342 results about "Model testing" patented technology

A system and method for multi-scale temporal attention processing in multimodal technology deep learning systems. This system processes time-series, textual, sentiment, and structured tabular data across three hierarchically-organized temporal streams—quarterly, weekly, and intraday levels—with bidirectional cross-temporal information flow. Scale-specific attention mechanisms are optimized for respective temporal granularities, while an adaptive controller dynamically weights each temporal level based on real-time market volatility indicators. A multi-scale fusion processor integrates attention-weighted representations to generate temporally unified representations preserving both short-term market dynamics and long-term trends. This approach enables superior forecasting and risk assessment by leveraging temporal correlations across multiple time scales while automatically adapting to changing market conditions. The system facilitates interpretable AI analysis through attention visualization and enables synthetic scenario generation for model testing.

A system and methods for processing diverse financial data types using a jointly trained Vector-quantized Variational Autoencoder (VQ-VAE) and neural upsampler. This system efficiently handles time-series, textual, sentiment, and structured tabular data through specialized encoding modules. A novel fusion module integrates these encodings, capturing cross-modal relationships via attention mechanisms and gated fusion units. The fused representation is compressed into a discrete latent space by the VQ-VAE encoder, then reconstructed and enhanced by the VQ-VAE decoder and neural upsampler, respectively. Joint training optimizes all components simultaneously, using a comprehensive loss function that balances reconstruction quality across modalities with upsampling performance. This approach enables superior data compression, reconstruction, and analysis, leveraging inter-modal correlations to improve financial forecasting, risk assessment, and decision-making. The system's ability to explore the latent space facilitates generation of new, synthetic financial scenarios for robust model testing and strategy development.

The invention discloses an out-of-focus identification and restoration method for a microscopic system. The method comprises the following steps: 1, preparing a microscopic out-of-focus image data set and carrying out degradation modeling; 2, constructing an out-of-focus parameter prediction network based on multi-label parameter reasoning and microscopic system priori knowledge and an image restoration network based on a generative adversarial network, performing independent training, and then performing merging training through a circulation system formed by mutual connection based on predicted point spread function convolution and deconvolution operation; 3, model fine tuning based on a specific system and a new data set; and 4, performing model testing, performing large-view image sliding window detection and collage fusion, and explicitly outputting defocus parameters of the system during image shooting. The method can be applied to out-of-focus fuzzy recognition and restoration of microscopic imaging of various systems, and is beneficial to the accuracy of functions such as particle counting and particle size statistics of the systems, thereby further promoting the application of the full-depth-of-view microscopic system in biomedical detection.

The invention discloses a feature fusion-based loaded coal and rock mass instability failure early warning method and system, and the method comprises the following steps: obtaining the information of an internal strain field and an external strain field through a coal and rock loading deformation failure test, and constructing a whole space-time sample data set; pre-training an auto-encoder for realizing feature extraction and constructing a multi-modal image; integrating depth features extracted from different source images by adopting a fusion strategy based on a self-attention mechanism so as to realize feature fusion; during training, inputting a multi-modal image in the data set into the neural network to obtain a strain field spatio-temporal evolution model; after the test is completed, the corresponding stage of the strain field measured by the sensor and the prediction result of the model are compared and verified, the relevant parameters of the model are adjusted according to the accuracy, and the fitting and generalization ability of the model is enhanced.

The invention provides a limit test method, system and device for an artificial intelligence model and a medium, and the method specifically comprises the steps: constructing a plurality of scene templates for different types of games, and generating a test case set by combining and transforming the scene templates according to a boundary condition input by a user; based on the test case set, according to a preset optimization objective function, adopting a heuristic search algorithm to determine an optimal test parameter combination meeting the artificial intelligence model; performing automatic testing on the artificial intelligence model according to the optimal test parameter combination, and generating a model test result; and analyzing the model test result, determining the performance of the artificial intelligence model under the boundary condition, generating a model test report, and carrying out optimization and re-test on the artificial intelligence model through the model test report. According to the method, full-process automation and intellectualization of game AI model testing are achieved, and the testing efficiency and the model quality are remarkably improved.

