39 results about "Domain testing" patented technology

The invention discloses an aerospace field test case generation method based on a knowledge graph. The method comprises the following steps: establishing a test field knowledge graph model comprising four layers of entities, namely a test item, a test case, a test process and a problem list; identifying test entities from a historical test document, and establishing inclusion, execution, generation and association relationships among the entities; filling the identified entities and relationships into the knowledge graph model to construct a knowledge graph, and optimizing the constructed knowledge graph; based on an input test item of a to-be-tested item, recommending a most relevant test case entity and a relevant test process and question list information thereof in the constructed and optimized knowledge graph; and inputting the recommended test case, test process and question list information into the large model to generate a structured test case text conforming to a project language style and a test environment. The invention aims to solve the problems of insufficient structuralization, weak reasoning ability and poor dynamic adaptability of the existing test case generation.

This invention discloses a multi-source information fusion-based equipment health diagnosis and management system, relating to the field of equipment management technology. It includes: an information acquisition module, a feature perception module, a processing and fusion module, an equipment modeling module, an evolution prediction module, a root cause diagnosis module, a cross-domain testing module, a learning and sharing module, a status assessment module, a collaborative decision-making module, a chain ledger module, and a visual decision-making module. This invention significantly improves the foresight and sensitivity of anomaly detection, reduces noise interference and sampling bias, makes the fused data more stable and physically consistent, supports differentiated diagnosis and individual-level prediction, improves the interpretability of diagnosis and the credibility of decision-making, avoids false alarms and missed alarms, enhances the reliability of early warnings, and achieves scientific, transparent, and traceable health management.

This invention discloses a software testing requirements analysis method based on knowledge graphs, comprising the following steps: Step 1: Collecting requirements-side and verification-side data to form a test requirements analysis corpus; Step 2: Extracting semantic objects of requirements, behaviors, and assertions; Step 3: Constructing a three-domain test knowledge graph; Step 4: Generating a test assertion tension field; Step 5: Inputting the test assertion tension field into an improved Hypergraph Transformer model to obtain a test requirement tension propagation feature map; Step 6: Generating a test requirement gap map; Step 7: Obtaining the test requirement decomposition results; Step 8: Updating the three-domain test knowledge graph and the test assertion tension field based on test execution feedback. This invention employs an assertion gap tension resonance mechanism and an improved Hypergraph Transformer model to improve the accuracy of software testing requirement gap identification and decomposition.

The invention belongs to the technical field of vibration data identification, and discloses a real-time adaptive learning bearing fault classification method and system based on prototype alignment and a parameter-by-parameter adaptive learning rate, and the method comprises the steps: inputting a source domain sample into a source domain pre-training module, obtaining a basic classification model through supervised training, and carrying out the training of a source domain pre-training module; extracting a feature mean value of each category to construct a source domain feature prototype set; in a target domain test stage, generating a relatively stable pseudo tag for a target domain sample by using an EMA Teamer module; then, an online self-adaptive updating process is executed on a target domain sample by using a pseudo tag, and feature alignment is performed in the process by combining symmetric cross entropy loss and prototype-based comparison loss; meanwhile, model parameters are stably updated by adopting a parameter-by-parameter adaptive learning rate strategy; and finally, outputting an accurate prediction label for a target domain sample by the model after adaptive training.

The application discloses a test case generation method and system, and belongs to the technical field of functional safety testing. The test case generation method automatically extracts structured data by performing semantic analysis on a functional safety requirement document, the structured data includes an ASIL level, a functional parameter threshold boundary and a safety target, and automatically determines a test method set based on the ASIL level and a preset rule, and automatically generates a test data set based on the ASIL level and the functional parameter threshold boundary, and then automatically generates a test case set in combination with the test method set, the test data set and the safety target, so that manual checking of standards or manual writing of cases are not needed in the whole process, and therefore, the generation time of functional safety test cases can be shortened, and the efficiency of test case generation can be improved.

