27 results about "Test optimization" patented technology

The invention is applicable to the technical field of interface testing, and provides an artificial intelligence-based interface testing optimization method, which comprises the following steps: constructing a multi-dimensional interface knowledge graph of a tested system, the multi-dimensional interface knowledge graph comprising but not limited to static structure information, business semantic information and running state feature information; based on the multi-dimensional interface knowledge graph, the driving state and the action space, deploying a test agent based on reinforcement learning; in the test agent, according to a preset multi-target reward function, executing a target-oriented exploration action, and generating a dynamic test case; according to the method, the twinborn test environment of the tested system is constructed for the test agent to perform exploratory test and generate the test case set, the value density of the test cases generated by the method is high, defects can be effectively found, the test cases are associated with interface service semantics and operation rules, and the reliability of the test cases is improved.

The invention relates to the technical field of software engineering and automatic testing, in particular to an E2E scene test optimization method and system based on target man-machine cooperation. According to the scheme, an optimization target is manually set, an initial test case and a business process are input, and a test resource library is constructed; the system automatically analyzes a service path, generates a use case dependency graph and a key path, and determines a core scene through manual verification; a candidate optimization strategy is generated by the system, and a target strategy is determined through manual screening according to resources and priorities; the system carries out simplification, merging and scheduling optimization on the use cases, and deploys and executes the use cases after manual sampling verification of coverage; the system collects execution data in real time and generates an evaluation report, and whether a use case is supplemented or a strategy is adjusted is determined after manual analysis; and finally updating the resource library and the strategy model, establishing a dynamic feedback mechanism, and triggering continuous optimization during system iteration. According to the method, the E2E test efficiency and the software delivery quality are remarkably improved through man-machine cooperation and closed-loop optimization.

The invention provides a fan blade lightning protection conduction testing method and system based on machine learning, and relates to the technical field of lightning protection conduction testing, and the method comprises the steps: carrying out the current testing of a fan blade lightning protection system through a multi-channel MOS tube control circuit; performing multi-dimensional feature extraction and local feature analysis on the current response data; carrying out feature enhancement and defect identification based on the local feature sequence, and carrying out multistage clustering on an identification result; and performing global health analysis and defect traceability of lightning protection conduction performance on the fan blade lightning protection system according to a defect classification result, generating a defect attribution point location for monitoring, performing feedback optimization on a multi-channel MOS tube control circuit, and constructing a test optimization closed loop. According to the invention, the technical problem that the lightning protection conduction test efficiency is low due to the fact that the static conduction test is difficult to identify early hidden defects in the prior art is solved, the current test is carried out through the multi-channel MOS tube control circuit, and the lightning protection conduction test efficiency is improved.

The invention discloses a motor testing system based on a sliding frame and a guide plate, and relates to the field of motor testing. The test rack module is welded by a Q235B steel plate and is fixed by an expansion bolt; the sliding frame module is composed of a sliding base, a connecting beam and a supporting platform and is connected with a spring for buffering. The guide plate module comprises symmetrical guide plates which are finely adjusted through a sliding block and an adjusting assembly. The motor fixing module comprises an L-shaped baffle, a spring and a clamping bolt, and a built-in pressure sensor is used for measuring clamping force; an output shaft of the test driving module is connected with a torque sensor and a tested motor, and is provided with a controller and an acquisition module; the data acquisition module synchronously acquires multiple parameters, and a report is generated through a touch screen display, storage and software setting process; the buffering and damping module reduces noise and vibration and corrects the level through a gasket, a foot pad and a sizing block, and stable operation of the device is guaranteed. The method is fast adaptive to multi-model motor testing, optimizes the traditional technology, improves the detection efficiency and accuracy, guarantees the testing stability and reliability, and meets the batch testing requirements.

The invention relates to the technical field of code testing, in particular to a software code testing system and device and a storage medium, and the testing system comprises a static analysis module which is used for carrying out grammar and semantic analysis on source codes and generating an abstract syntax tree and a control flow diagram; the dynamic test module is used for executing source codes, monitoring behaviors during operation and capturing behavior abnormal data and code performance data; the knowledge graph construction module is used for constructing a code defect knowledge graph based on a historical defect database, and the code defect knowledge graph comprises an incidence relation between a code mode and a defect type; the test optimization engine is used for dynamically adjusting a test case generation strategy according to the static analysis result and the code defect knowledge graph; and the multi-dimensional evaluation module outputs a code coverage rate, a defect detection rate and a performance bottleneck analysis report. According to the software code test system, the test efficiency of the test optimization engine is improved through combination of the static analysis module and the dynamic test module; 48% of redundancy tests are reduced through priority distribution, and the test efficiency is further improved.

