CN112215439A - Geological disaster emergency command data processing method and system based on GIS - Google Patents
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Abstract
The disclosure relates to the technical field of data processing combining geological disasters and emergency commands, in particular to a geological disaster emergency command data processing method and system based on a GIS. According to the method, monitoring dimension characteristic data are firstly obtained, first geological disaster monitoring data are then determined, and corresponding emergency command planning data are finally generated according to the first geological disaster monitoring data. Therefore, the first geological disaster monitoring data can be determined through different monitoring dimension characteristic data, so that the geological disaster change condition of the first geological disaster monitoring area is obtained, the possible types of geological disasters can be determined in advance, and the emergency command planning data can be further accurately generated. The invention can carry out emergency command based on the pre-generated emergency command planning data when the geological disaster occurs, thereby minimizing personal safety loss and property loss when the geological disaster occurs.
Description
Technical Field
The disclosure relates to the technical field of data processing combining geological disasters and emergency commands, in particular to a geological disaster emergency command data processing method and system based on a GIS.
Background
In recent years, with the deterioration of the environment, various geological disasters frequently occur, and serious threats are caused to the life safety and property safety of people. How to deal with these geological disasters to protect people's lives and property is very important.
The Geographic Information System (GIS) is a specific spatial Information System of great importance. The system is a technical system for collecting, storing, managing, operating, analyzing, displaying and describing relevant geographic distribution data in the whole or partial earth surface (including the atmosphere) space under the support of a computer hardware and software system.
In order to realize deep analysis of geological disasters to deal with geological disasters, a common technology generally combines and analyzes a GIS and geological monitoring data, but in this way, it is difficult to plan emergency treatment in advance and accurately, so that it is difficult to minimize loss when a geological disaster occurs.
Disclosure of Invention
In order to solve the technical problems in the related art, the present disclosure provides a geological disaster emergency command data processing method and system based on a GIS.
In a first aspect of the embodiments of the present invention, a method for processing geological disaster emergency command data based on a GIS is provided, which is applied to a GIS data processing server, and includes:
acquiring monitoring dimension characteristic data of a first geological disaster monitoring area in each remote sensing satellite image data included in a group of remote sensing satellite image data, wherein the monitoring dimension characteristic data comprises geological dimension characteristic data, vegetation coverage characteristic data and rainfall dimension characteristic data of the first geological disaster monitoring area;
determining first geological disaster monitoring data from the group of remote sensing satellite image data based on monitoring dimension characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data and a change track of data characteristics of the first geological disaster monitoring area in each remote sensing satellite image data, wherein the change track of the data characteristics is used for indicating a geological change state and state change information of the first geological disaster monitoring area;
and generating corresponding emergency command planning data according to the first geological disaster monitoring data.
Optionally, the obtaining of the monitoring dimension characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data included in the group of remote sensing satellite image data includes:
acquiring geological dimension characteristic data of a first geological disaster monitoring area in each remote sensing satellite image data included in a group of remote sensing satellite image data;
acquiring vegetation coverage characteristic data of a first geological disaster monitoring area in each remote sensing satellite image data included in a group of remote sensing satellite image data;
and acquiring rainfall dimension characteristic data in each remote sensing satellite image data included in the group of remote sensing satellite image data of the first geological disaster monitoring area.
Optionally, the obtaining of the geological dimension characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data included in the group of remote sensing satellite image data includes:
aiming at any remote sensing satellite image data in the group of remote sensing satellite image data, the following operations are executed to obtain geological dimension characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data included in the group of remote sensing satellite image data:
under the condition that the first image point cloud data included in the group of remote sensing satellite image data includes the set early warning label of the first geological disaster monitoring area, acquiring gradient change data of the first geological disaster monitoring area in the first image point cloud data;
determining geological dimension characteristic data of the first geological disaster monitoring area in the first image point cloud data based on the gradient change data and a gradient updating track, wherein the gradient updating track is used for indicating whether similar label characteristics exist in the set early warning label and other early warning labels of the first geological disaster monitoring area in the first image point cloud data under the condition that the first image point cloud data comprises other early warning labels of the first geological disaster monitoring area;
and under the condition that the first image point cloud data does not comprise other early warning labels of the first geological disaster monitoring area, determining geological dimension characteristic data of the first geological disaster monitoring area in the first image point cloud data as a first monitoring category for indicating that a state to be observed exists.
Optionally, determining geological dimension feature data of the first geological disaster monitoring area in the first image point cloud data based on the gradient change data and a gradient update track of the first image point cloud data and other image point cloud data includes:
determining geological dimension characteristic data of the first geological disaster monitoring area in the first image point cloud data as a second monitoring category for indicating that the first geological disaster monitoring area is in a pre-landslide state under the condition that the first geological disaster monitoring area is determined to be located in a central area of a point cloud coverage area of the first image point cloud data based on the gradient change data;
determining geological dimension characteristic data of the first geological disaster monitoring area in the first image point cloud data as a third monitoring category for indicating that the first geological disaster monitoring area is in a pre-ground trap state under the condition that the first geological disaster monitoring area is determined to be located in an outer area of a point cloud coverage area of the first image point cloud data based on the gradient change data and similar label characteristics exist between a set early warning label corresponding to the first geological disaster monitoring area and other existing early warning labels;
determining geological dimension characteristic data of the first geological disaster monitoring area in the first image point cloud data as a first monitoring category for indicating that the first geological disaster monitoring area is in a state to be observed under the condition that the first geological disaster monitoring area is determined to be located in an outer area of a point cloud coverage area based on the gradient change data, and no other early warning label exists or no similar label characteristic exists between a set early warning label corresponding to the first geological disaster monitoring area and the other existing early warning labels;
the central area of the point cloud coverage area is a pre-divided area which is located in the area corresponding to the first image point cloud data and has a point cloud clustering track, and the outer area of the point cloud coverage area is an area which is included in the first image point cloud data and is except the central area of the point cloud coverage area.
