24 results about "Population based" patented technology

A first risk score for each member in a population is generated using a first predictive model. A second risk score is generated for each member in the population using a second predictive model. The population is stratified into a plurality of risk groups based on: (i) predefined manageable medical conditions; (ii) the first risk score; and (iii) the second risk score. Based on the plurality of risk groups, the population is segmented into a high-risk group and a low-risk group. A panel of care management associates of a plurality of care management associates is assigned to each member in the member population based on whether the member belongs to the high-risk group or the low-risk group.

The invention provides a data recommendation method and device based on artificial intelligence, computer equipment and a storage medium. The method comprises the following steps: acquiring key business data of an initial crowd; analyzing the key business data based on an index analysis strategy to generate analysis reasons and optimization suggestions; performing data integration on the key business data, the analysis reasons, the optimization suggestions and the user behavior logs to obtain target data; based on the target data, performing crowd optimization processing on the initial crowd by using a preset large model, a diffusion model and a hybrid model to determine an optimized target crowd; performing effect prediction on the target crowd, and judging whether effect prediction data accords with expectation or not; if yes, binding the target crowd with a marketing strategy, and verifying the target crowd based on an AB experiment; and if the experiment result of the AB experiment is a forward result, performing product recommendation processing on the target crowd based on a total delivery strategy. According to the method, the marketing recommendation efficiency and accuracy are effectively improved.

The invention relates to the technical field of data processing, in particular to a community governance method and system based on population data, and the method comprises the steps: carrying out the preprocessing of collected multi-source population data, carrying out the desensitization of sensitive data through a differential privacy technology, and carrying out the data fusion, and obtaining a population data table; performing static or dynamic feature extraction on the population data table, and generating a governance feature portrait table by using a clustering algorithm and an association rule algorithm in sequence based on the extracted features; inputting the governance feature portrait table into a constructed strategy rule base for matching, and generating a governance strategy table in combination with space-time constraint conditions; and calculating a performance index according to an execution log generated by the governance strategy table, adjusting a weight parameter of a strategy rule base through a reinforcement learning model, updating the governance feature portrait table, and performing personalized and accurate community governance based on dynamic data of multi-source population data.

The invention provides an AISPN marker combination and a multi-dimensional ancestral and geographic inference model, and relates to the technical field of forensic medicine. According to the method, firstly, seven groups of ancestral inference SNP (Single Nucleotide Polymorphism) sites (AISNPs, AISNPs) of East Asia and Southeast Asia crowds are screened, the screened sites are respectively subjected to efficiency evaluation based on principal component analysis, ADMIXTURE and t-SNE methods, and a set of multi-dimensional ancestral and geographic source inference model is constructed in combination with six different machine learning methods. The AISPN marker combination provided by the invention comprises at least 50 AISPN sites, has relatively high differentiation in East Asia and Southeast Asia crowds, can distinguish nine crowds based on relatively few sites, provides site information for preparation of kits, and provides valuable information for research of forensic medicine, anthropology and population genetics.

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.

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.

The invention discloses a super-multi-objective optimization method and system based on dual-file coevolution and dual-distance indexes, and the method comprises the steps: generating an initial population based on the parameters of a power system, initializing the parameters and a reference vector set, and storing the population into a convergence file and a diversity file; calculating an ideal point and a worst point based on the convergence file, and updating the convergence file in combination with a double-distance index; maintaining population global distribution through a reference vector association operation; evaluating a population distribution state in combination with information entropy to determine a local neighborhood size; calculating individual local neighborhood density based on the parallel distance and updating a diversity file; constructing a mating pool based on the double archives and generating a filial generation population; and iteratively executing until the non-dominated solution set in the diversity file is output. According to the method, the convergence and diversity retention capability of the algorithm to the complex leading edge is enhanced, and a power system scheduling scheme with multi-target balance of the power generation cost, the transmission loss, the line utilization rate and the power generation adjustment amount can be efficiently generated.

The application discloses a village feature recognition method and device based on multi-source data, and belongs to the technical field of image recognition and artificial intelligence. The method comprises the following steps: generating a first population outflow characteristic value of a target village based on population census data; generating a second population outflow characteristic value of the target village based on a night light remote sensing image of the target village; generating a third population outflow characteristic value of the target village based on electricity consumption data; calculating a population outflow comprehensive characteristic value by adopting a multi-target linear weighting function method based on the first population outflow characteristic value, the second population outflow characteristic value and the third population outflow characteristic value; and identifying the hollowing degree of the village based on the population outflow comprehensive characteristic value. The application improves the accuracy of the hollowing degree recognition of the village, and the method is simple and fast.

