198 results about "Test phase" patented technology

The invention discloses a method, a system and equipment for predicting the stability of integrated circuit test equipment and a medium, and belongs to the technical field of integrated circuit test. The method comprises the following steps: firstly, carrying out preprocessing and feature selection on historical data in an FT test stage, and adopting a Gaussian mixture model (GMM) to cluster and identify different operation condition clusters of a test machine; then, establishing a health state GMM reference for each working condition cluster, calculating a KL divergence value of a normal sample and the reference, and setting a dynamic anomaly detection threshold by 99.73% quantile of the KL divergence value; and finally, in real-time monitoring, calculating a KL divergence value of real-time data and a corresponding working condition cluster benchmark, and comparing the KL divergence value with a dynamic threshold value to realize accurate anomaly marking. The method effectively solves the problem of abnormal detection of the test data of the integrated circuit under complex and changeable working conditions, and improves the monitoring accuracy and working condition adaptability.

The invention relates to the technical field of gas hydrate phase equilibrium tests, in particular to a gas hydrate phase equilibrium condition testing device and method under the normal pressure condition, and the testing device adopts a core framework integrating hydrate synthesis and phase equilibrium testing under the normal pressure into the same reaction kettle unit. And aiming at the functional difference of phase balance test under synthesis and normal pressure, independent high and low temperature control subsystems and pressure control modules are respectively arranged, so that the efficiency of a high-pressure synthesis stage is guaranteed, and the precision requirement of a low-pressure test stage is met. According to the testing method, a constant-volume gradual heating decomposition method is adopted, high-precision micro-pressure change monitoring is combined, and the phase equilibrium point of the hydrate under the low-pressure condition can be accurately captured. The problem that hydrate phase equilibrium experiment data in the area near the normal pressure is deficient is solved, and key data support can be provided for model verification and safety design.

The invention belongs to the technical field of mechanical fault diagnosis, and discloses a rolling bearing small sample fault diagnosis method and system based on an online soft label Gaussian prototype network. The method comprises the following steps: collecting bearing vibration signals under different working conditions to construct a data set, dividing the data set into a meta-training set and a meta-test set, and splitting the data set into a support set and a query set; constructing a Gaussian prototype network model containing an embedding module, a prototype calculation module and a classification module; in the meta-training stage, soft labels are dynamically generated by adopting an online soft label strategy, and multi-task training is carried out in combination with hard label loss to obtain optimal parameters; in the meta-test stage, a Gaussian prototype is constructed based on a support set, classification is achieved by calculating the Euclidean distance between a sample and the prototype, and prototype parameters are finely adjusted to adapt to cross-working-condition diagnosis when feature distribution drifts. According to the method, label noise interference is effectively relieved, the diagnosis precision can still be ensured under a small number of labeled samples, and the model generalization ability and the diagnosis stability are remarkably improved.

The invention discloses a man-machine interaction language model output post-processing system supporting field-level score comparison and a rule engine and an optimization method. And the post-processing system cooperatively works through an information recording module, a score comparison module, a modification module, a manual check confirmation module and a data output module. According to the post-processing optimization method, a framework and a prototype software product are built according to the post-processing logic of the test stage and the post-processing logic of the working stage. According to the post-processing optimization method, firstly, a generation result and a structured demand are collected and marked, then the text quality is evaluated through BLEU and ROUGE, optimization and modification are carried out according to a scoring result, finally, the text is output according to a standard format and is in butt joint with an industrial system, and it is ensured that the text is accurate and available. According to the method, traceable, intervening and integratable high-quality post-processing of the output result of the language model is realized, and the requirements of complex task scenes in industrial application on the content, structure and quality control of the intelligently generated text are met.

Examples provide a system and method for dynamically improving and validating automation plans using generative artificial intelligence (AI). An automation manager analyzes automation plans during the design phase and the testing phase. The automation plan makes suggestions for improving the automation plan in real time using natural language prompts. The automation plan validates the automation plan against guardrails defining restrictions on the actions performed by the system during automation plan execution. The system generates explainability data describing the next planned actions and reasons why each planned action is selected for execution during the automation to increase user confidence in the automation. The system requests user approval prior to performing each action for more accurate and efficient workflow automation while reducing system resource usage consumed during execution of inefficient or ineffective automation plans.

