33 results about "Socio economic data" patented technology

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 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 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 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 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 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 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 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 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 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.

The invention discloses an environmental risk monitoring and evaluating system and method adaptive to the total factors of the Yellow River basin, and relates to the technical field of environmental monitoring and evaluating, and the system comprises a multi-source data collection module which is used for collecting the ground sensor data and standardized satellite remote sensing data of the Yellow River basin and the social economic data of 9 provinces / regions of the basin; the data preprocessing and cleaning module is used for performing targeted preprocessing and cleaning on the acquired data; the data fusion and analysis module is used for performing multi-dimensional fusion and deep analysis on the preprocessed and cleaned data, mining an environmental risk association rule in combination with a GIS technology, and providing decision support for environmental risk prevention and control of the Yellow River basin; and the environment risk assessment and early warning module is used for performing deep application on the analysis result, and providing Yellow River basin environment risk assessment, accurate early warning and joint defense and joint control decision support by constructing an exclusive index system and optimizing an early warning model and a threshold value. The method is used for monitoring and evaluating the multi-environmental-factor risk of the Yellow River basin.

The application discloses an agricultural field attraction range definition method and system based on an improved breaking point theory, relates to the technical field of agricultural fields, collects multi-source agricultural geographic spatial data and social economic data in a research area, sequentially carries out data cleaning, spatial coordinate matching and attribute standardization processing, and establishes a standardized spatial database; an agricultural field comprehensive quality evaluation index system including four dimensions of agricultural production capacity, infrastructure level, scientific and technological service capacity and ecological environment quality is constructed, the shortest time distance between any two agricultural fields is calculated, and the improved breaking point formula is used to calculate the breaking point position between adjacent agricultural fields, so that the problems of the traditional division result deviating from the regional traffic status and the boundary demarcation not conforming to the actual agricultural service radiation law are effectively solved, the finally determined agricultural field attraction range is more suitable for the practical scene of agricultural product transportation, grass-roots agricultural technology popularization and agricultural convenient service landing, and the division result is more reasonable.

The invention provides a flood control management method based on a social and economic database of a bank flood storage area, and the method comprises the steps: proposing the regional research of social and economic data and underlying surface evolution laws of Middle Beach people in a flood control area; seamless integration and fusion among data such as different actually measured topographic data, high-resolution remote sensing images, flood control engineering design data and social and economic statistical data are realized, a Yangtze river main stream digital scene is constructed, drainage basin underlying surface and row flood storage space social and economic development changes are deduced based on GIS and RS, a row flood storage space database is established, and a flood control system is established. The evolution state of the flood control area is reflected, the Yangtze River main stream flood risk evolution process and driving mechanism can be revealed, and the adaptive flood control treatment method is systematically provided.

The application discloses a kind of water network dike overtopping flood simulation and disaster loss assessment method, belong to the technical field of flood control and disaster mitigation.The method comprises: based on hydrological geographic data, water power analysis is carried out, and potential overtopping area is determined;For potential overtopping area, based on cumulative erosion energy criterion simulation breach evolution, generate breach flow process;According to the breach flow process, simulate flood evolution, generate spatialized disaster intensity field;Combined with disaster intensity field and social and economic data, direct and indirect economic losses are assessed, and comprehensive disaster loss results are generated.The application introduces the physical criterion based on cumulative erosion energy, uses comprehensive intensity index, three-dimensional vulnerability surface and applies graph neural network to simulate cascading failure, solves the problems of missing breach trigger mechanism, single disaster intensity measurement and static disaster loss assessment.

This invention discloses a method for constructing an evaluation model for ecological carrying capacity and industrial layout suitability. By acquiring multi-source ecological environment and socio-economic data of the evaluation area, and constructing three subsystem indicator sets (stress, state, and response) based on the PSR framework, the comprehensive ecological carrying capacity score (ECC) of each evaluation unit is obtained. Industrial layout suitability indicators are extracted from four dimensions: natural suitability, resource support, environmental constraints, and economic accessibility. A combined weighting method using the analytic hierarchy process (AHP) and entropy weighting is employed to calculate the comprehensive industrial layout suitability score (ILS). Finally, the ECC and ILS are quantitatively coupled through a coupling coordination degree model, outputting a coupling coordination degree index D and hierarchical industrial layout decision recommendations. This invention achieves an integrated quantitative evaluation of ecological carrying capacity and industrial layout suitability, providing a scientific decision support tool for regional sustainable development planning.

