46 results about "Population based" 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 relates to the technical field of building digitization, in particular to a public service facility planning method and system based on population prediction. The planning method comprises the following steps: dividing residences in an object area into a plurality of residential areas, and predicting population scale data and population structure data of each residential area; determining the predicted number of people of each age group in each residential area within the preset age limit, and calculating the average value of the activity ability of each residential area in each year within the preset age limit; and determining the walking limit distance of the target group in each residential area in each year within a preset age limit according to the activity capability mean value, and taking the minimum value of the walking limit distance of each residential area within the preset age limit as a reference limit value. And determining the location of the public service facility in the object area, so that the distance value from each residential area in the object area to the location is smaller than or equal to the corresponding reference limit value. The method can improve the overall quality of urban planning, and has positive significance for saving social resources.

The invention provides a method for deducing the abundance of each family in a group based on mixed pool simplified genome sequencing, which comprises the following steps of: (1) establishing a parent 2b-rad library, and extracting SNP (Single Nucleotide Polymorphism) genotypes and sites by using a 2b-rad standard process; combining the SNP genotypes of the 2n parents into genotypes of n parent pairs according to a male parent and female parent relationship; comparing the genotypes of the parent pairs, and determining family specific tags; (2) extracting offspring individual tissues, mixing, and carrying out DNA extraction and 2b-rad library building sequencing on the mixed sample tissues; counting the sequencing depth of all the specific tag sites and calculating the secondary allele frequency of the specific tag of the mixed sample filial generation; and (3) calculating the proportion of each family in the offspring according to the sequencing depth of the specific tag site and the secondary allele frequency of the specific tag of the mixed offspring. According to the method, the overall feature distribution of the family can be accurately evaluated without sequencing each individual, so that the cost is greatly reduced.

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 belongs to the technical field of urban planning and construction, and discloses an evaluation method, system and device based on regional coordinated development. According to the method, a multi-dimensional evaluation index system comprising a target layer, a criterion layer and an index layer is established, after an index value and population data are obtained, a single-index regional coordination development difference index is obtained through a Wilson coefficient calculation function based on population weighting, and then an expert scoring method and an AHP analytic hierarchy process are combined to determine a weight, so that the regional coordination development difference index is obtained. And calculating a regional coordination development difference index to judge a regional coordination development level. According to the method, the problems of imperfect evaluation dimension and insufficient index comparability in the prior art are solved, the current situation characteristics of regional coordination development can be reflected more reasonably and accurately, and a scientific decision basis is provided for optimizing a regional coordination development policy.

The invention discloses a subgrade structure parameter intelligent optimization method and device fusing population inheritance and reverse learning, equipment and a storage medium, and relates to the technical field of land traffic engineering subgrade structure design, and the method comprises the steps: obtaining a subgrade engineering to-be-optimized parameter set, a subgrade engineering parameter optimization model and a self-adaptive hyper-heuristic algorithm; based on the roadbed engineering parameter set to be optimized, the roadbed engineering parameter optimization model and the adaptive hyper-heuristic algorithm, population inheritance and reverse learning are carried out to generate a population, and target population individual information is determined; based on the roadbed engineering parameter set to be optimized, the roadbed engineering parameter optimization model, the adaptive hyper-heuristic algorithm and the target population individual information, correlation analysis is carried out to adjust the search direction and the search step length, and target optimization population information is determined; and completing intelligent optimization of roadbed structure parameters based on the target optimization population information. The subgrade parameters are optimized by fusing population heredity and reverse learning, and the optimization efficiency is improved.

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 discloses a method and device for improving the accuracy of intelligent interview evaluation, and relates to the technical field of intelligent interview, and the method comprises the steps: obtaining the interview session data of candidates; comparing the answer of the candidate to the interview question with the plurality of typical answers corresponding to the question to obtain a comparison result of the answer of the candidate and each typical answer and an ability score corresponding to each typical answer; estimating the original ability score of the candidate under each investigation dimension based on the comparison result of the answer of the candidate and each typical answer and the ability score corresponding to each typical answer; calculating the ranking of the candidates in the norm population based on the original ability scores of the candidates under each investigation dimension; and converting the ranking into a standard ability score of the candidate under each investigation dimension. According to the invention, the overall accuracy of intelligent interview evaluation is improved.

