49 results about "Test failure" patented technology

This utility model discloses a manual testing circuit and device for an IPM driver board. The circuit includes VIN, VCC, GND, G, and A terminals, and UH, VH, WH upper bridge input switch groups, N-segment lower bridge input switch groups, and BR, UH, VH, WH, UL, VL, WL output switch groups, respectively connected to the IPM driver board under test. VIN, VCC, and GND terminals are connected to the UH, VH, WH upper bridge input switch groups and the N-segment lower bridge input switch groups, respectively. G and A terminals are connected to the BR, UH, VH, WH, UL, VL, WL output switch groups, respectively. Using the circuit provided by this utility model, the switching function of the IPM driver board under test can be tested individually. By setting the UH, VH, WH upper bridge switch groups to control the individual power supply of the upper bridge arms of the IPM driver board under test, and by switching the N-segment lower bridge input switch groups to control the individual power supply of each phase of the lower bridge arm, fault analysis of a single bridge arm can be achieved. This allows for the screening of problematic IPM module driver boards before assembly, reducing losses caused by overall module test failures.

This application discloses a software testing method and related equipment based on dual-model collaboration, relating to the field of software testing technology. The method includes: processing a natural language requirements document using a pre-defined large language model to obtain a test script for testing the software under test; if the software test result is a test failure, verifying and classifying the test result using a pre-defined algorithm model to obtain abnormal scripts and system bugs; correcting the abnormal scripts and system bugs; and iteratively testing the software under test based on the corrected test script and the corrected software under test until the software test is completed. This application, through a collaborative architecture of "large model understanding + small model judgment" and a semantic consistency detection mechanism, effectively corrects logical deviations in the generated content (test script), ensuring a high degree of matching between test cases and system functional behavior, significantly improving the reliability and executability of test cases, and thus achieving high-quality and high-efficiency software testing.

The present application belongs to the technical field of probe card batch detection, and provides a chip batch detection method and system based on a probe card multiplexing strategy, comprising: obtaining the number of times of multiplexing of the probe card, analyzing the change of temperature drift of the probe card, constructing a temperature drift prediction model based on a deep learning model; based on the temperature drift prediction model, taking the current number of multiplexing and the current working temperature of the probe card as input, outputting the temperature drift prediction value of the current number of multiplexing, adopting a preset temperature drift compensation strategy to perform temperature drift compensation, and performing quantitative deviation analysis on the temperature drift compensation to determine whether the temperature drift compensation is out of limit; the present application realizes dynamic monitoring of the whole life cycle of the probe card, guides the staff to reasonably dispose, and avoids test failure and cost waste.

A disclosed method defines root cause failure categories for a test case and associates each category with a corresponding test configuration property. Test configuration property information, indicative of the test configuration properties, are embedded in test case metadata. After generating test results, including test script messages, the test script messages assessed to identify the “N” most significant keywords in the message. The significance of a term in the test script message may be calculated based on an inverse document frequent parameter, independent of the term frequency within the document. Test result groups may then be determined by invoking a suitable clustering algorithm, e.g., a k-means clustering algorithm, to cluster the test script messages based on their corresponding keyword sets. Hypothesis test statistics may then be calculated for each test result group. A most probable root cause may then be identified for some or all of the test result groups.

Embodiments of the present disclosure disclose an interface test and electromagnetic lock replacement method, device, electronic equipment and readable medium. A specific embodiment of the method comprises: generating an atomized interface configuration file sequence corresponding to test request information; loading an environment variable pool from a preset storage; sequentially for each atomized interface configuration file in the atomized interface configuration file sequence, performing the following test steps: injecting the environment variable pool and the operation configuration parameter information in the atomized interface configuration file into the execution context of an external test script file; updating the current environment variable pool; initiating an operation request to the electromagnetic lock of the cabinet access control equipment; in response to determining that each interface test data generated indicates that the lock control operation test fails, controlling the relay and lock replacement mechanical arm on the cabinet access control equipment to replace the electromagnetic lock with a backup electromagnetic lock. This embodiment reduces the number of times the cabinet door cannot be normally opened or closed or the electromagnetic lock is replaced by mistake.

