45 results about "Pre testing" patented technology

The invention relates to the technical field of edge computing, electric automobile testing and the like, and provides an electric automobile instrument testing method based on end-cloud collaboration, which comprises the following steps of: constructing a lightweight virtualized resource isolation environment at an edge computing node, loading signal generation, communication agency and state monitoring micro-service; a test platform with elastic capacity expansion and contraction and fault self-recovery capability is formed; historical test data are collected at a cloud end, and an equipment health degree prediction model is trained and deployed to an edge node to support a local intelligent test; constructing a dynamic test service chain, generating and pre-distorting and compensating an enhanced test signal, and then acting the enhanced test signal on the instrument; through multi-modal signal acquisition and unified preprocessing, in combination with wavelet packet decomposition and a deep neural network, high-dimensional feature extraction is realized, a test report containing a health trend is finally output, and a test process is actively intervened based on a trend prediction result. According to the method, the authenticity of test signals, the health assessment precision and the safety and efficiency of the test process are effectively improved.

The invention discloses an automatic test method for a data link system, and belongs to the technical field of information transmission and processing. The method comprises the steps of test scheme selection and generation, test item conformity matching, test case automatic generation, test script automatic generation, test process automatic management and manual intervention, test instrument tool automatic control, test data automatic calling, test environment visual display, test result automatic collection and test conclusion automatic evaluation. And generating a system authentication conclusion and the like. Automatic testing of data link system authentication can be achieved to the maximum extent, and it is ensured that the testing process is reliable, safe and efficient.

The invention discloses a test case execution method and device, equipment and a medium, relates to the technical field of chip testing, is applied to a test server, and comprises the following steps: obtaining a target test case from a current test case set, and obtaining a pretest result of the target test case in a pretest environment; the number of the pretest environments is at least 2; if the pretest result represents that the test is successful, sending the target test case to a preset hardware auxiliary verification platform deployed with a chip design file of the target chip prototype, and testing the target test case on the preset hardware auxiliary verification platform to obtain a final test result; and if the pre-test result is null or the characterization test fails, sending the target test case to the pre-test environment, testing the target test case in the pre-test environment, and then skipping to the step of obtaining the pre-test result of the target test case in the pre-test environment again until the pre-test result characterizes that the test succeeds.

Disclosed herein are systems and methods to obtain representative formation pore pressure and formation mobility from pressure measurements with a formation testing tool. In some embodiments, the method includes performing a first pre-test in which a pressure sensor measures a drawdown pressure and a buildup pressure, calculating a statistical value for the measured buildup pressure, determining if the statistical value is above or below a threshold, shutting down a mud pulser if the statistical value is above or below the threshold, and performing a second pre-test in which the pressure sensor measures a second drawdown pressure and a second buildup pressure. In embodiments, the second pre-test includes a formation fluid drawdown flow rate that is different than a formation fluid drawdown flow rate in the first pre-test.

The invention discloses a lubricating equipment component fault prediction test system, which relates to the technical field of lubricating equipment state monitoring and comprises a multi-source sensing module, a particle feature resolving module, an entropy analysis module, a collaborative diagnosis module and a decision execution module. According to the lubricating equipment part fault prediction test system, by means of the particle feature resolving module, materials of metal and non-metal abrasion particles can be accurately recognized, the flow speed and the concentration of the particles can be calculated, a detailed material proportion, size distribution and a real-time concentration thermodynamic diagram can be generated, and the entropy value analysis module is used for analyzing the real-time temperature and pressure data according to the real-time temperature and pressure data. An infinitesimal body entropy change value is accurately calculated, the phase change condition of a lubricant is accurately judged in combination with a preset threshold value, a collaborative diagnosis module carries out deep fusion and analysis on multi-source data, time asynchronism is eliminated through dynamic time warping and other algorithms, an association rule base is established, confidence is calculated, and therefore the fault type of a lubricating equipment component is comprehensively predicted.

