844 results about "Assessment methods" patented technology

The invention discloses a construction site safety risk intelligent assessment method and system, and belongs to the technical field of engineering supervision. The method comprises the following steps: S100, collecting multi-source data including an equipment state, an image video, a personnel state, an equipment distance, an environment parameter and an engineering text in real time; s200, performing space-time alignment, preprocessing and cross-modal feature fusion on the multi-source data; s300, outputting a risk score and a risk level label based on the dynamic risk knowledge graph and a multi-model fusion algorithm; s400, generating a hierarchical disposal strategy according to the risk scoring hierarchy, and realizing risk closed-loop management and control through rectification verification and cooperation of multiple parties; and S500, outputting a three-dimensional visual risk distribution map and a compliance report. Through technologies of multi-source data fusion, dynamic risk mapping knowledge, intelligent closed-loop management and the like, comprehensive perception, accurate evaluation, efficient management and control and compliance landing of engineering supervision safety risks are realized, the occurrence rate of safety accidents is remarkably reduced, and reliable technical support is provided for intelligent construction site construction.

The invention discloses a screening and evaluating system and method for cognitive impairment of old people, and belongs to the technical field of intelligent medical treatment. According to the system, electroencephalogram, VR / AR interaction, physiology and gait data are collected through the multi-modal acquisition module, the data processing module carries out data processing, dynamically selects a scale and carries out fusion analysis, the man-machine interaction module realizes natural interaction, and the output module generates a visual report and carries out early warning. The core algorithm comprises dynamic scale selection, multi-modal fusion evaluation and the like. The evaluation method comprises the steps of data acquisition, scale selection, data fusion, confidence verification, output and the like. Compared with a traditional method, the method overcomes the defects that subjectivity is high, and static evaluation, data splitting and base layer application are limited. The cognitive impairment assessment method can accurately and dynamically assess cognitive impairment, improves assessment accuracy, realizes full utilization of data, is simple and convenient to operate, is suitable for various scenes, provides efficient and reliable support for diagnosis, monitoring and intervention of cognitive impairment, and promotes development of intelligent medical treatment.

The invention relates to the technical field of multi-modal data, provides a pilot competency dynamic evaluation method, system and device based on multi-modal data and a storage medium, and solves the problems of low accuracy of pilot competency evaluation and poor pertinence of training guidance. The method comprises the steps of collecting flight control data, physiological signal data, psychological assessment data, subjective scale data and international civil aviation organization core competency information, performing alignment fusion on multi-source data by adopting a time synchronization algorithm, and identifying an attention fixation mode based on a hidden Markov model. And constructing a workload index by fusing a subjective scale and a physiological entropy value, inputting the multi-modal features into a pre-trained competency assessment model, outputting three levels of psychological assessment indexes including a basic ability layer, a dynamic presentation layer and a risk early warning layer, and finally generating a personalized training report. According to the invention, the accuracy of pilot competency evaluation and the pertinence of training guidance are improved.

The invention provides a data asset multi-dimensional assessment method and system based on a block chain and machine learning, an assessment index system covering dimensions such as data quality, value, risk and security is constructed, a block chain technology is adopted to carry out assessment process and result evidence storage, a machine learning model is introduced to dynamically optimize index weights, and the assessment efficiency is improved. And a data asset marketization pricing mechanism is explored and formed. According to the method, the problems that an existing data asset evaluation method is single in dimension, high in subjectivity and lack of credibility are solved, scientific evaluation, marketization pricing and credible publication of the data asset value are achieved, important reference is provided for data element circulation, and the method has great significance in promoting prosperous development of the data element market.

