CN113506203A

CN113506203A - Data processing method, device, equipment and storage medium

Info

Publication number: CN113506203A
Application number: CN202110783037.6A
Authority: CN
Inventors: 佘金星; 南聪强; 董秀军; 李强; 陈江攀; 李辛结; 刘佳雨; 罗小明; 郑鹏; 蒋玲巧; 何柠稀; 李佳秘
Original assignee: Jingchuang Smart Technology Co ltd
Current assignee: Jingchuang Smart Technology Co ltd
Priority date: 2021-07-12
Filing date: 2021-07-12
Publication date: 2021-10-15

Abstract

The application provides a data processing method, a device, equipment and a storage medium, wherein the method comprises the following steps: constructing a three-dimensional interpretation environment by using the acquired point cloud data and the optical remote sensing image; determining at least one target geological disaster area, at least one influence factor in the target geological disaster area, and target parameters for describing the influence factor in the target geological disaster area in the three-dimensional interpretation environment; calculating an occurrence probability index of the target geological disaster area according to the weight of each influence factor and each target parameter in the target geological disaster area; determining an easiness-occurring grade area to which the target geological disaster area belongs according to the easiness-occurring index of the target geological disaster area and a threshold interval set for each easiness-occurring grade area; displaying each target geological disaster area in the designated area and the susceptibility grade area to which the target geological disaster area belongs; by the method, the manual workload is reduced, and the determination efficiency of the susceptibility index of the target geological disaster area is improved.

Description

Data processing method, device, equipment and storage medium

Technical Field

The application relates to the field of geological disaster interpretation analysis, in particular to a data processing method, device, equipment and storage medium.

Background

Geological disasters generally occur on the earth surface layer, cause great loss to the lives and properties of human beings, in order to reduce the loss, geological disaster prevention and control need to be carried out, to a geological disaster, this geological disaster point quantity is many, and the position distribution of each geological disaster point is long and short, the prevention and control work for this geological disaster has brought huge challenge, for this geological disaster of effectual prevention and control, the probability that this geological disaster takes place in each geological disaster point place area need be known, and prevent and control this geological disaster according to the order of probability from big to small, in order to improve the efficiency of geological disaster prevention and control.

In the prior art, a worker shoots a remote sensing image of a research area on the spot, then multiple experts analyze the obtained remote sensing image for multiple times by using a method from known to unknown, from area to local, from overall to individual, and from qualitative to quantitative according to own experience, set a score for each area where a geological disaster point is located after analysis, wherein the score represents the probability of occurrence of the geological disaster, but the method needs a lot of time and energy, so that the analysis efficiency of the probability of occurrence of the geological disaster is low, the types of the geological disaster are various, the number of the geological disaster points is large for each geological disaster, and the workload of workers is large.

Disclosure of Invention

In view of this, embodiments of the present application provide a data processing method, an apparatus, a device, and a storage medium, so as to reduce manual workload and improve analysis efficiency of occurrence probability of a geological disaster.

Mainly comprises the following aspects:

in a first aspect, an embodiment of the present application provides a data processing method, where the method includes:

constructing a three-dimensional interpretation environment by using the acquired point cloud data and the optical remote sensing image of the specified area, wherein the three-dimensional interpretation environment is used for restoring the real environment of the specified area;

determining at least one target geological disaster area in the three-dimensional interpretation environment and at least one influence factor related to occurrence of a target geological disaster in the target geological disaster area and each target parameter for describing each influence factor in each target geological disaster area according to survey data of the designated area and first geological data included in the three-dimensional interpretation environment, wherein the survey data are stored in a first database, and the survey data include natural environment data and human activity data;

aiming at each target geological disaster area, calculating an occurrence probability index of the target geological disaster area according to the weight of each influence factor in the target geological disaster area and each target parameter, wherein the occurrence probability index is used for indicating the possibility of occurrence of the target geological disaster in the target geological disaster area;

determining a target threshold interval to which the proneness index belongs according to the proneness index of the target geological disaster area and threshold intervals set for all the proneness grade areas, and determining the proneness grade area corresponding to the target threshold interval as the proneness grade area to which the target geological disaster area belongs;

and displaying each target geological disaster area in the designated area and the susceptibility grade area to which the target geological disaster area belongs.

Optionally, the constructing a three-dimensional interpretation environment by using the acquired point cloud data of the designated area and the optical remote sensing image includes:

obtaining a digital elevation model DEM of the designated area according to ground point cloud data extracted from the point cloud data, and obtaining a digital orthophoto map DOM of the designated area according to the optical remote sensing image;

obtaining a three-dimensional terrain, a mountain shadow map and second geological data of the designated area according to the DEM;

according to the relative position relation among target ground objects in the real environment of the designated area, the mountain shadow map and the DOM are superposed on the three-dimensional terrain to obtain a three-dimensional virtual environment;

and adding the second geological data to the three-dimensional virtual environment to obtain the three-dimensional interpretation environment.

Optionally, the determining, according to the survey data of the designated area stored in the first database and the first geological data included in the three-dimensional interpretation environment, at least one target geological disaster area in the three-dimensional interpretation environment, and at least one influence factor related to occurrence of a target geological disaster in the target geological disaster area, and each target parameter for describing each influence factor in each target geological disaster area includes:

performing remote sensing geological interpretation in the three-dimensional interpretation environment according to the survey data and the first geological data to establish at least one interpretation mark;

screening the interpretation marks according to morphological characteristics of the target geological disaster in the three-dimensional interpretation environment to obtain at least one target interpretation mark for interpreting the morphological characteristics;

determining a region represented by the target interpretation mark as the target geological disaster region in the designated region so as to obtain at least one target geological disaster region;

determining at least one target mark item which is the same as the preset influence factor in mark items included in the target interpretation mark according to the preset influence factor of the target geological disaster included in a second database, so as to take the target mark item as the influence factor;

and determining target parameters for describing the influence factors according to the interpretation data under the target mark item included in the target interpretation mark.

