CN-121983302-A - Population life expectancy estimation method, device, computer equipment and storage medium

CN121983302ACN 121983302 ACN121983302 ACN 121983302ACN-121983302-A

Abstract

The invention relates to the field of population life prediction, in particular to a population life expectancy estimation method, a population life expectancy estimation device, computer equipment and a storage medium, which are used for carrying out population age distribution grid data prediction based on population associated grid data and a pre-constructed nonlinear regression mapping model, and matching the population age distribution grid data with a model life table to obtain population birth life expectancy grid data, so that the necessary demand of the traditional method on death population is overcome, the spatial distribution characteristic of population life expectancy is represented, and the accuracy of population life expectancy estimation of a target area is improved.

Inventors

Liu Yangxiaoyue
TIAN YUAN

Assignees

中国科学院地理科学与资源研究所

Dates

Publication Date: 20260505
Application Date: 20251223

Claims (10)

1. A method of estimating life expectancy of a population, comprising the steps of: Obtaining population associated raster data of a target area, wherein the population associated raster data comprises a plurality of types of population associated parameters of a plurality of grids; Inputting the population associated raster data into a pre-constructed nonlinear regression mapping model to obtain population age distribution raster data, wherein the population age distribution raster data comprises population age distribution data of each grid; the nonlinear regression mapping model is a model constructed by taking population associated parameters of each type as explanatory variables and population numbers of a plurality of age groups of each gender in the population age distribution data as dependent variables; The population birth life expectancy raster data is obtained by matching the population age distribution raster data obtained by resampling the population age distribution raster data for a plurality of times and a preset model life table, wherein the population birth life expectancy raster data comprises the birth life expectancy of each gender of each grid; Performing quality control index calculation according to the population birth life expectancy grid data resampled for a plurality of times to obtain quality control indexes of each gender of each grid; respectively carrying out effective grid identification according to the quality control indexes of each gender of each grid to obtain effective grid tag data of each gender; and matching according to the population age distribution grid data, the valid grid label data of each gender and the model life table, and obtaining population birth life expectancy grid data of each gender as a population life expectancy estimation result of the target area.
2. The method of population life expectancy estimation according to claim 1, wherein said inputting the population associated raster data into a pre-constructed non-linear regression mapping model to obtain population age distribution raster data, further comprises the steps of: Accumulating the population numbers of all grids of the same age group with the same gender in the population age distribution grid data to obtain population forecast numbers of all age groups with all genders; dividing the population statistics number of the same age group of the same gender by the population forecast number to obtain scaling factors of the age groups of the sexes; Obtaining processed population age distribution grid data according to population numbers of age groups of each gender of each grid in the population age distribution grid data, scaling factors of each age group of each gender and a preset constraint scaling algorithm, wherein the constraint scaling algorithm is as follows: In the formula, The ith of the gender s of the processed ith grid The number of people in each age group, Gender s of the ith grid The number of people in each age group, Gender s The scaling factor for each age group, Is a numerical operation function.
3. The method of estimating life expectancy of population according to claim 2, wherein the matching is performed according to a number of resampled age distribution grid data of population and a preset model life table to obtain a number of resampled life expectancy grid data of population, comprising the steps of: according to the population age distribution grid data and a preset population proportion vector calculation algorithm, population proportion vectors of all age groups of all sexes of all grids are obtained, wherein the population proportion vector calculation algorithm is as follows: In the formula, Gender s of the ith grid Population ratio vector for each age group, Gender s of the ith grid The number of people in each age group, Population count for gender s of the ith grid; Obtaining a square error sum of each gender of each grid under each life expectancy according to population proportion vectors of each age group of each gender of each grid, population proportion vectors of each age group under each life expectancy in the model life table and a preset square error sum calculation algorithm, wherein the square error sum calculation algorithm is as follows: In the formula, Is the sum of square errors of the gender s of the ith grid at the life expectancy e, For the number of age groups, For life expectancy at birth Lower first Population proportion vector of individual age groups; And determining the birth life expectancy of each age segment of each gender of each grid according to the sum of square errors of each age segment of each gender of each grid under each birth life expectancy, wherein the sum of square errors is minimum, and obtaining the birth life expectancy of each age segment of each gender of each grid.
4. The method for estimating life expectancy of a population of claim 3, wherein said quality control indicator comprises a standard error; The quality control index calculation is performed according to the life expectancy raster data of the population birth, and the quality control index of each sex of each raster is obtained, comprising the following steps: obtaining the birth life expectancy of each gender of each grid according to the birth life expectancy of each gender of each grid in the resampled population birth life expectancy grid data for a plurality of times and a preset average value calculation algorithm, wherein the average value calculation algorithm is as follows: In the formula, For the life expectancy of birth for gender s of the ith grid, B is the number of resamples, Life expectancy raster data for the b-th resampled population the life expectancy of the gender s of the i-th grid; Obtaining standard errors of each gender of each grid according to the birth life expectancy, the birth life expectancy average life expectancy of each gender of each grid in the population birth life expectancy grid data resampled for a plurality of times and a preset standard error calculation algorithm, wherein the square error sum calculation algorithm is as follows: In the formula, Is the standard error of the gender s of the ith grid.