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 model preprocessing method and system for a lithology identification model, and the method comprises the steps: obtaining a hyper-parameter range of a plurality of hyper-parameters in a pre-constructed lithology identification model, constructing a first preset number of individuals based on the parameter range of the plurality of hyper-parameters, completing the initialization of a population, updating the initialized population in a preset round; each update round comprises global optimization and local optimization; in the global optimization step, the position of the individual is updated based on the distance between the individual and the target individual, and the updated position of the individual is obtained; in the step of local optimization, the position of each individual in the global optimization group is optimized again based on the individual influence parameter group, and local optimization is completed; and after updating of a preset round, screening a final individual through the fitness, performing model training on the lithology identification model by applying hyper-parameters of the individual, and performing lithology identification after training is completed.

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 invention discloses an urban land-population dynamic coupling simulation method and system based on a geographic element process, and the method comprises the steps: building a CA model based on a circle layer structure marking a city center; macroscopic process modeling is completed through a GMP model, and the expected value of growth of each circle layer of the microscopic layer is calculated; determining the demand of each circle layer based on the expected growth value of each circle layer and the actual growth value of each iteration, integrating a nonlinear function with a CA model, and establishing a land simulation penalty mechanism; under the framework of the ABM model, simulating behaviors of selecting a working place, a commuting mode and a residence place by a person; performing urban form control influenced by the social network and urban form control based on bearing capacity; and adjusting parameters of the GMP model by adopting a differential evolution algorithm, and carrying out iterative prediction on the influence of land use change on population distribution under various expansion scenes. According to the invention, high-accuracy land simulation, population simulation and multi-scene prediction can be realized.

The invention discloses a population spatio-temporal distribution prediction method and system considering behavior spatio-temporal heterogeneity. The method comprises the following steps: S1, constructing an explicit function relationship of spatio-temporal dependence between a social media digital footprint GDF and a population based on a TFDF (Tracking Frequency per capita) of the digital footprint; s2, based on a Transform encoder, fusing the static geographic features of the space unit, the dynamic environment features and a historical social media digital footprint GDF sequence, and generating space-time associated context features; s3, constructing a TFDF prediction model to predict a future period TFDF and constructing a population prediction model to predict a future period population; and training a TFDF prediction model and a population prediction model by taking the explicit function relationship of the space-time dependence between the social media digital footprint and the population as training constraints. According to the method, the spatial-temporal heterogeneity of the social media digital footprint and the population relationship is modeled, and the spatial heterogeneity of the population time sequence mode is considered based on the feature fusion of the multi-source geographic spatial data, so that the generalization ability of population spatial-temporal distribution prediction is improved by fully utilizing massive GDF data.

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.

Systems and methods predict disease risk in a sub-population based on characteristic data including demographic data and health data measured and collected from health monitoring devices. During training, a machine learning model receives biological outcomes for each subject of a population, and is trained using a resource limitation based loss function (“highest-k loss function”) using a soft sorting method to optimize accuracy of the machine learning model for various sub-populations of subjects who are at the highest risk for the biological outcomes. Subsequent data on available resources in a hospital are fed to the trained model for determining biological outcomes for the high-risk sub-population and ranking them for optimizing monitoring, treatment, care, resource allocation.

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.

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.

The invention provides an urban reflow population scale and structure identification method, which comprises the following steps of S1, based on population census data and household registration population data of a corresponding year, calculating household registration permanent resident population and outflow population scales, and counting distribution of genders, age groups, household registration attributes and village and town types; s2, screening and sorting effective sample data corresponding to the general survey year according to information in the microscopic survey data; s3, calculating the difference between the initial distribution and the overall distribution of the samples; optimizing the weight through multiple rounds of iteration until the relative error between the weighted sample distribution and the total distribution is smaller than a preset threshold value; s4, superimposing the generated data of a plurality of census years, and filling the weight data of the missing years by adopting a time sequence interpolation method; judging the flow state of each year according to the information, and generating panel data containing individual weights and flow states of continuous years; and S5, verifying the reasonability of the data, and if the verification is not passed, returning to S1 to adjust the classification condition and regenerating the data until the verification is passed.

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 present invention discloses a method, device, electronic device, and storage medium for solving seabed topography. The method includes: initializing an initial population based on preset rules; using the initial population as the first population; obtaining seabed topography data of a target search area through inversion based on the first population; then performing fitness evaluation to obtain the root mean square value of each individual in the first population; when an individual's root mean square value is less than or equal to an iterative optimal threshold, the corresponding individual is used as a target individual; otherwise, updating the population through a genetic algorithm, increasing the first loop count by 1, and returning to the step of obtaining seabed topography data of the target search area through inversion based on the first population until the target individual is obtained, or the first loop count equals a preset iteration count; and finally determining a solution result for the target search area based on the target individual. The embodiments of the present invention can efficiently and accurately solve seabed topography and can be widely applied in the field of data processing technology.

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.