88 results about "Test design" patented technology

The invention discloses a post-pumping air tank robustness collaborative design method considering hydraulic parameter time-varying characteristics, which comprises the following steps: defining a hydraulic parameter uncertainty fluctuation interval of a water delivery system in a full life cycle, and setting a body structure decision variable range of a post-pumping air tank; performing combined sampling in the water conservancy parameter uncertainty fluctuation interval and the body structure decision variable range by using a test design method to generate an initial sample set; performing steady-state and transient-state coupling simulation on the initial sample set, constructing a constant-flow operation condition of the water delivery system by using a hydraulic equation, updating a water pump working point and pipeline pressure distribution, performing transient simulation by using a characteristic line method to obtain a hydraulic response index, and training to obtain a water hammer response agent model; constructing a robustness optimization objective function based on failure probability constraint; and performing global optimization on the target function by using an intelligent optimization algorithm, calling the water hammer response agent model to perform random simulation, evaluating a failure probability, and outputting a target design scheme.

The invention provides a part lightweight design method based on a random forest model, and the method comprises the steps: defining a design variable and a response target, and generating a sample point through a test design sampling method; geometric modeling and updating, finite element preprocessing, solving calculation and result extraction are automatically completed through parameterized scripts, and a sample data set is formed; independently training a random forest regression model for each performance response by using the sample data set, and constructing a quality rapid calculation model; verifying the random forest agent model until all agent models meet preset precision requirements; on the basis of the proxy model, constructing an optimization problem, carrying out optimization solution by adopting a multi-objective optimization algorithm, and outputting a Pareto optimal solution set; and selecting a candidate design scheme from the Pareto optimal solution set, executing high-precision finite element simulation, comparing a prediction result and a simulation result of the proxy model, and if an error is within an acceptable range and all performances meet design requirements, determining a final lightweight design scheme.

The invention provides a small sample AI proxy model construction method based on sensitivity analysis and supplementary sampling, and the method comprises the steps: carrying out the global sensitivity analysis through the automatic simulation process of parameterized parts of wind power equipment in combination with test design, and removing insensitive variables, thereby guaranteeing the accuracy and standardization of a sample, avoiding the invalid consumption of irrelevant variables, and achieving the automatic simulation of the parameterized parts of the wind power equipment. Latin hypercube sampling is adopted based on key variables, a core design area is uniformly covered, the number of initial simulation times is greatly reduced, the cost is controlled, the problem that the generalization ability is poor due to uneven small sample distribution is solved, a deep neural network regression model is trained through a training set, the strong nonlinear fitting ability of the deep neural network regression model adapts to a complex mapping relation, the core law is efficiently learned, and the robustness is high. Based on initial model prediction precision and sensitivity information, samples are accurately supplemented in weak areas, samples are not increased blindly, investment is reduced, prediction blind areas are made up, errors are gradually reduced through iterative training closed-loop optimization, and finally the small sample AI proxy model meeting preset requirements is obtained.

The invention provides a hybrid level enhanced random algorithm based on double-loop optimization and a cross-platform implementation method thereof, and belongs to the technical field of modeling and parameter, and the method comprises the following steps: S1, initialization; s2, setting algorithm parameters; s3, executing internal circulation, and executing local search under a fixed threshold value; s4, judging constraint conditions; when there is no constraint condition, generating a candidate solution; s5, calculating a target function value; s6, judging updating of the matrix according to the target function value; s7, returning to an outer loop, and adjusting a threshold value Th according to the frequency for receiving the new design and the number of iterations; s8, after M iterations are completed, the acceptance rate and the improvement rate are calculated; s9, updating the threshold value Th; and S10, judging a condition for stopping iteration. According to the method, the problems that a general construction method is lacked, local optimum is easy to sink and cross-platform deployment is difficult to realize in a multi-factor mixing level test design are solved.

