A regional geological disaster early warning method and device, computer equipment and storage medium

Abstract

The application relates to a regional geological disaster early warning method and device, a storage medium and equipment, the method comprising the following steps: acquiring hydrological data and soil data detected by a detection device in a selected area, and acquiring geological and environmental data; acquiring hydrological data in the selected area and soil data in the selected area according to the geological and environmental data and the hydrological data and the soil data; performing numerical processing on the hydrological data in the selected area and the soil data in the selected area to obtain hydrological values in the selected area and soil values in the selected area; matching and associating each hydrological value in the selected area with each soil value in the selected area to obtain a geological factor value group; acquiring historical hydrological data and historical soil data, performing correlation analysis on the historical hydrological data and the historical soil data, and obtaining a disaster early warning model; and acquiring an early warning result according to the disaster early warning model. The application has the effects of improving the efficiency and accuracy of regional geological disaster early warning.

Description

Technical Field

[0001] This invention relates to the technical field of geological disaster early warning, and in particular to a regional geological disaster early warning method, device, computer equipment, and storage medium. Background Technology

[0002] Geological disasters refer to geological processes or phenomena that, under the influence of natural or human factors, cause loss of human life and property and damage to the environment. The temporal and spatial distribution patterns of geological disasters are subject to both the natural environment and human activities, and are often the result of the interaction between humans and nature.

[0003] Related geological disaster early warning technologies involve analyzing soil data below the surface within a specific area, acquiring images of the area through remote sensing technology, and measuring the absolute displacement of the objects in the area.

[0004] The existing technical solutions mentioned above have the following drawbacks: geological disaster early warning methods that detect some geological factors in the region and then analyze them are easily affected by environmental and other factors, which can lead to inaccurate measurement results. Furthermore, the processing of measurement data is relatively troublesome, which can lead to a complicated early warning process.

[0005] Regarding the aforementioned technologies, the inventors believe that they have drawbacks, such as the susceptibility to interference in accurately predicting geological disasters in the region and the cumbersome processing procedures. Summary of the Invention

[0006] To improve the efficiency and accuracy of regional geological disaster early warning, this application provides a regional geological disaster early warning method, device, computer equipment, and storage medium.

[0007] The above-mentioned objective of this application is achieved through the following technical solution:

[0008] A regional geological disaster early warning method, the regional geological disaster early warning method comprising:

[0009] Acquire hydrological and soil data obtained by the detection device within the selected area, and acquire geological and environmental data;

[0010] Based on the geological and environmental data and the hydrological data, hydrological data within the selected area is obtained; and based on the geological and environmental data and the soil data, soil data within the selected area is obtained.

[0011] The hydrological data and soil data within the selected area are digitized to obtain the hydrological values ​​and soil values ​​within the selected area.

[0012] The hydrological values ​​in each selected area are matched and associated with the soil values ​​in each selected area to obtain a set of geological factor values.

[0013] Historical hydrological data and historical soil data are acquired, and correlation analysis is performed on the historical hydrological data and historical soil data to obtain a disaster early warning model;

[0014] According to the disaster early warning model, a monitoring data set is obtained from the geological factor data set, and the monitoring data set is input into the disaster early warning model to obtain the early warning result.

[0015] By adopting the above technical solution, the regional geological disaster early warning method provided by this invention provides early warning of geological disasters in a specific area. Hydrological data detection devices and soil data detection devices are installed in this area. Hydrological and soil data for the area are obtained through these devices. Geological and environmental data for the area, such as rainfall, well location and depth, pond location and depth, river location and span, vegetation cover location and depth, building location, and foundation depth, are then obtained through information acquisition channels such as the internet. By combining the geological and environmental data, hydrological data, and soil data, and considering the impact of these data on the hydrological and soil data obtained by the detection devices, specific hydrological and soil data for the required area are obtained. This specific hydrological and soil data is then converted into specific numerical values ​​through numerical processing, and each specific hydrological value is compared with each... By matching and correlating specific soil values ​​to obtain a set of geological factor values, historical hydrological and soil data are acquired. A disaster early warning model is generated based on this data. Then, based on the disaster early warning model, the sets of values ​​to be monitored are selected from the geological factor value set to obtain a monitoring value set. This monitoring value set is then input into the disaster early warning model to obtain the early warning results. Therefore, by performing correlation analysis on historical hydrological and soil values ​​to generate a disaster early warning monitoring model and selecting monitoring value sets for regional geological disaster early warning, this method avoids relying on a single hydrological or soil factor for early warning. Such a single factor is easily affected by environmental factors, leading to a decrease in the accuracy of geological disaster early warnings. Furthermore, it avoids the need to re-detect the single factor or other factors due to interference with the detected values, thus improving the accuracy and efficiency of regional geological disaster early warnings.

[0016] In a preferred embodiment, this application can be further configured to: acquire historical hydrological data and historical soil data, perform correlation analysis on the historical hydrological data and the historical soil data, and obtain a disaster early warning model, specifically including:

[0017] The historical hydrological data and historical soil data within the selected area are digitized to obtain the historical hydrological values ​​and historical soil values ​​within the selected area.

[0018] The historical hydrological values ​​in each selected area are matched and associated with the historical soil values ​​in each selected area to obtain a set of historical geological factor values.

[0019] Correlation analysis was performed on each set of historical geological factor values, and the disaster correlation coefficient was obtained based on the correlation analysis results.

[0020] Based on the disaster correlation coefficient, obtain the disaster correlation value set from the historical geological factor value set;

[0021] Based on the disaster-related numerical group, obtain the disaster threshold;

[0022] A disaster early warning model is trained based on the disaster threshold and the disaster-related numerical group.

