697 results about "Assessment methods" patented technology

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 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 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 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 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 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 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 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 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.

The invention discloses a homework semantic evaluation method based on a multi-modal large model, and relates to the technical field of education intelligent evaluation, and the method comprises the steps: 1, normalizing multi-modal answers into a step sequence, a scoring key point map, an acceptable answer set and an initial scoring anchor point, and building a step and key point bidirectional index; 2, performing consultation according to teacher roles, generating candidate scores and key credits, and calculating a key point coverage degree, a consistency degree and a contradictory proportion; and step 3, reading the conditional reliability ledger, generating a comprehensive weight, aggregating the comprehensive weight to obtain a final score, and outputting an uncertainty index at the same time. 4, giving out hierarchical personalized explanation, determining a minimum correctable step, executing step-level echoing, and generating a training list; 5, carrying out anchor question online calibration and fusion parameter updating, and setting a trigger proportion upper limit and an early stop threshold according to a budget perception route; the link realizes unified measuring scale, steady decision, traceable learning and controllable cost.

The invention provides a quality evaluation method for a geological three-dimensional model, and the method comprises the steps: S11, carrying out the analysis of a historical geological project document, extracting evaluation indexes and data demands, and determining related modeling source data according to the type and target of a geological project; s12, learning an evaluation scheme of a historical successful geological project document, and generating a three-dimensional geological model evaluation scheme containing a verification data range and method; s13, monitoring and analyzing the modeling process of the three-dimensional geologic model based on the three-dimensional geologic model evaluation scheme; s14, determining the weight of each evaluation index in the three-dimensional geologic model evaluation scheme so as to determine the quality grade of the three-dimensional geologic model, and continuously adjusting the weight distribution of the evaluation indexes; and S15, according to the quality grade of the three-dimensional geologic model, determining whether the three-dimensional geologic model meets the acceptance requirement or not. The invention provides a systematic evaluation method for the quality evaluation of the three-dimensional geologic model, thereby promoting the application and development of the three-dimensional geologic model in various fields.

The invention belongs to the technical field of video quality evaluation, and particularly relates to a video quality evaluation method based on spatio-temporal feature fusion. Comprising the following steps: acquiring a video quality evaluation data set, preprocessing the video quality evaluation data set, and processing preprocessed video data by adopting a time feature extraction module to obtain short video features and long video features; processing the preprocessed video data by adopting a spatial feature extraction module to obtain high-level semantic features and distortion features; fusing the short video features, the long video features, the high-level semantic features and the distortion features by using a feature fusion module to obtain fused features; inputting the fusion feature into a multi-layer perceptron for processing to obtain a video quality score; calculating the total loss of the model and continuously adjusting model parameters according to the total loss of the model to obtain a trained video quality evaluation model; performing video quality evaluation by using the trained model; according to the method, the correlation between the spatial-temporal features and the inter-frame features is effectively utilized, so that the accuracy of quality evaluation is improved.

The invention relates to the technical field of psychological health, and discloses a psychological health multi-modal evaluation method, system and device and computer equipment, and the method comprises the steps: obtaining multi-modal data, and carrying out the feature extraction of the multi-modal data, and obtaining multi-modal features; performing emotion-oriented cross-modal attention mechanism analysis on the multi-modal features to respectively obtain a preset-dimension emotion scale vector, an emotion semantic feature, a multi-dimensional emotion feature and an uncertainty quantitative feature; fusing the preset dimension emotion scale vector, the emotion semantic feature, the multi-dimensional emotion feature and the uncertainty quantitative feature to obtain a multi-modal fusion feature; and obtaining a mental health assessment result based on the multi-modal fusion features and a preset multi-task learning framework. Through a cross-modal attention mechanism, deep semantic fusion of five modals of vision, audio, physiology, text and behavior is realized, and intelligent mapping from original multi-modal data to accurate psychological state judgment is realized through a deep learning technology.

The invention discloses a landslide disaster risk dynamic assessment method and system based on an improvement project progress, and relates to the technical field of natural disaster treatment. According to the invention, through integration of multi-source heterogeneous data, real-time dynamic assessment and a machine learning model, accurate and dynamic assessment of landslide disaster risks is realized, assessment precision and stability are improved, and timeliness and pertinence of decision making are enhanced; a feature disturbance stability mechanism is introduced into the SPY algorithm, and a pseudo-negative sample screening strategy with higher robustness and discriminability is constructed; an information entropy reduction amount is introduced, an acquisition function is improved, the efficiency of hyper-parameter optimization is improved, and a stacking generalization integration model is effectively trained; the influence of the regulation situation progress in the landslide disaster area is considered, the susceptibility correction coefficient is set, the landslide disaster susceptibility evaluation result is corrected, it is ensured that the landslide disaster susceptibility evaluation result can reflect the actual situation in time, and powerful technical support is provided for prevention and management of landslide disasters.

The invention discloses an environmental risk step-by-step assessment method in a multi-medium and multi-scale complex scene, and belongs to the technical field of environmental risk assessment. The method comprises the following steps: obtaining the heavy metal concentration of the waste residue through a leaching experiment, and determining the toxicity grade by using a single pollution index method and a potential ecological risk index method; dividing volume grades based on quantiles, dividing local diffusion risk grades by using an inverse distance weighted interpolation method and the like, dividing remote receptor sensitivity grades according to a surrounding land and water source construction model, and generating priority control grades by integrating the grades; and finally, fusing multi-source data to construct a spatio-temporal conjoint analysis model verification result. Therefore, by integrating pollution source toxicity, volume, diffusion risk and receptor sensitivity to carry out risk step-by-step assessment, a multi-source data fusion and spatio-temporal conjoint analysis method is proposed for the first time, and the problems of single assessment dimension, insufficient data fusion, lack of dynamic verification and the like of an existing assessment method in a multi-medium and multi-scale scene are effectively solved.

