Geological disaster monitoring and early warning method and system based on artificial intelligence

Abstract

The invention discloses a geological disaster monitoring and early warning method and system based on artificial intelligence, and relates to the technical field of geological data processing, and the method comprises the steps: building a geological disaster-geological feature matching relation, thereby accurately adapting to all related geological features corresponding to each geological disaster, and improving the accuracy of geological disaster monitoring and early warning. And a reliable basis is provided for subsequent monitoring and early warning of geological disasters. The geological disaster early warning method comprises the steps of setting a geological feature early warning value of each type of geological disasters under each geological disaster hidden danger point, integrating a plurality of geological disasters with an intersection or intersection relationship to form a plurality of geological disaster hidden danger point position conditions, evaluating the risk level of the geological disaster hidden danger point, and ensuring the timeliness of geological disaster early warning. The geological feature early warning value of the geological disaster is adjusted according to the influence chain of the geological disaster, the mutual influence between the geological disasters is considered, and the early warning value advance of the affected geological disaster is set, so that the early warning value is adjusted. And the geological disaster monitoring precision and the early warning advancement and timeliness are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of geological data processing, and in particular to a geological disaster monitoring and early warning method and system based on artificial intelligence. Background Art

[0002] With the intensification of global climate change and human activities, geological disasters occur frequently, posing a serious threat to people's lives and property. Traditional geological disaster monitoring methods have many limitations, such as limited monitoring range and insufficient early warning accuracy. The rapid development of artificial intelligence technology has provided new solutions for geological disaster monitoring and early warning. By integrating advanced technologies such as big data, the Internet of Things, and cloud computing, artificial intelligence can monitor and analyze massive environmental monitoring data in real time, such as rainfall, soil moisture, and seismic waveforms.

[0003] In the existing technology, geological disasters are often monitored based on the threshold of geological characteristics. This threshold is often fixed and does not take into account the mutual impact between different geological disasters, resulting in poor accuracy in geological disaster monitoring, low advance and timeliness of early warning, and failure to effectively guarantee safety protection against geological disasters.

[0004] Therefore, how to improve the accuracy of geological disaster monitoring, the advanceness and timeliness of early warning is a technical problem that needs to be solved. Summary of the invention

[0005] The purpose of the present invention is to solve the problem that the threshold of geological characteristics is fixed in the prior art, and the mutual influence between different geological disasters is not taken into account, resulting in poor accuracy of geological disaster monitoring, low advance and timeliness of early warning, and a geological disaster monitoring and early warning method based on artificial intelligence is proposed, which includes: Collect geological information in the target area, extract geological features of all categories of geological information, match geological feature categories with geological disaster categories, and establish a matching relationship between geological disasters and geological features; Construct a map of the target area, mark the geological hazards one by one on the map of the target area through the matching relationship between geological hazards and geological features, and determine the geological hazard potential points of different risk levels; Set the geological characteristic warning value of each type of geological disaster at each geological disaster risk point, and build a geological disaster impact chain for each geological disaster risk point; The geological characteristic warning value of geological disasters is adjusted according to the geological disaster impact chain to achieve monitoring and early warning of geological disasters.

[0006] In some embodiments of the present application, the geological feature category is matched with the geological disaster category to establish a matching relationship between geological disasters and geological features, including: Obtain the cause information of each geological disaster category, decompose the cause information to form multiple conditions, connect the multiple conditions to form a geological disaster process, use the conditions as nodes in the process, confirm the geological feature category under each node, and thus determine a type of geological feature; Record the geological features other than the first category of geological features as other geological features, calculate the linear correlation between the first category of geological features and other geological features, and screen out the second category of geological features; According to the first type of geological features, the function of the preset radial basis function kernel and other geological features, the nonlinear correlation is calculated to screen out the second type of geological features; Each geological hazard category is matched with its corresponding Class I geological feature and Class II geological feature, so as to establish a matching relationship between geological hazard and geological feature.