The invention belongs to the field of energy storage power station testing, relates to a data analysis technology, is used for solving the problem that the fault type occurrence frequency of an energy storage battery in different operation stages cannot be counted and analyzed in the prior art, and particularly relates to an energy storage power station preventive testing platform based on artificial intelligence. Comprising a stage evaluation module, a fault analysis module and a test optimization module which are in communication connection in sequence, and the stage evaluation module, the fault analysis module and the test optimization module are in communication connection with a database; according to the invention, the execution frequency of the test item can be dynamically adjusted according to the actual operation stage of the supervision object; the dynamic adaptive adjustment enables the test resources to be distributed more effectively, and the test resources are positively correlated with the fault occurrence probability, so that the waste of the test resources is avoided, the efficiency and the accuracy of the preventive test are improved, and the intelligent and refined operation and maintenance management of the energy storage power station is realized.

The invention discloses an automatic test optimization method and device based on reinforcement learning and a storage medium. According to the optimization method, a strategy model based on a neural network is utilized, corresponding first test action information is generated according to state information, and the state information comprises first feedback information currently fed back by an application program or an operating system; testing the application program according to the first test action information, and obtaining second feedback information corresponding to the test action information; generating reward information corresponding to the first test action information and the second feedback information; according to the state information, the first test action information and the reward information, track information used for optimizing a strategy model is constructed; and optimizing the strategy model by using a reinforcement learning method according to the track information. Intelligent and dynamic allocation of mobile application test resources is realized, so that the test resources are projected to functional modules with relatively high defect risks more accurately, and the overall efficiency and quality of automatic testing are improved.

The invention relates to the technical field of unmanned driving testing, in particular to a testing and evaluating method and system for airport unmanned driving equipment. The method comprises the following steps: calling a scene specificity parameter library according to airport operation data, obtaining a test scene and a quantitative index, and establishing a virtual-real test environment; triggering a multi-layer test request, and testing the equipment basic capability, the automatic driving basic capability, the equipment operation execution scene and the collaborative operation in sequence; obtaining layered test data, performing four-dimensional quantitative evaluation, and outputting a standard reaching condition according to an evaluation result; updating a scene specificity parameter library and a test parameter standard based on the evaluation result and the airport operation demand change, and precipitating data assets to form a reusable database; the system comprises a test preparation module, a hierarchical test execution module, a test result evaluation module and a test optimization iteration module. Through the mode, the operation precision is improved, the collaborative conflict is reduced, and the core demand of an airport for high-safety and high-efficiency operation is met.

The invention discloses a new energy automobile HIL double-engine cooperative automatic test method and system, and relates to the technical field of new energy automobile power testing, and the method comprises the steps: uploading collected configuration data, carrying out the automatic simulation configuration through an in-loop HIL simulation method, generating a test task, and distributing the test task to an HIL rack of each region, battery parameters and double-engine control parameters are obtained; according to the battery parameters and the double-engine control parameters, synchronous simulation of the battery and the double engines is conducted through an in-loop HIL simulation method, and real-time energy interaction data between the battery and the double engines is obtained; summarizing and analyzing according to the double-engine collaborative test data, and generating optimization suggestions of the double-engine collaborative automatic test. Through the dynamic power distribution method, power distribution of the motor and the internal combustion engine can be automatically adjusted according to the battery SOC and the double-engine control parameters collected in real time, cooperative work of the motor and the internal combustion engine is dynamically optimized, energy efficiency is improved in real time, and energy waste is reduced.

The invention provides a KPI comprehensive evaluation and test optimization method of an ADAS system. The method comprises the steps of obtaining a work log in an ADAS test process; based on the work log, calculating a KPI index used for evaluating the ADAS system function; evaluating the KPI index according to a preset judgment rule so as to identify a security attention event and a performance weakness event in the ADAS system; for each identified security attention event and performance weakness event, automatically intercepting data in a preset time window before and after occurrence of the events, and marking the data as attention events; and performing fault judgment on the concerned event, and generating a test case optimization suggestion. The technical problems that in the prior art, troubleshooting and test case optimization are disjointed, only single fault positioning can be carried out on recognized problems, and association logic among fault sources, scene elements and test cases is not established, so that quick tracing is difficult when sensing data exceptions occur in the test process are solved.