The invention provides a graph representation learning method based on sparse and far and adjacent node enhancement, and relates to the technical field of data enhancement. The method comprises the following steps: obtaining an initial node feature matrix, an adjacent matrix and an original classification label according to an obtained to-be-enhanced graph structure data set, and dividing a training set and a test set; reinforcing node features of the training set by using a feature enhancement technology to obtain a sparse enhanced graph structure data set; then, in combination with a node mixing technology, mixing of features and classification labels is performed on intra-class nodes and inter-class nodes, so that the accessibility of far-adjacent nodes is increased, and a far-adjacent enhanced graph structure data set is obtained; inputting the far-adjacent enhanced graph structure data set into the graph neural network model to carry out graph representation learning and training so as to obtain optimal node representation and a trained graph representation learning model; and finally, performing model test evaluation to obtain an evaluated model. According to the method, the defects of a traditional graph neural network in processing information aggregation of sparse nodes and far and adjacent nodes are overcome.

The invention discloses a cross-fault tunnel test simulation method under the combined action of ground vibration and fault movement, and belongs to the technical field of tunnel engineering model tests.The method comprises the following steps that firstly, the similarity relation of models is determined; 2, manufacturing a tunnel lining test model; 3, manufacturing a test model box; fourthly, surrounding rock is poured; 5, pressurizing the model box to simulate the tunnel burial depth; step 6, simulating fault dislocation; and step 7, applying seismic oscillation. In the invention, the designed horseshoe-shaped flexible model box replaces a rigid model box adopted by a conventional model test, the test cost is low, the test can be completed on one set of test system without designing several additional devices for cooperation, the operation process is simple, the feasibility is high, and the test efficiency is improved by considering the influence of ground stress aiming at the characteristic that the tunnel passes through the active fault. The deformation and failure evolution law of the tunnel under the combined action of seismic oscillation and fault movement is comprehensively analyzed, and the real site condition is better reflected.

The invention relates to safety compliance evaluation, in particular to a safety compliance evaluation system and method based on a multi-modal large model, and a functional module cluster, which are used for managing each functional module in the system, supporting cross-modal consistency detection and comparing answer consistency under different modals. The self-developed security system is responsible for execution of an assessment task, including resistance test enhancement and built-in multi-granularity data variation strategy, improves assessment robustness by simulating a real attack scene, realizes end-to-end automation from data generation, tested model test to risk assessment, supports configurable parameters and reproducible results, and has the advantages of high reliability and high reliability. The self-iteration and version management of the evaluation model are realized, and the self-iteration mechanism is deeply combined with a compliance label system, so that the risk identification capability under legal, ethical and industrial specifications can be continuously optimized; according to the technical scheme provided by the invention, the limitation that evaluation can be carried out only under a single mode and a single rule in the prior art can be effectively overcome.

The invention discloses a model testing method and device. A plurality of risk queries of an initial model are obtained, each risk query is split to obtain a query group, the query group comprises a plurality of sub-queries, the initial model is trained based on the query group to obtain a red team model, and a to-be-tested model is tested through the red team model. Therefore, a single round of query is divided into multiple rounds of query, so that a security protection mechanism of the to-be-tested model is bypassed, security vulnerabilities of the to-be-tested model are mined and exposed, and the effectiveness of model testing is improved.

The invention discloses a satellite orbit forecasting method based on deep learning physical constraint loss, and the method comprises the following steps: 1, carrying out the normalization preprocessing of input data, forming a training data set and a test data set, and constructing batch processing training data; and 2, performing dimension expansion on sample data points in each window in the batch processing data formed in the step 1, constructing a multi-dimensional feature space of the sample points, and forming a batch processing input data format capable of being introduced into the model. And 3, performing forward reasoning on the batch data formed in the step 2 by using a model, and obtaining a batch processing orbit prediction value output by the model at the next moment through a CNN lightweight spatial-temporal feature extraction module and a BiLSTM bidirectional time sequence neural network module. And 4, taking the track prediction value obtained in the step 3 and the truth value label in the training set obtained in the step 1 as input, calculating to obtain a loss value of a current training iteration batch through a multi-random learning loss module fusing physical constraints, and performing reverse updating of model parameters to complete model training. And step five, through the steps two to four, performing reasoning verification on the model by using the test set formed in the step one, and comparing with a truth value in the test set to obtain a model test result.