The invention provides a satellite-borne router network access test system and a test method thereof. The satellite-borne router network access test system comprises a constellation network node simulation platform, a performance test evaluation platform, an interface protocol conversion device, a constellation service traffic simulator and a network router digital model. Through joint simulation of the virtual nodes and the physical nodes, a simulation test platform can be provided for design and optimization of the satellite-borne router through scene simulation of a complex satellite network environment in a design stage, and the development efficiency is improved; on the other hand, in the product development and system test stage, the full-constellation operation scene can be simulated, the function and the performance of the satellite-borne router can be assessed and verified in the full-constellation scene in the ground environment, the method is particularly suitable for the low-orbit large-scale batch emission large-scale networking scene, the test coverage and the test effectiveness of the product are greatly improved, and the test cost is reduced. Development risks of insufficient verification, delayed verification time and the like before launching are effectively released, and meanwhile, the test verification cost is reduced.

The invention relates to the field of memory management, and provides a DDR memory frequency switching test method and device. The method comprises the following steps: acquiring a plurality of preset frequency gears of the DDR memory; constructing a multi-stage test flow of at least two different switching modes; for each test stage, generating target data before executing the frequency switching action, acquiring actual data from the DDR memory in the process of executing the frequency switching action, performing consistency comparison on the target data and the actual data, and detecting whether the frequency switching process causes data hopping or not; performing reliability judgment on a corresponding switching mode of the DDR memory based on a comparison result of each test stage, establishing a fault association relationship among the plurality of test stages, and when any test stage detects data hopping, determining a potential frequency switching fault currently existing in the DDR memory; and carrying out collaborative verification on the actual data in different switching modes in the multi-stage test process to form a frequency switching test report. The frequency switching test reliability and test efficiency can be improved.

The invention relates to a wire harness quality prediction and evaluation method fusing historical data, and the method comprises the steps: collecting the real-time data of wire harness design, production and test stages, and equipment operation, image collection and defect classification parameters, obtaining the historical data of the same type of wire harnesses according to batch specifications, and building a mapping index to form a basic data pool; extracting and fusing features to construct a multi-dimensional matrix; establishing a failure rule set and a weight model in combination with historical cases; inputting a trained machine learning model to generate a quality prediction result; and supplementing new data to update related rules and models after abnormal traceability to form a closed loop. According to the invention, by fusing real-time and historical data and combining feature extraction, model training and closed-loop updating, accurate prediction and efficient fault tracing of the wire harness quality are realized, the evaluation system is continuously optimized, and the reliability and production applicability of wire harness quality evaluation are remarkably improved.

The invention discloses a battery health state prediction method based on variational feature extraction and composite kernel function optimization Gaussian process regression. The method comprises the following steps: firstly, collecting battery cycle data, extracting features by utilizing VAE, screening out an optimal feature set by adopting PCC, and jointly dividing the optimal feature set and SOH data into a training set and a test set; in the training process, the optimal modal number K of the VMD is determined through the envelope entropy, a composite kernel function CKFGPR model fusing a linear kernel, a radial basis kernel and a periodic kernel is constructed, and hyper-parameters are optimized through maximum likelihood estimation. In the test stage, the features are decomposed into K intrinsic mode components by using VMD, the K intrinsic mode components are respectively input into the CKFGPR model for prediction and reconstruction, and finally the CKFGPR model is input again to realize SOH prediction. According to the method, the feature robustness is enhanced through cooperative application of the VAE and the VMD, the fitting capability of the GPR to the battery degradation behavior is improved in combination with the composite kernel function, and the SOH prediction precision is effectively improved.

