73 results about "Socio economic data" patented technology

The invention relates to the technical field of ecological environment protection, in particular to an ecological environment damage intelligent identification management method based on multi-source data fusion, which comprises the steps of data acquisition and preprocessing, data fusion and feature extraction, ecological environment damage evaluation model construction, damage dynamic analysis and trend prediction, restoration suggestion generation and visual display. The method comprises the following steps: collecting water quality, atmosphere, biological and social economic data in real time through a sensor network deployed in an ecological sensitive area, preprocessing the data, extracting key features by adopting a multi-modal data fusion technology and a deep learning model, and constructing an ecological damage assessment model; the change rule of ecological damage and the potential high-risk area are predicted based on a time sequence analysis method, and an optimal ecological restoration scheme is generated in combination with a particle swarm optimization algorithm. According to the method, the evaluation result and the restoration suggestion are displayed through the visual interface, a scientific basis is provided for decision makers, and the efficiency and the effect of ecological environment restoration work can be improved.

The invention discloses a territorial space planning toughness identification and evaluation method, and belongs to the technical field of territorial space planning. The method comprises the following steps: step 1, acquiring spatial information, social economic data and ecological environment elements of an evaluation area; 2, dividing functional units as network nodes, and constructing a weighted adjacent matrix to form a complete spatial relation network structure; 3, determining an index weight in combination with an analytic hierarchy process and an entropy evaluation method; 4, calculating the toughness index of each node, and comprehensively evaluating the overall toughness level of the network in combination with network topology analysis; 5, identifying key weak nodes and links based on a network topology structure and node importance analysis; step 6, adopting a multi-objective optimization algorithm to generate candidate schemes of specific improvement measures, and utilizing a Monte Carlo simulation method to evaluate the feasibility and effect of each candidate scheme to determine an optimal implementation path; and step 7, forming an evaluation-improvement-re-evaluation loop optimization mechanism.

The invention discloses a water network dam overtopping flooding simulation and disaster damage assessment method, and belongs to the technical field of flood control and disaster reduction. The method comprises the following steps: performing hydrodynamic analysis based on hydrological geographic data, and determining a potential overtopping area; for the potential overtopping area, simulating breach evolution based on a cumulative scouring energy criterion, and generating a breach flow process; simulating flood routing according to the breach flow process, and generating a spatialized disaster intensity field; and in combination with the disaster intensity field and social economic data, direct and indirect economic losses are evaluated, and a comprehensive disaster damage result is generated. According to the method, a physical criterion based on accumulated scouring energy is introduced, a comprehensive strength index, a three-dimensional vulnerability curved surface and an application graph neural network are adopted to simulate cascade failure, and the problems of breach trigger mechanism deficiency, single disaster strength measurement and disaster damage assessment staticization are solved.

The invention relates to the technical field of snowstorm disaster estimation, in particular to a complex terrain snowstorm disaster estimation method, and the method comprises the steps: obtaining high-temporal-spatial-resolution weather forecast data provided by an intelligent grid forecast product; acquiring historical snow disaster data, landform data and social economic data in the snow disaster risk census database; based on the weather forecast data and the snow disaster risk census data, performing data cleaning, fusion and feature extraction by using a machine learning algorithm; according to the extracted features, constructing an evaluation model reflecting a complex relationship between the snowfall and the snow disaster risk; according to the method, the intelligent grid forecasting product and the snow disaster risk census data are fused, so that the risk estimation with high temporal-spatial resolution is realized. The data fusion not only improves the precision of estimation, but also enables the estimation result to reflect the differentiated risk of different areas.

The invention provides an intelligent virtuality and reality combined psychological health service device based on digital elements, and aims to improve the accuracy and individuation level of psychological health management. The device firstly obtains the physiological indexes, behavior data, environmental factor data and social economic data of an individual, and carries out multi-modal fusion to form comprehensive feature data. Based on this, a psychological health environment factor model is constructed to quantify the influence of the external environment on the individual psychological state, and the calculation weight and prediction logic of the negative emotion large model are optimized. And the optimized negative emotion large model is used for identifying an individual emotion state, analyzing factors such as social environment, economic pressure and life events in combination with the mental health environment factor model, and generating an individual mental health assessment result. And according to an evaluation result, the virtual digital doctor provides intelligent pre-inquiry and other services. Through data-driven intelligent analysis and virtual-real combined intervention means, the accessibility, accuracy and intervention effect of psychological health services are improved.

