86 results about "Domain testing" patented technology

The invention relates to the technical field of artificial intelligence and databases, in particular to an NL2SQL method and system based on reinforcement learning, and the method comprises the following steps: natural language input preprocessing, semantic analysis and abstract representation construction, mode linking and candidate set determination, SQL generation, query execution and feedback optimization, and multi-round interaction processing. The method has the beneficial effects that complex scenes such as nested sub-query, multi-table JOIN and aggregation function combination are supported, and the SQL generation accuracy is improved by more than 30% compared with that of a traditional method (test data is from a spider data set); a mode link mechanism based on reinforcement learning can quickly adapt to a new database mode, and in a cross-domain test (such as switching from an e-commerce database to a medical database), the accuracy rate decrease amplitude is less than 15%.

The invention relates to the technical field of automatic testing, and discloses an automatic test case generation method and device and a medium, and the method comprises the steps: constructing test case knowledge bases of different fields, and carrying out dynamic and static joint feature analysis on code libraries corresponding to field test data; performing intention recognition on the test demand data, and performing atomization operation on the test demand data according to the test demand intention; identifying a test demand code block associated with the test demand data; generating a basic test case of the test demand code block according to the atomization parameter, and injecting an abnormal stream into the basic test case according to the atomization parameter to obtain a target test case; and selecting a similar test case corresponding to the target test case from the test case knowledge base, performing regression test on the test demand code block by using the regression test case, and optimizing the regression test case according to a test result to obtain an automatic test case. According to the invention, the coverage rate during test case generation can be improved.

The invention discloses a bearing residual service life prediction method based on joint domain adaptation, and the method comprises the construction of a prediction model, and the construction method of the prediction model comprises the following steps: S1, fusing an ECA-Net network and a CNN network, constructing an ECA-CNN feature extractor, and extracting the degradation features of bearing source domain data and target domain data; s2, migrating the model to a target domain, and carrying out the training on a target domain training set through employing a joint domain adaptation method, so as to improve the prediction performance of the model; and S3, after the training is completed, testing by using the target domain test set to obtain the predicted RUL of the bearing test set to evaluate the performance of the model. According to the method, the source domain and the target domain of the bearing are aligned on the feature level by adopting the MMSD measurement method, the difference between the source domain and the target domain is reduced, a high-quality pseudo label is generated, the method is used for guiding adversarial training of a discriminator and a generator of a weight adversarial training network model, and the prediction precision of the residual service life of the bearing under the unsupervised condition of the model is improved.

The invention discloses a rolling bearing depth universal domain self-adaptive cross-working-condition fault diagnosis method and device and a medium, and can be applied to the technical field of rolling bearing intelligent fault diagnosis. According to the method, the fault diagnosis model comprising the feature extractor, the binary classification network and the adversarial domain discriminator is trained through the source domain data containing the fault type label and the target domain data not containing the fault type label, and the loss function in the training process is calculated; adjusting model parameters of the fault diagnosis model according to the total loss function and a preset model parameter optimization algorithm so as to realize shared class feature distribution alignment between the source domain training set and the target domain training set, and identifying a private fault type of the target domain data set according to a confidence coefficient threshold; and when the number of training iterations of the fault diagnosis model is greater than or equal to the maximum number of iterations, the trained fault diagnosis model is tested through the target domain test set, so that the fault diagnosis accuracy of the fault diagnosis model can be improved.

The invention discloses a vertical field objective test data set construction method, system and device and a medium, and relates to the technical field of natural language processing, the construction method comprises the following steps: obtaining text data, and carrying out knowledge extraction and arrangement on the text data to obtain formatted text information; taking the formatted text information as input, and performing question generation, correct answer generation, interference option generation and analysis generation through a pre-established large model to obtain test data; performing quality detection on the obtained test data, and screening out the test data meeting the quality detection requirements; and performing self-inspection on the test data meeting the quality detection requirements, and retaining the test data passing the self-inspection to obtain a vertical field objective test data set. According to the method, the vertical domain test questions can be automatically generated on a large scale, the dependency degree of testers on professional domain knowledge is reduced, meanwhile, the labor cost is saved, the problem difficulty is controllable, and the professionality and accuracy of test question generation are improved.