The invention relates to the technical field of integrated circuits, and provides a verification platform, a verification method, electronic equipment and a medium. The verification platform comprises: an application layer bus function level model comprising a customized interface for a to-be-tested design for interacting with the to-be-tested design, the to-be-tested design being a PCIe protocol docking and protocol compliance test suite, and the to-be-tested design being a PCIe protocol docking and protocol compliance test suite; the PCIe protocol docking and protocol compliance test suite supports a verification demand of a physical layer intellectual property core and a verification demand of protocol consistency of a PCIe controller and a physical layer subsystem level; and the to-be-tested design interacts with the application layer bus function level model through the customized interface. Thus, the integration and through-flow debugging cost of subsystem protocol consistency verification is reduced, the verification period of a docking test of PCIe subsystem protocol standardization is shortened, and the completeness of docking verification of a full quantization protocol of a PCIe subsystem and the platform reuse efficiency are improved.

The invention discloses a communication debugging method and device, electronic equipment and a storage medium, and relates to the technical field of communication, and the method comprises the steps: integrating a to-be-tested design and a transaction processor to a top layer module of a simulation environment, and mapping the top layer module to a hardware acceleration simulation platform; a simulation database corresponding to the top layer module is generated on the hardware acceleration simulation platform; loading the simulation database to a hardware acceleration simulation platform, and starting a runtime environment; calling an application program interface provided by the transaction processor through the test code to set communication parameters; starting simulation to realize communication debugging between the to-be-tested design and the transaction processor; wherein the transaction processor is used for realizing a communication link in a virtualization mode. According to the method and the device, the dependence on physical peripherals is eliminated, and efficient and flexible communication debugging is realized on the premise that actual hardware connection is not needed.

This invention relates to the field of chip verification technology, and more particularly to a random verification method, electronic device, and medium based on register arrays. S1: Set m=1, execute S2; S2: Send data to RA in the target register array. m and RB m Input the same random test stimulus; S3, RA m Predict the random write value, and set F m Updated to RA m Predicted random write values; S4, RB m Write random numbers based on the input random test stimulus; S5, read the currently stored data in the RTL instances of all registers in the chip under test design in random order and compare it with the corresponding reference value. If they are all the same, proceed to S6; otherwise, the verification fails and the process ends; S6, compare m and M. If m

The invention relates to the technical field of test design, in particular to a key factor mining-based accommodating and arranging condition optimization method, medium and equipment, which are characterized in that accommodating and arranging conditions are generated by analyzing a target text, an initial simulation queue is constructed in combination with database retrieval, and baseline feature distribution is analyzed, so that representative research crowds can be obtained without real object recruitment; the test cost is reduced; a panoramic object feature matrix is constructed by integrating multi-dimensional data, the limitation of a single feature is broken through, and comprehensive data support is provided for key factor mining; the contribution degree of each characteristic variable to the preset key outcome is analyzed based on the matrix, and the key characteristic variables are positioned, so that the experience driving is changed into the data driving, and the scientificity and the pertinence of the accommodating and arranging condition design are improved; the accommodating and arranging conditions are dynamically adjusted through the closed-loop optimization process, it is ensured that the target accommodating and arranging condition set gives consideration to queue representativeness, feature pertinence and distribution rationality, and the design scientificity and success rate of clinical tests are improved.

The invention discloses a data processing model training and FCBGA packaging structure material matching method with a cover. According to the specific implementation scheme, a three-dimensional finite element model of a target packaging structure is constructed based on geometric parameters and material parameters of the target packaging structure; with the material parameters as design variables, inputting M groups of material parameter combinations generated through target test design based on the material parameters into the three-dimensional finite element model to obtain a data set; and extracting part of data from the data set based on the simulation test task as a training data set, and training the to-be-trained model based on the training data set to obtain a data processing model. According to the technical scheme, mechanical property analysis can be carried out on different materials according to the simulation result, so that the efficiency of evaluating the reliability of the packaging structure is improved, and the trial and error cost is reduced.

