450 results about "Test evaluation" patented technology

The invention discloses an automatic code generation and optimization system based on multiple modes, which relates to the technical field of automatic programming, and comprises a task analysis module for analyzing task description and constraint conditions in combination with a CLIPS rule engine and a knowledge graph and original data, identifying task targets and requirements, and outputting a task risk assessment report and a task intention set; the optimization decision module is used for selecting an optimal modal data subset by using a particle swarm optimization algorithm and a path planning algorithm, performing optimization adjustment according to task requirements, and outputting a code generation strategy; and the test evaluation module is used for evaluating and improving the unit test and the integration test by utilizing the variation test, carrying out quality and performance evaluation on the code through continuous integration and continuous delivery, and outputting a code evaluation report and an optimization suggestion. According to the method, the optimal modal data subset is dynamically screened, and the data transmission path is optimized, so that the code performance is ensured, the computing resource consumption is reduced, and the global optimization of the code generation strategy is realized.

The present invention relates to the technical field of electrical equipment monitoring, and in particular to an electrical equipment state monitoring and dynamic test evaluation system, comprising a data acquisition module, a data processing module, a feature extraction module, a dynamic test evaluation module and a state evaluation and prediction module; wherein: the data acquisition module is used for real-time acquisition of raw data during the operation of the electrical equipment; the data processing module is used for processing the raw data collected by the data acquisition module to generate a standardized data set; the feature extraction module is used for extracting relevant feature parameters reflecting fatigue damage accumulation from the raw data; the dynamic test evaluation module is used for simulating the fatigue damage evolution process of the electrical equipment under complex working conditions; the present invention, by real-time monitoring of fatigue damage accumulation of the electrical equipment, combines dynamic test evaluation with finite element simulation technology, accurately predicts the remaining life of the equipment and the time of occurrence of a fault, and improves the accuracy and early warning capability of the equipment health state evaluation.

The invention relates to the technical field of data processing, and discloses a power grid fault early warning system based on machine learning and a related device, and the system is characterized in that an initial monitoring module identifies and identifies abnormal data points through monitoring the voltage, current and load of a power grid, and obtains an abnormal mode identification result; the load fluctuation analysis module positions the abnormal node according to the abnormal mode identification result to obtain abnormal node positioning information; the trend analysis and prediction module evaluates and predicts fault nodes and time according to the abnormal node positioning information to obtain a risk prediction result; the strategy formulation and response module formulates an operation instruction based on the risk prediction result to obtain a fault prevention measure; the fault simulation and test module performs virtual fault simulation by adopting fault prevention measures, and performs iterative optimization on a strategy to obtain a test evaluation result; and the fault early warning and optimization module optimizes a fault prevention strategy according to a test evaluation result, and a more accurate target fault early warning and a faster and more effective target response strategy can be obtained.

The invention relates to an online multi-dimensional comprehensive test evaluation method for an automatic driving test. The method comprises the following steps: constructing a test scene library; constructing a test scene complexity model; building an intelligent driving joint simulation test platform, and embedding the intelligent driving algorithm into the intelligent driving joint simulation test platform; constructing an automatic driving test multi-dimensional evaluation index system for performing multi-dimensional performance evaluation on the automatic driving vehicle; an intelligent driving joint simulation test platform is utilized to obtain vehicle test data, an evaluation index system and a test scene complexity model are combined to calculate objective evaluation index weights, then comprehensive performance quantitative scores are calculated, and finally an automatic evaluation report is output and obtained. Compared with the prior art, the subjectivity of manual evaluation can be reduced, and the evaluation efficiency of the automatic driving vehicle is improved; comprehensive and objective quantitative scoring can be carried out on the multi-dimensional performance of the automatic driving vehicle, and the influence of different test scenes on the scoring difference of the automatic driving vehicle is reduced.