The invention discloses a cross-working-condition bearing fault diagnosis method based on a multi-module combination model, and the method comprises the following steps: 1, collecting bearing vibration time domain signals when different bearing faults are generated under different working conditions, dividing a source domain training set with fault type labels, and obtaining a source domain training set; the target domain training set and the target domain test set are not provided with fault type labels; step 2, constructing a diagnosis model, wherein the diagnosis model comprises a feature extraction module, a fault classifier module, a global domain confrontation module, a condition domain confrontation module and a feature weighted alignment module; 3, inputting the source domain training set, the target domain training set and the target domain test set into a diagnosis model for model training; and 4, carrying out bearing fault identification by adopting the trained diagnosis model. The generalization ability of the bearing fault diagnosis model is improved.

The invention relates to the field of fault diagnosis, and discloses a bearing fault diagnosis method and device based on a dynamic domain adaptation network, and a medium, and the method comprises the steps: obtaining the vibration signal data of bearings of different models under different working conditions, and taking the data as the data of a source domain and a target domain; a generative adversarial network based on time-frequency feature structure similarity is adopted to carry out data expansion, and a 1-D CNN network containing a CBAM module and a dynamic balance domain adaptation module is utilized to carry out feature extraction and classification. Source domain data are input into the network for feature extraction, and meanwhile, the cross entropy loss and the cross-domain feature are combined to adapt to the loss optimization model. And after model training is completed, target domain test data is input into the trained fault diagnosis network, a verification result shows that the model has high diagnosis accuracy and generalization ability on different data sets, and the reliability of rolling bearing fault diagnosis is remarkably improved.

The invention discloses a rotating machine cross-domain fault diagnosis method based on transfer learning. The method comprises the steps that a sensor is used for collecting source domain data and target domain data of a rotating machine; based on a feature extraction module, improving a local maximum mean value function, a weighting factor and a classifier, and constructing a fault diagnosis model; inputting the source domain data into a feature extraction module and a classifier, and obtaining an optimal weight and source domain classification loss through back propagation; inputting target domain data into the feature extraction module and the classifier after weight migration through weight migration to obtain a pseudo tag and classification loss of a target domain; obtaining domain alignment loss by using an improved local maximum mean value function; adjusting by using a weighting factor to obtain total loss; and inputting the target domain test data into the trained network model to obtain a fault result. According to the method, the fault features of the signals can be effectively extracted, and higher fault diagnosis accuracy and better generalization performance are shown under the cross-working condition.

The invention relates to the technical field of electronic testing, and discloses a USB module multi-station synchronous testing and data association method and system, and the method comprises the steps: constructing a global schedulable resource map of a testing station, so as to create a resource load balancing table and a global testing database of the testing station; configuring a synchronous test controller of the test station by using the test task flow generated by the test station and the global schedulable resource atlas; reading the unique identity label of the USB module so as to establish an association mapping network of the USB module in the global test database; and executing multi-station synchronous testing and data association processing of the USB module based on the global schedulable resource atlas, the resource load balancing table, the synchronous testing controller, the collaborative optimization strategy set and the association mapping network. According to the invention, accurate synchronous control of multi-station test and automatic correlation analysis of test data can be realized.

The invention relates to the technical field of industrial fault diagnosis, in particular to a multi-sensor and cross-working-condition industrial fault diagnosis method. The method comprises the following steps: S1, acquiring equipment operation data of a plurality of sensors under different working conditions through a data acquisition system, and dividing the equipment operation data into a training set, a verification set and a test set; s2, constructing the original data into a 3D space-time collaborative tensor as model input; s3, CBT, LMSCB, ESRM and KAN-TD are embedded into a network architecture, LightM-ConvKNet intelligent fault diagnosis modeling is completed, then pre-training of the model is completed by using a source domain sample, and fine tuning is performed on the model based on a target domain sample; and S4, inputting the target domain test set into the fine-tuned model to generate a fault diagnosis result. By adopting the method, the limitation of the CNN convolution kernel size is broken through, the local correlation among sensor data can be fully acquired, the parameter quantity is reduced, the calculation efficiency is improved, and the calculation complexity is reduced.

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.