The invention discloses an aerospace equipment data-real fusion test cross-domain scene-task generation and evaluation method, and belongs to the technical field of equipment digital testing. The method comprises the following steps: constructing a cross-domain test definition model of a layered architecture; analyzing a natural language demand by using a large language model in combination with a domain knowledge base, and generating a cross-domain task flow and a variable scene instance; generating a scene image conforming to a physical rule through a fine tuning generative model and a visual language model closed-loop verification mechanism, and reconstructing the scene image into a dynamic simulation scene with physical attributes; and finally, driving a digital-real fusion system to operate, and completing automatic evaluation through a digital-real synchronous verification mechanism and a multi-dimensional performance evaluation model. According to the method, the problems that scene construction depends on manpower and the number-real consistency is poor in aerospace equipment testing are effectively solved, and high-credibility and automatic number-real fusion testing is achieved.

The application provides a building group energy consumption prediction method and system based on space-time graph convolution and adversarial domain adaptation, comprising: obtaining source domain related data and target domain related data, preprocessing to obtain initial input data, etc.; constructing a space-time graph convolution network model and pre-training to obtain a pre-trained space-time graph convolution network model, splitting the model into a space-time feature extractor and a predictor; performing weight initialization and adversarial training on the target domain space-time feature extractor and the target domain predictor to obtain a trained target domain space-time feature extractor and a trained target domain predictor; inputting a target domain test set into the trained target domain space-time feature extractor and the trained target domain predictor to obtain a target domain regional building group energy consumption prediction result; and the application captures the spatial dependence relationship between buildings based on graph convolution and graph attention mechanism, adopts a transfer learning strategy to transfer source domain prediction related knowledge to a target domain, and accurately predicts energy consumption by using a small amount of data.

The application discloses a three-directional vibration high-end machine tool joint surface contact characteristic testing device and method, and belongs to the field of machine tool dynamic characteristic analysis. The testing device and process mainly comprise the following steps: before testing, a first support and a second support are fixed on a base through bolts, a tested piece is screwed on a boss of the base through a pre-tightening bolt, and a vibration exciter system is hung on the support through a rope; a vibration mode database in a computer is called, the number of sensors and a layout position scheme are determined according to the vibration mode of a tested piece with different structures; the sensors are pasted on the tested piece according to the obtained scheme, the output ends of the sensors are connected to the computer through a signal acquisition system, and the contact characteristic of the machine tool joint surface is obtained by processing the computer. The device has the advantages of simple structure, convenient loading and unloading, easy repeated experiment, sensor layout scheme according to the shape of the tested piece, and fast and accurate measurement of the dynamic characteristic of the machine tool joint surface.

A method and system for identifying small-sample bearing fault modes based on multi-source data integration, relating to the field of intelligent operation and maintenance and health management of mechanical equipment. It solves the problems of difficulty in identification and inaccurate identification due to the limited number of samples in existing bearing fault modes. The method includes: constructing multi-source data samples based on a public dataset; extracting features from the multi-source data samples to construct source domain training sets and target domain training sets; combining the target domain training sets and source domain training sets to obtain a combined source domain training set; training a base classifier based on the target domain test set and the combined multi-source domain sample set; calculating inter-domain distribution metrics and sample similarity; constructing a weight matrix; classifying the target domain test set using the base classifier to obtain class probabilities; and weighting and integrating the class probabilities according to the weight matrix to obtain the classification results of the target domain test set, thus completing fault mode identification. This invention is applied in the field of fault identification.

The invention provides a large language model cross-domain illusion detection method and system, and the method comprises the steps: screening an optimal template from a candidate template set according to the authenticity direction variance ratio of each candidate template; splicing the unknown field test text with the optimal template to obtain a spliced test text; performing feature extraction on the spliced test text through a large language model to obtain a to-be-detected feature vector; and detecting the to-be-detected feature vector through the trained detector to obtain reliability data of the cross-domain test. Compared with the prior art that a cross-domain consistency mode is difficult to capture by features directly extracted by LLM and generalization ability of a detector is limited, the method has the advantages that the optimal template is determined according to the authenticity direction variance ratio of the candidate template, and the plug-and-play optimal template is used as a prompt template to optimize the feature extraction process, so that the accuracy of feature extraction is improved. The significance and cross-domain consistency of the authenticity related structure are enhanced, and the generalization performance of the detector in the unknown field is improved.

