Collapse disaster early warning method and device

Abstract

The invention provides a collapse disaster early warning method and device, and relates to the technical field of geological disaster monitoring, and the method comprises the steps: obtaining the accumulated rainfall of a slope unit in a preset historical period, the soil moisture content before rainfall, and the collapse frequency of the slope unit in the preset historical period; the probability that the slope unit generates a rain pattern group larger than a collapse early warning threshold value within a preset future age limit and the probability that the slope unit collapses at the same time under the condition that the rain pattern group larger than the collapse early warning threshold value occurs within a preset historical age limit are obtained, and then the probability that the slope unit collapses at the same time within the preset future age limit is obtained. And under the condition that the rain pattern group greater than the collapse early warning threshold value occurs, the probability of collapse occurs at the same time. According to the collapse disaster early warning method provided by the invention, the accuracy and reliability of collapse disaster early warning are improved on the basis of combining the space-time law.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster monitoring, and in particular to a method and device for early warning of collapse disasters. Background Art

[0002] Landslide is one of the common sudden geological disasters, which often causes damage to facilities such as railways, roads, and residential houses. It is one of the main targets of geological disaster prevention and control.

[0003] As an effective means of disaster prevention and control, landslide prediction and early warning are the focus of domestic and foreign scholars. The existing landslide early warning model pays attention to the rainfall characteristics of landslide disaster events and warns of landslide disasters. However, due to the influence of multiple factors such as topography, geology and hydrology, and human engineering activities, landslide disasters have strong suddenness and uncertainty, which makes it difficult for the landslide early warning model to accurately predict landslide disasters. Summary of the invention

[0004] The present invention provides a landslide disaster early warning method and device, which are used to solve the technical problem of low accuracy in predicting landslide disasters in the prior art.

[0005] The present invention provides a landslide disaster early warning method, comprising the following steps: Obtaining the cumulative rainfall of the slope unit within a predetermined historical period, the soil moisture content before rainfall, and the number of collapses of the slope unit within the predetermined historical period; Based on the accumulated rainfall and the soil moisture content, the number of times the slope unit has a rainfall type group greater than a collapse warning threshold within a predetermined historical period is obtained; the rainfall type group is a data pair consisting of the accumulated rainfall and the soil moisture content corresponding to the accumulated rainfall; the collapse warning threshold is obtained using a collapse warning model; the collapse warning model is constructed based on the total number of collapses of multiple slope units, the accumulated rainfall within a predetermined historical period before the collapse occurs, and the soil moisture content before the rainfall; Based on the accumulated rainfall and the soil moisture content, a first probability is obtained through an exceedance probability model, the first probability being the probability that the slope unit will experience a rain type group greater than a collapse warning threshold within a predetermined future period; and based on the number of collapses that occurred in the slope unit within a predetermined historical period and the number of rain type groups greater than a collapse warning threshold that occurred in the slope unit within a predetermined historical period, a second probability is obtained, the second probability being the probability that the slope unit will experience a collapse at the same time when a rain type group greater than a collapse warning threshold occurs within a predetermined historical period; the exceedance probability model is established based on Poisson distribution; Based on the first probability and the second probability, a third probability is obtained; the third probability is the probability of the slope unit collapsing simultaneously within a predetermined future period of time when a rainfall pattern group greater than a collapse warning threshold occurs.

[0006] According to a landslide disaster early warning method provided by the present invention, the steps of constructing the landslide early warning model include: Obtain the total number of collapses of multiple slope units, the cumulative rainfall in a predetermined historical period before the collapse, and the soil moisture content before the rainfall; Determine a collapse warning threshold corresponding to the accumulated rainfall and the soil moisture content according to the proportion of the number of collapses corresponding to the accumulated rainfall and the soil moisture content in the total number of collapses in the plurality of slope units; The collapse warning model is established based on the corresponding relationship between the accumulated rainfall, the soil moisture content and the collapse warning threshold.