The invention discloses a grid scale basin flood toughness evaluation method and system based on underlying surface functions and endurance capability, and the method comprises the steps: obtaining high-resolution remote sensing image data and POI data, obtaining a fine classification result through multi-level classification processing, and carrying out the calculation through combining with social economic data, thereby obtaining various underlying surface function weight values; performing statistical analysis and gridding calculation on the fine classification result and historical disaster data to obtain a grid unit endurance capacity value; a coupling hydrodynamic model is constructed for simulation calculation, and grid submerging water depth and submerging time data are obtained; calculating a system performance curve, and extracting toughness characteristic values to obtain a grid toughness characteristic value set; and calculating comprehensive toughness based on the toughness characteristic value set, and performing spatial integration in combination with the functional weight values of the underlying surface at different moments to obtain toughness evaluation results of different spatial and temporal scales. Accurate decision support is provided for urban flood control and disaster reduction.

The invention provides a method and a system for evaluating the layout suitability of a wind power plant from a macroscopic perspective, and belongs to the technical field of prediction or optimization specially suitable for administrative or management purposes. Firstly, natural geographic data and social economic data are collected and preprocessed; determining a wind power plant site selection alternative area according to the preprocessed natural geographic data and social economic data; determining a wind power plant suitability evaluation index system and a weight; the wind power plant suitability evaluation index system comprises natural geographic factors and social economic factors of the wind power plant layout; based on natural geographic factors and social economic factors of the wind power plant layout, site selection natural condition optimization and social economic condition optimization are carried out on the wind power plant site selection alternative area; and performing overlay analysis and reclassification on respective optimization results of the wind power plant site selection natural condition optimization and the social economic condition optimization to obtain a final wind power plant site selection suitability evaluation result. According to the scheme, the suitability of the wind power plant layout can be evaluated macroscopically.

The invention discloses a black land water and soil resource bearing capacity evaluation system based on big data, which relates to the technical field of agricultural production and comprises a data acquisition module, a data preprocessing module, an evaluation index calculation module, a bearing capacity evaluation model module and a result analysis and display module. The data acquisition module is used for acquiring soil data, water resource data and social economic data of black land. According to the evaluation system for the bearing capacity of the water and soil resources of the black land, through integration of multi-source heterogeneous data, comprehensive and accurate evaluation of the bearing capacity of the water and soil resources of the black land is achieved, high-density deployment and real-time updating of the data acquisition module are achieved, the temporal-spatial resolution and reliability of an evaluation result are ensured, and the evaluation accuracy is improved. The problem of insufficient data acquisition density in the prior art is effectively solved, a data preprocessing and evaluation model construction technology is adopted, the dynamic adaptability and prediction precision of an evaluation model are improved, and visual and understandable chart display and decision support information are provided.

The invention discloses a multi-dimensional trade-off analysis method for watershed ecological service optimization, which belongs to the technical field of multi-dimensional trade-off analysis, and comprises the following steps: S1, adopting a multi-source heterogeneous data fusion algorithm, combining remote sensing, GIS (Geographic Information System), weather and social economic data, and extracting key ecological factors through a random forest; according to the method, Monte Carlo simulation is utilized to generate multiple groups of land utilization scenes, and key tradeoff pairs are identified in combination with principal component analysis, so that challenges of uncertainty and multi-target conflicts in ecological service optimization are effectively coped with; thousands of groups of land utilization patterns are generated through random sampling, ecological service values under different scenes are simulated, and the limitation of single scene hypothesis is avoided; principal component analysis eliminates dimensional differences through standardization processing, extracts dominant ecological service types, and provides a mathematical basis for quantification of a tradeoff relationship.

The present application provides a multi-objective material distribution center site selection and path planning method, which obtains material distribution data; the material distribution data includes geographic information data, traffic condition data, material demand data, meteorological data and socio-economic data; the material distribution data is processed by a missing data processing algorithm based on fuzzy clustering to obtain processed material distribution data; the processed material distribution data is analyzed by a deep learning model to evaluate the material demand characteristics and predict the material demand change trend; a path planning model based on multi-objective optimization is constructed according to the material demand characteristics and material demand change trend; the multi-objective function in the path planning model based on multi-objective optimization is solved based on a microhabitat genetic algorithm optimized by a marine predator algorithm to obtain a globally optimized distribution center site selection and planning path, thereby solving the problem of inaccurate material distribution center site selection and planning path.

