365 results about "Validation methods" patented technology

The invention discloses a verification method and system of a chip simulation model, a medium and a program product, and relates to the technical field of chip verification, and the method comprises the steps: obtaining a simulation model and a performance parameter index of a to-be-verified chip; generating model training data based on the historical performance indexes, the corresponding historical use case set and the historical verification result, and performing model training based on the model training data to obtain a use case generation model; inputting the performance parameter indexes to a case generation model to obtain a test case set and a test verification expectation; executing a plurality of test cases in the test case set based on the simulation model, and recording an actual response result of the simulation model; based on the actual response result and the test verification expectation, generating a test log, and determining an abnormal mark in the test log; and marking the fault features corresponding to the exception, taking the test cases as newly added training data, and optimizing the case generation model based on the newly added training data. According to the invention, the chip verification efficiency can be improved.

The invention belongs to the technical field of cross-data island consensus, and particularly relates to a cross-data island consensus verification method and system based on credibility scoring. According to the invention, by dynamically evaluating the trust degree of each data island and combining network state fluctuation, real-time verification and abnormal early warning of a consensus result are realized, the stability and safety of cross-data island cooperation are effectively improved, the data consistency is ensured, the data risk caused by network fluctuation is reduced, the self-adaptive capability of the system is enhanced, and the method is suitable for large-scale popularization and application. In the consensus process, historical interaction events and a current cooperation state of a data island are considered, efficient alignment of cross-island features is achieved through a gradient exchange mechanism, a standardized consensus data view is generated, in addition, self-adaptive adjustment of a consensus verification rule is ensured by pre-deploying a trusted execution environment, and the consensus verification efficiency is improved. And the consensus accuracy and efficiency are further improved.

The invention discloses a control method and system of automation equipment for industrial production, and particularly relates to the technical field of automation equipment control. The method comprises the following steps: extracting diversified samples of normal values, abnormal values and hidden defects from an existing data warehouse, labeling categories, and constructing a high-quality initial data set; evaluating the stability of the model by using a layered K-fold cross validation method, and testing and evaluating the consistency of the abnormal response capability and the historical mode through a high-load condition when the model is unstable; comprehensively analyzing the stability and the abnormal response capability of the model, and evaluating the capability of the AI for identifying error modes in historical data; for inaccurate identification, the dynamic time warping algorithm is used for detecting the deviation degree of the equipment operation parameters, an alarm is triggered in time, and the production process is paused to guarantee the safety of the equipment, so that the reliability and accuracy of the AI model in a dynamic environment are remarkably improved, and meanwhile, the risks of equipment damage and production safety accidents caused by optimization errors are effectively reduced.

The invention relates to a verification method and device of a DMA controller, computer equipment, a readable storage medium and a program product. The method comprises the following steps: in response to a starting instruction of a verification script, extracting a test case of a to-be-verified DMA controller based on a pre-constructed verification environment, sending the test case to the to-be-verified DMA controller and a corresponding reference model according to a target path of the test case, and obtaining a first running result of running the test case by the to-be-verified DMA controller, and obtaining a second running result of running the test case by the reference model, comparing the first running result with the second running result, and generating a verification result of the DMA controller to be verified. The test case is automatically generated, so that the workload of manual writing is reduced, and the efficiency and quality of the verification process are remarkably improved.

The invention relates to an airplane airborne BIT system testability verification method based on a correlation model, and the method comprises the steps: carrying out the testability modeling of an object system, and obtaining a dependency relation model which describes the relation between a fault mode of the object system and a test method of the object system; based on the dependency relationship model, obtaining a BIT theoretical fault report set of each fault mode under all test methods by utilizing reachability analysis, and deducing an optimal diagnosis fault fuzzy group; constructing a fault sample library, verifying the BIT theoretical fault report set by using samples in the library, and adjusting the dependency relationship model according to a verification result; and performing fault simulation based on the verified dependency matrix, and calculating a total fault detection rate and a fault isolation rate of the object system according to a generated fault report. According to the method, the isolation rate index of the fault can be effectively verified in the incomplete and unformed stage of the fault diagnosis strategy.

