CN114118576A - Regional geological disaster trend prediction method and system - Google Patents
Regional geological disaster trend prediction method and system Download PDFInfo
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Abstract
The invention provides a regional geological disaster trend prediction method and system, and relates to the field of data analysis. A regional geological disaster trend prediction method comprises the following steps: acquiring geological disaster parameter information of a target area and historical geological disaster parameter information of the target area; preprocessing geological disaster parameter information of a target area and historical geological disaster parameter information of the target area; training a geological disaster trend prediction model through the preprocessed parameter information; and inputting each sample point in the target area into the geological disaster trend prediction model to obtain a geological disaster trend prediction result of the target area. The accuracy of the prediction model can be guaranteed by obtaining a training set constructed by a large number of samples through the acquired historical data. In addition, the invention also provides a regional geological disaster trend prediction system, which comprises: the device comprises an acquisition module, a preprocessing module, a model training module and a prediction module.
Description
Technical Field
The invention relates to the field of data analysis, in particular to a regional geological disaster trend prediction method and system.
Background
The geological disaster has the characteristics of wide influence range, strong destructiveness and the like, and the geological landslide is used as one of the forms of the geological disaster, thereby bringing great threat to the nature and the life and property safety of people. Due to the burst property of the geological landslide, people evacuation and property protection are difficult to carry out in time when the landslide occurs, so that early prediction and identification before the occurrence of the geological landslide are very necessary, more evacuation time can be strived for people before the occurrence of the landslide through accurate early prediction and identification, and further the loss of lives and properties is reduced.
The traditional geological disaster early warning method mainly adopts a method of monitoring data of a geological disaster high-incidence section in real time by arranging sensors and immediately giving an alarm if the monitored data reaches a preset threshold value.
However, the conventional geological disaster warning method has the following disadvantages:
1. the monitoring mode is single: a method of firstly setting a fixed threshold, then monitoring data and finally resetting the fixed threshold based on a monitored data feedback result is adopted all the time, and the monitoring means is too single;
2. early warning lag: if the alarm threshold value is set to be too high, disasters often come temporarily, and enough time is not provided for withdrawing the disaster-stricken personnel and rescuing the disasters;
3. the accuracy is low: if the alarm threshold is set to be too low, the early warning usually has larger deviation, and unnecessary manpower and material resources are wasted;
4. environmental factors are not considered: different geographical environments and different periods often correspond to different early warning thresholds, so accurate early warning cannot be performed more times.
Therefore, how to avoid the problems that the existing geological disaster early warning method is low in accuracy, poor in real-time performance and too single in monitoring means is still a problem to be solved urgently by technical personnel in the field.
Disclosure of Invention
The invention aims to provide a regional geological disaster trend prediction method, which can ensure the accuracy of a prediction model by obtaining a training set constructed by a large number of samples through acquired historical data.
Another object of the present invention is to provide a regional geological disaster trend prediction system, which can operate a regional geological disaster trend prediction method.
The embodiment of the invention is realized by the following steps:
in a first aspect, an embodiment of the present application provides a regional geological disaster trend prediction method, which includes acquiring geological disaster parameter information of a target region and historical geological disaster parameter information of the target region; preprocessing geological disaster parameter information of a target area and historical geological disaster parameter information of the target area; training a geological disaster trend prediction model through the preprocessed parameter information; and inputting each sample point in the target area into the geological disaster trend prediction model to obtain a geological disaster trend prediction result of the target area.
In some embodiments of the present invention, the acquiring geological disaster parameter information of the target region and historical geological disaster parameter information of the target region includes: and acquiring geological disaster parameters of the target area in a time period preset before the current time node as historical geological disaster parameter information of the target area.
In some embodiments of the present invention, the above further includes: and acquiring geological rock group information, geological environment information, topographic and geomorphic information and geographical position information of the target area as geological disaster parameter information of the target area.