The invention relates to the technical field of intelligent terminals and network testing, in particular to an automatic testing system and method based on an AI large model.According to the system, parameters are configured and manually rechecked according to the testing type and the network environment, knowledge base data are retrieved through the RAG technology, an optimal testing case combination is dynamically generated in combination with the testing stage, the equipment model and the like, and the testing efficiency is improved. Testing of reliability, stability, performance and the like is automatically executed, key indexes are collected and re-checked by professionals after AI intelligent merging analysis, finally, a standardized report is output and fed back to a data optimization system and a knowledge base, a computing power-model-agent collaborative system is constructed, and automation of a whole test link is achieved; according to the method, the test period is shortened by more than 50%, the test precision and coverage are remarkably improved, the method is adaptive to multiple types of terminals and multiple scenes, test resources are optimized, it is ensured that results are objective and repeatable, and the method has sustainable evolution ability.

The invention discloses a multi-mode missing learning method based on dual-prompt cross-state alignment distillation and calibration adaptation, and belongs to the technical field of multi-mode learning. According to the method, various modal missing conditions can be effectively processed without depending on a complex modal reconstruction model, so that the calculation overhead and the storage cost are effectively reduced; the inter-modal shared structure is captured by fully utilizing scarce complete data, the inter-modal shared structure and task specific decision boundary knowledge are transmitted to an inference network through cross-state alignment distillation (feature level + prediction level), and the value of the scarce complete data is fully mined; according to the method, the model parameter efficiency is guaranteed, the performance of an incomplete multi-modal learning task is improved, and the method has good generalization ability for the unseen mode missing modes by combining a mask condition mixing module; meanwhile, the robustness of the model to the distribution offset is further improved through single-step calibration prompt adaptation in the test stage.

This invention discloses a control method, system, and medium for a DC offset compensation circuit of an optical transceiver driver chip, relating to the field of optoelectronic integrated circuit testing technology. The method includes the following steps: time-aligning the test waveform and bias trajectory of the optical transceiver driver chip; extracting the phase misalignment and amplitude jump between beats; and mapping the continuous changes to a timing conflict observation band for identifying beat overlap regions. Based on the phase misalignment trend of the timing conflict observation band, the beat overlap points of the DC offset compensation loop are determined, and an overlap beat index is generated based on the duration and polarity of the amplitude jump to identify interfering nodes. This invention achieves millisecond-level beat observation and microsecond-level overlap identification during the testing phase, accurately extracting interfering nodes and constructing a coupling strength map. Combined with beat rearrangement and energy unloading strategies, it suppresses nonlinear oscillations, reduces oscillation and overload risks, improves eye diagram quality and bit error rate stability, and enhances the accuracy and robustness of chip testing.

The invention aims to provide a dual-camera focusing and focus checking system based on a UVC mode, and the system achieves the automatic switching of an equivalent test distance between a main camera and an auxiliary camera through a cooperative control mechanism of an electric sliding rail type Chart platform and a teleconverter module. MTF threshold tables corresponding to different test stages are called according to different test stages of focusing, focus checking, final inspection and the like of the camera, so that the MTF values of the different test stages are compared; through adaptive focusing and focus checking of the double cameras based on the UVC mode, the cameras delivered to a user can work in an optimal use state.

The invention provides an interface checking method, belongs to the field of automobile electronic software development, and unifies interface naming specifications. Creating a software component model according to the interface naming specification; performing interface verification according to the software component model to ensure the interface consistency between the software components and avoid subsequent problems caused by interface mismatching; potential problems such as data type mismatching and parameter transmission errors are found in advance, restoration can be carried out in an earlier stage, and the later modification cost is reduced. The integration risk is minimized, normal and stable interaction between software components is ensured, and the workload and time cost in the integration test stage are reduced.

The invention discloses a direction consistency constraint-based adaptive image classification method during testing. The method comprises the following steps of: 1, constructing an image classification training data set and preprocessing the image classification training data set; 2, constructing a classification model and completing pre-training; 3, decomposing the weight of each linear layer in the pre-training model to obtain a modulus length matrix and a unit direction matrix, and introducing an orthogonal matrix to construct an effective weight structure for updating in a test stage; and 4, in a test stage, calculating the difference between the model output features and the source domain features in terms of statistics, constructing a loss function with statistics alignment as a target, and on the premise of keeping the unit direction matrix unchanged, updating the modulus length matrix and the orthogonal matrix to realize adaptive adjustment of the image classification model during test. According to the method, the problem of performance degradation caused by input distribution change is effectively relieved, and the classification precision of an image classification model under a complex disturbance condition is remarkably improved.