The embodiment of the invention provides a core grain verification method and related equipment. The core grain verification method comprises the following steps: acquiring data path modules of a plurality of core grains in a to-be-tested design and module verification environments corresponding to the data path modules; the data path modules of the plurality of core particles are in heterogeneous design; the module verification environment is used for indicating a verification environment corresponding to the data path module; compiling the obtained data path module of each core particle into different file libraries and connecting the file libraries to a top layer module of the to-be-tested design; combining the obtained module verification environments corresponding to the data path modules of the core particles into a top layer verification environment; and based on the top layer verification excitation of the top layer verification environment, executing verification of data path modules of a plurality of core particles in a to-be-tested design. According to the scheme, the test accuracy of the test scene of the cross-heterogeneous data path module can be ensured.

The invention discloses an AI auxiliary test case generation and verification method and a terminal, and the method comprises the steps: obtaining and analyzing game commodity data, filling a script template with the analyzed game commodity data, verifying and adjusting the script template through a trained AI model, recognizing a test scene according to the adjusted script template, and carrying out the verification of the test scene. A corresponding test case is generated; and executing the test case, verifying the execution result of the test case through database query and log analysis, and generating a test report, so that the method can adapt to tests of different scenes and requirements, and the reliability and execution efficiency of the test are improved.

The invention discloses a formal verification method based on a hierarchical verification framework, which comprises the following steps of: firstly, dividing a target program into a C code and an assembly code, modeling and verifying the assembly code, then modeling and verifying the C code, and in the modeling process of the C code, carrying out verification on the C code; and defining a specific state and an abstract state of each data structure, establishing mapping through a refinement relationship, defining a function model based on the abstract state, constructing a function API specification, and finally defining system properties for a target program according to the function model and verifying the system properties. According to the method, a hierarchical verification framework and a formalization tool are combined, formalization verification is carried out on the target program from specific implementation to abstract division into three layers, comprehensive verification from bottom-layer codes to high-layer system properties is realized, the risk of security vulnerabilities is reduced, and the reliability of the target program is improved.

The embodiment of the invention discloses a data verification method and device, electronic equipment, a medium and a program product. The method comprises the following steps: determining a starting date and an ending date of a corresponding target event in a current production environment according to preset event type information, and obtaining target request data corresponding to the event type information contained from the starting date to the ending date; acquiring a corresponding actual result of the target event in the production environment, and acquiring first field information corresponding to the starting date; comparing the latest date with the starting date to determine initial state data corresponding to the starting date; returning the initial state data and the target request data to a preset simulation environment to generate a simulation result; and generating a verification result of the to-be-verified data according to the first environment parameter of the production environment, the second environment parameter of the simulation environment, the actual result and the simulation result.

The invention discloses a register model and register transfer level generation and test method, device, equipment and medium, and relates to the technical field of integrated circuit design verification, and the method comprises the following steps: analyzing a comma separation value table file by using a register generation platform to generate a register model, a register transfer level and a register test vector; the comma separation value table file is a file used for describing register information; generating a general verification methodology environment by using a test environment generation platform, and integrating the general verification methodology environment based on the register model, the register transfer level and the register test vector to obtain a corresponding integrated test environment; and compiling the integrated test environment, if the compiling is successful, running the register test vector in the integrated test environment which is successfully compiled, and outputting a corresponding test result. Therefore, the automation degree of register model verification can be improved, and the verification efficiency of front-end verification is improved.

The invention provides a chip verification method, device and system. The method comprises the following steps: acquiring a configuration file of a current test scene; a constraint strategy involved in the current test scene is configured in the configuration file according to a set data structure; the constraint strategy is a predefined constraint condition corresponding to the test requirement; analyzing the configuration file to obtain an analysis result; generating a hardware programming language simulation data structure corresponding to the chip verification case based on the analysis result; and outputting the hardware programming language simulation data structure to a chip verification platform so as to perform chip function verification in a chip simulation environment. According to the automatic generation and configuration method of the test case in the technical scheme of the invention, the user friendliness of the verification environment is remarkably improved, so that a verification team is assisted to quickly master and efficiently utilize the verification environment.

