18 results about "Human population distribution" 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.

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.

Based on demographic information of the area being analyzed, the system estimates the distribution of people with user-specified attributes at a given location within that area. [Solution] The human flow information estimation device 2000' includes a calculation device 2100 that performs human flow information analysis based on predetermined demographic information, which includes the number of people located in each of a plurality of predetermined area meshes and information on the attributes of those people. The human flow information estimation unit 2103 identifies a set of unit sub-regions that cover the area to be analyzed without overlapping each other, and based on demographic information and predetermined lifestyle information, it distributes the number of people from the demographic information to the unit sub-regions at predetermined time intervals, based on the type of place and the size of the place corresponding to the classification of people's behavior in each area mesh, and estimates aggregated data for each unit sub-region. Based on the aggregated data, the human flow information estimation unit 2103 calculates a human absorption capacity value, which is the ratio of the number of people with the analyzed attributes for each unit sub-region to a predetermined value.

Embodiments of the present application provide a kind of starting point and end point population flow data generation method and device. Among them, the method comprises: the graph model corresponding to the target area is constructed, and graph neural network model is established according to the graph model;Using the historical population distribution data and the starting point and end point population flow data of each region in the target area, the graph neural network model is trained;Using the graph neural network model obtained after training, the population flow data of the target area is generated.Relative to prior art, the model of the embodiments of the present application can generate higher accuracy population flow data based on relatively easy to collect population distribution data, easy to implement engineering, with the advantages of small amount of calculation, lower implementation cost and the like.

The application discloses a community facility supply and demand matching analysis method and related device, and relates to the technical field of city physical examination, updating and management. The method comprises the following steps: acquiring population position service data and population portrait data in a target area, and determining population distribution data of different target age groups; acquiring community boundary data in the target area, combining the population distribution data of different target age groups, and obtaining population distribution data of different age groups in each community in the target area; acquiring community facility interest point data in the target area, combining community facility types suitable for different target age groups, determining community facility types and quantities configured for different target age groups in each community, and combining population distribution data of different age groups in each community in the target area, to determine community facility items to be improved for each target age group in each community in the target area. The application adopts a data-driven mode, and improves the accuracy of the community facility supply and demand matching analysis result.

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.

Technologies for population distribution-based drive testing are disclosed. An example method includes determining geographic units for drive testing based on a proposed coverage of the communications network and a population distribution of potential users of the communications network, and for each geographic unit: determining a mobile test pattern and a set of stationary test locations for drive testing, and causing performance of drive testing in accordance with the mobile test pattern and set of stationary test locations.

The invention relates to the technical field of urban public service facility evaluation and space planning, and discloses a living circle identification and full-age adaptive facility supply and demand evaluation method based on resident flow. The core of the method is to dynamically capture an actual travel track of a resident by using mobile phone signaling data so as to identify a life circle range highly matched with an actual behavior; a two-dimensional demand weight matrix of groups and facilities according to age groups is constructed, and differentiated demands of different age groups such as children, young adults and old people for various public service facilities are finely quantified; by fusing multi-source data such as population distribution, facility point locations and real-time road networks, accurate matching evaluation of facility supply and demand services is realized by adopting an improved space interaction model; according to the method, the limitation that traditional planning depends on a static administrative boundary is effectively overcome, life circle division is more scientific, and the fineness and fairness of facility configuration are remarkably improved from the perspective of all-age demand evaluation.

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.

Based on demographic information of the area being analyzed, the system estimates the distribution of people with user-specified attributes at a given location within that area. [Solution] The human flow information estimation device 2000' includes a calculation device 2100 that performs human flow information analysis based on predetermined demographic information, which includes the number of people located in each of a plurality of predetermined area meshes and information on the attributes of those people. The human flow information estimation unit 2103 identifies a set of unit sub-regions that cover the area to be analyzed without overlapping each other, and based on demographic information and predetermined lifestyle information, it distributes the number of people from the demographic information to the unit sub-regions at predetermined time intervals, based on the type of place and the size of the place corresponding to the classification of people's behavior in each area mesh, and estimates aggregated data for each unit sub-region. Based on the aggregated data, the human flow information estimation unit 2103 calculates a human absorption capacity value, which is the ratio of the number of people with the analysis target attributes for each unit sub-region to a predetermined value.