The application provides a CMG (control moment gyro) failure test design method based on Gaussian process regression, which is suitable for test design of CMG working characteristics in a multi-stress coupling environment. The method reflects the mapping relationship between test factors and test indexes by using a Gaussian process regression model, establishes a prediction model based on simulation data, constructs an utility function, expands a training set in a sequential manner, updates the prediction model until the accuracy requirement is met. All simulation sample points are subjected to cluster analysis (clustered into at least two categories), and then part of the sample points in each category are randomly selected for testing, so that the CMG working characteristics are verified, and efficient test verification of the CMG working characteristics can be realized.

The embodiment of the invention provides a document generation method and device. According to the method, the test item can be hierarchically split through mind map node arrangement provided by the editing window, so that a tester can more clearly control the test coverage range, the risk of case omission is reduced, and the document generation precision is improved. Based on the document template and the target mind map, a test design document and a test case document are generated, and a demand tracking table is generated in the test design document. Due to the fact that the test design document and the test case document are from the same target mind map, the problem of information splitting of traditional scattered documents is avoided in the mode, the test design, the test requirements and the test cases have strong relation, and efficient tracking is facilitated.

This invention relates to a simulation test method for grouting of prestressed concrete bridge ducts, characterized by the following specific test steps: S1: Test design; S2: Equipment and material preparation; S3: Test commencement. This method addresses the problem that common duct grouting construction techniques, processes, and requirements cannot meet the needs of new grouting materials and equipment, such as "grouting from the lowest point." Some construction techniques have theoretical flaws, such as "flushing the duct before grouting and blowing away accumulated water with high-pressure air," and some lack reliable theoretical and practical basis. Therefore, it is necessary to study the influencing factors of duct grouting construction techniques based on current grouting materials, summarize the main factors affecting duct grouting quality, and propose an operable grouting process method.

The invention provides a USB measurement and control system collaborative verification platform building method based on the combination of System Verilog and a C language, and relates to the technical field of FPGA testing, the method is based on a Cadence IES simulation environment to build a verification platform of a layered tree topology architecture, the verification platform is independent of a tested design project file, a three-level system structure is adopted, and the verification platform is provided with a three-level architecture; the rtl design layer is used for storing a source code file of a to-be-tested USB measurement and control system baseband processing FPGA (Field Programmable Gate Array) described by adopting a register transmission level; and the tb test platform layer adopts a modular design and comprises a dutif interface definition module, a dutrap design packaging module, a tb test control module and a tbtop top layer integration module. According to the USB measurement and control system collaborative verification platform building method based on the combination of the System Verilog and the C language, intelligent generation of a verification environment is achieved through an automatic script, and the configuration complexity is remarkably reduced; and a parameterized configuration mechanism is adopted, so that different types of USB measurement and control systems can be quickly adapted, and excellent adaptability is shown.

The application relates to the field of software engineering and discloses a natural language driven GUI test rehearsal and test script generation method based on an LLM, which comprises the following steps: S1: test operation design: a test personnel and a planning agent realize human-machine cyclic interaction, and convert a high-level test case described in natural language into a low-level and standardized test operation description; wherein the planning agent adopts a hybrid architecture based on retrieval enhancement generation, the test personnel reviews, adjusts and feeds back the test operation description, and the feedback information is stored in a context memory module of the planning agent as prior knowledge; S2: test automation rehearsal: an operation agent takes the test operation description generated in S1 as input. Through human-machine cyclic interaction between the planning agent and the test personnel, automatic conversion of a natural language test case into a standardized test operation is realized, so that a test personnel without a programming background can also participate in automatic test design.

The invention discloses a production line simulation test object automatic creation and data integration system and method, and belongs to the technical field of industrial simulation, and the system comprises a unified data center platform, a data interface adaptation layer, a simulation object construction engine, a test management and execution engine, and a data acquisition and optimization engine. The method comprises the following steps: automatically analyzing input data according to a predefined meta-model template, and instantiating the input data into a specific simulation object; automatically docking and converting multi-source data through an adapter mode, and injecting the multi-source data into corresponding object attributes; test design, operation monitoring, data acquisition and verification optimization are integrally completed; the simulation modeling efficiency and accuracy can be greatly improved, the dependence on professional engineers is reduced, and rapid simulation analysis and optimization decision support of a production system are realized.