The invention provides a power-down continuous test data consistency verification method and device, equipment and a medium. The method comprises the following steps: issuing a query instruction to obtain disk information of a currently connected SSD (Solid State Disk); according to the disk information, judging whether to issue a continuous test request; if a continuous test request is issued, returning a processing result according to the disk information and the continuous test request through a preset processing rule; selecting a continuous test request corresponding to a successful result as a verification request according to the processing result; and performing consistency verification on the disk information according to the verification request. By systematically executing the steps of disk information acquisition, continuous test request decision, request filtering and screening, final consistency comparison and the like, an automatic closed-loop test process is constructed, and the problem that the consistency of SSD power failure continuous test data cannot be verified by various IO tools at present is solved; and consistency verification can be carried out on the successfully written data before and after power failure.

The invention relates to the technical field of electronic design, and discloses a bit stream verification method and device, equipment, a storage medium and a program product.The bit stream verification method comprises the steps that an execution file corresponding to a target test case is executed, and a bit stream file and a routing file of the target test case are obtained; determining configuration information of the target test case based on the bit stream file and the routing file, and configuring a test file of the target test case based on the configuration information; based on the routing file, configuring an input signal and a reference model corresponding to the target test case; and based on the input signal, respectively testing the test file and the reference model, and determining a bit stream verification result of the target test case. Thus, simulation verification of the bit stream file of the target integrated circuit is deconstructed, simulation verification of the bit stream file is performed on each test unit in the target integrated circuit, simulation verification of the bit stream file is realized through the reference model, simulation of the bit stream file is simplified, simulation efficiency of the bit stream file is further improved, and simulation time is shortened.

The invention discloses a machine learning-based kernel data verification method, which comprises the following steps of: firstly, obtaining simulated effective proliferation factors and actually measured effective proliferation factors of a plurality of kernel experiment references, and calculating to obtain a difference value; 2, calculating nuclear data sensitivity characteristics of each experimental reference, wherein the nuclear data sensitivity characteristics comprise a sensitivity coefficient for a nuclear reaction cross section and experimental metadata; thirdly, constructing a machine learning model based on a random forest, and performing training by taking the kernel data sensitivity characteristics as input and the effective multiplication factor difference value as output; and finally, carrying out model feature importance analysis, and identifying kernel data sensitivity features which have significant influence on effective proliferation factor differences. The nuclear data verification method based on machine learning is established for the first time, and is used for identifying potential defects in nuclear reaction data or deviation of experimental references.

The invention relates to the technical field of artificial intelligence, and discloses an artificial intelligence algorithm model accuracy verification test method comprising the following steps: S1, data set preparation: collecting diversified data sets to cover more scenes and conditions; s2, whether cross validation is carried out or not is judged, if yes, a cross validation method is adopted, a data set is divided into a plurality of mutually exclusive subsets, a part of the subsets are cyclically used as a test set, the rest of the subsets are used as a training set for multiple times of verification and testing so as to evaluate the performance and stability of the model, and if not, the next step is carried out; and S3, selecting a proper evaluation index, and if cross validation is not selected, selecting a proper evaluation index. According to the method, the accuracy of the artificial intelligence algorithm model can be comprehensively and systematically evaluated, developers are helped to accurately understand the performance of the model in different data sets and scenes, and the model with better performance can be screened out by comparing the performance of different models in the same test set.