A computer-implemented method for runtime detection of a software error in a predetermined software element is disclosed. The method comprises the following steps: a) Creating at least one replica software element of the predetermined software element, in particular during a creation phase, such that a test set of executable software elements is created based on the predetermined software element, wherein one or more delay elements (716) are embedded in the at least one replica software element; b) Deployment of at least two executable software elements of the test set on at least one computing platform, in particular during a test phase; c) Comparing the processing results of the at least two executable software elements used on the computing platform in order to detect the software error.

The invention discloses a chip Trojan horse detection and shielding system and method based on multiple confusion links, and relates to the technical field of integrated circuit safety. According to the method, a plurality of confusion links which are large in structural difference, identical in function and similar in time delay are designed for sensitive critical paths in a chip. A write-only unreadable register is adopted to store link selection information, so that an attacker is prevented from stealing or tampering a currently activated path by means of scanning a chain, debugging an interface and the like; a dual-stage operation mechanism is adopted, a test stage is combined with high-precision delay measurement and anomaly analysis to accurately identify a path in which Trojan is implanted, and a formal stage is dynamically switched to a Trojan-free path based on safety link information stored in a register, so that a chip is ensured to automatically avoid malicious logic during operation, and active defense instead of passive detection is realized. The method is superior to a traditional hardware Trojan horse protection scheme in detection precision, operation safety and implementation cost, and an efficient, reliable and easy-to-integrate solution is provided for chip safety.

According to the test method and device based on call link identification, the medium and the computer equipment provided by the invention, the test environment deployment success event is firstly monitored, and the application mirror image is automatically loaded, so that the consistency of the analysis object and the final operation code is ensured. Then source codes and software packages on which the source codes depend are extracted, and all feasible calling links from each entry class to a target method are comprehensively and accurately identified in combination with a byte code scanning technology, so that omission possibly caused by manual tracing is fundamentally avoided, test cases of multiple service scenes are automatically constructed according to each complete calling link, and the test efficiency is improved. And full-scene verification of the change influence range is realized. Therefore, according to the scheme, the accuracy of call link discovery and the test coverage rate are improved, the human intervention cost is reduced by automatically generating and executing the test case, finally online faults caused by missed test are prevented in the early stage of the test stage, and the test reliability and the test efficiency are improved.

A method of testing phase-based ranging includes the following. A respective first phase correction term in response to a channel sounding procedure is measured for a first carrier frequency of a carrier signal by a device under test and a test and / or measurement instrument, respectively. A first channel phase offset is calculated for the first carrier frequency based on the measured first phase correction terms. A respective second phase correction term is measured for a second carrier frequency of the carrier signal by the device under test and the test and / or measurement instrument, respectively. A second channel phase offset is calculated for the second carrier frequency based on the measured second phase correction terms. A phase is altered in order to emulate a different distance between the test and / or measurement instrument and the device under test. Further, a test and / or measurement system is described.

The invention discloses an ASU helium detection test system, and relates to the field of automobile part sealing detection, and the system comprises a gas source supply unit which is used for providing at least two test gases with different properties; the gas circuit construction unit comprises a controlled multi-way valve group and is used for performing selective fluid connection on the gas source supply unit, the to-be-detected module, the pumping and draining unit and the detection unit; the pumping and discharging unit is used for carrying out vacuumizing treatment on the test gas path before the test and recovering the test gas after the test; the detection unit is used for collecting physical state parameters and leakage medium concentration parameters of the to-be-tested module in the testing process; and the control unit is configured to execute a sealing performance detection process comprising at least two different test stages. According to the scheme, large leakage pre-screening can be carried out by utilizing a low-cost medium so as to protect a helium detector and reduce medium consumption, and closed-loop gas recovery circulation is constructed so as to remarkably reduce the use cost of high-value tracer gas.