The invention provides a bidirectional recurrent neural network acoustic logging curve reconstruction method. The reconstruction method comprises the steps of S1, acquiring data and performing sequence segmentation; s2, carrying out normalization processing on the logging curve; s3, carrying out superposition networking and training on the bidirectional recurrent neural network structure block; s4, model testing and parameter storage; and S5, reading the model parameter file to reload the model, importing the to-be-reconstructed logging data, generating a prediction curve, and storing and exporting a result. A bidirectional recurrent neural network algorithm of artificial intelligence deep learning is adopted, bidirectional depth sequence information contained in a logging curve is effectively captured, the curve reconstruction accuracy is improved, unmeasured, missing and low-quality curves are reconstructed, multi-curve information is fused, and the sensitivity of a reconstructed acoustic curve is improved. The system has the advantages of being low in cost, high in efficiency and high in precision.

Example embodiments of the present disclosure are related to artificial intelligence / machine learning (AI / ML) model test. A first apparatus transmits test configuration information to a second apparatus, the test configuration information indicating a test mode of an AI / ML model with respect to at least one transmission and reception unit (TRP), the at least one TRP being arranged within an environment based on a test plan for at least one test channel indicator. The first apparatus receives, from the second apparatus, at least one predicted channel indicator for the at least one TRP, the at least one predicted channel indicator being derived by the second apparatus using the AI / ML model. The first apparatus determines a test result for the AI / ML model based on a comparison between the at least one predicted channel indicator and the at least one test channel indicator, the test result indicating whether the AI / ML model is validated.

The invention provides a large model test case generation method, system and device based on multi-Agent collaboration. The method comprises the steps of obtaining product information of target product demand information; if the product demand information is incomplete, performing structured supplement on the extracted product demand information according to a slot filling technology to obtain complete product demand information; performing assembly according to the complete product demand information and the background information, and determining a first prompt word used for requesting the large model to generate a test case; and generating a first edition test case according to the first prompt word by using the plurality of agents, reviewing the first edition test case, generating an optimization suggestion, and fusing the first edition test case based on the optimization suggestion to generate a final test case. Through knowledge injection and structured requirements, the test case not only covers functional requirements, but also improves the accuracy of the test case; dynamic verification and correction of the use cases are achieved through cooperation of the multiple agents, the generation efficiency of the test use cases is improved, and the coverage rate is increased.

The invention discloses a remote sensing target detection large model construction method based on context feature deep learning, and the method comprises the following steps: S1, dividing an original data set according to a strategy, forming a training set, a verification set and a test set, and guaranteeing that each subset has representativeness in sample number, scene diversity and target distribution; s2, constructing a remote sensing target detection large model by adopting a deep learning method based on context features, and fusing multi-level features and introducing a query optimization mechanism; and S3, performing model testing and performance evaluation, selecting optimal model parameters, performing comprehensive verification on the test set, and evaluating the comprehensive performance of the model in the aspects of detection precision, speed and robustness. According to the method, the pre-trained DINOv2 backbone network, the context feature enhancement module and the intensive supervision strategy are introduced, so that rich context information can be captured in a multi-scale and complex scene, and the detection precision is greatly improved.