5. The method of population expected life of claim 4 wherein the quality control indicator comprises a confidence interval width; The quality control index calculation is performed according to the life expectancy raster data of the population birth, and the quality control index of each sex of each raster is obtained, comprising the following steps: Sorting according to the birth life expectancy of the same gender of the same grid in the resampled population birth life expectancy grid data for a plurality of times, and obtaining the birth life expectancy sorting data of the sexes of the grids; Confidence interval construction is carried out according to the life expectancy sequencing data of each gender of each grid to obtain the confidence interval of each gender of each grid, and confidence interval width calculation is carried out according to the confidence interval of each gender of each grid to obtain the confidence interval width of each gender of each grid.
6. The population life expectancy estimation method according to claim 5, wherein the step of performing effective grid identification according to the quality control index of each gender of each grid to obtain effective grid tag data of each gender, respectively, comprises the steps of: 95% quantile calculation is carried out according to the standard errors of the sexes of the grids to obtain a standard error threshold; And according to the standard error, the confidence interval width, the standard error threshold and the preset confidence interval width threshold of each gender of each grid, if the standard error is smaller than the standard error threshold and the confidence interval width is smaller than the confidence interval width threshold, the grids are used as effective grids of the corresponding gender, and effective grid tag data of each gender is constructed.
7. The method of estimating life expectancy of a population according to claim 5, wherein the matching is performed according to the age distribution grid data of the population, the valid grid tag data of each gender and the model life table to obtain life expectancy grid data of each gender, comprising the steps of: according to the population number of each age group of each effective grid of each gender, population proportion calculation is carried out to obtain population proportion vectors of each age group of each effective grid of each gender; According to population proportion vectors of all age groups of all effective grids of all sexes and the model life table, square error sum calculation and life expectancy calculation are carried out, so that the square error sum of all effective grids of all sexes under all life expectancy and the life expectancy of all age groups of all effective grids of all sexes are obtained; Determining the minimum square error sum of each effective grid of each gender, and calculating 95% quantile according to the minimum square error sum of each effective grid of the same gender to obtain a square error sum threshold; And deleting the effective grids according to the least square error sum of the effective grids of each gender and the square error sum threshold, if the least square error sum is larger than the square error sum threshold, constructing population birth life expected life grid data according to the birth life expected life of each age group of each effective grid of each gender after deletion, and obtaining the population birth life expected life grid data of each gender.
8. A population expected life estimating apparatus, comprising: The data acquisition module is used for acquiring population associated raster data of a target area, wherein the population associated raster data comprises a plurality of types of population associated parameters of a plurality of grids; The model prediction module is used for inputting the population associated grid data into a pre-constructed nonlinear regression mapping model to obtain population age distribution grid data, wherein the population age distribution grid data comprises population age distribution data of each grid; the nonlinear regression mapping model is a model constructed by taking population associated parameters of each type as explanatory variables and population numbers of a plurality of age groups of each gender in the population age distribution data as dependent variables; The life prediction module is used for carrying out resampling for a plurality of times according to the population age distribution grid data to obtain population age distribution grid data of resampling for a plurality of times, and matching according to the population age distribution grid data of resampling for a plurality of times and a preset model life table to obtain population birth life expectancy grid data of resampling for a plurality of times, wherein the population birth life expectancy grid data comprises birth life expectancy of each gender of each grid; The index calculation module is used for carrying out quality control index calculation according to the population birth life expectancy grid data resampled for a plurality of times to obtain the quality control index of each gender of each grid; the effective grid identification module is used for respectively carrying out effective grid identification according to the quality control indexes of the sexes of the grids to obtain effective grid tag data of the sexes; And the life estimating module is used for matching according to the population age distribution grid data, the valid grid label data of each gender and the model life table to obtain population birth life expectancy grid data of each gender as a population life expectancy estimating result of the target area.
9. A computer device comprising a processor, a memory and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the population expected life estimation method of any one of claims 1 to 7 when the computer program is executed.
10. A storage medium storing a computer program which when executed by a processor implements the steps of the population expected life estimating method according to any of claims 1 to 7.