[0007] In some embodiments of the present application, geological hazards are marked one by one on a map of a target area through a matching relationship between geological hazards and geological features, including: Collect historical records of geological disasters, and mark them on the map of the target area according to the historical records of geological disasters. Match the first-class geological features with the second-class geological features on the map of the target area through the matching relationship between geological disasters and geological features, and calculate the matching degree of geological features; Conduct an overlay analysis of the historical records of geological disasters and the matching degree of geological features on the map of the target area to determine the type of geological disasters and their specific locations.

[0008] In some embodiments of the present application, determining geological disaster potential points of different risk levels includes: According to the specific location of geological disasters, geological disaster areas are formed, and the impact range and hazard degree of each geological disaster area are evaluated. The intersection between geological disaster areas or the intersection between the impact ranges of geological disaster areas are recorded as intersection relations, and the degree of intersection between different geological disasters in the intersection relations is calculated. Each intersection relationship corresponds to a geological disaster potential point; The risk index of the geological hazard potential point is calculated according to the hazard degree of each geological hazard area and the degree of intersection between different geological hazards in the intersection relationship, so as to determine the risk level of the geological hazard potential point; ; in, For the Risk indicators of geological disaster risk points, For the The number of geological disasters at each geological disaster risk point, For the The risk weight of each geological hazard is For the The geological disaster potential point The degree of hazard of geological disasters, , are the weights of the intersection degree and the impact degree of the geological disaster with the greatest impact, for The maximum value in is a preset constant.

[0009] In some embodiments of the present application, the geological characteristic warning value of each type of geological disaster at each geological disaster risk point is set, including: Collect the historical numerical range of each type of geological feature in the historical records of geological disasters, intercept the historical numerical range of each type of geological feature when the geological disaster occurs, determine the average historical numerical range of each type of geological feature, and determine the first warning value of each type of geological feature based on the average historical numerical range; The average value and standard deviation of the geological characteristics are calculated according to the historical numerical range of each type of geological characteristics when the geological disaster occurs, and a multiple is mapped through the risk level of the geological disaster potential point. The second warning value of each type of geological characteristics is determined by combining the average value, standard deviation and multiple of the geological characteristics; The geological characteristic warning value of each type of geological disaster is set according to the first warning value and the second warning value of each type of geological characteristic.

[0010] In some embodiments of the present application, a geological disaster impact chain is constructed for each geological disaster risk point, including: Analyze the causal relationship between multiple geological disasters under each geological hazard risk point, take each geological disaster as an event node in the geological disaster impact chain, connect the event nodes with line segments according to the causal relationship, and assign different lengths to the line segments according to the degree of influence between the event nodes, so as to construct the geological disaster impact chain.

[0011] In some embodiments of the present application, before adjusting the geological characteristic warning value of the geological disaster according to the geological disaster impact chain, the method further includes: Define two state spaces of each geological disaster, the non-occurrence state and the occurrence state, according to the geological characteristics, and calculate the state transition probability of the geological disaster from the non-occurrence state to the occurrence state at the current time according to the preset period; Based on the state transition probability, determine whether there is an impending geological disaster. If there is an impending geological disaster, adjust the geological characteristic warning value of the geological disaster according to the geological disaster impact chain; Otherwise, the geological characteristic warning value of geological disasters will not be adjusted.

[0012] In some embodiments of the present application, the geological characteristic warning value of the geological disaster is adjusted according to the geological disaster impact chain, including: Determine the geological hazards affected by the impending geological hazards and the extent of their impact in the geological hazard impact chain, and determine the advance amount of the geological characteristic warning value of the geological hazards affected by the impending geological hazards; ; in, For the The first The advance amount of geological characteristic warning value of geological disasters, The affected The state transition probability of a geological disaster, The first geological disaster that is about to occur will affect the The initial advance value of the geological characteristic warning value of geological hazards, For the The first The conversion coefficient for the impact of geological disasters is adjusted. For the The first The impact of geological disasters; The geological characteristic warning value can be adjusted by the advance amount of the geological characteristic warning value, so as to carry out timely monitoring and warning of geological disasters.

