30 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.

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 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 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 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 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.

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 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.

The application relates to the technical field of big data, and discloses a numerical control operation data intelligent supervision method and system based on big data, which comprises a flow rate test module, a gas detection module, a workpiece clamping module, a clamp adjusting module and an error control module. The flow rate test module is used for pretesting workpiece clamping; the gas detection module is used for determining the sensitivity of a gas detection device and performing gas detection on a workpiece; the workpiece clamping module is used for judging whether the workpiece clamping is in place; the clamp adjusting module is used for adjusting the workpiece clamping that is not in place; and the error control module is used for calculating the error caused by air tightness. The application can avoid production line shutdown and production delay caused by inaccurate clamping, improve production efficiency, reduce the waste rate in the numerical control machining process, save production cost, realize an automatic and intelligent detection process, and has an important role in guaranteeing production efficiency, product quality and safety.

The application provides a low-frequency transformer performance test method and device, electronic equipment and storage medium, and relates to the technical field of low-frequency transformer performance test. The method comprises the following steps: obtaining electrical performance parameter pre-test data of each transformer sample; calculating electrical performance parameter distribution characteristic values corresponding to the current batch of transformers; calculating test parameter adjustment amount, and adjusting the test parameter configuration of the low-frequency transformer performance test system based on the test parameter adjustment amount; using the adjusted test parameter configuration to perform batch electrical performance test on the current batch of low-frequency transformers, obtaining batch electrical performance test data, and determining whether the performance of the current batch of transformers is qualified according to the batch electrical performance test data. The method effectively avoids the misjudgment or missed judgment phenomenon caused by improper fixed parameter configuration, thereby significantly improving the effectiveness and reliability of the batch test result, and providing a more solid guarantee for product quality.

This application discloses a method, apparatus, device, and medium for predicting the number of erase / write cycles of flash memory. The method includes: performing a limited number of cycles of room temperature and high temperature testing on selected flash memory chips to obtain pre-test characteristic parameters, and determining bad block judgment parameters based on the pre-test characteristic parameters; determining logical bad blocks on the selected flash memory chips based on the bad block judgment parameters to obtain the number of logical bad blocks with a limited number of cycles at room temperature and the number of logical bad blocks with a limited number of cycles at high temperature; performing a high temperature cycle test on the selected flash memory chips and determining physical bad blocks to obtain the number of high temperature cycle erase / write cycles; performing the same room temperature and high temperature tests on the pre-test flash memory chips to obtain the number of pre-test logical bad blocks with a limited number of cycles at room temperature and the number of pre-test logical bad blocks with a limited number of cycles at high temperature; and determining the target number of erase / write cycles for the pre-test flash memory chips based on the obtained data. This technical solution achieves accurate evaluation of the number of erase / write cycles of flash memory chips, reduces losses, and improves efficiency.

The invention discloses a method for in-situ continuous testing of rock mechanical parameters in a well, and relates to the technical field of rock mechanical testing. The invention discloses a method for continuously testing mechanical parameters of rocks in situ in a well. The method comprises the following specific steps of wellbore preparation, equipment preparation, equipment entering the well, pre-testing, testing operation, data processing and analysis and result output. According to the invention, controllable continuous micro-etching is carried out on well wall rocks by using an underground device, in-situ dynamic testing of rock mechanical parameters is realized, and the limitation of discrete measurement is broken through; automatically optimizing control head motion parameters and repairing noise data; a temperature / pressure compensation module is arranged, the method adapts to the complex underground environment with the temperature larger than or equal to 150 DEG C and the pressure larger than or equal to 60 MPa, in-situ rapid measurement of rock mechanical parameters is achieved in combination with a mechanical model, well wall rocks are directly tested, the mechanical parameters of the rocks are obtained, and the problems that a traditional method is low in efficiency, high in cost and large in data deviation are solved. And the test efficiency and the accuracy and reliability of test results are improved.