To provide a verification apparatus, a verification method, and a verification program that are capable of verifying contents of sentences such as outputs of large language models, which superficially appear to be natural but include false contents, and performing appropriate processing.SOLUTION: A verification apparatus 64 includes: a text selection unit 132 that extracts a verification target portion from an inputted text; a support network creation unit 134 and a contradiction network creation unit 136 that are for collecting, from a set of existing texts, a support text that supports contents of the verification target portion and a contradiction text that contradicts the contents of the verification target portion; and a text editing device 108 that selectively executes, on the basis of whether or not a prescribed relationship is established between the support text and the contradiction text that are collected, processing for maintaining the verification target portion and processing for editing the verification target portion.SELECTED DRAWING: Figure 1

The embodiment of the invention provides a verification method and system for system degradation, equipment and a medium. A specific embodiment of the method comprises: in response to a degradation verification request of a user for a target system, starting a server to execute a corresponding target operation for the target system, and triggering a target event corresponding to the degradation verification request and for a verification server, event parameters comprising a test scene of the verification; in response to the target event, the verification server sends an event type and a test scene to the configuration server; the configuration server calls a test server to execute a function test task for the target system according to the event type and the test scene; the test server sends the execution result to a verification server for determining a verification result for the degraded verification request. By means of the method, the efficiency of degradation verification and the verification frequency in a specific verification period can be greatly improved, and then the capacity of discovering degradation faults of the system through degradation verification is improved.

The present application relates to an ownership verification method and processing method for a structured data set, a device, and a medium. The ownership verification method comprises: acquiring a structured data set; from a target object to be verified, acquiring secret information corresponding to the structured data set, a proportional label of watermark data, and specific mathematical properties corresponding to the watermark data, wherein by means of a preset rule, the probability that a verification value calculated from any data satisfying a predetermined data format and the secret information meets a preset mathematical feature is smaller than a first threshold, and the proportional label is greater than the first threshold; on the basis of the secret information and the specific mathematical properties, identifying the watermark data from the structured data set; and collecting statistics about a proportional result of the watermark data in the structured data set, and determining the ownership relationship between the target object and the structured data set on the basis of the proportional result and the proportional label.

The invention belongs to the technical field of power system safety analysis and control, and particularly relates to a data-driven power system transient stability evaluation method with interpretability, in the aspect of evaluation result interpretation, an SHAP (Shapley additive interpretation) method and an ALE (cumulative local effect) analysis are comprehensively applied, the SHAP method calculates marginal contribution of each feature based on the game theory, and the ALE method calculates the marginal contribution of each feature based on the game theory. And the nonlinear relation between the characteristic value and the model output is quantized. In addition, a local agent model (LIME) is adopted to train an interpretable model in a sample neighborhood to approximate black box model behaviors. In order to ensure the reliability of an explanation result, verification methods such as a characteristic sensitivity test, an anti-fact prevention and control experiment and a migration scene robustness test are adopted, and the credibility of an explanation conclusion is systematically verified in manners of disturbing key characteristics, adjusting operating parameters, constructing different working conditions and the like.

The invention discloses a workflow analysis linkage verification method and device based on log analysis, equipment and a medium, and relates to the technical field of workflow verification, and the method comprises the following steps: collecting operation log data, and carrying out time sequence reconstruction and event extraction to obtain a structured log record; performing semantic analysis on the structured log record, extracting a log semantic feature vector of each event, and constructing a semantic feature matrix; based on the semantic feature matrix, performing semantic modeling on nodes in a preset process model to obtain a process node semantic vector; matching the log semantic feature vector with the flow node semantic vector, and constructing and optimizing an initial mapping relation matrix according to a matching result; executing flow rule compliance and rationality joint verification according to an optimization result; according to the method, deep coupling of the log, the process model and the business rule is realized, so that the problems that log analysis and rule coupling are insufficient and process compliance and rationality verification are difficult to accurately carry out are solved.