The invention provides an evaluation method, system and device for a large language model and a storage medium, and the method comprises the steps: constructing a sample data set which comprises input data and corresponding reference answers, inputting the input data into an evaluated model, and generating an output result; a multi-dimensional evaluation index system is designed according to task requirements, a dynamic weight is allocated to each evaluation index, each evaluation index is provided with scoring standard description, and the evaluation indexes comprise at least two items of context correlation, term consistency, language fluency and expression accuracy; combining the input data, the reference answer, the output result and the scoring standard description into a standardized input instruction, and calling an evaluation model to perform multi-dimensional scoring on the standardized input instruction to generate an evaluation result; and analyzing the evaluation result according to a preset threshold value, and generating a structured feedback suggestion containing an improvement direction. According to the method, rapid optimization and iteration of the large language model can be effectively supported.

The invention discloses a lightweight concrete column beam durability evaluation method and device based on twinborn simulation, and relates to the technical field of twinborn simulation, and the method comprises the steps: collecting the degradation characteristics of a lightweight concrete column beam, and constructing a three-dimensional multi-scale structure model; executing coupling simulation to generate a twin simulation model; monitoring a column beam through a multi-source sensor, and determining a real-time performance degradation parameter; updating the twinborn simulation model by using the degradation parameters; and collecting future environment and load data, inputting the future environment and load data into the updated model, simulating a degradation evolution process, and generating a durability evaluation result. The technical problem that a prediction result and actual service performance have significant deviation due to the fact that a traditional durability evaluation method is difficult to dynamically couple a multi-physical field environment and a material microcosmic degradation mechanism is solved, and a dynamic evaluation system for virtual-real mapping is constructed through twinborn simulation. And the prediction precision of the degradation process of the lightweight concrete column beam and the dynamic deduction capability of the service life are improved.

The invention relates to the technical field of intelligent equipment evaluation and fault prediction, and discloses a dynamic equipment quality evaluation method based on multi-agent collaborative reinforcement learning, and the method comprises the steps: obtaining original data from equipment and a sensor, and carrying out the preprocessing; analyzing the preprocessed data, and identifying key factors influencing the quality of the equipment; constructing a plurality of expert models, and predicting and evaluating an analysis result; fusing the weight of each expert model to obtain a comprehensive evaluation result; and performing risk assessment on the comprehensive assessment result, extracting key influence factors and decision rules, and generating an assessment result. Efficient fusion of multi-source data and automatic identification of key factors are realized, and the accuracy and reliability of equipment quality evaluation are remarkably improved; a model fusion result is optimized through dynamic weight distribution, so that the prediction performance is improved; the problems that in the prior art, data fusion is difficult, key factor identification depends on experience, and the interpretability of an evaluation result is insufficient are solved.

The invention discloses a personalized hypertensive nephropathy risk assessment method and system based on a graph convolutional network, and the method comprises the steps: collecting modal data related to hypertensive nephropathy, and carrying out the preprocessing; constructing a patient similarity graph; performing multi-source information fusion and domain knowledge integration; sparse processing is carried out on the graph structure, and standardization processing is carried out on an adjacent matrix; modeling through a time sequence diagram convolutional network and a long-short term memory network, and capturing the evolution trend of patient characteristics along with time; through a self-attention mechanism and a graph regularization item, the transmission of important features is enhanced; a multi-head attention mechanism is adopted, and different types of relation modes are captured; optimizing model parameters by using an Adam optimization algorithm and a regularization method; and a risk assessment result is optimized by adopting weighted stacking and a self-adaptive attention mechanism. According to the risk assessment method, accurate assessment of the risk of hypertensive nephropathy is realized by constructing the multi-dimensional and dynamically changing patient similarity graph.

The invention discloses a mine safety production risk monitoring and early warning system and method, and relates to the technical field of mine safety, and the system comprises a data collection module, a data integration and storage module, a correlation analysis and model construction module, a real-time monitoring and early warning module and an emergency decision support module. According to the method, a multi-dimensional data acquisition system is constructed, multi-source data of geology, production, equipment, personnel and the like are fused, deep analysis is carried out by applying a multi-dimensional association algorithm based on mutual information, potential relationships among the data are comprehensively mined, comprehensive and accurate assessment of mine safety production risks is realized, and a real-time dynamic risk assessment model is utilized to realize comprehensive and accurate assessment of mine safety production risks. The risk state is updated in real time according to latest collected multi-dimensional data, a server-side timed task obtains data from a data integration and storage module according to the minute-level frequency and inputs the data into a model, an early warning mechanism is triggered immediately when the risk level calculated by the model exceeds a preset threshold value, risk mutation can be perceived in time, and the hysteresis of a traditional assessment mode is overcome.