Optionally, before the calculating, for each target geological disaster area, an index of vulnerability of the target geological disaster area according to the weight of each influencing factor in the target geological disaster area and each target parameter, the method further includes:

determining the weight of each influence factor by using an analytic hierarchy process according to the corresponding relation among the influence factors and the influence degree of each influence factor on the target geological disaster;

the calculating, for each target geological disaster area, an index of vulnerability of the target geological disaster area according to the weight of each influencing factor and each target parameter in the target geological disaster area includes:

for each target geological disaster area, respectively grading each target parameter in the target geological disaster area;

determining the area of the target geological disaster area under the parameter level and the number of the target geological disasters in the area according to the parameter level to which each target parameter belongs;

for each influence factor described by the target parameter, calculating a certainty coefficient of the influence factor in the target geological disaster area according to the area, the number, the total area of the specified area and the total number of the target geological disasters in the specified area, wherein the certainty coefficient refers to the probability of the target geological disasters in the target geological disaster area occurring under the influence factor;

and carrying out weighted summation on the certainty factor of each influence factor in the target geological disaster area and the corresponding weight to obtain the susceptibility index of the target geological disaster area.

Optionally, the data processing method further includes:

setting a target geological disaster area corresponding to the probability index being greater than or equal to a preset threshold as an area where the target geological disaster occurs, and setting a target geological disaster area corresponding to the probability index being less than the preset threshold as an area where the target geological disaster does not occur;

according to preset test data of the target geological disaster in the designated area, taking the correctly predicted proportion of the area where the target geological disaster occurs as data on a vertical axis, taking the correctly predicted proportion of the area where the target geological disaster does not occur as data on a horizontal axis and a vertical axis, and establishing a Receiver Operating Characteristic (ROC) curve;

calculating an area AUC under the ROC curve in the ROC curve to serve as reference data for reference by a user, wherein the AUC is used for representing the accuracy of the volatility index;

displaying the ROC curve or the reference data.

In a second aspect, an embodiment of the present application provides a data processing apparatus, where the apparatus includes:

the building module is used for building a three-dimensional interpretation environment by using the acquired point cloud data and the optical remote sensing image of the specified area, wherein the three-dimensional interpretation environment is used for restoring the real environment of the specified area;

a determining module, configured to determine, according to survey data of the specified area stored in a first database and first geological data included in the three-dimensional interpretation environment, at least one target geological disaster area in the three-dimensional interpretation environment, and at least one influencing factor related to occurrence of a target geological disaster in the target geological disaster area, and each target parameter for describing each influencing factor in each target geological disaster area, where the survey data includes natural environment data and human activity data;

a calculating module, configured to calculate, for each target geological disaster area, an index of easiness of occurrence of the target geological disaster area according to the weight of each influencing factor in the target geological disaster area and each target parameter, where the index of easiness is used to indicate a possibility of occurrence of the target geological disaster in the target geological disaster area;

the partitioning module is used for determining a target threshold interval to which the proneness index belongs according to the proneness index of the target geological disaster area and threshold intervals set for all the proneness grade areas, so that the proneness grade area corresponding to the target threshold interval is determined as the proneness grade area to which the target geological disaster area belongs;

and the first display module is used for displaying each target geological disaster area in the designated area and the susceptibility grade area to which the target geological disaster area belongs.

Optionally, the configuration of the building module, when being used for building a three-dimensional interpretation environment by using the acquired point cloud data and the optical remote sensing image of the specified area, includes:

Optionally, the configuration of the determining module, when configured to determine, according to the survey data of the designated area stored in the first database and the first geological data included in the three-dimensional interpretation environment, at least one target geological disaster area in the three-dimensional interpretation environment, and at least one influencing factor related to occurrence of a target geological disaster in the target geological disaster area, and each target parameter used for describing each influencing factor in each target geological disaster area, includes:

Optionally, before the configuration of the calculation module is configured to calculate, for each target geological disaster area, an index of susceptibility of the target geological disaster area according to the weight of each influence factor in the target geological disaster area and each target parameter, the configuration further includes:

the configuration of the calculation module, when configured to calculate, for each of the target geological disaster areas, an index of vulnerability of the target geological disaster area according to the weight of each of the influencing factors in the target geological disaster area and each of the target parameters, includes:

Optionally, the data processing apparatus further includes:

the setting module is used for setting a target geological disaster area corresponding to the probability index being greater than or equal to a preset threshold as an area where the target geological disaster occurs, and setting a target geological disaster area corresponding to the probability index being smaller than the preset threshold as an area where the target geological disaster does not occur;

the processing module is used for establishing a receiver operating characteristic ROC curve by taking the correctly predicted proportion of the area where the target geological disaster occurs as data on a vertical axis and taking the correctly predicted proportion of the area where the target geological disaster does not occur as data on a horizontal axis and a vertical axis according to preset test data of the target geological disaster in the specified area;

an analysis module, configured to calculate an AUC of an area under the ROC curve in the ROC curve to serve as reference data for a user to refer to, wherein the AUC is used to represent an accuracy of the vulnerability index;

and the second display module is used for displaying the ROC curve or the reference data.

In a third aspect, an embodiment of the present application provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the data processing method according to any one of the first aspect when executing the computer program.

In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps of the data processing method in any one of the above first aspects.

The technical scheme provided by the embodiment of the application can have the following beneficial effects:

the data processing method provided by the embodiment of the application aims at a geological disaster in a specified area, namely: the method comprises the steps of constructing a three-dimensional interpretation environment of a designated area by utilizing acquired point cloud data and optical image data of the designated area, wherein the point cloud data can accurately represent the collective position and ground surface detail characteristics of each target in the designated area, the three-dimensional interpretation environment constructed by using the point cloud data and the remote sensing image data not only can more accurately restore the real environment of the designated area, but also can represent the ground surface detail characteristics of the designated area, so that the three-dimensional interpretation environment comprises first geological data of the designated area, but factors influencing the occurrence of the target geological disasters are not limited to geological factors, but also can be natural environmental factors or human activity factors, so that the two factors are required to be pre-stored in a first database as investigation data, and after the three-dimensional interpretation environment is constructed, at least one target geological disaster area can be more accurately determined in the designated area according to the investigation data and the first geological data, and at least one influence factor in each target geological disaster area and a target parameter corresponding to each influence factor, and then for each target geological disaster area, the influence degrees of different influence factors on the target geological disaster in the target geological disaster area are different, the influence degrees of the same influence factor, different values of the target parameter and different target geological disasters in the target geological disaster area are also different, so that the probability of occurrence of the target geological disaster in the target geological disaster area can be determined according to the determined weight of each influence factor in the target geological disaster area and the target parameter corresponding to the weight, namely: the method is completed by the server in the whole process, so that the manual workload is reduced, the determining efficiency of the proneness index of the target geological disaster area is improved, and in addition, compared with the remote sensing image used in the prior art, the method utilizes the optical remote sensing image and the point cloud data, so that the used initial data precision is higher, and the accuracy of the proneness index of the target geological disaster area is improved; meanwhile, after the occurrence probability indexes of the target geological disaster areas are obtained, the target geological disaster areas are divided into different occurrence probability grade areas according to the occurrence probability indexes of the target geological disaster areas and the threshold value intervals set for the occurrence probability grade areas, dividing results are displayed, and more visual reference data are provided for the prevention and treatment work of the target geological disaster.