Optionally, the acquiring vegetation coverage characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data included in the group of remote sensing satellite image data includes:
aiming at any remote sensing satellite image data in the group of remote sensing satellite image data, the following operations are executed to obtain vegetation coverage characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data included in the group of remote sensing satellite image data:
under the condition that it is determined that first image point cloud data included in the group of remote sensing satellite image data includes a vegetation observation index of the first geological disaster monitoring area, acquiring a vegetation distribution characteristic matrix of the first geological disaster monitoring area in the first image point cloud data;
determining vegetation coverage characteristic data of the first geological disaster monitoring area in the first image point cloud data based on the vegetation distribution characteristic matrix;
the vegetation distribution characteristic matrix is a characteristic matrix corresponding to a difference value of a vegetation change track index of the first geological disaster monitoring area in the first image point cloud data and a vegetation change track index of the first geological disaster monitoring area in the second image point cloud data, and the second image point cloud data is image point cloud data which is included in the group of remote sensing satellite image data and is located in the previous frame of the first image point cloud data.
Optionally, determining vegetation coverage characteristic data of the first geological disaster monitoring area in the first image point cloud data based on the vegetation distribution characteristic matrix includes:
under the condition that the weighted value of the dynamic matrix element of the vegetation distribution characteristic matrix is 0, determining that vegetation coverage characteristic data of the first geological disaster monitoring area in the first image point cloud data are first vegetation coverage characteristic data used for indicating that the first geological disaster monitoring area is in a first vegetation coverage state;
determining that vegetation coverage characteristic data of the first geological disaster monitoring area in the first image point cloud data is second vegetation coverage characteristic data used for indicating that the first geological disaster monitoring area is in a second vegetation coverage state under the condition that the weighted value of the static matrix element of the vegetation distribution characteristic matrix is 0 and the weighted value of the dynamic matrix element is less than or equal to 0;
and under the condition that the weighted value of the static matrix element of the vegetation distribution characteristic matrix is 0 and the weighted value of the dynamic matrix element is greater than 0, determining that the vegetation coverage characteristic data of the first geological disaster monitoring area in the first image point cloud data is third vegetation coverage characteristic data for indicating that the first geological disaster monitoring area is in a third vegetation coverage state.
Optionally, the obtaining rainfall dimension characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data included in the group of remote sensing satellite image data includes:
aiming at any remote sensing satellite image data in the group of remote sensing satellite image data, the following operations are executed to obtain rainfall dimension characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data included in the group of remote sensing satellite image data:
acquiring rainfall area distribution information of a first geological disaster monitoring area in first image point cloud data under the condition that the first image point cloud data included in the group of remote sensing satellite image data includes a rainfall label of the first geological disaster monitoring area;
and determining rainfall dimension characteristic data of the first geological disaster monitoring area in the first image point cloud data based on the rainfall area distribution information.
Optionally, determining first geological disaster monitoring data from the group of remote sensing satellite image data based on monitoring dimension characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data and a variation trajectory of data characteristics of the first geological disaster monitoring area in each remote sensing satellite image data includes:
determining address disaster evaluation coefficients respectively corresponding to the geological dimension characteristic data, the vegetation coverage characteristic data and the rainfall dimension characteristic data which are configured in advance;
determining a comprehensive evaluation coefficient of the first geological disaster monitoring area in each remote sensing satellite image data according to the geological dimension characteristic data and the corresponding address disaster evaluation coefficient thereof, the vegetation coverage characteristic data and the corresponding address disaster evaluation coefficient thereof, and the rainfall dimension characteristic data machine corresponding address disaster evaluation coefficient thereof in each remote sensing satellite image data of the first geological disaster monitoring area;
and determining the first geological disaster monitoring data based on the comprehensive evaluation coefficient of the first geological disaster monitoring area in each remote sensing satellite image data and the change track of the data characteristics of the first geological disaster monitoring area in each remote sensing satellite image data.