Methods and apparatus to apply household-level weights to audience measurement data at a household-member level are disclosed. An example method to determine demographics of populations to measure media audiences of populations includes determining demographics for members of a first household of a sub-population. First demographics of a first member of the first household are different than second demographics of a second member of the first household. The example method includes calculating a first household-level weight for the first household based on a demographics distribution of the sub-population and aggregate demographics of a population. The example method includes applying the first household-level weight to the first demographics of the first member, applying the first household-level weight to the second demographics of the second member, and estimating a demographics distribution of the population to measure a media audience of the population based on the weighted first demographics and the weighted second demographics.

A computing system for generating handicap-adjusted health scores processes multi-source health metric data collected from heterogeneous devices and activity modalities. The system normalizes and calibrates unstructured health data using schema-conformant transformation logic and device-specific correction parameters. User-specific baseline values are computed over configurable rolling windows and compared to cohort-level reference parameters derived from demographic and device-based population segmentation. Deviation values are corrected using demographic and device normalization models to generate structured adjustment vectors. The system applies context-aware scoring logic to compute normalized health scores that reflect individual performance patterns relative to statistically similar user cohorts. Scoring outputs are configured for competitive presentation via a user interface and may support leaderboard ranking, team-based scoring, or context-specific evaluations across diverse activity types.

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 medical data feature selection method, system and device and a storage medium, and relates to the technical field of data feature selection, and the method comprises the following steps: generating an initial population comprising a plurality of individuals according to a plurality of original features, obtaining the fitness value of each individual in the initial population through a fitness function, and obtaining the fitness value of each individual in the initial population; weighting the fitness entropy and the position entropy to obtain a comprehensive entropy; defining the current population as an exploration type population or a development type population based on the comprehensive entropy; determining an individual historical weight and a population historical weight according to the historical update success times and the total attempt times of each individual, and defining the individuals in the new population as exploration type individuals or development type individuals through the individual historical weight and the population historical weight; and judging whether the current iteration meets a termination condition or not, and if the current iteration meets the termination condition, obtaining a feature combination corresponding to the global optimal individual as an optimal feature selection result to be output. According to the invention, medical features can be accurately screened, and missing of medical key features is effectively avoided.

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.

An object of a system according to an embodiment is to efficiently formulate, evaluate, and review a location optimization plan for realizing sustainable development of a local government.SOLUTION: A system includes a GIS data collection part, a population dynamics analysis part, a location optimization plan generation part, and an evaluation review part. The GIS data collection unit collects GIS data. The demographic analysis unit analyzes the GIS data collected by the GIS data collection unit. A location optimization plan generation part generates a custom-made location optimization plan on the basis of the population dynamics and human flow information analyzed by the population dynamics analysis part. The evaluation review unit performs continuous evaluation of the location optimization plan and review of the plan as necessary.SELECTED DRAWING: Figure 1

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.

This application relates to the field of accelerometer calibration, and more particularly to an accelerometer calibration method, apparatus, and related equipment. The method includes: constructing a population comprising multiple individuals based on multiple parameters of the accelerometer to be calibrated; determining the current diversity value of the current population based on the current fitness value of each current individual in the current population during the current evolutionary process of the population's iterative evolution; determining the current evolutionary strategy of the current population based on the current diversity value; controlling the current population to evolve based on the current evolutionary strategy to obtain a target population; if the target population meets the termination condition of population evolution, determining the calibration values ​​of multiple parameters based on the candidate values ​​carried by the target individuals in the target population. This application can solve the technical problem that the accuracy of the determined mathematical mapping relationship of the accelerometer is low due to the problem that traditional genetic algorithms are prone to getting trapped in local optima.

The invention discloses an electric power heavy load and overload early warning method and system based on population flow, and relates to the technical field of electric power load early warning, the electric power heavy load and overload early warning method based on population flow specifically comprises the following steps: 1, collecting dynamic characteristics covering population flow and multi-source data associated with power consumption behaviors, and realizing multi-dimensional cross validation; 2, further processing the multi-source data, analyzing and calculating a multi-source overlapping correction coefficient based on the overlapping degree of the monitoring data, and integrating the parameter values of each data source to determine the final number of active terminals; according to the method, the flow intensity index is constructed through the population relative flow rate and the direction consistency coefficient, the dynamic change of population flow is incorporated into the load prediction model, and the static defect of traditional prediction based on historical mean values is made up.