In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.

Fig. 1 is a flowchart illustrating a data processing method according to a first embodiment of the present application;

FIG. 2 is a flow chart of another data processing method provided in the first embodiment of the present application;

FIG. 3 is a flow chart of another data processing method provided in the first embodiment of the present application;

FIG. 4 is a flow chart of another data processing method provided in the first embodiment of the present application;

FIG. 5 shows a ROC plot provided in accordance with one embodiment of the present application;

fig. 6 is a schematic structural diagram illustrating a data processing apparatus according to a second embodiment of the present application;

fig. 7 shows a schematic structural diagram of a computer device provided in the third embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.

Based on this, embodiments of the present application provide a data processing method, an apparatus, a device, and a storage medium, which are described below by way of embodiments.

Example one

Fig. 1 shows a flowchart of a data processing method provided in a first embodiment of the present application, and as shown in fig. 1, the method includes the following steps:

step S101: and constructing a three-dimensional interpretation environment by using the acquired point cloud data and the optical remote sensing image of the specified area, wherein the three-dimensional interpretation environment is used for restoring the real environment of the specified area.

Specifically, the designated area refers to an area to be researched selected from an area needing geological disaster research, and the point cloud data comprises ground point cloud data and non-ground point cloud data.

It should be noted that the three-dimensional interpretation environment can restore the real environment of the designated area, the point cloud data can describe the geometric position of the target in the form of three-dimensional coordinate points, the surface detail characteristics of the target can be represented by the color of the three-dimensional coordinate points, and the image characteristics of the optical remote sensing image can reflect the characteristics of the components, the structure, the properties and the like of the target, so that the three-dimensional interpretation environment of the designated area can be constructed according to the point cloud data and the optical remote sensing image after the point cloud data and the optical remote sensing image of the designated area are acquired.

It should be noted again that the manner Of acquiring the point cloud data and the optical remote sensing image Of the designated area may be set according to actual conditions, for example, the point cloud data and the optical remote sensing image Of the designated area may be acquired according to a stereo camera or a TOF (Time-Of-Flight) camera, or the point cloud data and the optical remote sensing image Of the designated area may be acquired by an airborne radar, and the specific manner Of acquiring is not specifically limited herein, wherein, by the airborne radar method, after the emission frequency Of the laser emitted by the sensor is manually set, the sensor is mounted by an unmanned aerial vehicle or a human-machine to reach the designated area, the sensor emits the laser to the designated area, and receives the laser reflected by ground, vegetation, buildings and other ground objects in the designated area, and stores the stored data, obtaining point cloud data of a high-density designated area, and then obtaining an optical remote sensing image of the designated area; the airborne radar technology is not influenced by environmental factors such as illumination, weather and the like, so that point cloud data and optical remote sensing images of designated areas can be acquired in real time, the technical resolution of the airborne radar is higher, and data of vegetation canopies, vegetation shrubs, erosion layers and ground surface layers of the designated areas can be acquired respectively, so that point cloud data and optical remote sensing images with higher accuracy can be acquired.

Step S102: according to survey data of the designated area stored in a first database and first geological data included in the three-dimensional interpretation environment, at least one target geological disaster area and at least one influence factor related to occurrence of a target geological disaster in the target geological disaster area are determined in the three-dimensional interpretation environment, and each target parameter for describing each influence factor in each target geological disaster area is determined, wherein the survey data comprises natural environment data and human activity data.

Specifically, the natural environment data of the designated area comprises data of natural disasters, meteorological hydrology and the like in the designated area, the human activity data comprises human activity data of underground water exploitation, trees felling and the like, and the natural environment data and the human activity data cannot be analyzed from the acquired point cloud data and the acquired optical remote sensing image, so that the three-dimensional interpretation environment constructed according to the point cloud data and the optical remote sensing image does not comprise the human activity data and the natural environment data, the two data need to be obtained by expert field investigation of the designated area, and the obtained investigation data is stored in the first database; the target geological disaster area refers to an area where a target geological disaster occurs, the area where the target geological disaster occurs in a designated area is not necessarily concentrated in one area, so that at least one target geological disaster area can be determined in the designated area, after the target geological disaster area is determined, in order to judge the easiness degree of the target geological disaster in the target geological disaster area, the influence factor of the target geological disaster occurring in the target geological disaster area needs to be known, while the occurrence of the target geological disaster is not necessarily influenced by only one factor, and the target geological disaster may be caused to occur due to the influence of multiple factors, so that at least one influence factor needs to be determined in the target geological disaster area, after the influence factor is determined, the influence factor needs to be specifically analyzed, so that a target parameter corresponding to the influence factor needs to be determined, the target parameter is used for specifically describing the specific situation of the influencing factor in the target geological disaster area.

It should be noted that the determined at least one target geological disaster area may be a grid, and the smaller the area of the grid is, the greater the number of the target geological disaster areas determined in the designated area is, the more accurate the index of the determined target geological disaster area is, so that the final partitioning result is more accurate.

It should be noted again that, for the first geological data in the three-dimensional interpretation environment, the first geological data are different for different target geological disasters, for example, when the target geological disaster is a collapse, the first geological data include geological data such as a slope, a slope direction, an elevation, a river pitch, a slope type, a rock group, a slope height, and the like, and specific first geological data are not specifically limited herein.

Step S103: and calculating an occurrence probability index of each target geological disaster area according to the weight of each influence factor in the target geological disaster area and each target parameter, wherein the occurrence probability index is used for indicating the possibility of occurrence of the target geological disaster in the target geological disaster area.