[0007] According to a landslide disaster early warning method provided by the present invention, obtaining a first probability based on the accumulated rainfall and the soil moisture content includes: Based on the accumulated rainfall of the slope unit within a predetermined historical period and the soil moisture content before rainfall, determining the number of times the accumulated rainfall and the soil moisture content are greater than a collapse warning threshold value corresponding to the accumulated rainfall and the soil moisture content; Based on the number of times the cumulative rainfall and the soil moisture content are greater than the collapse warning threshold corresponding to the cumulative rainfall and the soil moisture content and the number of years of the predetermined historical period, the frequency of the occurrence of a rainfall type group greater than the collapse warning threshold in the slope unit within the predetermined historical period is obtained; The first probability is calculated based on the frequency of the occurrence of rainfall pattern groups greater than the collapse warning threshold value in the slope unit within a predetermined historical period of time, using an exceedance probability model.

[0008] According to a landslide disaster warning method provided by the present invention, the frequency of the occurrence of a rainfall type group greater than the landslide warning threshold value corresponding to the cumulative rainfall and the soil moisture content and the number of years of the predetermined historical period in the slope unit within the predetermined historical period is obtained based on the number of times the cumulative rainfall and the soil moisture content are greater than the landslide warning threshold value and the number of years of the predetermined historical period, including: The result of dividing the number of times the cumulative rainfall and the soil moisture content are greater than the collapse warning threshold corresponding to the cumulative rainfall and the soil moisture content by the number of years of the predetermined historical period is taken as the frequency of the occurrence of a rainfall type group greater than the collapse warning threshold in the slope unit within the predetermined historical period.

[0009] According to a landslide disaster early warning method provided by the present invention, the second probability is obtained based on the number of times the slope unit has collapsed within a predetermined historical period and the number of times the slope unit has experienced a rainfall pattern group greater than a landslide early warning threshold within a predetermined historical period, including: The second probability is obtained by dividing the number of times the slope unit collapses within a predetermined historical period by the number of times the slope unit experiences a rainfall pattern greater than a collapse warning threshold within a predetermined historical period.

[0010] According to a collapse disaster early warning method provided by the present invention, obtaining a third probability based on the first probability and the second probability includes: The result of multiplying the first probability by the second probability is used as the third probability.

[0011] The present invention also provides a collapse disaster warning device, comprising the following modules: An acquisition module, used to acquire the accumulated rainfall of the slope unit within a predetermined historical period, the soil moisture content before rainfall, and the number of collapses of the slope unit within the predetermined historical period; A first calculation module is used to obtain the number of times that the slope unit has a rainfall type group greater than a collapse warning threshold value within a predetermined historical period based on the accumulated rainfall and the soil moisture content; the rainfall type group is a data pair consisting of the accumulated rainfall and the soil moisture content corresponding to the accumulated rainfall; the collapse warning threshold value is obtained using a collapse warning model; the collapse warning model is constructed based on the total number of times that a collapse occurs in multiple slope units, the accumulated rainfall within a predetermined historical period before the collapse occurs, and the soil moisture content before the rainfall; The second calculation module is used to obtain a first probability based on the accumulated rainfall and the soil moisture content through a exceedance probability model, wherein the first probability is the probability that the slope unit will experience a rain type group greater than a collapse warning threshold within a predetermined future period; and, based on the number of collapses that occurred in the slope unit within a predetermined historical period and the number of rain type groups greater than a collapse warning threshold that occurred in the slope unit within a predetermined historical period, obtain a second probability, wherein the second probability is the probability that the slope unit will experience a collapse at the same time when a rain type group greater than a collapse warning threshold occurs within a predetermined historical period; the exceedance probability model is established based on Poisson distribution; The early warning module is used to obtain a third probability based on the first probability and the second probability; the third probability is the probability that the slope unit will collapse simultaneously within a predetermined future period of time when a rainfall type group greater than a collapse early warning threshold occurs.

[0012] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, any of the above-mentioned collapse disaster warning methods is implemented.

[0013] The present invention also provides a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the collapse disaster warning method as described in any one of the above is implemented.

[0014] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements any of the above-mentioned collapse disaster warning methods.

[0015] The present invention provides a landslide disaster early warning method. The method obtains the number of rain type groups greater than the landslide early warning threshold value in the slope unit within the predetermined historical period by acquiring the cumulative rainfall of the slope unit within the predetermined historical period, the soil moisture content before rainfall, and the number of times the slope unit has collapsed within the predetermined historical period; then, based on the cumulative rainfall and the soil moisture content, a first probability is obtained through a exceedance probability model; a second probability is obtained based on the number of times the slope unit has collapsed within the predetermined historical period and the number of times the slope unit has collapsed within the predetermined historical period. By comprehensively considering the cumulative rainfall of the slope unit within the predetermined historical period and the soil moisture content before rainfall, the randomness problem of the landslide disaster caused by the uncertainty of the rainfall time and rainfall and the spatial heterogeneity problem of the soil texture of different slope units are solved, so that in combination with the spatiotemporal law, the probability of the slope unit collapsing at the same time in the case of the rainfall type group greater than the landslide early warning threshold value within the predetermined future period is accurately predicted, thereby improving the accuracy and reliability of the landslide disaster early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a flow chart of a collapse disaster early warning method provided by the present invention.

