565results about How to "Improve assessment accuracy" patented technology

The invention relates to the technical field of intelligent wearable devices, in particular to an intelligent wearable device data-driven old people falling risk real-time assessment system, which executes the following steps of: acquiring acceleration and angular acceleration signals of an old patient based on a wearable device; inputting the acceleration and angular acceleration signals into the reconstructed gravity center motion state space model to obtain a predicted gravity center position; inputting the predicted center-of-gravity position into a gait analysis model to obtain posture and foot position information; inputting the characteristic parameters into a risk analysis model, and outputting an offline fall risk assessment value; calculating a residual strength index based on calf muscle strength; an entropy index based on the compensation reference point and the plantar pressure compensation gravity center is obtained; the method comprises the following steps of: inputting the current falling risk into a risk analysis model, estimating and correcting the current falling risk, outputting the online falling risk, integrating multi-source data such as acceleration, angular velocity, electromyographic signals and the like, and combining an innovative entropy index, so that the system can comprehensively evaluate the balance state of the old people.

The invention provides a comprehensive evaluation method and system for the remediation effect of heavy metal contaminated soil, and relates to the technical field of environmental engineering.The method specifically comprises the steps that remote sensing data of a contaminated area is obtained, a soil sample is collected, the microbial respiration rate and the soil physicochemical property of the soil sample are measured, the remote sensing data are processed, and a normalized vegetation index is obtained; generating a soil health assessment index based on the collected physical and chemical properties of the soil; integrating the soil evaluation index, the microbial respiration rate and the normalized vegetation index to construct a soil remediation evaluation index; and comparing the remediation evaluation index with a preset evaluation threshold value, and evaluating the remediation effect of the heavy metal contaminated soil. According to the method, the soil health evaluation index and the remediation evaluation index are constructed, so that the evaluation accuracy of the remediation effect of the heavy metal contaminated soil is effectively improved. Through multivariate correlation analysis, biological, chemical and physical characteristics of the soil can be comprehensively reflected, and a scientific basis is provided for subsequent management and remediation measures.

The invention discloses a regional risk prediction method and system based on toxic gas diffusion analysis, and provides a regional risk prediction method based on toxic gas diffusion analysis on the basis of source item traceability prediction of a leakage source. The content of the invention mainly relates to development and improvement of a gas diffusion model, data acquisition and processing, construction of a risk prediction model, uncertainty analysis, research of an emergency response strategy, case analysis and verification and system integration and demonstration application, and through comprehensive consideration of factors such as gas properties, meteorological conditions and topographic features, prediction accuracy is improved, and the risk of the gas diffusion model is predicted. Scientific basis is provided for risk management, and practicability and popularization of the prediction system are finally realized.

The invention discloses a scoliosis evaluation system and device based on multi-modal information fusion, and belongs to the field of spine health. A back pressure electromyographic signal acquisition module acquires stress electromyographic information of a back muscle group; the thermal imaging module obtains a thermal imaging information graph of a human body; the plantar pressure acquisition module acquires plantar pressure information; the multi-mode data fusion module inputs the stress myoelectricity information, the thermal imaging information graph and the plantar pressure information into the encoder layer, a cross attention mechanism is adopted in the feature matching layer to fuse features from different modules, a classifier model is used for scoliosis evaluation, and classification is conducted according to the scoliosis degree. According to the application, the collaborative mechanism of the stress myoelectricity information, the thermal imaging information graph and the plantar pressure information of the back during scoliosis is utilized, multi-modal data fusion is performed on the stress myoelectricity information, the thermal imaging information graph and the plantar pressure information of the back, full-automatic evaluation is realized through a multi-modal data fusion model, the evaluation accuracy is high, and no radiation is generated.