[0013] Correspondingly, the present application also provides a geological disaster monitoring and early warning system based on artificial intelligence, including: The matching module is used to collect geological information in the target area, extract geological features of all categories of geological information, match the geological feature categories with the geological disaster categories, and establish a matching relationship between geological disasters and geological features; The risk module is used to construct a map of the target area, mark the geological hazards one by one on the map of the target area through the matching relationship between geological hazards and geological features, and determine the geological hazard potential points of different risk levels; A construction module is used to set the geological characteristic warning value of each type of geological disaster at each geological disaster risk point, and to construct a geological disaster impact chain for each geological disaster risk point; The monitoring module is used to adjust the geological characteristic warning value of geological disasters according to the geological disaster impact chain, so as to realize the monitoring and early warning of geological disasters.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. Establish a matching relationship between geological hazards and geological features, so as to accurately adapt all relevant geological features corresponding to each geological hazard, and provide a reliable basis for the monitoring and early warning of subsequent geological hazards. Determine geological hazard risk points of different risk levels, set the geological feature warning value of each type of geological hazard under each geological hazard risk point, integrate multiple geological hazards with cross-or intersection relationships, form the location of multiple geological hazard risk points, and evaluate the risk level of geological hazard risk points, so as to facilitate the setting of the warning value of subsequent geological features and ensure the timeliness of geological hazard warning.

[0015] 2. Construct a geological disaster impact chain, adjust the geological characteristics warning value of geological disasters according to the geological disaster impact chain, consider the mutual influence between geological disasters, set the advance amount of the warning value of the affected geological disasters, and adjust the warning value accordingly. This improves the accuracy of geological disaster monitoring, the advance and timeliness of warning, ensures that geological disasters can be monitored in a timely, effective and accurate manner, and facilitates the early setting of safety measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of the process of a geological disaster monitoring and early warning method based on artificial intelligence proposed by the present invention; Figure 2 This is a structural schematic diagram of a geological disaster monitoring and early warning system based on artificial intelligence proposed by the present invention. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0018] Reference Figure 1 , a geological disaster monitoring and early warning method based on artificial intelligence, comprising the following steps: Step S101, collect geological information in the target area, extract geological features of all categories of geological information, match geological feature categories with geological disaster categories, and establish a matching relationship between geological disasters and geological features.

[0019] In this embodiment, it is necessary to collect geological information in the target area from multiple channels, including geological survey reports, remote sensing image data, historical geological disaster records, meteorological data, etc. These data are the basis for subsequent analysis. Using GIS (geographic information system) technology, remote sensing technology and geological knowledge, the collated geological information is characterized to extract geological features such as stratum lithology, geological structure, topography, hydrogeological conditions, etc. General classification of features: The extracted geological features are classified according to their properties, such as physical features, chemical features, mechanical features, etc., and then classified into detailed categories for subsequent matching with geological disaster categories.

[0020] In some embodiments of the present application, the geological feature category is matched with the geological disaster category to establish a matching relationship between geological disasters and geological features, including: Obtain the cause information of each geological disaster category, decompose the cause information to form multiple conditions, connect the multiple conditions to form a geological disaster process, use the conditions as nodes in the process, confirm the geological feature category under each node, and thus determine a type of geological feature; Record the geological features other than the first category of geological features as other geological features, calculate the linear correlation between the first category of geological features and other geological features, and screen out the second category of geological features; According to the first type of geological features, the function of the preset radial basis function kernel and other geological features, the nonlinear correlation is calculated to screen out the second type of geological features; Each geological hazard category is matched with its corresponding Class I geological feature and Class II geological feature, so as to establish a matching relationship between geological hazard and geological feature.

[0021] In this embodiment, the causal mechanism of each type of geological disaster is studied in depth, including the physical, chemical and mechanical processes of its occurrence. The geological features directly related to the causes of geological disasters are identified, such as topography, stratum lithology, geological structure, hydrogeological conditions, etc., so as to extract a class of geological features. According to the results of the analysis of the causal mechanism, a dynamic process is constructed, and each node in the process represents a key geological feature or condition. The nodes are connected in the logical order of the occurrence of geological disasters to form a complete process. For example, for landslide disasters, a class of geological features may include steep terrain, loose rock and soil layers, abundant groundwater, etc. Taking landslide disasters as an example, the dynamic process may include: increase in terrain slope → loose rock and soil layers → groundwater infiltration → landslide occurs.

