Geological disaster monitoring and responding method and system

Abstract

The invention relates to the technical field of artificial intelligence, in particular to a geological disaster monitoring and responding method and system.The geological disaster monitoring and responding method comprises the steps that geological data are collected from multiple data sources, multivariate analysis is conducted on the collected address data, and disaster influence factors are screened out; the disaster influence factors are graded and quantified through mechanical learning, danger grading is carried out on a target area through a K-means algorithm to generate a danger grading map, then the danger grading map of the target area is analyzed and positioned through a GIS system, and a geological disaster risk zoning map marked with coordinate information is obtained. And the obtained address disaster risk linear partition map is sent to the command center through the communication system for display and response, so that the command center can clearly know the risk level of each position of the target area and the specific position of each risk level, thereby improving the rescue efficiency and avoiding economic loss to a certain extent.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster monitoring, and in particular to a geological disaster monitoring and response method and system. Background Art

[0002] Sudden geological disasters such as landslides, collapses, mud-rock flows and ground subsidence pose a huge threat to economic development and the safety of people's lives and property. In order to reduce the damage, economic losses and casualties caused by geological disasters, various geological disaster prevention measures have been taken in various places, such as carpet inspections of geological disaster potential points, manual inspections of key disaster areas, and the construction of a group monitoring and prevention system, which have achieved good results. However, manual inspections and group monitoring and prevention work are mainly based on manual inspections and manual alarms, requiring a large number of inspection and monitoring personnel, and this method is inefficient and cannot meet the needs of modern disaster prevention work. Therefore, it is urgent to realize a geological disaster monitoring and early warning method and system that can dynamically display, monitor in real time and issue early warnings. Summary of the invention

[0003] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a geological disaster monitoring and response method and system, which can realize geological disaster monitoring and early warning with dynamic display, real-time monitoring and timely early warning.

[0004] In order to implement the above technical solution, in a first aspect, the present invention provides a geological disaster monitoring and response method, comprising the following steps: Step 1: Collect geological data of the target area from multiple data sources; Step 2: Conduct multivariate analysis on geological data to screen out disaster influencing factors; Step 3: Classify and quantify the selected disaster impact factors through machine learning models; Step 4: Based on the graded and quantified influencing factors, the target area is divided into danger levels using the K-means algorithm to generate a danger level division map for the target area; Step 5: Analyze and locate the hazard level classification map of the target area through the GIS system to obtain the geological disaster risk zoning map; Step 6: Send the obtained address disaster risk linear zoning map to the command center through the communication system for display and response.

[0005] Furthermore, the step 1 comprises: Identify and remove outliers in the collected historical geological data through box plots to ensure data quality and consistency; Classifying the geological data after removing outliers to obtain multiple categories of geological data, wherein each category of geological data includes multiple influencing factors; Get the time information of each impact factor.

[0006] Furthermore, the step 2 includes: A. Choose two impact factors at random and record them as impact factor L1 and impact factor L2; B. Calculate the Pearson correlation coefficient of the selected influencing factors L1 and L2; The calculation formula of Pearson correlation coefficient is as follows: ; Where R is the Pearson correlation coefficient, dimensionless; , are the observed values ​​of the two variables, dimensionless; is the mean of two variables, dimensionless; k is the number of samples, pieces; C. Save the calculated Pearson correlation coefficient value; D. Repeat steps AC until all influencing factors have been calculated for Pearson correlation coefficients; E. Compare the saved Pearson correlation coefficient with the impact factor of the preset threshold, and screen out the impact factors greater than the preset threshold as disaster impact factors.

[0007] Furthermore, the danger level classification of the target area by using the improved K-means algorithm includes the following steps: (1) First, select the number of clusters, give the starting cluster center matrix and possibly give the particle V initial; (2) Obtain the membership of all particles, determine the cluster center again, and find the fitness value and extreme value of the particles; (3) Determine the global extreme value and its position based on the extreme values ​​of all particles; (4) Re-determine the particle's velocity and require that it does not exceed Vmax, and then re-determine the particle's position through the position relationship; (5) If the iteration conditions are not met, go to (2) again; if they are met, the position of the optimal particle is obtained.

