1185 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 relates to the technical field of data processing, and discloses a road infrastructure health monitoring and evaluation method and device, equipment and a medium. The method comprises the following steps: performing multi-mode acquisition and multi-path transmission processing on data acquired by an intelligent sensor network to obtain an original monitoring data set; cleaning, standardizing and fusing the original monitoring data set to obtain preprocessed structured data; performing multi-dimensional spatial correlation analysis on the structured data through a multi-scale time sequence analysis algorithm to obtain health state evaluation data of the road infrastructure; based on the health state evaluation data, performing dynamic threshold calculation and intelligent diagnosis on the road infrastructure to obtain early warning information and a preliminary diagnosis result; and performing expert knowledge base matching and multi-criterion decision analysis on the early warning information and the preliminary diagnosis result to obtain a maintenance decision suggestion and an optimization scheme. The road infrastructure health monitoring and evaluation efficiency and accuracy are improved.

The invention provides an evaluation method and device of a large language model system and related equipment, and relates to the technical field of computers. The method comprises the following steps: generating multiple groups of question and answer pairs, wherein each group of question and answer pair comprises an evaluation question and a standard answer; inputting the evaluation question of each question and answer pair into a to-be-evaluated large language model question and answer system, and obtaining a question answer output by the large language model question and answer system; and based on a pre-constructed quantitative evaluation model, according to the evaluation questions and the standard answers of the multiple groups of question and answer pairs, evaluating the questions and answers output by the large language model question and answer system. According to the method, the pre-constructed quantitative evaluation model is utilized, the answers output by the model are scored according to the standard answers, the problems of subjectivity and inconsistency in a traditional evaluation method are solved, the evaluation efficiency is improved, a large amount of data is automatically processed, and manpower and time cost is saved. The objectivity of evaluation is enhanced, personal errors are reduced, and the reliability of evaluation results is ensured.

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 discloses a multi-modal rehabilitation data intelligent evaluation method and system based on a large model, and relates to the field related to data processing, and the method comprises the steps: carrying out the data collection based on a plurality of data sources, obtaining and integrating multi-modal data collection results, obtaining a multi-modal data set, synchronizing the multi-modal data set to an information extraction model, and carrying out the retrieval, a rehabilitation training scheme is executed to track the rehabilitation progress of a target user in real time; rehabilitation effect data is obtained and synchronized to the rehabilitation evaluation large model; a multi-dimensional evaluation result is obtained to perform feedback optimization on the rehabilitation training scheme; and a rehabilitation optimization training scheme is determined. The technical problem that traditional rehabilitation evaluation depends on professionals and lacks enough professional knowledge and experience, so that the accuracy and efficiency of evaluation are affected is solved, and the technical effects of performing intelligent personalized evaluation on rehabilitation training and improving the precision and efficiency of rehabilitation evaluation are achieved.

The invention discloses a surrounding environment risk assessment method based on a cover excavation subway foundation pit, and relates to the technical field of subway construction. The evaluation method comprises the following steps of multi-source data acquisition and preprocessing: collecting geological survey data, environment monitoring data, construction monitoring data, existing building data, pipeline data and electric power facility data of a foundation pit area. The deep analysis of the complex interaction between the foundation pit construction and the surrounding environment is realized, the innovative method overcomes the limitations of single data and one-sided analysis in the traditional risk assessment, the comprehensiveness and accuracy of the risk assessment are remarkably improved, and the risk assessment efficiency is improved through comprehensive analysis. The potential influence of foundation pit construction on the surrounding environment can be accurately monitored in real time, the reasonable early warning threshold value is set, and once the monitoring data exceed the preset threshold value, the early warning signal is triggered immediately, so that the safety of the surrounding environment is effectively guaranteed, and the risk in the construction process is reduced.

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 relates to a method for evaluating the situation awareness ability of a pilot based on a Ti-SA model. The Ti-SA model comprises an eye movement trace multi-dimensional attention enhancement module, an AOI multi-scale comprehensive feature module and a fusion module. The evaluation method comprises the steps of collecting eye movement trace time sequence data and flight training videos of a pilot at continuous moments in real time, and obtaining corresponding AOI fixation time sequence data based on the eye movement trace time sequence data, the flight training videos and a preset AOI subdivision area; preprocessing the eye movement trace time sequence data and the AOI fixation time sequence data; inputting the preprocessed eye movement trace time sequence data into an eye movement trace multi-dimensional attention enhancement module to obtain a category probability value corresponding to the eye movement trace time sequence data; inputting the preprocessed AOI fixation time sequence data into an AOI multi-scale comprehensive feature module to obtain a category probability value corresponding to an AOI fusion feature; and inputting the two category probability values into a fusion module to obtain a predicted situation awareness level.

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 field of data processing, in particular to a psychological assessment method based on a large model and a related device. The method comprises the following steps: generating a psychological evaluation strategy according to a current interview task and a corresponding psychological evaluation index; generating multi-round dialogue information through an evaluation interaction model based on a psychological evaluation strategy, and interacting with the target object through the multi-round dialogue information to complete a psychological evaluation process corresponding to the current interview task; performing multi-modal interaction mode analysis on the target object based on the multi-round dialogue information and the corresponding response information to obtain a psychological assessment report of the target object; the psychological assessment report at least comprises a quantitative analysis result and a qualitative psychological analysis result aiming at the dialogue content. According to the method, by accurately guiding dialogue content generation and adjusting the evaluation strategy in real time, the psychological state of the target object is more accurately touched, individual differences are adapted, related personnel are assisted in completing psychological evaluation tasks, the accuracy of the evaluation result is improved, and the psychological evaluation efficiency is improved.