[0022] It can be understood that the second-class geological features are geological features with a high correlation with the first-class geological features, and can also indirectly reflect the geological disaster situation. The linear correlation between the first-class geological features and other features is calculated using statistical methods (such as the Pearson correlation coefficient). In addition to linear correlation, nonlinear correlation also needs to be considered. Nonlinear correlation indicates that there is a more complex relationship between two variables, which cannot be described by a straight line. The kernel function is a technique used in support vector machines to process nonlinear data. It can map the original data to a high-dimensional space, so that in this high-dimensional space, the data that was originally linearly inseparable in the low-dimensional space becomes linearly separable. Radial basis function (RBF) kernel: also known as the Gaussian kernel, is one of the most commonly used kernel functions in SVM, which can handle complex nonlinear relationships and thus calculate nonlinear correlation.

[0023] Step S102, constructing a map of the target area, marking the geological disasters one by one on the map of the target area through the matching relationship between geological disasters and geological features, and determining geological disaster potential points of different risk levels.

[0024] In this embodiment, GIS technology is used to construct a digital map of the target area, including a topographic map, a geological map, etc. According to the matching relationship between geological hazards and geological features, the geological hazards are marked one by one on the map, indicating the type, location, scale and other information of the disaster. Geological hazards with similar locations or mutual influence are integrated into a geological hazard potential point (area), and the risk level is comprehensively described by the intersection of geological hazards and the degree of hazard.

[0025] In some embodiments of the present application, geological hazards are marked one by one on a map of a target area through a matching relationship between geological hazards and geological features, including: Collect historical records of geological disasters, and mark them on the map of the target area according to the historical records of geological disasters. Match the first-class geological features with the second-class geological features on the map of the target area through the matching relationship between geological disasters and geological features, and calculate the matching degree of geological features; Conduct an overlay analysis of the historical records of geological disasters and the matching degree of geological features on the map of the target area to determine the type of geological disasters and their specific locations.

[0026] In this embodiment, historical geological disaster records are collected, including information such as disaster type (such as landslide, earthquake, mudslide, etc.), occurrence time, location, scale, impact range, loss, etc. Match the first and second geological features, and calculate the matching degree of the geological features (the matching degree is calculated by the category matching of the first and second geological features on the map). According to the type and mechanism of geological disasters, determine which geological features are critical and need to be used for matching. For example, for landslide disasters, you may need to pay attention to terrain slope, rock and soil type, groundwater conditions, etc.; for earthquake disasters, you may focus on crustal movement, fault distribution, etc. The geological disaster records and geological feature matching results are superimposed and analyzed to find out the areas where historical geological disasters are prone to occur and the areas where the geological features match, and determine the areas where geological disasters may occur.

[0027] In some embodiments of the present application, determining geological disaster potential points of different risk levels includes: According to the specific location of geological disasters, geological disaster areas are formed, and the impact range and hazard degree of each geological disaster area are evaluated. The intersection between geological disaster areas or the intersection between the impact ranges of geological disaster areas are recorded as intersection relations, and the degree of intersection between different geological disasters in the intersection relations is calculated. Each intersection relationship corresponds to a geological disaster potential point; The risk index of the geological hazard potential point is calculated according to the hazard degree of each geological hazard area and the degree of intersection between different geological hazards in the intersection relationship, so as to determine the risk level of the geological hazard potential point; ; in, For the Risk indicators of geological disaster risk points, For the The number of geological disasters at each geological disaster risk point, For the The risk weight of each geological hazard is For the The geological disaster potential point The degree of hazard of geological disasters, , are the weights of the intersection degree and the impact degree of the geological disaster with the greatest impact, for The maximum value in is a preset constant.