This invention discloses an adaptive RGBT target tracking method, apparatus, device, and storage medium based on confidence-gated testing. The invention initializes the bimodal template in the first frame and saves the initial anchor points of the predicted head batch normalized affine parameters. The bimodal search region is cropped frame by frame, and the BN statistics of the current frame are updated based on the confidence level of the previous frame. Quality estimation is performed on the bimodality, and modal differences are calculated, adaptively configuring the statistic update rate. A response map and target prediction boxes are output after a single forward inference. Peak values, salience, and distribution entropy are extracted from the response map to construct a confidence score, which is divided into high, medium, and low levels. Only high-confidence frames undergo single-step gradient updates of the predicted head BN affine parameters through self-supervised target optimization; the remaining network parameters are frozen throughout, and anchor point regularization is used to suppress parameter drift. This invention achieves unlabeled adaptive adaptation during the testing phase and controllably suppresses the cumulative drift of erroneous updates.

The invention relates to controller power-on test equipment, a test method and a manufacturing system. The controller power-on test equipment comprises one or more power-on test parts, a multi-axis displacement unit, an image acquisition part and an image test part, the power-on test part is used for placing, positioning and fixing a target test controller and powering on the target test controller through the test interface; the multi-axis displacement unit is used for executing multi-degree-of-freedom motion according to a preset or dynamically adjusted acquisition track; and the image acquisition part is used for acquiring test image data displayed by one or more image test parts according to preset shooting parameters in the motion process of the multi-axis displacement unit and transmitting the test image data to a target terminal in real time. According to the method and the device, the acquisition track can be dynamically adjusted according to the test image data, and the test stage of the target test controller is identified based on the continuous image feature change, so that key image acquisition and test judgment are completed in a proper stage, automation and adaptive detection of the power-on test process are realized, and the power-on test effect is improved.

The application belongs to the technical field of mechanical fault diagnosis, and discloses a rolling bearing small sample fault diagnosis method and system based on an online soft label Gaussian prototype network. The method collects bearing vibration signals under different working conditions to construct a data set, divides the data set into a meta-training set and a meta-testing set, and splits the data set into a support set and a query set; a Gaussian prototype network model containing embedding, prototype calculation and classification modules is constructed; in the meta-training stage, soft labels are dynamically generated by using an online soft label strategy, hard label loss is combined to carry out multi-task training to obtain optimal parameters; in the meta-testing stage, Gaussian prototypes are constructed based on the support set, classification is realized by calculating the Euclidean distance between samples and prototypes, and when the feature distribution drifts, the prototype parameters are fine-tuned to adapt to cross-condition diagnosis. The method effectively alleviates label noise interference, can still guarantee diagnosis accuracy under a small number of labeled samples, and significantly improves the model generalization ability and diagnosis stability.

The invention provides a dual confidence calibration method and system for a neural network classifier, equipment and a medium, and effectively solves the problems that an existing single-stage calibration method is difficult to give consideration to excessive confidence, under confidence, class imbalance sensitivity and the like. A mixed loss function fusing bifocus loss and difference between multi-class confidence and accuracy is introduced in a training stage, so that a neural network classifier is promoted to generate well-calibrated prediction distribution; a class-by-class multi-partition temperature scaling model optimized based on a coupling simulated annealing method is adopted in the reasoning stage, and calibration requirements of different classes and different confidence intervals are more accurately met compared with a traditional temperature scaling technology of a single temperature coefficient; in the training process, classifiers of different rounds are stored, multi-model calibration results are averaged class by class in the test stage, the calibration stability is effectively improved, prediction confidence errors are reduced, and the method is particularly suitable for the safety key fields such as medical diagnosis and automatic driving which have extremely high requirements for prediction reliability.

The application discloses a kind of new energy battery module structural member strength detection equipment of car, belong to structural member strength detection technical field, including base, the side surface of the base top is fixedly installed with side shell, the top end of the side shell is fixedly installed with top shell, the base is fixedly installed with side plate between top shell, the top surface of the top shell is provided with pressure detection assembly, one side of the pressure detection assembly is provided with walking assembly;In the application, by matching pressure detection assembly and walking assembly are provided, by the design, the strength detection method of stage type is used, the stage and accuracy of test result can be effectively guaranteed, and in the testing process, a certain side air flow can be generated, and the airflow can be accelerated to a certain extent. These air flows can effectively eliminate the heat generated inside the deformation structure in the previous test phase, thereby ensuring the test effect and accuracy of the subsequent test, greatly improving the actual application effect of the equipment.