[0023] By adopting the above technical solution, historical hydrological data and historical soil data are converted into specific historical hydrological and soil values. These values ​​are then matched and correlated to form a historical geological factor value group. Correlation analysis is performed on this group, and a disaster correlation coefficient is obtained based on the results. This coefficient represents the correlation between geological disasters and the historical hydrological and soil values ​​within the historical geological factor value group. Based on this correlation coefficient, a historical geological factor value group with a high correlation to the occurrence of geological disasters is obtained—the disaster-related value group. Further analysis of the historical hydrological and soil values ​​within this disaster-related value group yields a disaster threshold that can trigger a geological disaster early warning. Using this disaster threshold and the disaster-related value group, a disaster early warning model is trained. Therefore, by acquiring historical hydrological and soil values ​​within a region, performing correlation analysis on these values, and then forming a disaster early warning model, the model is matched with two types of disaster factors, improving its accuracy.

[0024] In a preferred embodiment, this application can be further configured such that: obtaining the disaster correlation value set from the historical geological factor value set based on the disaster correlation coefficient specifically includes:

[0025] Based on the disaster correlation coefficient, obtain the coefficient threshold;

[0026] Each set of historical geological factor values ​​is associated with its corresponding disaster correlation coefficient.

[0027] By filtering the historical geological factor value groups whose disaster correlation coefficients are greater than the coefficient threshold, disaster correlation value groups are obtained.

[0028] By employing the aforementioned technical solution, correlation analysis is performed on historical hydrological and soil values ​​within historical geological factor value sets. The resulting disaster correlation coefficients are then analyzed to determine their relationship with the occurrence of geological disasters. A threshold coefficient indicating the occurrence of a geological disaster is determined when this coefficient exceeds a certain threshold. The historical geological factor value sets are then matched with the disaster correlation coefficients obtained from the correlation analysis of those sets. Furthermore, the disaster correlation coefficients of each historical geological factor value set are compared with the threshold coefficients to identify disaster-related value sets that are correlated with the occurrence of geological disasters. Therefore, by analyzing the disaster correlation coefficients, disaster-related value sets within historical geological factor value sets that are relevant to the occurrence of geological disasters are selected. This saves time and effort in subsequent steps of regional geological disaster early warning, avoiding the need to rely on historical geological factor value sets unrelated to the occurrence of geological disasters when developing a disaster early warning model based on historical geological factor value sets. This improves the efficiency of regional geological disaster early warning.

[0029] In a preferred embodiment, this application can be further configured as follows: the historical hydrological values ​​within the selected area include daily hydrological values ​​and disaster hydrological values ​​within the selected area; the historical soil values ​​within the selected area include daily soil values ​​and disaster soil values ​​within the selected area; and obtaining the disaster threshold based on the disaster-related value group specifically includes:

[0030] Based on the daily hydrological values ​​within the selected area and the disaster hydrological values ​​within the selected area in the disaster-related numerical group, obtain the hydrological numerical disaster threshold.

[0031] Based on the daily soil values ​​and disaster soil values ​​within the selected area in the disaster-related numerical group, the soil numerical disaster threshold is obtained;

[0032] The disaster threshold is obtained based on the hydrological numerical disaster threshold and the soil numerical disaster threshold.

[0033] By adopting the above technical solution, the obtained historical hydrological data includes daily hydrological data within the selected area when no geological disasters occur, and disaster hydrological data within the selected area when geological disasters occur. Based on the daily and disaster hydrological data within the selected area in the disaster-related data group, the hydrological disaster threshold for geological disasters when the hydrological data reaches a certain value is obtained. Based on the daily and disaster soil data within the selected area in the disaster-related data group, the soil disaster threshold for geological disasters when the soil data reaches a certain value is obtained. Finally, based on the hydrological disaster threshold and the soil disaster threshold, the disaster threshold is obtained. Therefore, by setting the disaster threshold, the values ​​detected within the area can be compared with the disaster threshold to realize the judgment and analysis of the values ​​detected within the area, simplifying the procedure for geological disaster early warning.

[0034] In a preferred embodiment, this application can be further configured such that: training a disaster early warning model based on the disaster threshold and the disaster-related numerical group specifically includes:

[0035] The disaster threshold is matched and associated with the corresponding disaster-related value group to obtain the early warning value group;

[0036] The disaster early warning model is obtained by training a preset model based on the aforementioned early warning numerical data set.

[0037] By adopting the above technical solution, after obtaining the disaster threshold, the disaster threshold is matched and associated with the corresponding disaster-related numerical group to obtain the warning numerical group. The preset model is trained by using the disaster-related numerical group and the disaster threshold in all the warning numerical groups to obtain the disaster warning model. Therefore, training the model by the warning numerical group improves the accuracy of the disaster warning model for geological disaster warning. Using the disaster warning model for geological disaster warning saves the steps of processing detection data and improves the efficiency of regional geological disaster warning.

[0038] In a preferred embodiment, this application can be further configured to: obtain a monitoring data set from the geological factor data set according to the disaster early warning model, input the monitoring data set into the disaster early warning model, and obtain early warning results, specifically including:

[0039] Obtain the disaster-related numerical set from the disaster early warning model, and obtain the monitoring numerical set from the geological factor numerical set based on the disaster-related numerical set;

[0040] Based on the hydrological values ​​and soil values ​​within the selected area in the monitoring value group, the early warning monitoring value is obtained;

[0041] The warning monitoring value is compared with the disaster threshold of the corresponding disaster-related value group in the disaster warning model. If the warning monitoring value is greater than the disaster threshold, a disaster warning command is triggered.

[0042] By adopting the above technical solution, disaster-related numerical sets associated with geological disasters are obtained from the disaster early warning model. All numerical sets in the geological factor numerical set are compared with the disaster-related numerical sets to obtain the monitoring numerical sets that need to be monitored in relation to geological disasters. Then, based on the hydrological and soil values ​​in the selected area from the monitoring numerical sets, the final early warning monitoring values ​​that need to be input into the disaster early warning model are obtained. These early warning monitoring values ​​are input into the disaster early warning model and compared with the disaster threshold of the corresponding disaster-related numerical set in the disaster early warning model to obtain the early warning result. Therefore, by filtering out the values ​​that need to be monitored from the disaster-related numerical sets in the disaster early warning model and then inputting these values ​​into the model to obtain the early warning result, the number of numerical sets that the model needs to process is reduced, and the efficiency of geological disaster early warning is improved.