The invention discloses a batch verification maximization fault diagnosis system and method for life stage migration. The dependence on target domain labeling is remarkably reduced while cross-stage high-precision fault recognition is achieved. The system comprises a life stage pseudo-domain division label generation module which inputs source domain and target domain test data containing timestamps or residual service life information and outputs data subsets corresponding to all stages; the stage perception sharing-specific feature coding module is used for inputting original signal data of each stage and a corresponding stage label, and outputting a shared feature and a stage specific feature; the channel-stage fusion selection module is used for receiving the shared features and the stage specific features from the stage perception sharing-specific feature coding module and outputting fused features; and the multi-stage batch nuclear norm maximization migration module is used for inputting fusion features and stage label information, respectively calculating a Softmax output matrix for each pseudo-domain, and maximizing the batch nuclear norm of the Softmax output matrix so as to enhance cross-stage discrimination and feature consistency.

The embodiment of the invention provides a test case generation method and device, a test method and device, electronic equipment and a computer readable storage medium, and relates to the field of natural language processing. The test case generation method comprises the following steps: obtaining test information; repeatedly executing the first operation until the trap text output by the first large language model meets a preset condition, and taking the trap text meeting the preset condition as a test case of the RAG system; the first operation comprises obtaining a trap text based on the test information and the first prompt information through a first large language model, and the trap text comprises semantic information of each trap fragment in the fragment set; the preset condition comprises that the output trap text comprises a correct answer, and the first relevancy between the output trap text and the question text is not lower than a relevancy threshold value. According to the embodiment of the invention, a basis is provided for analyzing and generating semantic understanding of the model from more levels.

The application discloses an IGBT module state prediction method based on a domain adversarial long short-term memory network, and comprises the following steps: collecting aging data of different IGBT modules respectively, cutting different sample sequences from sample data of known IGBT modules respectively, and labeling after normalization processing; an adversarial transfer state prediction network for unknown IGBT modules is constructed; in the training stage, source domain and target domain data are mapped into a high-dimensional feature space to obtain data feature distribution; domain invariant features of the two domains are learned by using a domain adversarial module to perform feature distribution matching; a weighting discrimination mechanism is further provided to evaluate the similarity degree of target domain sample data and source domain data, discriminate the transferability of data, and thus effectively improve the classification performance of the state category; target domain test data is input into the trained prediction model for testing, the current running state of the equipment is discriminated by calculating the obtained weight value, and the final classification prediction result is output.

The application discloses a total nitrogen and total phosphorus inversion method based on sample migration and lasso regression, which comprises the following steps: step 1, collecting water quality data of multiple sites and sorting the water quality data of each site according to time respectively; step 2, pre-processing; step 3, for each site, dividing a target domain training set and a target domain test set respectively; and determining a source domain auxiliary training set; step 4, constructing a TrAdaboost transfer learning model based on a weak learner lasso algorithm, training a model corresponding to the site by using a combination of the source domain auxiliary training set and the target domain training set, obtaining a trained sample transfer learning model corresponding to each site, and inputting the target domain test set corresponding to the site into the trained sample transfer learning model to obtain a model output of total nitrogen and total phosphorus results corresponding to the site. The application can effectively improve the detection efficiency and the detection precision of measurement results.