The invention provides a senile dementia clock drawing experiment automatic evaluation method and system based on non-rigid registration, and belongs to the technical field of artificial intelligence, and the method comprises the steps: S1, obtaining clock drawing experiment data of a testee, and carrying out the preprocessing of the obtained clock drawing experiment data, and obtaining a first point cloud image; s2, performing non-rigid registration on the first point cloud image and a preset standard template to obtain a second point cloud image after non-rigid registration; s3, performing clock drawing experiment analysis on the second point cloud image to obtain an analysis result; and S4, scoring the analysis result according to a preset scoring standard to obtain a total score of the senile dementia scale of the testee. The non-rigid registration method based on machine vision has the advantages that automatic and intelligent evaluation of the senile dementia scale clock drawing experiment is achieved, evaluation efficiency and accuracy are improved, a more effective auxiliary means is provided for diagnosis and treatment of senile dementia patients, and the non-rigid registration method based on machine vision has wide application prospects.

The invention relates to a ship collision risk assessment method and system based on multi-source heterogeneous data, and the method comprises the steps: obtaining a plurality of continuous frames of images shot at a fixed position, and extracting the center pixel coordinates of all ships in each frame of image; acquiring AIS data of the ship; aiming at the center pixel coordinates of all ships in each frame of image, identifying the ships with breakpoints in the tracks and the ships detected by mistake, and realizing the correction of the ship tracks; based on the pixel coordinates of the corrected ship trajectory, converting the pixel coordinates into world coordinates to obtain visual data; obtaining fusion data by matching the AIS data with the visual data; and based on the fusion data, ship collision risk assessment is realized. Compared with the prior art, the method has the advantages of improving the evaluation accuracy under the condition that the AIS data is partially missing, overcoming the problem of feature drift and the like.

The invention relates to a network security situation awareness method based on a deep neural network. The method comprises the following steps: acquiring and preprocessing a network security data set to obtain an initial data set; constructing a situation element extraction model, and inputting the initial data set into the situation element extraction model to obtain network security attack elements; carrying out feature extraction based on the network security attack elements, and carrying out feature-level data fusion to obtain a fused feature vector; constructing a network security situation assessment model, and inputting the fusion feature vector into the network security situation assessment model to obtain a situation assessment result; forming a multi-dimensional time sequence data set based on the network security attack elements and the situation assessment result, and training a deep neural network security situation prediction model based on a sample convolution and interaction module based on the multi-dimensional time sequence data set; and inputting the current situation data into the trained deep neural network security situation prediction model, and outputting a network situation prediction result. And the network security situation awareness evaluation and prediction accuracy is improved.

The invention provides a goaf tunnel lining structure safety evaluation method and system under heavy rainfall, and relates to the technical field of underground and tunnel engineering.The method comprises the steps that a tunnel evaluation index is generated by obtaining rainfall intensity, rainfall duration, geological characteristics, soil permeability and other data; the method comprises the following steps: analyzing interaction of rainfall moisture on lining water pressure and surrounding soil deformation, calculating surrounding rock stress, calculating water pressure generated after rainfall permeation, quantifying dynamic influence of the water pressure on the lining water pressure, and calculating a safety coefficient of a lining structure by integrating a tunnel evaluation index, the surrounding rock stress and the water pressure after permeation. And obtaining lining displacement, crack development speed and water flow permeation data, generating a risk coefficient, comparing the risk coefficient with a safety coefficient, and judging whether to give out early warning or not. According to the method, related data of the tunnel lining are comprehensively analyzed, a tunnel safety evaluation and early warning mechanism is established, and the safety of the goaf tunnel lining structure under the heavy rainfall condition is ensured.

The invention belongs to the field of artificial intelligence, finance and medical treatment, and relates to an algorithm model verification and precision evaluation method, which comprises the steps of calling a pre-constructed original algorithm model to process an original sample file set to obtain a to-be-evaluated model result, and associating an original sample file with the to-be-evaluated model result to obtain a to-be-evaluated model result; performing annotation task allocation according to the generated annotation task allocation scheme, receiving a truth value result submitted by the annotation personnel, comparing a to-be-evaluated model result with the corresponding truth value result to obtain an initial model precision evaluation result, optimizing the model according to the initial model precision evaluation result, and evaluating the optimization algorithm model to obtain an evaluation result. And generating a model performance comparison report. The invention further provides an algorithm model verification and precision evaluation device, equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the original sample file can be stored in a block chain. According to the method, the model iteration efficiency can be greatly improved, and the precision and performance of the model are improved.