Specifically, for each target geological disaster area, the target parameters corresponding to the same influencing factor in the target geological disaster area have different values, the degree of influence on the occurrence of the target geological disaster in the target geological disaster area is different for different influencing factors, the degree of influence is large, and the weight of the corresponding influencing factor is larger, so that in order to know the probability of the occurrence of the target geological disaster in the target geological disaster area, the probability index of the target geological disaster area needs to be calculated according to the weight of each influencing factor in the target geological disaster area and the corresponding target parameter.

Step S104: and determining a target threshold interval to which the proneness index belongs according to the proneness index of the target geological disaster area and the threshold intervals set for the respective proneness grade areas, so as to determine the proneness grade area corresponding to the target threshold interval as the proneness grade area to which the target geological disaster area belongs.

Specifically, after determining the susceptibility index of each target geological disaster area, each target geological disaster area is compared with the threshold value interval set in each susceptibility grade area, the expression form of the threshold interval is the easy-to-send index range, if the easy-to-send index is found to be in the easy-to-send index range corresponding to a certain threshold interval after comparison, the target geological disaster area corresponding to the susceptibility index belongs to the susceptibility grade area corresponding to the threshold interval, wherein the set of each susceptibility index range corresponding to each susceptibility rank region includes the range specified for the susceptibility index, e.g., the range specified for the susceptibility index is [0,1], the set of the ranges of the respective susceptibility indexes corresponding to the respective susceptibility rank areas is any range including [0,1], which may be [0,1], or [0,5 ].

For example, the determined vulnerability index of a certain target geological disaster area is 0.5, the preset vulnerability grade area includes an extremely low vulnerability area, a medium vulnerability area, a high vulnerability area and an extremely high vulnerability area, the threshold interval corresponding to the extremely low vulnerability area is [0,0.1], the threshold interval corresponding to the low vulnerability area is (0.1,0.3], the threshold interval corresponding to the medium vulnerability area is (0.3,0.7], the threshold interval corresponding to the high vulnerability area is (0.7,0.9], the threshold interval corresponding to the extremely high vulnerability area is (0.9, 1), and the comparison shows that the vulnerability index of the target geological disaster area belongs to the threshold interval corresponding to the medium vulnerability area, so that the target address disaster area belongs to the medium vulnerability area.

Step S105: and displaying each target geological disaster area in the designated area and the susceptibility grade area to which the target geological disaster area belongs.

Specifically, the display mode of each target geological disaster area in the designated area and the susceptibility rank area to which the target geological disaster area belongs may be set according to actual conditions, for example, the designated area may be displayed in the form of an electronic map, the susceptibility rank areas of different ranks may be represented by different colors in the electronic map of the designated area, the coordinates of the area edge point of each target geological disaster area in the designated area and the susceptibility rank area to which the target geological disaster area belongs may be displayed in the form of text, the form of the electronic map and the form of text may be used together for display, and the specific display mode is not specifically limited herein.

The data processing method provided by the figure one aims at a geological disaster in a specified area, namely: the method comprises the steps of constructing a three-dimensional interpretation environment of a designated area by utilizing acquired point cloud data and optical image data of the designated area, wherein the point cloud data can accurately represent the collective position and ground surface detail characteristics of each target in the designated area, the three-dimensional interpretation environment constructed by using the point cloud data and the remote sensing image data not only can more accurately restore the real environment of the designated area, but also can represent the ground surface detail characteristics of the designated area, so that the three-dimensional interpretation environment comprises first geological data of the designated area, but factors influencing the occurrence of the target geological disasters are not limited to geological factors, but also can be natural environmental factors or human activity factors, so that the two factors are required to be pre-stored in a first database as investigation data, and after the three-dimensional interpretation environment is constructed, at least one target geological disaster area can be more accurately determined in the designated area according to the investigation data and the first geological data, and at least one influence factor in each target geological disaster area and a target parameter corresponding to each influence factor, and then for each target geological disaster area, the influence degrees of different influence factors on the target geological disaster in the target geological disaster area are different, the influence degrees of the same influence factor, different values of the target parameter and different target geological disasters in the target geological disaster area are also different, so that the probability of occurrence of the target geological disaster in the target geological disaster area can be determined according to the determined weight of each influence factor in the target geological disaster area and the target parameter corresponding to the weight, namely: the method is completed by the server in the whole process, so that the manual workload is reduced, the determining efficiency of the proneness index of the target geological disaster area is improved, and in addition, compared with the remote sensing image used in the prior art, the method utilizes the optical remote sensing image and the point cloud data, so that the used initial data precision is higher, and the accuracy of the proneness index of the target geological disaster area is improved; meanwhile, after the occurrence probability indexes of the target geological disaster areas are obtained, the target geological disaster areas are divided into different occurrence probability grade areas according to the occurrence probability indexes of the target geological disaster areas and the threshold value intervals set for the occurrence probability grade areas, dividing results are displayed, and more visual reference data are provided for the prevention and treatment work of the target geological disaster.

In a possible implementation scheme, after each volatility index is calculated, the volatility indexes with different sizes can be represented by colors with different depths on an electronic map, the higher the color is, the larger the volatility index is, and then the target electronic map represented by the colors is displayed to provide a more intuitive reference basis for a user.

In a possible embodiment, for the construction of the three-dimensional interpretation environment in step S101, the following method can be used to implement:

and obtaining a digital elevation model DEM of the designated area according to ground point cloud data extracted from the point cloud data, and obtaining a digital orthophoto map DOM of the designated area according to the optical remote sensing image.

And obtaining the three-dimensional terrain, the mountain shadow map and second geological data of the designated area according to the DEM.

And according to the relative position relation between the target ground objects in the real environment of the designated area, superposing the mountain shadow map and the DOM on the three-dimensional terrain to obtain a three-dimensional virtual environment.

Specifically, for second geological data of the designated area, different target geological disasters are performed, the second geological data are different, for example, when the target geological disasters are collapse, the second geological data include geological data such as gradient, slope direction, elevation, river distance, slope type, rock group, slope height and the like, and the specific second geological data are not specifically limited herein; the target features in the designated area may also vary from designated area to designated area, for example, the target features in the designated area 1 include features such as rocks and trees, and the target features in the designated area 2 include features such as buildings and constructed roads.