[0018] Figure 2 It is a schematic diagram of a collapse warning threshold line based on soil moisture content and accumulated rainfall in a collapse disaster warning method provided by the present invention.

[0019] Figure 3It is a structural schematic diagram of a collapse disaster early warning device provided by the present invention.

[0020] Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0021] Landslide is one of the common sudden geological disasters, which often causes damage to facilities such as railways, roads, and residential houses. It is one of the main targets of geological disaster prevention and control.

[0022] Landslide prediction and warning are effective means of disaster prevention and control. However, due to the influence of multiple factors such as topography, geology and hydrology, and human engineering activities, landslide disasters are highly sudden and uncertain. Therefore, it is difficult for existing landslide warning models to accurately predict landslide disasters.

[0023] Based on the probability characteristics of collapse disasters in space and time, more accurate prediction and analysis of collapse disasters can be carried out. Spatial probability is generally based on collapse potential analysis, and temporal probability analysis methods are mainly based on exceedance probability method and physical model method. When the study area has multi-year collapse disaster samples, assuming that future collapse events maintain the same frequency as in the past, the Poisson distribution can be used to estimate the spatiotemporal recurrence probability of collapse disasters.

[0024] In order to improve the accuracy and reliability of landslide disaster warning, the present invention proposes to use the soil moisture content, which characterizes the absolute water content of the soil, as the early effective rainfall indicator, and the cumulative rainfall in the number of times that trigger the disaster as the direct trigger indicator, to construct a landslide disaster warning threshold line, analyze the rainfall field data of the past 10 years, calculate the number of rainfall events exceeding the threshold, and use the Poisson distribution to evaluate the time probability warning model of future landslides, so as to realize the spatiotemporal prediction and warning of landslide disasters.

[0025] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] Combine the following Figures 1 to 4 The present invention describes a landslide disaster early warning method and device.

[0027] Figure 1 Schematic diagram of a method for early warning of landslide disasters provided by the present invention. Figure 1 As shown, the method comprises the following steps: Step 101: Obtain the cumulative rainfall of the slope unit within a predetermined historical period, the soil moisture content before rainfall, and the number of collapses of the slope unit within the predetermined historical period.

[0028] Hydrological conditions are important triggering factors that affect the occurrence of landslides. When precipitation infiltrates into the rock and soil, it increases the weight of the rock and soil on the one hand, and lubricates the contact surface between the soil and the rock on the other hand, reducing friction, thereby causing landslides. Therefore, the moisture content in the rock and soil has an important influence on whether landslides occur or not. The precipitation that causes landslide disasters mainly comes from the previous effective rainfall and the triggering rainfall when the disaster occurs. Among them, the triggering rainfall can be obtained from rainfall monitoring, while the previous rainfall days and attenuation coefficient in the previous effective rainfall are difficult to determine.

[0029] To address the above issues, the existing technology usually uses different numbers of days such as 1, 3, 5, 7, 15, and 30 and an attenuation coefficient ranging from 0.7 to 0.84 to calculate the model of previous effective rainfall. However, due to the influence of indicators such as soil texture, land use, and rainfall intensity, the rainfall infiltration characteristics of each region are different. It is unreasonable to use a unified number of days and attenuation coefficient for previous effective rainfall.

[0030] In addition, due to the uncertainty of landslide disasters, even when the rainfall event exceeds the threshold, a disaster may not necessarily occur. Therefore, the early warning model in the existing technology only focuses on the rainfall characteristics of disaster events and ignores the analysis of rainfall events where no disaster occurs, which will result in a high false alarm rate of the early warning.

[0031] In order to increase the analysis of rain events without disasters and improve the accuracy of landslide disaster warnings, before conducting landslide disaster warning analysis on any study area, the embodiment of the present invention first obtains the cumulative rainfall in the study area within a predetermined historical period, the soil moisture content before rainfall, and the number of landslides that occurred in the study area within a predetermined historical period.