The invention discloses a data quality management method and system, a storage medium and electronic equipment, and relates to the technical field of data management. The method comprises the steps of obtaining a target plan of a military-civilian fusion project, and determining key sub-projects in the target plan; collecting target data of each key sub-project, and determining associated data of the target data; checking and evaluating the target data to obtain a first evaluation result; evaluating the associated data based on the first evaluation result to obtain a second evaluation result of the target data; and generating a data quality report based on the first evaluation result and the second evaluation result, and visually displaying the data quality report. By implementing the technical scheme provided by the invention, the accuracy of data quality evaluation can be improved.

The invention belongs to the technical field of aging state evaluation, and discloses a high-voltage dry-type bushing insulation aging state evaluation method based on frequency domain characteristics. According to the method, the FDS is adopted, and the aging state of the insulating material is evaluated according to the change of the complex capacitance, the complex dielectric constant, the dielectric loss factor and the like of a medium along with the frequency under a low-voltage sine alternating electric field. The FDS shows remarkable superiority in a plurality of aspects. The method comprises the following steps: firstly, measuring a dielectric constant and dielectric loss of an insulating medium in a wide frequency range by an FDS method so as to obtain response characteristics of the insulating material under different frequencies; according to the method, the frequency domain dielectric spectroscopy FDS is combined with the advantages of wide frequency range measurement, frequency domain feature recognition, comprehensive reflection of electrical characteristics and the like, so that accurate evaluation of the insulation aging state of the high-voltage dry-type bushing is realized. The safety and the stability of a power system can be improved, and faults and accidents caused by insulation aging can be reduced.

The invention discloses a data-based XGBoost model construction method for evaluating the cerebral hemorrhage recurrence risk of a patient, and the method comprises the steps: S10, reading and processing the data of the cerebral hemorrhage patient, and obtaining the data of a training set; s20, receiving the training set data, processing the training set data by adopting LASSO regression and a step-by-step recursion program, and outputting a plurality of variable features; s30, judging whether the p value of each characteristic variable is less than 0.05 or not; s40, when the p value of the characteristic variable is smaller than 0.05, outputting the variable characteristic; s50, constructing and optimizing parameters of the first model, and outputting an XGBoost model; and S60, carrying out visual interpretation on an evaluation result of the XGBoost model by using an SHAP (Secure Hardware Application Program) and an LIME (Local Instrumentation Model Element) interpreter Compared with the prior art, the method provided by the invention has the advantages that the model resolution AUC is 0.92 and the prediction accuracy is 0.95 by taking nine characteristic factors as internal verification of the model; the model resolution ratio AUC tested by the test set is 0.93, and the prediction accuracy is 0.91.

The invention relates to a GIS particle defect evaluation method and equipment fusing vibration and partial discharge, and the method comprises the steps: obtaining an ultrasonic signal and an ultrahigh frequency signal caused by the partial discharge of a GIS, generating a phase distribution diagram of the partial discharge, comparing the phase distribution diagram with a reference map, and judging whether a particle defect exists or not; for particle defects, the particle size is calculated based on the average discharge capacity and the maximum discharge capacity of the ultrahigh-frequency signals; and acquiring a vibration signal, combining the calculated particle size, outputting the predicted particle horizontal diffusion area and the maximum jumping height, and calculating a danger degree index of the particle defect so as to evaluate the danger degree of the GIS particle defect. Compared with the prior art, the method has the advantages that the sensitivity, the accuracy and the real-time performance of detection are improved, and more comprehensive evaluation is provided for the health state of the GIS equipment.