The utility model provides a testing device, and relates to the technical field of electronic atomization. The testing device comprises a main body and a clamping assembly, the main body comprises a placing part and a mounting part, the placing part forms a testing surface for placing an object to be tested, and the mounting part is arranged on the placing part and surrounds the testing surface; the clamping assembly is movably connected with the installation part and used for clamping the to-be-tested object so that the included angle between the to-be-tested object and the test surface can be set to be a preset test angle. The clamping assembly movably connected to the mounting part can be used for clamping the to-be-tested object, so that the included angle between the to-be-tested object and the test surface is set to be the preset test angle, and the test device can adjust the to-be-tested object to each angle according to test requirements, so that each part of the to-be-tested object is measured, and the test efficiency is improved. For example, the liquid leakage condition of each part of the to-be-tested object can be detected, the posture of the to-be-tested object is not prone to change in the testing process, the testing accuracy and reproducibility can be improved, and the testing efficiency can be improved.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a GUIAgent task planning method and system based on multi-agent collaboration. The method comprises the following steps: S1, constructing multi-agent collaboration data: automatically generating training data after verification, repair and rule optimization through multi-agent collaboration and feedback control of a closed loop; the multi-agent comprises a training sample generation module, a data quality verification module, a data restoration module and a rule extraction module; s2, constructing a training sample taking an end point node as a prediction target based on the unique offline map node by adopting an end point prediction data format and a model training method, and performing fine tuning on the pre-trained large language model by adopting an inference-free NoThink training mode to obtain a fine-tuned model; and S3, constructing a training set, a same-domain test set and a cross-domain test set, and performing performance evaluation and generalization ability verification on the fine-tuned model.

The invention discloses a test vector generation device and an aging test system, and relates to the technical field of circuits, the test vector generation device comprises a plurality of sequence generators, a multiplexer and a control circuit; the control circuit is used for respectively sending test sequence generation signals to the plurality of sequence generators under the condition that a to-be-tested chip where the test vector generation device is located is connected with the aging test bench; the sequence generators are used for respectively generating candidate test sequences under the condition that the test sequence generation signals are received, each sequence generator generates one candidate test sequence, and the candidate test sequences generated by the sequence generators are different; the multiplexer is used for selecting a target test sequence from the plurality of candidate test sequences, and the target test sequence is used for carrying out aging test on the to-be-tested chip. Compared with the prior art, the method does not need a test board to provide a test vector for the to-be-tested chip, increases the flexibility of the aging test, and improves the user experience.

The invention provides a test method, a test device, computing equipment, a storage medium and a program product, and relates to the technical field of communication. The test method comprises the following steps: acquiring encoded first attribute information of traffic participants, and extracting and analyzing the first attribute information into decoded second attribute information of the traffic participants based on a predefined data structure type; in response to the received analyzed second attribute information, processing the second attribute information to obtain third attribute information of the traffic participant, the third attribute information being decoded; and in response to receiving the processed third attribute information, sending the third attribute information.

The present invention relates to a test management system for managing, through a dedicated application, a process of requesting and conducting test services in the field of life science, the test management system comprising: a manager terminal used by a manager of a company that produces and provides the dedicated application; a CRO master terminal used by managers including a team leader and a sales representative of a CRO company that has been commissioned to provide the test service; a CRO terminal used by a person in charge who performs tests at the CRO company; a client terminal used by a client company representative who has commissioned the test service to the CRO company; and a server that provides the dedicated application to the manager terminal, the CRO master terminal, the CRO terminal, and the client terminal, provides various functions related to the tests through the dedicated application, and enables real-time sharing of data and information. According to the present invention, tests can be easily and quickly conducted, thereby contributing to the convenience of CRO companies, client companies, and the like involved in the tests.

The application discloses a security test method of a baseboard management processor and electronic equipment, relates to the technical field of testing, and comprises the following steps: performing multi-domain hierarchical mapping on a baseboard management controller security scheme, generating a hierarchical test object set of a security domain, and extracting test objects corresponding to each domain security scheme; constructing an attack path across at least two types of test object sets and instantiating the attack path into a scene sample, reproducing remote firmware upgrade and access control linkage complex working conditions; carrying out multi-dimensional cross-domain testing containing firmware upgrade and access control interaction based on the scene sample, collecting interactive test data sets containing execution data flow, actual response and expected response, and completely retaining multi-module linkage test data; performing feature analysis and result aggregation on the data set to generate a verification output set, so that the protection capability is systematically verified, the overall protection effect in a complex scene is accurately evaluated, and the defects that related technologies lack systematic testing means, protection capability verification and accurate evaluation of protection effect in a complex scene are solved.