[0043] The second objective of this invention is achieved through the following technical solution:

[0044] A regional geological disaster early warning device, the regional geological disaster early warning device comprising:

[0045] The data acquisition module is used to acquire hydrological and soil data, as well as geological and environmental data, obtained by the detection device within the selected area.

[0046] The data processing module is used to obtain hydrological data within the selected area based on the geological and environmental data and the hydrological data, and to obtain soil data within the selected area based on the geological and environmental data and the soil data.

[0047] The data quantification module is used to quantify the hydrological data and soil data within the selected area to obtain the hydrological values ​​and soil values ​​within the selected area.

[0048] The numerical group acquisition module is used to match and associate the hydrological values ​​in each selected area with the soil values ​​in each selected area to obtain a geological factor numerical group.

[0049] The early warning model acquisition module is used to acquire historical hydrological data and historical soil data within the selected area, perform correlation analysis on the historical hydrological data and historical soil data within the selected area, and obtain a disaster early warning model.

[0050] The early warning module is used to obtain a monitoring data set from the geological factor data set according to the disaster early warning model, input the monitoring data set into the disaster early warning model, and obtain the early warning result.

[0051] By adopting the above technical solution, hydrological and soil data for the area are obtained through a detection device. Geological and environmental data for the area, such as rainfall, well location and depth, pond location and depth, river location and span, vegetation cover location and depth, building location, and foundation depth, are then acquired through the internet or other information acquisition channels. This data is combined with the geological and environmental data, hydrological data, and soil data, and the impact of these data on the hydrological and soil data obtained by the detection device, to obtain specific hydrological and soil data for the required area. This specific hydrological and soil data is then converted into numerical values ​​through numerical processing. Each specific hydrological value is matched and correlated with each specific soil value to obtain a set of geological factor values. Historical hydrological and soil data are collected, and a disaster early warning model is generated based on this data. Then, based on the disaster early warning model, a set of monitoring values ​​is selected from the geological factor numerical data set. This set of monitoring values ​​is then input into the disaster early warning model to obtain the early warning results. Therefore, by performing correlation analysis on historical hydrological and soil values ​​to generate a disaster early warning monitoring model and selecting monitoring value sets for regional geological disaster early warning, this approach avoids relying on a single hydrological or soil factor for early warning. Such a single factor is easily affected by environmental factors, leading to a decrease in the accuracy of geological disaster early warnings. Furthermore, it avoids the need to re-detect the single factor or other factors due to interference with the detected values, thus improving the accuracy and efficiency of regional geological disaster early warning.

[0052] The above-mentioned objective three of this application is achieved through the following technical solution:

[0053] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the aforementioned regional geological disaster early warning method.

[0054] The fourth objective of this application is achieved through the following technical solution:

[0055] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the aforementioned regional geological disaster early warning method.

[0056] In summary, this application includes at least one of the following beneficial technical effects:

[0057] 1. The regional geological disaster early warning method provided by this invention provides early warning of geological disasters in a specific area. Hydrological data detection devices and soil data detection devices are installed within this area. Hydrological and soil data for the area are obtained through these devices. Geological and environmental data for the area, such as rainfall, well location and depth, pond location and depth, river location and span, vegetation cover location and depth, building location, and foundation depth, are then acquired through the internet or other information acquisition channels. By combining the geological and environmental data, hydrological data, and soil data, and considering the impact of these data on the hydrological and soil data obtained by the detection devices, specific hydrological and soil data for the required area are obtained. This specific hydrological and soil data is then converted into specific numerical values ​​through numerical processing, and each specific hydrological value is compared with each specific... By matching and correlating soil values, a geological factor value group is obtained. Historical hydrological and soil data are acquired, and a disaster early warning model is generated based on these data. Then, based on the disaster early warning model, the value groups that need to be monitored are selected from the geological factor value group to obtain the monitoring value group. The monitoring value group is then input into the disaster early warning model to obtain the early warning results. Therefore, by performing correlation analysis on historical hydrological and soil values ​​to generate a disaster early warning monitoring model and selecting monitoring value groups for regional geological disaster early warning, the method avoids early warning based on a single hydrological or soil factor, which is easily affected by environmental factors, leading to a decrease in the accuracy of geological disaster early warning. It also avoids the need to re-detect the single factor or other factors due to interference with the detected values ​​of a single factor, thus improving the accuracy and efficiency of regional geological disaster early warning.

[0058] 2. Historical hydrological and soil data are converted into specific historical hydrological and soil values. These values ​​are then matched and correlated to form a historical geological factor value group. Correlation analysis is performed on this group to obtain a disaster correlation coefficient, which represents the correlation between geological disasters and the historical hydrological and soil values ​​within that group. Based on this correlation coefficient, a historical geological factor value group with a high correlation to geological disasters is obtained—the disaster-related value group. Further analysis of the historical hydrological and soil values ​​within this disaster-related value group yields a disaster threshold that can trigger a geological disaster warning. Using this threshold and the disaster-related value group, a disaster warning model is trained. Therefore, by acquiring historical hydrological and soil values ​​within the region, performing correlation analysis, and then forming a disaster warning model, the model is matched with two types of disaster factors, improving its accuracy.

[0059] 3. By adopting the above technical solution, disaster-related numerical sets concerning geological disasters are obtained from the disaster early warning model. All numerical sets in the geological factor numerical set are compared with the disaster-related numerical sets to obtain the monitoring numerical sets that need to be monitored in relation to geological disasters. Then, based on the hydrological and soil values ​​within the selected area in the monitoring numerical sets, the final early warning monitoring values ​​that need to be input into the disaster early warning model are obtained. These early warning monitoring values ​​are input into the disaster early warning model and compared with the disaster threshold of the corresponding disaster-related numerical set in the disaster early warning model to obtain the early warning result. Therefore, by filtering out the values ​​that need to be monitored from the disaster-related numerical sets in the disaster early warning model and then inputting these values ​​into the model to obtain the early warning result, the number of numerical sets that the model needs to process is reduced, and the efficiency of geological disaster early warning is improved. Attached Figure Description

[0060] Figure 1 This is a flowchart of a regional geological disaster early warning method in an embodiment of this application;

