57 results about "Population data" patented technology

Discussed herein are methods and systems for an interdependent series / suite of AI models. In one embodiment, a processor receives projection assumption inputs from a user device and executes a population builder machine learning model to predict a dynamic adjustment table. It applies the model to population data to generate a value population file, which simulates a subset of the population based on the predicted table. The file contains value cells representing instances of a product. The device then runs a mortality machine learning model to determine mortality data for the product using the simulated population. Finally, it executes a flow projection model to generate a projection report for the product, incorporating mortality data and projection assumptions.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a spatial-temporal feature attention mechanism-based population thermodynamic data prediction method, which comprises the following steps of: acquiring a target area map, and performing node division on the target area map to generate a target area node network; obtaining population data information of each node in the target area node network; calculating the population data information of each node by using a neural network to obtain time sequence features and spatial features of each node; generating a time feature vector and a space feature vector of each node according to the time sequence features and the space features; and generating predicted population thermodynamic data information according to the time feature vector and the space feature vector, thereby solving the problem that a traditional model is insufficient in spatial and temporal feature capture capability, solving the problem of spatial and temporal feature calculation redundancy in a deep learning algorithm, reducing model parameters by introducing spatial and temporal feature attention, and improving the prediction accuracy of the population thermodynamic data information. And the calculation efficiency of large-range and big-data population thermodynamic data can be well improved.

The invention discloses a future grid population distribution prediction method, which comprises the following steps of: a) acquiring fine spatial-temporal scale population data in a historical period, and processing the fine spatial-temporal scale population data in the historical period to obtain a population elasticity coefficient; b) acquiring a ratio of crop yield or water resource quantity of each age in the future to a steady-state value of a reference year, and acquiring an environment variable under the influence of climate change; and c) according to the population elasticity coefficient and the environment variable under the climate change influence, generating future urban population and rural population distribution change based on the shared social economy SSPs and the shared social economy and representative forced path SSPs-RCPs composite scene, and finally synthesizing population distribution change. According to the future grid population distribution prediction method provided by the invention, the influence of the natural environment on the population distribution change is combined on the basis that the social and economic change influences the population distribution change, and the future grid population distribution can be predicted more accurately.

The embodiment of the invention relates to the technical field of population activity monitoring, and particularly discloses a population activity quantity extraction method and system based on cloud computing and big data utilization. According to the embodiment of the invention, multi-source population data of a target monitoring area is collected and uploaded; based on a cloud computing technology, performing data standardization and fusion processing on the multi-source population data; predicting the number of regional population, and performing dynamic visual display; and determining a plurality of population early warning rules, carrying out abnormal risk judgment on the number of regional population, and carrying out early warning pushing and linkage response when an abnormal risk exists. According to the method, a dynamic fusion mechanism of an emotion-flow coupling anomaly thermodynamic diagram and a flow mutation grid map with conflict indexes is constructed, so that silent abnormal points and false abnormal points are accurately captured.

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 provides a dynamic population analysis and medical resource optimization method and system based on mobile phone signaling data and medical insurance data, and the method comprises the steps: recognizing a residence place, a working place and a medical staying point of a user through a space-time double-layer clustering algorithm based on the mobile phone signaling data, constructing a dynamic population distribution model, and carrying out the calculation of the dynamic population distribution model. The dynamic population data, the OD matrix data and the medical insurance data are subjected to association analysis, regional-level medical resource requirements are predicted, the defects in the prior art are overcome, and the method has important research significance and use value.

The invention relates to the technical field of population prediction, and discloses a population prediction method and device based on combination of an ARIMA model and a queue element method. The method comprises the steps of obtaining historical migration population data of a to-be-predicted region, determining a difference order through ADF test, preliminarily determining the range of an autoregression order and a moving average order by using an autocorrelation function and a partial autocorrelation function, and constructing an ARIMA model; the optimized ARIMA model is adopted to fit historical migration population data, a migration population prediction result is obtained, and migration population data of each age group in the target prediction time period is output in combination with age structure data of floating population of the reference year; taking the migration population data of each age group as input parameters of migration population in a queue element method; according to a preset sum growth rate, selecting a western mode of a numeral-demann region model life table as a death model; and setting the average life expectancy according to a preset birth ratio, and realizing permanent resident population prediction of the to-be-predicted region.