The invention discloses an ecological geology dynamic monitoring and evaluation method based on multi-source data fusion, and belongs to the field of geological environment monitoring, and the monitoring and evaluation method specifically comprises the following steps: S1, collecting different types of geological monitoring data from satellite remote sensing, ground sensor data sources and geological survey, and carrying out preprocessing research; s2, dynamically adjusting the weight of each type of geological monitoring data, carrying out fusion, and carrying out data modeling on an incomplete collection region; s3, according to the processed geological monitoring data, evaluating an ecological geological problem of the target area, simulating an evolutionary path of the ecological geological problem, and identifying potential risks; according to the method, multi-dimensional data collaboration can be realized, the monitoring accuracy of the change of the ecogeological problem is improved, the real-time response capability is enhanced, the early warning response time is shortened, the problem of scale fracture in a traditional method is effectively solved, and comprehensive evaluation of the ecogeological problem is realized.

The invention discloses an occupational information evaluation method based on rule judgment and a large language model, and belongs to the technical field of information processing, and the method comprises the following steps: S1, obtaining a multi-modal document uploaded by a user, converting the multi-modal document into a Markdown format, and completing a shunting operation and a converging operation; s2, extracting a named entity of the multi-modal document after the confluence operation, and constructing a knowledge graph; s3, obtaining query content, and matching most relevant regulation content by using the knowledge graph; s4, generating specific improvement suggestions by utilizing the rule model according to the most relevant regulation content; and S5, adjusting the specific improvement suggestions by using a large language model to obtain a final evaluation result. Through the OCR technology, regular expression analysis and context-associated picture analysis, unified structured processing of texts, tables and pictures is achieved, and the problem that unstructured data are difficult to integrate in a traditional method is effectively solved.

The invention particularly relates to a meteorological equipment health degree assessment method based on regression and fuzzy comprehensive analysis. The method comprises the following steps: constructing a meteorological equipment health degree assessment index system; the method comprises the steps of collecting multi-source data of meteorological equipment, and completing data preprocessing through missing value filling and abnormal value correction; performing feature extraction on the multi-level indexes by adopting regression analysis, screening out core parameters strongly related to the health degree, and quantifying priorities; combining an analytic hierarchy process, integrating expert experience and feature importance results, constructing a hierarchical judgment matrix, and calculating the dynamic weight of each hierarchy index; and based on a fuzzy comprehensive evaluation method, establishing a multi-dimensional comment set and a membership function for the quantitative index and the qualitative index, fusing the weight matrix and the membership data, and outputting a quantitative score of the health state of the meteorological equipment. According to the method, the accuracy of health assessment of the meteorological equipment in severe environments such as extremely cold environments can be realized, and reliable technical support is provided for intelligent operation and maintenance of the meteorological equipment.

The invention discloses a target threat assessment method based on a large language model, and the method comprises the following steps: carrying out the modeling of a confrontation environment, integrating multi-dimensional information, and determining a plurality of threat assessment targets; generating an evaluation index through the large language model; evaluating the generated evaluation indexes in combination with expert knowledge, if the evaluation indexes do not meet the requirements, further generating the evaluation indexes by the large language model, and if the evaluation indexes meet the requirements, generating an evaluation matrix by the large language model; performing evaluation by combining an evaluation matrix symmetrically formed by expert knowledge, if the evaluation matrix does not meet requirements, further generating the evaluation matrix by the large language model, and if the evaluation matrix meets the requirements, calculating the distance between each target and the positive and negative ideal solutions; and performing threat level sorting on the target according to the distance. By optimizing the decision process, more efficient threat assessment in a complex confrontation environment is realized, and decision support with higher reference value is provided for decision makers.

The invention discloses portable intelligent assessment and rehabilitation training integrated equipment for cognitive disorder patients, and belongs to the technical field of cognitive disorder assessment. Comprising a device body composed of a base and a display screen, and the device body is electrically connected with an intelligent evaluation training system. The problem that an existing assessment method has limitation is solved, and the portability of existing medical equipment is improved by performing integrated design on the equipment in a notebook computer form; multi-dimensional evaluation data of the patient are collected through a MoCA Monte-Evaluation scale, scores and change trends of all cognitive functions are extracted by using a correlation analysis method, and the scores and change trends are predicted through a neural network model to obtain a prediction result of the patient; corresponding rehabilitation training suggestions are retrieved from the decision tree classification model, optimization is carried out in combination with actual conditions, and then rehabilitation training is carried out on the patient; and by introducing an intelligent technology, personalized design and comprehensive intervention, the evaluation and rehabilitation training effects are further improved.

The invention relates to a self-interpretation multi-modal personality assessment method based on a multi-agent hierarchical reasoning chain of a large language model, which comprises the following steps of: firstly, unifying multi-modal features such as voice and vision to a text space through modal self-interpretation personality traits representation of a unified space, and generating text description codes related to personality traits; constructing a causal graph model by using a Bayesian causal reasoning theory, analyzing specific contribution of each modal data to personality traits evaluation, and generating a clear reasoning chain and explanation; a two-stage hierarchical reasoning framework is utilized, coarse granularity personality classification is completed in the first stage, psychological measurement norm data is introduced in the second stage, a'classification-scoring 'dynamic mapping mechanism is established, and fine granularity scoring calibration is achieved. Meanwhile, the interpretability of the model is deepened through a hierarchical confidence transfer mechanism, and it is ensured that each scoring result can be traced to the original feature, the classification credibility and the adjustment rule; according to the method, a multi-modal personality assessment special large language model is constructed, and a more transparent, explainable and credible technical basis is provided for personality assessment.