The invention discloses an intelligent redevelopment research method for low-efficiency land use, which comprises the following steps of: acquiring multi-source data including land change survey data, topographic data, urban planning data, remote sensing data, point-of-interest data, mobile signaling data and social economic data in a research area, and inputting the multi-source data into a land use identification model; obtaining a spatial distribution diagram of low-efficiency land in the research area and corresponding low-efficiency factors; inputting the spatial distribution diagram and the low-efficiency factors into a redevelopment potential calculation model to obtain a redevelopment comprehensive potential value of the low-efficiency land; a plurality of redevelopment schemes are obtained based on the redevelopment comprehensive potential value, the rigid constraint of the low-efficiency land during redevelopment and the redevelopment mode library; and obtaining respective corresponding benefit index difference values after the low-efficiency land is redeveloped by using the plurality of redevelopment schemes, and obtaining an optimal redevelopment scheme from the plurality of redevelopment schemes by using a multi-attribute decision algorithm and the benefit index difference values.

The invention belongs to the technical field of water quality monitoring, and discloses a big data water quality dynamic monitoring and early warning method and system, a'water environment-social economy-ecosystem 'ternary data system is constructed through a cross-domain data symbiosis module, social and economic data such as enterprise pollution discharge, agricultural irrigation and the like and ecological microscopic data such as algae gene expression and the like are fused, and the water quality dynamic monitoring and early warning system is established. Implicit factor dominance is realized in combination with a dynamic weight algorithm; meanwhile, the quantum dot marking technology breaks through chemical analysis blind areas, accurately locates multi-source pollution, greatly improves the novel combined pollution identification capability, solves the problem that the traditional technology is lagged in response to complex pollution, and provides omnibearing data support for pollution traceability; in extreme weather, a parameter mutation adaptation mechanism is started, sub-models are rapidly switched, and an enhanced feature set generated by a cross-domain feature distillation step is combined, so that the model can accurately simulate the coupling effect of pollutants and a biologic chain, the long-term prediction stability is remarkably improved, and a reliable trend basis is provided for early warning decision making.

The invention discloses an industrial chain node anomaly detection method and system based on electric power time sequence characteristics, and belongs to the technical field of industrial chain data processing, and the method comprises the steps: obtaining electric power data, social and economic data of different industries, and upstream and downstream associated data in the industries in real time; performing season adjustment on the index time sequence in the primary selection index pool by using a time sequence model; performing association analysis to obtain an industrial node association rule, analyzing a time difference earlier stage or lagging stage of indexes of upstream and downstream nodes according to the industrial node association rule, and dividing into consistent, first and lagging index groups; calculating a diffusion index and a synthesis index, and analyzing supply and demand conduction indexes of upstream and downstream of the industry to form a prosperity sequence; comparing the distribution difference between the current sequence and the reference business sequence through KS test to judge whether abnormity exists or not; and tracing the source link with the abnormal condition according to the abnormal test result, and quantifying the influence degree of the source link on the whole industry chain.

The invention discloses an atmospheric greenhouse gas delta13C isotope observation construction method, which belongs to the technical field of atmospheric environment monitoring and is realized through three steps of monitoring data acquisition, traceability data acquisition and data processing standardization. According to the invention, through a double-site cooperative monitoring network and in combination with data acquisition of background sites and urban sites, the atmospheric CH4 concentration can be monitored in real time, and the delta 13C isotope value of CH4 can be accurately analyzed. And the source of CH4 can be qualitatively identified by utilizing a Kearing plot model, and high precision and high credibility of source analysis are ensured. According to the method, the Bayesian mixture model and the machine learning technology are adopted, the CH4 emission contributions of different sources are quantitatively estimated, and the emission sources and driving factors of CH4 are comprehensively analyzed in combination with multi-source tracer data and social economic data.

The invention discloses a low-efficiency land use identification method and device based on a pressure-state-response framework, and the method comprises the steps: obtaining the geographic position data, land use type data, social and economic data, population data, ecological environment data, infrastructure data and regulation and control response data of a plurality of target regions; calculating a spatial correlation coefficient between the first target area and the second target area based on the geographic position data, the land use type data, the social economic data and the ecological environment data to obtain a spatial correlation matrix; based on the social economic data, the population data, the ecological environment data, the infrastructure data, the regulation and control response data and the spatial incidence matrix, constructing a low-efficiency land use evaluation index system; determining a comprehensive evaluation index set of each target area through the low-efficiency land use evaluation index system and the land use type data; and determining the low-efficiency land use level of each target area based on the comprehensive evaluation index set, and obtaining a low-efficiency land use identification result. According to the invention, the accuracy of low-efficiency land use identification can be improved.

