199 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.

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 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.

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 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 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 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 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 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.

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 discloses a multi-period non-stationary hydrological variable valuation method, and relates to the technical field of hydrological prediction. Comprising the steps of obtaining hydrological variables of a plurality of observation stations in a drainage basin at different time points as hydrological time sequences of the observation stations; constructing an experimental variation function according to the hydrological time sequence of each observation station; selecting a drift model through a trend surface analysis method and model inspection; constructing a multi-period non-stationary variable equation set comprising a Lagrange multiplier and a hydrological variable weight based on the experimental variation function and the drift model; expressing the multi-period non-stationary variable equation set as a matrix form, and calculating a Lagrange multiplier and a hydrological variable weight through a matrix inversion method; and constructing an estimation formula according to the calculation result of the Lagrange multiplier and the hydrological variable weight, and performing multi-period spatial estimation on the hydrological variable of the to-be-measured drainage basin through the estimation formula. Dependence on the number of observation stations can be reduced, and accurate estimation can be achieved only through a small amount of observation station data.

Provided in the embodiments of the present specification are a task processing method and apparatus, a question answering processing method and apparatus in a target domain, a domain task model test method and apparatus, and a computing device, a computer-readable storage medium and a computer program product. The task processing method comprises: acquiring task data of a target task, wherein the target task comprises at least one task stage, and the task stage comprises a thinking sub-task and a decision-making sub-task; inputting the task data into a domain task model, and executing the thinking sub-task, so as to obtain a target thinking result of the domain task model with respect to the task data; and inputting the target thinking result into the domain task model, and executing the decision-making sub-task, so as to obtain a target task result corresponding to the target task, wherein the domain task model is obtained by means of performing training on the basis of a test result of the thinking sub-task and a test result of the decision-making sub-task. By means of step-by-step interaction with a model, the thinking analysis capability and the task processing capability of the model with respect to a target task can be improved on the basis of a chain of thought of the model, thereby improving the accuracy of a target task result.

The invention discloses a privacy protection and robustness test method and system for large model fine tuning, and belongs to the technical field of machine learning security. The method comprises the steps that a three-layer distributed architecture comprising an edge server, a cloud server and a plurality of edge clients is constructed, the edge clients distill local privacy data and cooperate with the edge server to train a global model, and a candidate detection sample set is formed; screening a sample set based on the potential feature deviation evaluation index, and sending the sample set to a cloud server for vulnerability detection to obtain an optimal backdoor detection candidate sample set; and multi-trigger parallel and progressive trigger sequence backdoor implantation is respectively used for scenes of single fine tuning and multiple fine tuning of the large model, an optimal backdoor detection candidate sample set is combined with a preset trigger to generate a backdoor test sample set, the backdoor test sample set is mixed with a clean data set, and then the robustness of the backdoor test sample set is tested through fine tuning of the large model. Large model fine tuning and robustness testing of privacy protection can be realized in a heterogeneous model cooperative training environment.

The invention discloses a model training method and device, and the method comprises the steps: judging whether a target training model meets a model test condition or not according to a model test plan and the current training state of the target training model; if yes, copying the target training model to obtain at least one target test model; and performing model training on the target training model, performing asynchronous and parallel model testing on the target testing model, and judging whether the model training of the target training model is completed or not according to a model testing result. The resource utilization rate can be improved, and the model training efficiency is improved.

The invention relates to an air conditioner load prediction method based on a self-adaptive double-flow graph attention network, and belongs to the technical field of building energy conservation and intelligent control. The method comprises the following steps: collecting historical power and environmental data of an air conditioner, and after preprocessing and normalization, constructing an input sequence through a sliding window and dividing a data set according to time; a prediction model is constructed, and a causal graph learning module, a multi-scale graph structure learning module, a self-adaptive space-time attention module, an uncertainty quantization module and a self-adaptive sampling module are integrated; a training set and a joint loss function training model are adopted, and a load prediction result and uncertainty estimation are output through Monte Carlo Dropout during testing. According to the method, the dynamic causal relationship between variables and multi-scale space-time dependence can be adaptively learned, reliable uncertainty quantification is provided while the prediction precision is improved, and the method is suitable for intelligent regulation and control and energy efficiency optimization of the air conditioning system.