This application discloses a mobile terminal automated testing method and apparatus, relating to the field of automated testing. It retrieves test knowledge related to test requirements from a pre-built knowledge base. Based on the test requirements and test knowledge, it generates automated test scripts executable at the mobile terminal automation driver layer using a large language model. The automated test scripts are then run to execute the tests. If a test fails, a patch script is generated based on the failure evidence package collected during script execution. This patch script is then used as a temporary script to re-execute the test until the test termination condition is met. If the test passes when the termination condition is met, the passed patch script is compared with the automated test script, and the comparison result is submitted for manual review. If the review is successful, the passed patch script is updated in the knowledge base. This application achieves intelligent script generation and automatic patch repair while ensuring script standardization and executability.

The application discloses a test method, device, equipment and medium, relates to the computer technical field, and particularly relates to the software test technical field. The method comprises the following steps: acquiring test data of a first service instance of a test object; the test data of the first service instance carries a test environment identifier, and the test environment identifier is used for indicating a preset test environment of the test object; according to the test environment identifier, an actual test environment of the first service instance is determined, and the first service instance is tested based on the test data of the first service instance in the actual test environment; and if the test result of the first service instance in the actual test environment is failure, the first service instance is tested in an alternative test environment of the first service instance. The test flow interruption problem of the test object caused by the test failure of the service instance can be solved in the architecture for avoiding the repeated deployment of the service instance.

Systems and methods for intelligent machine learning model monitoring may include executing statistical test(s) for metric(s) to determine whether at least one of data used or an output in each machine learning model is statistically similar to expectations based on at least one of training data or test data for each model; when a first test failure of a tested model of a plurality of machine learning models occurs based on the statistical test(s) for the metric(s) exceeding a first test threshold, automatically executing an additional test based on the first test failure and additional historical data; and automatically generating an alert of test failure for the tested model based on the additional test exceeding an additional test threshold as a second test failure. The second test failure is representative of verification of the first test failure.

As various systems evolve towards autonomous self-evolution, self-optimization capability has become a crucial characteristic of modern systems. Systems need to be able to propose improvement suggestions and automatically modify their own code or configuration based on operational data, user feedback, or external instructions. However, existing technologies suffer from deficiencies such as a lack of secure testing mechanisms, data corruption during testing, a stateless branching concept, a lack of conflict handling mechanisms, and insufficient rollback capabilities. This application provides a secure testing method, system, and computer-readable storage medium for system self-optimization, aiming to enable the target system to test improvement suggestions in an isolated environment, independently store state changes generated during testing, intelligently merge them into the main system after successful testing, safely discard them upon test failure, and support precise rollback, ensuring the system maintains stability and data integrity while autonomously evolving.

This application provides a method and apparatus for diagnosing communication link test failures. When a test failure of at least one communication link of an onboard controller is detected, a diagnostic message for diagnosing the failed communication link is determined based on a link mapping file compiled from the onboard controller. Failure status data returned by the onboard controller in response to the diagnostic message is obtained. The failure status data is parsed to generate a failure diagnosis report corresponding to the onboard controller. The failure diagnosis report characterizes the status information of failure variables in the failed communication link. By accurately locating the diagnostic message and obtaining and parsing the failure status data based on the link mapping file compiled from the onboard controller to generate a failure diagnosis report containing failure variable status information, the method can efficiently and accurately diagnose the cause of communication link test failures, improving the efficiency and accuracy of fault diagnosis.

The utility model discloses an unmanned plane test flight platform, through the design of first safety rope and second safety rope, the flight range of unmanned plane is limited from two directions of up and down, under normal control test, two safety ropes will not interfere with the flight of unmanned plane, when unmanned plane appears misoperation or out of control condition, first safety rope cooperates counterweight and limit ring, can ensure that unmanned plane down falls and does not hit test base, second safety rope cooperates with ground anchor, can prevent unmanned plane from moving up and colliding with truss. This double protection mechanism greatly reduces the risk of falling or collision damage of unmanned plane in the test process due to misoperation or out of control, reduces the economic loss caused by test failure, provides reliable guarantee for the pre-test of batch production of unmanned plane, and helps to improve the stability and economic benefit of the whole production process.

The present application relates to an edge scene generation method, device, equipment, storage medium and program product, and relates to the field of vehicle algorithm testing. The method comprises: obtaining a scene data set of automatic driving algorithm test failure and an initial scene, a target feature combination related to test failure can be extracted from the scene data set, based on the target feature combination and the initial scene, the multi-modal processing capability of a large model can be applied to generate an edge scene meeting the test requirements. This way does not need to generate a model by a large amount of historical data self-training, but extracts key failure feature combinations based on the real failure behavior of the automatic driving algorithm, decouples the extraction and processing of the failure features from the traditional generation model, so as to generate an edge scene meeting the training requirements of the automatic driving algorithm by using the inference capability of the general large model, batch acquisition of the edge scene can be realized in less time and at lower cost, and the rapid iteration requirement of the automatic driving algorithm is met.