The invention discloses a land-sea overall land and land space planning method and system, and the method comprises the steps: collecting remote sensing data, marine environment monitoring data and basic geographic and social economic data of a research region, and carrying out the denoising, format conversion and time-space registration preprocessing of the data; fusing the preprocessed data by adopting an enhanced self-adaptive space-time reflectivity fusion model to generate a unified data set with high space-time resolution; constructing a three-level classification system based on the fused data set, carrying out land space suitability dynamic evaluation by adopting an adaptive weighting algorithm, and outputting a classification result and a suitability score; and taking the suitability scoring result as input, performing multi-objective optimization solution by adopting an improved non-dominant genetic algorithm, and generating and outputting a land-sea land space planning scheme. According to the method, the problem of differences of land and sea data in source, format, resolution and updating frequency is effectively solved, a unified space-time reference is established, and the data integration capability and the accuracy of a planning foundation are remarkably improved.

The present invention discloses a grid-scale watershed flood resilience evaluation method and system based on the function and tolerance of underlying surfaces. The method includes obtaining high-resolution remote sensing image data and POI data, obtaining a fine classification result through multi-level classification processing, and calculating the function weight values of various underlying surfaces in combination with socioeconomic data; performing statistical analysis and grid calculation on the fine classification result and historical disaster data to obtain the tolerance capacity values of grid cells; constructing a coupled hydrodynamic model for simulation calculation to obtain grid inundation depth and inundation time data; calculating the system performance curve, extracting the resilience characteristic values, and obtaining a set of grid resilience characteristic values; calculating the comprehensive resilience based on the set of resilience characteristic values, and performing spatial integration in combination with the function weight values of underlying surfaces at different times to obtain the resilience evaluation results at different spatio-temporal scales. The present invention provides accurate decision-making support for urban flood control and disaster reduction.

The invention provides an ecological risk evaluation and prediction evaluation method based on a multi-source evaluation framework, and relates to the technical field of ecological risk evaluation, and the method specifically comprises the steps: dividing a target region into grid units, and collecting landscape pattern and environmental social economic data; analyzing landscape data through a sliding window method, and calculating a landscape ecological risk value of each unit; constructing a regional ecological risk assessment index system based on social economic data, determining an index weight by adopting Bayesian BWM and CRITIC methods, and performing weighted calculation to obtain a regional ecological risk value; integrating the two types of risk values to form a spatial distribution diagram, and superposing to obtain a unit comprehensive ecological risk value; and performing risk grading by adopting a natural breakpoint method, generating a grading graph, predicting risk early warning time of each unit based on a historical trend, and realizing ecological risk dynamic evaluation and early warning.

The invention relates to the technical field of drainage networks, in particular to a drainage network siltation risk assessment method based on a random forest algorithm, and the method comprises the steps: obtaining pipeline basic data, siltation detection data, land utilization data and social economic data of a drainage network; constructing a pipeline siltation regression model based on a random forest algorithm and obtaining a pipeline siltation degree; calculating the importance degree of input factors by using an OOB sample during training of the pipeline siltation regression model; calculating a pipeline risk factor according to the importance degree of the input factor, the value of the input factor and the pipeline siltation degree; fitting is conducted on the pipeline siltation degree and the pipeline danger factors through a logistic curve, and a curve fitting result is obtained; and calculating a pipeline risk factor corresponding to a preset pipeline siltation degree based on a curve fitting result, and obtaining a pipeline siltation risk assessment result according to the pipeline risk factor and a preset siltation risk grade. According to the method, multi-dimensional comprehensive assessment of the sedimentation risk of the drainage pipeline is realized, and the efficiency and accuracy of risk assessment are greatly improved.

The invention discloses a method, a device and equipment for defining an ecological underground water level in a desertification region, and relates to the field of underground water level localization, and the method comprises the following steps: carrying out underground water dynamic simulation according to meteorological data, hydrological data, geological data and social economic data; correlation analysis is carried out according to underground water level change conditions and ecological data in different media in the desertification region, and key ecological variables are obtained; determining an ecological health threshold value according to underground water level change conditions and key ecological variables in different media in the desertification region; according to the meteorological data, the hydrological data and the geological data, determining key environment variables influencing ecological system response characteristics under different underground water level conditions; and determining the ecological underground water level by using a multivariate statistical method according to the underground water level change condition, the key ecological variable, the ecological health threshold value and the key environment variable in different media in the desertification region. According to the invention, the underground water range required for maintaining the health of the ecological system in the research area can be defined.

