CN-116523378-B - Visual assessment method and system for risk hidden danger of massive houses

CN116523378BCN 116523378 BCN116523378 BCN 116523378BCN-116523378-B

Abstract

The invention belongs to the field of house data processing, and discloses a method and a system for visualizing and evaluating risk hidden danger of massive houses, wherein the method comprises the steps of performing comprehensive risk evaluation of buildings of single houses and calculating risk hidden danger coefficients of the single houses; the method comprises the steps of carrying out space aggregation treatment on massive houses from a plurality of administrative region levels to different degrees to obtain aggregated point house risk hidden danger distribution situations, converting discrete vector points into continuous grid network data for display through nuclear density analysis, and carrying out result analysis and visual rendering display according to heat space distribution characteristic grid network data. According to the invention, the heat distribution is calculated based on the density weight of the risk hidden danger index, so that the geographical space distribution characteristic display effect of the house risk hidden danger index is enhanced, and the technical problems of stuck state, full memory and the like in rendering of hundred million-level building object surfaces and attribute data are solved.

Inventors

CHEN MINGHUA
KONG XIANJUAN
DENG MINGLIANG
CAI JINYI
Liang Nanfeng
HUANG GUANGXU

Assignees

奥格科技股份有限公司

Dates

Publication Date: 20260512
Application Date: 20230331

Claims (7)