After the point cloud data of the designated area is acquired, in order to improve the accuracy of the point cloud data, preprocessing the point cloud data, wherein the preprocessing includes processing operations such as post-POS differential processing, resolving, denoising filtering, and fairway leveling, after the preprocessing, classifying the preprocessed point cloud data, separating ground point cloud data from non-ground point cloud data in the point cloud data, extracting the ground point cloud data of the designated area from the separated point cloud data, using the extracted ground point cloud data as input data of an ArcGIS (geographic information system), and generating a DEM (Digital Elevation Model) of the designated area in the ArcGIS; after the optical remote sensing image of the designated area is acquired, the optical remote sensing image is corrected, embedded, cut and the like, and then a Digital ortho-Map (DOM) of the designated area is obtained.

After obtaining the DEM and DOM of the designated area, taking the DEM as input data of ArcGIS to obtain a mountain shadow map of the designated area, analyzing the DEM in three-dimensional digital earth software Earth Survy to obtain second geological data, then taking the DEM as input data of Earth Survy to obtain three-dimensional terrain of the designated area, and after obtaining the three-dimensional terrain of the designated area, taking the three-dimensional terrain, the mountain shadow map and the second geological data as input data of Earth Survy, wherein the Earth Survy constructs a three-dimensional interpretation environment according to the preset relative position relationship among target land objects in the designated area, the three-dimensional interpretation environment not only restores the real environment condition of the designated area, but also comprises second geological data of the designated area, and the second geological data can exist in the three-dimensional interpretation environment in the form of annotation, wherein the Earth Survy simulates a real world simulated three-dimensional scene by using a large amount of remote sensing image data, digital elevation data and other two-dimensional data The software functions as: the functions of three-dimensional data browsing, data editing, real-time coordinate and elevation information reading, structural plane measurement, distance measurement, contour line one-key generation, gradient map generation and the like are realized.

In a possible implementation, fig. 2 shows a flowchart of another data processing method provided in the first embodiment of the present application, and as shown in fig. 2, when step S102 is executed, the following steps may be implemented:

step S201: and performing remote sensing geological interpretation in the three-dimensional interpretation environment according to the survey data and the first geological data to establish at least one interpretation mark.

Specifically, the survey data includes natural environment data and human activity data of each region in the designated region, the first geological data includes geological data of each region in the designated region, when remote sensing geological interpretation is performed in the three-dimensional interpretation environment, an interpretation tag including the natural environment data, the human activity data and the first geological data of the region is added to each region in the three-dimensional interpretation environment, so that the created interpretation tag includes at least one tag item and interpretation data under the tag item, wherein the interpretation data under the tag item includes at least one data, such as: the sign item is elevation, the interpretation data under the sign item is 544m, the sign item is natural disaster, and the interpretation data under the sign item is earthquake and flood.

Step S202: and screening the interpretation marks according to the morphological characteristics of the target geological disaster in the three-dimensional interpretation environment to obtain at least one target interpretation mark for interpreting the morphological characteristics.

Step S203: and in the designated area, determining the area represented by the target interpretation mark as the target geological disaster area so as to obtain at least one target geological disaster area.

Specifically, at least one morphological feature of the target geological disaster is prestored in the three-dimensional interpretation environment, the morphological feature may be in a form of text description or a form of numerical value interval, the expression form of the specific morphological feature is not specifically limited herein, after the interpretation mark is established, for each interpretation mark, the interpretation data under the mark item of the interpretation mark and the corresponding morphological feature are compared, and if the interpretation data belongs to the morphological feature, the interpretation mark corresponding to the interpretation data is screened out to be used as the target interpretation mark.

For example, if the target geological disaster is a collapse, the collapse is represented in the three-dimensional interpretation environment in the form of vegetation: coverage area [0,50], slope: [20 °,50 ° ], natural disasters: earthquake; the sign item of the interpretation sign in the three-dimensional interpretation environment and the corresponding interpretation data thereof comprise that the vegetation coverage area is 60, the gradient is 40 degrees, the natural disaster is earthquake, after comparing the vegetation coverage area in the expression form with the vegetation coverage area in the three-dimensional interpretation environment, determining that the vegetation is not a target interpretation mark, comparing the slope in the representation with the slope in the three-dimensional interpretation environment, determining that the slope is the target interpretation mark, therefore, the area for establishing the interpretation mark with the gradient of 40 degrees is determined as the target geological disaster area, the natural disaster in the expression form is compared with the natural disaster in the three-dimensional interpretation environment, the natural disaster is determined as the target interpretation mark, therefore, the area of the interpretation mark for establishing the natural disaster as the earthquake is determined as the target geological disaster area, and in conclusion, two target geological disaster areas are determined in the designated area.

Step S204: and determining at least one target mark item which is the same as the preset influence factor in mark items included in the target interpretation mark according to the preset influence factor of the target geological disaster included in a second database, so as to take the target mark item as the influence factor.

Step S205: and determining target parameters for describing the influence factors according to the interpretation data under the target mark item included in the target interpretation mark.

Specifically, the preset influence factors are factors influencing occurrence of the target geological disaster, which are obtained through pre-investigation, for example, the target geological disaster is a collapse, the target interpretation flags include flag items and interpretation data under the flag items, and the factors such as a slope, an elevation, a rock group and the like obtained through the pre-investigation can influence the occurrence of the collapse, so the factors are taken as the preset influence factors, the preset influence factors are pre-stored in the second database, after the target interpretation flags are determined, at least one target flag item is screened out from the flag items of each target interpretation flag, and the target flag item is taken as the influence factor of the target geological disaster, wherein the name of the target flag item is the same as the name of the preset influence factor, that is: the target mark item is the same as the preset influence factor, such as: if the preset influence factor is an elevation and the mark item is also the elevation, the mark item is the same as the preset influence factor, the mark item is a target mark item, and the target mark item is used as the influence factor; because the target interpretation flag comprises the flag item and the interpretation data under the flag item, the interpretation data under the target flag item can be used as the target parameter for describing the influencing factors, wherein the interpretation data can be numerical values or character expressions, and the target parameter can be numerical values or character expressions.

It should be noted that, the screening method of the target flag item may be set according to actual conditions, and for the flag item in each target interpretation flag, whether a preset influence factor identical to the flag item exists or not may be queried in the second database, if so, the flag item is determined as the target flag item, and if not, the flag item is not the target flag item; and for each preset influence factor, searching a mark item which is the same as the preset influence factor in the mark items of each target interpretation mark, if the same mark item is found, determining the found mark item as a target mark item, and if the same mark item is not found, skipping the preset influence factor.

