Geographic community portrait missing prediction method based on Monte Carlo method

A technology of Monte Carlo method and prediction method, which is applied in geographic information database, special data processing application, electronic digital data processing and other directions, can solve the problem of supplementing missing fields in community portraits, and achieves safe and convenient use, scientific and reasonable structure, and high efficiency. The effect of production output

Inactive Publication Date: 2021-09-03
亿景智联(苏州)科技有限公司
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Problems solved by technology

[0004] The present invention provides a method for predicting the absence of geographical community portraits based on the Monte Carlo method, which can effectively solve the problem of supplementing the missing fields of different community portraits in the above-mentioned background technology, and cannot efficiently supplement missing values. For the analysis of the surrounding areas of geographical communities , and the problem that the improvement of data quality has greatly helped

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  • Geographic community portrait missing prediction method based on Monte Carlo method
  • Geographic community portrait missing prediction method based on Monte Carlo method
  • Geographic community portrait missing prediction method based on Monte Carlo method

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[0024] Example: such as Figure 1-2 As shown, the present invention provides a technical solution, a method for predicting the absence of geographical community portraits based on the Monte Carlo method, including the following steps:

[0025] S1. By constructing a multi-dimensional and multi-region hypothetical data set, the hypothetical region and multi-dimensional missing positions are filled with the median and covariance, and the rectangular area S is randomly divided into four small areas according to the data set 1, 2, 3 , 4;

[0026] S2. Construct a regression model using the median value and covariance for different spatial region sets, and solve S according to the regional regression model 0 +S β , where S β is the random residual value;

[0027] S3. Calculate the median and covariance process of all regions and dimensions, and then draw a sample from the middle posterior distribution;

[0028] S4. Steps S2 and S3 are repeated until convergence, and the last int...

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Abstract

The invention discloses a geographic community portrait missing prediction method based on a Monte Carlo method, and the method comprises the following steps: S1, carrying out the filling of a median and a covariance through constructing a multi-dimensional multi-region hypothesis data set, a hypothesis region and a multi-dimensional missing position, and averagely and randomly dividing a rectangular region S into four small regions 1, 2, 3 and 4 according to the data set; S2, for different space region sets, using a median value and a covariance to construct a regression model, and according to the region regression model, solving S0 + S[beta], wherein S[beta] is a random residual value. According to the invention, the method is scientific and reasonable in structure and safe and convenient to use, complicated mathematical derivation and calculation processes can be omitted by adopting the Monte Carlo method, and simulation times of different equal magnitudes can be adopted according to the requirements of actual conditions on result precision, so that more efficient production output can be displayed according to actual requirements in an actual application scene.

Description

technical field [0001] The invention relates to the technical field of intelligent spatial big data, in particular to a method for predicting the absence of geographical community portraits based on the Monte Carlo method. Background technique [0002] In the era driven by big data and led by intelligence, the quality optimization of massive spatio-temporal data has always been a difficult problem to solve. Simple manual supplementation and repair often overflow the cost. The missing community data is predicted and supplemented, the data quality is gradually and efficiently improved, and a more complete data ecology is built; [0003] However, for different community portrait dimensions, how to predict relatively accurate missing values ​​is a difficult problem. For the analysis of the surrounding areas of geographical regions, whether it is possible to automatically predict high-quality missing locations. Contents of the invention [0004] The present invention provides ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/215G06F16/29G06F16/28
CPCG06F16/215G06F16/29G06F16/283
Inventor 朱与墨田鹏飞吴丹
Owner 亿景智联(苏州)科技有限公司
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