In some embodiments of the present invention, the preprocessing the geological disaster parameter information of the target region and the historical geological disaster parameter information of the target region includes: and performing analog-to-digital conversion on the acquired geological disaster parameter information of the target area and the historical geological disaster parameter information of the target area to obtain a characteristic mean value of the characteristic value of the parameter information.
In some embodiments of the present invention, the above further includes: and performing zero equalization preprocessing on all the parameter information characteristic values according to the characteristic average values of the parameter information characteristic values.
In some embodiments of the present invention, the training the geological disaster trend prediction model through the preprocessed parameter information includes: inputting the preprocessed parameter information into a pre-trained initial prediction model, and determining a prediction label corresponding to the current input information according to an output result of the geological disaster trend prediction model, wherein the geological disaster trend prediction model is obtained by training based on preprocessed parameter information samples.
In some embodiments of the present invention, the inputting each sample point in the target area into the geological disaster trend prediction model to obtain the geological disaster trend prediction result of the target area includes: and generating a warning when the geological disaster trend prediction result of the target area is within the preset geological disaster threshold range.
In a second aspect, an embodiment of the present application provides a regional geological disaster trend prediction system, which includes an obtaining module, configured to obtain geological disaster parameter information of a target region and historical geological disaster parameter information of the target region;
the preprocessing module is used for preprocessing the geological disaster parameter information of the target area and the historical geological disaster parameter information of the target area;
the model training module is used for training the geological disaster trend prediction model through the preprocessed parameter information;
and the prediction module is used for inputting each sample point in the target area into the geological disaster trend prediction model to obtain a geological disaster trend prediction result of the target area.
In some embodiments of the invention, the above includes: at least one memory for storing computer instructions; at least one processor in communication with the memory, wherein the at least one processor, when executing the computer instructions, causes the system to: the device comprises an acquisition module, a preprocessing module, a model training module and a prediction module.
In a third aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements a method as any one of regional geological disaster trend prediction methods.
Compared with the prior art, the embodiment of the invention has at least the following advantages or beneficial effects:
the density cluster map of the target area can be verified according to the actual geological disaster occurrence condition of the target area, so that the first geological disaster prediction model is verified, the accuracy, authenticity and reliability of the first geological disaster prediction model are guaranteed, and the accuracy, authenticity and reliability of geological disaster prediction of the monitored area by adopting the first geological disaster prediction model are further guaranteed. Comparing geological disaster trend information of the target area with preset threshold value information to obtain a comparison result; and then carrying out disaster early warning on the target area according to the comparison result to generate texture disaster early warning information. Therefore, disaster early warning can be carried out through the obtained geological disaster trend information of the target area, and loss is reduced. The accuracy of the prediction model can be guaranteed by obtaining a training set constructed by a large number of samples through the acquired historical data, and the data acquired in real time is input into the prediction model and can output the prediction result in real time to guarantee the real-time performance of prediction.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed 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 invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a schematic diagram illustrating steps of a regional geological disaster trend prediction method according to an embodiment of the present invention;
fig. 2 is a detailed step diagram of a regional geological disaster trend prediction method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a regional geological disaster trend prediction system according to an embodiment of the present invention;
fig. 4 is an electronic device according to an embodiment of the present invention.
Icon: 10-an acquisition module; 20-a pre-processing module; 30-a model training module; 40-a prediction module; 101-a memory; 102-a processor; 103-communication interface.
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 some embodiments of the present application, but not all 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 given herein without making any creative effort, shall fall within the protection scope of the present application.
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.
It is to be noted that the term "comprises," "comprising," or any other variation thereof is intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the individual features of the embodiments can be combined with one another without conflict.
Example 1
Referring to fig. 1, fig. 1 is a schematic diagram illustrating steps of a regional geological disaster trend prediction method according to an embodiment of the present invention, which is shown as follows:
step S100, acquiring geological disaster parameter information of a target area and historical geological disaster parameter information of the target area;
in some embodiments, historical data of geological disaster occurrence in the target area is collected, and basic parameters of the geological disaster are obtained, wherein the basic parameters include a geological disaster type parameter, a grade parameter, a spatial distribution parameter and a geological environment parameter before the disaster occurrence. The geological parameter information of the target area can be directly input or obtained from other systems. The geological parameter information of the target area comprises geological rock group information, topographic and geomorphic information, historical geological disaster information, geographic information and the like of the target area.