The invention provides a test case dynamic scheduling method and system based on an ant colony algorithm, and relates to the technical field of software test optimization. The method comprises the steps that core variables and parameters are initialized; analyzing the dependency relationship of the test cases; estimating the execution time of the test case according to the dependency relationship of the test case; selecting a to-be-executed test node according to the test case execution time; selecting a test case to be executed according to the task-node distribution table; according to the to-be-executed test case, updating a node operation state; according to the task execution result, the total completion time and the pheromone matrix, updating a test case execution state and the pheromone matrix; and obtaining an optimal scheduling scheme according to the current number of iterations or the total completion time fluctuation of continuous multiple iterations. According to the method, a load balancing problem is converted into an integer programming problem through mathematical modeling, optimal task allocation is realized by utilizing a heuristic search mechanism of the ant colony algorithm, and the efficiency and stability of a test system facing uncertain factors are remarkably improved.

The invention discloses a current transformer electromagnetic compatibility test optimization method and device and a computer program product, and the method comprises the steps: carrying out a test in a pre-constructed lightning stroke simulation scene, and collecting simulation test data; the current offset degree is calculated based on the time sequence and the sliding window analysis technology, the dynamic response type of the mutual inductor is identified, and a peak report containing the immunity is generated; synchronously adjusting the initial average thickness and the nearest point distance of the metal shielding layer by using a full-factor design method, constructing a two-dimensional parameter matrix, and repeating the lightning stroke simulation test; and comparing each group of parameter test results with a shielding-layer-free reference result to calculate an interference suppression ratio, screening an optimal parameter combination by adopting a multi-objective optimization algorithm, and outputting a test report. According to the method, the coupling rule of shielding geometric layout and electromagnetic interference is quantified, the technical spanning from qualitative protection to quantitative optimization is realized, the test accuracy and the engineering applicability of an optimization scheme are improved, and the operation requirements of a smart grid on the current transformer are met.

The invention relates to the technical field of integrated circuit testing, in particular to a test optimization method for a three-dimensional stacked integrated circuit, which comprises the following steps of: constructing a theoretical total cost model, setting algorithm parameters, initializing a population, executing differential evolution global search and self-adaptive multi-strategy local search. According to the method, the theoretical total cost model for correcting the discarding cost deviation is constructed, and the hybrid enhanced differential evolution algorithm fusing dynamic reverse learning and adaptive multi-strategy local search is designed, so that the direct test cost is reduced, the potential discarding risk caused by manufacturing parameter fluctuation is effectively controlled, and the method is suitable for popularization and application. Therefore, the total cost of the comprehensive theory of the three-dimensional stacked integrated circuit is reduced, the global exploration and local development capabilities of the algorithm are balanced by using a hybrid enhancement strategy, and the search process is effectively prevented from falling into local optimum; and efficient and robust optimization of the three-dimensional integrated circuit test process under complex constraints is realized.

The application provides a two-thirds wiring GIS protection transformation linkage test optimization method, and belongs to the technical field of power system relay protection device acceptance. The method first completes the test precondition confirmation, and then adopts a two-stage layered architecture to carry out the test: the indirect verification test without switch passes through the monitoring of the operation box action contact and the terminal conduction state, verifies the integrity of all protection circuits, and does not need the actual opening and closing of the circuit breaker; the direct verification test with switch is only carried out on the core necessary verification circuit, ensures that the two sets of trip / coil of the circuit breaker are operated once, and the new protection device is operated with the switch at least once; the test whole process follows the standardized process control, if unqualified, the corresponding fault is checked and then retested, if qualified, the test report is formed after the pressure plate is restored. The application greatly reduces the number of circuit breaker operations, shortens the test period, reduces the comprehensive cost, realizes the full coverage verification of the protection circuit, and improves the test efficiency, accuracy and safety.

The invention discloses a wheel lateral rigidity test optimization method and device, and belongs to the field of detection equipment. The device provided by the invention is composed of an axis positioning mechanism and a locking scale mechanism which are connected through a central shaft. The axis positioning mechanism utilizes a slide rail, a slide block and a positioning claw to cooperate with a slide rail mounting disc and a bottom plate to realize axis positioning of a to-be-measured wheel center hole, and realizes rapid and accurate positioning of the center of the mounting hole of the to-be-measured wheel. The locking scale mechanism ensures that excitation points are uniformly distributed through an angle dial and a length scale, and the connecting line of the two pairs of excitation points passes through the circle center of the center hole of the wheel to be measured. The device provided by the invention can be operated by a single hand, is simple in structure and convenient to operate, effectively solves the problem of measurement deviation caused by traditional visual point selection, and remarkably improves the accuracy and working efficiency of wheel lateral rigidity testing.