[0028] In this embodiment, each geological disaster area is delineated according to the location of the geological disaster. The hazard level of each area is assessed based on factors such as the type, scale, historical frequency of occurrence, and potential losses of the geological disaster, and the possible impact range of each geological disaster area is assessed using technical means such as the Geographic Information System (GIS). The intersection relationship indicates that the two geological disasters are located close to each other or have mutual influence. The degree of intersection is the degree of intersection of geological disaster areas or the degree of intersection of influence ranges (regional intersection area). A comprehensive value determined by combining all the intersection degrees is the degree of intersection. The geological hazard risk point is essentially an area in which there are multiple different geological disasters with an intersection relationship.

[0029] Understandably, It represents the correction of the sum of the hazards of multiple geological hazards by combining the intersection degree and the geological hazard with the greatest impact. In order to balance the size of the correction function, the risk index intervals of different geological hazard risk points correspond to different risk levels of geological hazard risk points.

[0030] Step S103, setting the geological characteristic warning value of each type of geological disaster at each geological disaster risk point, and constructing a geological disaster impact chain for each geological disaster risk point.

[0031] In this embodiment, the warning value is a threshold value of the geological characteristic. When the value of the geological characteristic is close to the threshold value, it does not mean that a geological disaster has occurred, but that a geological disaster is about to occur and is in a dangerous state.

[0032] In some embodiments of the present application, the geological characteristic warning value of each type of geological disaster at each geological disaster risk point is set, including: Collect the historical numerical range of each type of geological feature in the historical records of geological disasters, intercept the historical numerical range of each type of geological feature when the geological disaster occurs, determine the average historical numerical range of each type of geological feature, and determine the first warning value of each type of geological feature based on the average historical numerical range; The average value and standard deviation of the geological characteristics are calculated according to the historical numerical range of each type of geological characteristics when the geological disaster occurs, and a multiple is mapped through the risk level of the geological disaster potential point. The second warning value of each type of geological characteristics is determined by combining the average value, standard deviation and multiple of the geological characteristics; The geological characteristic warning value of each type of geological disaster is set according to the first warning value and the second warning value of each type of geological characteristic.

[0033] In this embodiment, based on the historical numerical range of each type of geological feature when a geological disaster occurs, an average historical numerical range is determined to determine the first warning value of the geological feature. Different risk levels are mapped with different multiples, and a warning threshold is preliminarily set according to the mean and standard deviation of the geological feature value. For example, the mean plus a certain multiple of the standard deviation (such as 1.5 times or 2 times) can be used as the warning threshold. The geological feature warning value (weighted average) of each type of geological disaster is set according to the first warning value and the second warning value of each type of geological feature.

[0034] In some embodiments of the present application, a geological disaster impact chain is constructed for each geological disaster risk point, including: Analyze the causal relationship between multiple geological disasters under each geological hazard risk point, take each geological disaster as an event node in the geological disaster impact chain, connect the event nodes with line segments according to the causal relationship, and assign different lengths to the line segments according to the degree of influence between the event nodes, so as to construct the geological disaster impact chain.

[0035] In this embodiment, the relationship between different types of geological disasters under the same geological disaster risk point is analyzed to identify possible geological disaster chains. For example, landslides may trigger mudslides, and earthquakes may trigger landslides, collapses, etc. It is clear that all types of geological disasters that may be involved under each geological disaster risk point, such as landslides, mudslides, earthquakes, collapses, etc., are identified. Each type of disaster is regarded as an independent event node and recorded in detail. Each event node is characterized, including its occurrence conditions, triggering factors, historical occurrence frequency, impact range, etc. This information will help to analyze the causal relationship and impact degree between nodes in the subsequent analysis. Through historical data, expert experience and field investigation, the causal relationship between each event node is analyzed. Determine which disasters may trigger other disasters, as well as the triggering mechanism and sequence between them. According to the causal relationship analysis, the event nodes with causal relationships are connected with line segments. The direction of the line segment indicates the direction of the impact, that is, from a disaster node to the next disaster node that it may trigger. Quantify the impact degree between each event node, and assign different lengths to the line segments connecting the event nodes according to the quantification results of the impact degree. The greater the impact, the longer the line segment is, to intuitively show the relative impact intensity between different disasters.