The invention provides a catering take-out consumption information prediction method based on a large language model and text mining. The method comprises the steps of obtaining catering take-out consumption data, social economic data and environmental data of a target area in a target historical time period; based on a text mining model, performing vectorization processing on the catering take-out consumption data to determine a catering take-out consumption preference vector corresponding to the catering take-out consumption data; on the basis of a large language model, according to the social economic data and the environmental data, predicting the catering take-out consumption amount of the target area; and based on a catering take-out consumption information prediction model, according to the catering take-out consumption data, the catering take-out consumption preference vector and the catering take-out consumption amount, determining catering take-out consumption information of the to-be-predicted area. According to the invention, the prediction accuracy and fineness of the catering take-out consumption information can be improved.

The invention discloses a flood loss assessment method and system based on carrier attributes and submerging characteristics. The method comprises the following steps: obtaining carrier spatial data, submerging characteristic data and social economic data, and performing three-dimensional coordinate space conversion to realize space-time alignment; calculating and obtaining a submerged overlapping region based on the aligned carrier space data and submerged feature data, constructing a four-dimensional enhanced layer for the submerged overlapping region, and calculating and obtaining the enhancement strength of the four-dimensional enhanced layer; calculating submerged population and submerged areas by combining the aligned social economic data and adopting a space measurement economic model; and creating a multi-scale evaluation model, and evaluating by taking the enhanced strength, the submerged population and the submerged area as input to obtain a flood loss value. Compared with a traditional method, the method has the advantages that the coverage degree is improved, the whole-process quantification from physical damage when a disaster occurs to social and economic linkage influence caused by the disaster to post-disaster reconstruction cost is realized, and the one-sidedness that the traditional method only pays attention to direct loss in the disaster is avoided.

The application discloses a dengue fever prediction method and device considering extreme weather driving and a medium. The method comprises the following steps: acquiring case data and multi-source data of a target area, wherein the multi-source data comprises meteorological data, environmental data and socio-economic data; constructing extreme weather characteristics according to the meteorological data and a preset extreme weather threshold; obtaining epidemic spatiotemporal lag data according to the spatial autocorrelation relationship and the temporal autocorrelation relationship of the case data; adding the epidemic spatiotemporal lag term in a zero-inflated negative binomial regression model to obtain an initial epidemic prediction model; the epidemic prediction model comprises a quadratic term of the extreme weather characteristics; and performing parameter fitting on the initial epidemic prediction model based on the case data, the extreme weather characteristics, the epidemic spatiotemporal lag data and the multi-source data to obtain a fitted epidemic prediction model, so as to predict future epidemics. The application can effectively improve the credibility of regional dengue fever epidemic prediction under the background of climate change.

The invention discloses a deep learning enhanced cultivated land pattern history reconstruction system and method. The method belongs to the technical field of historical cultivated land data reconstruction. The system comprises a multivariate data input module which is used for acquiring land utilization / coverage data, natural environment data and social economic data of a to-be-cultivated land pattern reconstruction region in a certain historical period; obtaining a cultivated land expansion map through the land utilization / coverage data, obtaining a natural environment driving factor through the natural environment data, and obtaining a social economic driving factor through the social economic data; the historical cultivated land area reconstruction module is used for constructing a grain yield-cultivated land area regression model of a research region, performing regional cultivated land area reconstruction based on the grain yield-cultivated land area regression model, and obtaining the annual net increment and decrement cultivated land pixel quantity by calculating the cultivated land area difference value of adjacent years; and the deep learning enhanced space simulation module is used for carrying out year-by-year farmland area reconstruction from the last year of the reconstruction year through a reverse simulation strategy based on a PLUS model.

A deep learning enhanced cultivated land pattern history reconstruction system and method. It belongs to the technical field of historical cultivated land data reconstruction. The system includes a multi-element data input module: obtaining land use / cover data, natural environment data and social economic data of a historical period of the cultivated land pattern reconstruction area; obtaining cultivated land expansion atlas through land use / cover data, obtaining natural environment driving factors through natural environment data, and obtaining social economic driving factors through social economic data; a historical cultivated land area reconstruction module: constructing a grain yield-cultivated land area regression model of the research area, reconstructing the cultivated land area of the region based on the grain yield-cultivated land area regression model, calculating the difference of cultivated land area between adjacent years to obtain the number of annual net increased and decreased cultivated land pixels; a deep learning enhanced spatial simulation module: starting from the last year of the reconstruction year, the cultivated land area is reconstructed year by year through the reverse simulation strategy based on the PLUS model.