The present invention discloses a cross-domain person re-identification method and system with dynamic confidence, which relates to the field of person re-identification. The method comprises pre-training a ResNet-50 network using a source domain training set; determining the mean average performance (mAP) of the current model on a source domain test set; and estimating the confidence of the current model on a target domain unlabeled dataset using mAP; extracting features of the target domain unlabeled dataset using the current model; clustering the extracted features using the DBSCAN algorithm, and using the clustered cluster IDs as pseudo-labels for corresponding pedestrian images in the target domain unlabeled dataset; initializing a memory bank using outlier features generated by clustering; training the current model using the target domain pseudo-labeled dataset, confidence, and memory bank, and then reconstructing the target domain pseudo-labeled dataset, confidence, and memory bank until the current model reaches an optimal state; and identifying the pedestrian images to be identified using the cross-domain person re-identification model. The present invention can improve the performance of person re-identification.

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.

The invention discloses a depth estimation method and system based on domain memory perception guidance, and the method comprises the steps: obtaining a source domain data set and a target domain data set which respectively comprise a segmentation label true value and a depth label true value; dividing the target domain data set into a target domain fine tuning set and a target domain test set; inputting the target domain fine tuning set into a pre-trained source domain model for cross-domain adjustment to obtain a cross-domain fine tuning model; wherein the pre-trained source domain model is trained by a source domain data set, a high-order polynomial memory operator HPM, a hierarchical memory interaction mechanism and a Gaussian guidance factor, and the cross-domain adjustment is the same as the training process of the source domain model; and inputting a target domain test set into the cross-domain fine tuning model to obtain a target domain depth estimation prediction result. According to the method, the dependency problem of a large-scale depth data set is effectively avoided, and the application bottleneck of small sample depth estimation is broken through.

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 application relates to a visual language model test adaptation method based on logarithmic calibration and consistency cache, and relates to the technical fields of computer vision, pattern recognition, machine learning and artificial intelligence. The method aims at the problems of class prediction bias, insufficient cache sample coverage and low utilization rate of boundary samples of a visual language model in a target domain test stage, and constructs an online adaptation framework composed of an image encoder, a text encoder, a dynamic logarithmic calibration module, a consistency guide exploration cache module and a cross-modal joint optimization module. The method improves the identification opportunities of difficult classes and low-frequency classes through a dynamic logarithmic calibration mechanism, and improves the overall class balance and classification stability. Through the consistency guide exploration cache mechanism, the coverage range of the cache to the real distribution of the target domain is expanded, and the perception ability of the model to the decision boundary region is enhanced, so that the adaptation effect in a complex distribution offset scene is improved.

The application discloses a test device, a test cabinet and a test method, and relates to the technical field of testing. The test device comprises a supporting piece, a containing groove for containing a tested piece and limiting the tested piece; a base in sliding connection with the supporting piece; a test terminal fixed to the base; a moving assembly for driving the supporting piece to slide on the base so that a physical interface of the tested piece is plugged with or separated from the test terminal; a first supporting assembly with a first end rotationally arranged on the supporting piece; and a linkage assembly connecting the moving assembly and the first supporting assembly. The application drives the supporting piece to slide by the moving assembly so that the physical interface is plugged with or separated from the test terminal, and the power of the moving assembly is transmitted to the first supporting assembly by the linkage assembly. When the moving assembly drives the supporting piece to slide away from the test terminal, the first supporting assembly is rotated to the second end supported on the bearing surface of the bearing base, so that the physical interface and the test terminal are stably separated and are prevented from being damaged.

The invention discloses a test case generation method and device, a medium and a product, and relates to the technical field of automatic testing, and the test case generation method comprises the steps: obtaining a Schema file of a financial industry specification standard for generating a test case; under the condition that the Schema file is a first version Schema file, analyzing the first version Schema file to obtain first target field information; under the condition that the Schema file is a second version Schema file, comparing an old version Schema file with the second version Schema file, determining difference contents, and analyzing the difference contents to obtain first target field information; and generating a test case according to the first target field information. According to the invention, automatic generation of the corresponding test case after the financial industry specification standard is changed is realized.