The invention relates to the field of mechanical fault identification, and discloses a rotating machine fault diagnosis method and system based on a multi-granularity knowledge supervision comparison optimization strategy, and the method comprises the steps: obtaining an original vibration signal, carrying out the window interception, and dividing a training data set and a test data set; performing normalization and data enhancement on each sample in the training data set twice to obtain a data enhancement sample for model training; samples in the test data set are only subjected to normalization processing and are used for model testing; building a granularity knowledge migration supervision comparison model; pre-training a feature extractor; training a granularity knowledge migration supervision comparison model by adopting the data enhancement sample according to a given granularity knowledge migration classification training step, a classification loss function and a back propagation optimization algorithm to obtain trained model parameters; and inputting a test sample into the trained granularity knowledge migration supervision comparison model to obtain a multi-fault classification and diagnosis result.

The embodiment of the invention discloses an automatic test method and system for an intelligent cockpit large model, and the method comprises the steps: uploading a to-be-tested case document, and generating a test input signal according to the to-be-tested case document; receiving the test input signal, executing a test task for the test input signal, and generating a test response result; collecting a test response result, wherein the test response result comprises a voice recognition result, a human-computer interaction interface screenshot and a system operation log; performing semantic comparison and scoring on the test response result through a judgment large model, and outputting a related index corresponding to the test response result; and generating a test result evaluation report according to the related indexes corresponding to the test response result, and outputting a typical error case list according to the test cases which do not reach the standard in the test result evaluation report. According to the method, the accuracy, the efficiency and the reproducibility of the intelligent cabin model test can be improved.

The invention relates to a test time training method based on invariant graph learning, and the method comprises the following steps: 1, a joint training stage, a joint training encoder fg, a main task classification head pi m and self-supervision task classification heads pi s1 and pi s2 are trained through a graph classification task and an auxiliary self-supervision task by using marked training set data, and the joint training stage is divided into two parts including invariant graph recognition and multi-level graph comparison learning; step 2, in a test time training stage, realizing self-adaption of the model on test data by minimizing the difference of feature distribution of a training domain and a test domain; and step 3, a test stage: in a model test stage, carrying out specific processes and strategies of model evaluation and fine adjustment by using a test sample. According to the scheme, through a test time training method, the model is finely adjusted by using label-free test data distribution, and the generalization performance of the model in a distribution offset scene is improved.

A robustness measurement method for LeNet-5 networks based on adversarial boundary constraints is presented, relating to the field of deep learning model testing. The main steps include: for each training sample, dynamically generating adversarial examples based on the model's current state during training iterations; constructing a composite loss function based on standard cross-entropy loss and dynamic boundary constraint loss; performing end-to-end training on all parameters of the LeNet-5 network; and using the overall approximate robustness boundary as a measure of model robustness after training. This method effectively improves the resistance of the LeNet-5 model to fast gradient sign-based adversarial attacks without altering the basic structure of the LeNet-5 network by designing a new loss function.

To provide a test precondition data derivation apparatus capable of easily deriving test precondition data corresponding to a test case.SOLUTION: Teacher data storage means stores teacher data used when generating, by machine learning, a model for deriving test precondition data that is either an SQL statement for registering data in a database of a system under test before execution of a test, or a file to be stored in a predetermined folder of the system under test before execution of the test, or both. Machine learning means generates a model for deriving the test precondition data by machine learning using the teacher data. Test precondition data derivation means derives test precondition data corresponding to a test case by applying the input test case to the model.SELECTED DRAWING: Figure 4

An application or software tool is used to test a device model with a physical device. The tool runs a test on the physical device via the network connection using the input of the test data. The test data includes the inputs and corresponding output values of the device model. The tool compares an output value of the physical device generated by the test with an output value of the device model to generate a result of the test. The test results are displayed on a display of the computing device.

This invention relates to the field of natural gas development and discloses a method, apparatus, storage medium, and processor for predicting the production of bottom-water gas reservoirs. The method includes: training a model based on a training set of production data from the bottom-water gas reservoir to obtain a first long short-term memory (LSTM) neural network model; optimizing hyperparameters based on a validation set to obtain a second LTM neural network model and a corresponding first error; testing the model based on a test set to obtain a corresponding second error; determining the generalization degree based on the first and second errors, and using the second LTM neural network model with a generalization degree meeting preset requirements as the target bottom-water gas reservoir production prediction model; and predicting the production of the bottom-water gas reservoir based on the target bottom-water gas reservoir production prediction model. This method captures long-term dependencies in time-series data using a LTM neural network, which can significantly improve the accuracy of bottom-water gas reservoir production prediction, thereby providing reliable support for oil and gas exploration and production decisions.