1. A visual assessment method for hidden danger of massive houses is characterized by comprising the following steps: Building comprehensive risk assessment of the single house is carried out, a plurality of house building attributes of the single house are weighted and summed, and risk hidden danger coefficients of the single house are calculated; Carrying out spatial aggregation treatment on massive houses from a plurality of administrative region levels to reduce the overall time consumption of aggregated data analysis and obtain the risk hidden danger distribution situation of aggregated houses; Converting discrete vector points into continuous grid network data for display through nuclear density analysis, wherein the grid network data is obtained by taking coordinates of central points of grid pixels of a designated row and column as prediction points, setting corresponding search radii for different spatial scales of a plurality of administrative region levels respectively, and carrying out weighted nuclear density analysis calculation on the prediction points by using a kernel function to obtain heat space distribution characteristic grid network data of transformation or demolition cost in a designated administrative region range; according to the heat space distribution characteristic grid network data, carrying out result analysis and visual rendering display; Converting the house monomer transformation or dismantling cost based on the house risk hidden danger coefficient; after carrying out space polymerization treatment on mass houses from a plurality of administrative region levels to different degrees, obtaining the cost of modifying or dismantling the polymerized house monomers at the polymerization point, wherein the process of the space polymerization treatment comprises the following steps: acquiring a central point of a spot face element of a house and a corresponding reconstruction or demolition cost attribute, reducing the number of original points in an administrative region level range based on an aggregation algorithm, aggregating the original points according to spatial distribution, summing the reconstruction cost of house monomers of the original points contained in the aggregation points, averaging, and then assigning the average value to the aggregation points to serve as the reconstruction or demolition cost of the house monomers of the aggregation points; The specific steps of the space aggregation treatment comprise: randomly selecting the central points of the K house pattern surface elements, and marking the central points as ; Defining a loss function: ; Wherein, the Representing the original center point of the ith house, Is that The cluster to which the cluster belongs is selected, Representing the cluster center point corresponding to the cluster, Counting the number of house centers aggregated to the cluster; the following procedure is repeated until the loss function Converging or reaching a set threshold value to obtain final cluster center points, namely, the original center point of each house is obtained Assigned to the cluster center closest thereto Recalculating the cluster center point for each cluster ; Calculating the sum average value of the reconstruction or demolition cost of the house center points aggregated by each cluster, and assigning the sum average value to finally generate the cluster center points Is to be modified or removed 。
2. The visual assessment method according to claim 1, wherein the calculation formula of the risk potential risk coefficient of the single house is: ; Is a risk hidden danger coefficient of a single house, The total number of house building attributes; To building attributes Is assigned to the value of (a), Is a property of building construction Is a hidden danger weight of (1).
3. The visual assessment method according to claim 1 or 2, wherein the house monomer modification or removal cost is converted based on a house risk potential factor, wherein the house monomer modification or removal cost is converted according to a monomer modification cost construction function, wherein the monomer modification cost construction function is: ; where o is a house risk potential factor, For a single-piece room area, The cost for modifying or dismantling the house; And (3) carrying out space polymerization treatment on the mass houses from a plurality of administrative region levels to different degrees to obtain the cost of modifying or dismantling the polymerized house monomers at the polymerization points.
4. The visual assessment method according to claim 1, wherein the kernel function is a fourth-order kernel function.
5. The visual assessment method according to claim 1, wherein the house monomer modification or removal cost is converted based on a house risk potential risk coefficient; Space polymerization treatment is carried out on the mass houses respectively from a plurality of administrative region levels to different degrees, so that the cost of modifying or dismantling the house monomers of the polymerized polymerization points is obtained; A process for converting discrete vector points into continuous raster data for presentation by nuclear density analysis, comprising: Constructing a grid network through the coordinate range of the cluster center points after aggregation; Traversing pixels in the grid network, searching that the center distance from the pixels is within a searching radius All cluster center points within; calculating the distance between the center of the pixel and the center point of the cluster House monomer reconstruction cost according to kernel function formula and cluster center point And obtaining the sum of the house monomer reconstruction cost probability densities of all cluster center points and the predicted points, namely obtaining the heat predicted value of the cluster center point.
6. The visual assessment method according to claim 5, wherein the heat prediction value The calculation formula of (2) is as follows: ; ; Wherein, the For the total number of predicted points, Px is the x coordinate of the pixel center point, and py is the y coordinate of the pixel center point; To search for the hit cluster center point x coordinate, Y coordinates of cluster center points for search hits.
7. The visual evaluation system for the risk hidden danger of the massive houses is characterized by comprising the following modules: the single risk evaluation module is used for carrying out comprehensive risk evaluation on the construction of the single house, weighting and summing a plurality of house construction attributes of the single house, and calculating a risk hidden danger coefficient of the single house; The monomer cost conversion module converts the house monomer transformation or dismantling cost based on the house risk hidden danger coefficient; The aggregation module is used for carrying out space aggregation treatment on a large number of houses from a plurality of administrative region levels to reduce the overall time consumption of aggregated data analysis and obtain the modification or dismantling cost of aggregated house monomers; The nuclear density analysis module converts discrete vector points into continuous grid network data for display through nuclear density analysis, wherein the grid network data is obtained by taking the coordinates of the central points of grid pixels of a designated row and column as prediction points, setting corresponding search radiuses for different spatial scales of a plurality of administrative region levels respectively, and carrying out weighted nuclear density analysis calculation on the prediction points by using a kernel function to obtain heat space distribution characteristic grid network data of transformation or demolition cost in a designated administrative region; The visual output module is used for carrying out result analysis and visual rendering display according to the heat space distribution characteristic grid network data; the process of space aggregation treatment by the aggregation module comprises the following steps: The method comprises the specific steps of acquiring a central point of a spot face element of a house and corresponding modification or dismantling cost attributes, reducing the number of original points in an administrative region level range based on an aggregation algorithm, aggregating the original points according to spatial distribution, summing the modification cost of house monomers of the original points contained in the aggregation points, averaging, and then assigning to the aggregation points to serve as the modification or dismantling cost of the house monomers of the aggregation points, wherein the specific steps comprise: randomly selecting the central points of the K house pattern surface elements, and marking the central points as ; Defining a loss function: ; Wherein, the Representing the original center point of the ith house, Is that The cluster to which the cluster belongs is selected, Representing the cluster center point corresponding to the cluster, Counting the number of house centers aggregated to the cluster; the following procedure is repeated until the loss function Converging or reaching a set threshold value to obtain final cluster center points, namely, the original center point of each house is obtained Assigned to the cluster center closest thereto Recalculating the cluster center point for each cluster ; Calculating the sum average value of the reconstruction or demolition cost of the house center points aggregated by each cluster, and assigning the sum average value to finally generate the cluster center points Is to be modified or removed 。