The embodiment of the invention provides testing equipment and a network equipment testing method, and relates to the technical field of testing, the testing equipment comprises a testing board, and the testing board comprises a programmable logic device; the programmable logic device loads a simulation program corresponding to the frame-type network equipment to be tested, when the frame-type network equipment is tested, the programmable logic device is in communication connection with a main control board in the frame-type network equipment, and the simulation program simulates working processes of all service board cards to be configured in the frame-type network equipment; the programmable logic device is used for responding to a test instruction issued by the main control board, running the simulation program to process a first signal for testing indicated by the test instruction to obtain a first processing result, and sending the first processing result to the main control board, and the main control board determines whether a link for processing the first signal between the main control board and the to-be-configured service board card is abnormal based on the first processing result. By applying the scheme provided by the embodiment of the invention, board card resources during ESS testing can be saved.

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 present invention provides a weighted multi-source domain transfer learning method for bearing remaining life prediction, comprising: obtaining vibration signals over the entire bearing life cycle to determine a bearing degradation dataset; dividing the bearing degradation dataset into a source domain and a target domain, and dividing the target domain into a target domain training portion and a target domain test portion, thereby constructing a multi-source domain transfer learning task dataset; training a source prediction model based on the source domain; fine-tuning the source prediction model based on the target domain training portion to obtain a bearing target prediction model; and obtaining a final bearing remaining life prediction, final prediction uncertainty, and root mean square error based on the target domain test portion and the bearing target prediction model. Taking into account the data scarcity of the target domain, the present invention incorporates a calibration term into the loss function to calibrate the prediction uncertainty during model training. This calibration term can be used to identify negative transfer and quantify the contribution of different prediction models to the target domain prediction task, thereby achieving better remaining life prediction.

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 invention discloses a test case generation method and device, a vehicle and a storage medium, and the test case generation method comprises the steps: obtaining a target demand document; determining target demand information according to the target demand document; determining an initial test path according to the target demand information; performing generalization operation on the initial test path to obtain a plurality of target test paths; and generating a target test case according to the plurality of target test paths. The technical problem of low precision of the vehicle body domain test case generated by a large model in the prior art is solved.

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.

An offshore wind turbine gearbox cross-domain fault diagnosis method based on wavelet transform and a multi-scale residual network comprises the steps that collected data of different fault types serve as input, then wavelet transform is used for processing, and signals obtained after noise reduction are obtained; designing a multi-scale residual network, and inputting the denoised signal into the multi-scale residual network for feature extraction; in a full connection layer of the network, using a maximum mean difference (MMD) to reduce the difference between a source domain and a target domain; inputting the training sets of the source domain and the target domain into a multi-scale residual network to obtain a gearbox fault diagnosis model; and S5, inputting the target domain test set into the model in the S5 for fault classification, thereby identifying different fault types of the gearbox. The method comprises the following steps: carrying out data processing on an original vibration signal by using wavelet transform to obtain a denoised signal, constructing a multi-scale residual network to carry out feature extraction on the denoised signal, and classifying the extracted features through a Softmax function so as to realize identification of a fault type.

The invention relates to the technical field of electrical equipment diagnosis, and particularly discloses a sample scarcity-oriented transformer fault identification and generalization improvement method and system, and the method comprises the steps: constructing a frequency domain wave equation and an impedance boundary condition of a transformer; embedding a frequency domain wave equation and an impedance boundary condition into a neural network node and a loss function of the initial physical information network model to obtain a target physical information network model, and constructing a training sample set and a target domain test set of multiple meta-learning tasks based on historical voiceprint data and fault sample data, training the target physical information network model by adopting model-independent meta-learning, and performing generalization ability test on the trained model based on the target domain test set to obtain a target meta-model; the target meta-model is embedded into an online voiceprint recognition system for feature extraction and fault type recognition, transformer fault tags are output, and the method improves the generalization ability under the conditions of fault sample scarcity, cross-equipment and cross-working conditions.

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.

The application relates to the technical field of wind power generation equipment predictive maintenance, in particular to a wind power generation key component multi-working condition residual life prediction method S and a target domain sample set D T ; a feature extractor module is constructed; a multi-feature adaptive module is constructed; a migratable attention mechanism module is constructed; a residual service life predictor module is constructed; a domain adaptation module is constructed; a residual service life prediction model is trained; residual service life of a wind power generation key component under a target domain working condition is predicted, target domain test sample sets are input into the established network model, and residual service life prediction results of the key component at each time are output. The wind power generation key component multi-working condition residual life prediction method proposes a migratable attention mechanism module, the module can dynamically activate degradation features with high migration in model training, so that the generalization capability of the model is improved.

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.