The invention discloses a communication engineering safety management evaluation method, which comprises the following steps of: designing a communication engineering safety management questionnaire survey table, collecting real-time data through a field automatic monitoring system, and cleaning the real-time data; determining evaluation factors according to the questionnaire data and the monitoring data, and constructing a fuzzy evaluation matrix; weight vectors of the evaluation factors are determined, and the contribution degree of each evaluation factor to the safety management condition is calculated through a fuzzy comprehensive evaluation method; establishing a communication engineering safety management knowledge graph by using historical safety accident cases, and identifying key influence factors; determining the contribution degree of a correction factor to the safety management condition by using the key influence factor, and correcting the contribution degree; and performing communication engineering safety evaluation by using the contribution degree of the corrected evaluation factor. According to the method, the questionnaire data and the monitoring data are combined, the fuzzy evaluation matrix is constructed, the knowledge graph is constructed in combination with the historical safety accident cases, the key influence factors are identified, and the correction factors are determined, so that the pertinence and practicability of the evaluation result are improved.

The invention relates to the technical field of speech recognition effect evaluation, in particular to a speech recognition evaluation method based on artificial intelligence. The method comprises the following steps: acquiring to-be-evaluated voice data, analyzing the to-be-evaluated voice data to obtain a voice fluency index, a voice clarity index and a voice accuracy index, and calculating to obtain a first voice quality index of the to-be-evaluated voice data based on the voice fluency index, the voice clarity index and the voice accuracy index; inputting the to-be-evaluated voice data into the voice content recognition model, outputting a prediction text of the to-be-evaluated voice data, and performing comparative analysis on the prediction text and the labeled text to obtain a second voice quality index of the to-be-evaluated voice data; and obtaining a voice recognition evaluation result corresponding to the to-be-evaluated voice data based on the first voice quality index and the second voice quality index. According to the invention, the evaluation efficiency and accuracy of the speech recognition effect can be improved.

The invention provides a potential risk event intelligent analysis method based on comparative learning, and belongs to the technical field of risk event intelligent analysis. A memory unit is regarded as a prototype of a normal video mode, and on the basis of memorizing video data features, contrast learning is introduced into the memory unit for training. A data enhancement strategy is adopted: different video sections in the same video belong to positive samples; data enhancement is performed on a video, and spatio-temporal information of the video, such as disorder, random rotation of video frames, continuous repetition of single video frames and the like, is destroyed as a negative sample sequence. Reconstruction errors are used as a standard for judging the drowning event, the similarity between an input video stream and a memory unit is used as an auxiliary standard for judging the drowning event, and the specific mixed decision-making method is completed in a weighting mode. According to the method, the general problem of network reconstruction and the problem of a memory model in anomaly detection are solved.

The invention provides a question and answer model training method, an object analysis method and related equipment. The question and answer model training method is used for solving the problem that question and answer accuracy of a target question and answer model obtained through training is low. The method at least comprises the following steps: splitting a selected sample question into a plurality of sub-questions by adopting a pre-trained question and answer model, and generating a predicted answer of the sample question based on the respective sub-answers of the plurality of queried sub-questions; based on a plurality of preset question and answer evaluation strategies, respectively executing the following steps: respectively evaluating the contribution value of each inquired sub-answer when the predicted answer is generated by adopting one question and answer evaluation strategy to obtain an evaluation result; and when determining that the plurality of obtained evaluation results meet training screening conditions, adjusting model parameters of the pre-training question and answer model based on the sub-answers. The whole question and answer process is comprehensively evaluated, the question and answer process meeting the training screening condition is adopted for training, and the question and answer accuracy of the target question and answer model obtained through training can be improved.