Optionally, generating corresponding emergency command planning data according to the first geological disaster monitoring data includes:
dividing the first geological disaster monitoring data according to geological disaster categories to obtain first disaster classification data and second disaster classification data; generating a first disaster occurrence rate distribution list corresponding to the first disaster classification data and a second disaster occurrence rate distribution list corresponding to the second disaster classification data, and determining a plurality of disaster node data with different disaster safety influence levels respectively included in the first disaster occurrence rate distribution list and the second disaster occurrence rate distribution list;
determining node updating information of the first disaster classification data in any disaster node data of the first disaster incidence distribution list, and determining disaster node data with the minimum disaster safety influence level in the second disaster incidence distribution list as target disaster node data; mapping the node updating information to the target disaster node data according to the data iteration track of the first geological disaster monitoring data, and obtaining disaster prediction information in the target disaster node data; generating a geological disaster association list between the first disaster classification data and the second disaster classification data based on the node update information and the disaster prediction information;
acquiring disaster trigger information from the target disaster node data by taking the disaster prediction information as reference information, mapping the disaster trigger information to disaster node data where the node update information is located according to a geological disaster transfer path corresponding to the geological disaster association list, so as to obtain disaster early warning information corresponding to the disaster trigger information from the disaster node data where the node update information is located, and determining disaster coping planning data corresponding to the disaster early warning information;
acquiring a real-time mapping path for mapping the node updating information to the target disaster node data; according to a matching coefficient between the disaster early warning information and node attribute information corresponding to a plurality of mapping nodes to be processed on the real-time mapping path, traversing disaster loss measurement data corresponding to the disaster response planning data in the second disaster occurrence rate distribution list until the disaster damage level of the disaster node data where the disaster loss measurement data is located is consistent with the disaster damage level of the disaster response planning data in the first disaster occurrence rate distribution list, stopping obtaining the disaster loss measurement data in the next disaster node data, establishing a corresponding relationship between the disaster response planning data and the disaster loss measurement data obtained last time, and determining emergency command planning data corresponding to the disaster response planning data based on the corresponding relationship.
In a second aspect of the embodiments of the present invention, a geological disaster emergency command data processing system based on a GIS is provided, including a GIS data processing server and a GIS data acquisition terminal, which are in communication with each other; wherein, the GIS data processing server is used for:
acquiring monitoring dimension characteristic data of a first geological disaster monitoring area in each remote sensing satellite image data included in a group of remote sensing satellite image data from the GIS data acquisition terminal, wherein the monitoring dimension characteristic data comprises geological dimension characteristic data, vegetation coverage characteristic data and rainfall dimension characteristic data of the first geological disaster monitoring area;
determining first geological disaster monitoring data from the group of remote sensing satellite image data based on monitoring dimension characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data and a change track of data characteristics of the first geological disaster monitoring area in each remote sensing satellite image data, wherein the change track of the data characteristics is used for indicating a geological change state and state change information of the first geological disaster monitoring area;
and generating corresponding emergency command planning data according to the first geological disaster monitoring data.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects: the method comprises the steps of firstly obtaining monitoring dimension characteristic data of a first geological disaster monitoring area in each remote sensing satellite image data included in a group of remote sensing satellite image data, secondly determining first geological disaster monitoring data from the group of remote sensing satellite image data based on the monitoring dimension characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data and the variation track of the data characteristic of the first geological disaster monitoring area in each remote sensing satellite image data, and finally generating corresponding emergency planning command data according to the first geological disaster monitoring data. Therefore, the first geological disaster monitoring data can be determined through different monitoring dimension characteristic data, so that the geological disaster change condition of the first geological disaster monitoring area is obtained, the possible types of geological disasters can be determined in advance, and the emergency command planning data can be further accurately generated. Therefore, emergency command can be carried out based on the pre-generated emergency command planning data when the geological disaster occurs, and further, the personal safety loss and the property loss are minimized when the geological disaster occurs.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a schematic illustration of an implementation environment according to the present disclosure.
Fig. 2 is a flowchart illustrating a method for processing emergency command data for geological disaster based on GIS according to an exemplary embodiment.
Fig. 3 is a schematic diagram illustrating a hardware structure of a GIS data processing server according to an exemplary embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
Referring first to fig. 1, there is shown an architectural diagram of a GIS-based geological disaster emergency command data processing system 100, which may include a GIS data processing server 200 and a GIS data collection terminal 300 in communication with each other; wherein, the GIS data processing server is used for:
acquiring monitoring dimension characteristic data of a first geological disaster monitoring area in each remote sensing satellite image data included in a group of remote sensing satellite image data from the GIS data acquisition terminal, wherein the monitoring dimension characteristic data comprises geological dimension characteristic data, vegetation coverage characteristic data and rainfall dimension characteristic data of the first geological disaster monitoring area; determining first geological disaster monitoring data from the group of remote sensing satellite image data based on monitoring dimension characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data and a change track of data characteristics of the first geological disaster monitoring area in each remote sensing satellite image data, wherein the change track of the data characteristics is used for indicating a geological change state and state change information of the first geological disaster monitoring area; and generating corresponding emergency command planning data according to the first geological disaster monitoring data.
Further, based on the same inventive concept as above, please refer to fig. 2, which shows a flow chart of a method for processing emergency command data of geological disaster based on GIS, which can be applied to the GIS data processing server 200 in fig. 1, and which may exemplarily include the contents described in the following steps S21-S23.
And step S21, acquiring monitoring dimension characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data included in the group of remote sensing satellite image data.
For example, the monitoring dimensional characteristic data includes geological dimensional characteristic data, vegetation coverage characteristic data, and rainfall dimensional characteristic data of the first geological disaster monitoring area.