The invention relates to the technical field of electrical variable testing, in particular to a high-precision performance testing processing method and system for a low-impedance voltage transformer. The method comprises the following steps: constructing a controllable test excitation signal, and synchronously collecting a corresponding primary side voltage and a secondary side voltage of a mutual inductor; space electromagnetism elimination is carried out according to the primary side voltage and the secondary side voltage of the mutual inductor, and frequency domain characteristic decomposition and signal instantaneous offset calculation are carried out at the same time, so that a mutual inductor signal characteristic matrix is constructed; acquiring environment parameters corresponding to the low-impedance voltage transformer; constructing a neural network model based on Bayesian optimization, and predicting and outputting a performance parameter set corresponding to frequency response and load characteristics; and comparing the performance parameter set with a preset standard threshold value, feeding back, generating a calibration compensation coefficient, testing and evaluating, and generating a high-precision performance test report corresponding to the low-impedance voltage transformer. According to the invention, high-precision performance testing of the low-impedance voltage transformer can be realized.

The invention discloses an automatic driving test case generation method based on a large model retrieval enhancement technology, and aims to improve the generation efficiency of an automatic driving test scene. The method comprises the following steps: firstly, cutting a preprocessed data text into blocks, and vectorizing the text through an embedded model to construct an efficient vector database; secondly, preliminarily retrieving related text blocks by using an improved hybrid retrieval algorithm, and reordering by using a BERT cross encoder to improve the relevance of retrieval results; aiming at the complex task requirement in the automatic driving test, the method is divided into a plurality of sub-tasks to be executed in parallel, so that the retrieval efficiency and accuracy are improved. And then, a LoRA strategy is adopted to finely adjust the large language model so as to adapt to an automatic driving scene generation task. And finally, jointly inputting the cue word template and the reordering result into a fine tuning model to generate a test case. According to the invention, a brand new technical scheme is provided for automatic driving test scene generation, and the method is of great significance in improving the test evaluation level of automatic driving and accelerating the landing of the automatic driving technology.

The invention discloses a test evaluation method and device for an industrial park integrated management system. The method comprises the following steps: acquiring a performance index data set of the industrial park integrated management system; the performance index data set comprises a performance index test data subset of each subsystem; the performance index test data subset comprises a test data sequence of each performance index; pre-processing the performance index data set to obtain a to-be-evaluated data set; and performing test evaluation processing on the to-be-evaluated data set to obtain a performance evaluation result value of the industrial park integrated management system.

The invention discloses an automatic software function testing and evaluating method based on a multi-modal large model, and particularly relates to the field of non-embedded software function testing and evaluating, which comprises the following steps: collecting software texts, images and structured data, generating corresponding feature vectors, aligning through a cross-modal comparison algorithm, and constructing a multi-modal testing and evaluating data set; deep features of all modes are extracted through a mode exclusive encoder, and a unified multi-mode function vector is output based on information entropy dynamic weighted fusion; constructing triple training data, and optimizing the multi-modal large model by combining a joint loss function with an enhanced training strategy; and finally, deploying the model at a cloud end and an edge node, carrying out real-time triggering test by docking a CI / CD process, and realizing functional integrity verification, interactive compliance detection and accurate defect positioning through semantic vector comparison, graph isomorphism rate calculation and cross-modal attention backtracking. According to the scheme, the test comprehensiveness and precision are improved, and the rapid iteration requirement of software is met.

The invention discloses a personalized learning system based on multiple agents and retrieval enhancement generation. The system comprises a knowledge extraction agent, a retrieval enhancement generation module, a planning teaching agent, a knowledge consolidation agent and a test evaluation agent. The knowledge extraction agent extracts document information from the original learning materials and converts the document information into semi-structured data; the retrieval enhancement generation module is used for storing the semi-structured data into a vector library in a vector form, retrieving information in the vector library according to a request sent by a user, and inputting enhancement information combined by the retrieval information and the request into a downstream agent; and the planning teaching agent, the knowledge consolidation agent and the test evaluation agent are respectively used for constructing a knowledge graph according to the received information to generate a personalized teaching plan, generating exercise questions to consolidate learned knowledge, evaluating a learning effect and providing feedback suggestions. The learning efficiency, the learning effect and the learning experience of the learner can be remarkably improved, and personalized learning can be met.

