407results about How to "Realize quantitative evaluation" patented technology

The invention relates to a high-precision multi-channel phased array radio measurement calibration method and a high-precision multi-channel phased array radio measurement calibration system. The method comprises the following steps: carrying out electromagnetic characteristic modeling on N array element channels and an antenna housing of the multi-channel phased-array antenna to obtain a channel measurement model; carrying out electromagnetic field numerical calculation on the multi-channel phased-array antenna to obtain an initial error data matrix; mapping the initial error data matrix to a two-dimensional weight matrix, and performing optimal matching solution on the two-dimensional weight matrix to obtain a theoretical calibration coefficient matrix; connecting the standard gain antenna and the multi-channel phased-array antenna by adopting a vector network analyzer, and performing far-field scanning measurement on the multi-channel phased-array antenna to obtain actually measured amplitude-phase characteristic data; and performing weighted least square iterative calculation on the actually measured amplitude-phase characteristic data and the theoretical calibration coefficient matrix to obtain a target calibration compensation coefficient. According to the invention, accurate measurement of the amplitude-phase characteristics of the multi-channel phased-array antenna is realized, and the stability of the calibration process is improved.

The invention discloses a waterproof coating formula optimization method and system based on cloud computing, and relates to the technical field of coating formula optimization. A waterproof coating formula optimization system based on cloud computing comprises a data acquisition and management module, a data model construction module, a formula performance optimization module, a formula cost performance optimization module, a new formula verification module and a data recording and loop optimization module. The performance data model and the environment data model are constructed, and the performance range index of the waterproof coating is combined with the use environment adaptability; a forward optimization channel and a reverse optimization channel are designed, and optimization is carried out for improvement of existing formula performance and adaptation of target environment requirements; by combining the performance evaluation model and the optimization algorithm, efficient searching and updating of the formula parameters are achieved, and the formula optimization efficiency is greatly improved.

The invention relates to the technical field of financial management and financial analysis, in particular to a financial calculation system and method based on a financing project, and the system comprises a multi-dimensional data source integration module, a big data analysis processing module, a machine learning prediction model construction module, a dynamic adjustment and real-time prediction module and a risk assessment and strategy suggestion module. By introducing the dynamic adjustment and real-time prediction module, real-time monitoring and automatic updating of an external data source are realized, and the intelligent dynamic adjustment mechanism not only can quickly capture key factors such as market changes, policy and regulation updating and the like, but also can automatically optimize prediction model parameters according to latest data. Compared with a traditional static prediction method, the dynamic adjustment strategy can significantly improve the precision and real-time performance of financial prediction, helps enterprises to quickly make accurate financial decisions in a complex and changeable market environment, and effectively avoids potential risks.

The invention relates to the technical field of coating processes, in particular to an automobile coating process parameter self-adaptive regulation and control method and device, and the method comprises the steps: collecting a process environment real-time variable set, a spraying equipment state real-time variable set and a coating self-characteristic real-time variable set in a coating workshop; calculating deviation degrees between the process environment real-time variable set, the spraying equipment state real-time variable set and the coating self-characteristic real-time variable set and a pre-input process environment standard variable set, a spraying equipment state standard variable set and a coating self-characteristic standard variable set; obtaining a real-time deviation multi-modal vector of the coating process; inputting the coating process real-time deviation multi-modal vector into a pre-constructed coating process deviation analysis model to obtain a coating process deviation evaluation quantized value; by accurately controlling coating process parameters, energy consumption is reduced, and material waste is reduced.

The invention discloses an application method for parking project calculation, and relates to the technical field of parking lot control. The application method of parking project measurement and calculation comprises the following steps: S1, data acquisition; s2, evaluating a network state; s3, acquiring an environment compensation coefficient; and S4, evaluating the operation state of the camera. According to the invention, the network abnormity evaluation value is obtained through the obtained network performance data, whether network optimization is carried out or not is judged based on the network abnormity evaluation value and the preset network abnormity threshold value, and then the operation state data of the main camera and the environment data obtained after optimization are analyzed to obtain the environment compensation coefficient. And according to the operation state data of the main camera, the environment compensation coefficient and the network abnormity evaluation value, obtaining an operation abnormity evaluation value, and based on the operation abnormity evaluation value and a preset operation abnormity threshold value, determining whether to adjust the main camera, thereby improving parking space guiding accuracy. The problem of low parking space guiding accuracy in parking space guiding application of parking project measurement and calculation in the prior art is solved.