The present application provides a method and an apparatus for determining a failure probability in a simulation test for autonomous driving and relates to the technical field of autonomous driving, wherein the method comprises: performing a high-coverage uniform sampling in a high-dimensional parameter space corresponding to a scene of autonomous driving logic to obtain a collection of first test scenes; performing a simulation test for each first test scene in the collection of first test scenes to obtain a first test indicator value of a system under test of a vehicle under test in each first test scene; determining a scene weighting of each first test scene in the high-dimensional parameter space; determining a first test failure probability of the system under test based on the scene weighting and the first test indicator value.Performing a sensitivity analysis for initial scene parameters that influence the first test indicator value to determine sensitive scene parameters; reducing the high-dimensional parameter space to a low-dimensional parameter space consisting of the sensitive scene parameters if the first test failure probability does not meet the accuracy requirement; and performing a reliability analysis of the low-dimensional parameter space to determine a second test failure probability of the system under test, using the second test failure probability as a final failure probability. The method and apparatus for determining a failure probability in an autonomous driving simulation test solves the problem that the relevant failure probability determination methods have low computational efficiency.

The application discloses a kind of automated self-healing UI test method and system based on multi-agent AI, belong to automated UI test technical field, including: through test inference interpretation class, original test script is semantized, and output expected intent;When test fails, multiple-source data is synchronously collected and multi-modal feature is extracted, fault propagation graph is constructed according to expected intent and multi-modal feature, fault propagation analysis is carried out using graph attention network, and fault diagnosis result is obtained;According to fault diagnosis result, repair strategy is negotiated and decided using deep Q network, greedy strategy and byzantine fault tolerance algorithm by distributed agent collaborative mechanism, and feedback is obtained;Based on feedback, repair strategy and test inference interpretation class are optimized by meta-learning algorithm, and dynamically adjusted;Through the construction test inference interpretation class, test intent is deeply understood, and distributed agent collaboration is used to complete fault diagnosis and automatic repair, improve semantic understanding ability, diagnostic accuracy and repair success rate.

The application discloses a testing device and method of a rail vehicle braking system. The device comprises a testing control module, a braking control module and a testing verification module. The testing control module is used for receiving a testing start signal of a braking test item and generating a corresponding testing control instruction. The braking control module is used for driving the braking system to perform a testing action according to the instruction and collecting testing execution state information. The testing verification module is used for checking the state information based on a preset testing standard and determining whether the testing passes. The testing device of the application changes a semi-automatic braking test into a full-automatic testing process, and the driver does not need to manually operate related devices, so that the problem of test failure caused by manual operation failure and overtime is eliminated, the testing time is shortened, the testing pass rate and efficiency are improved, and the on-time departure and on-line operation of the motor train unit are ensured.

This application relates to the field of cable performance testing technology, and particularly to a method for testing the tracking resistance performance of insulation layers in new energy cables. The method includes: a control device inputting test information to multiple testing devices at preset time points so that a parameter parsing unit can acquire the test information; if the control device inputs test information to one of the multiple testing devices and a test failure is detected, then at a specified time point, a test instruction generated by one of the multiple testing devices is acquired; upon acquiring the test instruction, the control device inputs new test information to the multiple testing devices at a first time point so that the multiple testing devices can perform the cable insulation tracking resistance performance test. The method provided by this application can solve the problem that if test information fails to be parsed successfully in a certain testing device during the testing process, it will affect the accuracy of the test results of a normally functioning testing device.

This invention relates to the field of brushless motor testing technology, and discloses a brushless motor testing device and method, including a device body, a testing component, and an unlocking mechanism. The device body is connected to a support rod and two sets of mounting plates. Each mounting plate has a movable block, and both sets of movable blocks are connected to racks with meshing gears. Each set of movable blocks is connected to a testing component and a torque sensor. A sliding rod is mounted on the support rod, and unlocking mechanisms are installed at both ends of the sliding rod. A connecting component is provided between the unlocking mechanism and the testing base. Two wedge blocks are connected to the sliding rod, and a push rod is connected to the movable base. This invention achieves simultaneous testing on one side and material loading and positioning on the other side through dual-station cooperation. Relying on the linkage structure of the push rod, wedge blocks, sliding rod, and unlocking component, the circuit is automatically turned on after the motor under test is positioned, and the power connector is simultaneously unlocked and disconnected after testing, avoiding test failure due to missed connections.