The invention discloses a multifunctional radar working mode recognition method based on a GAN-SA-GRU, and the method comprises the steps: building a multifunctional radar pre-data set with a label based on the pulse characteristic parameter setting of an airborne phased array radar, dividing the multifunctional radar pre-data set into a pre-training set, a pre-verification set and a pre-test set, building a multifunctional radar working mode recognition model based on the GAN-SA-GRU, and carrying out the recognition of the working mode of the multifunctional radar based on the GAN-SA-GRU. Inputting the pre-training set and the pre-verification set for training to obtain a trained multifunctional radar working mode recognition model, finally inputting the model into the pre-test set for testing, outputting a test result, and evaluating the accuracy of model recognition. According to the method, by introducing the GAN, self-training with random noise is achieved, non-ideal factors are added into the training set, data set expansion is achieved, the data set obtained through self-training is added into the GRU network for secondary training, and the data set expansion efficiency is improved. And a model which is more stable under a non-ideal condition can be obtained to realize the recognition of the working mode of the multifunctional radar.

The invention provides a low-frequency transformer performance testing method and device, electronic equipment and a storage medium, and relates to the technical field of low-frequency transformer performance testing. The method comprises the following steps: acquiring electrical performance parameter pretest data of each transformer sample; calculating an electrical performance parameter distribution characteristic value corresponding to the current batch of transformers; calculating a test parameter adjustment amount, and adjusting test parameter configuration of the low-frequency transformer performance test system based on the test parameter adjustment amount; and performing batch electrical performance test on the low-frequency transformer of the current batch by adopting the adjusted test parameter configuration to obtain batch electrical performance test data, and judging whether the performance of the transformer of the current batch is qualified or not according to the batch electrical performance test data. According to the method, the phenomenon of misjudgment or missed judgment caused by improper configuration of fixed parameters is effectively avoided, so that the effectiveness and the reliability of batch test results are remarkably improved, and a firmer guarantee is provided for the product quality.

The invention discloses an engineering plastic performance prediction method, and relates to the field of data processing. The method comprises the following steps: reading basic elements of pre-test engineering plastic; determining a first performance prediction result in combination with the intelligent derivation model; interacting multiple application scenes of the pretest engineering plastic, and determining a second performance prediction result; based on a controllable deviation range, performing mutual inspection on the first performance prediction result and the second performance prediction result, and determining an effective inspection result; and if the effective test result is yes, carrying out mean value processing, and taking the result as a performance prediction result. By the adoption of the method, the technical problem that in the prior art, due to the fact that performance prediction of engineering plastics is carried out under specific conditions, actual conditions are not fully considered, and prediction result errors are large is solved, performance prediction is carried out in a finite element reasoning and multi-scene joint simulation test mode, the prediction results are mutually verified, and the performance prediction accuracy is improved. And a prediction result with higher precision is obtained.

The invention provides a transformer fault maintenance virtual training method and system based on multi-modal cooperation. The method comprises the following steps: constructing a multi-modal knowledge graph of transformer fault data; pre-testing the trainee by adopting a preset collaborative training system, determining the initial proficiency of the trainee, and determining an eye movement triggering threshold of the trainee by adopting a preset fault complexity optimization algorithm and a preset eye movement triggering threshold algorithm; according to the eye movement data of the trainee and the eye movement triggering threshold value of the trainee, fault associated data in the multi-mode knowledge graph are called, a preset virtual-real feedback intensity algorithm is adopted, fault interaction data of the trainee and the collaborative training system are obtained, and then a personalized training scheme is generated. According to the invention, trainees are assisted to construct a complete fault maintenance knowledge framework, knowledge fragmentation is avoided, association understanding is enhanced, the problem of missing practical operation is solved, a personalized training scheme is generated, the training pertinence and efficiency are improved, and the skill improvement period is shortened.

An analysis device specifies a cause when a test of test target software fails in a test environment that is an environment for executing a test. In the test, execution of test target software and confirmation of an execution result are executed by a test script without human intervention. Test data is data read into the test target software and the test script when the test is executed. The analysis device includes: a score calculation unit that calculates a failure cause score for each of test target software, test data, a test script, and a test environment based on the presence or absence of a change; and an estimation unit that estimates, based on a failure cause score, whether the test has failed due to test target software, test data, a test script, or a test environment.