[0032] In the collapse hazard probability analysis, the first thing to do is to select the analysis unit. The analysis unit represents a sub-area after a region (i.e. the above-mentioned study area) is divided and should include the ground features of the region. Different segmentation methods will affect the processing of input data uncertainty and the fit of the model. Therefore, the analysis unit needs to be able to represent the homogeneity within the largest unit and the heterogeneity between the largest units.

[0033] At present, the commonly used units are grid units, terrain units, uniform condition units, slope units, topographic hydrological units, terrain units and administrative unit. Among them, the slope unit can divide the area into different hydrological zones based on drainage channels and watersheds as boundaries, and maintain the characteristics of the terrain, which can present the characteristics of the slope and is suitable for collapse potential analysis.

[0034] In order to maintain the characteristics of the terrain, the embodiment of the present invention first divides the study area into multiple slope units (i.e., a type of slope unit) for early warning analysis of landslide disasters, obtains the cumulative rainfall of the slope unit within a predetermined historical period by searching for rain gauges near the slope unit, and obtains the soil moisture content of the slope unit before rainfall by searching for moisture meters near the slope unit. At the same time, the rain gauges and moisture meters corresponding to the slope units are paired one-to-one and one-to-many to form data pairs of cumulative rainfall and soil moisture content for subsequent analysis.

[0035] The embodiment of the present invention also collects the number, time and specific location of collapse disasters that occurred in the slope unit within a predetermined historical period through historical data records, thereby completing the collection of historical disaster samples.

[0036] The following description takes the past 10 years as an example to represent the predetermined historical period. In other cases, the predetermined historical period may also be other numbers such as 15 years or 20 years, which is not limited in the embodiment of the present invention.

[0037] Step 102: based on the accumulated rainfall and the soil moisture content, obtain the number of times the slope unit has a rainfall type group greater than a collapse warning threshold within a predetermined historical period; the rainfall type group is a data pair consisting of the accumulated rainfall and the soil moisture content corresponding to the accumulated rainfall; the collapse warning threshold is obtained using a collapse warning model; the collapse warning model is constructed based on the total number of collapses of multiple slope units, the accumulated rainfall within a predetermined historical period before the collapse occurs, and the soil moisture content before the rainfall.

[0038] In an embodiment of the present invention, firstly, sample data of previous landslide disasters in the study area are statistically analyzed to extract the time of disaster occurrence, including the total number of landslides in multiple slope units, the cumulative rainfall within a predetermined historical period (which can be any number of years such as 1 year, 5 years or 10 years) before the landslide (the time of landslide occurrence is determined based on historical data records), and the soil moisture content before rainfall. According to the analysis, the corresponding relationship between the cumulative rainfall, soil moisture content and the corresponding number of landslides is obtained, and then the landslide warning threshold is set according to the corresponding relationship, thereby constructing a landslide warning model.

[0039] When analyzing the collapse disaster warning of the slope unit, the collapse warning threshold determined by the collapse warning model is compared with the cumulative rainfall and soil moisture content of the slope unit in the past 10 years, so as to obtain the number of rain type groups with a value greater than the collapse warning threshold in the slope unit in the past 10 years. The rain type group is a data pair consisting of the cumulative rainfall and soil moisture content obtained by pairing the rain gauge and moisture meter corresponding to the slope unit one-to-one and one-to-many.

[0040] The embodiment of the present invention uses soil moisture content and rainfall data (i.e., cumulative rainfall) to construct a landslide warning model, which reduces the warning error caused by the uncertainty of rainfall time and rainfall amount caused by the use of rainfall attenuation coefficient and attenuation days in the prior art. At the same time, the spatial heterogeneity of indicators such as soil texture and land use of different analysis units in the study area can be reflected according to the soil moisture content, which solves the problem of inaccurate landslide disaster prediction caused by using the same rainfall attenuation coefficient for different analysis units in a study area in the prior art without considering the spatial heterogeneity of different analysis units, thereby improving the accuracy and reliability of landslide disaster warning.