The application discloses a kind of urban sprawl space-time evaluation method and system based on multi-source remote sensing and population data, the method constructs urban sprawl index based on spatial vector cosine similarity by fusing night light and gridded population data, accurately quantifies the spatial dispersion degree of population and economic activity;Using recursive quantization analysis technology, extract certainty, laminar flow and recursive entropy from time series, synthesis recursive trend intensity index, deep diagnosis of the internal dynamics characteristics and driving force intensity of sprawl trend;Combined with terrain constraint identification and LISA space-time path analysis, realize the classification and spatial stability evaluation of sprawl mode;And using geographic detector, the differential driving force interaction detection of mode is carried out, and finally the decision support report of the fusion multi-dimensional evaluation conclusion is automatically generated.The application realizes the integrated, fine and dynamic evaluation of urban sprawl from intensity, trend, mode to driving mechanism.

The present invention discloses a method, system, equipment and medium for evaluating the coupling coordination between public sports facilities and population, including obtaining public sports facilities data, population data and urban development statistical yearbook; calculating the spatial geographic aggregation of public sports facilities and population according to the public sports facilities data and population data; calculating the development level evaluation value; allocating evaluation weights to the spatial geographic aggregation of public sports facilities and population according to the development level evaluation value; calculating the coupling coordination between public sports facilities and population according to the spatial geographic aggregation of public sports facilities and population and the corresponding evaluation weights. The present invention incorporates the development level of each region into the evaluation process, can more realistically measure the current coupling and coordinated development status of public sports facilities and population, and provides a reference for improving the service level of public sports facilities and promoting high-quality urban development.

This invention relates to the field of ecological protection zone identification technology, and particularly to a method for improving the accuracy of the Human Footprint Index. The method comprises the following steps: S1, Population Grid Redistribution: A construction land mask is extracted from land cover data. Population statistics data from the construction land mask and population grid data are input into the iURBAN algorithm to redistribute the population grid data. Population statistics data are then used to verify the accuracy of the population grid data before and after redistribution. S2, Fragmentation Index Scoring: A movement speed grid is calculated. City tiers are classified based on land cover data. Based on the city tier classification and movement speed grid, different levels of urban patches representing urban edges are obtained. Landscape pattern indicators representing fragmentation are calculated, and fragmentation index scores are assigned based on the landscape pattern indicator calculation results. S3, Human Footprint Index Calculation. This invention redistributes population data from the population grid to optimize the population distribution and improve the accuracy of the population grid data.

The invention provides a talent flow analysis method based on population data. The talent flow analysis method comprises the steps of obtaining talent big data; based on a pre-trained talent flow analysis model, performing talent flow analysis according to the talent big data to obtain an analysis result; and outputting and displaying the analysis result. According to the talent flow analysis method based on the population data, accurate description and foresight prediction of the talent flow condition are achieved, powerful data support is provided for government talent policy making, enterprise human resource management, personal occupational planning and the like, talent resource configuration optimization is facilitated, and talent efficiency is improved.

The invention discloses a plot scale population prediction method based on an improved queue element prediction method, and relates to the field of urban and rural planning and design technology and demography, and the method comprises the following steps: collecting and processing plot scale population data to form a plot scale population database; constructing a plot scale population prediction model based on a traditional queue element prediction method, and correcting fertility data and death data input; on the basis of calculating the normalized population mobility, considering the influence of major industrial projects, and correcting the plot scale population mobility; the aspects of data input, operation speed, output result and result precision of the plot scale population prediction model are improved, the calculation precision and speed of a traditional queue element prediction method are optimized, and plot scale population fine prediction is achieved. According to the method, mastering of the population development situation by government departments can be improved, development policy making and space resource allocation are assisted, and objectivity and scientificity of urban government are improved.

The invention discloses a city spread space-time evaluation method and system based on multi-source remote sensing and population data, and the method comprises the steps: constructing a city spread index based on spatial vector cosine similarity through fusing night light and grid population data, and precisely quantifying the spatial dispersion degree of population and economic activities; a recursive quantitative analysis technology is adopted, characteristics such as certainty, laminar flow and recursive entropy are extracted from the time sequence, a recursive trend intensity index is synthesized, and the inherent dynamic characteristics and driving force intensity of the spreading trend are deeply diagnosed; in combination with terrain constraint recognition and LISA space-time path analysis, spread mode classification and spatial stability evaluation are realized; and carrying out differentiated driving force interactive detection in different modes by utilizing a geographic detector, and finally automatically generating a decision support report fused with a multi-dimensional evaluation conclusion. According to the invention, integrated, refined and dynamic evaluation of city spread from intensity, trend and mode to a driving mechanism is realized.