The invention belongs to the technical field of city power system operation and maintenance, and discloses a city power system earthquake damage toughness determination method and system based on multi-dimensional data analysis, and the method comprises the steps: building a multi-source heterogeneous evaluation database through obtaining earthquake parameters, power facility information, geographical topographic data and social economic data of each county area of a city; and based on the seismic oscillation parameters and the power facility vulnerability function, calculating the damage probability and the function expectation loss rate of the transformer substations and the power transmission lines of different voltage levels, and generating a regional power supply residual function matrix. And calculating the post-earthquake power supply gap rate by combining the real-time power demand model and the power supply capacity recovery curve, evaluating the power transmission line repair cost by using topographic features, and quantifying the influence degree of the earthquake on economic loss, affected population and social stability. According to the invention, the toughness and risk management capability of the power system in earthquake disasters can be effectively improved, and technical support is provided for rapid power recovery and resource optimization configuration after earthquakes.

The invention discloses an urban and rural landscape sustainable geographic design method based on random optimization and deep learning. According to the method, land utilization type data, geospatial data, environmental data and social economic data of a to-be-optimized area are utilized, four ecological system services including grain supply amount, carbon reserve, water conservation amount and culture service value are taken as optimization indexes, and calculation mode information of the indexes is input into an urban and rural landscape sustainable geographic framework; and predicting a high-probability change region through an artificial neural network model, performing iterative optimization on a current land utilization scheme by taking landscape sustainability as a target guide through a simulated annealing algorithm, and driving generation of an urban and rural landscape sustainability global optimal land utilization scheme. According to the method, a land utilization scheme with remarkable and better ecological indexes can be generated, multi-party benefit conflicts are efficiently coordinated, a flexible framework supports multi-target extension, and support can be provided for complex planning decision making.

The present application relates to a kind of river shape reservoir safety risk area social economic data investigation range acquisition method, system, medium and equipment, it includes: according to the multiple period remote sensing image of the area where the water level observation station of reservoir main dam upstream main dam is located and its synchronous water level monitoring data, water level elevation correction value is obtained to correct reservoir check flood level, flood control high water level;According to DEM data, the intersection of river channel line and reservoir management range surface in the direction of reservoir tail is calculated, i.e.the elevation of reservoir tail;According to the elevation of reservoir tail and the corrected reservoir check flood level, flood control high water level, according to the elevation reference value of the extraction of each area investigation range, according to the number of partition, along river channel, the reservoir area is partitioned, and according to the elevation reference value of the extraction of each area investigation range, the investigation range is extracted in each area, to obtain final investigation range.The present application can realize the fast, accurate acquisition of river shape reservoir safety risk area social economic data investigation range.

The present application relates to a kind of geological disaster risk assessment method and system fusing random forest with attention, the method includes the following steps: step A: basic geographic information data, meteorological data and social and economic data are collected as risk assessment index, geological disaster risk grade, geological disaster type as label, training set TS is constructed;Step B: using training set TS, random forest model RF and deep learning network model G based on attention mechanism are trained, for predicting geological disaster risk grade;Step C: risk assessment index and geological disaster type are input into random forest model RF and deep learning network model G, obtain geological disaster risk grade.The method and system are beneficial to improve the accuracy of geological disaster risk assessment.

The invention discloses an urban low-efficiency land identification and analysis method and system based on multivariate data, and relates to the technical field of urban land planning, and the method comprises the steps: obtaining and preprocessing basic land data, extracting a target type of land, and carrying out the attribute association and spatial linkage of the target type of land and multi-source social economic data; constructing a qualitative and quantitative low-efficiency land identification evaluation index system, and judging whether various types of lands are low-efficiency lands or not through an expert weighting and comprehensive evaluation method to obtain a preliminary low-efficiency land identification result; carrying out multi-subject and multi-type field check on the preliminary identification result, integrating multi-source data to form a correction data set, carrying out refined screening and boundary correction on the land parcel, and generating a final low-efficiency land identification result layer; and performing standardized storage on the final affirmation result, establishing an annual data dynamic updating mechanism, and performing iterative updating on the existing final affirmation result set based on the latest multi-source data. According to the invention, personal errors can be greatly reduced, and the recognition efficiency and accuracy are improved.