One or more embodiments of the invention provide a security situation assessment method of a power network and related equipment. The method comprises the following steps: acquiring network security situation data of a plurality of nodes in a power network, the network security situation data being extracted according to a preset fixed field; constructing a security situation data knowledge graph according to the network security situation data; inputting the network security situation data set into a situation assessment model to obtain a situation assessment result; wherein the situation assessment model comprises a graph convolutional neural network layer and a transformer layer. Through the method, the power network security situation assessment effect can be effectively improved, so that the security performance of the power network is improved.

The invention provides a performance evaluation method and device for satellite-based enhanced navigation, and relates to the technical field of high-precision positioning. The method comprises the steps that the area range of a to-be-evaluated area is acquired, grid point division operation is carried out on the to-be-evaluated area according to the area range, and the grid point division operation is used for dividing the to-be-evaluated area into a plurality of sampling grid points; acquiring enhanced information data and broadcast ephemeris data of the target satellite, and calculating an enhanced orbit error and an enhanced clock error of the target satellite according to the enhanced information data and the broadcast ephemeris data; the enhanced orbit error and the enhanced clock error of the target satellite and the positions of the sampling grid points serve as input, multiple sets of positioning errors of the target satellite on the multiple sampling grid points are calculated through a precise positioning model, and one sampling grid point corresponds to one set of positioning errors; constructing a positioning error sequence through the multiple groups of positioning errors, and calculating convergence time and positioning precision when convergence conditions are met according to the positioning error sequence; and sending the convergence time and the positioning precision as performance evaluation results to the terminal equipment. According to the method, the problem that the positioning performance evaluation result cannot comprehensively and objectively reflect the actual capability of the enhanced service at the service area boundary or even under the condition that no actual measurement station is available due to the fact that the existing satellite-based enhanced service positioning performance evaluation method cannot uniformly select the tracking stations in the specified service area to perform positioning performance evaluation is solved.

The invention relates to the technical field of sewage treatment, in particular to a sewage treatment effect assessment method and system based on data analysis, which improve the quality of sewage treatment effect assessment and realize accurate assessment of sewage treatment effect. The method comprises the following steps: acquiring biochemical data information of a water quality sample before and after sewage treatment and microscopic image information of the water quality sample after sewage treatment; performing water quality biochemical feature extraction on the water quality sample biochemical data information before and after sewage treatment, and calculating to obtain water quality biochemical feature change vectors before and after sewage treatment; inputting the water quality biochemical characteristic change vector into a water quality treatment biochemical effect evaluation model to obtain a first-order sewage treatment effect evaluation result; performing water quality image feature extraction on the water quality sample microscopic image information after sewage treatment to obtain a water quality image feature set; and aiming at each water quality image feature in the water quality image feature set, respectively comparing with a corresponding preset image feature.

The invention provides a potato yield evaluation method and device, electronic equipment and a storage medium, and relates to the technical field of agriculture, and the method comprises the steps: obtaining remote sensing image data, meteorological data, a soil nutrient comprehensive evaluation index and potato yield data of a potato planting region in a key growth period; acquiring various vegetation indexes of the potato planting area according to the remote sensing image data; screening out a plurality of second vegetation indexes with relatively high correlation from the plurality of vegetation indexes according to correlation coefficients of each vegetation index in the plurality of vegetation indexes and the potato yield data, and then constructing a plurality of initial yield evaluation models in one-to-one correspondence with the plurality of second vegetation indexes; determining the initial yield evaluation model with the highest precision in the plurality of initial yield evaluation models as a target yield evaluation model; and predicting the potato yield in the potato planting area according to the target yield evaluation model. The potato yield evaluation efficiency and evaluation accuracy can be improved.