The invention discloses a population prediction method and device, electronic equipment and a storage medium. Comprising the following steps: acquiring satellite image data, night light image data and base station communication data of a target area; determining plane area data, floor number and building type of the building in the target area based on the satellite image data; for any building, determining the population density of the building based on the plane area data, the floor number and the building type of the building, and determining an initial population prediction result based on the plane area data, the floor number and the population density of the building; and determining a population calibration coefficient of the target area based on at least one of the night light image data, the base station communication data and the initial population prediction result of each building, so as to calibrate the initial population prediction result corresponding to each building, and obtain a corresponding target population data prediction result. Dynamic calibration of the population prediction data is realized, and the accuracy of the population prediction result is improved.

The embodiment of the application discloses a kind of city shrinkage identification methods based on timing population data change point detection, it is related to geographic information technical field, the method comprises: obtaining land use spatial grid data and population spatial grid data;Land use spatial grid data, population spatial grid data are respectively converted into matrix;Determine town land grid based on land use spatial matrix, extract population data from population spatial matrix, construct to obtain one-dimensional population array, obtain smoothed timing data after smoothing processing;Change trend mutation node of population data is identified based on smoothed timing data, each one-dimensional population array is segmented;Population data change trend of the array after identification is segmented, determine city shrinkage grid.The application uses change point detection to automatically divide period, can more objectively identify the start and end time of city shrinkage, can provide data support for future town land evolution simulation and regional development policy formulation.

The invention relates to a refined earthquake buried population estimation method and a medium, and relates to the technical field of population data calculation. Firstly, function types of buildings in a target area are identified based on remote sensing data, so that population weights of the buildings of all the function types are obtained, and after an earthquake disaster occurs, rescue force can be accurately positioned to each high-risk building instead of a fuzzy area. Secondly, for each building, based on the function type, the building area and the population weight of the building, the building scale population number is calculated, and the collapse rate and the person in-room rate when an earthquake occurs are obtained, so that the estimated number of buried people can be dynamically obtained; therefore, more refined buried population spatial distribution can be obtained by integrating various complex nonlinear potential influence factor relationships, and a more scientific and effective decision basis is provided for earthquake emergency response.

The invention discloses a multi-dimensional population data dynamic fusion analysis system and method, and the system comprises a multi-source data fusion module which is used for receiving multi-source data and carrying out the alignment of the multi-source data, and obtaining the aligned data; and the intelligent analysis module is connected with the multi-source data fusion module and is used for receiving the aligned data and generating a bus scheduling or path optimization scheme based on the aligned data and the dynamic job-housing index model. The analysis accuracy and effect can be improved.

The invention discloses a population data research and judgment method and system, and the method comprises the steps: collecting multi-source data of a plurality of data formats according to a collection rule, and carrying out the preprocessing of the multi-source data, and obtaining standardized target multi-source data; performing data fusion on the target multi-source data to obtain a unified population data set, and performing population feature learning and extraction on the population data set by using graph embedding and a graph neural network to extract population feature vectors; inputting the population feature vector into an analysis, research and judgment model for analysis and prediction to obtain a prediction result for predicting a population data transformation trend, and displaying the prediction result through an sql statement; multi-source data changes are monitored in real time, and the analysis, research and judgment model and the corresponding prediction result are dynamically updated.

The invention provides a large-scale demographic data synchronization method, system and device and a medium, and belongs to the technical field of big data processing. The method comprises the steps that multi-source demographic data change information is received and analyzed, and a standardized event stream is generated; event distribution and tracking are carried out through a routing buffer layer; performing association sorting, duplicate removal and screening on the event stream to form a fusion event block; transmitting the data to a target node through a multi-protocol network and storing the data into hierarchical storage; executing multi-level storage optimization and establishing a query identifier; and finally generating a synchronization report. According to the method, efficient and reliable synchronization and management of ten millions of population data are realized. Full-automatic synchronization and integration of multi-source population data are realized, and data accuracy and storage efficiency are improved through intelligent deduplication and multi-level storage optimization; and real-time processing and historical tracing are supported, and high-standard requirements of large-scale population data management on timeliness and accuracy are met.

