10 results about "Population structure" patented technology

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 discloses a corn selective breeding effect analysis method integrating population inheritance and deep learning, and the method comprises the steps: collecting corn germplasm resource data, including phenotype data, genotype data and environmental data; performing standardization processing and missing value filling on the phenotypic data, and calculating population genetic parameters including allele frequency, linkage imbalance and population structure stability; performing correlation analysis on the population genetic parameters and phenotypic data by using a deep learning model, predicting phenotypic values of candidate parents by using the trained model, calculating breeding indexes in combination with the population genetic parameters, and providing guidance for corn breeding decisions based on prediction results; through organic combination of a deep learning technology and a population genetics principle, accurate prediction and evaluation of the corn selective breeding effect are realized, the breeding efficiency is improved, and a new technical path is provided for corn variety improvement.

The present invention discloses a method for efficiently identifying ancient ancestral introgressed genes in poplar trees. The method comprises the following steps: obtaining a SNP dataset for a poplar population, obtaining the population structure of the poplar population, preliminarily locating the introgressed regions of the poplar population, locating candidate ancestral introgressed regions of the poplar population, obtaining the ancient ancestral introgressed regions of the poplar population, and obtaining ancient ancestral introgressed genes of the poplar population. The method disclosed in the present invention can effectively reduce false positives, reduce computational complexity, and increase identification speed. It can also obtain ancestral gene flow in multiple poplar populations, accurately identify ancient ancestral introgressed genes in poplar trees, and has a wide range of applicability.

The invention discloses a method for efficiently identifying an ancestral ancient introgression gene of a poplar. The method comprises the following steps: obtaining an SNP data set of a poplar population, obtaining a population structure of the poplar population, preliminarily positioning an introgression region of the poplar population, positioning a candidate ancestor introgression region of the poplar population, obtaining an ancestor ancient introgression region of the poplar population, and obtaining an ancestor ancient introgression gene of the poplar population. The method disclosed by the invention can effectively reduce false positive, reduce the calculation amount and improve the identification speed, can obtain the progenitor gene flow of multiple populations of the poplar, can accurately identify the progenitor paleo-introgression gene of the poplar, and is wide in application range.

The invention relates to the technical field of optimal scheduling, and discloses a material transfer optimal scheduling method and system based on a large language model. The method comprises the steps of initializing a population, constructing cue words, calling a large language model to generate a filial generation, detecting validity, evaluating and selecting fitness, carrying out linkage adjustment based on a temperature parameter and a population structure, and outputting a current optimal solution. The system corresponds to the method. According to the invention, through deep fusion of the large language model and the material transfer optimization scheduling, the production workshop material transfer efficiency is significantly improved.

This invention provides a method, system, and storage medium for predicting and dynamically allocating childcare service personnel demand based on population structure changes. The invention first acquires population-related data and policy-related data by administrative division, constructs a set of scenario parameters to generate a baseline scenario, an optimistic scenario, and a conservative scenario, and determines the parameter value range; for each scenario, it generates childcare service demand prediction results, and outputs personnel demand intervals for different job positions by combining job conversion parameters; based on actual observation data and prediction results, it constructs scenario trigger indicators and determines scenario switching rules, switching to the target scenario output when threshold conditions are met; further, it combines existing personnel status data to generate a dynamic allocation scheme and updates the personnel status data, thereby enabling multi-scenario prediction of childcare service demand under different population change scenarios and achieving dynamic adjustment of personnel allocation, improving the accuracy of childcare service personnel demand prediction and the rationality of allocation.

The invention provides a method and system for obtaining the contribution rate of population migration to regional population structure change, and the method comprises the steps: extracting an effective residential population sample, dividing a plurality of space units in a research regional range, and obtaining the contribution rate of population migration to regional population structure change according to the multi-dimensional attributes of the effective residential population sample. Dividing each spatial unit population in the selected time period into a target group and a non-target group, identifying population cross-spatial unit migration data, further measuring and calculating a target grouping index of target group migration on each spatial unit population structure, and classifying the target grouping index by using a threshold classification method to judge a target grouping type caused by migration. Calculating the contribution rate of population migration of each space unit to the population structure change of the target group of the target space unit, and identifying the space unit which has a leading effect on the population structure change of the target group of the target space unit. According to the method, large-scale, refined and long-time-sequence target grouping quantitative measurement can be carried out, and the contribution strength of other spaces to the population structure change of a target space unit is measured.

The invention discloses a school-level academic pressure prediction method and device, electronic equipment and a storage medium, and belongs to the field of data processing, and the method comprises the steps: obtaining a sex-based age structure of a reference year, a population natural change parameter of each prediction year, and a population density control threshold value of each administrative region; and school education resource attributes and space coordinates, and residential space coordinates. The regional migration rate is determined based on the population structure and the density threshold value, year-by-year school age population of each administrative region is calculated in combination with the natural change parameters, and then the year-by-year number of students to be entered is obtained through distribution according to the residence point population proportion. Inputting the educational resource attributes into a multi-layer perceptron model to generate school attraction features, and calculating an enrollment probability in combination with a general learning distance; and predicting the number of years-by-year students in each school, and generating a school pressure index based on the maximum bearing capacity of the school. By implementing the method, the problem that the supply and demand contradiction of a single school cannot be accurately reflected due to the fact that the school pressure prediction granularity is rough in the prior art can be solved.

The invention provides an intelligent prediction method for dynamic evolution of a living type heritage system. The intelligent prediction method comprises the following steps: establishing a heritage material spatial form evolution model, a community multi-subject dynamic interaction model and a culture factor propagation and diffusion model; coupling the heritage material spatial form evolution model, the community multi-subject dynamic interaction model and the culture factor propagation and diffusion model to form a multi-modal coupling model; and finally, predicting a future contextual model based on the multi-modal coupling model. According to the method, time sequence modeling and trend prediction of core elements such as a space structure, a population structure and cultural activities in a human settlement system are realized.

The invention discloses an epidemic first arrival time prediction method based on individual migration and application thereof, and belongs to the technical field of epidemic prediction. In order to accurately predict the first arrival time of the sub-population, a sub-population reaction diffusion process model of the epidemic disease is constructed; based on the obtained sub-population reaction diffusion process model of the epidemic disease, establishing a dual-population structure epidemic disease first arrival time prediction method; based on the obtained sub-population reaction diffusion process model of the epidemic disease and a dual-population structure epidemic disease first arrival time prediction method, establishing a hub structure epidemic disease first arrival time prediction method; establishing a one-dimensional linear structure epidemic first arrival time prediction method; and establishing a multi-path structure epidemic first arrival time prediction method. According to the method, the epidemic first arrival time between any sub-populations on a general composite population network can be accurately predicted.