[0036] In some embodiments of the present application, before adjusting the geological characteristic warning value of the geological disaster according to the geological disaster impact chain, the method further includes: Define two state spaces of each geological disaster, the non-occurrence state and the occurrence state, according to the geological characteristics, and calculate the state transition probability of the geological disaster from the non-occurrence state to the occurrence state at the current time according to the preset period; Based on the state transition probability, determine whether there is an impending geological disaster. If there is an impending geological disaster, adjust the geological characteristic warning value of the geological disaster according to the geological disaster impact chain; Otherwise, the geological characteristic warning value of geological disasters will not be adjusted.

[0037] In this embodiment, a state is defined for each geological disaster, including two states of "not occurring" and "occurring". A state space is formed, in which each state represents a specific situation of a geological disaster. The probability of each geological disaster transferring from the "not occurring" state to the "occurring" state is calculated. These probabilities constitute a transition probability matrix, and each element in the matrix represents the probability of transferring from one state to another. The probability of each geological disaster transferring to the occurrence state in the current state can be calculated by the steady-state distribution or time evolution formula of the Markov chain. Based on the state transition probability, it is judged whether there is an impending geological disaster. When there is an impending geological disaster, it means that the geological disaster may occur immediately. At this time, the warning value of other geological disasters affected by this geological disaster is adjusted so that the geological disaster situation can be monitored in time.

[0038] Step S104, adjusting the geological characteristic warning value of the geological disaster according to the geological disaster impact chain, so as to realize the monitoring and early warning of the geological disaster.

[0039] In this embodiment, the advance amount of the geological characteristic warning value here is the comprehensive advance amount of the geological characteristic warning value of this type of geological disaster, and the comprehensive advance amount is specifically allocated according to the matching relationship between the geological characteristics and the geological disaster.

[0040] In some embodiments of the present application, the geological characteristic warning value of the geological disaster is adjusted according to the geological disaster impact chain, including: Determine the geological hazards affected by the impending geological hazards and the extent of their impact in the geological hazard impact chain, and determine the advance amount of the geological characteristic warning value of the geological hazards affected by the impending geological hazards; ; in, For the The first The advance amount of geological characteristic warning value of geological disasters, The affected The state transition probability of a geological disaster, The first geological disaster that is about to occur will affect the The initial advance value of the geological characteristic warning value of geological hazards, For the The first The conversion factor for adjusting the impact of geological disasters is For the The first The impact of geological disasters; The geological characteristic warning value can be adjusted by the advance amount of the geological characteristic warning value, so as to carry out timely monitoring and warning of geological disasters.

[0041] In this embodiment, “Early warning threshold lead time” emphasizes the advance time of the warning value. Indicates the degree of influence on the correction of the initial lead.

[0042] Correspondingly, this application also provides a geological disaster monitoring and early warning system based on artificial intelligence, such as Figure 2 As shown, including, The matching module is used to collect geological information in the target area, extract geological features of all categories of geological information, match the geological feature categories with the geological disaster categories, and establish a matching relationship between geological disasters and geological features; The risk module is used to construct a map of the target area, mark the geological hazards one by one on the map of the target area through the matching relationship between geological hazards and geological features, and determine the geological hazard potential points of different risk levels; A construction module is used to set the geological characteristic warning value of each type of geological disaster at each geological disaster risk point, and to construct a geological disaster impact chain for each geological disaster risk point; The monitoring module is used to adjust the geological characteristic warning value of geological disasters according to the geological disaster impact chain, so as to realize the monitoring and early warning of geological disasters.