The invention provides an AI-based servo driver fault on-line rapid diagnosis method and system, and the method achieves the quantitative evaluation of fault data through obtaining the candidate system fault monitoring data of a servo driver control system, and converting the candidate system fault monitoring data into a diagnosis difficulty index and a fault description knowledge attribute. When the diagnosis difficulty index exceeds the preset index and the fault data lacks dynamic change information, the method can further utilize a plurality of fault penetration derivation data and the associated fault knowledge networks matched with the data, and effectively identifies and integrates similar fault modes by calculating the state feature distance between the associated fault knowledge networks, thereby improving the fault diagnosis accuracy. Compared with the prior art, the method has the advantages that the accuracy and efficiency of fault diagnosis are remarkably improved, particularly in a complex and changeable fault scene, the fault reason can be quickly positioned, and data support is provided for instant maintenance and recovery of the servo driver, so that the downtime of a system is shortened, and the overall operation reliability and stability of equipment are improved.

The invention relates to the technical field of geological ecological monitoring, in particular to a geological ecological monitoring system and method. The method comprises the following steps: acquiring a topographic map of a monitoring area; performing real-time data acquisition by using a sensor network according to the topographic map of the monitored area, and constructing a rock-soil physical characteristic matrix to obtain the rock-soil physical characteristic matrix; performing rock-soil mechanical response spectrum construction according to the rock-soil physical characteristic matrix to obtain a rock-soil mechanical response spectrum; according to the monitored area topographic map, point cloud data processing is utilized to construct a side slope geometric model, and a side slope three-dimensional geometric model is obtained; performing unit stress analysis according to the three-dimensional geometric model of the side slope to obtain unit stress data; and performing strength reduction calculation according to the unit stress data to obtain a reduction strength parameter. The geological ecological system is comprehensively, dynamically and systematically monitored and evaluated, so that geological disaster prevention and control and ecological environment protection are better served.

The invention discloses a private domain user management method and system for multiple touch channels, a medium and a program product, and the method comprises the steps: obtaining the historical behavior data of a user in each touch channel; identifying the behavior relevance of the user among the arriving channels; constructing an inter-channel behavior mapping relation of migration paths of the user among the touch channels based on behavior relevance; according to the behavior mapping relation between the channels, the overlapping ratio of user behaviors between the reaching channels, the difference of conversion effects and the overlapping ratio of time sequences are calculated; based on the overlapping rate, the difference and the coincidence degree, determining the reaching channel complementation degree; grouping and clustering the reaching channels according to the reaching channel complementation degree to obtain a plurality of channel collaboration units; and the optimal time difference of the arriving users in each channel cooperation unit is calculated, and the marketing resources are allocated among the arriving channels based on the optimal time difference, so that the accuracy of private domain operation management is improved.

The invention discloses a new energy automobile charging equipment test method and system, and relates to the technical field of new energy automobile charging, and the method comprises the steps: controlling to-be-tested charging equipment to charge a simulation battery device according to preset charging parameters, and monitoring and recording the charging efficiency of the to-be-tested charging equipment in real time through a data collection device; at a predetermined SOC point, a programmable load device is utilized to simulate various load working conditions, and the dynamic response characteristics of the to-be-tested charging equipment are tested; calculating a charging performance index according to the charging efficiency and the dynamic response characteristic; and generating a multi-dimensional test report based on the charging performance index. According to the invention, dynamic sampling frequency adjustment and multi-SOC point dynamic load testing are introduced, so that the capturing capability of transient response characteristics of the charging equipment and the testing practicability are improved.

The invention discloses a steel-concrete joint surface defect identification method based on air coupling ultrasonic guided waves, which comprises the following steps: S1, selecting an ultrasonic guided wave mode and a probe frequency, and determining an incident angle theta of an air coupling excitation probe; s2, constructing a detection system according to the parameters in the step S1; s3, constructing a data set, wherein the acquisition mode of the sample of the ultrasonic guided wave signal in the data set comprises the following steps of: performing actual detection by adopting the detection system to obtain the sample of the ultrasonic guided wave signal; by solving the air coupling ultrasonic three-dimensional finite element calculation model, obtaining a sample of an ultrasonic guided wave signal under the condition that interference factors and joint surface defects exist; s4, training the recognition model by using the data set; the input of the recognition model is the time domain feature and the frequency domain feature of the ultrasonic guided wave signal, and the output of the recognition model is a defect probability value of a measuring point corresponding to the ultrasonic guided wave signal; and S5, identifying the ultrasonic guided wave signal of each measuring point by adopting the trained identification model so as to calculate the defect probability of each measuring point.