Step S22, determining first geological disaster monitoring data from the group of remote sensing satellite image data based on monitoring dimension characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data and variation tracks of data characteristics of the first geological disaster monitoring area in each remote sensing satellite image data.
For example, the change trajectory of the data feature is used to indicate a geological change state and state change information of the first geological disaster monitoring area.
And step S23, generating corresponding emergency command planning data according to the first geological disaster monitoring data.
For example, the first geological disaster monitoring data is used to characterize the occurrence (occurring or not) of a geological disaster in the first geological disaster monitoring area. The emergency command planning data is used to instruct crowd evacuation and emergency handling.
It can be understood that, by executing the contents described in the above steps S21-S23, first obtaining the monitoring dimension feature data of the first geological disaster monitoring area in each remote sensing satellite image data included in the set of remote sensing satellite image data, then determining the first geological disaster monitoring data from the set of remote sensing satellite image data based on the monitoring dimension feature data of the first geological disaster monitoring area in each remote sensing satellite image data and the change trajectory of the data feature of the first geological disaster monitoring area in each remote sensing satellite image data, and finally generating the corresponding emergency command planning data according to the first geological disaster monitoring data. Therefore, the first geological disaster monitoring data can be determined through different monitoring dimension characteristic data, so that the geological disaster change condition of the first geological disaster monitoring area is obtained, the possible types of geological disasters can be determined in advance, and the emergency command planning data can be further accurately generated. Therefore, emergency command can be carried out based on the pre-generated emergency command planning data when the geological disaster occurs, and further, the personal safety loss and the property loss are minimized when the geological disaster occurs.
In one example, the acquiring of the monitoring dimension characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data included in the set of remote sensing satellite image data, which is described in step S21, includes the following steps S211 to S213.
Step S211, acquiring geological dimension characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data included in the group of remote sensing satellite image data.
Step S212, vegetation coverage characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data included in the group of remote sensing satellite image data is obtained.
Step S213, rainfall dimension characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data included in the group of remote sensing satellite image data is obtained.
Therefore, a plurality of different monitoring dimension characteristic data can be obtained, and a comprehensive data base is provided for the subsequent generation of the first geological disaster monitoring data.
Further, the acquiring of the geological dimension characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data included in the set of remote sensing satellite image data described in step S211 includes the following contents: and executing the following steps S2111 to S2113 aiming at any remote sensing satellite image data in the group of remote sensing satellite image data so as to obtain geological dimension characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data included in the group of remote sensing satellite image data.
Step S2111, acquiring gradient change data of the first geological disaster monitoring area in the first image point cloud data under the condition that the first image point cloud data included in the group of remote sensing satellite image data includes the set early warning label of the first geological disaster monitoring area.
Step S2112, determining geological dimension characteristic data of the first geological disaster monitoring area in the first image point cloud data based on the gradient change data and the slope updating track.
For example, the slope updating trajectory is used to indicate whether similar tag features exist in the set early warning tag and other early warning tags of the first geological disaster monitoring area in the first image point cloud data when the first image point cloud data includes other early warning tags of the first geological disaster monitoring area.
Step S2113, under the condition that the first image point cloud data does not include other early warning labels of the first geological disaster monitoring area, determining geological dimension characteristic data of the first geological disaster monitoring area in the first image point cloud data as a first monitoring category for indicating that a state to be observed exists.
Therefore, the geological dimension characteristic data of the first geological disaster monitoring area in the first image point cloud data can be determined according to the slope change data and the slope updating track, and therefore the geological dimension characteristic data can comprehensively and accurately reflect geological change conditions.
Further, on the basis of the steps S2111 to S2113, determining the geological dimension characteristic data of the first geological disaster monitoring area in the first image point cloud data based on the gradient change data and the gradient update track of the first image point cloud data and other image point cloud data may further include the following steps a1 to c 1.
Step a1, determining that the geological dimension characteristic data of the first geological disaster monitoring area in the first image point cloud data is a second monitoring category for indicating that the first geological disaster monitoring area is in a pre-landslide state under the condition that the first geological disaster monitoring area is determined to be located in the central area of the point cloud coverage area of the first image point cloud data based on the gradient change data.
Step b1, determining geological dimension characteristic data of the first geological disaster monitoring area in the first image point cloud data as a third monitoring category for indicating that the first geological disaster monitoring area is in a pre-ground trap state under the condition that it is determined that the first geological disaster monitoring area is located in an outer area of a point cloud coverage area of the first image point cloud data based on the gradient change data, and similar label characteristics exist between a set early warning label corresponding to the first geological disaster monitoring area and other existing early warning labels.
Step c1, determining geological dimension characteristic data of the first geological disaster monitoring area in the first image point cloud data as a first monitoring category for indicating that the first geological disaster monitoring area is in a state to be observed under the condition that the first geological disaster monitoring area is determined to be located in an outer area of the point cloud coverage area based on the gradient change data, and no other early warning label exists or no similar label characteristic exists between a set early warning label corresponding to the first geological disaster monitoring area and the other existing early warning labels.