The invention relates to the technical field of street cleanliness evaluation, and discloses a street cleanliness real-time evaluation method based on multi-modal data fusion. The method comprises the following steps: firstly, acquiring initial acquisition parameters and original monitoring data of the multi-modal sensing equipment, wherein the data comprises overlapped region environment information and is acquired according to a preset direction; then performing multi-modal preprocessing on the original data, extracting heterogeneous feature difference points and generating an incidence relation graph; dividing an effective monitoring range based on the atlas, and adjusting acquisition parameters to form an optimized parameter set; performing space-time alignment on the adjusted data to generate a multi-source data sequence; and finally, performing fusion processing on the overlapped region, eliminating feature conflicts and information redundancy, and generating a real-time evaluation result. In addition, the incidence relation graph can be corrected in combination with manual inspection records. According to the method, multi-modal data can be effectively fused, real-time and accurate evaluation of the street cleanliness is realized, and the evaluation reliability and adaptability are improved.

The invention relates to the technical field of environmental parameter measurement, in particular to an indoor environment quality intelligent evaluation method based on multi-dimensional data fusion, which comprises the following steps: step 1, performing time-space synchronous acquisition on multi-source data; 2, a dynamic correlation model is constructed, a parameter coupling matrix is generated based on inter-parameter time-delay cross-correlation analysis, the time-delay cross-correlation analysis is achieved through sequence translation in a sliding time window and correlation coefficient calculation, an environment state class is constructed according to a parameter mean vector, fluctuation intensity and abnormal event marks, and a dynamic correlation model is constructed; each environment state class is bound with an independent parameter contribution degree weight set; step 3, performing double-stage environment quality evaluation; and step 4, adaptive increment optimization: when the deviation between the comprehensive evaluation index and the subjective evaluation exceeds a threshold value, adjusting a parameter contribution degree weight set of an environment state class, and periodically reconstructing a parameter coupling matrix. The prediction capability is improved by considering the time-delay coupling between the parameters; the system has self-learning and optimization capabilities, and the stability of long-term operation of the system is improved.

The invention provides a multi-dimensional quality evaluation method, device and equipment for carbon footprint data and a storage medium, and the method comprises the steps: obtaining multi-source heterogeneous data of a product, the multi-source heterogeneous data comprising basic information of the product and carbon footprint related data of the whole life cycle of the product; generating carbon footprint data of the product based on the multi-source heterogeneous data; and performing multi-dimensional quality evaluation on the carbon footprint data based on a pre-constructed carbon footprint knowledge graph, a time dimension detection model, a space dimension detection model and a cross-dimension detection rule. According to the invention, the carbon footprint knowledge graph, the time dimension detection model, the space dimension detection model and the cross-dimension detection rule are introduced to realize semantic, time, space and cross-dimension multi-dimensional quality evaluation of the carbon footprint data, and the detection dimension can cover the multi-dimensional features required by the carbon footprint calculation. Therefore, it is ensured that the carbon footprint data quality evaluation result can reflect the influence on the carbon footprint calculation reliability more accurately.