The invention discloses an arid region lake basin water environment bearing capacity evaluation and prediction system, and relates to the technical field of lake drought early warning. A training data collection module is arranged to collect historical meteorological data, historical water level label data, historical meteorological characteristic data and historical drought duration label data; a training sample construction module is set to construct a plurality of lake water level training samples and a plurality of drought duration training samples, a model training module is set to train a water level sequence prediction model and a drought duration prediction model, and a bearing capacity prediction module is set to output future drought duration based on future meteorological characteristic data and the drought duration prediction model. Outputting a future lake water level sequence based on the future meteorological data, the future drought duration, the water level sequence prediction model and the real-time lake water level, and performing early warning on the lake bearing capacity based on the future lake water level sequence; and the prediction accuracy of lake water level change and drought occurrence is improved.

The invention provides a resource management method and system for a server operating system, and the method comprises the steps: carrying out the double-layer graph modeling of process and resource data in the server operating system, and obtaining a process competition graph layer and a resource dependence graph layer; utilizing a preset graph neural network to perform feature extraction on the node information of the process competition graph layer and the node information of the resource dependence graph layer to obtain a node feature vector containing a competition relation and a distribution state; according to the node feature vectors, deadlock probability calculation is carried out through a multi-layer perceptron, and a process priority adjustment scheme is obtained; according to the node feature vector and a process priority adjustment scheme, performing resource allocation strategy learning to obtain a first resource allocation scheme; according to the first resource allocation scheme, performing multi-level queue allocation on the process to obtain a second resource allocation scheme; and performing resource management on the server according to the second resource allocation scheme.

The invention provides a peaceful treatment and care effect evaluation method, which belongs to the technical field of peaceful treatment and care effect evaluation, and comprises the following steps: establishing a physiological index anomaly detection model and removing abnormal data by collecting physiological index data and subjective score data of a patient in real time and applying technologies such as multivariate mixed decomposition and time sequence feature analysis. And establishing a function relationship between the physiological index stable component and the subjective score data, calculating a physiological fluctuation influence coefficient matrix, and performing weighted fusion calculation by using the corrected physiological stability coefficient matrix and subjective score coefficient matrix according to a personalized reference range in a patient baseline database to obtain a comprehensive score of the calming treatment and protection effect. And finally, according to a preset scoring interval, generating an evaluation result of the calming treatment and protection effect. According to the method, the physiological index data and the subjective score data are fully utilized, and the problem that the current calming treatment effect depends on subjective evaluation of medical staff and is difficult to reflect accurately is solved.

The present invention belongs to the technical field of construction risk assessment, and specifically discloses a subway construction safety risk intelligent assessment system, which includes a field data acquisition module, an equipment data import module, a construction data analysis module, a construction risk assessment module, a risk sorting and display module, a database, and an early warning execution and feedback terminal. The present invention solves the current deficiencies in data collection scope and depth by setting up key areas, and performing real-time construction site data acquisition and importing the accumulated construction time and real-time equipment monitoring data of key areas. At the same time, risk assessment is performed through three key aspects of personnel, equipment and structure, forming an independent and complete risk indicator analysis system, and by constructing risk indication maps in each key area, it is convenient for construction management personnel to conduct comprehensive risk monitoring and management, and improves the effectiveness of risk control and feedback links.

The invention provides a multi-scale feature fused textile quality detection method, which comprises the following steps of: aiming at multi-scale feature representation, obtaining a torsion angle and a torsion direction feature descriptor which are kept unchanged under image rotation transformation by calculating an invariant moment of a feature vector, and generating a multi-scale torsion feature vector; calculating a correlation coefficient between the torsion frequency and the torsion density in the multi-scale torsion feature vector and the brightness gradient and the brightness change rate in the brightness feature vector, and determining the correlation degree of the torsion feature and the brightness feature; according to the comprehensive feature vector, a support vector machine classifier is adopted to classify the textile image, and whether a defect area with an abnormal torsion area or an abnormal brightness dynamic range exists in the textile is judged; and in the defect area, local features of a torsion angle, a torsion amplitude, average brightness and brightness contrast are extracted, the torsion degree and the brightness abnormal degree of the defect area are judged through threshold comparison, and quantitative description of the defect is obtained.