[0041] Step 103, based on the accumulated rainfall and the soil moisture content, a first probability is obtained through an exceedance probability model, the first probability being the probability that the slope unit will experience a rain pattern group greater than a collapse warning threshold within a predetermined future period of time; and based on the number of times the slope unit has collapsed within a predetermined historical period of time and the number of times the slope unit has experienced a rain pattern group greater than a collapse warning threshold within a predetermined historical period of time, a second probability is obtained, the second probability being the probability that the slope unit will experience a collapse simultaneously with a rain pattern group greater than a collapse warning threshold within a predetermined historical period of time; the exceedance probability model is established based on Poisson distribution.

[0042] In the embodiment of the present invention, a exceedance probability model is established based on Poisson distribution.

[0043] Poisson distribution can predict the probability of an event occurring in the future by the frequency of its past occurrence. Assuming that the frequency of future events is the same as in the past, the expression of Poisson distribution of n events occurring in time interval t is: In the formula, represents the probability of n events occurring within t years, represents the frequency of this type of event occurring in the past. is a natural constant.

[0044] Based on the above Poisson distribution, the exceedance probability model established in the embodiment of the present invention is: The exceedance probability model describes the probability of one or more events of a certain type occurring within a time interval t. In the formula, when n=1, that is, , which represents the probability of a certain type of event occurring more than once within t years, is called the exceedance probability.

[0045] The accumulated rainfall in the past 10 years and the soil moisture content corresponding to the accumulated rainfall form a rain type group. The number of occurrences of the rain type group greater than the collapse warning threshold is divided by 10 to obtain the frequency of occurrence of the rain type group greater than the collapse warning threshold. , and then the probability (i.e., the first probability) P1 of a rain pattern group with a value greater than the collapse warning threshold value occurring within a predetermined future period can be calculated by using the exceedance probability model. In the embodiment of the present invention, the predetermined future period (which can be any number of years such as 1 year, 5 years, or 10 years) is described by taking the future year as an example, i.e., t=1.

[0046] Based on the above embodiment, the number of times the slope unit has collapsed in the past 10 years is divided by the number of times the slope unit has experienced a rainfall pattern group greater than the collapse warning threshold in the past 10 years, and the probability (i.e., the second probability) P2 of the slope unit collapsing simultaneously with the occurrence of a rainfall pattern group greater than the collapse warning threshold in the past 10 years can be obtained.

[0047] Step 104: Based on the first probability and the second probability, a third probability is obtained; the third probability is the probability that the slope unit will collapse simultaneously in a predetermined future period of time when a rainfall pattern group greater than a collapse warning threshold occurs.

[0048] By multiplying P1 and P2 calculated above, we can obtain the probability (i.e., the third probability) P of the slope unit collapsing simultaneously in the next year when a rainfall group greater than the collapse warning threshold occurs.

[0049] The embodiment of the present invention first uses the exceedance probability model to calculate the probability P1 of a rain type group greater than the collapse warning threshold value occurring in the next year, and then divides the number of collapses occurring in each slope unit in the past 10 years by the number of rain type groups exceeding the threshold value to calculate the probability P2 of collapse occurring when a rain type group greater than the collapse warning threshold value occurs. P1 and P2 are multiplied to obtain the probability of collapse occurring when a rain type group greater than the collapse warning threshold value occurs in the next year.

[0050] The embodiment of the present invention fully considers the randomness of landslide disasters caused by uncertainty of rainfall, and uses the exceedance probability model to study the spatiotemporal laws of landslide disasters. Compared with the prior art, the present invention realizes real-time tracking under different rainfall conditions by hanging rain gauges on slope units, reflecting the difference in probability of landslide disasters in the control areas of different rain gauges, and improving the accuracy of landslide disaster warning.

[0051] The present invention provides a landslide disaster early warning method. The method obtains the number of rain type groups greater than the landslide early warning threshold value in the slope unit within the predetermined historical period by acquiring the cumulative rainfall of the slope unit within the predetermined historical period, the soil moisture content before rainfall, and the number of times the slope unit has collapsed within the predetermined historical period; then, based on the cumulative rainfall and the soil moisture content, a first probability is obtained through a exceedance probability model; a second probability is obtained based on the number of times the slope unit has collapsed within the predetermined historical period and the number of times the slope unit has collapsed within the predetermined historical period. By comprehensively considering the cumulative rainfall of the slope unit within the predetermined historical period and the soil moisture content before rainfall, the randomness problem of the landslide disaster caused by the uncertainty of the rainfall time and rainfall and the spatial heterogeneity problem of the soil texture of different slope units are solved, so that in combination with the spatiotemporal law, the probability of the slope unit collapsing at the same time in the case of the rainfall type group greater than the landslide early warning threshold value within the predetermined future period is accurately predicted, thereby improving the accuracy and reliability of the landslide disaster early warning.

