39 results about "Population data" patented technology

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.

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 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 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 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 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.

The invention discloses a historical population space reconstruction method and system based on altitude driving, a terminal and a storage medium, and the method comprises the steps: obtaining population statistical data in a historical period, and carrying out the preprocessing, and obtaining target historical population data; acquiring historical spatial data, performing matching processing on the historical spatial data and the target historical population data to obtain a three-dimensional structure database, and performing mask extraction and resampling processing to obtain target altitude data; constructing a population and altitude quantitative mapping relation model according to the target historical population data and the target altitude data; and according to the population and altitude quantitative mapping relation model, performing preliminary space distribution processing and loop iterative calculation on the target historical population data to obtain a historical population space reconstruction result. According to the method, the population distribution is quantitatively associated with the altitude, so that the population number in the historical period can be subjected to refined space distribution in the geographic unit, and the accuracy of generation of the historical population space reconstruction result is effectively improved.

The present disclosure provides a population standard address matching method and system based on an analytical data warehouse, and relates to the technical field of standard address matching, which comprises the following steps: collecting and sorting a StarRocks data warehouse containing standard addresses; obtaining external population data and preprocessing the external population data; matching the preprocessed population data information with the population address information in the StarRocks data warehouse containing standard addresses; adopting a multi-level address matching algorithm to associate the areas matched to the standard addresses, and storing the areas not matched to the standard addresses in a special address library; updating and maintaining the address data in the special address library, and incorporating the address data in the special address library into the StarRocks data warehouse containing standard addresses after dynamic updating; and the present disclosure has high accuracy and consistency in address matching.

This invention relates to the ontology of population data, and proposes a method for ontology of zonal population data entities. The steps include: 1) acquiring zonal population data and dividing the zonal population data into individual indicators according to different zonal population domain concepts, and using the specific description or classification of the concepts corresponding to the individual indicators as the corresponding indicator sub-features; 2) constructing a zonal population data ontology, which includes classes, attributes, and values. Classes correspond to concepts in the population domain, attributes correspond to individual indicators and indicator sub-features of the zonal population data, and values ​​correspond to the specific values ​​of individual indicators and indicator sub-features in the zonal population data; 3) mapping the zonal population data onto the zonal population data ontology, mapping the individual indicators and indicator sub-features of the zonal population data into the corresponding attributes under the corresponding classes in the zonal population data ontology, and mapping the specific values ​​of the indicators and indicator sub-features in the zonal population data into the values ​​of the ontology to achieve ontology of the zonal population data.

The invention belongs to the technical field of natural disaster assessment. A flood-affected population assessment method based on three-dimensional building characteristics and spatial indexes comprises the following steps: establishing a three-dimensional building response model, and calculating an effective base area of a building through height / floor conversion; calculating a population density based on the effective base area and the population data; establishing multi-stage water depth submerging area statistics, and adopting an R-tree spatial index technology to realize rapid spatial analysis of submerging elements and buildings; calculating a submerged area through layer intersection; and generating a submerged population statistical table by taking the administrative division as a unit. The method is used for solving the technical problems that an existing disaster damage assessment method cannot reflect the limitation of three-dimensional characteristics of a building, and an existing flood disaster damage assessment method is low in calculation efficiency and precision.

Embodiments of the present invention relate to the technical field of population activity monitoring, specifically disclosing a method and system for extracting population activity data based on cloud computing and big data utilization. The embodiments of the present invention collect and upload multi-source population data for a target monitoring area; standardize and fuse the multi-source population data based on cloud computing technology; predict regional population numbers and dynamically visualize them; determine multiple population warning rules, assess the risk of abnormal population numbers in a region, and, when abnormal risks exist, issue warning push notifications and initiate a coordinated response. The present invention achieves precise capture of silent and falsely reported anomalies by constructing a dynamic fusion mechanism that combines an emotion-traffic coupling anomaly heat map with a traffic mutation grid map with conflict indicators.

The embodiment of the invention provides a data optimization method and device, equipment and a storage medium. The method comprises the steps that an initial population data set is obtained, the initial population data set comprises at least one piece of individual data, and the individual data comprises at least one design parameter; performing reverse learning processing on the initial population data set to obtain a first population data set; performing differential evolution processing and cross processing on the first population data set to obtain a second population data set; and performing non-dominated sorting processing and clustering processing on the second population data set to obtain a target population data set which is used for performing multi-target optimization. Through multi-working-condition constraint processing and multi-mode solution set maintenance, the output target population data set can meet design requirements under different working conditions, diversified candidate schemes are provided for engineers, the design period is shortened, and the comprehensive performance of the suspension system is improved.

The present application relates to the technical field of load prediction, and particularly relates to a load prediction method and system for power distribution areas, which comprises the following steps: collecting population signaling data and performing anonymous preprocessing on the collected data to obtain anonymized regional population data and an anonymized inter-grid population flow matrix; the population signaling data comprises regional population count and a cross-grid moving event sequence set; a mapping relationship between a grid g and an area a of a power distribution network is established in combination with GIS data and grid positions of the power distribution network, and an estimated population flow amount between areas is calculated; a CNN-LSTM-Attention-GNN population prediction model is established, and area population data, weather data, date data and the estimated population flow amount between areas are collected to input the population prediction model to obtain a population prediction value at a future time; and a load estimation model is constructed according to the population prediction value at the future time and a per capita load to realize area-level load prediction. The present application improves the precision of load prediction.

The application discloses a population distribution data determination method and related equipment, and relates to the technical field of data processing. The method comprises the following steps: acquiring a multi-source population data set of a target region; performing sub-regional category division on the target region according to a population aggregation degree, to obtain N preset sub-regions with different aggregation degrees; performing data division on the multi-source population data set based on the N preset sub-regions, to obtain N sub-regional data sets corresponding to the N preset sub-regions; using a preset algorithm corresponding to the aggregation degree to respectively perform data integration on the N sub-regional data sets, to obtain N sub-regional population distribution data; and performing data merging on the N sub-regional population distribution data, to obtain target population distribution data of the target region. According to the application, the region is divided according to the population aggregation degree, and the data integration is performed by using the adaptive algorithm, so that the accuracy and quality of the population data of different regions are optimized, and the accuracy of the population multi-source data fusion is improved.

The present application relates to the technical field of machine learning and population prediction, and particularly relates to a population prediction method and system based on adaptive ridge regression and multi-model stacking; firstly, the original population data is preprocessed by anomaly value repair, first-order difference, standardization and PCA dimension reduction to generate time series features; then, three models of multilayer perceptron, random forest and linear regression are constructed, cross-validation and grid search are used to optimize the hyperparameters and complete the training; then, through the hierarchical stacking strategy, the prediction output of the basic model is input into the ridge regression sub-model as a new feature; finally, based on the performance of each basic model in different population characteristic prediction tasks, the weight proportion is dynamically adjusted through the regularization parameter, and the accurate prediction of multi-dimensional population characteristics is realized. The present application effectively integrates the advantages of each model, improves the prediction accuracy and stability of indicators such as total population, gender structure, urban-rural flow, and provides a scientific basis for population strategy formulation, public resource planning and risk warning.