The invention provides a software system dependency defect tracking method and system based on a graph database, and relates to the technical field of software, and the method comprises the steps: parsing a source code into an abstract syntax tree, and extracting a dependency; constructing a dependency graph data model; establishing a defect mode feature library; performing pattern matching in the graph data model to identify potential defects; calculating a defect influence range and severity; and generating a defect tracking report, including defect priority ranking, complete call chain construction, defect detailed information recording and optimization suggestion generation. According to the method, the dependency relationship defect in a software system can be effectively identified and tracked, and the software quality is improved.

The invention belongs to the technical field of construction risk assessment, and particularly discloses and provides a subway construction safety risk intelligent assessment system, which comprises a field data acquisition module, an equipment data import module, a construction data analysis module, a construction risk assessment module, a risk arrangement display module, a database and an early warning execution and feedback terminal. According to the method, the key area is set, real-time construction site data acquisition is carried out, and accumulated construction duration and real-time equipment monitoring data of the key area are imported, so that the problem of deficiency in the aspects of data acquisition range and depth at present is solved, and risk assessment is carried out through three key aspects of personnel, equipment and structure, so that the risk assessment accuracy is improved. An independent and complete risk index analysis system is formed, and the risk indication diagrams in the key areas are constructed, so that the construction management personnel can conveniently carry out comprehensive risk monitoring and management, and the effectiveness of risk management and control and feedback links is improved.

The invention provides a drilling and blasting method mechanized tunnel surrounding rock pressure arch evolution and pressure calculation method, which specifically comprises the following steps: constructing a mechanized tunnel finite difference calculation model, and setting tunnel engineering calculation parameters; determining surrounding rock pressure arch inner and outer boundaries based on change characteristics of surrounding rock stress before and after excavation; automatically monitoring the stress change in the surrounding rock excavation process through Flac3D software, storing a calculation model of each excavation stage, and automatically calculating the upper and lower boundaries of a pressure arch of the model of each stage; performing curve fitting and correction on the key points of the upper and lower boundaries of the pressure arch to obtain a calculation formula of the pressure arch of each excavation construction method; and respectively calculating the internal loosening pressure of the surrounding rock pressure arch and the external deformation pressure of the pressure arch to obtain a mechanical tunnel surrounding rock pressure calculation formula. According to the method, the pressure arch range formed by mechanical tunnel construction disturbance is determined more accurately, quantitative evaluation of the mechanical tunnel construction disturbance range is achieved, and a basis is provided for feasibility of large-section and long-footage construction of mechanical tunnel surrounding rock.

The invention discloses a network security early warning method and system based on big data, and relates to the technical field of network security, and the main scheme is that multiple types of data including network traffic, user behaviors, system logs and hardware state data are collected, and then index calculation is carried out based on the data; a network attack risk index, a user data exception risk index, a system security risk index and a hardware fault risk index are calculated, a network security comprehensive risk index is further calculated comprehensively, finally, a network security early warning threshold value is preset, the comprehensive index is compared with the preset network security early warning threshold value, and whether an early warning mechanism is triggered or not is judged according to a comparison result. And generating corresponding early warning information.

The invention discloses a coal sampling method based on near infrared spectroscopy, and belongs to the field of coal quality detection.The coal sampling method comprises the steps that a near infrared spectrogram and coal quality parameters of a coal sample are obtained, a data set S1 is constructed, preprocessing is carried out, and a data set S2 is obtained; a U-Net network model is constructed; training a U-Net network model by using the data set S2; the method comprises the following steps: acquiring coal samples initially collected on a sampling site, crushing and grouping to obtain multiple groups of subsamples; adopting a near infrared spectrometer to measure the near infrared spectrogram of each subsample as input, and utilizing the trained U-Net network model to predict corresponding coal quality parameters; performing cross grouping, pairing and weighing on each group of subsamples, and calculating the relative deviation of the weight difference of each pair of coal samples; calculating the sampling precision according to the relative deviation of the weight difference and the predicted coal quality parameters; and comparing the calculated sampling precision with the target precision, and adjusting the sampling scheme to obtain a coal sampling result. Aiming at the low coal sampling efficiency in the prior art, the sampling efficiency is improved.