[0043] Compared with the prior art, the present invention has the following beneficial effects: 1. Establish a matching relationship between geological hazards and geological features, so as to accurately adapt all relevant geological features corresponding to each geological hazard, and provide a reliable basis for the monitoring and early warning of subsequent geological hazards. Determine geological hazard risk points of different risk levels, set the geological feature warning value of each type of geological hazard under each geological hazard risk point, integrate multiple geological hazards with cross-or intersection relationships, form the location of multiple geological hazard risk points, and evaluate the risk level of geological hazard risk points, so as to facilitate the setting of the warning value of subsequent geological features and ensure the timeliness of geological hazard warning.

[0044] 2. Construct a geological disaster impact chain, adjust the geological characteristics warning value of geological disasters according to the geological disaster impact chain, consider the mutual influence between geological disasters, set the advance amount of the warning value of the affected geological disasters, and adjust the warning value accordingly. This improves the accuracy of geological disaster monitoring, the advance and timeliness of warning, ensures that geological disasters can be monitored in a timely, effective and accurate manner, and facilitates the early setting of safety measures.

[0045] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present invention can be implemented by hardware, or by software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present invention.

[0046] Those skilled in the art will appreciate that the accompanying drawings are merely schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily required for implementing the present invention.

[0047] Those skilled in the art will appreciate that the modules in the system in the implementation scenario can be distributed in the system of the implementation scenario according to the implementation scenario description, or can be changed accordingly and located in one or more systems different from the implementation scenario. The modules in the above implementation scenario can be combined into one module, or can be further split into multiple submodules.

[0048] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A geological disaster monitoring and early warning method based on artificial intelligence, characterized in that: include, Collect geological information in the target area, extract geological features of all categories of geological information, match geological feature categories with geological disaster categories, and establish a matching relationship between geological disasters and geological features; Construct a map of the target area, mark the geological hazards one by one on the map of the target area through the matching relationship between geological hazards and geological features, and determine the geological hazard potential points of different risk levels; Set the geological characteristic warning value of each type of geological disaster at each geological disaster risk point, and build a geological disaster impact chain for each geological disaster risk point; The geological characteristic warning value of geological disasters is adjusted according to the geological disaster impact chain to achieve monitoring and early warning of geological disasters.

2. The artificial intelligence-based geological disaster monitoring and early warning method according to claim 1 is characterized in that: And match the geological feature categories with the geological disaster categories to establish a matching relationship between geological disasters and geological features. include, Obtain the cause information of each geological disaster category, decompose the cause information to form multiple conditions, connect the multiple conditions to form a geological disaster process, use the conditions as nodes in the process, confirm the geological feature category under each node, and thus determine a type of geological feature; Record the geological features other than the first category geological features as other geological features, calculate the linear correlation between the first category geological features and other geological features, and screen out the second category geological features; According to the first type of geological features, the function of the preset radial basis function kernel and other geological features, the nonlinear correlation is calculated to screen out the second type of geological features; Each geological hazard category is matched with its corresponding Class I geological feature and Class II geological feature, so as to establish a matching relationship between geological hazard and geological feature.

3. The artificial intelligence-based geological disaster monitoring and early warning method according to claim 2 is characterized in that: Geological hazards are marked one by one on the map of the target area through the matching relationship between geological hazards and geological features, including: Collect historical records of geological disasters, and mark them on the map of the target area according to the historical records of geological disasters. Match the first-class geological features with the second-class geological features on the map of the target area through the matching relationship between geological disasters and geological features, and calculate the matching degree of geological features; Conduct an overlay analysis of the historical records of geological disasters and the matching degree of geological features on the map of the target area to determine the type of geological disasters and their specific locations.

4. The artificial intelligence-based geological disaster monitoring and early warning method according to claim 3 is characterized in that: Identify geological disaster risk points of different risk levels, including: According to the specific location of geological disasters, geological disaster areas are formed, and the impact range and hazard degree of each geological disaster area are evaluated. The intersection between geological disaster areas or the intersection between the impact ranges of geological disaster areas are recorded as intersection relations, and the degree of intersection between different geological disasters in the intersection relations is calculated. Each intersection relationship corresponds to a geological disaster potential point; The risk index of the geological hazard potential point is calculated according to the hazard degree of each geological hazard area and the degree of intersection between different geological hazards in the intersection relationship, so as to determine the risk level of the geological hazard potential point; ； in, For the Risk indicators of geological disaster risk points, For the The number of geological disasters at each geological disaster risk point, For the The risk weight of each geological hazard is For the The geological disaster potential point The degree of hazard of geological disasters, , are the weights of the intersection degree and the impact degree of the geological disaster with the greatest impact, for The maximum value in is a preset constant.