The invention provides an automatic testing method and system based on an Internet of Things model, electronic equipment and a storage medium, and aims to solve the problems of low efficiency and incomplete test of compiling a model test case. The method comprises the following steps: exporting a specific format file of a product model on an Internet of Things platform, and analyzing the file to obtain a product model test case; extracting each field content under attributes, events and service function types; respectively storing the extracted field contents according to events, attributes and services; and automatically generating test input data for each test item in combination with the data type and the parameter value range in the field content through an effective equivalence class and boundary value method, and storing the automatically generated test input data in the test input data item of the corresponding test case file to complete the automatic generation of the test case. The test case can be automatically generated, the test efficiency is improved, and the timeliness, stability and reliability of the test case are effectively improved.

The invention discloses a model performance test method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence. The theoretical maximum lexical throughput of a target large language model is calculated based on the video memory bandwidth of a graphics processor, the model parameter quantity, the byte number corresponding to the quantization precision and the video memory bandwidth utilization rate; meanwhile, the benchmark performance throughput is obtained, a theoretical corresponding first concurrency number is calculated in combination with the theoretical maximum lexical unit throughput and the concurrency competition loss coefficient, then the model test is executed based on the first concurrency number to obtain the actual maximum lexical unit throughput and a corresponding second concurrency number, and a model performance test result is generated. The problems that in the prior art, due to the fact that manual testing is conducted depending on manual intervention, a continuous approaching attempt mode is adopted, a reasonable test starting point is not deduced in combination with hardware core bottlenecks and key parameters, evaluation is time-consuming and labor-consuming, the result is prone to being affected by artificial factors, and accuracy and consistency are poor can be solved.

The embodiment of the invention provides a model testing method and device, equipment and a storage medium. The method comprises the steps that a plurality of local groups are created for a plurality of candidate defect types respectively; obtaining a plurality of target images, respectively inputting the plurality of target images into the defect detection model for detection to obtain a defect detection result of each target image, and for each target image, obtaining a defect detection result of a target defect type corresponding to the target image based on the first confidence interval and the target confidence of the target defect type corresponding to the target image; determining a first target group in each local group corresponding to the target defect type, and dividing the target image into the first target group; and for each candidate defect type, determining a first detection group in each local group corresponding to the candidate defect type, and determining a test result of the target model based on a relationship between the number of the target images corresponding to each first detection group and the total number of the target images. According to the embodiment of the invention, the stability and robustness of the model in a production field can be improved.

The invention discloses a mechanical arm fault prediction system based on digital twinning, and belongs to the technical field of mechanical arms. The system comprises a digital twin model construction module, an experimental simulation module, a data collection module, a model training module, a model test module, a fault prediction module, a prediction result output module, a result analysis module, an alarm prompt module, an aging prediction module, a durability analysis module, a digital model display module and a durability identification module for each part. The mechanical arm fault prediction platform is constructed on the basis of the digital twinning technology by means of cooperation of the multiple component state identification modules, the pre-arranged plan execution module and the pre-arranged plan setting storage module, and the faults, the aging degree and the durability of all the components of the mechanical arm can be predicted.

The invention relates to the technical field of model testing, in particular to a model testing method and device and electronic equipment, and is used for solving the problems of long test data combination time consumption and low coverage rate of a model testing mode in the related technology. The multiple features are classified, a core feature set, a common feature set and an edge feature set are obtained, and the model can be a deep learning model; generating a test set of the model according to a differentiated data generation strategy of feature importance on the basis of the rule set of the service type adapted to the model, the core feature set, the common feature set and the edge feature set, and performing model performance test on the model by using the test set to obtain a test report; therefore, the efficiency of model algorithm testing is improved.

The invention proposes an entity extraction method and system based on a dynamic window attention mechanism, and the method comprises the steps: data collection, data preprocessing, and automatic labeling training, and during the automatic labeling training, based on the dynamic window attention mechanism, through improving the attention extraction weight of a TMN frame, the automatic labeling training is carried out. Trigger training, entity recognition model training and entity recognition model testing are carried out. From the perspective, the problem of key information omission or redundancy possibly caused by a fixed window is avoided by introducing a dynamic window attention mechanism, so that the model is more efficient when processing long sentences and complex sentence patterns, and the text information capture capability and the feature extraction effect of the model are remarkably improved. Besides, by combining the similarity matching optimization between the trigger and the sentence, the distinguishing capability of the model in a trigger classification task is remarkably improved, so that the distinguishing of a matched sample and an unmatched sample is more accurate.