The invention discloses a multi-target highway reconstruction and extension construction period traffic organization scheme quantitative comparison and selection method. The method comprises the steps of calculating a service level score of a construction period traffic organization scheme; calculating a project cost score of the traffic organization scheme in the construction period; calculating a construction period score of the construction period traffic organization scheme; calculating a traffic conversion score of the traffic organization scheme in the construction period; calculating a traffic safety risk score of the traffic organization scheme in the construction period; calculating the multi-target comparison and selection weight of the traffic organization scheme in the construction period; and calculating a comprehensive score of the traffic organization scheme in the construction period, and making a decision. According to the method, a multi-target quantitative evaluation system is established based on rich highway reconstruction and extension traffic organization design experience, and the method is suitable for evaluation and comparison and selection of traffic organization schemes in the construction period of highway reconstruction and extension projects with traffic passing and traffic keeping at the same time. Comprehensive evaluation is carried out from multiple aspects of service level, construction period, project cost and traffic safety, and the method can be flexibly suitable for traffic organization scheme comparison and selection of different reconstruction and extension projects.

The invention relates to the field of video inspection, in particular to an active video inspection system based on Internet of Things driving, which comprises a current analysis module, an image input module, an image input detection module, an image output detection module and an audio detection module. According to the method, the current of the conference room equipment is monitored, abnormal equipment is found in time, rapid fault diagnosis is achieved, the effectiveness of the video stream is effectively judged by obtaining and processing the video stream and analyzing the grayscale image, it is ensured that the input picture is always in an available state, the blank screen phenomenon and picture distortion are avoided, and the user experience is improved. The image output detection module verifies the display effect, ensures the accuracy of an output picture and determines an abnormal area when the output picture is abnormal, the audio detection module plays a test audio and collects feedback, the sound receiving effect of a microphone is effectively evaluated, the conference room equipment is automatically and actively inspected, the equipment state is continuously monitored and evaluated, and the sound receiving efficiency is improved. Potential faults can be found and prevented in time, and the sudden fault risk of equipment is remarkably reduced.

The invention relates to the technical field of network security, and discloses a vector-based network security operation index measurement neural network algorithm, which comprises the steps of collecting network transmission data to establish a to-be-analyzed data group, and obtaining a data fluctuation value; comparing and screening the data fluctuation value with a fluctuation threshold value, and performing abnormal value calculation on the screened data to determine abnormal data; analyzing correlation among the abnormal types, when data with the correlation greater than a correlation threshold exists among all the abnormal types, extracting the abnormal types of the correlation group to establish a feature vector, and inputting the feature vector into a pre-established neural network model to obtain an abnormal condition type; extracting an abnormal value of the correlation group to determine a proportion vector, and obtaining an abnormal condition coefficient according to the proportion vector and the abnormal value; and performing network early warning according to the abnormal condition coefficient and the abnormal condition type. According to the invention, based on dual analysis of the abnormal condition type and the abnormal condition coefficient, intelligence and response efficiency of network security operation are improved.

The invention discloses a distribution network reactive power support capability evaluation method and system considering uncertainty new energy output, and belongs to the technical field of power grid power generation dispatching, and the method comprises the steps: constructing a distribution network reactive power deviation index considering active and reactive power coupling, and fusing the index into a distribution network reactive power support capability evaluation calculation process; constructing reactive power reserve models of various traditional energy sources and uncertain new energy sources, wherein the reactive power reserve models comprise a generator set, a capacitor bank, wind power generation, photovoltaic power generation, energy storage, a flexible load and an electric vehicle; constructing a typical scene set which comprehensively considers the output of all the models; constructing a distribution network reactive power support capability calculation model considering the uncertainty new energy output; and solving by adopting a genetic algorithm to obtain the reactive power support capability of the distribution network. According to the method, multiple types of reactive power resources on the power distribution side are fully transferred, the reactive power reserve potential evaluation model for the active power distribution network individuals is established, and quantitative evaluation of the reactive power voltage supporting capacity of the power distribution network is achieved.

The invention discloses a sugar net recognition system based on a block chain and federal learning, and the system comprises a data collection module which is used for collecting fundus image data and related medical data; the federal learning module is connected with the data acquisition module, the federal learning module comprises a plurality of participant nodes, and each participant node performs model training based on local data; the block chain module is connected with the federated learning module and used for storing model parameters, training data abstracts and data access record key information in the federated learning process; the sugar network identification module is used for receiving the global sugar network identification model trained by the federal learning module; and the result output and feedback module is connected with the sugar net identification module. According to the method, the data collaborative training model is integrated through federal learning, so that the sugar net recognition accuracy is improved, misdiagnosis and missed diagnosis are reduced, the block chain ensures the data to be safe and credible, the innovative formula assists optimization, the system application is wide, clinical and remote medical treatment is facilitated, and the sugar net diagnosis service quality is improved.