For example, the central area of the point cloud coverage area is a pre-divided area which is located in the area corresponding to the first image point cloud data and has a point cloud clustering track, and the outer area of the point cloud coverage area is an area which is included in the first image point cloud data and is other than the central area of the point cloud coverage area.
In this way, by executing the steps a 1-a 3, the first monitoring category of the first geological disaster monitoring area in the state to be observed, the second monitoring category of the pre-landslide state and the third monitoring category of the pre-ground fault state can be determined, so that accurate and complete determination of different geological disaster categories is realized, and reliable data basis can be provided for generation of subsequent emergency command planning data.
Optionally, the acquiring vegetation coverage characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data included in the set of remote sensing satellite image data in step S212 may include: and executing the following steps S2121 and S2122 aiming at any remote sensing satellite image data in the group of remote sensing satellite image data to acquire vegetation coverage characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data included in the group of remote sensing satellite image data.
Step S2121, under the condition that it is determined that the vegetation observation index of the first geological disaster monitoring area is included in the first image point cloud data included in the group of remote sensing satellite image data, acquiring a vegetation distribution characteristic matrix of the first geological disaster monitoring area in the first image point cloud data.
Step S2122, determining vegetation coverage characteristic data of the first geological disaster monitoring area in the first image point cloud data based on the vegetation distribution characteristic matrix.
For example, the vegetation distribution feature matrix is a feature matrix corresponding to a difference between a vegetation change track index of the first geological disaster monitoring area in the first image point cloud data and a vegetation change track index of the first geological disaster monitoring area in second image point cloud data, and the second image point cloud data is image point cloud data which is included in the set of remote sensing satellite image data and is located in a frame before the first image point cloud data.
It is understood that by performing the above steps S2121 and S2122, vegetation coverage characteristic data can be determined in real time based on the vegetation distribution characteristic matrix, thereby taking into account the variation in vegetation coverage.
Further, the determining of vegetation coverage characteristic data of the first geological disaster monitoring area in the first image point cloud data based on the vegetation distribution characteristic matrix as described in step S2122 may exemplarily include the following steps a 2-c 2.
Step a2, under the condition that the weighted value of the dynamic matrix element of the vegetation distribution characteristic matrix is 0, determining that the vegetation coverage characteristic data of the first geological disaster monitoring area in the first image point cloud data is first vegetation coverage characteristic data used for indicating that the first geological disaster monitoring area is in a first vegetation coverage state.
B2, determining that the vegetation coverage characteristic data of the first geological disaster monitoring area in the first image point cloud data is the second vegetation coverage characteristic data used for indicating that the first geological disaster monitoring area is in the second vegetation coverage state under the condition that the weighted value of the static matrix element of the vegetation distribution characteristic matrix is 0 and the weighted value of the dynamic matrix element is less than or equal to 0.
And c2, determining that the vegetation coverage characteristic data of the first geological disaster monitoring area in the first image point cloud data is third vegetation coverage characteristic data for indicating that the first geological disaster monitoring area is in a third vegetation coverage state under the condition that the weighted value of the static matrix element of the vegetation distribution characteristic matrix is 0 and the weighted value of the dynamic matrix element is greater than 0.
Therefore, the dynamic matrix elements and the static matrix elements based on the vegetation distribution characteristic matrix can confirm different vegetation coverage states, so that the influence of different vegetation coverage conditions on the geology can be considered, and the first geological disaster monitoring data can be accurately and reliably determined in the later stage.
In one possible example, the acquiring of the rainfall dimension characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data included in the set of remote sensing satellite image data described in step S213 includes: aiming at any remote sensing satellite image data in the group of remote sensing satellite image data, the following operations are executed to obtain rainfall dimension characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data included in the group of remote sensing satellite image data: acquiring rainfall area distribution information of a first geological disaster monitoring area in first image point cloud data under the condition that the first image point cloud data included in the group of remote sensing satellite image data includes a rainfall label of the first geological disaster monitoring area; and determining rainfall dimension characteristic data of the first geological disaster monitoring area in the first image point cloud data based on the rainfall area distribution information. Therefore, the rainfall dimension characteristic data can be accurately divided according to different rainfall areas, and the data accuracy and the characteristic identification degree of the rainfall dimension characteristic data are improved.
In one possible implementation, the determining, by step S22, first geological disaster monitoring data from the set of remote sensing satellite image data based on the monitoring dimension characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data and the variation trajectory of the data characteristic of the first geological disaster monitoring area in each remote sensing satellite image data may further include the following steps S221 to S223.
Step S221, determining address disaster evaluation coefficients corresponding to the geological dimension characteristic data, the vegetation coverage characteristic data and the rainfall dimension characteristic data which are configured in advance.
Step S222, determining a comprehensive evaluation coefficient of the first geological disaster monitoring area in each remote sensing satellite image data according to the geological dimension characteristic data and the corresponding address disaster evaluation coefficient of the first geological disaster monitoring area in each remote sensing satellite image data, the vegetation coverage characteristic data and the corresponding address disaster evaluation coefficient of the vegetation coverage characteristic data, and the corresponding address disaster evaluation coefficient of the rainfall dimension characteristic data machine.