A first risk score for each member in a population is generated using a first predictive model. A second risk score is generated for each member in the population using a second predictive model. The population is stratified into a plurality of risk groups based on: (i) predefined manageable medical conditions; (ii) the first risk score; and (iii) the second risk score. Based on the plurality of risk groups, the population is segmented into a high-risk group and a low-risk group. A panel of care management associates of a plurality of care management associates is assigned to each member in the member population based on whether the member belongs to the high-risk group or the low-risk group.

The present disclosure relates to the technical field of computers, and provides a medical risk prediction method and device, a computer readable storage medium and an electronic device. The method comprises: determining a sampling feature according to the correlation degree between a plurality of first candidate features of a historical insured population of a target city and claim information of urban insurance; determining distribution information of the sampling feature of the historical insured population, and extracting a target population from the population having medical records in the target city according to the distribution information; training a personal medical expense prediction model according to the feature values of the target features of the target population in a first historical period and the personal medical expenses of the target population in a second historical period; and predicting the target medical expenses of a to-be-insured population based on the personal medical expense prediction model, so as to determine the medical risk of the to-be-insured population according to the target medical expenses. The present scheme can improve the accuracy of medical risk prediction.

This invention provides a method for predicting the elderly category structure in disease populations based on grey component prediction modeling. The method includes: collecting multi-component elderly population structure data influenced by specific diseases and analyzing the characteristics of the component data; constructing a modeling framework that integrates a time elasticity mechanism and component structure analysis based on the data characteristics; establishing a novel dynamic nonlinear grey component prediction model based on the framework, which is adapted to irregular time-series component data and optimized with multiple parameters; solving the grey component prediction model parameters and estimating the structural evolution of each component of the elderly disease population, thereby solving the problem of predicting the elderly category structure and managing health in disease populations with small sample sequences. The method proposed in this invention can accurately depict the proportional changes of elderly people in different health states within a disease population, enabling health managers to more accurately grasp the changing trends of the elderly population in the disease burden, and thus formulate more reasonable resource allocation and intervention strategies.

The invention relates to the field of population life prediction, in particular to a population life expectancy estimation method and device, computer equipment and a storage medium, population age distribution raster data prediction is performed based on population association raster data and a pre-constructed nonlinear regression mapping model, and matching is performed in combination with a model life table. The population life expectancy raster data is obtained, the necessary requirement for the number of dead people in a traditional method is overcome, the spatial distribution characteristics of the population life expectancy are represented, and the accuracy of population life expectancy estimation of the target area is improved.

A data processing system for recommending insurance plans implements obtaining an electronic copy of demographic information associated with a user; analyzing the demographic information with a first machine learning model to recommend a bundle of insurance policies based on the demographic information, wherein the first machine learning model is configured to group insured people having similar demographics into clusters and to generate the bundle of insurance policies based on predicted medical insurance consumption associated with a respective group into which the model predicts that the first user falls; customizing the recommended bundle of insurance policies based on the demographic information associated with the user to generate a customized bundle of insurance policies; generating an insurance recommendation report that presents the customized bundle of insurance policies to the user; and causing a user interface of a display of a computing device associated with the user to present the insurance recommendation report.

This invention discloses a method for assessing the affected population based on dynamically corrected disaster-reported population data. The method includes the following steps: Step 1, collecting basic data of the study area; Step 2, initializing the population density field; Step 3, constructing a spatial adjacency graph; Step 4, determining the dynamic flood inundation range; Step 5, comparing the reported data to correct population errors. This method, even with limited reported data, dynamically optimizes local population density estimation by combining the spatiotemporal patterns of flood evolution, while ensuring total population conservation, non-negativity, and spatial smoothness. It achieves dynamic updates of population density at the grid scale within administrative regions, thereby obtaining a more accurate and realistic spatial distribution of the population and the number of affected people. This provides a basis for decision support such as population relocation and precise rescue, demonstrating strong practicality.

The invention provides a forensic model for medium-non-flowing population source identification based on oral microorganisms and a construction method of the forensic model, and belongs to the technical field of medical model construction. The identification model provided by the invention can accurately distinguish groups living in China for a long time, groups living in African for a long time and floating crowds migrating from African to China. The model can be directly applied to analysis of biological samples such as saliva spots in a crime scene, key clues about sources and flow behaviors of suspects are provided for investigation organizations, the investigation direction can be quickly locked in cross-regional cases related to overseas personnel, the case solving efficiency is improved, and the method has important forensic practice value and social safety significance.