[0008] Furthermore, the step five comprises: Divide the target area into security levels. For example, the target area can be divided into high-risk area, medium-risk area and low-risk area; high-risk area is indicated by red, medium-risk area is indicated by blue, and low-risk area is indicated by green; Based on the target area hazard level division map, the data of each divided area is analyzed to obtain the risk scale information and risk probability information of each divided area; Based on the risk scale information and risk probability information of each area, the coordinate information of each area is marked, and a geological disaster risk zoning map with coordinate information is generated.

[0009] In a second aspect, the present invention provides a geological disaster monitoring and response system, comprising: Multiple data sources are used to collect geological data of the target area from different aspects through different methods; A data collection module, used to collect geological data collected from multiple data sources; The geological data analysis module is used to perform multivariate analysis on the collected geological data to screen out disaster influencing factors; The quantification module is used to classify and quantify the selected disaster impact factors through a machine learning model; A danger level classification module is used to classify the target area into danger levels based on the classified and quantified influencing factors by using the K-means algorithm to generate a danger level classification map for the target area; The analysis and positioning module is used to analyze and locate the hazard level classification map of the target area through the GIS system to obtain the geological disaster risk zoning map; Communication system, used to send geological hazard risk zoning maps to the command center; The command center is used to display the geological hazard risk zoning map and respond.

[0010] In a third aspect, the present invention provides a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the geological disaster monitoring and response method described above.

[0011] The beneficial effects of the present invention are: (1) The present invention collects geological data from multiple data sources and performs multivariate analysis on the collected address data to screen out disaster influencing factors, classifies and quantifies the disaster influencing factors through machine learning, and divides the target area into risk levels through the K-means algorithm to generate a risk level division map, then uses a GIS system to analyze and locate the target area risk level division map to obtain a geological disaster risk zoning map marked with coordinate information, and sends the obtained address disaster risk linear zoning map to the command center through a communication system for display and response, so that the command center can clearly understand the risk level at each location in the target area and the specific location of each risk level, thereby improving rescue efficiency and avoiding economic losses to a certain extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0013] Figure 1 The present invention is a flow chart of a geological disaster monitoring and response method. DETAILED DESCRIPTION

[0014] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0015] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further explanation of the present invention. Unless otherwise specified, each technical and scientific term used in this embodiment has the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0016] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0017] In the present invention, terms such as "upper", "lower", "left", "right", "front", "back", "vertical", "horizontal", "side", "bottom", etc. indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings. They are relational words determined only for the convenience of describing the structural relationships of the various parts or elements of the present invention, and do not specifically refer to any part or element in the present invention and should not be understood as limitations on the present invention.

[0018] In the present invention, terms such as "fixed connection", "connected", "connection", etc. should be understood in a broad sense, indicating that it can be fixedly connected, integrally connected or detachably connected; it can be directly connected or indirectly connected through an intermediate medium. Relevant scientific research or technical personnel in this field can determine the specific meanings of the above terms in the present invention according to specific circumstances, and they should not be understood as limiting the present invention.

[0019] Embodiment 1: like Figure 1 As shown, this embodiment provides a geological disaster monitoring and response method, including the following steps: S1: Collect geological data of the target area from multiple data sources.

[0020] S1-1: Identify and remove outliers in the collected geological data by means of box plots to ensure data quality and consistency.

[0021] S1-2: Classify the geological data after removing outliers to obtain multiple categories of geological data, wherein each category of geological data includes multiple influencing factors.

[0022] Among them, the categories of geological data include: topographic data, meteorological data, and hydrological data, etc.

[0023] Topography and landforms include: vegetation cover, stratum lithology, soil conditions, curvature, elevation, slope and aspect, etc.; Meteorological data include: temperature, humidity, wind speed, wind direction and atmospheric pressure and other influencing factors; hydrological data include precipitation, evaporation, transpiration and runoff and other influencing factors.

[0024] S1-3: Obtain the time information of each influencing factor.

[0025] In this embodiment, by obtaining the time information of each influencing factor, when constructing the hazard level prediction model, comparison can be made based on data at the same time, which helps to improve the accuracy of the prediction model.

[0026] S2: Perform multivariate analysis on geological data and screen out influencing factors greater than the preset threshold by calculating the Pearson correlation coefficient, and mark them as disaster influencing factors.