The invention provides a reliability test evaluation method and system for an integrated circuit chip, and relates to the technical field of integrated circuit chip test.The method comprises the steps that a test target and a test index are defined; constructing a multi-level test environment; performing a dynamic stress test in the constructed multi-level test environment; based on the historical test data and the monitoring data, establishing a prediction model by using a machine learning algorithm to predict performance changes and potential faults of the integrated circuit chip under different conditions; performing intelligent diagnosis on faults occurring in the test process by using the established prediction model, and positioning a fault source; and continuously monitoring the test process through the online remote monitoring platform, and dynamically adjusting the test parameters and the test strategy according to the monitoring data and the prediction result of the prediction model. The reliability of the integrated circuit chip is comprehensively and accurately evaluated, and the test efficiency and the fault prevention capability are improved through a multi-level test environment, machine learning prediction and intelligent diagnosis.

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 discloses an automatic test evaluation construction method of a multi-modal large model and a related device, relates to the technical field of multi-modal large models, and is used for detecting the performance of the multi-modal large model. The automatic test evaluation construction method for the multi-modal large model comprises the steps of obtaining sample data; constructing an adversarial network architecture; inputting the sample data into an adversarial network architecture for training to obtain target test data; judging whether the target test data is repeated with data in a preset block chain database or not; if not, storing the target test data to a block chain database; positioning the starting node and a task node corresponding to the target application scene in a pre-created knowledge graph; obtaining a target test index according to the starting node and the task node, and allocating a weight to the target test index according to the target application scene; inputting the target test data into the multi-modal large model for testing to obtain a test result; and generating an evaluation report according to the test result and the target test index.

The invention provides an electromagnetic safety test system and method for switch integrated equipment, and relates to the technical field of power system switch equipment. The system is used for solving the problems that multi-parameter dynamic coupling analysis of a traditional test is insufficient, transient and gradual interference is difficult to capture by a fixed window, and comprehensive quantitative evaluation is lacked, and comprises a test module, an interference analysis module and a test evaluation module, calculating a total coupling interference variable; the interference analysis module draws a change curve based on the total coupling interference variable, analyzes a threshold over-area, a threshold duration and a peak value, and generates an interference risk index; and the test evaluation module calculates a total evaluation value and a threshold super-window proportion by combining the risk indexes of the windows, and divides safety levels. According to the invention, multi-parameter dynamic coupling simulation is realized, different types of interferences are accurately captured, a full-period dynamic evaluation system is constructed, and the accuracy of electromagnetic safety performance judgment of equipment is improved.

The invention relates to a microcomputer system performance test and evaluation method, device and equipment and a medium, and the microcomputer system performance test and evaluation method comprises the steps: if it is detected that a key event of a microcomputer system has occurred, executing a corresponding test cycle operation according to the key event; and converting and generating uniformly formatted real-time log data based on each test result in the test cycle operation, clustering and matching the real-time log data to a predefined abnormal state, and matching corresponding adjustment parameters based on the abnormal state to obtain the power state of the microcomputer system. Executing a first dynamic adjustment operation of the power state based on the performance adjustment parameter; according to the method, standardized real-time log data is generated from each test result, and the log data can be intelligently clustered and matched to a predefined abnormal state, so that the abnormity can be quickly identified, the adjustment parameters are generated, the power state and the test process of the system are dynamically optimized, and the flexibility of abnormity response and power consumption adjustment is improved.

Embodiments of the invention disclose a document classification method and apparatus, a device and a medium. The method comprises the steps of obtaining a pre-generated classification corpus; obtaining a field vocabulary of the specified enterprise; obtaining keywords of the documents based on relevancy between preset vocabularies in the field vocabulary and the documents; respectively converting the training set documents and the test set documents in the plurality of document features into a first feature vector of each training set document formed by the relevancy of the keywords of the training set documents and a second feature vector of each test set document formed by the relevancy of the keywords of the test set documents; based on the first feature vector of each training set document, training to obtain a document classifier, the document classifier being constructed through a machine learning model; testing and evaluating the document classifier based on the second feature vector; and if the document classifier passes the test evaluation, deploying the document classifier so as to classify the newly added documents in the classification corpus through the document classifier.