Embodiments of the present application provide a chip test method, a test platform and equipment, the method comprising: obtaining a simulation verification mode, the simulation verification mode comprising: Virtio module level verification and PCIe-to-Virtio subsystem level verification; compiling corresponding DUT code and verification environment code according to the simulation verification mode; performing simulation verification by using test cases, a Host side Virtio driver simulating a test scene set according to the test cases, calling a corresponding register adapter, and verifying different simulation verification modes based on the same test cases. In the embodiments of the present application, automatic adaptation of a to-be-tested design DUT and a verification environment instantiation is realized, and when Virtio module level verification and PCIe-to-Virtio subsystem level verification are performed, the test cases can be fully reused, thereby accelerating chip verification convergence and shortening the verification period.

A simulation failure identification and physical acceleration fusion-based MEMS sensor accelerated life test method comprises the following steps: aiming at environmental stress information of an MEMS sensor in a predetermined task process, identifying a failure form and a weak part by using structure simulation, and carrying out simulation evaluation on candidate acceleration stress working conditions constructed around the weak part; according to a preset life, a reliability requirement and a test resource constraint, selecting a combination of a stress level, test time and a sample number from the candidate working conditions to form an accelerated life test design scheme of the MEMS sensor; according to the method, the test period can be shortened on the premise that the test representativeness and accuracy are guaranteed, the test cost is reduced, and the effectiveness and universality of the reliability test design of the MEMS sensor are improved.

Certain aspects of the present disclosure provide a method to define at least one register transfer language (RTL) level parameter for a design for test (DFT) wrapper module including at least one DFT sub-module including an RTL logic. A number of instances of the at least one DFT sub-module may be based on a value of the at least one RTL level parameter. The at least one DFT sub-module may be used for testing a system-on-a-chip (SoC) chip.

The application discloses a kind of meta-heuristic test design optimization methods based on matrix transformation, belong to computer-aided test design and statistical modeling technical field, this method uses a two-stage optimization framework: first, in global exploration stage, with minimizing global uniformity index as target to carry out unconstrained optimization, to obtain well space coverage sample set;Subsequently in local development stage, with minimizing adjacent distance variance as target, optimization is carried out under the constraint of maintaining the good global coverage obtained, to fine adjustment sample point local spacing distribution, improve uniformity;The application encodes test design scheme into matrix form individual, is updated iteratively by meta-heuristic algorithm, and adopts global normalization strategy to ensure that sample is always located in design space;The application can systematically consider the global coverage of sample and local uniformity, significantly improve the sample distribution quality in high-dimensional space, generate comprehensive performance excellent test design scheme.

The invention provides a concrete permeability prediction model construction method based on deep learning, and relates to the technical field of constructional engineering. The method comprises the following steps: performing sample preparation and test design; carrying out a multi-working-condition permeability test; test data are integrated and cleaned, and modeling preparation processing is carried out; carrying out deep learning model training and optimization; and verifying the model precision, and proposing a prediction formula applied to engineering. Compared with a traditional algorithm, the optimization efficiency of the method is improved by 3.5 times, the training period is shortened to be within 30 minutes, the test set Rreaches 0.999, the equipment cost is reduced by 40% or above, rapid and accurate prediction of the permeability under the complex working condition is achieved, the application threshold is reduced through engineering expression, the method conforms to the green engineering and the double-carbon concept, and the method is suitable for large-scale popularization and application. And the anti-seepage design requirements of engineering scenes such as island-reef hydraulic reclamation and gas storage facilities are met.

A test case generation method, comprising: receiving test requirements of a user; retrieving a knowledge base on the basis of the test requirements to obtain test objects related to the test requirements, wherein the test objects related to the test requirements include test objects corresponding to similar requirements; deriving use case generation assistance information by means of a language model on the basis of at least one of: the test objects related to the test requirements, a requirement document of the test requirements, or associated information, wherein the associated information comprises at least one of associated functions or scenarios, and the use case generation assistance information comprises at least one of: at least one test point or at least one test-related factor; and then, on the basis of the at least one test point or the at least one test-related factor, generating test cases corresponding to the test requirements. In the method, by recommending a test point or a test-related factor on the basis of a knowledge base, and integrating the recommendation of the test point or the test-related factor into a test operation process, quick reference to a historical test design process can be achieved, thereby improving test design efficiency.