For example, if the target geological disaster is a collapse disaster, the preset influence factors in the second database include elevation, gradient, slope direction and rainfall, the marker items of each target interpretation marker include elevation, gradient and earthquake, screening is performed in the first screening manner, for an elevation marker item, a preset influence factor elevation the same as that of the elevation marker item is found in the second database, the elevation marker item is a target item, for a gradient marker item, a preset influence factor gradient the same as that of the gradient marker item is found in the second database, the gradient marker item is a target item, for an earthquake marker item, a preset influence factor the same as that of the earthquake marker item is not found in the second database, and the earthquake marker item is not a target marker item; and screening in the second screening manner, if the preset influence factor elevation has the same sign item elevation as the preset influence factor elevation, the sign item is the target sign item, if the preset influence factor gradient has the same sign item gradient as the preset influence factor elevation, the sign item is the target sign item, and if the preset influence factor gradient and the precipitation amount have no sign item which is the same as the preset influence factor gradient, the sign item is not determined, and the two preset influence factors are skipped.

It should be noted again that the second database may be the same database as the first database, or may be different databases.

In a possible embodiment, before performing step S103, a weight of each of the influencing factors is determined by using an analytic hierarchy process according to the corresponding relationship between the influencing factors and the degree of influence of each of the influencing factors on the target geological disaster.

Specifically, the expression form of the influence degree of the influence factors on the target geological disaster is a score, the corresponding relationship includes two influence factors belonging to the same type, the two influence factors do not belong to the same type, for example, in a collapse geological disaster, both the elevation and the gradient belong to topographic and topographic factors, both the earthquake and the flood belong to natural disaster factors, and the gradient and the earthquake do not belong to the same type of factors, before determining the weight of each influence factor by using an analytic hierarchy process, the pre-stored score assigned to each influence factor and the preset corresponding relationship between each influence factor are obtained, and after the score of each influence factor and the corresponding relationship between each influence factor are obtained, the weight of each influence factor is determined according to an analytic hierarchy process.

After determining the weight of each influence factor by using a chromatography, verifying the accuracy of the weight of each influence factor by using a preset fractal dimension value of each influence factor in order to improve the accuracy of the determined weight of each influence factor, wherein the fractal dimension value is the preset weight of each influence factor, and the preset weight is obtained by artificially scoring; when the weight of each influence factor is verified, the influence factors are arranged according to the sequence of the weight of the influence factors from large to small to obtain a first arrangement sequence, then the influence factors are arranged according to the sequence of the fractal dimension values of the influence factors from large to small to obtain a second arrangement sequence, finally the first arrangement sequence and the second arrangement sequence are compared, if the first arrangement sequence is not consistent with the second arrangement sequence, the accuracy of the determined weight of each influence factor is low, the weight of each influence factor is determined by using an analytic hierarchy process after the influence degree of each influence factor is adjusted, and the verification operation is continued after the weight is determined until the first arrangement sequence is consistent with the second arrangement sequence to obtain the weight of each influence factor.

Fig. 3 is a flowchart illustrating another data processing method provided in the first embodiment of the present application, and as shown in fig. 3, when step S103 is executed, the following steps may be implemented:

step S301: and aiming at each target geological disaster area, grading each target parameter in the target geological disaster area respectively.

Step S302: and aiming at the parameter grade to which each target parameter belongs, determining the area of the target geological disaster area under the parameter grade and the number of the target geological disasters in the area.

Specifically, for each target geological disaster area, at least one influence factor exists in the target geological disaster area, for each influence factor in the target area, according to a parameter grade table preset for a target parameter of the influence factor, a parameter grade to which a target parameter of the influence factor belongs in the parameter grade table is determined, the parameter grade in the parameter grade table corresponds to a preset parameter range, if the target parameter belongs to a certain target parameter range in each parameter range, the target parameter belongs to a parameter grade corresponding to the target parameter range, after the parameter grade of the target parameter is determined, the geological area of the target geological disaster area under the parameter grade needs to be determined, a first area corresponding to the target geological disaster area in the parameter range corresponding to the parameter grade is determined, and then the area of the first area is calculated, and after determining the area of the target geological disaster area under the parameter level, determining at least one target geological disaster point in the determined area from target geological disaster points of a designated area included in pre-stored experimental data, calculating the number of the at least one target geological disaster point, and taking the number as the number of target geological disasters in the area, wherein the target geological disaster point is a position coordinate where the target geological disaster occurs, and the pre-stored experimental data includes a target address disaster point in the at least one designated area.

Taking a collapse geological disaster as an example, aiming at each collapse geological disaster area, the influence factors existing in the collapse geological disaster area comprise elevation, gradient and slope direction, aiming at the influence factor elevation in the collapse geological disaster area, the target parameter of the elevation is 1480, and the parameter grade table preset for the influence factor elevation comprises a first parameter grade: elevation range (— infinity, 1380 m)]And the second parameter grade: elevation range (1380m, 1420 m)]And the third parameter level: elevation range (1420m,1460 m)]Fourth parameter level: elevation range (1460m,1550 m)]And the fifth parameter grade: finding that the target parameter of the elevation is in the elevation range corresponding to the fourth parameter level after comparison in the elevation range (1550m, infinity,) so that the parameter level corresponding to the target parameter of the elevation is the fourth parameter level, and for the fourth parameter level, locating the collapse geological disaster area in the elevation range (1460m,1550m,) in the collapse geological disaster area]The area of the lower region is 7km²The number of collapsed geological disasters in the area of the region is 3.

Step S303: and calculating a certainty coefficient of the influence factor in the target geological disaster area according to the area, the number, the total area of the specified area and the total number of the target geological disasters in the specified area aiming at the influence factor described by each target parameter, wherein the certainty coefficient refers to the probability of the target geological disasters in the target geological disaster area occurring under the influence factor.

Specifically, if the calculated certainty factor of the influencing factor is positive, it is determined that the influencing factor is an influencing factor that promotes the occurrence of the target geological disaster, and if the calculated certainty factor of the influencing factor is negative, it is determined that the influencing factor is an influencing factor that suppresses the occurrence of the target geological disaster, and the calculation method is: calculating the ratio of the number to the area as a first ratio a, calculating the ratio of the total number to the total area as a second ratio b, and then calculating the certainty factor CF of the influencing factor in the target geological disaster area according to the following formula:

for example, if the number is 3, the area is 6km²If the first ratio a is 0.5, the total number is 20, and the total area is 50km²If the second ratio b is 0.4, the deterministic coefficient CF of the influencing factor in the target geological disaster area is 1/3 by using the above calculation formula; if the number is 2, the area is 5km²If the first ratio a is 0.4, the total number is 30, and the total area is 60km²Then the second ratio b is 0.5, and the certainty factor CF of the influencing factor in the target geological disaster area is-1/3 using the above calculation formula.