The invention discloses a nuclear leakage event identification and positioning method, device, equipment and medium, and relates to the technical field of identification and localization, the method comprises the following steps: firstly, monitoring an OLR abnormal region caused by a nuclear leakage event by using an OLR abnormal monitoring technology to obtain an OLR abnormal result of a research region; performing anomaly monitoring on the relative humidity data and the relative temperature data respectively, then performing comprehensive judgment on an OLR anomaly region, a relative humidity anomaly region and a relative temperature anomaly region, identifying the event and positioning the occurrence position of the event, and finally, determining the occurrence position of the event. And comprehensively evaluating the influence caused by the event in combination with social data such as annual average population data of the event observation year. According to the method, the nuclear leakage event is identified and positioned by using the remote sensing data, all-weather leakage monitoring can be realized, and the method has remarkable economic and social benefits.

The invention provides a medical and health facility supply and population matching degree evaluation method. The method comprises the following steps: constructing a medical and health facility supply evaluation index system from two dimensions of space and use; acquiring medical and health facility data, residential area data and population data required by the evaluation indexes; calculating each evaluation index value according to the acquired data; according to each evaluation index value, determining each index weight by adopting an entropy weight method, and calculating a supply value; and according to the calculated supply value and population data, a location entropy analysis method is adopted to diagnose the matching degree of the medical and health facility supply and population distribution. According to the method, the medical and health facility supply level is comprehensively quantified, the matching condition of the current medical and health facility supply and population can be measured more practically, and reference is provided for scientific planning and configuration optimization of regional medical and health facilities.

The invention provides an urban population spatialization correction method under the support of authority reconciliation building table data, which comprises the following steps of: S1, determining an area range and a space reference for implementing correction, and obtaining area boundary data, authority survey data, population spatialization result data and population statistical data; s2, determining a statistical grid; s3, unifying spatial references of the region boundary data, the authority survey data and the population spatialization result data; s4, cutting population spatialization result data; s5, matching the right survey data, the cut population spatialization result data and the statistical grid to obtain grid population data and grid building data; s6, calculating the per household population of the grid; and step S7, correcting the grid population data. According to the method, the influence of signaling data quality, a residence time threshold value, uneven base station density, a space threshold value and the like on urban population spatialization can be eliminated, a regional population data spatialization result is corrected, and related city updating work is supported.

The invention discloses a population data generation method and device and a computer readable storage medium, and the method comprises the steps: carrying out the conversion of an initial population sample from an attribute domain to a copula domain, obtaining a copula domain sample, carrying out the vectorization coding representation of the copula domain sample, and obtaining a matrix sample; performing diffusion model training and sample generation based on the matrix sample to obtain a new matrix sample; and decoding the new matrix sample to obtain a copula domain vector sample, and performing quantile function mapping on the copula domain vector sample to obtain an attribute domain demographic data generation sample. According to the method, the initial population sample is converted to the copula domain, the correlation mode between the attribute variables is reflected through the copula density function, the influence of edge distribution is avoided, the intrinsic dependency constraint relation between the variables can be effectively captured, and the population sample data with high feasibility and diversity is generated.

The application discloses a power load prediction method based on population flow, relates to the technical field of data processing, and determines an OD flow matrix of a preset area and historical load data of the preset area; regards each target area in the preset area as a node, regards historical population flow data in the OD flow matrix as an edge, regards historical load data of each target area, historical time in the OD flow matrix, historical population data, regional characteristic data and meteorological data as attributes of the node, and constructs a space-time dynamic graph; processes the space-time dynamic graph through a population distribution prediction model to obtain predicted population data of the space-time dynamic graph; processes historical load data, historical time, meteorological data and the predicted population data of the space-time dynamic graph to obtain a predicted load distribution graph of the preset area. Through the technical scheme, the characteristic data of each target area in the time scale and the characteristic data of each target area in the space scale are matched, and the accuracy of the predicted load distribution graph can be improved.