The invention relates to the technical field of reservoir flood control early warning, and discloses a reservoir flood control risk assessment and early warning system based on big data. A data aggregation unit of the system continuously collects historical hydrology, real-time meteorology, reservoir area geographic information and surrounding social economic data; the data preprocessing unit performs multi-dimensional cleaning and standardization on the data to generate an assessment input data set with a unified format; the risk dynamic analysis unit constructs a flood occurrence probability prediction model by applying a machine learning algorithm on the basis of the data set, constructs a submerging range simulation model in combination with geographic space characteristics, and calculates a potential economic loss quantized value; the early warning grade mapping unit inputs the result into an early warning grading rule base to generate an overall flood control risk early warning grade; a plan matching decision-making unit retrieves a flood control coping plan set with the highest matching degree according to the early warning level; and the early warning information issuing unit converts the pre-arranged plan into visual guidance information, and broadcasts and pushes the visual guidance information to an emergency management department and downstream resident terminals.

The invention discloses a shared bicycle passenger flow prediction method based on a geographically weighted regression model and a related device. The method comprises the following steps: collecting spatial data and social economic data of a shared bicycle putting point of a target city; inputting the effective independent variable data set, the spatial autocorrelation of the effective independent variables, the multi-collinearity test result and the housing price factor data into a least square regression model and a geographical weighted regression model, and outputting a regression coefficient, a significance level and a goodness of fit; and according to the regression coefficient, the significance level, the goodness of fit, the delivery point category and the actual shared bicycle passenger flow data, calculating the prediction precision and the lifting amplitude at different delivery point categories. According to the method, the spatial heterogeneity relationship between the shared bicycle passenger flow volume and the influence factors is considered through the geographically weighted regression model, the regression coefficient is adjusted according to different geographic positions, the influence characteristics of a local area are reflected more accurately, and the prediction precision is remarkably improved.

The invention relates to the technical field of territorial space planning, and provides a territorial space planning risk assessment method which comprises the following steps: step 1, based on a space planning target of a target area, dividing multiple levels of assessment units, and constructing a three-dimensional risk assessment index system comprising a risk source index, a risk bearing body index and a risk conduction path index; 2, collecting multi-source heterogeneous data including remote sensing images, geological and geographic data and social and economic data, and generating a spatial database through data fusion; and step 3, classifying the data by adopting a Jenks natural breakpoint method, calculating an index weight in combination with an empowerment method, and constructing a dynamic risk assessment index matrix. 12 types of indexes are integrated through a risk source-bearing body-conduction path three-dimensional model, and the assessment coverage rate is increased by 40%; multi-scale trend distribution point correction enables the remote sensing image registration error to be reduced to 1.2 pixels, and the disaster identification false alarm rate is reduced by 35%.

The application discloses a kind of building disaster loss evaluation uncertainty optimization method of multi-source data and random distribution coupling, comprising the following steps: collecting the historical flood data of different cities, different types of buildings;The evaluation area is divided into grid;High-resolution water depth data of each grid unit is simulated and generated using HiPIMS model, and disaster loss data of each grid is generated by combining remote sensing data verification;The cost of different types of buildings is generalized using building cost data in city statistical yearbook;Building flood vulnerability is evaluated based on the gridding method;The evaluation uncertainty of building flood vulnerability is quantified using confidence level analysis method.The application solves the problem of multi-source data fusion and building accurate classification, unifies the scale difference of flood characteristics and socio-economic data, introduces confidence level analysis method to quantify uncertainty, significantly improves the evaluation accuracy and reliability, and provides scientific support for urban disaster prevention and mitigation, risk management and planning.

The invention relates to a high-precision particulate matter quantity and concentration prediction method, device and equipment based on Stacking ensemble learning, a storage medium and a program product. The method comprises the steps that to-be-processed data are obtained, and the to-be-processed data comprise environmental data and social economic data; the to-be-processed data is input into a pre-trained stack integration model, a first layer of the stack integration model comprises at least two preselected base learners, and a second layer of the stack integration model comprises a meta learner; processing the to-be-processed data through the at least two preselection base learners to obtain at least two preliminary prediction results of the particle quantity concentration; and processing the at least two preliminary prediction results through the meta-learner to obtain a particle quantity concentration prediction result. The monitoring cost of the particulate matter number concentration can be reduced, and the space coverage rate and the real-time performance of particulate matter number concentration monitoring can be improved.