The invention provides a system and a method for testing the fire extinguishing concentration of various fire extinguishing agents, and belongs to the field of fire extinguishing effect testing of fire extinguishing agents, the testing method comprises the following steps: determining the critical fire extinguishing mass concentration required when each fire extinguishing agent is independently applied through the fire extinguishing concentration testing system; the critical fire extinguishing mass concentration required by the combined application of each fire extinguishing agent is determined through a fire extinguishing concentration testing system; comparing the collaboration factor with a collaboration threshold value; if the collaborative factor is larger than a collaborative threshold value, it is judged that the collaborative fire extinguishing effects of the multiple types of fire extinguishing agents are mutual inhibition; and if the cooperation factor is smaller than the cooperation threshold value, it is judged that the cooperation fire extinguishing effects of the multiple kinds of fire extinguishing agents are mutually promoted. The method provided by the invention can be used for developing and evaluating the efficient fire extinguishing agent and evaluating the combined use effect of various fire extinguishing agents.

The invention relates to the technical field of block chains and security technologies, and particularly provides a security protection detection method and system for a smart contract based on a block chain. The security indexes at least comprise a password economics security index, a dynamic behavior characteristic index, a formalized verification index and a consensus layer security index; and performing quantitative evaluation on the collected multi-dimensional security index to obtain a comprehensive security score of the target block chain, scoring the cryptoeconomics security index through a preset cryptoeconomics security index scoring model to obtain a cryptoeconomics security score, and sending the cryptoeconomics security score to the target block chain. The current comprehensive security score of the target block chain is compared with the adaptive dynamic risk threshold, and if the current comprehensive security score is higher than the adaptive dynamic risk threshold, an early warning signal is sent out, and the effect of comprehensively evaluating the block chain smart contract is achieved.

The invention discloses a coal power and clean energy multi-energy complementary system flexibility quantitative evaluation method comprising the following steps: collecting wind / light output historical power prediction errors of a wind power station / photovoltaic station as an error set, and sampling the error set; superposing the extracted wind / light output historical power prediction error value on the input wind and light data to obtain a wind and light load prediction curve; based on the minimum value of the total loss cost of the abandoned electricity quantity and the load shedding quantity in the coal power and clean energy power generation system as a target function, forming an optimal economic dispatching model in combination with constraint conditions; and calculating a flexibility quantitative evaluation index by taking the abandoned electricity quantity and the load shedding quantity as operation intermediate quantities, setting a threshold value of the flexibility quantitative evaluation index, and performing evaluation. Quantitative evaluation of the flexibility of the coal-water wind-light power generation system is realized, and subsequent guidance of setting of the reserve capacity of the power generation system according to the evaluation result is facilitated.

The invention relates to the technical field of visual health management, in particular to a ciliary muscle adjustment training system and method based on eye movement tracking, and the method comprises the steps: collecting eye movement data of a fixation point track, an adjustment amplitude change rate and pupil response time in real time, and combining personalized parameters such as the age of a user, diopter and a training target; inputting a ciliary muscle training dynamic planning algorithm module, dynamically generating a stage scheme containing a training action type, a single-group duration, a stimulation interval and a focal length adjustment strategy, and adaptively adjusting the strategy according to real-time adjustment stability and a historical ability curve; through multi-dimensional matching analysis of a pre-stored standard eye movement template, a training specification score is calculated from the track goodness of fit, speed uniformity, amplitude standard-reaching rate and binocular coordination, visual fatigue parameters are obtained based on continuous training duration, pupil fluctuation and retina reflex change, a scoring result is dynamically corrected, and a visual fatigue evaluation result is obtained. Personalized, self-adaptive and quantitative safety control of the training process is realized.

The embodiment of the invention provides a method and device for analyzing the influence of policies on economy based on a CGE model, and the method comprises the steps: setting model parameters of the CGE model and the policy scene of each region in a domestic region set through constructing the CGE model and a solving equation set of the CGE model; and according to the model parameters and the policy scenario of each region in the domestic region set, solving the equation set through the CGE model to obtain the economic condition of the target region. Quantitative evaluation of regional economic and environmental benefits by green power development of a target region is realized, and comprehensive benefits of power structure adjustment, carbon emission reduction target realization and industrial optimization and upgrading under different policy scenarios can be accurately quantified, so that an enterprise is assisted in accurately identifying a green power value; and the government is assisted to scientifically formulate policies for promoting industrial green development and economic structure transformation.