This invention relates to the field of data storage technology and discloses a method for testing the signal quality of the I / O interface of a memory module. The method includes: verifying the signal integrity of the I / O interface in the memory module based on eye diagram analysis; after verifying signal integrity, quantitatively comparing the morphological characteristics of the input and output eye diagrams, and determining whether morphological changes have occurred during signal transmission. These morphological changes include at least logic level flipping errors, timing recognition errors, and signal quality degradation. If the morphological characteristics of the input and output eye diagrams are consistent, the test is successful; if they are inconsistent, the test fails. This invention achieves visualized and quantitative verification of signal integrity through eye diagram analysis, covering multiple dimensions such as voltage, timing, and noise, providing a more comprehensive reflection of signal transmission quality compared to single-parameter testing.

This application provides a method, apparatus, equipment, and medium for judging failure measurement data in static strength tests of aircraft structures, belonging to the technical field of static strength testing of aircraft structures. The method includes marking failure measurement points already determined by the data acquisition system's self-check or other methods before the test begins; during the test, for each set of data acquired, a sliding window method is used to cyclically judge the failure data for each data point in that set. The failure data judgment includes: determining whether the current measurement point has already been marked as a failure measurement point; if so, judging the next measurement point according to preset judgment parameters; the preset judgment parameters include an upper limit for the measurement value, a lower limit for the measurement value, a starting load, an upper limit for the load, a number of fitting blocks, a first-check coefficient, and a lower limit for the coefficient. Through the processing scheme of this application, automatic judgment and marking of test failure data are achieved, improving the efficiency of failure measurement data judgment.

This application relates to a chip cycle testing device, comprising: a test board; a control board; a connection module including a first adapter board, a second adapter board, and an electrical connector, the electrical connector connecting the first adapter board and the second adapter board, the first adapter board connecting to the test board, and the second adapter board connecting to the control board; and a temperature chamber including an inner chamber and an outer chamber. The inner chamber has a working area for mounting the test board; the outer chamber has a transition area for accommodating the connection module, with openings on both sides of the transition area to allow the connection module to connect to the control board outside the outer chamber and to the test board in the working area. The inner chamber has an opening in the chamber wall adjacent to the working area to allow the test board to connect to the connection module. This invention can isolate heat / cold air in the inner chamber during high / low temperature testing, preventing the control board from overheating and condensing, thus preventing damage to the control board and test failure.

This invention relates to the field of smart meter technology and relates to an automatic testing method for electricity meter event recording, comprising: triggering an event; generating event recording data at the time of the event occurrence; ending the event trigger; repeating the above steps; storing the event recording data at the time of the event occurrence; obtaining the occurrence time T1 of the previous recording data, and the next occurrence time T2 = T1 + 1 hour; obtaining the occurrence time Tn of the previous n recording data; determining whether T2 - Tn equals n hours, if yes, continuing the test, otherwise returning a test failure; for the previous n recording data, calculating the theoretical complete recording data based on the obtained occurrence time Tn, comparing the theoretical complete recording data with the currently obtained actual recording data, determining whether the currently obtained actual recording data is correct, if incorrect, returning a test failure; incrementing n by 1, repeating the above steps until n equals the maximum number of records. This invention is simple, reliable, and consumes few resources, realizing automatic testing of all event records.

This invention relates to the fields of software engineering and artificial intelligence, and particularly to an automated management system for the entire software development lifecycle, including a requirements analysis and architecture design module, a development and testing integration module, a deployment and operation monitoring module, and a visual management module. In the requirements phase, the invention performs semantic parsing of natural language requirements to generate structured documents, intelligently matches historical architecture patterns, and establishes a two-way traceability relationship. In the development and testing phase, it automatically generates code frameworks and test cases, performs incremental code review based on a defect prediction model, and automatically completes log cleaning, fault location, root cause analysis, and defect ticket closure when tests fail. In the deployment and operation phase, it intelligently generates canary release strategies, constructs a digital twin operation model, and enables multi-dimensional tracing and rapid handling of operational anomalies. This invention breaks down data barriers throughout the entire process with a unified digital mainline data layer, significantly improving software development and delivery efficiency and system operational stability.