5. The method for monitoring and early warning of geological disasters based on artificial intelligence according to claim 1 is characterized in that: Set the geological characteristic warning value for each type of geological disaster at each geological disaster risk point, including: Collect the historical numerical range of each type of geological feature in the historical records of geological disasters, intercept the historical numerical range of each type of geological feature when the geological disaster occurs, determine the average historical numerical range of each type of geological feature, and determine the first warning value of each type of geological feature based on the average historical numerical range; The average value and standard deviation of the geological characteristics are calculated according to the historical numerical range of each type of geological characteristics when the geological disaster occurs, and a multiple is mapped through the risk level of the geological disaster potential point. The second warning value of each type of geological characteristics is determined by combining the average value, standard deviation and multiple of the geological characteristics; The geological characteristic warning value of each type of geological disaster is set according to the first warning value and the second warning value of each type of geological characteristic.

6. The method for monitoring and early warning of geological disasters based on artificial intelligence according to claim 1 is characterized in that: Construct a geological disaster impact chain for each geological disaster risk point, including: Analyze the causal relationship between multiple geological disasters under each geological hazard risk point, take each geological disaster as an event node in the geological disaster impact chain, connect the event nodes with line segments according to the causal relationship, and assign different lengths to the line segments according to the degree of influence between the event nodes, so as to construct the geological disaster impact chain.

7. The artificial intelligence-based geological disaster monitoring and early warning method according to claim 6 is characterized in that: Before adjusting the geological characteristic warning value of the geological disaster according to the geological disaster impact chain, the method further includes: Define two state spaces of each geological disaster, the non-occurrence state and the occurrence state, according to the geological characteristics, and calculate the state transition probability of the evolution of the non-occurrence state of the geological disaster to the occurrence state at the current time according to the preset period; Based on the state transition probability, determine whether there is an impending geological disaster. If there is an impending geological disaster, adjust the geological characteristic warning value of the geological disaster according to the geological disaster impact chain; Otherwise, the geological characteristic warning value of geological disasters will not be adjusted.

8. The artificial intelligence-based geological disaster monitoring and early warning method according to claim 7 is characterized in that: Adjust the geological characteristic warning value of geological disasters according to the geological disaster impact chain, including: Determine the geological hazards affected by the impending geological hazards and the extent of their impact in the geological hazard impact chain, and determine the advance amount of the geological characteristic warning value of the geological hazards affected by the impending geological hazards; ； in, For the The first The advance amount of geological characteristic warning value of geological disasters, The affected The state transition probability of a geological disaster, The first geological disaster that is about to occur will affect the The initial advance value of the geological characteristic warning value of geological hazards, For the The first The conversion coefficient for the impact of geological disasters is adjusted. For the The first The impact of geological disasters; The geological characteristic warning value can be adjusted by the advance amount of the geological characteristic warning value, so as to carry out timely monitoring and warning of geological disasters.

9. A geological disaster monitoring and early warning system based on artificial intelligence, characterized in that: include, The matching module is used to collect geological information in the target area, extract geological features of all categories of geological information, match the geological feature categories with the geological disaster categories, and establish a matching relationship between geological disasters and geological features; The risk module is used to construct a map of the target area, mark the geological hazards one by one on the map of the target area through the matching relationship between geological hazards and geological features, and determine the geological hazard potential points of different risk levels; A construction module is used to set the geological characteristic warning value of each type of geological disaster at each geological disaster risk point, and to construct a geological disaster impact chain for each geological disaster risk point; The monitoring module is used to adjust the geological characteristic warning value of geological disasters according to the geological disaster impact chain, so as to realize the monitoring and early warning of geological disasters.

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