set into a target test data format, and generating a target test data set meeting diversified test scene requirements after verification. According to the invention, the requirements of diversified banking business test scenes on high-quality test data can be better met.

The invention belongs to the field of automatic driving, and particularly relates to a multi-scene fusion automatic driving analog simulation test method and system, and the method comprises the steps: obtaining multi-source heterogeneous driving data, carrying out the preprocessing and fusion of the multi-source heterogeneous driving data, and obtaining standardized scene data; based on the standardized scene data, constructing a basic simulation scene by adopting a photo-level real-time rendering technology, and optimizing and constructing the basic simulation scene into a three-dimensional scene model by adopting a 3D Gaussian splashing technology; based on a preset scene generation rule and through a 4D space-time modeling algorithm, performing expansion and variation on the three-dimensional scene model, and automatically generating a multi-dimensional test scene set; executing a simulation test corresponding to the multi-dimensional test scene set, and collecting test result data of the automatic driving system; and establishing a scene optimization and algorithm iteration closed-loop mechanism based on the test result data, and dynamically optimizing the multi-dimensional test scene set and the automatic driving system algorithm.

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.

This invention relates to systems and methods for real-time dynamic classification and orchestration of test automation components in distributed DevOps environments. The system features an Auto Identify Automation (AIA) engine that leverages supervised learning, Multi-Modal Artificial Intelligence (AI), and Generative AI technologies. It includes a Smart Scenario Designer interface that allows users to author test scenarios using handwriting and voice inputs, which are processed in real-time by AI-driven handwriting recognition, voice recognition, and Natural Language Processing (NLP). The system dynamically suggests relevant automated components via a smart bubble pane, facilitating rapid scenario creation. The architecture is tool-agnostic and scalable, with a Shared Workbench Engine that supports real-time collaboration and conflict resolution. The system continuously adapts and improves, ensuring that the automation suite remains consistent, up-to-date, and aligned with evolving software requirements, enabling efficient and user-friendly management of complex test automation processes.

The embodiment of the invention discloses a risk awareness test arrangement system and method. Wherein the scene modeling engine is used for constructing a standardized business test scene model according to distributed tracking data of a production or test environment; the risk analysis engine is connected with the scene modeling engine and is used for acquiring a business test scene model, calculating a dynamic risk score for each business test scene and generating a test priority queue according to a score result; the intelligent arrangement engine is connected with the risk analysis engine and is used for automatically creating an isolated containerized test environment for the high-priority business test scene according to the test priority queue and automatically scheduling an end-to-end dial test task based on a topological structure of a business test scene model; and the observability closed-loop engine is connected with the intelligent arrangement engine and is used for collecting full-link observability data, analyzing and asserting an execution result and feeding back an analysis result to the risk analysis engine and the scene modeling engine during the execution period of the dial testing task.

The invention discloses a method and equipment for automatically testing a webpage based on a multi-modal large model, and the method comprises the steps: navigating to a target webpage according to an input instruction of a user task, and obtaining a webpage screenshot and a webpage structure; according to the layout information of each element in the webpage screenshot, identifying and marking each interactive element according to the identification position; obtaining interactive nodes and text elements in the webpage structure, and constructing a document object model tree; associated text elements corresponding to the interactive nodes are screened out from the text elements, and page description information is generated; and inputting the user task, the marked webpage screenshot and the page description information into a multi-modal large model to obtain structured operation content, converting the structured operation content into an automatic script, and testing the target webpage, so that manual intervention is reduced through full-process automation, the test coverage, the adaptability and the execution efficiency are improved, a test result is ensured to be more reliable, and the test efficiency is improved. The method is especially suitable for a complex webpage structure and a high-frequency iteration test scene.

The invention discloses an intelligent and automatic test case generation method based on a large language model, and relates to the technical field of large language model application, and the method comprises the following steps: collecting a software demand description file and an interface standardization file of a project, carrying out semantic analysis and mapping, generating a logic constraint set, and establishing a path mapping table; inputting the path mapping table into a large language model, generating a test scene and assertion, and executing semantic drift detection; when semantic drift is detected, a drift report is generated, a reverse correction process is executed, a patch prompt is generated, and the generation chain is executed again; and after the semantic distance of the prompt chain is detected to reach a convergence state, outputting a stable test case set. According to the method, the generation structure is controlled through the deterministic prompt sequence, so that the stability and controllability of test scene and assertion generation are realized; and through semantic drift detection and a reverse correction mechanism, real-time correction of semantic offset of the prompt chain is realized, and the accuracy and stability of the test case are improved.

The invention relates to the technical field of safety testing, and particularly provides a method, device and equipment for generating an expected function safety testing scene library, and the method comprises the steps: obtaining a parameter combination of key parameters in a pre-constructed basic scene; wherein the key parameters comprise environment parameters, traffic dynamic parameters and vehicle system parameters; generating a plurality of test scenes based on the parameter combination; after screening the plurality of test scenes, constructing a test scene library; wherein the test scene library is used for an expected function safety test of the intelligent driving vehicle. According to the embodiment of the invention, a rich expected function security test scene library can be constructed, and support is provided for expected function security test.