The invention discloses an intelligent assessment method and device for body rehabilitation of a stroke patient, and relates to the technical field of rehabilitation intelligent assessment, and the method comprises the steps: connecting a medical information management system, and obtaining the stroke medical record information of the current patient; performing identification according to the stroke medical record information; the multi-modal sensing module is connected with the rehabilitation device, performs rehabilitation motion sensing on the limb on the affected side according to the multi-modal sensor, and outputs a multi-modal sensing data set; collecting a multi-modal sensing sample set of a healthy limb corresponding to the limb on the affected side; a rehabilitation evaluation model is constructed, the rehabilitation evaluation model evaluates the multi-modal sensing data set, and rehabilitation level indexes are obtained; the rehabilitation level indexes are fed back to the rehabilitation device for training mode optimization. The technical problems that in the prior art, body rehabilitation evaluation depends on subjective judgment, the limb function recovery condition of the patient is difficult to comprehensively reflect, rehabilitation evaluation efficiency is poor, and the result is inaccurate are solved, and the technical effect of improving rehabilitation efficiency and evaluation accuracy is achieved.

The invention relates to the technical field of safety simulation training drilling, in particular to an intelligent assessment and evaluation method for mine accident emergency risk avoiding, which comprises the following steps of: S1, acquiring multi-dimensional data in an emergency risk avoiding capability drilling process of a student; s2, performing data preprocessing on the multi-dimensional data, and constructing an evaluation data set; s3, constructing an emergency risk avoiding capability evaluation system, wherein the system comprises evaluation items and distribution weights of the evaluation items; s4, establishing a weight evaluation model, and dynamically adjusting the distribution weight of the evaluation item in combination with the environmental data; and S5, scoring is completed according to the emergency risk avoiding capability evaluation system, and a personalized evaluation report is generated. According to the invention, comprehensive, objective and real-time evaluation of the emergency risk avoiding capability of the miner is realized, and the influence of human factors on the evaluation result can be reduced; the method can flexibly adapt to different environments and scenes through dynamic adjustment of the evaluation model, provides personalized evaluation reports and improvement suggestions, and effectively assists students in improving the emergency risk avoiding capability.

The invention discloses an engineering quality safety risk intelligent assessment method and system, relates to the technical field of engineering quality safety assessment, adopts multiple types of sensors to collect data, breaks through the limitation that the traditional structure monitoring only depends on a single index to judge the risk, and realizes cross-parameter and cross-component structure state comprehensive perception. The problem that the structure risk is commonly evolved by multiple factors is solved, and the overall recognition precision is improved. A sliding window and a periodic variable coefficient calculation mechanism are introduced, and the system can dynamically extract fluctuation characteristics of structure response and identify different evolution forms of short-time fluctuation, medium-term creep and long-term mutation. And the defects of insufficient abnormal precursor identification capability and serious hysteresis response in the traditional method are overcome. According to the method, an abnormal mutation index AEI and an evolution acceleration factor EAF are constructed, the formation process and the development path of the risk are identified no longer only on the basis of whether a certain moment value exceeds a threshold value or not, and the risk process perception capability is improved.

The invention provides an intelligent safety and stability assessment method, system and device for an underground cavern and a medium, and the method comprises the following steps: S1, building a three-dimensional geological model of the underground cavern according to initial geological exploration data; s2, multiple monitoring instruments are pre-buried in key parts of the underground cavern, and monitoring data are collected in real time; s3, constructing an underground cavern safety and stability evaluation model based on a deep learning algorithm; s4, inputting real-time monitoring data into an evaluation model, combining historical monitoring data and geological model information, analyzing and comparing with a three-dimensional numerical calculation result, adjusting geological parameters, performing reconstruction evaluation, and outputting a safety and stability level; and S5, according to an evaluation result, automatically generating early warning information and processing suggestions. According to the method, the problems of poor real-time performance and low data utilization rate of traditional evaluation under complex geological conditions are solved, and the intelligent level of construction safety monitoring of underground cavern groups such as hydropower stations and pumped storage projects can be effectively improved.