The invention belongs to the technical field of urban planning, and particularly relates to a traffic station region land utilization efficiency evaluation method, which comprises the following steps of: acquiring and preprocessing point-of-interest data and a traffic station region in an analysis region; extracting land utilization layout features based on the null data set; constructing a multi-mode traffic network including walking, bicycles, buses and rail transit based on the layout features; establishing a land coupling simulation model of traffic based on the regional reachability index; causal inference and model calibration are carried out based on historical traffic intervention events of dynamic evolution; adopting a multi-objective optimization algorithm to generate traffic and land utilization configuration schemes meeting different planning objectives; according to the method, the evaluation result is subjected to visual display and decision output, unified modeling of traffic and social economic data is achieved through multi-source heterogeneous data fusion and standardization processing, and the effect of improving data space-time matching precision and calculation reliability is achieved.

The embodiment of the present invention relates to an analysis method of the spatial pattern of the out-of-town patient's medical treatment rate, the method comprising: obtaining case data, geographic element data and socioeconomic data of out-of-town patients seeking medical treatment in a target city; determining multiple first influencing factors of out-of-town patients seeking medical treatment based on the case data, geographic element data and socioeconomic data; screening multiple first influencing factors using a multiple regression model to obtain multiple second influencing factors; the outflow of patients from a prefecture-level city to a target city's medical treatment rate is a dependent variable, and multiple second influencing factors are independent variables, and fitting analysis is performed on the dependent variable and the independent variable using an OLS regression model, a GWR regression model and an MGWR regression model to determine the target regression model with the highest degree of fit; based on the significance test variable of the target regression model, determining the target influencing factor from the second influencing factors to analyze the spatial pattern of the medical treatment rate. The technical solution provided by the present invention can accurately analyze the spatial pattern of the out-of-town patient's medical treatment rate.

The application relates to a water resource pressure assessment method integrating social economy and climate change. It relates to the technical field of water resource pressure assessment in arid river basins. The method comprises the following steps: obtaining historical values and predicted values of social economic data, correcting the predicted values of the social economy to obtain corrected predicted values; calculating potential evapotranspiration in different scenarios based on historical scenario meteorological data and corrected future climate mode data; calculating future basin water resources based on the correlation between basin rainfall and water resources; extracting drought intensity characteristics according to the change trend of the historical scenario and the future scenario basin drought index; constructing a basin water resource pressure index calculation formula; and according to the calculated water resource pressure index of the water resource region, standardizing the index to obtain a standardized index, and evaluating the change trend of the water resource pressure of the region in different scenarios according to the standardized index. The method solves the problem of quantification and evaluation of basin water resource pressure under complex conditions.

The invention relates to a dynamic disease prediction system based on a real-time search trend and public data, belongs to the technical field of disease prediction, and solves the problems that during existing disease prediction, data acquisition is difficult, hysteresis exists, and a dynamic change relation between data is not considered, so that a prediction result is deviated. The system comprises a data acquisition module which is used for acquiring a public data set of continuous time with a city as a unit and performing preprocessing; the public data set comprises disease search indexes, meteorological data and social economic data; the dynamic disease analysis module is used for constructing and applying a multi-dimensional dynamic analysis model on the basis of the multi-dimensional public data set, and identifying and quantifying the data influencing the search index of the disease and the influence degree; wherein the multi-dimensional dynamic analysis model is used for capturing the dynamic relationship of the data and obtaining the time sequence relationship among the data; and the disease prediction module is used for predicting the disease development trend based on the data influencing the search index of the disease and the influence degree.

The present application belongs to the field of urban power system operation and maintenance technology, and discloses a city power system earthquake disaster resilience determination method and system based on multi-dimensional data analysis. The present application obtains the earthquake parameters, power facility information, geographic data and social economic data of each county in the city, and constructs a multi-source heterogeneous evaluation database. Based on the seismic motion parameters and the power facility vulnerability function, the damage probability and the expected loss rate of the function of different voltage level substations and transmission lines are calculated, and the residual function matrix of regional power supply is generated. Combined with the real-time power demand model and the power supply capacity recovery curve, the post-earthquake power supply gap rate is calculated, and the influence of the terrain characteristics on the transmission line repair cost is evaluated to quantify the influence of the earthquake on the economic loss, the affected population and the social stability. The present application can effectively improve the resilience and risk management capability of the power system in the earthquake disaster, and provide technical support for the post-earthquake power rapid recovery and resource optimization allocation.