Step S223, determining the first geological disaster monitoring data based on the comprehensive evaluation coefficient of the first geological disaster monitoring area in each remote sensing satellite image data and the change track of the data characteristics of the first geological disaster monitoring area in each remote sensing satellite image data.
Therefore, the first geological disaster monitoring data under various geological changes and geological environment conditions can be determined based on the steps S221 to S223, so that a timely and reliable data basis is provided for the subsequent generation of emergency command planning data.
Based on the above, the generation of the corresponding emergency command planning data according to the first geological disaster monitoring data in step S23 may further include the following steps S231-S234.
Step S231, dividing the first geological disaster monitoring data according to geological disaster categories to obtain first disaster classification data and second disaster classification data; generating a first disaster occurrence rate distribution list corresponding to the first disaster classification data and a second disaster occurrence rate distribution list corresponding to the second disaster classification data, and determining a plurality of disaster node data with different disaster safety influence levels respectively included in the first disaster occurrence rate distribution list and the second disaster occurrence rate distribution list.
Step S232, determining node update information of the first disaster classification data in any disaster node data of the first disaster occurrence rate distribution list, and determining the disaster node data having the smallest disaster safety influence level in the second disaster occurrence rate distribution list as target disaster node data; mapping the node updating information to the target disaster node data according to the data iteration track of the first geological disaster monitoring data, and obtaining disaster prediction information in the target disaster node data; and generating a geological disaster association list between the first disaster classification data and the second disaster classification data based on the node updating information and the disaster prediction information.
Step S233, obtaining disaster trigger information from the target disaster node data using the disaster prediction information as reference information, mapping the disaster trigger information to disaster node data in which the node update information is located according to a geological disaster transfer path corresponding to the geological disaster association list, obtaining disaster early warning information corresponding to the disaster trigger information from the disaster node data in which the node update information is located, and determining disaster response planning data corresponding to the disaster early warning information.
Step S234, acquiring a real-time mapping path of the node updating information mapped to the target disaster node data; according to a matching coefficient between the disaster early warning information and node attribute information corresponding to a plurality of mapping nodes to be processed on the real-time mapping path, traversing disaster loss measurement data corresponding to the disaster response planning data in the second disaster occurrence rate distribution list until the disaster damage level of the disaster node data where the disaster loss measurement data is located is consistent with the disaster damage level of the disaster response planning data in the first disaster occurrence rate distribution list, stopping obtaining the disaster loss measurement data in the next disaster node data, establishing a corresponding relationship between the disaster response planning data and the disaster loss measurement data obtained last time, and determining emergency command planning data corresponding to the disaster response planning data based on the corresponding relationship.
In this way, by performing the above steps S231 to S234, it is possible to take into account the disaster damage measurement data and the disaster response planning data when generating the emergency command planning data, thereby ensuring that the generated emergency command planning data can minimize the personal safety and property damage when used at a later stage, and thus ensuring the feasibility of emergency command.
On the basis of the above, please refer to fig. 3 in combination, a hardware structure diagram of a GIS data processing server 200 is also provided, and the GIS data processing server 200 may include a processor 210 and a memory 220 which are in communication with each other. Wherein the processor 210 realizes the method shown in fig. 2 by calling the computer program from the memory 220 and running.
In summary, according to the method and the system, monitoring dimension characteristic data of a first geological disaster monitoring area in each remote sensing satellite image data included in a set of remote sensing satellite image data is firstly acquired, first geological disaster monitoring data is determined from the set of remote sensing satellite image data based on the monitoring dimension characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data and a change track of data characteristics of the first geological disaster monitoring area in each remote sensing satellite image data, and corresponding emergency command planning data is finally generated according to the first geological disaster monitoring data. Therefore, the first geological disaster monitoring data can be determined through different monitoring dimension characteristic data, so that the geological disaster change condition of the first geological disaster monitoring area is obtained, the possible types of geological disasters can be determined in advance, and the emergency command planning data can be further accurately generated. Therefore, emergency command can be carried out based on the pre-generated emergency command planning data when the geological disaster occurs, and further, the personal safety loss and the property loss are minimized when the geological disaster occurs.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (10)
1. A geological disaster emergency command data processing method based on GIS is characterized in that the method is applied to a GIS data processing server and comprises the following steps:
acquiring monitoring dimension characteristic data of a first geological disaster monitoring area in each remote sensing satellite image data included in a group of remote sensing satellite image data, wherein the monitoring dimension characteristic data comprises geological dimension characteristic data, vegetation coverage characteristic data and rainfall dimension characteristic data of the first geological disaster monitoring area;
determining first geological disaster monitoring data from the group of remote sensing satellite image data based on monitoring dimension characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data and a change track of data characteristics of the first geological disaster monitoring area in each remote sensing satellite image data, wherein the change track of the data characteristics is used for indicating a geological change state and state change information of the first geological disaster monitoring area;
and generating corresponding emergency command planning data according to the first geological disaster monitoring data.
2. The method of claim 1, wherein obtaining monitoring dimension feature data of the first geological disaster monitoring area in each remote sensing satellite image data included in the set of remote sensing satellite image data comprises:
acquiring geological dimension characteristic data of a first geological disaster monitoring area in each remote sensing satellite image data included in a group of remote sensing satellite image data;
acquiring vegetation coverage characteristic data of a first geological disaster monitoring area in each remote sensing satellite image data included in a group of remote sensing satellite image data;
and acquiring rainfall dimension characteristic data in each remote sensing satellite image data included in the group of remote sensing satellite image data of the first geological disaster monitoring area.