The invention relates to a cross validation method and device for BMC and BIOS adaptability of a server and a storage medium, and the method comprises the steps: firstly, constructing a firmware resource library, and uploading m versions of BMCs and n versions of BIOSs to the firmware resource library; the server obtains the combination of the BMC and the BIOS of the first version from a firmware resource library and executes updating operation on the BMC and the BIOS of the first version; verifying the version information of the BIOS of the first updated version, obtaining hardware information of the server, performing verification processing, verifying the performance of the web server of the BMC of the first updated version so as to verify the suitability of the BMC of the first updated version and the BIOS, and recording a first adaptation result; traversing m * (n-1) versions of BMC and BIOS combinations stored in the firmware resource library, performing updating operation on the m * (n-1) versions of BMC and BIOS, verifying the suitability of each BMC and BIOS combination, and recording m * (n-1) adaptation results; and forming a BMC and BIOS version compatibility list according to the first adaptation result and m * n-1 adaptation results. The efficiency and accuracy of the BMC and BIOS suitability verification method are improved.

The invention relates to the technical field of data storage, and discloses a method for verifying average fault-free time of a solid state disk, which comprises the following steps of: initializing a test environment of the solid state disk; a dynamic comprehensive stress model is constructed, acceleration factors are calculated based on self-adaptive parameters of reliability, in the testing process, the acceleration factors are added, different read-write load combinations are circularly switched according to set time slices, and testing of the solid state disk is accelerated; judging whether the test reaches a preset duration or a trigger fault condition; if yes, analyzing solid state disk fault diagnosis and data analysis, and if not, returning to continue accelerated testing of the solid state disk. According to the method, the dynamic comprehensive stress model is constructed, and the temperature stress, the voltage stress, the read-write load stress and other multi-dimensional stress are combined, so that the limitation that only single static high-temperature stress is applied in a traditional test method is broken through, comprehensive stress test on the solid state disk is realized, and the test comprehensiveness and accuracy are improved.

The invention provides a product full life cycle-oriented demand verification management system and method, and the system comprises a verification planning module which obtains a demand item, and obtains a product verification stage, a verification method and a verification task according to the demand item; the verification execution module is used for constructing an incidence relation among the requirement item, the verification method and the verification task, obtaining a verification deliverable associated with the verification task according to the verification task, obtaining a verification result according to a checking and signing condition of the verification task, updating a verification state of the verification task and forming a complete tracing chain; and the verification monitoring module is used for generating a demand verification view and a traceability chain view corresponding to the complete traceability chain according to the demand item, the verification state, the verification method, the verification task, the verification result and the verification deliverable. Through the technical scheme provided by the invention, the coverage rate, the traceability and the execution efficiency of demand verification are greatly improved, and the project quality risk is effectively reduced.

The invention belongs to the crossing field of network space security, software engineering and artificial intelligence, and discloses a dynamic and static cooperative vulnerability full-link intelligent analysis and verification method based on a large language model and an MCP protocol, and the method comprises the steps: S1, task initialization and context construction; s2, target code deep analysis and static taint preanalysis are carried out; s3, carrying out Fuzzing Harness directional generation and iterative optimization on the basis of the stain paths; s4, performing dynamic and static collaborative sandbox execution and dynamic stain tracking; and S5, vulnerability accessibility judgment and report output of multi-source data fusion are carried out. According to the method, a dynamic and static combination mechanism is adopted, vulnerability analysis and verification can be automatically completed for software target codes, and accurate technical support is provided for vulnerability risk grading and repairing priority ranking.

The invention provides an SoC chip verification automation system based on task flow. The SoC chip verification automation system comprises a task flow management module, an automatic evaluation and selection module, an automatic execution module, a real-time optimization adjustment module, a result analysis and report generation module and a verification method tool library. According to the invention, the automation degree of verification is improved, flow chaos and human errors are reduced, and the verification efficiency and reliability are improved; the pertinence and effect of verification are improved, and the coverage rate and effectiveness of verification are ensured; and the cost and the risk in the verification process are reduced, so that the SoC verification method has important application and advantages in the SoC verification field.