[0061] Figure 2 This is a flowchart illustrating the implementation of S50 of the regional geological disaster early warning method in this application embodiment;

[0062] Figure 3 This is a flowchart illustrating the implementation of S54 of the regional geological disaster early warning method in this application embodiment;

[0063] Figure 4 This is a flowchart illustrating the implementation of S55 of the regional geological disaster early warning method in this application embodiment;

[0064] Figure 5 This is a flowchart illustrating the implementation of S56 of the regional geological disaster early warning method in this application embodiment;

[0065] Figure 6 This is a flowchart illustrating the implementation of S60 of the regional geological disaster early warning method in this application embodiment;

[0066] Figure 7 This is a schematic diagram of a device for a regional geological disaster early warning method in an embodiment of this application;

[0067] Figure 8 This is an internal structural diagram of the computer device used in the regional geological disaster early warning method in this application embodiment. Detailed Implementation

[0068] The present application will be further described in detail below with reference to the accompanying drawings.

[0069] In one embodiment, such as Figure 1 As shown, this application discloses a regional geological disaster early warning method, which specifically includes the following steps:

[0070] S10: Acquire hydrological and soil data obtained by the detection device within the selected area, and acquire geological and environmental data.

[0071] In this embodiment, the regional geological disaster early warning method targets a specific area for geological disaster warning. Within this area, detection devices are installed to monitor corresponding hydrological and soil data. Hydrological data refers to the data on hydrological factors within the area detected by the detection devices. Soil data refers to the data on soil factors within the area detected by the detection devices. Geological and environmental data refers to the meteorological and geological conditions within the area.

[0072] Specifically, the data obtained includes hydrological and soil data from the detection devices installed within the region that detect factors related to geological hazards. Hydrological data includes the carbon dioxide content, pH, temperature, pressure, flow velocity, and sediment content of water sources within the region. Soil data includes soil hardness, mass, moisture content, temperature, pH, and stress. Meteorological and atmospheric data and environmental geological data are also obtained through the internet or other information channels. These include data on temperature, humidity, air pressure, wind direction, wind speed, and rainfall, as well as the location and depth of wells, ponds, rivers, vegetation cover, buildings, and foundation depth within the region.

[0073] S20: Obtain hydrological data within the selected area based on geological and environmental data and hydrological data, and obtain soil data within the selected area based on geological and environmental data and soil data.

[0074] In this embodiment, hydrological data within the selected area refers to hydrological factor data after superimposing geological and environmental data on hydrological data. Soil data within the selected area refers to soil factor data after superimposing geological and environmental data on soil data.

[0075] Specifically, based on the geological and environmental data and hydrological data within the region, the impact of geological and environmental data on the detected hydrological data is analyzed. Furthermore, by combining geological and environmental data with hydrological data, such as the impact of vegetation root growth on water carbon dioxide content, and the impact of temperature or rainfall on water temperature, more accurate hydrological data is obtained, i.e., hydrological data within the selected area. Similarly, based on the geological and environmental data and soil data within the region, the impact of geological and environmental data on the detected soil data is analyzed. Furthermore, by combining geological and environmental data with soil data, such as the impact of well location and depth on soil stress, and the impact of rainfall on soil moisture content, more accurate soil data is obtained, i.e., soil data within the selected area.

[0076] Furthermore, due to the limited detection range of geological hazard detection devices in the region, several detection devices are installed in the region. The geological and environmental data of different detection devices may differ. Therefore, each hydrological and soil data is analyzed in conjunction with the corresponding geological and environmental data.

[0077] S30: Numericalize the hydrological and soil data within the selected area to obtain the hydrological and soil values ​​within the selected area.

[0078] In this embodiment, the hydrological values ​​within the selected area refer to the values ​​obtained by numerical processing of the hydrological data within the selected area. The soil values ​​within the selected area refer to the values ​​obtained by numerical processing of the soil data within the selected area.

[0079] Specifically, hydrological data and soil data within the selected area at the same location and time are converted into specific numerical values. For example, the carbon dioxide content in the water is converted into the amount of carbon dioxide in the same volume, and the soil stress is converted into the resultant force of the forces acting on the soil in all directions, thus obtaining the hydrological values ​​and soil values ​​within the selected area.

[0080] S40: Match and correlate the hydrological values ​​in each selected area with the soil values ​​in each selected area to obtain a set of geological factor values.

[0081] In this embodiment, the geological factor numerical group refers to a numerical group in which hydrological values ​​within a selected area are matched and correlated with soil values ​​within a selected area.

[0082] Specifically, the hydrological values ​​in each selected area are associated and combined with the soil values ​​in each selected area to obtain a set of values ​​including one hydrological value and one soil value in each selected area, namely, a set of geological factor values. The hydrological values ​​in each selected area are matched and associated with all soil values ​​in all selected areas. Therefore, there are multiple sets of geological factor values, and each set of geological factor values ​​is different.

[0083] S50: Obtain historical hydrological data and historical soil data within the selected area, conduct correlation analysis on the historical hydrological data and historical soil data within the selected area, and obtain a disaster early warning model.

[0084] In this embodiment, historical hydrological data within the selected area refers to hydrological data within a first preset time period prior to the issuance of a regional geological disaster early warning. Historical soil data within the selected area refers to soil data within the first preset time period prior to the issuance of a regional geological disaster early warning. The disaster early warning model refers to a model used for regional geological disaster early warning.

[0085] Specifically, hydrological and soil data from the first preset time period are acquired, i.e., historical hydrological and soil data within the selected area. Correlation analysis is then performed on these historical hydrological and soil data, i.e., through mathematical operations, such as data fitting or calculating correlation coefficients, to analyze the relationship between the correlation of historical hydrological and soil data within the selected area and the occurrence of geological disasters. After obtaining the analysis results, a disaster early warning model is obtained based on the correlation between the correlation of historical hydrological and soil data within the selected area and the occurrence of geological disasters.

[0086] S60: Based on the disaster early warning model, obtain the monitoring data set from the geological factor data set, input the monitoring data set into the disaster early warning model, and obtain the early warning results.