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.

This invention relates to the field of population spatiotemporal distribution modeling technology, and proposes a population model generation method based on crowdsourced data, comprising the following steps: Step 1, collecting socioeconomic and natural environment data to obtain standardized multi-source raster data; Step 2, based on the standardized data, constructing a daytime population weight layer by training a heterogeneous basic regression model with five-fold cross-validation and building an adaptive spatial weighted support vector regression meta-learner; Step 3, extracting building volume and nighttime light intensity raster layers from the standardized data to construct a nighttime population weight layer; Step 4, constructing a non-working day population weight layer by weighted fusion based on the day and night weight layers; Step 5, constructing basic spatial mapping units to complete high-scale downscaling reconstruction of population space, obtaining initial high-resolution population distribution results; Step 6, introducing a time smoothing model to correct the temporal inconsistency problem of the initial results, generating the final population distribution model. This invention effectively solves the problems of insufficient spatiotemporal resolution, poor time-segment adaptability, and unreasonable assignment of missing data areas in traditional population distribution models, providing accurate, comprehensive, and highly usable population distribution data support for scenarios such as urban planning, resource allocation, and emergency management.

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 application provides a flood disaster injury trend prediction method and system and a life integrated machine, and relates to the technical field of flood disaster injury trend prediction. The method comprises the following steps: based on geographic spatial data, population census data and mobile phone signaling data of a disaster area, the population quantity distribution is evaluated; the population quantity distribution is projected into a digital elevation model, and after the actual disaster population distribution is determined in combination with rainfall distribution data, on-site rescue is performed based on a preset rescue plan; after a preset time interval, on-site injury data is counted; and based on the on-site injury data, the life length of the wounded is predicted through a prediction model. According to the scheme, the population quantity distribution of the disaster area can be accurately calculated, the actual disaster population distribution can be calculated in combination with the rainfall distribution, and the life length of the wounded can be reliably predicted, so that a scientific basis is provided for disaster prevention, emergency evacuation, medical rescue, command and scheduling and risk management.

The application provides a flood disaster population exposure calculation method and system based on space constraints, relating to the technical field of flood disasters, which comprises the following steps: determining the current water accumulation mask space distribution of a target city for model prediction according to an obtained rainfall observation sequence; inputting the current multivariate state information of each resident into a pre-trained reinforcement learning model; predicting the risk avoidance action currently performed by each resident based on the estimation of the state transition probability distribution of the reinforcement learning model; determining the spatial position of each resident at the next moment according to the predicted risk avoidance action; determining the population distribution of each spatial unit of the target city at the next moment according to the spatial position of each resident at the next moment; and determining the flood disaster population exposure value of the target city at the target moment according to the population distribution of each spatial unit of the target city at the target moment. The application aims to improve the calculation accuracy of population exposure.

The invention relates to a population distribution prediction sample generation method fusing multi-source remote sensing and social perception data, particularly relates to the field of computers, and remarkably improves the precision and reliability of a population distribution prediction sample through multi-source data fusion and cooperative processing. According to the method, the problems of spatial-temporal inconsistency, noise interference, insufficient regional generalization ability and the like of multi-source data are effectively solved, the generated sample can accurately reflect the spatial-temporal distribution characteristics of the population and has good adaptability, and the technology provides a high-quality data basis for applications such as earthquake risk assessment and supports long-term disaster prevention planning and resource optimization configuration.

The invention provides a social humanity data spatialization method based on multi-source data, and the method comprises the following steps: carrying out land utilization type area statistics based on land utilization classification data, and constructing an initial population model through a multiple linear regression method in combination with administrative division population data; reversely calculating population data according to the initial population model, and performing coefficient correction on the initial population model in combination with population statistical data; population distribution values in different administrative regions are obtained through calculation based on the corrected initial population model, and Kriging interpolation is carried out on the population distribution value of each grid after grid data are utilized to carry out gridding processing; according to the correlation between the map POI data and the administrative division population data, constructing a grid population redistribution model by using a quadtree spatial index algorithm; and based on a grid population redistribution model, redistributing the population distribution value of each grid obtained through Kriging interpolation, and generating a final spatial population data gridding result.