The invention relates to the technical field of meteorological engineering, and discloses a meteorological engineering project management risk assessment method, which comprises the following steps of: firstly, collecting historical risk event data of a meteorological engineering project and related meteorological conditions, engineering parameters and management measures, and performing data cleaning and integration to form a standardized risk data set; and based on the data set, constructing a risk prediction model, and predicting types and probabilities of risks possibly encountered by the project in the future. Meanwhile, a risk influence assessment system is established in combination with historical data, and the risk is comprehensively assessed from three dimensions of risk severity, occurrence possibility and controllability. The risk prediction and the assessment system are combined, the risk comprehensive influence value is calculated, then the risks are sorted and graded, and coping strategies are made for high-risk and medium-risk links. And finally, the risk assessment result and strategy are presented in a visual report form, comprehensive risk assessment support is provided for project management personnel, and the risk prevention and control capability of meteorological engineering project management is effectively improved.

The embodiment of the invention provides an estuary region storm surge disaster assessment method and device. The assessment method comprises the steps of obtaining basic geographic data, meteorological data, tide and wave data and disaster-bearing body data of a preset region of an estuary region; constructing a discrete grid according to the basic geographic data; according to the tide and wave data, constructing a storm surge and wave numerical simulation model in a discrete grid; simulating different typhoon scenes in a numerical simulation model of the storm surge and the waves according to the meteorological data to obtain simulation results; evaluating according to the simulation result to obtain the danger level of each area; evaluating according to the disaster-bearing body data to obtain the vulnerability level of each region; and determining the comprehensive risk level of each area according to the danger level and the vulnerability level. According to the embodiment of the invention, the evolution process of storm surge disasters can be simulated more accurately, and then the comprehensive risk level of each area of the estuary region is determined.

The invention provides a bridge state assessment method and system based on multi-source sensing data, and relates to the technical field of bridge state assessment, and the method comprises the steps: collecting historical monitoring data at a starting point, a center and an end point of a bridge, carrying out the preprocessing of the data, and taking a mean value for subsequent assessment; and updating the state value of the bridge part through Kalman filtering to obtain acceleration and displacement data after state updating. And then, an expert scoring method is adopted to carry out state evaluation on the data, and a random forest model is constructed to carry out state prediction. The current monitoring data is evaluated through the model, if the current moment state predicted by the model is lower than the state threshold value, the abnormal index of the bridge is calculated and then compared with the preset abnormal threshold value, and if the abnormal index exceeds the standard, early warning is given out. According to the method, multi-source data are effectively integrated, the accuracy and real-time performance of bridge monitoring are improved, and a scientific basis is provided for safe operation of a bridge.

The invention discloses a multi-dimensional drainage port overflow risk assessment system and assessment method, and belongs to the technical field of drainage port overflow risk assessment systems, and the system integrates ground monitoring and satellite remote sensing multi-source data, and collects related information of a drainage port in an omnibearing manner. The ground monitoring data provides accurate local hydrology, water quality and drainage facility operation state information, the satellite remote sensing data realizes large-range monitoring, the advantages of the ground monitoring data and the satellite remote sensing data are complementary, the data are fused through a weighted average method and a Kalman filtering method, the data accuracy and reliability are improved, and the weighted average method distributes weights according to data characteristics for preliminary fusion, so that the accuracy and reliability of the data are improved. The Kalman filtering method adapts to data dynamic change, such as water level data fusion according to a state equation and an observation equation optimization result, combines the advantages of ground high-precision monitoring and satellite wide-range coverage, accurately reflects water level change, quantifies the overflow risk of a drainage port, and provides effective support for urban water fine supervision.

The invention relates to the technical field of English language ability assessment, in particular to an English language ability assessment method and system. The method comprises the following steps: collecting a writing text uploaded by a student, and analyzing the literature and style category and sentence pattern structure of the writing text to obtain a content literature and style category sentence pattern structure; thirdly, identifying logic relation deviation in the text based on the structures, performing clustering, further quantizing the context fuzzy degree, and generating context fuzzy degree quantized data; and finally, designing an English language ability evaluation architecture by adopting a Q-learning algorithm. According to the invention, the English language ability evaluation technology is optimized, so that the English language ability evaluation technology is more accurate.