Step S110, preprocessing geological disaster parameter information of a target area and historical geological disaster parameter information of the target area;
in some embodiments, the acquired geological disaster parameter information of the target area and the historical geological disaster parameter information of the target area are subjected to analog-to-digital conversion to obtain a feature mean value of the feature value of the parameter information. And performing zero equalization preprocessing on all the parameter information characteristic values according to the characteristic average values of the parameter information characteristic values.
Step S120, training a geological disaster trend prediction model through the preprocessed parameter information;
in some embodiments, the current input information of the user may be input into the prediction model in training for each training sample in turn, and the parameters of the prediction model may be adjusted according to the comparison between the corresponding target label and the output result of the prediction model. In this way, when the prediction model is initially trained, a small number of standard samples are used, and further, the initial prediction model can be adjusted by fully utilizing user selection data in an actual scene. The selected data can be automatically marked under the guidance of an initial prediction model obtained by training, a large number of training samples are generated, the manual workload is reduced, and the efficiency is improved. Meanwhile, the large amount of training samples are derived from the actual scene, so that the method is more beneficial to training a prediction model which is more adaptive to the actual scene and more effective.
And S130, inputting each sample point in the target area into the geological disaster trend prediction model to obtain a geological disaster trend prediction result of the target area.
In some embodiments, the reference region information is obtained by obtaining the geological parameter information of the target region and then screening in a preset reference terrain library according to the geological parameter information of the target region; the reference region information has a certain correlation with the target region, and then historical geological disaster information in the target region geological parameter information and the historical geological disaster information in the reference region information are extracted and trained to obtain a predicted geological disaster trend model; the historical geological disaster information in the reference region information is also used as sample information, so that the number of samples trained in the geological disaster trend forecasting model is increased, the obtained geological disaster trend forecasting model is more accurate, and finally, the geological parameter information of the target region is input into the geological disaster trend forecasting model to obtain the geological disaster trend information of the target region.
Example 2
Referring to fig. 2, fig. 2 is a detailed step diagram of a regional geological disaster trend prediction method according to an embodiment of the present invention, which is shown as follows:
and step S200, acquiring geological disaster parameters of a target area in a time period preset before the current time node as historical geological disaster parameter information of the target area.
Step S210, obtaining geological rock group information, geological environment information, topographic and geomorphic information and geographical position information of the target area as geological disaster parameter information of the target area.
Step S220, performing analog-to-digital conversion on the acquired geological disaster parameter information of the target area and the historical geological disaster parameter information of the target area to obtain a characteristic mean value of the characteristic value of the parameter information.
And step S230, performing zero-equalization preprocessing on all the parameter information characteristic values according to the characteristic average values of the parameter information characteristic values.
And S240, inputting the preprocessed parameter information into a pre-trained initial prediction model, and determining a prediction label corresponding to the current input information according to an output result of the geological disaster trend prediction model, wherein the geological disaster trend prediction model is obtained by training based on the preprocessed parameter information sample.
And step S250, generating a warning when the geological disaster trend prediction result of the target area is within the preset geological disaster threshold range.
In some embodiments, if the geological disaster trend prediction result of the target area is within the preset geological disaster threshold range, it can be predicted that a geological disaster may occur in the monitored area. Therefore, the target area can be determined according to the displacement data of the measuring points, and then the correlation analysis is carried out on the natural environment parameters and the abnormal points of the target area by adopting a Pearson correlation analysis method, so that a multivariate data correlation analysis model is established, and the formation rule of geological disasters (landslides) is further excavated and perfected.