The invention provides an expansion sample verification method and device based on a neural network, equipment and a medium, and relates to the technical field of neural networks. The method comprises the following steps: evaluating a plurality of trained first layered feed-forward neural networks to obtain a first evaluation result, and determining an optimal first layered feed-forward neural network; utilizing the optimal first layered feedforward neural network to predict the extended input parameters to form extended experimental data, and combining the extended experimental data with the training sample set to form a synthetic sample set; training a plurality of second layered feedforward neural networks based on the synthetic sample set; and evaluating the plurality of trained second layered feedforward neural networks to obtain a second evaluation result, and determining the validity of the extended experimental data based on the second evaluation result and the first evaluation result. Based on the content, the problem of low reliability of verification of the expanded sample in the prior art can be improved.

The invention discloses a dynamic self-verification method based on service flow-test flow homologous heterogeneous execution. The method comprises the following steps: uniformly describing service logic and a corresponding interface test contract through a strong type of domain specific language (DSL); the method comprises the following steps: simultaneously compiling homologous DSL descriptions into stateful business workflows executed by a process engine and stateless and combinable atomic test contract sets called by a test executor by utilizing a double-path compiling engine; during operation, loose coupling synchronous triggering and execution of a service flow and a test flow are realized based on a high-throughput event bus, and real-time and high-fidelity contract verification, performance monitoring and chaos probe testing are performed on cross-system interface calling in a production environment. According to the invention, the traditional post-event and off-line integration test is converted into the in-event, on-line and continuous system endogenous capability, and the industrial problems that the complex enterprise system group integration test is difficult and the operation and maintenance monitoring is passive are effectively solved.

The invention relates to a data set generation and answer verification method and device and a medium, and the data set generation method comprises the following steps: obtaining a question, a standard answer and a pre-selection large model, and generating a large language model reply by using the pre-selection large model; based on the questions, the standard answers and the large language model replies, an initial data set is constructed through multi-stage data screening and labeling, and the initial data set comprises the questions, the standard answers, the large language model replies and judgment results; and performing data enhancement on the initial data set in combination with multiple technology enhancement of complex formula equivalent generation, error pattern adversarial training and cross-domain prompt disturbance to generate a final data set. Compared with the prior art, the method has the advantages that more robust and high-accuracy answer verification in multiple fields can be realized, and the model confrontation capability is high.

The invention discloses a mechanical part reliability index verification method under virtual-real fusion, and belongs to the technical field of reliability engineering. Aiming at the problem that the test period is long when the reliability index of a mechanical part is tested through a physical experiment in the prior art, a test bed is built for the highest-risk failure mode of the mechanical part to carry out the physical experiment, and the damage value monitoring quantity in the highest-risk failure mode is measured; establishing a preliminary damage model under the highest risk failure mode of the mechanical part; then establishing a damage simulation model, and continuously updating unknown parameter random variables in the damage simulation model according to physical test data and Bayesian inference to obtain a parameterized damage simulation model; and finally, comparing and verifying the parameterized damage simulation model according to the reliability index of the mechanical part in the highest risk failure mode. According to the method, samples required for reliability index verification of the mechanical parts can be effectively reduced, the test cost is reduced, and the test period is shortened.

The invention discloses a characterization optimization and soft label construction method for a drug-target interaction prediction model, and the method comprises the following steps: (1) constructing a molecular characterization space based on a Molformer pre-training model, carrying out the feature space similarity analysis of a negative sample through employing a K-Means clustering algorithm, and eliminating redundant samples through setting a dynamic threshold value, thereby achieving the equalization of positive and negative samples; (2) designing a soft label optimization mechanism based on a logarithmic function, converting a pKd value in a DAVIS data set into soft labels continuously distributed in a (0, 1) interval, and reserving binding strength gradient information to enhance the sensitivity of the model to a weak binding compound; and (3) integrating the above data enhancement strategies, and training a drug target interaction prediction model through a five-fold cross validation method to improve the model prediction performance. The method verifies the significant advantages of the method in relieving the problems of data offset and information loss, and also provides a high-robustness data processing framework for virtual drug screening of deep learning.