Method (700) comprising: Performing (B710) several instances of a failure test on a hardware component (102) of a computer platform (100) by applying different values ​​of a physical operating parameter to the hardware component (102) for each of the instances; Determining (B720), from the results (208) of the several instances of the failure test, a value of the physical operating parameter at which the hardware component (102) transitions from passing the failure test to failing the failure test; Determining (730) a degradation rate (212), specifying a change magnitude of a limit value of the physical operating parameter at which the hardware component (102) is able to pass the failure test, wherein the change magnitude is relative to the elapsed time, based at least partially on the value of the physical operating parameter; Determining (740) that the degradation rate (212) exceeds a reference degradation rate;and determining one or more remedial measures based at least partly on the degradation rate (212) that exceeds the reference degradation rate.;

The application relates to the cross field of software engineering and artificial intelligence, and in particular to an AI-based test failure root cause analysis and automatic repair method, which constructs a space-time anchored tensor based on multi-modal original data of test failure; the evidence matching degree of a reverse cause-effect chain is calculated in combination with objective evidence, a root cause result is truncated, an initial repair code is matched, a history track is extracted to eliminate pseudo-associated cases to evaluate risks, and a global configuration change with high risk is downgraded to a local private configuration; the influence domain safety degree and a repair cost penalty value are calculated based on real associated cases, and an access evaluation result is obtained through weighted comparison; retesting is performed, if the retesting fails, cross-generation residual features with repaired redundant features are extracted, new data is injected, the above steps are iteratively executed until the fault is eliminated. The application effectively filters out AI deduction illusions and pseudo-associated influences, and improves the accurate positioning and safe self-repairing capability of complex faults.

This invention relates to a method for cross-domain migration diagnosis of aero-engine test faults based on continuous diffusion. Existing diagnostic models suffer from low accuracy and insufficient reliability under varying operating conditions due to inconsistencies in feature distributions between the source and target domains. Aero-engine test signals are severely affected by environmental interference, and large differences in cross-domain features result in weak generalization ability of traditional models, making them prone to misdiagnosis. To address this issue, this invention proposes a fractional gradient modeling method based on continuous diffusion. First, forward diffusion of stochastic differential equations is used to construct a global fractional network model of the source and target domains. Then, dual-path gradient modeling is performed on the signal, and the cross-domain divergence degree is calculated. Finally, the diagnostic results and confidence evaluation are output. This method can effectively extract long-range time-series features and achieve distribution alignment, improving diagnostic accuracy under varying operating conditions. This invention is applied to the cross-domain migration diagnosis of aero-engine test faults.

This disclosure provides a method, apparatus, and electronic device for attributing test anomalies, relating to the field of computer technology, particularly artificial intelligence, large-scale models, natural language processing, intelligent agents, deep learning, and mapping. The specific implementation scheme is as follows: Failed test cases corresponding to the failed target test task are clustered to obtain at least one cluster; for any cluster, multi-source operation and maintenance data related to the failed test cases within the cluster are acquired; using a large-scale model, based on the multi-source operation and maintenance data, failure attribution analysis is performed on the failed test cases within the cluster to obtain the failure cause analysis results of the target test task; wherein, the failure cause analysis results include the test failure root cause type and attribution description information.

A system that includes a failed test detector and a task updater can automatically update tasks associated with consistent failures of software tests in a software development management platform. The failed test detector can use a set of evidence of test (EOT) files that indicate software testing results over a period of time to identify tests that are consistently failing when executed against versions of a software application. The task updater can automatically create tasks associated with such consistently-failing tests in the software development management platform. The task updater can also automatically close existing tasks associated with tests, in the software development management system, if the failed test detector determines that those tests are no longer failing consistently.

The application relates to the field of simulation design optimization of high-pressure test platforms, and discloses a comprehensive optimization design method of an extra-high voltage test platform based on electric field and mechanical simulation, which comprises the following steps: key parameters of the extra-high voltage test platform are simulated in simulation software to construct a simulation model, which is used to simulate whether electric field design and mechanical design of the extra-high voltage test platform have design defects or shortcomings, including problems such as excessive local field intensity, excessive insulation spacing, unreasonable electric field distribution gradient and the like; problems such as gravity center deviation, excessive height, excessive symmetry, poor acceleration resistance, insufficient local design strength and the like; and according to simulation data, the design parameters of the extra-high voltage test platform are optimized and simulated again, and finally, the result is optimized. The application can realize the best balance between electrical performance and mechanical performance, greatly reduces the production frequency of a physical prototype and the risk of test failure, and has the advantages of high precision, high efficiency, high safety and global optimal solution.