The invention belongs to the technical field of electricity consumption prediction, and particularly provides a WBGT-based provincial resident living electricity consumption prediction method, system and device and a storage medium, and the method comprises the steps: collecting the multi-source original data of a provincial scale in a target region, calculating the hourly wet bulb and black bulb temperature, and generating a WBGT feature index; building a bidirectional fixed effect panel model containing a province fixed effect and a time fixed effect by taking provincial resident living electricity consumption data as dependent variables and WBGT characteristic indexes and social economic data as independent variables; training the constructed model through a parameter estimation method to obtain a trained prediction model; and defining a climate-social economic scene to be predicted, inputting the WBGT characteristic index and social economic data in the scene into the trained prediction model, and calculating to obtain a resident living electricity consumption estimated value. According to the method, the WBGT is adopted to carry out attribution analysis or scene simulation on the historical electricity consumption, and the estimation precision and reliability are improved.

The invention discloses a high-temporal-spatial-resolution atmospheric particulate matter source analysis method based on observation constraint. The method comprises the following steps: simulating set time, and setting regional particulate matter PM2.5 and concentration of each component thereof; assimilating the observed PM2.5 and component concentration data of the PM2.5 in the set time and the set region and the PM2.5 and component concentration data of the PM2.5 obtained through CMAQ simulation; performing source analysis based on the assimilated data; and constructing a machine learning model by taking the source contribution of the source analysis as a dependent variable and taking the meteorological data, the geographic and social economic data and the road traffic data in each grid of the set time and the set region as independent variables, and simulating the source contribution concentration of different sources through the constructed model in a scale reduction manner. By adopting the method provided by the invention, an accurate source analysis result with high temporal-spatial resolution can be obtained, so that a scientific basis is provided for accurately formulating an atmospheric pollution prevention and control policy, a treatment strategy is optimized, the treatment cost is reduced, and the treatment efficiency is improved.

The application discloses a method for measuring non-equilibrium and quantitative attribution of environmental basic public services in farming and pastoral areas, which takes a town resident site as a research object, adopts three characteristic indexes of life garbage treatment, life sewage treatment and sanitary toilet, adopts an environmental basic public service allocation level index and a factor allocation rate algorithm, constructs a village environmental basic public service and social economic data set in farming and pastoral areas, and measures non-equilibrium on the basis of multi-scale description, and uses a logistic regression model to measure the causes of environmental basic public service allocation failure and the heterogeneity among factors. The application can quantitatively measure the overall and factor non-equilibrium of the village environmental basic public service on a multi-scale, and can quantitatively analyze the internal and external causes of the non-equilibrium, thereby laying a foundation for optimizing the allocation mode in farming and pastoral areas and customizing an environmental risk adaptive management scheme.

The invention discloses an urban population dynamic distribution prediction method and system based on a built environment. The method comprises the following steps: firstly, constructing an initial index system containing built-up environment and social economic data; performing feature selection by using an XGBoost model subjected to hyper-parameter optimization, and screening out key influence factors; the core lies in that an improved MLP model is constructed and trained, and the model effectively models the spatial overflow effect of building environmental factors in adjacent areas by introducing a spatial receptive field and a feature fusion layer. And finally, inputting the key influence factors of the year to be predicted into the trained improved MLP model to realize dynamic and accurate prediction of the population scale of each space unit in the city. The method overcomes the defects that a traditional method neglects a multivariable interaction effect, spatial correlation and nonlinearity, significantly improves the precision and reliability of population distribution prediction, and provides a scientific basis for smart city planning and management.

This invention relates to the field of suitability assessment and prediction technology, providing a machine learning-based method for predicting the suitability of carbon sequestration in coal mine goafs. It constructs a multi-source feature dictionary for goafs from five dimensions: mine safety, formation stability, formation sealing, resource reservability, and economic suitability. Subsequently, it processes historical exploration data, real-time monitoring data, and socio-economic data through missing value processing, outlier removal, standardization, and encoding to construct a standardized dataset. The dataset is trained using a multi-model framework based on random forest, support vector machine, extreme gradient boosting model, and logistic regression model, and hierarchical cross-validation is used to select the optimal model, thereby predicting the suitability assessment level for carbon sequestration in goafs. This invention fills the gap in the integration of carbon sequestration suitability assessment with machine learning, providing new methods and technical support for subsequent carbon sequestration in deep coal mine goafs.