The invention discloses a multi-dimensional large model test evaluation method and system, belongs to the technical field of artificial intelligence testing, integrates a basic model, platform capability and application performance to realize multi-dimensional large model test evaluation, and comprises the following steps: basic model capability evaluation, including sensitive lexicon + BERT semantic double-engine security detection and context dependence test chain; evaluating the capability of the model platform, including presetting LLM intelligent labels and double-person back-to-back data backflow; application performance evaluation, including character disturbance robustness test and LIME interpretability grading evaluation; large-model full-stack testing is achieved through a RESTful heterogeneous model nanotube interface, and localized compatibility adaptation verification is achieved based on GPU attenuation rate quantification. The method solves the problem of lack of evaluation of platform engineering capability, safety compliance and localization support in the prior art, realizes three-dimensional capability coupling evaluation, solves the problem of localization adaptation, and fills the blank of a large-model full-stack test technology.

The invention provides an IPS log deduplication merging method based on machine learning. The IPS log deduplication merging method comprises the following steps of A, data collection and preparation; b, data feature processing; c, dividing a data set; d, selecting a classification model; e, model training; f, performing model evaluation; g, testing and deploying the model; h, de-duplicating and combining the logs; according to the IPS log deduplication and combination method, the model is fitted through the machine learning technology, and the accuracy can be optimized through continuous iteration; manual log deduplication and combination work can be greatly reduced, and the labor cost is reduced; according to the method, the data collected through real attacks has better universality, and meanwhile, the data can also be used for research and application of other projects in the later period; the model provided by the invention has multiple labels, the conditions of merging and de-duplication can be changed as required, and the de-duplication and merging efficiency of logs is improved.

The invention relates to the technical field of risk management, and discloses a financial AI large model-oriented multi-dimensional test intelligent risk management and control method, which comprises the steps of integrating channel test, data test, AI test and AI large model test by constructing a financial AI large model test special database; traditional financial sensitive fields and AI test specific sensitive fields are accurately identified based on data consanguinity analysis, and dynamic desensitization is realized in combination with a lightweight desensitization engine; calling a preset risk scene library, defining a risk index system of each scene, dynamically calculating a risk level through a weighted scoring algorithm, and carrying out modeling by using a graph database to identify hidden linkage risks; triggering a grading response strategy according to the risk grade, and dynamically optimizing resource allocation; and after the test is finished, a multi-dimensional risk thermodynamic diagram and a root cause analysis report are generated, and processing experience is fed back to the scene library optimization strategy. According to the invention, the efficiency and safety of risk management and control can be improved.

The invention provides a target detection model robustness test method based on cross-task learning, features extracted by a semantic segmentation model are teacher features, features extracted by a target detection model are student features, the similarity between the teacher features and the student features is evaluated by using a query-key attention mechanism, and the robustness of the target detection model is tested. The optimization process of stabilizing the gradient by adopting a historical gradient stabilization strategy is adopted, the conflict gradient direction is adjusted through conflict gradient projection, better gradient compatibility is obtained in combination with alternate joint task element optimization, and the test performance is improved. According to the method, diversified gradient information with strong correlation required by the target detection task is distilled from the semantic segmentation task, so that the target detection model can learn more universal and robust characteristics, the gradient direction is further stabilized and optimized, and the performance of the model robustness test is improved.

The invention relates to a large model test method and device, computer equipment, a readable storage medium and a program product, and the method comprises the steps: responding to a test instruction, and reading at least one corresponding test task from a task queue; segmenting and / or merging the at least one test task to obtain a processed test task, and obtaining access information of test data corresponding to the processed test task; sending the processed test task and the access information to a computing node of a large model corresponding to the test instruction; and the computing node is used for acquiring test data according to the access information so as to execute the test of the processed test task. By adopting the method, the test efficiency and accuracy of the large model can be improved.