[0027] By calculating the Pearson correlation coefficient, the influencing factors greater than the preset threshold are screened out and marked as disaster influencing factors, including the following steps: S2-1: arbitrarily select two impact factors, which are recorded as impact factor L1 and impact factor L2; S2-2: Calculate the Pearson correlation coefficient between the selected influencing factors L1 and L2; The calculation formula of Pearson correlation coefficient is as follows: ; Where R is the Pearson correlation coefficient, dimensionless; , are the observed values ​​of the two variables, dimensionless; is the mean of the two variables, dimensionless; k is the number of samples, pieces.

[0028] In this embodiment, represents the observed value of the impact factor L1, is the observed value of the impact factor L2; They are the mean of the impact factor L1 and the mean of the impact factor L2 respectively.

[0029] S2-3: Save the calculated Pearson correlation coefficient.

[0030] S2-4: Repeat steps S2-1 to S2-3 until all influencing factors are subjected to Pearson correlation coefficient calculation.

[0031] For example, the Pearson correlation coefficients among the influencing factors: curvature, aspect, slope, precipitation, elevation, stratum lithology, landform type and soil category are calculated, and the storage form is shown in Table 1.

[0032] Table 1

[0033] S2-5: Compare the saved Pearson correlation coefficient with the preset threshold, and screen out the influencing factors that are greater than the preset threshold as disaster influencing factors.

[0034] S3: Classify and quantify the screened impact factors through machine learning models.

[0035] S4: Based on the graded and quantified influencing factors, the target area is divided into danger levels using the K-means algorithm to generate a danger level division map of the target area.

[0036] The step of dividing the target area into dangerous levels by using the K-means algorithm comprises the following steps: (1) First, select the number of clusters, give the starting cluster center matrix and possibly give the particle V initial.

[0037] (2) Obtain the membership of all particles, determine the cluster center again, and calculate the fitness value and extreme value of the particles.

[0038] (3) Based on the extreme values ​​of all particles, determine the global extreme value and its position. (4) Re-determine the particle's velocity and require that it does not exceed Vmax, and then re-determine the particle's position through the position relationship.

[0039] (5) If the iteration conditions are not met, go to (2) again to execute; if they are met, the position of the optimal particle is obtained.

[0040] S5: Analyze and locate the hazard level classification map of the target area through the GIS system to obtain a geological disaster risk zoning map.

[0041] Specifically, the steps include: S5-1: Divide the target area into safety levels. For example, the target area can be divided into high-risk area, medium-risk area and low-risk area; high-risk area is represented by red, medium-risk area is represented by blue, and low-risk area is represented by green.

[0042] S5-2: Analyze the data of each divided area based on the hazard level division map of the target area to obtain the risk scale information and risk probability information of each divided area; S5-3: Based on the risk scale information and risk probability information of each area, mark the coordinate information of each area through the GIS system and generate a geological hazard risk zoning map with coordinate information.

[0043] S6: Send the obtained linear zoning map of geological hazard risk to the command center through the communication system for display and warning.

[0044] Embodiment 2: This embodiment provides a geological hazard monitoring and response system, including: Multiple data sources, used to collect geological data of the target area from different aspects through different methods; A data collection module, used to collect the geological data collected by multiple data sources; A geological data analysis module, used to perform multivariate analysis on the collected geological data to screen out disaster impact factors; A quantization module, used to classify and quantify the screened disaster impact factors through a machine learning model; A hazard level division module, used to divide the hazard level of the target area through the K-means algorithm based on the classified and quantified impact factors to generate a hazard level division map of the target area; An analysis and positioning module, used to analyze and position the hazard level division map of the target area through the GIS system to obtain a geological hazard risk zoning map; A communication system, used to send the geological hazard risk zoning map to the command center; A command center, used to display the geological hazard risk zoning map and make a response.

[0045] Embodiment 3: This embodiment provides a computer-readable storage medium. The computer-readable storage medium includes a stored program, wherein when the program runs, it controls the device where the computer-readable storage medium is located to execute the geological hazard monitoring and response method described in Embodiment 1.

[0046] For the same and similar parts between the various embodiments in this specification, reference can be made to each other. In particular, for the terminal embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and for the relevant parts, reference can be made to the description in the method embodiments.