[0052] Optionally, the steps of constructing the collapse warning model include: Obtain the total number of collapses of multiple slope units, the cumulative rainfall in a predetermined historical period before the collapse, and the soil moisture content before the rainfall; Determine a collapse warning threshold corresponding to the accumulated rainfall and the soil moisture content according to the proportion of the number of collapses corresponding to the accumulated rainfall and the soil moisture content in the total number of collapses in the plurality of slope units; The collapse warning model is established based on the corresponding relationship between the accumulated rainfall, the soil moisture content and the collapse warning threshold.

[0053] Specifically, the embodiment of the present invention uses the soil moisture content that characterizes the absolute moisture content of the soil as an indicator of the early effective rainfall, and the accumulated rainfall in the disaster-triggering events as a direct triggering indicator to construct a landslide warning model in order to determine the landslide warning thresholds corresponding to the accumulated rainfall and the soil moisture content.

[0054] Figure 2 Schematic diagram of a collapse warning threshold line based on soil moisture content and accumulated rainfall in a collapse disaster warning method provided by the present invention. Figure 2 shown.

[0055] By collecting historical collapse disaster sample data of multiple slope units (which can be historical collapse disaster sample data within any number of years such as 1 year, 5 years or 10 years), the time of collapse disaster is extracted, and the cumulative rainfall R before the collapse disaster is calculated, the soil moisture content w before rainfall is obtained, and then the soil moisture content w is used as the horizontal coordinate and the cumulative rainfall R is used as the vertical coordinate. The proportion of the number of collapses corresponding to the cumulative rainfall R and the soil moisture content w in the total number of collapses in multiple slope units is used to determine the collapse warning threshold corresponding to the cumulative rainfall and soil moisture content.

[0056] For example, in the embodiment of the present invention, the number of collapses corresponding to the cumulative rainfall R and the soil moisture content w is 20%, 40%, 70% and 90% of the total number of collapses as blue, yellow, orange and red warning curves, respectively, to construct collapse warning expressions: In the formula, It indicates the cumulative rainfall when the number of times the slope unit collapses is 90% of the total number of times the collapse occurs. It indicates the cumulative rainfall when the number of times the slope unit collapses is 70% of the total number of times the collapse occurs. It indicates the cumulative rainfall when the number of collapses of the slope unit is 40% of the total number of collapses. It represents the cumulative rainfall when the number of collapses of the slope unit reaches 20% of the total number of collapses, and w represents the soil moisture content corresponding to each cumulative rainfall.

[0057] In some embodiments, other collapse warning thresholds may also be set according to other proportions of the number of collapses corresponding to the accumulated rainfall R of the slope unit and the soil moisture content w in the total number of collapses of multiple slope units.

[0058] The embodiment of the present invention uses the soil moisture rate that characterizes the absolute moisture content of the soil and the cumulative rainfall that characterizes the cumulative rainfall in the number of times that triggered the disaster as sample data to construct a landslide warning model, thereby reducing the warning error caused by the uncertainty of rainfall time and rainfall caused by the use of rainfall attenuation coefficients and attenuation days in the prior art. At the same time, the spatial heterogeneity of indicators such as soil texture and land use in different analysis units in the study area can be reflected according to the soil moisture rate, which solves the problem of inaccurate prediction of landslide disasters caused by using the same rainfall attenuation coefficient for different analysis units in a study area in the prior art without considering the spatial heterogeneity of different analysis units.

[0059] On the basis of determining the collapse warning threshold, by calculating the rainfall field data of all rain gauges in the past 10 years, the start and end time of rainfall can be accurately captured, and the accumulated rainfall of each session can be calculated. The soil moisture content before the start of rainfall is obtained, and the soil moisture content before rainfall and the accumulated rainfall of each session are combined into a data pair (w, R). Then, the frequency of data pairs (w, R) (i.e., rain type groups) greater than the collapse warning threshold in the past 10 years can be calculated and substituted into the exceedance probability model, which provides a basis for predicting and analyzing the probability of collapse, thereby improving the accuracy and reliability of subsequent collapse disaster warnings.