The invention discloses a test evaluation method for an English learning process, and relates to the field of adaptive learning systems. The method comprises the following steps: S1, constructing a hierarchical knowledge graph based on a knowledge point dependency relationship; after the student completes the task or the test, the knowledge point mastery degree in the knowledge graph is updated in real time; s2, the updated knowledge graph is input into a DTN network model, and the DTN network model combines a direct dependency relationship and an indirect dependency relationship to automatically judge a task currently needing to be learned by a student; and S3, by calculating the knowledge mastery degree and the path dependency matrix of the student, recommending learning content and practice most suitable for the current level of the student. According to the method, by introducing the path dependency matrix and the depth sequential network model, indirect dependency modeling between knowledge points is realized, and the intelligent and personalized level of an adaptive language learning system is improved.

The invention relates to the technical field of automobile testing and evaluation, in particular to a method and device for testing and evaluating the safety of a whole automobile steering function, an automobile and a medium. Obtaining vehicle controllability evaluation, a maximum lateral acceleration value, vehicle lateral displacement in target time, fault takeover time, steering wheel torque, a steering wheel angle and time domain curve and vehicle data information of a yaw angle and time domain curve when the test vehicle fails under the target steering test working condition; and according to a test evaluation criterion corresponding to the target steering test working condition, analyzing the multiple pieces of vehicle data information, thereby generating controllability of the test vehicle under multiple dimensions, and further generating a steering system safety evaluation report of the test vehicle. Therefore, the problems that the error of a test evaluation result is increased while the labor cost is increased and the steering system safety mechanism cannot be efficiently and accurately verified and judged when the safety performance is evaluated manually in the related technology are solved.

The invention discloses an artillery bore opening flow field proxy model construction method based on a physical information neural network, and belongs to the technical field of test evaluation. According to the artillery bore opening flow field proxy model construction method based on the physical information neural network, N-S solution loss and empirical formula loss in a loss function are combined through the hard constraint thought, and the loss function is simplified. The method firstly describes a basic framework of the method, secondly introduces a specific formula of an improved loss function, and finally verifies the superiority of the method through experiments. According to the method, the shock wave field reconstruction has obvious advantages when the data volume is low, response values of all design points in the space can be quickly obtained, and the optimization efficiency is greatly improved.

Systems and methods for testing, evaluating, and scoring IT products (e.g., software) and product updates from a technology provider are disclosed herein. More specifically, organizational assessment may be performed to evaluate the provider's development lifecycle processes and generate organization maturity scores. Architecture assessment may be performed to evaluate the system-level and software-level architectures associated with the application and generate architecture maturity scores. Product verification may be performed via automated testing and penetration testing to generate verification maturity scores. The organization maturity scores, architecture maturity scores, and verification maturity scores may be used to provide recommendations to the provider and also combined into an overall maturity score, which may serve as a comprehensive summary of the evaluation. These generated scores inform and expedite testing of future iterations of the product.

The invention provides a test evaluation method and device in agile iteration and a storage medium. The method mainly comprises the steps of data acquisition, index analysis, dynamic early warning, visual display and feedback optimization. The device comprises a data acquisition module, an index analysis module, a dynamic early warning module, a visualization module and a feedback optimization module. The storage medium implements the test evaluation method when the executable instruction is executed by the processor. By the adoption of the technical scheme, the test index early warning system is designed according to the agile development characteristics, through a series of real-time data evaluation indexes, team conditions and service conditions are dynamically combined, test activities can be accurately tracked, contribution and efficiency of testers can be quantified in real time, and the test efficiency can be improved. Intelligent index early warning can be provided to help to optimize task allocation, iteration strategies and the like, and project progress, team efficiency and product quality are improved; and meanwhile, a closed-loop feedback mechanism is also constructed, and the early warning rule is continuously optimized.

The invention discloses a cryptographic algorithm and protocol test system, the system is configured into two deployment modes: a transparent mode and a man-in-the-middle mode, the transparent mode can hide an IP or MAC address of a device, and certificate and algorithm information in a cryptographic protocol is identified and analyzed through a passive traffic analysis mode; the man-in-the-middle mode provides a dynamic packet changing function, realizes a dynamic interaction function with a server in an agent mode, can be used for application scenarios such as identity authentication, data encryption and decryption, protocol vulnerability analysis and performance test, can effectively improve the evaluation integrity and accuracy of a cryptographic algorithm and a cryptographic protocol, forms a complete and accurate test evaluation evidence chain, and improves the evaluation efficiency of the cryptographic algorithm and the cryptographic protocol. And the detection evaluation result is effectively supported. The system can be accessed to a production network to capture, identify and analyze network traffic, so that an international / national cryptographic algorithm and protocol security analysis function is realized, and verification and analysis on compliance, correctness, effectiveness and the like of password application in an important network and information system can be realized.