The invention discloses a capacitor online testing method and system based on electrical parameter dynamic feature fusion, and relates to the technical field of electronic component testing. The test method comprises the following steps: performing three-stage tests of constant-current charging, constant-voltage holding and constant-current discharging on the to-be-tested multi-layer ceramic capacitor (MLCC) in sequence, and synchronously acquiring voltage and current dynamic response data; fitting a curve through linear and nonlinear least square methods, and extracting characteristic parameters such as capacitance, equivalent series resistance, leakage current, dielectric polarization intensity and polarization time constant; and substituting the parameters into a comprehensive health index (CHI) function, and realizing intelligent diagnosis and grading of the reliability of the MLCC according to a comparison result of a CHI value and a preset threshold value. The test system comprises each hardware module for realizing the test method. The test method and the test system provided by the invention are used for improving the screening accuracy and the product reliability and are adaptive to the test requirements of MLCC of different specifications.

The invention discloses an identity recognition method and device based on an ECG signal, equipment and a medium, and the method comprises the steps: inputting a target domain test set into a target identity recognition model, and obtaining a classification result; wherein the target identity recognition model is determined through the following steps: obtaining a domain confrontation model, and the domain confrontation model comprises an initial feature extraction module; based on the domain adversarial model, removing the initial feature module, performing feature extraction through a double-branch architecture combining grouping and halving convolution and residual error, and introducing a dynamic space attention mechanism to perform feature enhancement to obtain an improved intermediate model; and performing iterative training on the intermediate model by using the source domain training set and the target domain training set to obtain a target identity recognition model. According to the invention, the identity recognition efficiency and precision can be improved.

The present application belongs to the technical field of fault diagnosis based on transfer learning, and more particularly relates to a gearbox fault diagnosis method based on deep transfer learning. The method comprises collecting original vibration signals of a gearbox under different operating conditions by using an accelerometer; dividing the collected data into source domain data and target domain data and performing preprocessing; constructing a pre-training model and inputting the source domain training set and the target domain training set for pre-training, and saving the best pre-training model with the highest accuracy; reading the best pre-training model, constructing a domain discriminator, an auxiliary discrimination module and a multi-label classifier after a feature extractor to obtain a fault diagnosis model and performing adversarial training; inputting the source domain test set and the target domain test set into the fault diagnosis model, and obtaining a fault diagnosis result. The present application solves the problems of the prior art, such as the limitation that the fault types of the gearbox between two fields must be the same, the limitation that the data set must be large and well labeled, and the gradient disappearance and explosion existing in the adversarial training process.

The application provides a task-level and sample-level double-layer fusion cross-domain multi-modal sentiment recognition method and system based on meta learning. The method is aimed at the domain difference and annotation cost problem between different sentiment data sets, adopts a model-independent meta learning (MAML) framework to realize rapid domain adaptation, and specifically includes multi-modal feature coding, double-layer adaptive fusion, meta learning adaptation and sentiment classification. The double-layer adaptive fusion includes a task-level fusion mechanism and a sample-level fusion mechanism. The former generates domain-level modal weights through an attention mechanism, and the latter dynamically adjusts the weights according to the characteristics of each sample through a gating mechanism. The meta learning adaptation learns the initialization parameters through inner and outer double-layer optimization. The leave-one-domain test protocol is adopted to verify the method on four public data sets. The method realizes high-precision cross-domain sentiment recognition under the condition of 20 samples, has a sample efficiency improvement of 2-4 times compared with traditional supervised learning, and significantly improves the cross-domain generalization ability of the model.