Description

Population life expectancy estimation method, device, computer equipment and storage medium Technical Field The present invention relates to the field of population life prediction, and in particular, to a population life expectancy estimation method, apparatus, computer device, and storage medium. Background Population life expectancy refers to the average number of years that a person born in the same period would be expected to continue to survive given the current age group mortality remains unchanged. The comprehensive representation of medical health, people health, life quality and social development condition is one of three synthetic indexes of the human development index of the united nations, and is also one of the international general indexes for evaluating the health level of residents. Population life expectancy is typically calculated using a Jiang Qinglang abbreviated life chart, with age-divided, sex-divided existing population and death population. The census data disclosed by the current statistics department mainly relates to the existing population of the sub-age group and the sub-gender, and the death population of the sub-age group and the sub-gender is difficult to obtain. Resulting in a spatially distributed characteristic that makes estimation of the population's expected life span difficult. Disclosure of Invention Based on this, an objective of the present invention is to provide a population life expectancy estimation method, apparatus, computer device and storage medium, which predict population age distribution grid data based on population associated grid data and a pre-constructed nonlinear regression mapping model, and match with a model life table to obtain population life expectancy grid data, so as to overcome the necessity of the conventional method in terms of death population number, to characterize spatial distribution characteristics of population life expectancy, and to improve accuracy of population life expectancy estimation in a target area. In a first aspect, an embodiment of the present application provides a population life expectancy estimation method, including the steps of: Obtaining population associated raster data of a target area, wherein the population associated raster data comprises a plurality of types of population associated parameters of a plurality of grids; Inputting the population associated raster data into a pre-constructed nonlinear regression mapping model to obtain population age distribution raster data, wherein the population age distribution raster data comprises population age distribution data of each grid; the nonlinear regression mapping model is a model constructed by taking population associated parameters of each type as explanatory variables and population numbers of a plurality of age groups of each gender in the population age distribution data as dependent variables; The population birth life expectancy raster data is obtained by matching the population age distribution raster data obtained by resampling the population age distribution raster data for a plurality of times and a preset model life table, wherein the population birth life expectancy raster data comprises the birth life expectancy of each gender of each grid; Performing quality control index calculation according to the population birth life expectancy grid data resampled for a plurality of times to obtain quality control indexes of each gender of each grid; respectively carrying out effective grid identification according to the quality control indexes of each gender of each grid to obtain effective grid tag data of each gender; and matching according to the population age distribution grid data, the valid grid label data of each gender and the model life table, and obtaining population birth life expectancy grid data of each gender as a population life expectancy estimation result of the target area. In a second aspect, an embodiment of the present application provides a population expected life estimating apparatus, comprising: The data acquisition module is used for acquiring population associated raster data of a target area, wherein the population associated raster data comprises a plurality of types of population associated parameters of a plurality of grids; The model prediction module is used for inputting the population associated grid data into a pre-constructed nonlinear regression mapping model to obtain population age distribution grid data, wherein the population age distribution grid data comprises population age distribution data of each grid; the nonlinear regression mapping model is a model constructed by taking population associated parameters of each type as explanatory variables and population numbers of a plurality of age groups of each gender in the population age distribution data as dependent variables; The life prediction module is used for carrying out resampling for a plurality of times according to the population age distribution