The invention provides a full-automatic intelligent testing method and system for a hand-operated direction valve for a hydraulic support, and belongs to the technical field of hydraulic support direction valve testing, and the method comprises the steps: calling a corresponding digital twinborn model according to the model information of a tested hand-operated direction valve, and obtaining standard test parameters to carry out the pretest of the tested hand-operated direction valve; calibrating the digital twin model through a parameter optimization algorithm; and executing a reversing performance test on the tested hand-operated reversing valve based on the calibrated digital twinborn model. Automatically switching test items according to a preset test sequence, reconstructing a corresponding hydraulic test loop, and solving an optimal control instruction based on the calibrated digital twin model and a safety constraint optimization algorithm; and after all the test items are completed, comprehensively analyzing the commutation performance test data and other item test data, and generating a test report. According to the invention, automatic adaptation and precise testing of different types of hand-operated direction valves can be realized, and the testing precision, efficiency and automation level are improved.

The present disclosure provides a knowledge point prediction method and device, electronic equipment and storage medium, the method comprises: obtaining text data of a test question to be predicted; using the output results of the self-attention module and the knowledge point attention module of the pre-trained knowledge point prediction model to process the text data respectively to determine the representation vector of the test question to be predicted; using the output module of the knowledge point prediction model, classifying the knowledge points according to the representation vector to obtain a classification result; using the post-processing module of the knowledge point prediction model, determining the prediction probability of each knowledge point in all preset knowledge points to which the test question to be predicted belongs according to the classification result, and determining the knowledge point number corresponding to the test question to be predicted according to the prediction probability; querying the preset correspondence between the number and the knowledge point to determine the target knowledge point corresponding to the knowledge point number. The present scheme can accurately predict the knowledge points in the test question.

The invention discloses a module IO port test and diagnosis automation system and method. The system is composed of a module IO detection tool, a to-be-detected module adapter plate and a computer terminal. The module IO detection tool integrates an MCU, an analog multiplexer, a level conversion chip, an LDO and other modules, the module adapter plate to be detected adopts a one-plate multi-adaptation design, and the computer terminal issues a control instruction through a serial port. The system can realize module IO output detection, input detection and diagnosis of influence of IO port pull-up or pull-down on module downloading and starting, and covers multiple links of research and development testing, production pre-testing and after-sales analysis. The method can adapt to various modules without replacing a detection tool, is high in automation degree, and meets the requirements of multi-scene detection.

The present application relates to the technical field of hydrogen cylinder safety detection, and in particular to a method for building a safety model for high-pressure hydrogen cylinders and a safety detection method, which includes: setting up several groups of bottle body solutions, loading several groups of bottle body solutions into different test environments for pre-testing, the bottle body solutions include bottle body materials, initial pressure, initial temperature, bottle body process, and bottle body structure; obtaining corresponding test results of different bottle body solutions based on pre-testing, and the test results include test temperature, test pressure, bottle body defects, and hydrogen leakage results; obtaining historical survey information to generate several safety reference tables, each safety reference table corresponds to a bottle body solution, and the safety reference table contains several safety test objects; based on the bottle body solution, test results and safety reference table, a safety model including an input layer, a hidden layer and an output layer is generated. The present application is used to achieve strong correlation of multiple detection results of hydrogen cylinders to improve detection results.

The invention provides a test question judgment method and device, electronic equipment, a storage medium and a product, and the method comprises the steps: carrying out the test question judgment of pre-collected test question data through a pre-trained test question judgment model, obtaining a judgment result corresponding to the test question data, and enabling the judgment result to comprise a score rate and a judgment label which represents whether the test question data can be judged or not; if the evaluation label shows that the evaluation label can be evaluated, taking the score rate as an evaluation output result corresponding to the test question data; and if the evaluation label shows that the test question data cannot be evaluated, taking preset rejection prompt information as an evaluation output result corresponding to the test question data. By adopting the technical scheme, whether the test question can be evaluated or not can be predicted while the score rate of the test question is predicted, the score rate of the test question is output only when the test question can be evaluated, and test question rejection is realized when the test question cannot be evaluated, so that the test question misjudgment rate is reduced, and the test question evaluation accuracy is improved.

The application discloses a method, device and equipment for testing software and a computer readable medium, and relates to the technical field of computers. A specific embodiment of the method comprises the following steps: in response to a client code submission success message in a distributed version control system, calling a controller to receive testing software through a network application programming interface, wherein the testing software comprises a client production resource file and a source code resource file; constructing a server production resource file according to production resource code of the source code resource file; determining that the client production resource file is consistent with the server production resource file, and that a code snapshot of the source code resource file is consistent with a code snapshot of a historical source code resource file; loading the testing software by using a browser, and feeding back a test result of the testing software based on display content of the browser. The embodiment can avoid human intervention in the testing software process, and improve software testing efficiency.