The utility model discloses a testing tool and a testing system, and relates to the technical field of testing, the testing tool comprises an interface assembly, a signal processing assembly, a first connecting end and a second connecting end, the interface assembly is used for accessing an SIM card to be tested; the signal processing assembly is electrically connected with the interface assembly; the first connecting end is electrically connected with the signal processing assembly and is used for accessing a function test module; the second connecting end is electrically connected with the signal processing assembly and is used for accessing a parameter modification tool; the signal processing component is used for establishing a data path between the interface component and the first connecting end; or the data path establishing module is used for establishing a data path between the interface component and the second connecting end. The utility model aims to realize automatic operation of rewriting and testing processes in the SIM card testing process, reduce manual operation, improve user experience and reduce testing cost.

The application discloses a test vector generation device and an aging test system, and relates to the technical field of circuits. The test vector generation device comprises a plurality of sequence generators, a multiplexer and a control circuit. The control circuit is configured to send test sequence generation signals to the plurality of sequence generators respectively under the condition that a chip to be tested where the test vector generation device is located is connected with an aging test platform. The sequence generators are configured to generate candidate test sequences respectively under the condition that the test sequence generation signals are received. Each sequence generator generates one candidate test sequence, and the candidate test sequences generated by each sequence generator are different. The multiplexer is configured to select a target test sequence from the plurality of candidate test sequences. The target test sequence is used for aging test of the chip to be tested. Compared with the prior art, the application does not need the test platform to provide test vectors to the chip to be tested, increases the flexibility of the aging test, and improves user experience.

An adversarial domain adaptive cross-domain fault diagnosis method based on instance-weighted feature matching is presented, belonging to the field of rotating machinery fault diagnosis. First, data is collected, a transfer task is constructed, and the data is partitioned to obtain source domain training samples, target domain training samples, and target domain test samples. Second, an adversarial domain adaptive network model based on instance-weighted feature matching is constructed. Third, the training process is implemented by minimizing a comprehensive loss function, ultimately obtaining the trained adversarial domain adaptive network model. Finally, fault diagnosis and performance evaluation are performed. This invention is the first to simultaneously consider the alignment of marginal and conditional distributions in adversarial domain adaptation, effectively solving the negative transfer problem caused by class misalignment and significantly improving the accuracy and robustness of cross-domain diagnosis. Furthermore, end-to-end reliable training accelerates model convergence and improves final performance. It is independent of specific network structures and has good versatility and promotional value.

The application discloses a kind of nuclear power instrument control system network attack detection method and system based on confrontation migration, other industrial control field disclosed network attack data set is selected to construct source domain data set, and target domain data set is constructed by nuclear power instrument control system semi-physical simulation;Source domain feature extractor and source domain classifier are constructed, and it is trained using source domain training data set, and the model saved after training;Adaptive model of confrontation domain is constructed, and confrontation training is completed using target domain data set, unknown attack classifier is also constructed, and it is cascaded into nuclear power instrument control system network attack detection model with the saved target domain feature extractor;The detection performance of nuclear power instrument control system network attack detection model is tested and verified using target domain test set.The application uses the idea of confrontation domain adaptation, realizes the migration reuse of source domain attack knowledge in target domain, solves the problem that nuclear power instrument control system network attack data is few and characteristic is deficient, and shows superior detection performance.

The embodiment of the invention provides a multi-domain test resource dynamic scheduling method, electronic equipment and a program product, and relates to the technical field of hardware-in-the-loop test. The method comprises the following steps: acquiring complexity characteristics of a multi-domain test scene, wherein the complexity characteristics comprise calculation complexity, input and output complexity and cross-domain synchronization complexity; constructing a quantization model based on the complexity features, and generating a scene complexity quantization value; and according to the scene complexity quantized value and the real-time resource state, generating a dynamic resource allocation strategy through a reinforcement learning algorithm, and executing the dynamic resource allocation strategy. Based on the method, the real-time performance, the system-level synchronism and the resource utilization rate of the multi-domain hardware-in-the-loop test can be improved when the hardware-in-the-loop test is carried out on multiple domains.