The invention discloses a three-dimensional deformation-based land subsidence and disaster risk assessment method, system and device, and relates to the field of land subsidence disaster prevention, and the method comprises the steps: obtaining a synthetic aperture radar image set, underground water level dynamic characteristics and global navigation satellite system reference station data of a research region; based on the synthetic aperture radar image set, determining an unwrapping graph queue by adopting a coherence-based small baseline subset differential interference synthetic aperture radar method; estimating a vertical rate and a horizontal rate according to the unwrapping graph queue and the reference station data of the global navigation satellite system; performing high-coherence point selection and two-dimensional deformation decomposition on the direction data, further determining the surface displacement of the stratum in the research area, and describing the surface subsidence intensity of the research area by adopting subsidence difference and angle deformation; and evaluating the disaster risk of land subsidence in the research area according to the earth surface displacement of the stratum in the research area and the dynamic characteristics of the underground water level. According to the invention, the accuracy of land subsidence and disaster risk assessment is improved.

The invention provides a power grid risk early warning method and system based on artificial intelligence, and belongs to the technical field of power systems, and the method comprises the steps: determining a first type of power access node and a second type of power access node in a to-be-monitored power grid system, determining the output support capability of the first type of power supply access nodes and the output fluctuation state of the second type of power supply access nodes, and constructing a plurality of risk observation areas according to the matching result of the output support capability of the first type of power supply access nodes and the output fluctuation state of the second type of power supply access nodes, and determining a comprehensive risk level of each risk observation area, determining a risk level of the to-be-monitored power grid system according to a combination result of the comprehensive risk levels of the plurality of risk observation areas, determining an early warning strategy matched with the risk level of the to-be-monitored power grid system, and executing the early warning strategy. The invention aims to improve the problems of low early warning accuracy and poor universality of the existing scheme.

The invention discloses a building construction progress monitoring method and system, and relates to the field of data analysis, and the main scheme is as follows: calculating a progress deviation index # imgabs4 # according to a progress and engineering quantity equilibrium deviation index # imgabs0 #, a quality and resource comprehensive deviation index # imgabs1 #, a safety and design deviation index # imgabs2 # and a comprehensive operation deviation index # imgabs3 #; according to the method, progress deviation evaluation can be carried out from multiple dimensions, and the problems that the real progress deviation condition cannot be reflected and the effectiveness of a resource allocation decision is reduced due to the fact that the data type for evaluating the construction progress deviation is single and is lack of diversity and the relation among various factors is ignored are solved; the construction progress deviation grade is judged according to the deviation grade threshold value set and the progress deviation index # imgabs5 #, the deviation degree can be accurately judged, the construction progress adjustment method is determined according to the construction progress deviation grade, resources can be reasonably allocated, and adjustment measures can better fit the actual situation.

The invention relates to an InSAR-based underground excavation space geometric parameter inversion method and system. The method comprises the steps of obtaining a ground surface deformation field of a research area where an underground excavation space is located based on an InSAR technology; underground space positioning prior information is collected according to the earth surface deformation field; geological prior information of the underground space is collected; fusing to obtain a priori information set of underground space geometric parameters; according to the prior information set, constructing a function relationship between InSAR observation deformation and ground surface three-dimensional deformation induced by underground space through a probability integral method; establishing a likelihood function based on the InSAR observation data and the earth surface deformation data; inverting the underground space geometric parameters based on the Bayesian theorem and a likelihood function to obtain underground space geometric parameter posterior distribution; and solving the underground space geometric parameter posterior distribution by using a differential evolution adaptive Metropolis algorithm to obtain an inversion result. And the accuracy and precision of the inversion result are improved.

The invention provides a heart conduction health degree assessment method, medium and system, and belongs to the technical field of electrical digital data processing.The heart conduction health degree assessment method comprises the steps that firstly, heart conduction signals of multiple testers and expert assessment results are collected; splitting the signals by using an empirical mode decomposition method to obtain a standard signal matrix and a non-standard signal matrix; then calculating the wavelet feature of the standard signal and the Fourier feature of the non-standard signal; based on the features, an improved convolutional neural network model is constructed, including a spatial pyramid pooling layer, an attention mechanism, residual connection and multi-scale feature fusion. After being trained, the model can be used for evaluating the health degree of a to-be-evaluated heart conduction signal. According to the method, the time-frequency characteristic of the signal is fully utilized, more accurate health condition assessment is realized through improvement of the deep learning model, and the problem that the overall health condition is difficult to comprehensively and objectively assess since most of the prior art only focuses on the local characteristic of the heart conduction system is solved.