[0087] In this embodiment, the monitoring value group refers to the portion of the geological factor value group that is related to the occurrence of geological disasters. The early warning result refers to the result of the early warning of regional geological disasters.

[0088] Specifically, based on the disaster early warning model, the relationship between historical geological factor numerical sets and the occurrence of geological disasters is obtained from the disaster early warning model. Based on this relationship, numerical sets related to the occurrence of geological disasters are screened from the geological factor numerical sets to obtain monitoring numerical sets. The monitoring numerical sets are then input into the disaster early warning model, which analyzes and judges the monitoring numerical sets to obtain early warning results.

[0089] In one embodiment, such as Figure 2 As shown, in step S50, historical hydrological data and historical soil data within the selected area are acquired. Correlation analysis is performed on the historical hydrological data and historical soil data within the selected area to obtain a disaster early warning model, specifically including:

[0090] S51: Convert historical hydrological data and historical soil data within the selected area into numerical values ​​to obtain historical hydrological values ​​and historical soil values ​​within the selected area.

[0091] In this embodiment, historical hydrological values ​​within the selected area refer to the hydrological data within the first preset time period prior to the issuance of a regional geological disaster warning. Historical soil values ​​within the selected area refer to the soil data within the first preset time period prior to the issuance of a regional geological disaster warning.

[0092] Specifically, historical hydrological data and soil data within the same selected area are converted into specific numerical values ​​to obtain historical hydrological values ​​and historical soil values ​​within the selected area.

[0093] S52: Match and correlate the historical hydrological values ​​in each selected area with the historical soil values ​​in each selected area to obtain the historical geological factor value group.

[0094] In this embodiment, the historical geological factor numerical group refers to a set of historical hydrological values ​​within the selected area and a set of historical soil values ​​within the selected area that are matched and correlated.

[0095] Specifically, the historical hydrological values ​​in each selection area are associated and combined with the historical soil values ​​in each selection area to obtain a set of historical geological factor values, which includes one set of historical hydrological values ​​and one set of historical soil values ​​in the selection area. Each set of historical hydrological values ​​in the selection area is matched and associated with all historical soil values ​​in the selection area. Therefore, there are multiple sets of historical geological factor values, and each set of historical geological factor values ​​is different.

[0096] S53: Conduct correlation analysis on each group of historical geological factor numerical data, and obtain the disaster correlation coefficient based on the correlation analysis results.

[0097] In this embodiment, the disaster correlation coefficient refers to the numerical value of the correlation between the historical geological factor value set and the occurrence of geological disasters.

[0098] Specifically, a correlation analysis is performed on the historical hydrological values ​​and historical soil values ​​within the selected area in the historical geological factor numerical group. This involves using mathematical operations, such as data fitting or calculating correlation coefficients, to calculate the degree to which the historical soil values ​​within the selected area change when the historical hydrological values ​​change. Based on the calculation results, a numerical value representing the degree to which the historical soil values ​​within the selected area change when the historical hydrological values ​​change is obtained, namely, the disaster correlation coefficient.

[0099] S54: Obtain disaster correlation data sets from historical geological factor data sets based on disaster correlation coefficients.

[0100] In this embodiment, the disaster-related numerical group refers to the portion of the historical geological factor numerical group that is related to the occurrence of geological disasters.

[0101] Specifically, based on the disaster correlation coefficient, it is determined whether there is a correlation between the historical geological factor numerical group and the occurrence of geological disasters, and then the numerical group that is related to the occurrence of geological disasters in the historical geological factor numerical group is selected to obtain the disaster correlation numerical group.

[0102] S55: Obtain the disaster threshold based on the disaster-related numerical group.

[0103] In this embodiment, the disaster threshold refers to the value used to determine whether a geological disaster will occur based on the relevant values ​​of the disaster-related numerical group.

[0104] Specifically, the disaster-related numerical set is a set of numerical values ​​identified in the previous steps that are related to the occurrence of geological disasters. Based on the historical hydrological values ​​and historical soil values ​​within the selected area in the disaster-related numerical set, the values ​​are combined with the geological disasters that occurred within the first preset time period for analysis. The result is a value obtained by combining the historical hydrological values ​​and historical soil values ​​within the selected area in the disaster-related numerical set, and this value can be used to determine the occurrence of geological disasters, i.e., the disaster threshold.

[0105] S56: A disaster early warning model is trained based on the disaster threshold and the disaster-related numerical group.

[0106] Specifically, a disaster early warning model is trained based on a disaster threshold and a set of disaster-related numerical values ​​corresponding to that threshold.

[0107] In one embodiment, such as Figure 3 As shown, in step S54, based on the disaster correlation coefficient, a disaster correlation value set is obtained from the historical geological factor value set, specifically including:

[0108] S541: Obtain the coefficient threshold based on the disaster correlation coefficient.

[0109] In this embodiment, the coefficient threshold refers to the value used to determine the correlation between historical geological factor numerical groups and the occurrence of geological disasters.

[0110] Specifically, based on the disaster correlation coefficient, which is the correlation between the historical hydrological values ​​and historical soil values ​​within the selected area and the occurrence of geological disasters, and combined with the occurrence of geological disasters, we can obtain the value at which the value reaches a certain level and is related to the occurrence of geological disasters. This leads to the value for judging the correlation between the historical geological factor value group and the occurrence of geological disasters, i.e., the coefficient threshold. Conversely, if the value does not reach a certain level, it is not related to the occurrence of geological disasters.

[0111] S542: Associate each set of historical geological factor values ​​with the corresponding disaster correlation coefficient.

[0112] Specifically, each set of historical geological factor values ​​is associated with its corresponding disaster correlation coefficient, and each set of historical geological factor values ​​is associated with the disaster correlation coefficient obtained from that set of historical geological factor values.

[0113] S543: Filter historical geological factor numerical groups with disaster correlation coefficients greater than the coefficient threshold to obtain disaster correlation numerical groups.