[0047] In several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can It can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or units, which can be electrical, mechanical or other forms.

[0048] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0049] In addition, it should be noted that the flowchart in the accompanying drawings shows the method of the embodiment of the present disclosure. In the corresponding description in the flowchart or block diagram in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in an order different from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be performed substantially in parallel, or sometimes in the opposite order, which may depend on the functions involved. Each block in the block diagram and / or flow chart, and the combination of blocks in the block diagram and / or flow chart, can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0050] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A geological disaster monitoring and response method, characterized in that: The following steps are involved: Step 1: Collect geological data of the target area from multiple data sources; Step 2: Conduct multivariate analysis on geological data to screen out disaster influencing factors; Step 3: Classify and quantify the selected disaster impact factors through machine learning models; Step 4: Based on the graded and quantified influencing factors, the target area is divided into danger levels using the K-means algorithm to generate a danger level division map for the target area; Step 5: Analyze and locate the hazard level classification map of the target area through the GIS system to obtain the geological disaster risk zoning map; Step 6: Send the obtained address disaster risk linear zoning map to the command center through the communication system for display and response.

2. The geological disaster monitoring and response method according to claim 1, characterized in that: The step one comprises: Identify and remove outliers in the collected historical geological data through box plots to ensure data quality and consistency; Classifying the geological data after removing outliers to obtain multiple categories of geological data, wherein each category of geological data includes multiple influencing factors; Get the time information of each impact factor.

3. The geological disaster monitoring and response method according to claim 1, characterized in that: The second step comprises: A. Choose two impact factors at random and record them as impact factor L1 and impact factor L2; B. Calculate the Pearson correlation coefficient of the selected influencing factors L1 and L2; The calculation formula of Pearson correlation coefficient is as follows: Where R is the Pearson correlation coefficient, dimensionless; , are the observed values ​​of the two variables, dimensionless; is the mean of two variables, dimensionless; k is the number of samples, pieces; C. Save the calculated Pearson correlation coefficient value; D. Repeat steps AC until all influencing factors have been calculated for Pearson correlation coefficients; E. Compare the saved Pearson correlation coefficient with the impact factor of the preset threshold, and screen out the impact factors greater than the preset threshold as disaster impact factors.

4. The geological disaster monitoring and response method according to claim 1, characterized in that: The method of dividing the target area into dangerous levels by improving the K-means algorithm includes the following steps: (1) First, select the number of clusters, give the starting cluster center matrix and possibly give the particle V initial; (2) Obtain the membership of all particles, determine the cluster center again, and find the fitness value and extreme value of the particles; (3) Determine the global extreme value and its position based on the extreme values ​​of all particles; (4) Re-determine the particle's velocity and require that it does not exceed Vmax, and then re-determine the particle's position through the position relationship; (5) If the iteration conditions are not met, go to (2) again; if they are met, the position of the optimal particle is obtained.

5. The geological disaster monitoring and response method according to claim 1, characterized in that: The step five comprises: Divide the target area into security levels. For example, the target area can be divided into high-risk area, medium-risk area and low-risk area; high-risk area is represented by red, medium-risk area is represented by blue, and low-risk area is represented by green; Based on the target area hazard level division map, the data of each divided area is analyzed to obtain the risk scale information and risk probability information of each divided area; Based on the risk scale information and risk probability information of each area, the coordinate information of each area is marked, and a geological disaster risk zoning map with coordinate information is generated.

6. A geological disaster monitoring and response system, characterized in that: include: Multiple data sources are used to collect geological data of the target area from different aspects through different methods; A data collection module, used to collect geological data collected from multiple data sources; The geological data analysis module is used to perform multivariate analysis on the collected geological data to screen out disaster influencing factors; The quantification module is used to classify and quantify the selected disaster impact factors through a machine learning model; A danger level classification module is used to classify the target area into danger levels based on the classified and quantified influencing factors by using the K-means algorithm to generate a danger level classification map for the target area; The analysis and positioning module is used to analyze and locate the hazard level classification map of the target area through the GIS system to obtain the geological disaster risk zoning map; Communication system, used to send geological hazard risk zoning maps to the command center; The command center is used to display the geological hazard risk zoning map and respond.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the geological disaster monitoring and response method described in any one of claims 1 to 5.

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