The invention relates to the technical field of electrical safety test, and discloses a comprehensive test device and test method applied to a power filter, and the method comprises the steps: carrying out the closed-loop correction of the contact impedance of a test loop before the application of a test voltage; intelligently selecting a parallel or sequence excitation mode to apply high voltage according to a pretest result; collecting a leakage current waveform in real time in a test process, comparing the leakage current waveform with a preset insulation failure precursor characteristic, and rapidly cutting off high voltage in hundreds of microseconds through an independent hardware channel during matching; and after the test is completed, calculating the dispersion of the batch leakage current, and comparing the dispersion with a threshold adaptively updated based on historical data. The corresponding device comprises a high-voltage matrix switch network, a self-adaptive tool matrix, a safety monitoring module and an upper computer. According to the invention, reliability guarantee of test connection, active safety protection in a test process and intelligent optimization capability of a test strategy are realized.

The invention discloses a method and a system for evaluating adaptability influence factors of cancer patient return work. The method comprises the following steps: preliminarily testing questionnaires, including literature review and evaluation tool review; pretesting, including expert consultation and trial survey; performing project analysis; analyzing exploratory factors; analyzing reliability and content validity; carrying out verification factor analysis; and extracting average variance and combining validity. The method is high in reliability and validity, good in language universality and item understandability, capable of providing an evaluation practical tool for clinical and occupational rehabilitation and capable of laying a foundation for making a personalized occupational rehabilitation plan in the future.

The application discloses an information processing method and device, a storage medium and a processor. The method comprises the following steps: obtaining test information; obtaining target text associated with the test information in a target database; extracting at least one target subtext from the target text, wherein the target subtext is used to represent a first test result, and the correlation degree of the target subtext with the test information is higher than a target threshold; predicting a second test result corresponding to the test information based on the target subtext; and outputting the second test result. The application solves the technical problem that the prediction of the test result consumes a lot of resources due to the dependence on manual labeling.

The invention relates to the technical field of test equipment, and provides a multi-factor coupled alloy corrosion behavior test device and method, and the method comprises the steps: obtaining multi-factor sensing data of a test sample in a salt spray test, analyzing the contribution degree of each factor to the corrosion rate of the test sample, and calculating the grey correlation degree; determining the influence weight of each environmental factor on the corrosion process; and constructing a weighted multivariable input matrix based on a grey relational degree result, predicting a corrosion rate evolution trend of the test sample through the trained LSTM neural network model, and reversely deducing a life degradation curve in combination with an Arrhenius acceleration model to obtain a life prediction value of the test sample. According to the invention, the comprehensive test of multi-factor corrosion environment simulation, multi-parameter collaborative monitoring and data integration analysis can be realized, and the deep learning model is combined with the Arrhenius acceleration model to realize accurate prediction of the service life of the test sample piece in the actual use environment.

This disclosure provides a pre-testing method and system for storage devices, applicable to testing systems. After powering on the test board, the control device sets the enable pins of each storage device under test (DUT) to a pre-test level, causing multiple DUTs to enter a pre-test mode instead of the target test mode. Next, the control device performs a scan operation on the multiple DUTs, switching the scan pins corresponding to the DUTs from a first level to a second level, reading the indicator signals output by the DUTs through indicator pins, and restoring the corresponding scan pins to the first level. Finally, the control device determines the contact state between each DUT and its corresponding test socket based on the indicator signals, and determines whether to allow multiple DUTs to enter the target test mode based on the contact state. This disclosure achieves non-invasive contact detection through pin level combinations, preventing DUTs with abnormal contact from entering the target test mode, thus improving test yield and efficiency.

The invention discloses a flash memory erasing frequency prediction method and device, equipment and a medium, and the method comprises the steps: carrying out a normal temperature test and a high temperature test of limited cycle times on extracted flash memory particles to obtain pre-test characteristic parameters, and determining bad block judgment parameters based on the pre-test characteristic parameters; performing logic bad block judgment on the extracted flash memory particles based on the bad block judgment parameters to obtain the number of normal-temperature finite loop logic bad blocks and the number of high-temperature finite loop logic bad blocks; carrying out high-temperature cycle test on the extracted flash memory particles and carrying out physical bad block judgment to obtain high-temperature cycle erasing times; executing the same normal-temperature test and high-temperature test on the pre-tested flash memory particles to obtain the number of pre-tested normal-temperature finite cycle logic bad blocks and the number of pre-tested high-temperature finite cycle logic bad blocks; and determining a target erasable number of times of the pre-test flash memory particles based on the obtained data. According to the technical scheme, accurate evaluation of the erasable times of the flash memory particles is realized, the loss is reduced, and the efficiency is improved.