The invention provides a self-adaptive question bank test paper formation and difficulty evaluation method and system based on a knowledge point tree, and relates to the technical field of data processing, and the method comprises the steps: constructing a knowledge point relation tree, calculating a knowledge point path to group and cluster test questions, building a multi-layer test question index structure, and calculating a real-time difficulty correction coefficient in combination with historical answer data of examinees; dynamically adjusting the priority of the test questions, and finally screening and generating target test paper according to the distribution proportion of knowledge points. According to the method, personalized test paper composition is realized, the accuracy of test question difficulty evaluation and the rationality of test paper knowledge point coverage are improved, and the requirements of adaptive learning and accurate evaluation are met.

The invention discloses a wrist joint spasm assessment method and a wrist joint spasm assessment device based on multi-source data fusion. The rotating speed data, the rotating angular speed data and the angle change data of the wrist joint, the pressure change data of the whole palm, the acceleration change data of the palm, the torque change data of the wrist joint and the electromyographic change data of related muscles in the movement process are measured, and scoring is carried out in combination with an expert system. The evaluation of wrist spasm is realized by combining a multiple linear regression algorithm with a differential evolution algorithm; and based on the multi-source data fusion, establishing an evaluation model by using a Bi-LSTM network so as to realize spasm evaluation. The wrist serves as a spasm detection part, and compared with traditional spasm detection equipment, a more convenient data acquisition mode is achieved while the evaluation precision is guaranteed.

The invention provides a biodiversity influence comprehensive evaluation method and system based on multi-source ecological factors, and relates to the technical field of biodiversity analysis, and the method comprises the steps: obtaining multi-source ecological factor data of a geological disaster control influence region, and carrying out the maximum likelihood estimation and kernel function learning processing of the multi-source ecological factor data to obtain a space-time intensity field; performing adaptive time-frequency decomposition processing based on the time-space intensity field and the time sequence of the community functional traits to obtain a multi-scale modal set; performing low-rank dynamic recognition processing on the multi-scale modal set to obtain an approximate operator and control gain pair; performing sparse regression modeling processing to obtain a nonlinear state equation set; and carrying out distribution displacement measurement processing based on the nonlinear state equation set and preset quadrat baseline distribution to obtain a comprehensive evaluation result of the biodiversity influence. According to the method, the problems of single index, rough modeling and lack of multi-scene prediction capability in the prior art are solved.

The invention discloses a multi-dimensional evaluation method for credential environment migration, and particularly relates to the field of evaluation. According to the method, an evaluation index system covering infrastructure, an application system, data resources and security compliance is constructed, and comprehensive scanning is realized through instruction set detection, binary analysis and equal security compliance inspection; dynamically calculating the weight by adopting an AHP-entropy weight hybrid model, and triggering weight rebalance based on the volatility; through combination of static scanning and a dynamic probe, the compatibility, performance and safety differences are quantitatively analyzed; and finally, generating a visual risk assessment report, integrating data of each stage to calculate a compatibility score, a performance loss rate and a security level, providing instruction level difference comparison, a performance flame graph and a vulnerability attack path interaction view, and outputting a migration suggestion comprising a priority reconstruction list, an NEON instruction optimization scheme and a staged reinforcement plan.

The invention provides a multi-modal large model content security assessment method and device, and the method comprises the steps: inputting a multi-modal harmful data set into a to-be-assessed multi-modal large model, and obtaining an output response of the multi-modal large model; initializing a plurality of large-scale language models, and endowing the plurality of large-scale language models with different role features to form a heterogeneity evaluation system; performing independent evaluation on the multi-modal large model in the aspect of content security by utilizing a plurality of large language models; a plurality of large language models are used for jointly reviewing independent evaluation results, and consistent evaluation suggestions are achieved through cooperative discussion among the models; and obtaining a final evaluation result by using a hard voting mechanism and a soft voting mechanism. According to the method, a plurality of large language models with different role features are introduced for cooperative assessment, and multi-view and multi-level security risk assessment is realized. And through a mode of combining independent evaluation and collaborative review, diversified view angles are effectively integrated, and the reliability and consistency of evaluation results are remarkably improved.