[0114] Specifically, based on the comparison between the disaster correlation coefficient and the coefficient threshold of each historical geological factor numerical group, historical geological factor numerical groups with disaster correlation coefficients greater than the coefficient threshold are selected to obtain historical geological factor numerical groups related to the occurrence of geological disasters, namely disaster correlation numerical groups.

[0115] In one embodiment, historical hydrological data within the selected area include routine hydrological data and hazardous hydrological data within the selected area; historical soil data within the selected area includes routine soil data and hazardous soil data within the selected area, such as... Figure 4 As shown, in step S55, the disaster threshold is obtained based on the disaster-related numerical group, specifically including:

[0116] S551: Obtain the hydrological disaster threshold based on the daily hydrological values ​​and disaster hydrological values ​​within the selected area in the disaster-related numerical group.

[0117] In this embodiment, the daily hydrological values ​​within the selected area refer to the hydrological values ​​during a second preset time period in which no geological disaster occurs. The disaster hydrological values ​​within the selected area refer to the hydrological values ​​during a third preset time period in which a geological disaster occurs. The hydrological disaster threshold refers to the value at which a geological disaster occurs when the hydrological value reaches a critical value.

[0118] Specifically, the first preset time period includes a second preset time period and a third preset time period. Based on the hydrological values ​​before and during the geological disaster, the changes in the hydrological values ​​within the second and third preset time periods in the first preset time period are analyzed to obtain the value at which the hydrological value reaches a critical value and a geological disaster occurs, i.e., the hydrological value disaster threshold.

[0119] S552: Obtain the soil numerical disaster threshold based on the daily soil values ​​and disaster soil values ​​within the selected area in the disaster-related numerical group.

[0120] In this embodiment, the daily soil value within the selected area refers to the soil value during a second preset time period when no geological disaster occurs. The disaster soil value within the selected area refers to the soil value during a third preset time period when a geological disaster occurs. The soil value disaster threshold refers to the value at which a geological disaster occurs when the soil value reaches a critical value.

[0121] Specifically, based on the hydrological values ​​before and during a geological disaster, the changes in soil values ​​within the second and third preset time periods in the first preset time period are analyzed to obtain the value at which a geological disaster occurs when the soil value reaches a critical value, i.e., the soil value disaster threshold.

[0122] S553: ​​Obtain the disaster threshold based on the hydrological numerical disaster threshold and the soil numerical disaster threshold.

[0123] Specifically, based on the corresponding hydrological numerical disaster threshold and soil numerical disaster threshold within each disaster-related numerical group, values ​​are obtained to determine whether a geological disaster will occur.

[0124] In one embodiment, such as Figure 5 As shown, in step S56, a disaster early warning model is trained based on the disaster threshold and the disaster-related numerical group, specifically including:

[0125] S561: Match and associate the disaster threshold with the corresponding disaster-related numerical group to obtain the warning numerical group.

[0126] In this embodiment, the early warning numerical group refers to a numerical group that includes disaster-related numerical groups and corresponding disaster thresholds.

[0127] Specifically, each disaster-related numerical group is matched and associated with its corresponding disaster threshold to obtain a numerical group that includes a disaster-related numerical group and a corresponding disaster threshold, namely, an early warning numerical group. There are multiple early warning numerical groups, and each early warning numerical group is different.

[0128] S562: Train the preset model based on the early warning numerical data to obtain a disaster early warning model.

[0129] Specifically, a disaster early warning model is obtained by training a preset model based on the disaster-related numerical groups and corresponding disaster thresholds in the early warning numerical group.

[0130] In one embodiment, such as Figure 6 As shown, in step S60, according to the disaster early warning model, a monitoring data set is obtained from the geological factor numerical set, and the monitoring data set is input into the disaster early warning model to obtain the early warning result. Specifically, this includes:

[0131] S61: Obtain disaster-related numerical sets from the disaster early warning model, and obtain monitoring numerical sets from the geological factor numerical sets based on the disaster-related numerical sets.

[0132] Specifically, based on the disaster early warning model, disaster-related numerical sets are extracted from the disaster early warning model. From the disaster-related numerical sets, the types of hydrological factors and soil factors corresponding to historical hydrological values ​​and historical soil values ​​within the selected area are extracted. From the geological factor numerical sets, the types of hydrological factors and soil factors corresponding to hydrological values ​​and soil values ​​within the selected area are extracted. The types corresponding to the above numerical values ​​are compared, and the geological factor numerical sets whose types are the same as those corresponding to the numerical values ​​in the disaster-related numerical sets are selected to obtain the monitoring numerical sets.

[0133] S62: Obtain early warning monitoring values ​​based on the hydrological and soil values ​​within the selected area in the monitoring value group.

[0134] In this embodiment, the early warning monitoring value refers to the value input into the disaster early warning model for regional geological disaster early warning.

[0135] Specifically, based on the disaster thresholds corresponding to the hydrological and soil numerical disaster thresholds within each disaster-related numerical group in the disaster early warning model, the hydrological and soil numerical values ​​within the selected area are processed in the same way to obtain numerical values ​​of the same type as the disaster thresholds in the disaster early warning model, which are used for regional geological disaster early warning, i.e., early warning monitoring values.

[0136] S63: Compare the early warning monitoring value with the disaster threshold of the corresponding disaster-related numerical group in the disaster early warning model. If the early warning monitoring value is greater than the disaster threshold, trigger the disaster early warning command.

[0137] In this embodiment, the disaster warning instruction refers to the instruction information that determines that a geological disaster will occur.

[0138] Specifically, the warning monitoring value corresponding to the monitoring value group is compared with the disaster threshold of the disaster-related value group in the disaster early warning model. If the warning monitoring value is greater than the disaster threshold, it is determined that a geological disaster will occur.

[0139] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0140] In one embodiment, a regional geological disaster early warning method device is provided, which corresponds one-to-one with the regional geological disaster early warning method method in the above embodiments. For example... Figure 7 As shown, the geological disaster early warning method and device for this area includes a data acquisition module, a data processing module, a data quantification module, a numerical data acquisition module, an early warning model acquisition module, and an early warning module. Detailed descriptions of each functional module are as follows:

[0141] The data acquisition module is used to acquire hydrological and soil data, as well as geological and environmental data, obtained by the detection device within the selected area.