Step S304: and carrying out weighted summation on the certainty factor of each influence factor in the target geological disaster area and the corresponding weight to obtain the susceptibility index of the target geological disaster area.

Taking a collapsed geological disaster as an example, the influence factors in the collapsed geological disaster area include a slope, precipitation amount and elevation, wherein the certainty factor of the slope is 0.3, the weight of the slope is 0.2, the certainty factor of the precipitation amount is 0.4, the weight of the precipitation amount is 0.5, the certainty factor of the elevation is 0.6, and the weight of the elevation is 0.3, so that the probability index of the collapsed geological disaster area is 0.3 × 0.2+0.4 × 0.5+0.6 × 0.3 — 0.44.

In a possible embodiment, in order to show the user a more accurate reference, the data processing method further comprises:

the experimental data, each susceptibility rating area in the designated area, each target geological disaster area included in each susceptibility rating area, each influence factor included in each target geological disaster area and the corresponding target parameter are used as input parameters of ArcGIS software to obtain output data, the output data comprises data such as area corresponding to each susceptibility rating area, ratio of the area to the total area of the designated area, target number of the target geological disasters in the area, ratio of the target number to the total number of the target geological disasters in the designated area included in the experimental data, development density of the target geological disasters in the area, and the like, and the obtained output data is displayed, wherein the development density refers to the aggregation degree of the target geological disasters.

The display mode of the output data may be set according to actual conditions, for example, the output data may be displayed in a table form or an annotation form, and the specific display mode is not specifically limited herein.

In a possible implementation, fig. 4 shows a flowchart of another data processing method provided in the first embodiment of the present application, and as shown in fig. 4, the data processing method further includes the following steps:

step S401: setting the target geological disaster area with the probability index larger than or equal to a preset threshold value as the area where the target geological disaster occurs, and setting the target geological disaster area with the probability index smaller than the preset threshold value as the area where the target geological disaster does not occur.

Step S402: and according to preset test data of the target geological disaster in the specified region, taking the correctly predicted proportion of the region where the target geological disaster occurs as data on a vertical axis, taking the correctly predicted proportion of the region where the target geological disaster does not occur as data on a horizontal axis and a vertical axis, and establishing a Receiver Operating Characteristic (ROC) curve.

Step S403: calculating an area AUC under the ROC curve in the ROC curve to use the AUC as reference data for reference by a user, wherein the AUC is used for representing the accuracy of the volatility index.

Step S404: displaying the ROC curve or the reference data.

Specifically, for each target geological disaster area in the designated area, judging whether the probability index of the target geological disaster area is greater than a preset threshold value, if so, determining the target geological disaster area as the area where the target geological disaster occurs, and if not, determining the target geological disaster area as the area where the target geological disaster does not occur; the test data comprises at least one second target geological disaster point in the designated area and test areas divided for the second target geological disaster points, wherein the second target geological disaster points comprise disaster points where target geological disasters occur and disaster points where the target geological disasters do not occur, so the test areas comprise test areas of two different area categories, the test area of the first area category is a test area where the target geological disasters occur, and the test area of the second area category is a second test area where the target geological disasters do not occur.

After obtaining each test area, calculating the test susceptibility index of each test area, arranging each test area and the corresponding test susceptibility index in the descending order of the test susceptibility indexes, regarding each arranged test area, taking the test susceptibility index of the first test area as a first preset threshold, setting the test area with the test susceptibility index larger than or equal to the first preset threshold as a first target test area with target geological disasters, setting the test area with the test susceptibility index smaller than the first preset threshold as a second target test area without target geological disasters, regarding each test area, judging whether the target test area to which the test area belongs is the same as the area type thereof according to the area type of the test area, if so, determining the target test area to which the test area belongs, if the target test area is a first target test area, increasing one first number of correctly predicted areas with target geological disasters, if the target test area is a second target test area, increasing one second number of correctly predicted areas without target geological disasters, after all the test areas are judged, counting the final value of the first number and the final value of the second number, calculating the ratio of the final value of the first number to the total number of the test areas to obtain a first ratio of correctly predicted areas with target geological disasters, calculating the ratio of the final value of the second number to the total number of the test areas to obtain a second ratio of correctly predicted areas with target geological disasters.

Taking the test susceptibility index of the second test area as a second preset threshold, calculating a third ratio of the area where the target geological disaster occurs and a fourth ratio of the area where the target geological disaster does not occur, which are corresponding to the second preset threshold, wherein the third ratio is correctly predicted, and the specific calculation mode refers to the calculation mode described for the first test area, and is not described again herein; by analogy, the test susceptibility index of the last test area is used as a qth preset threshold, the mth proportion of the area where the target geological disaster occurs and the nth proportion of the area where the target geological disaster does not occur, which correspond to the qth preset threshold, are correctly predicted, and the specific calculation mode refers to the calculation mode described for the first test area, which is not described herein again.

The method comprises the steps of taking the correctly predicted proportion of each region with target geological disasters and the correctly predicted proportion of each region without the target geological disasters as input data of MATLAB (matrix laboratory) software, taking the correctly predicted proportion of each region with the target geological disasters as data on a vertical axis in the MATLAB, taking the correctly predicted proportion of each region without the target geological disasters as data on a horizontal axis and a vertical axis, establishing a receiver operating characteristic ROC curve, calculating area AUC under the ROC curve, and taking the AUC as reference data for a user to refer to, so that the AUC can be directly displayed, the ROC curve can be directly displayed, both the AUC and the ROC curve can be displayed, and reference data can be more intuitively provided for the user.

It should be noted that AUC is used to represent the accuracy of the volatility index, and the larger the AUC is, the more accurate the calculated volatility index is, the smaller the AUC is, the less accurate the calculated volatility index is, and the AUC value range is [0.5, 1 ]; the susceptibility index indicates the degree of susceptibility of the target geological disaster, and the greater the susceptibility index, the more susceptible the target geological disaster occurs, and the smaller the susceptibility index, the less susceptible the target geological disaster occurs.