The invention discloses an evacuation capability evaluation method, device and equipment for an external emergency refuge space. The method comprises the following steps: obtaining building information in a first target range; acquiring real-time population distribution data and census population data in a first target range in the to-be-evaluated time period; obtaining real population distribution data in a first target range according to the real-time population distribution data and the census population data; obtaining a plurality of evacuation points and to-be-evacuated information of each evacuation point according to the real population distribution data and the building information; all external emergency refuge spaces in a second target range with the evacuation point as the center are obtained; and based on a preset escape speed data group, according to the to-be-evacuated information and the road information corresponding to the evacuation point, obtaining a first evacuation capability evaluation result whether each external emergency refuge space is reachable within a preset time threshold. According to the method provided by the invention, the actual evacuation effect of the emergency refuge space in the to-be-evaluated time period can be accurately evaluated.

The invention discloses a power load prediction method based on population flow, and relates to the technical field of data processing, and the method comprises the steps: determining an OD flow matrix of a preset region and historical load data of the preset region; taking each target region in the preset region as a node, taking historical population flow data in the OD flow matrix as an edge, taking historical load data of each target region, and historical time, historical population data, region feature data and meteorological data in the OD flow matrix as attributes of the nodes, and constructing a space-time dynamic graph; processing the space-time dynamic graph through a population distribution prediction model to obtain predicted population data of the space-time dynamic graph; and processing the historical load data, the historical time, the meteorological data and the predicted population data of the space-time dynamic graph to obtain a predicted load distribution graph of the preset area. According to the technical scheme, the feature data of each target region on the time scale and the feature data of each target region on the spatial scale are matched, and the accuracy of predicting the load distribution diagram can be improved.

This invention relates to a refined method and medium for estimating the population buried in earthquakes, belonging to the field of population data extrapolation technology. First, based on remote sensing data, the functional types of buildings in the target area are identified, thereby obtaining the population weight of each functional type of building. This allows rescue forces to accurately locate each high-risk building after an earthquake, rather than a vague area. Second, for each building, based on its functional type, building area, and population weight, the building-scale population is calculated, and the collapse rate and occupancy rate at the time of the earthquake are obtained, thus dynamically estimating the number of people buried. In this way, a more refined spatial distribution of the buried population can be obtained by integrating various complex nonlinear potential influencing factors, providing a more scientific and effective decision-making basis for earthquake emergency response.

The present invention relates to the technical field of biological population prediction at nuclear power water intakes, and discloses a method for predicting biological populations at nuclear power water intakes based on deep learning. The method first collects data such as environmental factors, biological distribution, and historical prediction deviations, constructs a model selection correlation equation, selects the current prediction model based on this data, and then determines whether the model is optimized based on actual biological population data, ultimately obtaining an optimized prediction model structure. The method defines the monitoring area and target biological species, sets the statistical time period and key parameters, utilizes swarm intelligence optimization technology to construct correlation equations and weighted data sets, determines the model by comparing the success rate, and determines whether the model structure is optimized based on the critical value of the effect difference. This method can improve prediction accuracy and adaptability, providing an effective solution for predicting biological populations at nuclear power water intakes.

The present disclosure is about a method and device, storage medium, and electronic device for adjusting intervention actions for infectious diseases, and relates to the field of big data processing technology. The method includes: obtaining social demographic data of a target area; wherein the social demographic data includes one or more of age data, family data, employment data, workplace data, school data, and commuting data; constructing an individual simulation model based on the social demographic data, and using the individual simulation model to calculate the regional state data after the current intervention action is performed in the target area; wherein the regional state data includes the number of newly confirmed cases per day, the cumulative number of confirmed cases, the number of vaccine injections, and multiple historical intervention actions; inputting the regional state data into a trained intervention action prediction model to obtain a predicted intervention level, and adjusting the current intervention action of the target area based on the predicted intervention level. The present disclosure improves the accuracy of the predicted intervention level.

The application provides a method for calculating multi-scale land cover change contribution degree and its correlation, wherein the method for calculating the contribution degree comprises the following steps: taking a national region, a natural regionalization region, a province and a typical urban area as multi-scale division regions; obtaining multiple land cover changes according to land cover categories; calculating population contribution degrees in multiple comparison modes according to population data in the multi-scale division regions; and calculating land cover change contribution degrees in multiple comparison modes according to the area of each land cover change in the multi-scale division regions. The land cover change in the multi-scale division regions and the population factor can be used to calculate the contribution degrees in multiple comparison modes. The multi-scale and quantitative method for calculating the contribution degrees is not easily affected by a few data deviations, the conclusion is closer to the actual situation, and thus the reference demand of the current land cover change on land policy tendency can be met.