The invention relates to the technical field of safety engineering and artificial intelligence, in particular to a limited space operation safety risk assessment method and system, and aims to effectively improve the core defects of the prior art through a machine learning technology and a data analysis method. Due to the real-time monitoring and evaluation capability, the system can respond to environmental changes in time; the model can identify and adapt to new risk factors due to high adaptability; the accuracy and objectivity of evaluation are improved through a data driving method; an iterative optimization mechanism ensures that the model can be evolved continuously, the effectiveness of risk management is enhanced, a more efficient, more accurate and more reliable safety risk assessment method and system are provided for limited space operation, a solid technical support is provided for guaranteeing operation safety, and the method and the system have good application prospects. And an important step of intelligent and digitized development of the security risk assessment field is marked.

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 relates to a multi-dimensional seismic risk assessment method based on seismic oscillation space-time distribution. The method comprises the following steps: S1, selecting IM indexes; s2, key structure parameters are extracted; s3, recording an engineering demand parameter EDP; s4, selecting an optimal IM index; s5, establishing an agent model of structural seismic response; s6, establishing an earthquake prediction equation; s7, establishing an aftershock risk analysis model; s8, obtaining aftershock intensity samples of the positions of the target structures; s9, obtaining an aftershock vulnerability curve; and S10, determining repair time and repair cost according to the repair of the structure and the repair of the structure function so as to finally determine the total repair cost of the structure of the regional line and the anti-seismic capacity after repair. Compared with the prior art, the method has the advantages of improving the accuracy of earthquake risk estimation and the like.

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 discloses an English composition evaluation method and device, belongs to the technical field of artificial intelligence in the education industry, and aims to solve the problem that a scoring result of an existing English composition automatic scoring system is greatly different from a manual scoring result. Comprising the following steps: obtaining a to-be-scored composition; semantic analysis is carried out on the to-be-scored composition, wherein the semantic analysis comprises high-level vocabulary analysis, semantic coherence analysis, logic hierarchy analysis, sentence pattern analysis and theme consistency analysis; obtaining a high-level vocabulary score, a semantic coherence score, a logic hierarchy score, a theme consistency score and a high-level sentence pattern score; based on a preset weight coefficient, performing weighted summation on the high-level vocabulary score, the semantic coherence score, the logic hierarchy score, the high-level sentence pattern score and the theme consistency score to obtain a final score of the to-be-scored composition; according to the invention, the accuracy and reliability of English composition scoring can be effectively 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 invention discloses an evaluation method and system of a large language model, electronic equipment and a storage medium, and relates to the technical field of model evaluation, and the method comprises the steps: generating a plurality of candidate answers of a preset question; generating an answer to the preset question by using the to-be-evaluated large language model; calculating a similarity mean value between the generated answer and each candidate answer by using each preset similarity calculation model; and according to the maximum similarity mean value, evaluating the to-be-evaluated large language model to obtain an evaluation result. According to the method, interference caused by subjective factors can be avoided, and compared with a keyword comparison mode, the method utilizes multiple similarity calculation models and evaluates the evaluation large language model with the maximum similarity mean value, so that the evaluation result is more accurate and fair, and the evaluation efficiency is improved. And the applicability of the large language model in specific fields such as the insurance field can be accurately evaluated.

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 an urban safety risk assessment method and system based on a digital standard, and the method comprises the steps: obtaining urban safety risk monitoring data and historical data, constructing a risk index event library according to the historical data, carrying out the abnormal screening and classification of the monitoring data, and obtaining risk indexes, matching the risk indicator with the risk indicator event library to determine a first risk event, predicting an uncertain risk event according to the first risk event, and constructing a city safety risk assessment model according to the first risk event, the uncertain risk event and the risk event weight, and inputting to-be-assessed city safety risk monitoring data and historical data into the city safety risk assessment model to obtain a city safety risk assessment result. The method not only can improve the efficiency and accuracy of urban safety risk assessment, but also has good interpretability, and can be directly applied to an urban safety risk assessment system.

The invention relates to an urban park physical examination evaluation system construction method based on a machine learning algorithm. The method comprises the following steps: S1) selecting an evaluation index set; s2) taking a park system, an urban comprehensive park, an urban community park and an urban micro-greenbelt collaborative group as evaluation scales, and selecting evaluation indexes from the evaluation index set; s3) acquiring specific data of evaluation indexes to form an index data set; s4) screening repeated indexes with complex collinear features in all index data sets by using a variance expansion coefficient; s5) clustering by using a K-medoids clustering algorithm, and S6) based on an SHAP machine learning algorithm, performing full sample analysis and outputting the importance degree of the index factors, and constructing an urban park physical examination assessment system based on the importance degree of the index factors.According to the method, subjectivity and uncertainty of a traditional assessment method can be avoided, the assessment efficiency is improved, and rapid updating can be performed.