The invention discloses a method and system for testing the critical speed of abnormal impact sound of automobile parts. The method comprises the steps of S1, testing preparation; s2, carrying out pre-test; s3, the excitation is adjusted according to an excitation adjusting strategy, so that the test part is in a critical state, a slight impact state and a severe impact state which are defined in advance, and images of the three impact states are recorded through a camera; s4, calculating the displacement of the interior part by using a DIC algorithm, generating a speed-time curve of three impact states, and determining a speed peak value and a peak value moment; and S5, calculating vibration energy corresponding to peak moments of the three impact states, and judging the impact abnormal sound critical speed of the part. According to the method, a reliable basis is provided for formulating a CAE simulation analysis target, so that the impact abnormal sound risk of the automobile parts is well controlled in the early CAE stage, the situation that more cost is paid in the later stage to solve the abnormal sound problem is avoided, and finally the impact abnormal sound problem generated by relative movement of the automobile parts is effectively avoided.

The invention provides a high-precision EMI diagnosis and pretest system based on a vector signal analyzer, and belongs to the technical field of data processing, and the system specifically comprises the steps: according to EMI interference signal source data in an interference signal source combination in different superposition risk types, determining an interference signal in a darkroom when it is determined that an environment interference signal in the darkroom needs to be considered; the method comprises the following steps: acquiring a deviation condition of vector signal characteristics of interference signal source combinations between different test targets, determining an identification strategy for identifying interference signals in environment interference signals according to the deviation condition, and determining the identification strategy for identifying the interference signals in the environment interference signals according to different identification interference signals. According to the invention, the method determines the diagnosis test method of the test target according to the coincidence condition between the superposed interference signals of the test target and other test targets in combination with the recognition strategy for recognizing the interference signals, and improves the reliability of test processing.

A testing method and system for electronic devices are provided. The testing method for electronic devices includes receiving multiple first test data for multiple first test cases and multiple second test data for multiple second test cases; generating multiple first local scores based on the multiple first test data; generating multiple second local scores based on the multiple second test data; generating a global score for a common test case of the multiple first test cases and the multiple second test cases; replacing at least some of the multiple first local scores with the global score to generate multiple first test scores; replacing at least some of the multiple second local scores with the global score to generate multiple second test scores; and performing a first mutation operation based on a first optimal test case corresponding to the highest score among the multiple first test scores to generate a first pre-test set.

The invention relates to an MEMS chip wafer level packaging vacuum degree detection method and system, and the method comprises the steps: 1, placing a wafer level packaging MEMS device on a vacuum probe table for calibration, adjusting the vacuum degree of a cavity, and recording the vacuum degree Pc at the moment; recording thermal conductance Gth under different vacuum conditions, and performing linear fitting according to Gth = APc + B to obtain A and B values; (2) placing the MEMS device on a vacuum probe table, and carrying out thermal conductivity pre-test in the same way in the step (1); (3) the resistance value Ri and the resistance value Rf at the maximum current and the minimum current are observed in the test data, and if (Ri-Rf) / Ri is larger than or equal to 8% and smaller than or equal to 12%, the thermal conductance Gth given by a test data table is recorded; otherwise, the current in the thermal conductivity test program is increased or decreased, so that the value is within the range of 8%-12%; recording the thermal conductance Gth given by the final test data table; and (4) calculating the vacuum degree Pd of the device by using the calibrated A and B and # imgabs0 #. The method is suitable for detecting the vacuum degree of all MEMS and other types of chip packaging cavities which can be integrated with the micro-bridge structure micro vacuum gauge.