For example, fig. 5 shows a ROC graph provided in an embodiment of the present invention, as shown in fig. 5, the horizontal axis of the ROC curve indicates a ratio of correctly predicted cells (cells, i.e., regions) not suffering from geological disaster, the horizontal axis of the ROC curve indicates a ratio of correctly predicted cells (cells, i.e., regions) suffering from geological disaster, the vertical axis of the ROC curve indicates a ratio of correctly predicted cells suffering from geological disaster, the vertical axis of the ROC curve indicates a ratio of 0.2, and the numerical value ranges of 0,1, the vertical axis of the ROC curve indicates a reference curve, the ROC graph indicates a solid line, and the AUC indicates a value range of 0.5, 1, the AUC value of this ROC curve can be calculated by MATLAB to be 0.708.

Example two

Fig. 6 is a schematic structural diagram of a data processing apparatus according to a second embodiment of the present application, and as shown in fig. 6, the data processing apparatus includes:

the building module 601 is configured to build a three-dimensional interpretation environment by using the acquired point cloud data and the optical remote sensing image of the designated area, where the three-dimensional interpretation environment is used to restore a real environment of the designated area;

a determining module 602, configured to determine, in the three-dimensional interpretation environment, at least one target geological disaster area and at least one influencing factor related to occurrence of a target geological disaster in the target geological disaster area, and each target parameter for describing each influencing factor in each target geological disaster area according to survey data of the designated area stored in a first database and first geological data included in the three-dimensional interpretation environment, wherein the survey data includes natural environment data and human activity data;

a calculating module 603, configured to calculate, for each target geological disaster area, an index of easiness of occurrence of the target geological disaster area according to the weight of each influencing factor in the target geological disaster area and each target parameter, where the index of easiness is used to indicate a possibility of occurrence of the target geological disaster in the target geological disaster area;

a partitioning module 604, configured to determine, according to the probability index of the target geological disaster area and the threshold interval set for each probability grade area, a target threshold interval to which the probability index belongs, so as to determine, as the probability grade area to which the target geological disaster area belongs, the probability grade area corresponding to the target threshold interval;

a first display module 605, configured to display each target geological disaster area in the designated area and the occurrence-prone rank area to which the target geological disaster area belongs.

In a possible embodiment, the configuration of the building module 601, when used for building a three-dimensional interpretation environment by using the acquired point cloud data of the designated area and the optical remote sensing image, includes:

In a possible embodiment, the configuration of the determining module 602, when configured to determine, in the three-dimensional interpretation environment, at least one target geological disaster area and at least one influencing factor related to occurrence of a target geological disaster in the target geological disaster area according to the survey data of the designated area stored in the first database and the first geological data included in the three-dimensional interpretation environment, and each target parameter for describing each influencing factor in each target geological disaster area, includes:

In a possible embodiment, before the configuration of the calculating module 603 is configured to calculate, for each of the target geological disaster areas, a vulnerability index of the target geological disaster area according to the weight of each influencing factor in the target geological disaster area and each target parameter, the method further includes:

In a possible embodiment, the data processing apparatus further comprises:

The apparatus provided in the embodiments of the present application may be specific hardware on a device, or software or firmware installed on a device, etc. The device provided by the embodiment of the present application has the same implementation principle and technical effect as the foregoing method embodiments, and for the sake of brief description, reference may be made to the corresponding contents in the foregoing method embodiments where no part of the device embodiments is mentioned. It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the foregoing systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

EXAMPLE III

Fig. 7 shows a schematic structural diagram of a computer device provided in the third embodiment of the present application, and as shown in fig. 7, the device includes a memory 701, a processor 702, and a computer program stored in the memory 701 and operable on the processor 702, where the processor 702 implements the data processing method when executing the computer program.

Specifically, the memory 701 and the processor 702 can be general memories and processors, which are not limited in particular, and when the processor 702 runs a computer program stored in the memory 701, the data processing method can be executed, so that the workload of workers is reduced, the determination efficiency of the index of the target geological disaster area is improved, and the accuracy of the index of the target geological disaster area is improved.

Example four

The embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program performs the steps of the data processing method.

Specifically, the storage medium can be a general storage medium, such as a mobile disk, a hard disk, or the like, and when a computer program on the storage medium is executed, the data processing method can be executed, so that the workload of workers is reduced, the efficiency of determining the susceptibility index of the target geological disaster area is improved, and the accuracy of the susceptibility index of the target geological disaster area is improved.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus once an item is defined in one figure, it need not be further defined and explained in subsequent figures, and moreover, the terms "first", "second", "third", etc. are used merely to distinguish one description from another and are not to be construed as indicating or implying relative importance.

Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the present disclosure, which should be construed in light of the above teachings. Are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A data processing method, comprising:

2. The method of claim 1, wherein the constructing a three-dimensional interpretation environment using the acquired point cloud data of the specified area and the optical remote sensing image comprises:

3. The method of claim 1, wherein the determining at least one target geological disaster area in the three-dimensional interpretation environment and at least one influencing factor related to occurrence of a target geological disaster in the target geological disaster area and each target parameter for describing each influencing factor in each target geological disaster area according to the survey data of the designated area stored in the first database and the first geological data included in the three-dimensional interpretation environment comprises:

4. The method of claim 1, wherein before calculating, for each of the target geological disaster areas, a susceptibility index for the target geological disaster area based on the weight of each of the influencing factors in the target geological disaster area and each of the target parameters, the method further comprises:

5. The method of claim 1, wherein the data processing method further comprises:

displaying the ROC curve or the reference data.

6. A data processing apparatus, comprising:

7. The apparatus of claim 6, wherein the configuration of the construction module, when used to construct the three-dimensional interpretation environment using the acquired point cloud data and optical remote sensing images of the specified area, comprises:

8. The apparatus of claim 6, wherein the determining module is configured to determine at least one target geological disaster area in the three-dimensional interpretation environment and at least one influencing factor related to occurrence of a target geological disaster in the target geological disaster area according to the survey data of the designated area stored in the first database and the first geological data included in the three-dimensional interpretation environment, and each target parameter for describing each influencing factor in each target geological disaster area, and comprises:

9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of the preceding claims 1-5 are implemented when the computer program is executed by the processor.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of the claims 1-5.