Example 3
Referring to fig. 3, fig. 3 is a schematic diagram of a regional geological disaster trend prediction system according to an embodiment of the present invention, which is shown as follows:
the acquisition module 10 is configured to acquire geological disaster parameter information of a target area and historical geological disaster parameter information of the target area;
the preprocessing module 20 is configured to preprocess the geological disaster parameter information of the target area and the historical geological disaster parameter information of the target area;
the model training module 30 is used for training the geological disaster trend prediction model through the preprocessed parameter information;
and the prediction module 40 is used for inputting each sample point in the target area into the geological disaster trend prediction model to obtain a geological disaster trend prediction result of the target area.
As shown in fig. 4, an embodiment of the present application provides an electronic device, which includes a memory 101 for storing one or more programs; a processor 102. The one or more programs, when executed by the processor 102, implement the method of any of the first aspects as described above.
Also included is a communication interface 103, and the memory 101, processor 102 and communication interface 103 are electrically connected to each other, directly or indirectly, to enable transfer or interaction of data. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 101 may be used to store software programs and modules, and the processor 102 executes the software programs and modules stored in the memory 101 to thereby execute various functional applications and data processing. The communication interface 103 may be used for communicating signaling or data with other node devices.
The Memory 101 may be, but is not limited to, a Random Access Memory 101 (RAM), a Read Only Memory 101 (ROM), a Programmable Read Only Memory 101 (PROM), an Erasable Read Only Memory 101 (EPROM), an electrically Erasable Read Only Memory 101 (EEPROM), and the like.
The processor 102 may be an integrated circuit chip having signal processing capabilities. The Processor 102 may be a general-purpose Processor 102, including a Central Processing Unit (CPU) 102, a Network Processor 102 (NP), and the like; but may also be a Digital Signal processor 102 (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware components.
In the embodiments provided in the present application, it should be understood that the disclosed method and system and method can be implemented in other ways. The method and system embodiments described above are merely illustrative, for example, the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
In another aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, which, when executed by the processor 102, implements the method according to any one of the first aspect described above. The functions, if implemented in the form of software functional modules 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 101 (ROM), a Random Access Memory 101 (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In summary, the regional geological disaster trend prediction method and system provided by the embodiment of the application can verify the density cluster map of the target region according to the actual geological disaster occurrence condition of the target region, so that the verification of the first geological disaster prediction model is realized, the accuracy, authenticity and reliability of the first geological disaster prediction model are ensured, and the accuracy, authenticity and reliability of geological disaster prediction of the monitored region by adopting the first geological disaster prediction model are further ensured. Comparing geological disaster trend information of the target area with preset threshold value information to obtain a comparison result; and then carrying out disaster early warning on the target area according to the comparison result to generate texture disaster early warning information. Therefore, disaster early warning can be carried out through the obtained geological disaster trend information of the target area, and loss is reduced. The accuracy of the prediction model can be guaranteed by obtaining a training set constructed by a large number of samples through the acquired historical data, and the data acquired in real time is input into the prediction model and can output the prediction result in real time to guarantee the real-time performance of prediction.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (10)
1. A regional geological disaster trend prediction method is characterized by comprising the following steps:
acquiring geological disaster parameter information of a target area and historical geological disaster parameter information of the target area;
preprocessing geological disaster parameter information of a target area and historical geological disaster parameter information of the target area;
training a geological disaster trend prediction model through the preprocessed parameter information;
and inputting each sample point in the target area into the geological disaster trend prediction model to obtain a geological disaster trend prediction result of the target area.
2. The method for predicting regional geological disaster trend as claimed in claim 1, wherein the acquiring geological disaster parameter information of the target region and historical geological disaster parameter information of the target region comprises:
and acquiring geological disaster parameters of the target area in a time period preset before the current time node as historical geological disaster parameter information of the target area.
3. The method for predicting regional geological disaster trends as claimed in claim 2, further comprising:
and acquiring geological rock group information, geological environment information, topographic and geomorphic information and geographical position information of the target area as geological disaster parameter information of the target area.