The invention discloses an environment assessment method and system based on mine ecological restoration, and relates to the technical field of mine ecological restoration management. According to the environment assessment method based on mine ecological restoration, when mine ecology is restored, ecological restoration data are obtained after each restoration stage of a to-be-assessed and restored mine is finished, data analysis is carried out respectively, and a geological environment restoration assessment index, a water quality environment restoration assessment index and a plant environment restoration assessment index are obtained; according to the method, the comprehensive environment restoration evaluation index after each restoration stage is finished is compared and analyzed with the preset comprehensive environment restoration evaluation index threshold value, and the corresponding restoration and remediation measures are taken based on the comparison and analysis result, so that the comprehensive environment restoration evaluation index after each restoration stage is finished is obtained. Therefore, the restoration effect is dynamically monitored, the timeliness in mine ecological restoration is ensured, and the mine ecological restoration effect after each restoration stage is comprehensively evaluated.

The invention belongs to the technical field of power system risk assessment, and particularly relates to a new energy station broadband oscillation risk redisk assessment method based on multivariate data, which comprises the following steps: performing data acquisition and data preprocessing on a new energy station-power grid coupling system to obtain a historical data set; extracting oscillation characteristic indexes to form index vectors; carrying out oscillation frequency source positioning analysis, calculating a time-frequency spectrum energy function to obtain an oscillation energy spectrum, carrying out main frequency energy track extraction, and introducing a frequency-power cross analysis mechanism to realize accurate identification of an oscillation source device; constructing a source-network coupled transfer function, performing stability margin calculation, and judging the stable state of the coupled system; and based on the index vector and the stability margin, carrying out oscillation risk grade division, and combining a source positioning result to generate an oscillation redisk map. According to the invention, panoramic recovery and risk quantitative evaluation of the broadband oscillation problem of the new energy field station can be realized.

The invention discloses a multi-agent interpretable text psychological health level assessment method and system, and the method comprises the steps: training an original agent model according to a multi-field data set and a psychological dialogue assessment data set, and obtaining a dialogue reference model and an assessment reference model; according to the two reference models, respectively processing the user question data and the multi-round dialogue data to obtain a multi-dimensional dialogue feedback data set and an evaluation feedback data set; according to the multi-dimensional dialogue feedback data set and the evaluation feedback data set, training the two reference models to obtain a dialogue effect evaluation model and an evaluation effect evaluation model; according to the two evaluation models and the psychological dialogue evaluation data set, training the two reference models to obtain a target dialogue model and a target evaluation model; according to the target dialogue model and the target evaluation model, mental health level evaluation is completed. According to the invention, through the multi-dimensional feedback mechanism training model and the cooperative work of multiple models, the interpretability and confidence of the psychological health assessment system are improved.

The invention relates to the technical field of environment evaluation, and discloses a multi-dimensional environment comprehensive evaluation method and system, and the method comprises the steps: carrying out the comprehensive image shooting of an environment through an unmanned plane carrying device, constructing an environment real scene model through combining laser scanning point cloud data and an algorithm, and providing a precise reference for the layout of a sensor. The method comprises the following steps: selecting proper sensors according to requirements and arranging the sensors; ensuring comprehensive coverage; setting an evaluation period; collecting environmental parameter values, constructing Bayesian network fusion data to obtain parameter evaluation values, calculating actual comprehensive evaluation values by combining subjective weights and objective weights, reflecting environmental conditions, and collecting historical data. And constructing an ARI MA model to predict an environmental parameter evaluation value in the next evaluation period, evaluating the prediction precision, if the precision is low, generating a prediction deviation signal, collecting data in real time in the evaluation period, constructing a complex time sequence, training a CNN-LSTM model for prediction, obtaining a prediction comprehensive evaluation value, and performing comprehensive evaluation on an environmental evaluation area.