The invention discloses a ceramic firing performance prediction method based on a GA-BP neural network, and the method comprises the steps: data collection and preprocessing, gray correlation analysis and screening of key process parameters, construction of a GA-BP neural network model, model training and verification, prediction model testing, and firing performance index prediction. By means of unique intelligent algorithm fusion, the problems of high experience dependence, high experiment cost, long period, low efficiency, time consumption, energy consumption, high efficiency and the like of a method for judging the ceramic firing performance through artificial experience in actual ceramic firing production are effectively solved. The technical problem that multi-target collaborative optimization is difficult to realize at the same time in the prior art is solved, remarkable advantages are shown in the aspects of prediction precision, intelligent degree and the like, the requirements of intelligent, green and high-quality ceramic firing process optimization are met, and the method has wide application prospects and important practical value.

The invention discloses a solar power generation power prediction method based on improved elastic network regression, and belongs to the field of solar power generation system output prediction. The problem that an existing solar power generation potential prediction method is poor in accuracy is solved. The method comprises the following steps: collecting meteorological influence factor data and network access power real-time power data of a place where a target solar power generation system is located, establishing an output-meteorological characteristic data set, determining a tradeoff coefficient r and a regularization strength parameter lambda by using a grid search algorithm in combination with k-fold cross validation, and constructing an elastic network regression model; and training the regression model, optimizing a trade-off coefficient r and a regularization strength parameter lambda of the elastic network regression model by using a TPE optimization algorithm to obtain a solar power generation prediction model, testing the power generation prediction model by using a test set, and finally performing power generation power prediction by using the tested power generation prediction model. The method is suitable for solar power generation power prediction.

This disclosure describes a policy consistency system that determines when RBAC policies are inconsistently implemented across different devices, platforms, services, and environments. For example, the policy consistency system uses a model-based testing framework to identify cases where user requirement inputs produce different outputs when the policy is implemented in different environments. In some implementations, the policy consistency system utilizes symbolic abstraction models and satisfiability solver models to efficiently identify inconsistencies in the implementation of a policy.

The invention relates to the technical field of embedded artificial intelligence, and discloses an arc discharge detection model training method and system based on quantitative perception training, and the method comprises the steps: constructing an initial floating point precision neural network model for DC arc discharge fault detection, and defining a plurality of quantization functions for the model to convert floating point parameters; a FakeQuant pseudo quantization strategy is self-defined, a linear calculation layer in an original model is replaced with a quantifiable calculation layer, and a quantization operator is created through the quantifiable calculation layer; performing layer-by-layer differentiation quantitative perception training on the replaced model based on a training data set, and simulating a forward calculation behavior of a low-precision fixed-point type in actual reasoning while keeping floating-point precision back propagation through a quantization operator, so that the weight of the model is adaptive to a quantization error; and converting the trained model parameters into a target low-precision fixed-point type, and generating an end-side deployable arc discharge detection model through model testing and computing power evaluation, thereby realizing high-fidelity conversion from a floating-point model to a low-precision fixed-point model.

The invention relates to the technical field of medical science, in particular to a framework model based on neural network learning and an application method of the framework model. According to the model, a graph convolutional network (GCN, Transform architecture and long short-term memory network (LSTM) are fused, a multi-level analysis process is constructed, the spatial structure and sequence dynamics of drug molecules are deeply explored, and the model comprises the following specific steps: model training: performing model training by adopting data sets collected by people such as Cui, Q.Cui2020Improved and the like; the model test selects three independent test sets to comprehensively evaluate the performance of the model; according to the data preprocessing, an RDKit library is used, SMILES character strings are converted into molecular objects, a feature tensor is constructed and used for storing core features of each molecule in detail, data splitting is carried out, and the data splitting adopts versions, such as Ahmad, divided by people based on a Cui2020 data set to carry out training and testing of a machine learning model.