4. The method for predicting regional geological disaster trend as claimed in claim 1, wherein the preprocessing the geological disaster parameter information of the target region and the historical geological disaster parameter information of the target region comprises:
and performing analog-to-digital conversion on the acquired geological disaster parameter information of the target area and the historical geological disaster parameter information of the target area to obtain a characteristic mean value of the characteristic value of the parameter information.
5. The method of predicting regional geologic hazard trend of claim 4, further comprising:
and performing zero equalization preprocessing on all the parameter information characteristic values according to the characteristic average values of the parameter information characteristic values.
6. The method for predicting regional geological disaster tendency as claimed in claim 1, wherein the training of the geological disaster tendency prediction model by the preprocessed parameter information comprises:
inputting the preprocessed parameter information into a pre-trained initial prediction model, and determining a prediction label corresponding to the current input information according to an output result of the geological disaster trend prediction model, wherein the geological disaster trend prediction model is obtained by training based on preprocessed parameter information samples.
7. The method for predicting the geological disaster tendency of the area according to claim 1, wherein the step of inputting each sample point in the target area into the geological disaster tendency prediction model to obtain the result of predicting the geological disaster tendency of the target area comprises:
and generating a warning when the geological disaster trend prediction result of the target area is within the preset geological disaster threshold range.
8. A regional geological disaster trend prediction system, comprising:
the acquisition module is used for acquiring geological disaster parameter information of a target area and historical geological disaster parameter information of the target area;
the preprocessing module is used for preprocessing the geological disaster parameter information of the target area and the historical geological disaster parameter information of the target area;
the model training module is used for training the geological disaster trend prediction model through the preprocessed parameter information;
and the prediction module is used for inputting each sample point in the target area into the geological disaster trend prediction model to obtain a geological disaster trend prediction result of the target area.
9. A regional geologic hazard trend prediction system as defined in claim 8 and comprising:
at least one memory for storing computer instructions;
at least one processor in communication with the memory, wherein the at least one processor, when executing the computer instructions, causes the system to perform: the device comprises an acquisition module, a preprocessing module, a model training module and a prediction module.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116050824A (en) * | 2022-12-14 | 2023-05-02 | 应急管理部国家自然灾害防治研究院 | Situation awareness and emergency evaluation method and system for composite disasters |
| CN116524699A (en) * | 2023-06-26 | 2023-08-01 | 四川省华地建设工程有限责任公司 | Geological disaster early warning method and system based on regional analysis |
| CN116976837A (en) * | 2023-09-25 | 2023-10-31 | 中国铁塔股份有限公司吉林省分公司 | Ground disaster situation analysis method and system applied to data sharing |
| CN116050824B (en) * | 2022-12-14 | 2024-04-30 | 应急管理部国家自然灾害防治研究院 | Situation awareness and emergency evaluation method and system for composite disasters |
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2021
- 2021-11-25 CN CN202111415737.6A patent/CN114118576A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116050824A (en) * | 2022-12-14 | 2023-05-02 | 应急管理部国家自然灾害防治研究院 | Situation awareness and emergency evaluation method and system for composite disasters |
| CN116050824B (en) * | 2022-12-14 | 2024-04-30 | 应急管理部国家自然灾害防治研究院 | Situation awareness and emergency evaluation method and system for composite disasters |
| CN116524699A (en) * | 2023-06-26 | 2023-08-01 | 四川省华地建设工程有限责任公司 | Geological disaster early warning method and system based on regional analysis |
| CN116524699B (en) * | 2023-06-26 | 2023-09-05 | 四川省华地建设工程有限责任公司 | Geological disaster early warning method and system based on regional analysis |
| CN116976837A (en) * | 2023-09-25 | 2023-10-31 | 中国铁塔股份有限公司吉林省分公司 | Ground disaster situation analysis method and system applied to data sharing |
| CN116976837B (en) * | 2023-09-25 | 2024-01-30 | 中国铁塔股份有限公司吉林省分公司 | Ground disaster situation analysis method and system applied to data sharing |
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