The invention provides a digital twinborn simulation test method and system applied to a building system, and the method comprises the steps: firstly, carrying out the construction processing of a knowledge graph of physical parameters of the building system, generating a physical parameter knowledge graph comprising structure, equipment, environment parameter nodes and a plurality of correlation edges, and then constructing a digital twinborn graph model based on the knowledge graph, the method comprises the following steps of: generating a graph test scene configuration sequence according to a model, including a node parameter disturbance rule and the like, executing a graph coupling simulation test by inputting the sequence into the model, and generating a graph test response data set including a node response sequence and the like; and finally, outputting a digital twin simulation test evaluation report based on the data set, wherein the report covers node response characteristics and the like. According to the method, the building system is comprehensively and accurately simulated, and the test efficiency, accuracy and comprehensiveness are improved.

The invention provides a composite material bearing structure storage life evaluation method considering an acceleration stress lower limit, and the method comprises the steps: 1, carrying out the initialization modeling: enabling the service life of a product to obey the logarithmic normal distribution, and building a three-parameter inverse power law acceleration model of the service life feature theta and the stress level S; 2, estimating parameters: estimating unknown parameters by applying a maximum likelihood estimation method for the three-parameter inverse power law acceleration model; 3, model selection: comparing the calculated likelihood function values, wherein the larger the likelihood function value is, the more stable and more accurate the model is; meanwhile, the variation coefficients of the acceleration factors are calculated in combination with test data for comparison, and the model with the small variation coefficient is more accurate; and 4, accelerated life evaluation: giving reliability, and evaluating the storage life of the three-parameter inverse power law model. The method is suitable for accelerated test evaluation of a product with a threshold effect; the three-parameter inverse power law model provided by the invention can show a better model fitting effect when the acceleration effect does not meet the linear characteristic.

The invention provides a performance test and evaluation method, system and device for a power grid credential environment and a medium, and the method comprises the steps: obtaining real-time data of each node through employing a distributed test probe and a data flow distribution strategy, and constructing a digital twinborn body of the power grid credential environment; the digital twin body comprises twin nodes corresponding to the nodes; extracting multi-dimensional performance test data of each twin node under the performance test load by adopting a classification dimension convergence method; and based on the performance test prediction data of each twin node, performing risk assessment on each twin node by adopting an entropy weight-comprehensive sorting algorithm. According to the method, dynamic load balance of each node is ensured through a data traffic distribution strategy, normal operation of the power grid credential environment is prevented from being influenced during test evaluation, and the method is suitable for a complex scene in which the power grid credential environment is easily impacted by a high load; data evaluation is carried out through performance test prediction data, data exception can be captured, and potential performance risk bottlenecks of a power grid credential environment can be found in advance.

The invention provides a GIS equipment withstand voltage test system and method, and relates to the technical field of GIS equipment withstand voltage test. Comprising the steps of obtaining parameter information and installation environment data of GIS equipment, constructing a three-dimensional digital twinborn model of the GIS equipment in combination with an electric field simulation engine, generating a withstand voltage test scheme and planning an optimal path. Acquiring partial discharge data and insulation resistance data under different voltage stresses by using an AI noise reduction algorithm according to the optimal path, carrying out multi-modal fusion processing on the partial discharge data and the insulation resistance data, extracting features by using a deep learning model, obtaining insulation defect and performance attenuation data, and carrying out multi-modal fusion processing on the insulation defect and performance attenuation data. And carrying out space mapping association with the three-dimensional digital twinborn model to generate a GIS equipment withstand voltage test evaluation report, thereby providing a powerful basis for equipment operation and maintenance. According to the invention, the three-dimensional digital twinborn model is constructed to generate the GIS equipment voltage withstanding test evaluation report, so that the accuracy of the GIS equipment voltage withstanding test is improved.