The invention discloses a language model evaluation method and device, electronic equipment and a computer readable storage medium. The method comprises the steps of obtaining at least one question answer pair in a preset vertical field; the question-answer pairs comprise questions and answers corresponding to the questions; performing anthropomorphic processing on the target question through a natural language processing model to obtain at least one question text of the target question in different question modes; the target question is a question in any question answer pair; questioning a question answering model corresponding to a preset vertical field based on the at least one question text, and obtaining at least one reply correspondingly output by the question answering model; and performing quality evaluation on the question answering model according to the answer corresponding to the target question and the at least one reply to obtain a quality evaluation result of the question answering model. Therefore, the efficiency and accuracy of evaluating the question answering model can be effectively improved, and meanwhile, the question answering model can be comprehensively evaluated.

The invention discloses an attenuation analysis method and an attenuation analysis system for smoke component filtering performance, and relates to the technical field of performance detection.The method comprises the steps that a tracking medium is introduced, and a particle non-targeting rule of mainstream smoke components is determined; carrying out physical modeling on the filter assembly to construct a hydrodynamic model; smoke filtering dynamic simulation is carried out by taking the suction characteristics as simulation variables, and a first space section is monitored; determining particle distribution characteristics of the mainstream smoke components in the test period according to the second space section; and constructing a filtering performance attenuation curve based on particle distribution characteristics. According to the method, the technical problem that the design and operation parameters of the filter assembly are difficult to effectively optimize due to the lack of accurate analysis means for the attenuation law of the smoke component filtering performance in the prior art is solved, and the change law of the smoke component filtering performance under different conditions is comprehensively mastered; and the performance evaluation accuracy and reliability of the flue gas filtering system are effectively improved.

The invention relates to the field of image detection, in particular to a multi-dimensional data shrimp group health automatic evaluation method, which comprises the following steps of: arranging a main monitor and an auxiliary monitoring cluster above a shrimp group to be monitored, shooting the shrimp group to be monitored, forming a corresponding health evaluation image, numbering according to a shooting time sequence, and pre-learning to obtain a health evaluation result. The health learning model learns the main assessment pre-data, generates a bubble number change diagram, compares the bubble number change diagram with a number threshold value, generates a corresponding health assessment result according to a comparison result, and learns the auxiliary assessment pre-data to generate an auxiliary judgment diagram when the health assessment result is unqualified; and performing weighted average calculation on the bubble number change diagram and the auxiliary judgment diagram according to a preset proportion, comparing an evaluation average value with a number threshold value, generating a final health evaluation result, and automatically evaluating the health condition of the shrimp group by utilizing machine learning, thereby improving the evaluation efficiency and accuracy.

The invention relates to a vehicle insurance premium evaluation method and device, a vehicle and a storage medium. The method comprises the following steps: acquiring vehicle driving data and battery state data of a vehicle in a previous preset period; according to the vehicle driving data, determining a vehicle purpose coefficient of the vehicle, according to the battery state data, determining a standing duration coefficient of a battery and determining a charging habit coefficient of a user, and according to the vehicle driving data and the battery state data, determining a driving style coefficient of the user; calculating a comprehensive coefficient by adopting the vehicle purpose coefficient, the standing duration coefficient, the charging habit coefficient and the driving style coefficient; the comprehensive coefficient is used for evaluating the use risk of the vehicle and the health state of the battery; and based on the comprehensive coefficient, evaluating the vehicle insurance premium required by the user for insurance. According to the scheme provided by the invention, the use risk of the vehicle and the health state of the battery can be comprehensively and accurately evaluated, so that the evaluation accuracy of the vehicle premium is improved.