[0142] The data processing module is used to obtain hydrological data within the selected area based on geological and environmental data and hydrological data, and to obtain soil data within the selected area based on geological and environmental data and soil data.

[0143] The data quantification module is used to quantify the hydrological data and soil data within the selected area to obtain the hydrological values ​​and soil values ​​within the selected area.

[0144] The numerical group acquisition module is used to match and associate the hydrological values ​​in each selected area with the soil values ​​in each selected area to obtain the geological factor numerical group.

[0145] The early warning model acquisition module is used to acquire historical hydrological data and historical soil data within the selected area, perform correlation analysis on the historical hydrological data and historical soil data within the selected area, and obtain a disaster early warning model.

[0146] The early warning module is used to obtain monitoring data sets from the geological factor data set according to the disaster early warning model, input the monitoring data sets into the disaster early warning model, and obtain early warning results.

[0147] Optionally, the early warning model acquisition module includes:

[0148] The historical data quantification submodule is used to quantify historical hydrological data and historical soil data to obtain historical hydrological values ​​and historical soil values ​​within the selected area.

[0149] The historical data group acquisition submodule is used to match and associate the historical hydrological data in each selected area with the historical soil data in each selected area to obtain the historical geological factor data group.

[0150] The historical numerical data processing submodule is used to perform correlation analysis on each historical geological factor numerical data group and obtain the disaster correlation coefficient based on the correlation analysis results.

[0151] The disaster-related numerical group acquisition submodule is used to obtain disaster-related numerical groups from historical geological factor numerical groups based on disaster-related coefficients.

[0152] The disaster threshold acquisition submodule is used to obtain the disaster threshold based on the disaster-related numerical group;

[0153] The early warning model acquisition submodule is used to train a disaster early warning model based on disaster thresholds and disaster-related numerical groups.

[0154] Optionally, the disaster-related numerical group acquisition submodule includes:

[0155] The coefficient threshold acquisition unit is used to obtain the coefficient threshold based on the disaster correlation coefficient;

[0156] The disaster correlation coefficient association unit is used to associate each set of historical geological factor values ​​with the corresponding disaster correlation coefficient.

[0157] The historical geological factor numerical group screening unit is used to screen historical geological factor numerical groups with disaster correlation coefficients greater than the coefficient threshold to obtain disaster correlation numerical groups.

[0158] Optionally, historical hydrological data within the selected area include daily hydrological data and disaster hydrological data within the selected area; historical soil data within the selected area includes daily soil data and disaster soil data within the selected area; the disaster threshold acquisition submodule includes:

[0159] The hydrological numerical disaster threshold acquisition unit is used to acquire the hydrological numerical disaster threshold based on the daily hydrological values ​​and disaster hydrological values ​​within the selected area in the disaster-related numerical group.

[0160] The soil numerical hazard threshold acquisition unit is used to acquire the soil numerical hazard threshold based on the daily soil values ​​and hazard soil values ​​within the selected area in the hazard-related numerical group.

[0161] The disaster threshold analysis and acquisition unit is used to obtain disaster thresholds based on hydrological numerical disaster thresholds and soil numerical disaster thresholds.

[0162] Optionally, the early warning model acquisition submodule includes:

[0163] The disaster-related numerical group association unit is used to match and associate disaster thresholds with corresponding disaster-related numerical groups to obtain early warning numerical groups.

[0164] The model training unit is used to train a preset model based on the early warning numerical data set to obtain a disaster early warning model.

[0165] Optionally, the early warning module includes:

[0166] The monitoring data set acquisition submodule is used to obtain disaster-related data sets from the disaster early warning model, and obtain monitoring data sets from the geological factor data sets based on the disaster-related data sets.

[0167] The early warning monitoring value acquisition submodule is used to acquire early warning monitoring values ​​based on the hydrological values ​​and soil values ​​within the selected area in the monitoring value group;

[0168] The disaster threshold comparison submodule is used to compare the warning monitoring value with the disaster threshold of the corresponding disaster-related numerical group in the disaster warning model. If the warning monitoring value is greater than the disaster threshold, a disaster warning instruction is triggered.

[0169] Specific limitations regarding the regional geological disaster early warning method and device can be found in the above-mentioned limitations on the regional geological disaster early warning method, and will not be repeated here. Each module in the aforementioned regional geological disaster early warning method and device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0170] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores geological and environmental data, hydrological values ​​within the selected area, soil values ​​within the selected area, geological factor value groups, historical hydrological values ​​within the selected area, historical soil values ​​within the selected area, disaster correlation coefficients, disaster thresholds, coefficient thresholds, and disaster early warning models. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a regional geological disaster early warning method.

[0171] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0172] Acquire hydrological and soil data obtained by the detection device within the selected area, and acquire geological and environmental data;

[0173] Based on geological and environmental data and hydrological data, hydrological data within the selected area was obtained, and based on geological and environmental data and soil data, soil data within the selected area was obtained.

[0174] The hydrological data and soil data within the selected area are digitized to obtain the hydrological values ​​and soil values ​​within the selected area.

[0175] The hydrological values ​​in each selected area are matched and correlated with the soil values ​​in each selected area to obtain a set of geological factor values.

[0176] Historical hydrological data and historical soil data within the selected area were acquired. Correlation analysis was performed on the historical hydrological data and historical soil data within the selected area to obtain a disaster early warning model.

[0177] Based on the disaster early warning model, a monitoring data set is obtained from the geological factor data set, and the monitoring data set is input into the disaster early warning model to obtain the early warning results.

[0178] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0179] Acquire hydrological and soil data obtained by the detection device within the selected area, and acquire geological and environmental data;

[0180] Based on geological and environmental data and hydrological data, hydrological data within the selected area was obtained, and based on geological and environmental data and soil data, soil data within the selected area was obtained.

[0181] The hydrological data and soil data within the selected area are digitized to obtain the hydrological values ​​and soil values ​​within the selected area.

[0182] The hydrological values ​​in each selected area are matched and correlated with the soil values ​​in each selected area to obtain a set of geological factor values.