The invention relates to the technical field of intelligent automobile test and evaluation, in particular to an intelligent networked automobile automatic driving performance evaluation method based on simulation test, which comprises the following steps: S1, generating a reference steady-state traffic flow in a simulation platform; s2, injecting traffic flow disturbance parameters into the reference steady-state traffic flow, and recording whole-course operation data of the reference steady-state traffic flow; s3, extracting performance indexes of the tested vehicle; s4, calculating a deviation degree, and quantifying the deviation degree as a performance attenuation rate; s5, performing weighted fusion to obtain a comprehensive disturbance tolerance index; and S6, outputting an evaluation conclusion of the automatic driving performance of the tested vehicle according to the comprehensive disturbance tolerance index. According to the method, the reference traffic flow is constructed, the disturbance is injected, the performance attenuation rate is quantified, and then the disturbance tolerance index is generated through weighting, so that objective, comparable and interpretable comprehensive evaluation of the automatic driving performance of the intelligent networked automobile is realized.

The invention discloses an automatic driving scene library dynamic generation method based on multi-modal data fusion, belongs to the field of automatic driving testing, and solves the problems of insufficient coverage of extreme working conditions, data splitting and single evaluation of a static scene library. According to the technical scheme, the method comprises the following steps: establishing a distributed data acquisition layer, and accessing a vehicle-mounted terminal and a V2X roadside equipment data stream through a federated learning framework; constructing a multi-modal semantic coding layer, fusing data extraction features, converting laser radar point cloud data into a space-time constraint matrix, and outputting a scene DNA coding sequence; deploying a dynamic evolution engine, mapping the DNA coding sequence into a traffic flow state parameter, and calculating an entropy gradient field; and starting a virtual-real co-occurrence scene generator, receiving the coordinates of the high-entropy region and the physical interference intensity parameters, performing simulation to generate a controlled physical interference test scene, injecting a group interaction rule based on behavior uncertainty, and finally outputting a dynamically updated scene library and a risk report for automatic driving system test evaluation.

The invention provides a storage equipment testing method and device and electronic equipment, and can be applied to the technical field of computers. The method comprises the steps that an initial test script is executed, target configuration parameters and target test strategies of the storage device are obtained, the initial test script comprises a diagnosis script and a test strategy matching script, the target configuration parameters are obtained by executing the diagnosis script, and the test strategy matching script is used for selecting the target test strategies based on the target configuration parameters; according to the target configuration parameters and the expected use duration of the equipment, a target test period is determined, and the target test period is a test period obtained after an initial test period in the initial test script is compressed; and obtaining a test evaluation report by using test execution information obtained by testing the storage device based on the target test period and the target test strategy.

The invention discloses a multi-protocol compatible intelligent test method and system for a power distribution automation terminal, and relates to the technical field of power distribution terminal test. The method comprises the following steps: acquiring basic information and historical test data of a tested terminal; automatically identifying and matching a communication protocol through a multi-protocol adaptive identification model, and generating a protocol conformance test template; according to the template, executing signal injection and collecting real-time response data, and calculating a terminal response matching degree and a protocol analysis accuracy parameter; and inputting the parameters into the test result evaluation model, and outputting a test result. Comprising an information acquisition module, a protocol identification and template construction module, a test execution and data acquisition module and a test evaluation module. According to the invention, automatic identification, standardized testing and intelligent evaluation of the multi-protocol terminal are realized, the testing efficiency, consistency and accuracy are improved, and the method is suitable for testing and diagnosis of distribution automation terminals of different models.

The invention discloses a special vehicle driving task performance and sitting posture comfort coupling-oriented test evaluation system and method, and the system comprises a data collection module which is used for obtaining multi-modal driving data; the index calculation module is used for extracting and quantifying characteristic indexes from the multi-modal driving data to obtain a driving task performance evaluation index set and a driving sitting posture comfort evaluation index set; the comprehensive evaluation and time domain modeling module constructs a driving task performance-sitting posture comfort coupling-oriented comprehensive quantitative model based on the driving task performance quantitative model and the driving sitting posture comfort quantitative model; and finally, comprehensively scoring the state of the driver by using the comprehensive quantitative model, and establishing a nonlinear mapping relationship between the driver state comprehensive score and the task time to obtain a dynamic change trend of the driver state comprehensive score.