The invention discloses a closed data space model test self-adaption method based on double-stage feature whitening, and belongs to the field of model self-adaption, and the method comprises the steps: inputting each enhanced view in an enhanced data set composed of a first enhanced view and a second enhanced view into a data processing model, obtaining an intermediate layer activation feature and an output result respectively corresponding to each enhanced view; determining a first whitening feature corresponding to each intermediate layer activation feature, and determining a second whitening feature corresponding to each first whitening feature after projection mapping based on all the first whitening features; and performing parameter updating on a target data layer in the data processing model based on joint model loss constructed by the output result and the second whitening feature. According to the method, a two-stage feature whitening mechanism is introduced, so that the model can obtain better adaptive ability in a closed data space, and the generalization performance and reasoning stability of the model in a complex test environment are improved.

The invention relates to a model detection method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: according to a first preset model, a to-be-processed data set and a first preset data set, updating an initial weight parameter of the first preset model to obtain a target weight parameter; then, according to the target weight parameter, a second preset data set and a first preset model, gradient information of the second preset data set is determined; and then, according to the gradient information, updating the to-be-processed data set to obtain a target data set. And further, training a second preset model according to the target data set to obtain a test model for attack test. Wherein the first preset data set does not comprise the abnormal training data, and the second preset data set comprises the abnormal training data. The method is used for testing the safety and robustness of the model.

The invention belongs to the technical field of intelligent medical big data disease risk prediction, and provides a VTE risk prediction method and system based on gradient increasing, and the method comprises the following steps: collecting a sample feature data group through a hospital system; preprocessing the collected sample feature data; performing correlation analysis on the processed sample feature data; calculating a risk threshold value of the numerical type sample features; establishing a machine learning model by adopting a gradient increasing algorithm; testing a model prediction effect; and performing VTE risk prediction by using a gradient increasing model. According to the method, feature data related to VTE diseases are doubly screened by combining feature importance ranking of the Pearson's correlation coefficient and the gradient increasing model, and linear and nonlinear correlation features are captured at the same time; the problems of prediction deviation and insufficient interpretability caused by one-sided feature screening and separation of threshold setting from diagnosis and treatment practice of an existing machine learning model are solved.

This invention provides a method for automatically generating test cases based on SysML state machine diagrams, comprising the following steps: S1, SysML state machine diagram parsing; S2, model conversion, converting the SysML state machine diagram into an intermediate model that is easy to analyze; S3, test path generation; S4, test data and test case generation; S5, test document generation. This invention also provides a readable storage medium. The beneficial effects of this invention are: it provides a method for automatically generating test cases based on SysML state machine diagrams, which can discover potential problems as early as possible in the development process, improve testing efficiency, reduce testing time and cost, and better meet the sufficiency requirements of testing.

The present invention proposes a hypergravity environment simulator using a lifting device and a test method thereof, belonging to the technical field of scaled model testing of submerged vehicles. The invention solves the problems of centrifugal technology's difficulty in simulating complex three-dimensional flows, restrictions on the size and shape of test pieces, and the introduction of additional rotational torque during hypergravity scaled model testing. The invention comprises a launch device, a vehicle, and a test device. The test device comprises a small-scale test device or a large-scale test device. The vehicle is disposed in a launch area. The small-scale test device achieves the lifting and lowering of a water tank by a linear motor and control system disposed in a slide elevator. The large-scale lifting device achieves the lifting and lowering of a water tank by a driving device disposed in a towing device, driving a lifting bracket. The invention is mainly used to simulate a hypergravity environment.

The utility model discloses a test device for simulating the influence of aircraft taxiing and landing on foundation settlement, which relates to the technical field of foundation treatment and model test and comprises a trolley, a telescopic smooth guide rail, a thrust device, a vertical telescopic smooth platform, a buffer device, a monitoring sensor, a model runway and a model soil layer. The device creatively integrates a runway model box to reproduce a runway surface structure layer and a pile-net composite foundation, a taxiing landing device is adopted to simulate the taxiing landing process of an aircraft, and braking is achieved through a buffer device. A drainage device is embedded in the device, the sliding trolley is arranged in a front three-point mode to simulate a main landing gear of an airplane, and a 3-degree gliding angle is kept to be close to the real sliding condition. The test device not only considers the transverse impact force of an aircraft, but also performs adaptive design for different tire configurations, and provides a valuable simulation and monitoring means for exploring the dynamic characteristics of aircraft taxiing-landing integration on the pile-net composite foundation.