[0183] Historical hydrological data and historical soil data within the selected area were acquired. Correlation analysis was performed on the historical hydrological data and historical soil data within the selected area to obtain a disaster early warning model.

[0184] Based on the disaster early warning model, a monitoring data set is obtained from the geological factor data set, and the monitoring data set is input into the disaster early warning model to obtain the early warning results.

[0185] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0186] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0187] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for early warning of regional geological disasters, characterized in that, The regional geological disaster early warning methods include: Acquire hydrological and soil data obtained by the detection device within the selected area, and acquire geological and environmental data; Based on the geological and environmental data and the hydrological data, hydrological data within the selected area is obtained; and based on the geological and environmental data and the soil data, soil data within the selected area is obtained. The hydrological data and soil data within the selected area are digitized to obtain the hydrological values ​​and soil values ​​within the selected area. The hydrological values ​​in each selected area are matched and associated with the soil values ​​in each selected area to obtain a set of geological factor values. Historical hydrological data and historical soil data within the selected area are acquired. Correlation analysis is performed on the historical hydrological data and historical soil data within the selected area to obtain a disaster early warning model, specifically including: The historical hydrological data and historical soil data within the selected area are digitized to obtain the historical hydrological values ​​and historical soil values ​​within the selected area. The historical hydrological values ​​in each selected area are matched and correlated with the historical soil values ​​in each selected area to obtain a set of historical geological factor values. Correlation analysis was performed on each set of historical geological factor values, and the disaster correlation coefficient was obtained based on the correlation analysis results. Based on the disaster correlation coefficient, obtain the disaster correlation value set from the historical geological factor value set; Based on the disaster-related numerical group, obtain the disaster threshold; A disaster early warning model is trained based on the disaster threshold and the disaster-related numerical group. According to the disaster early warning model, a monitoring data set is obtained from the geological factor data set, and the monitoring data set is input into the disaster early warning model to obtain the early warning result.

2. The regional geological disaster early warning method according to claim 1, characterized in that, Based on the disaster correlation coefficient, a disaster correlation value set is obtained from the historical geological factor value set, specifically including: Based on the disaster correlation coefficient, obtain the coefficient threshold; Each set of historical geological factor values ​​is associated with its corresponding disaster correlation coefficient. By filtering the historical geological factor value groups whose disaster correlation coefficients are greater than the coefficient threshold, disaster correlation value groups are obtained.

3. The regional geological disaster early warning method according to claim 1, characterized in that, The historical hydrological data within the selected area includes daily hydrological data and disaster-related hydrological data. The historical soil data within the selected area includes daily soil data and disaster-related soil data. Based on the disaster-related data set, a disaster threshold is obtained, specifically including: Based on the daily hydrological values ​​within the selected area and the disaster hydrological values ​​within the selected area in the disaster-related numerical group, obtain the hydrological numerical disaster threshold. Based on the daily soil values ​​and disaster soil values ​​within the selected area in the disaster-related numerical group, the soil numerical disaster threshold is obtained; The disaster threshold is obtained based on the hydrological numerical disaster threshold and the soil numerical disaster threshold.

4. The regional geological disaster early warning method according to claim 1, characterized in that, Based on the disaster threshold and the disaster-related numerical group, a disaster early warning model is trained, specifically including: The disaster threshold is matched and associated with the corresponding disaster-related value group to obtain the early warning value group; The disaster early warning model is obtained by training a preset model based on the aforementioned early warning numerical data set.

5. The regional geological disaster early warning method according to claim 1, characterized in that, Based on the disaster early warning model, a monitoring data set is obtained from the geological factor data set, and the monitoring data set is input into the disaster early warning model to obtain the early warning result, specifically including: Obtain the disaster-related numerical group from the disaster early warning model, and obtain the monitoring numerical group from the geological factor numerical group based on the disaster-related numerical group; Based on the hydrological values ​​and soil values ​​within the selected area in the monitoring value group, the early warning monitoring value is obtained; The warning monitoring value is compared with the disaster threshold of the corresponding disaster-related value group in the disaster warning model. If the warning monitoring value is greater than the disaster threshold, a disaster warning command is triggered.

6. A regional geological disaster early warning device, characterized in that, The regional geological disaster early warning device includes: The data acquisition module is used to acquire hydrological and soil data, as well as geological and environmental data, obtained by the detection device within the selected area. The data processing module is used to obtain hydrological data within the selected area based on the geological and environmental data and the hydrological data, and to obtain soil data within the selected area based on the geological and environmental data and the soil data. The data quantification module is used to quantify the hydrological data and soil data within the selected area to obtain the hydrological values ​​and soil values ​​within the selected area. The numerical group acquisition module is used to match and associate the hydrological values ​​in each selected area with the soil values ​​in each selected area to obtain a geological factor numerical group. The early warning model acquisition module is used to acquire historical hydrological data and historical soil data, perform correlation analysis on the historical hydrological data and historical soil data, and obtain a disaster early warning model. The early warning model acquisition module includes: The historical data quantification submodule is used to quantify the historical hydrological data and the historical soil data to obtain historical hydrological values ​​and historical soil values ​​within the selected area. The historical data group acquisition submodule is used to match and associate the historical hydrological data in each selected area with the historical soil data in each selected area to obtain a historical geological factor data group. The historical data group processing submodule is used to perform correlation analysis on each of the historical geological factor data groups and obtain the disaster correlation coefficient based on the correlation analysis results. The disaster-related numerical group acquisition submodule is used to acquire disaster-related numerical groups from the historical geological factor numerical groups based on the disaster-related coefficients. The disaster threshold acquisition submodule is used to acquire the disaster threshold based on the disaster-related value group; The early warning model acquisition submodule is used to train a disaster early warning model based on the disaster threshold and the disaster-related numerical group. The early warning module is used to obtain a monitoring data set from the geological factor data set according to the disaster early warning model, input the monitoring data set into the disaster early warning model, and obtain the early warning result.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the regional geological disaster early warning method as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the regional geological disaster early warning method as described in any one of claims 1 to 5.

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