The application discloses an optimization method and system based on adaptive test design and design space compression, which firstly sets an initial design optimization target and a design region, sets corresponding optimization termination conditions, generates a plurality of test points based on the current design region to form a corresponding test point set. Then, test analysis is carried out on the generated test points, the optimal point in the current test point set is searched, and based on the current design region, the existing test points are adaptively generated to continue generating new test points to form a new test point set. Finally, test analysis is carried out on the test points in the newly generated test point set, new test points are searched, and it is judged whether the searched new optimal point is better than the last optimal point. If yes, the design region is rearranged based on the new optimal point; if no, the current design region is compressed. Finally, it is judged whether the set optimization termination conditions are met, so as to judge whether to continue optimization.

The invention discloses a rapid equivalent analysis method and system for a heat dissipation cold plate based on a Kriging agent model, and belongs to the field of heat dissipation design of electronic equipment, and the method comprises the following steps: obtaining a geometric model of the heat dissipation cold plate, and carrying out grid division and simulation to obtain design points of a grid model of the heat dissipation cold plate; after the key simulation data and the corresponding temperature response values serve as a training set to be normalized, a distance matrix is obtained through calculation, a regression matrix and a correlation matrix are constructed in combination with the key simulation data of the design points, finally, parameters of a Kriging agent model are optimized in combination with boundaries of the key simulation data, and the Kriging agent model is obtained. And obtaining a Kriging agent model for rapid equivalent analysis of the heat dissipation cold plate, and inputting a to-be-tested design point into the Kriging agent model to obtain a predicted temperature response value. According to the method, the processes of geometric modeling, grid division and numerical solution do not need to be carried out again after subsequent parameter adjustment, and the efficiency of equivalent analysis of the heat exchange performance of the heat dissipation cold plate is improved.

The air bridge simulation analysis method of a parameterized model, electronic equipment and a storage medium belong to the technical field of wind tunnel test design. In order to realize an air bridge simulation analysis method which is convenient to operate and simple to calculate. The air bridge simulation model parameters are set based on an L-shaped air bridge, including air bridge simulation model overall layout parameters, air bridge simulation model local size parameters, effectiveness detection is performed, and a parameterized air bridge simulation model is constructed; wind tunnel test load parameters are loaded, and parameterized air bridge simulation model verification analysis is performed; sensitive parameters are extracted from simulation model verification analysis results by using a parameter sensitivity analysis method, a sensitive parameter list is obtained, air bridge simulation analysis of the parameterized model is performed, and response surface optimization method is used to optimize simulation analysis results. The method for establishing a parameterized model is used to complete full-automatic establishment of the model by modifying parameters, realize automatic implementation of finite element simulation, and automatically extract calculation results.

The invention discloses a chip verification method and device, and relates to the technical field of integrated circuit design verification. The method comprises the following steps: determining an environment data class and an environment configuration class in a current verification environment; determining a reference algorithm class corresponding to the environment configuration class from algorithm classes of a pre-constructed transaction-level algorithm library according to the environment configuration class, and instantiating the reference algorithm class to obtain a reference model; constructing a reference model system comprising a reference model based on a common interface of a reference algorithm class and an object handle declared by the common interface; obtaining an environment data class and an environment configuration class through a model input agent of the reference model system, determining input of a reference model and a to-be-tested design, and obtaining final reference output of the reference model system and final test output of the to-be-tested design; and comparing the final reference output with the final test output to verify the chip. According to the embodiment, cross-level verification multiplexing can be realized, and the reusability and transportability of the reference model are improved.