The invention discloses an automatic driving scene library dynamic generation method based on multi-modal data fusion, belongs to the field of automatic driving testing, and solves the problems of insufficient coverage of extreme working conditions, data splitting and single evaluation of a static scene library. According to the technical scheme, the method comprises the following steps: establishing a distributed data acquisition layer, and accessing a vehicle-mounted terminal and a V2X roadside equipment data stream through a federated learning framework; constructing a multi-modal semantic coding layer, fusing data extraction features, converting laser radar point cloud data into a space-time constraint matrix, and outputting a scene DNA coding sequence; deploying a dynamic evolution engine, mapping the DNA coding sequence into a traffic flow state parameter, and calculating an entropy gradient field; and starting a virtual-real co-occurrence scene generator, receiving the coordinates of the high-entropy region and the physical interference intensity parameters, performing simulation to generate a controlled physical interference test scene, injecting a group interaction rule based on behavior uncertainty, and finally outputting a dynamically updated scene library and a risk report for automatic driving system test evaluation.

The invention relates to the technical field of software testing, and discloses an agent-based multi-dimensional test sample automatic generation method and system.The agent-based multi-dimensional test sample automatic generation method comprises the steps that code changes are captured, interface definitions are extracted, and multi-source data are fused for consistency verification; constructing a multi-dimensional dependency graph and analyzing the change influence to obtain an associated interface list; generating a multi-dimensional test scene; performing parameter traceability and consistency derivation, and initializing test data and environment to obtain a complete test data set; generating a use case code, and performing hierarchical optimization and executable verification; test arrangement optimization and automatic execution are carried out, and faults are monitored and diagnosed in real time; carrying out multi-dimensional analysis and defect identification, dynamically evaluating the value of a test case and optimizing a test strategy to obtain a comprehensive test report; according to the invention, full-process automation of test case generation is realized, the test efficiency and quality are improved, and the test maintenance cost is reduced.

[Problem] To support automation of a test. [Solution] This information processing system is characterized by including: a scenario generation unit for generating a scenario of a test by giving a first prompt instructing generation of the scenario to a large-scale language model on the basis of a test case describing an outline of the test; and a script generation unit for generating a script by giving a second prompt to the large-scale language model, the second prompt instructing generation of the script that causes a computer to execute the test on the basis of the scenario.

The invention relates to the technical field of circuit board analysis, in particular to an integrated circuit defect analysis method and system, and the method comprises the steps: extracting and screening E-test data of a target product from a big data platform for integrated circuit manufacturing; performing defect classification modeling processing on the screened E-test test data; calculating a defect correlation index according to the classification accuracy and the correlation metric value of each candidate E-test item; determining a defect judgment threshold value and a numerical value distribution direction related to the defect based on a logic regression classifier result of the key test item, and generating a binary defect distribution diagram corresponding to each jointed board; calculating the image similarity between the bad jointed board graph and each good jointed board graph; judging whether the image similarity is higher than a preset threshold value or not, and adding the jointed boards of the good jointed board graph into a virtual bad jointed board list; and based on the virtual bad jointed board list, analyzing and comparing the manufacturing process flow data of the virtual bad jointed board and the original defective jointed board, and determining the potential root cause causing the defect. According to the method, the problems that in the E-test scene of the circuit board, the number of defect samples is extremely small, categories are extremely unbalanced, an experience threshold value is difficult to set, and root causes are difficult to trace can be solved.

The application discloses a vehicle test boundary scene generation method and system based on a pre-boundary scene, comprising the following steps: obtaining a first feature data set; obtaining a real boundary scene and a real pre-boundary scene based on the first feature data set and a preset risk discrimination criterion; constructing a second feature data set based on real pre-boundary scene data and the real boundary scene data; obtaining generated pre-boundary scene data by using a generation model based on the real pre-boundary scene data; training a prediction model based on the real pre-boundary scene data and the real boundary scene data; and obtaining test prediction boundary scene data based on the generated pre-boundary scene data and the prediction model. The method provided by the application obtains test prediction boundary scene data based on real pre-boundary scene data, and uses a generation model and a prediction model to expand real boundary scene data, thereby solving the technical problem that existing test scene data is sparse due to the limited space of generated scene data.

The invention provides a Beidou satellite navigation positioning terminal dynamic test system and method, and the system comprises a power divider which is connected with a high-precision navigation positioning antenna, and is connected with a standard global satellite navigation system high-precision positioning terminal and a tested Beidou satellite navigation positioning terminal at the same time. The signal receiving module is used for enabling the standard global satellite navigation system high-precision positioning terminal and the detected Beidou satellite navigation positioning terminal to receive consistent satellite navigation signals; the post-data resolving and processing computer is used for being connected with the standard global satellite navigation system high-precision positioning terminal and the tested Beidou satellite navigation positioning terminal and receiving and obtaining test data of the standard global satellite navigation system high-precision positioning terminal and the tested Beidou satellite navigation positioning terminal. And post-resolving processing is carried out based on the test data. The system is simple in structure, easy to deploy, reliable in test result and suitable for a dynamic test scene, and the positioning precision and the test efficiency are remarkably improved.

The invention relates to an MBSE multi-field-based virtual-real fusion simulation platform and method, and the method comprises the steps: an intelligent matching module is used for receiving a to-be-detected algorithm model according to a user interface module, determining an MBSE multi-field model from a model management module, and achieving the MBSE multi-field model through the MBSE multi-field model; the virtual-real fusion test module is used for determining a plurality of test scenes constructed according to real scene data according to the MBSE multi-field model, performing full-life-cycle verification on the MBSE multi-field model and the to-be-detected algorithm model according to each test scene to obtain a corresponding verification result, and when the verification result meets a preset condition, executing the virtual-real fusion test on the to-be-detected algorithm model. The to-be-detected algorithm model is stored in the model management module, the full-life-cycle automatic verification process is achieved through the virtual-real fusion test module, the problems that virtual simulation and real object test are separated, and evaluation standards are not uniform are solved, and the method has the advantages that the test efficiency is improved, and the virtual-real fusion test capability is enhanced.