The 2.5D integrated circuit test path optimization method based on decomposition multi-objective evolutionary algorithm belongs to the field of integrated circuit test optimization, and aims to solve the problems that the traditional optimization method cannot efficiently solve the multi-objective optimization problem and the effect of balancing test time and test hardware overhead is not ideal because the gradient information of the problem is usually required. The method comprises the following steps: step one, initialization, generating an initial population for the test path configuration scheme of the 2.5D integrated circuit, and evaluating the initial population index according to the multi-objective evolutionary algorithm, and selecting the optimal fitness index as the ideal point of the initial population; step two, generating a new solution by using a crossover and mutation operator, and evaluating the new solution according to the multi-objective evolutionary algorithm and updating the ideal point; step three, environment selection, including updating the neighborhood solution and the elite solution set; step four, executing the iteration process of steps two and three for multiple times until the iteration is completed, and outputting the elite solution set.

The invention discloses a test optimization method for a shimmy damper, and the method comprises the steps: simulating the working state of the shimmy damper, and obtaining the test results of a damping performance test, a high-frequency fatigue test, a sealing performance test and a running-in test of the shimmy damper; and determining the linear stiffness in the X and Y directions and the torsional stiffness in the Z direction. All parameter variables influencing the performance of the shimmy damper are collected, corresponding test performance is obtained, and key parameter variables are determined based on the random forest model; and predicting the performance of the shimmy damper based on the key parameter variables, and obtaining an optimal key parameter combination through a genetic algorithm based on the error between the predicted performance of the shimmy damper and the actual test performance, the constraint condition and the optimization target. According to the method, performance prediction and correction of the shimmy damper at the beginning of design can be achieved, the design period is shortened, and good practicability is achieved.

2.5D integrated circuit time delay defect detection method based on differential evolution algorithm clustering belongs to the technical field of integrated circuit test optimization, and aims to solve the problems of large test cost and slow test speed of traditional 2.5D integrated circuit time delay defect detection method. The method comprises the following steps: step one, reading 2.5D integrated circuit critical path data as clustering data; step two, using the differential evolution algorithm to process the clustering data in the register, and obtaining the optimal clustering scheme; without setting the grouping number in advance, the best grouping number can be determined in the running process, the clustering process is optimized through mutation, crossover and selection operations, and the best clustering scheme is determined; step three, according to the optimal clustering scheme, the clustering center in each subclass is selected as the representative critical path, the delay on the representative critical path is measured and mapped to other critical paths, and the time delay defect on the integrated circuit critical path is detected.

The application discloses an intelligent robot arm control system and method based on an Internet of Things, relates to the technical field of equipment control, and comprises an equipment operation test and optimization module, a historical task information analysis module, an information comparison module and an abnormal early warning processing module. The equipment operation test and optimization module is used for performing equipment operation test on the intelligent robot arm and performing test optimization. The historical task information analysis module is used for predicting the time required by the intelligent robot arm to complete a current object grabbing task, which is recorded as a grabbing prediction time. The information comparison module is used for collecting load information of a current object to be grabbed, judging a range in which the load of the current object to be grabbed is located, and calling test information, which is recorded as a temperature rise test time. The grabbing prediction time and the temperature rise test time are compared. When the grabbing prediction time is greater than the temperature rise test time, the abnormal early warning processing module is used for predicting that there is a risk of object falling and performing abnormal early warning, so that the probability of failure of the robot arm operation task is reduced.

The invention provides an unmanned vehicle adaptive test system based on digital twinning and reinforcement learning, and the system comprises a digital twinning modeling module which is used for constructing an unmanned vehicle digital twinning model and a digital twinning test scene, and the digital twinning model is used for mapping the state of a virtual unmanned vehicle in real time, and carrying out the collision and comfort detection of the virtual unmanned vehicle, the digital twin test scene is used for simulating a physical test environment; the reinforcement learning test module is used for performing path planning test on the virtual unmanned vehicle by utilizing a reinforcement learning algorithm to generate a test case; and the test optimization module is used for comparing the deviation between the digital twin test scene and the physical real test environment in the test case and the reaction deviation between the virtual unmanned vehicle and the entity unmanned vehicle, and optimizing the test case. The physical test environment and the unmanned vehicle state are mapped in real time, the driving path of the unmanned vehicle is dynamically adjusted, the optimal test case is generated, the test strategy is continuously optimized, and the unmanned vehicle test comprehensiveness, efficiency and accuracy are improved.