The application provides a precision and temperature compensation function test method of an intelligent pressure transmitter, comprising the following steps: fitting and establishing a quantitative mathematical model according to experimental data collected by a pre-test device; building a virtual test device about the pressure transmitter to be tested, so as to simulate different pressure working conditions and temperature working conditions, and synchronously collect feedback signals output by the pressure transmitter to be tested; comparing and analyzing the collected feedback signals with standard virtual stimulation signals, and determining the measurement accuracy of the pressure transmitter and evaluating the effectiveness of the temperature compensation function of the pressure transmitter according to the comparison and analysis results; the application has the following beneficial effects: the application directly stimulates the core circuit of the transmitter by constructing a "pseudo-physical environment" equivalent to the real working condition, and can complete full-scene testing without relying on special physical equipment, so the application can not only greatly reduce the dependence of testing on the physical environment and shorten the research and development and production test cycle, but also can break through the test bottleneck of extreme scenes.

The invention provides an aviation equipment ground safety accident prediction method and system based on space-time factors, and relates to the technical field of resource allocation and risk assessment, and the method comprises the steps: S1, recognizing a test environment safety accident failure mode, and obtaining a failure mode of an aviation equipment ground test environment; s2, setting a safety standard and establishing a safety accident statistical prediction distribution mathematical model according to historical safety accident data; s3, solving the safety accident statistical prediction distribution mathematical model based on test data to obtain safety accident occurrence condition probability model parameters; and S4, predicting the overall safety accident probability of the test environment to determine the safety of the test environment. According to the method, a high-risk safety accident probability estimation problem is converted into a test problem of key parameters, time, distance and the like under controllable application constraints, and quantitative prediction based on small sample data is realized by establishing a mathematical model of a safety accident occurrence condition probability density function and a safety accident probability density function.

The invention discloses a multi-stress coupling screening method and system for a silicon carbide device, and relates to the technical field of semiconductor devices, and the method comprises the following specific steps: S10, carrying out the pretest of a packaged sample device, and obtaining an expected value and an expected ratio of main electrical parameters of the device; s20, testing the limiting temperature of the to-be-tested device to obtain key electrical parameters of the to-be-tested packaging device at the limiting temperature; s30, testing the to-be-tested device at normal temperature to obtain key electrical parameters of the packaged device at normal temperature; and S40, carrying out data comparison and abnormity judgment on the to-be-detected device. According to the method, the multi-stress coupling screening method and system have defect coverage comprehensiveness, defects of gate oxide, packaging, channels and the like are captured at the same time, and a blind area of traditional single stress screening is avoided; through multi-stress coupling and double-stage testing, high-precision screening is realized in a relatively short time; the scheme frame can be adapted to all packaging forms of the silicon carbide power device at the current stage and the silicon carbide power devices with various voltage and current grade specifications.

The present invention discloses a scenario-adaptive aging test method and system for ultra-high voltage MOS, which relates to the technical field of MOS testing. The method includes: establishing a device aging test platform; parsing test conditions for the application scenario information of the target ultra-high voltage MOS device; performing pre-test adjustment on the target ultra-high voltage MOS device to determine an aging test update parameter table; monitoring the aging test of the target ultra-high voltage MOS device; invoking an ultra-high voltage MOS aging self-adaptive evaluator to process and evaluate the device aging scenario test data stream set to obtain the device scenario aging performance test result. It solves the technical problems in the prior art that the MOS aging test lacks scenario adaptability and it is difficult to accurately simulate the aging process of MOS devices in the actual application environment, resulting in insufficient test pertinence and poor result accuracy, and achieves the technical effect of improving the pertinence and result accuracy of the MOS device aging test.

This invention relates to the field of electrical safety testing technology, and discloses a comprehensive testing device and method for power supply filters. The method includes: performing closed-loop correction of the contact impedance of the test circuit before applying the test voltage; intelligently selecting parallel or sequential excitation mode to apply high voltage based on pre-test results; acquiring leakage current waveforms in real time during the test, comparing them with preset insulation failure precursor characteristics, and rapidly cutting off the high voltage within microseconds through an independent hardware channel during matching; calculating the dispersion of batch leakage current after the test and comparing it with a threshold adaptively updated based on historical data. The corresponding device includes a high-voltage matrix switch network, an adaptive tooling matrix, a safety monitoring module, and a host computer. This invention achieves reliable test connection assurance, proactive safety protection during the test process, and intelligent optimization capabilities for the test strategy.