3. The method of claim 2, wherein obtaining geological dimensional feature data of the first geological disaster monitoring area in each remote sensing satellite image data included in the set of remote sensing satellite image data comprises:
aiming at any remote sensing satellite image data in the group of remote sensing satellite image data, the following operations are executed to obtain geological dimension characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data included in the group of remote sensing satellite image data:
under the condition that the first image point cloud data included in the group of remote sensing satellite image data includes the set early warning label of the first geological disaster monitoring area, acquiring gradient change data of the first geological disaster monitoring area in the first image point cloud data;
determining geological dimension characteristic data of the first geological disaster monitoring area in the first image point cloud data based on the gradient change data and a gradient updating track, wherein the gradient updating track is used for indicating whether similar label characteristics exist in the set early warning label and other early warning labels of the first geological disaster monitoring area in the first image point cloud data under the condition that the first image point cloud data comprises other early warning labels of the first geological disaster monitoring area;
and under the condition that the first image point cloud data does not comprise other early warning labels of the first geological disaster monitoring area, determining geological dimension characteristic data of the first geological disaster monitoring area in the first image point cloud data as a first monitoring category for indicating that a state to be observed exists.
4. The method of claim 3, wherein determining geological dimensional feature data of the first geological disaster monitoring area in the first image point cloud data based on the slope change data and a slope update trajectory of the first image point cloud data with other image point cloud data comprises:
determining geological dimension characteristic data of the first geological disaster monitoring area in the first image point cloud data as a second monitoring category for indicating that the first geological disaster monitoring area is in a pre-landslide state under the condition that the first geological disaster monitoring area is determined to be located in a central area of a point cloud coverage area of the first image point cloud data based on the gradient change data;
determining geological dimension characteristic data of the first geological disaster monitoring area in the first image point cloud data as a third monitoring category for indicating that the first geological disaster monitoring area is in a pre-ground trap state under the condition that the first geological disaster monitoring area is determined to be located in an outer area of a point cloud coverage area of the first image point cloud data based on the gradient change data and similar label characteristics exist between a set early warning label corresponding to the first geological disaster monitoring area and other existing early warning labels;
determining geological dimension characteristic data of the first geological disaster monitoring area in the first image point cloud data as a first monitoring category for indicating that the first geological disaster monitoring area is in a state to be observed under the condition that the first geological disaster monitoring area is determined to be located in an outer area of a point cloud coverage area based on the gradient change data, and no other early warning label exists or no similar label characteristic exists between a set early warning label corresponding to the first geological disaster monitoring area and the other existing early warning labels;
the central area of the point cloud coverage area is a pre-divided area which is located in the area corresponding to the first image point cloud data and has a point cloud clustering track, and the outer area of the point cloud coverage area is an area which is included in the first image point cloud data and is except the central area of the point cloud coverage area.
5. The method of claim 2, wherein obtaining vegetation coverage characteristic data for the first geological disaster monitoring area in each remote sensing satellite image data included in the set of remote sensing satellite image data comprises:
aiming at any remote sensing satellite image data in the group of remote sensing satellite image data, the following operations are executed to obtain vegetation coverage characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data included in the group of remote sensing satellite image data:
under the condition that it is determined that first image point cloud data included in the group of remote sensing satellite image data includes a vegetation observation index of the first geological disaster monitoring area, acquiring a vegetation distribution characteristic matrix of the first geological disaster monitoring area in the first image point cloud data;
determining vegetation coverage characteristic data of the first geological disaster monitoring area in the first image point cloud data based on the vegetation distribution characteristic matrix;
the vegetation distribution characteristic matrix is a characteristic matrix corresponding to a difference value of a vegetation change track index of the first geological disaster monitoring area in the first image point cloud data and a vegetation change track index of the first geological disaster monitoring area in the second image point cloud data, and the second image point cloud data is image point cloud data which is included in the group of remote sensing satellite image data and is located in the previous frame of the first image point cloud data.
6. The method of claim 5, wherein determining vegetation coverage characteristic data of the first geological disaster monitoring area in the first image point cloud data based on the vegetation distribution characteristic matrix comprises:
under the condition that the weighted value of the dynamic matrix element of the vegetation distribution characteristic matrix is 0, determining that vegetation coverage characteristic data of the first geological disaster monitoring area in the first image point cloud data are first vegetation coverage characteristic data used for indicating that the first geological disaster monitoring area is in a first vegetation coverage state;
determining that vegetation coverage characteristic data of the first geological disaster monitoring area in the first image point cloud data is second vegetation coverage characteristic data used for indicating that the first geological disaster monitoring area is in a second vegetation coverage state under the condition that the weighted value of the static matrix element of the vegetation distribution characteristic matrix is 0 and the weighted value of the dynamic matrix element is less than or equal to 0;
and under the condition that the weighted value of the static matrix element of the vegetation distribution characteristic matrix is 0 and the weighted value of the dynamic matrix element is greater than 0, determining that the vegetation coverage characteristic data of the first geological disaster monitoring area in the first image point cloud data is third vegetation coverage characteristic data for indicating that the first geological disaster monitoring area is in a third vegetation coverage state.