Description

Visual assessment method and system for risk hidden danger of massive houses Technical Field The invention belongs to the field of house data processing, and particularly relates to a method and a system for visually evaluating hidden danger of massive houses. Background In the existing house risk disaster visualization system, houses are presented in a discrete building surface element form and are used for establishing space position information of single houses in the application of house general investigation, cadastral investigation and the like, but users are difficult to intuitively perceive space distribution rules of the houses under large space scales of nationwide, provinces, cities and the like. In addition, performance problems such as blocking, memory fullness and the like easily occur in processing and rendering of massive space data, and page display effect is affected. Therefore, the house attribute information is often displayed in a statistical chart such as a contour map or a histogram, and is used for displaying data distribution under different administrative level scales, but the spatial variation trend under fine granularity and among administrative areas is difficult to be reflected. The existing thermodynamic diagram based on nuclear density analysis is often used for displaying the spatial distribution characteristics of geographic elements, and the application of the thermodynamic diagram is biased to qualitative analysis, and the interpretation of quantitative information of the thermodynamic diagram is lacking, so that quantitative comparison results are difficult to obtain among the thermodynamic diagrams of different time or space, such as the common thermodynamic diagram only displays thermodynamic distribution conditions, and a user cannot acquire quantitative information through specific numerical values and cannot compare the change conditions of regional thermodynamic quantity at different time points. Disclosure of Invention In order to solve the defects existing in the prior art, the invention provides a visual assessment method and a visual assessment system for hidden danger of massive houses, and solves the technical problems of blocking, memory fullness and the like in rendering of hundred million-level building object planes and attribute data. The method is realized by the following technical scheme that the method for visually evaluating the risk hidden danger of the massive houses comprises the following steps: Building comprehensive risk assessment of the single house is carried out, a plurality of house building attributes of the single house are weighted and summed, and risk hidden danger coefficients of the single house are calculated; Carrying out spatial aggregation treatment on massive houses from a plurality of administrative region levels to reduce the overall time consumption of aggregated data analysis and obtain the risk hidden danger distribution situation of aggregated houses; Converting discrete vector points into continuous grid network data for display through nuclear density analysis, wherein the grid network data is obtained by taking coordinates of central points of grid pixels of a designated row and column as prediction points, setting corresponding search radii for different spatial scales of a plurality of administrative region levels respectively, and carrying out weighted nuclear density analysis calculation on the prediction points by using a kernel function to obtain heat space distribution characteristic grid network data of transformation or demolition cost in a designated administrative region range; and carrying out result analysis and visual rendering display according to the heat space distribution characteristic grid network data. Preferably, the calculation formula of the risk hidden danger coefficient of the single house is as follows: Wherein, the method is that the risk hidden danger coefficient of the single house is represented by the formula I, N is the total number of house building attributes, a i is the assignment of the house building attribute i, and w i is the hidden danger weight of the house building attribute i. Further preferably, the house monomer modification or removal cost is converted based on the house risk potential factor, and the house monomer modification or removal cost is converted according to a monomer modification cost construction function, wherein the monomer modification cost construction function is as follows: Wherein o is a house risk hidden danger coefficient, a is a single house area, and f (o) is the cost for modifying or dismantling the house; And (3) carrying out space polymerization treatment on the mass houses from a plurality of administrative region levels to different degrees to obtain the cost of modifying or dismantling the polymerized house monomers at the polymerization points. Preferably, converting the house monomer modification or dismantling cost based on the house risk h