The present invention discloses a multi-function radar working mode recognition method based on GAN-SA-GRU. First, based on the pulse characteristic parameter setting of the airborne phased array radar, a multi-function radar pre-data set with labels is constructed and divided into a pre-training set, a pre-verification set, and a pre-test set. Then, a multi-function radar working mode recognition model based on GAN-SA-GRU is constructed. The pre-training set and the pre-verification set are input for training to obtain a trained multi-function radar working mode recognition model. Finally, the pre-test set is input for testing, the test results are output, and the accuracy of model recognition is evaluated. The method of the present invention realizes self-training with random noise by introducing GAN, adds non-ideal factors to the training set, realizes the expansion of the data set, and adds the data set obtained by self-training to the GRU network for secondary training. It can obtain a more stable model under non-ideal conditions to realize the recognition of the multi-function radar working mode.

The invention discloses a test question multi-knowledge-point labeling method and system based on sequence generation, and relates to the field of natural language processing. Firstly, a BERT model is adopted to extract a test question text vector, a global attention feature represented by a test question is extracted through an attention mechanism, the global attention feature is input into an LSTM, and a plurality of knowledge points of the test question are predicted in a sequence generation mode. Meanwhile, when the knowledge point sequence is generated in each step, the extracted knowledge point prior information feature vectors are fused by using a sequence generation-based multi-knowledge point marking method, so that the purpose of correcting the generation of the knowledge point sequence is achieved. And finally, completing a test question knowledge point labeling task in a sequence generation mode. According to the method, the marking accuracy can be comprehensively and scientifically improved on the multi-knowledge-point marking task of the mathematical test questions, and an auxiliary effect is provided for teaching of teachers.

The invention discloses a UDS diagnostic protocol automatic test method, test system and test device, and the test method comprises the steps: constructing a test environment communication link, and initializing test parameter configuration information; reading the target test item, and executing the automatic test case; and performing closed-loop verification on the test result, and recording and analyzing the test result. According to the invention, by constructing a power supply control-diagnostic service-closed loop verification time sequence collaborative architecture, full-process automation of UDS protocol testing is realized, manual intervention of the testing process is effectively avoided, and the testing efficiency and accuracy are effectively improved; meanwhile, request data and response results can be recorded in real time based on test execution data and closed-loop verification results, a traceable data set corresponding to the test process is formed, and a test report is output in a standard mode in a preset format; in addition, after the test is finished, the power supply can be automatically powered off, the safe reset of the test environment is ensured, and the test repeatability is improved.

The invention discloses an open reading understanding problem difficulty estimation method and system based on an LLMs large language model, and the method mainly comprises the steps: taking a multi-capability heterogeneous LLM as a pretest subject, enabling a teacher agent to carry out the scoring according to an automatic index, carrying out the monotone probability calibration, and enabling a score to be mapped into an approximate correct probability; under the IRT framework, an expert agent jointly estimates the subject ability theta and the question difficulty beta through EM, EM maximum likelihood solution is supported, and weighted aggregation and K-level discretization labeling of continuous difficulty are carried out on the question difficulty according to the model confidence coefficient; furthermore, the monotonous relationship of'difficulty sequence-score sequence 'is verified and checked through self-playing verification of sequence consistency and weighting consistency, so that the non-manual quality acceptance and backtracking are realized. On the premise of unifying the theoretical scale and interpretability, the method has the engineering characteristics of parallelism, friendliness and near-linear expansion, and the robustness and generalization ability are improved; and the obtained difficulty labels are approximately in normal distribution, so that paper composition and hierarchical teaching are facilitated.

The application provides a model prediction compensation high heat flux test control method, which takes a graphite heating finite element model as a prediction model for controlling a high heat flux test system, firstly expresses a process of radiation heating of a test piece by a graphite heating element according to a virtual work principle, then establishes a transfer function between input electric power and output radiation heat of a controlled object, and substitutes a time-varying temperature field of graphite heating calculated by the prediction model into closed-loop control of the test system; finally, a time sequence prediction model is established, errors generated by the test system are compared with a test heating target temperature field, and the errors are applied to a controller of the system as input to pre-adjust and compensate parameters of the controller. The model prediction compensation high heat flux test control method provided by the application solves a control problem of large inertia, nonlinearity and time variation of a thermal test system composed of graphite heating elements, and ensures precision of construction of a ground aerodynamic heat simulation test environment.