7. The method of claim 2, wherein obtaining rainfall dimension feature data for the first geological disaster monitoring area in each remote sensing satellite image data included in the set of remote sensing satellite image data comprises:
aiming at any remote sensing satellite image data in the group of remote sensing satellite image data, the following operations are executed to obtain rainfall dimension characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data included in the group of remote sensing satellite image data:
acquiring rainfall area distribution information of a first geological disaster monitoring area in first image point cloud data under the condition that the first image point cloud data included in the group of remote sensing satellite image data includes a rainfall label of the first geological disaster monitoring area;
and determining rainfall dimension characteristic data of the first geological disaster monitoring area in the first image point cloud data based on the rainfall area distribution information.
8. The method of claim 1, wherein determining first geological disaster monitoring data from the set of remote sensing satellite image data based on the monitoring dimension characteristic data of the first geological disaster monitoring area in the remote sensing satellite image data and the variation trajectory of the data characteristic of the first geological disaster monitoring area in the remote sensing satellite image data comprises:
determining address disaster evaluation coefficients respectively corresponding to the geological dimension characteristic data, the vegetation coverage characteristic data and the rainfall dimension characteristic data which are configured in advance;
determining a comprehensive evaluation coefficient of the first geological disaster monitoring area in each remote sensing satellite image data according to the geological dimension characteristic data and the corresponding address disaster evaluation coefficient thereof, the vegetation coverage characteristic data and the corresponding address disaster evaluation coefficient thereof, and the rainfall dimension characteristic data machine corresponding address disaster evaluation coefficient thereof in each remote sensing satellite image data of the first geological disaster monitoring area;
and determining the first geological disaster monitoring data based on the comprehensive evaluation coefficient of the first geological disaster monitoring area in each remote sensing satellite image data and the change track of the data characteristics of the first geological disaster monitoring area in each remote sensing satellite image data.
9. The method of claim 1, wherein generating corresponding contingency conductor planning data from the first geological disaster monitoring data comprises:
dividing the first geological disaster monitoring data according to geological disaster categories to obtain first disaster classification data and second disaster classification data; generating a first disaster occurrence rate distribution list corresponding to the first disaster classification data and a second disaster occurrence rate distribution list corresponding to the second disaster classification data, and determining a plurality of disaster node data with different disaster safety influence levels respectively included in the first disaster occurrence rate distribution list and the second disaster occurrence rate distribution list;
determining node updating information of the first disaster classification data in any disaster node data of the first disaster incidence distribution list, and determining disaster node data with the minimum disaster safety influence level in the second disaster incidence distribution list as target disaster node data; mapping the node updating information to the target disaster node data according to the data iteration track of the first geological disaster monitoring data, and obtaining disaster prediction information in the target disaster node data; generating a geological disaster association list between the first disaster classification data and the second disaster classification data based on the node update information and the disaster prediction information;
acquiring disaster trigger information from the target disaster node data by taking the disaster prediction information as reference information, mapping the disaster trigger information to disaster node data where the node update information is located according to a geological disaster transfer path corresponding to the geological disaster association list, so as to obtain disaster early warning information corresponding to the disaster trigger information from the disaster node data where the node update information is located, and determining disaster coping planning data corresponding to the disaster early warning information;
acquiring a real-time mapping path for mapping the node updating information to the target disaster node data; according to a matching coefficient between the disaster early warning information and node attribute information corresponding to a plurality of mapping nodes to be processed on the real-time mapping path, traversing disaster loss measurement data corresponding to the disaster response planning data in the second disaster occurrence rate distribution list until the disaster damage level of the disaster node data where the disaster loss measurement data is located is consistent with the disaster damage level of the disaster response planning data in the first disaster occurrence rate distribution list, stopping obtaining the disaster loss measurement data in the next disaster node data, establishing a corresponding relationship between the disaster response planning data and the disaster loss measurement data obtained last time, and determining emergency command planning data corresponding to the disaster response planning data based on the corresponding relationship.
10. A geological disaster emergency command data processing system based on GIS is characterized by comprising a GIS data processing server and a GIS data acquisition terminal which are communicated with each other; wherein, the GIS data processing server is used for:
acquiring monitoring dimension characteristic data of a first geological disaster monitoring area in each remote sensing satellite image data included in a group of remote sensing satellite image data from the GIS data acquisition terminal, wherein the monitoring dimension characteristic data comprises geological dimension characteristic data, vegetation coverage characteristic data and rainfall dimension characteristic data of the first geological disaster monitoring area;
determining first geological disaster monitoring data from the group of remote sensing satellite image data based on monitoring dimension characteristic data of the first geological disaster monitoring area in each remote sensing satellite image data and a change track of data characteristics of the first geological disaster monitoring area in each remote sensing satellite image data, wherein the change track of the data characteristics is used for indicating a geological change state and state change information of the first geological disaster monitoring area;
and generating corresponding emergency command planning data according to the first geological disaster monitoring data.
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