The invention provides an ARC test temperature rise rate calculation method and device, and belongs to the technical field of ARC test. The ARC test temperature rise rate calculation method comprises the following steps: S1, obtaining original time temperature data of an ARC test search stage; s2, smoothing the original time and temperature data based on a non-parametric smoothing method to obtain a central trend curve, and scanning the central trend curve based on a change point detection algorithm to extract a plurality of data segments; s3, fitting all the data segments based on a robust regression algorithm to obtain a target linear model; and S4, obtaining the slope of the target linear model, and taking the slope as the temperature rise rate of the ARC test search stage. According to the method, the technical problem that the calculation result is inaccurate and unstable due to data noise and abnormal values when the temperature rise rate at the Search stage of the ARC test is calculated by adopting an end point method in the prior art is solved, the accuracy is improved, the robustness and the repeatability are relatively high, the hardware dependence is reduced, and automatic and intelligent analysis is realized.

The present application relates to the technical field of speech processing, and particularly relates to a speech enhancement method based on dynamic convolution and narrowband Conformer, which comprises a training stage and a test stage and can realize high-quality speech enhancement.The speech enhancement model proposed in the present application is composed of a generator and a discriminator, the narrowband Conformer network is used in the generator to improve the extraction ability of the model to speech spectrum information, and the dynamic convolution is further used to replace the traditional convolution, so that the parameter quantity and the calculation quantity of the model are greatly reduced, the noise reduction effect is improved, and the operation efficiency of the algorithm and the stability and reliability of the model are effectively improved.

This invention discloses a few-shot industrial anomaly detection method based on dual-guided contrast, relating to the fields of computer vision and industrial defect detection. The method first constructs a few-shot reference set, extracts image features using a pre-trained visual encoder, and then performs dual-guided branch fusion after normal-anomaly guided branching. During the training phase, a global contrast aggregation module and pixel-level contrast loss are introduced, and during the testing phase, the anomaly detection results are output. Compared with existing technologies, this invention can fully utilize the guiding information from a small number of normal and anomaly samples, improving anomaly detection accuracy and cross-class generalization ability, and exhibiting faster inference speed.

The application discloses a phase shifter test and control word optimization method based on automatic test, which comprises the following steps: obtaining a control word table according to the corresponding normalized amplitude and phase information of the I / Q control word full space combination of a to-be-tested phase shifter; using an automatic test device to scan and test the control word table state by state to obtain the amplitude and phase information of the gain; taking the test result of the reference state control word as a benchmark to calculate the relative phase of each scanning state in a target frequency band, and performing phase winding processing to establish a relative phase data table; according to the amplitude and relative phase data of each scanning state in the target working frequency band, sequentially performing in-band gain flatness screening and center frequency point gain consistency screening to construct a candidate optimization set, searching for the optimized control word corresponding to each ideal target phase, and forming an optimized control word table corresponding to each phase state of the phase shifter. The application adopts a brand-new design strategy and efficiently and accurately realizes the automatic calibration and optimization of the multi-state control word of the phase shifter.

The invention discloses an intelligent agent life cycle management method and system based on an eight-stage closed loop. The method comprises the following steps: determining eight life cycles, which are connected in sequence, of an intelligent agent; implementing a Prompt stage, and receiving and analyzing a target function demand; in the implementation Code stage, automatic programming is carried out, and implementation codes meeting the target function requirements are created; implementing the Build stage, and integrating the codes generated in the Code stage into a target agent capable of running; implementing a Test stage, and performing automatic simulation and evaluation of non-determinacy behaviors on the target agent; in the Release implementation stage, a target agent is released, and check before deployment is executed; implementing a Deploy stage, and deploying the target agent to a target operation environment; implementing an Operate stage, and continuously detecting the running state of the target intelligent agent; according to the method, a Retrain stage is implemented, a large language model on which a target agent depends is optimized on the basis of operation data collected in an Opate stage, the improved large language model acts on a Prompt stage, and a new round of development and optimization circulation is driven.