The invention relates to a method for optimizing the setup of a test bench test of a vehicle assembly.

The application discloses a kind of wheel lateral stiffness test optimization method and its device, belong to detection equipment field.The device provided by the application is composed of axis positioning mechanism and locking scale mechanism, and the two are connected by center shaft.Axial positioning mechanism utilizes slide rail, slider and positioning claw to cooperate with slide rail mounting disc and bottom plate to realize the axial positioning of the center hole of the wheel to be measured, and simultaneously realizes the quick and accurate positioning of the center of the mounting hole of the wheel to be measured.Locking scale mechanism ensures that the distribution of excitation points is uniform, and the line connecting the two excitation points of the two pairs passes through the center of the center hole of the wheel to be measured.The device provided by the application can be operated with one hand, has simple structure and is convenient to operate, effectively solves the measurement deviation problem caused by traditional visual selection, and significantly improves the accuracy and work efficiency of wheel lateral stiffness test.

The present application relates to the field of software engineering and automatic test technology, more particularly, to an E2E scene test optimization method and system based on target man-machine cooperation. The scheme sets an optimization target by hand and inputs initial test cases and business processes to build a test resource library; the system automatically analyzes business paths to generate a case dependency graph and a key path, and determines core scenes through manual verification; then the system generates candidate optimization strategies, and determines target strategies according to resource and priority screening by hand; the system optimizes the cases according to this, and deploys and executes after manual sampling verification coverage; the system collects execution data in real time and generates an evaluation report, and decides whether to supplement the cases or adjust the strategies after manual analysis; finally, the resource library and the strategy model are updated, and a dynamic feedback mechanism is established to trigger continuous optimization when the system iterates. This method significantly improves the E2E test efficiency and software delivery quality through man-machine cooperation and closed-loop optimization.

This application discloses a fault injection test optimization method and apparatus based on test vector analysis. The method includes: performing functional simulation for each test case in a test case set, collecting trigger information generated after the fault injection moment during the functional simulation; marking this information on the circuit topology diagram of the circuit under test, and analyzing and determining the set of effective fault test points corresponding to the test case; summarizing and analyzing the sets of effective fault test points corresponding to each test case to determine the set of test cases with the most fault test point coverage and the fewest test cases, as the target test case set; and performing fault injection testing on the circuit under test based on the target test cases and the set of effective fault test points corresponding to each target test case. This application accurately achieves effective reduction and optimization of the test case set and the corresponding fault test points, significantly reducing the testing workload and improving testing efficiency.

The application discloses a kind of multi-die interconnection integrity test optimization method and system, it is related to die test technical field, including the weighted connection structure model of CTE mismatch, aspect ratio and hollow density correction is constructed, four kinds of failure of sensitivity model are established to thermal fatigue, TSV leakage, bonding hollow and electromigration, risk index is calculated and three-level coverage baseline is divided by fusing FMEA and safety level, compressed test vector is generated according to clock domain clustering, dynamic rerouting is realized through IEEE1838 hierarchical TAP, online response freezes and extracts adjacent line to form fault cluster, execute hardware level isolation and directional diagnosis, update weight based on fault result, monitor process drift and dynamically adjust threshold, offline update topology graph forms closed loop iteration.The application realizes cross-die differentiation test strategy, improves test efficiency and fault detection rate.

The application provides a fan blade lightning protection conduction test method and system based on machine learning, relating to the technical field of lightning protection conduction test, the method comprising: current test on the fan blade lightning protection system through a multi-channel MOS tube control circuit; multi-dimensional feature extraction and local feature analysis on current response data; feature enhancement and defect identification based on local feature sequence, multi-level clustering on the identification result; global health analysis and defect tracing of lightning protection conduction performance on the fan blade lightning protection system according to the defect classification result, generation of defect attribution points for monitoring, feedback optimization in the multi-channel MOS tube control circuit, and construction of a test optimization closed loop. The application solves the technical problem of low lightning protection conduction test efficiency in the prior art due to the difficulty of identifying early hidden defects in static conduction test, and improves the lightning protection conduction test efficiency through current test in the multi-channel MOS tube control circuit.