The invention discloses an intelligent test case generation method and device, a terminal and a storage medium. The method comprises the following steps: extracting entities of a demand document, mining an association relationship between the entities and carding out a complete business process link, and converting the demand document into standardized structured demand data; nodes and edges are constructed through the industry knowledge base and historical test case data, and a domain knowledge graph is generated according to the nodes and the edges; selecting a test design method based on the structured demand data and the domain knowledge graph, and generating a test strategy according to the selected test design method; and generating an initial test case according to the test strategy, and verifying and optimizing the initial test case to obtain an optimized test case. According to the method, a full-process automatic link of demand analysis, test design, case generation and verification optimization is constructed, multi-tool switching is not needed from demand document processing to final optimization test case output, manual intervention links are greatly reduced, and an end-to-end automatic closed loop of test case generation is realized.

The invention discloses a die-casting structural member rapid high-precision simulation construction method, and relates to the technical field of material testing, and the method comprises the steps: determining a target change range of target material parameters of a simulation group die-casting structural member as a search space; sampling in the search space by adopting a test design method, and obtaining sample point response data through simulation calculation; determining a first force and displacement curve of the die-casting structural member of the simulation group, and setting a target function as a constraint to minimize the difference between the first force and displacement curve and a second force and displacement curve of the die-casting structural member of the experimental group; under the constraint of a target function, searching in a search space for global optimization to obtain an approximate optimal solution of a target material parameter; the prediction precision of the finite element model with the approximate optimal solution is verified; and determining an optimal parameter value of the target material parameter according to a preset condition and the prediction precision. According to the method, the parameters can be adjusted and optimized in a closed-loop mode, and the applicability of the optimal parameter value to the same batch of die-casting structural parts is improved.

The invention provides a method for predicting mechanical properties of a flexible structure, which comprises the following steps of: analyzing functional requirements of the structure, uniformly sampling in a load range of the flexible structure by adopting test design, obtaining a load set comprising a plurality of groups of load working conditions, and establishing a mechanical property data set of the flexible structure under the plurality of working conditions, constructing a coordinate feature matrix and a load feature matrix as input samples, and designing a multi-branch topological structure neural network based on physical information; introducing a stress weighting mechanism, establishing a physical-oriented composite loss function, adjusting neural network parameters based on the minimum loss function, establishing a Gaussian process regression model, predicting the residual error of the neural network model, and compensating the model to obtain a neural network prediction model after residual error compensation. According to the method, the calculation speed of solving the mechanical properties of the flexible mechanism can be effectively increased, the related data of the mechanical properties of the flexible structure can be given with relatively high precision, and the problem of low calculation precision of a traditional finite element method for the flexible mechanism is solved.

The invention provides a soft reset verification method, a logic device and data processing equipment, when a message ending signal of a tested design is monitored, an ideal model is triggered to start a processing flow, and when it is monitored that soft reset is completed, a global variable is set. And under the condition that the ideal model is triggered, obtaining the message data from the specified address and caching the message data. And if the global variable is not in the set state, the cached message data is used as expected message data to be sent to a comparator, and the comparator compares actual message data obtained from the detected and identified data with the expected message data to obtain a comparison result. And if the global variable is in the set state, the ideal model stops sending the message data to the comparator, and clears the cached message data and descriptors. According to the scheme, data comparison failure is avoided by establishing time sequence synchronization between the ideal model and the tested equipment, and a cache clearing mechanism is executed after soft reset is finished, so that a single test case can support multiple soft reset operations.

The application provides a software test case generation method and system based on domain knowledge constraints. A domain knowledge base covering domain basic knowledge and industry testing criteria is constructed; the original requirement text is normalized, the semantic understanding ability of the knowledge base and the large language model is combined, the requirement type is identified and the structured decomposition rule is dynamically matched, and a structured requirement model is constructed; the test focus features in the requirement are extracted, and the test item decomposition requirements are formulated; the large language model is driven to expand the scene and systematically disassemble the requirement, and the systematic test items are generated according to the test item decomposition requirements; the test cases are generated in multiple dimensions with the test items as the input, and the traceable relationship among the requirements, the test items and the test cases is established; the integrity check and consistency check are performed, and the test design result is output. The application enables the large language model to perform the test design task in a controlled and professional knowledge environment, and realizes the systematic generation from the natural language requirement to the executable test case.