[0060] Optionally, obtaining a first probability based on the accumulated rainfall and the soil moisture content includes: Based on the accumulated rainfall of the slope unit within a predetermined historical period and the soil moisture content before rainfall, determining the number of times the accumulated rainfall and the soil moisture content are greater than a collapse warning threshold value corresponding to the accumulated rainfall and the soil moisture content; Based on the number of times the cumulative rainfall and the soil moisture content are greater than the collapse warning threshold corresponding to the cumulative rainfall and the soil moisture content and the number of years of the predetermined historical period, the frequency of the occurrence of a rainfall type group greater than the collapse warning threshold in the slope unit within the predetermined historical period is obtained; The first probability is calculated based on the frequency of the occurrence of rainfall pattern groups greater than the collapse warning threshold value in the slope unit within a predetermined historical period of time, using an exceedance probability model.

[0061] Specifically, firstly, the accumulated rainfall of the slope unit in the past 10 years (or other years such as 5 years, 15 years or 20 years, and the embodiment of the present invention is still described here by taking 10 years as an example) and the soil moisture content before rainfall are counted, and the soil moisture content before rainfall and the accumulated rainfall in each session are combined into a data pair, i.e., a rain type group. Combined with the collapse warning threshold determined by the collapse warning model, the number of times the rain type group is greater than the collapse warning threshold in the past 10 years is calculated; Secondly, based on the number of times the rainfall type group was greater than the collapse warning threshold in the past 10 years, divided by the number of years (10 years), the frequency of the rainfall type group greater than the collapse warning threshold in the slope unit in the past 10 years can be obtained; Finally, the frequency of rainfall type groups greater than the collapse warning threshold occurring in the slope unit in the past 10 years is substituted into the exceedance probability model to calculate the probability of rainfall type groups greater than the collapse warning threshold occurring in a predetermined future number of years. When the predetermined future number of years t=1, it is the probability (i.e., the first probability) P1 of the rainfall type group greater than the collapse warning threshold occurring in the next year in the embodiment of the present invention.

[0062] Optionally, the obtaining, based on the number of times that the cumulative rainfall and the soil moisture content are greater than the collapse warning threshold corresponding to the cumulative rainfall and the soil moisture content and the number of years of the predetermined historical period, of a frequency of a rainfall type group greater than the collapse warning threshold occurring in the slope unit within a predetermined historical period, comprises: The result of dividing the number of times the cumulative rainfall and the soil moisture content are greater than the collapse warning threshold corresponding to the cumulative rainfall and the soil moisture content by the number of years of the predetermined historical period is taken as the frequency of the occurrence of a rainfall type group greater than the collapse warning threshold in the slope unit within the predetermined historical period.

[0063] Specifically, firstly, based on historical data, the number of times the cumulative rainfall and soil moisture content in the past 10 years exceeded the collapse warning threshold was counted M, and then divided by the years of rainfall data N = 10 years. Finally, the frequency of the rainfall type group with a slope unit greater than the collapse warning threshold was calculated. ,Right now =M / N.

[0064] Optionally, obtaining the second probability based on the number of collapses of the slope unit within a predetermined historical period and the number of rain pattern groups greater than a collapse warning threshold value occurring in the slope unit within a predetermined historical period comprises: The second probability is obtained by dividing the number of times the slope unit collapses within a predetermined historical period by the number of times the slope unit experiences a rainfall pattern greater than a collapse warning threshold within a predetermined historical period.

[0065] Specifically, based on the slope unit, the number of collapse events F that occurred in each slope unit in the past 10 years is counted, and divided by the number of rainfall types M that occurred in the slope unit in the past 10 years that were greater than the collapse warning threshold. In this way, the probability (i.e., the second probability) P2=F / M of collapse occurring simultaneously in the slope unit in the past 10 years when a rainfall type group greater than the collapse warning threshold occurs can be obtained.

[0066] Optionally, obtaining a third probability based on the first probability and the second probability includes: The result of multiplying the first probability by the second probability is used as the third probability.

[0067] Specifically, the embodiment of the present invention takes the probability of collapse occurring when a rainfall type exceeding a threshold occurs within the next year as an example for explanation. Then, the probability (i.e., the third probability) P of collapse occurring at the same time when a rainfall type group exceeding the collapse warning threshold occurs within the next year of the slope unit is calculated as follows: P=P1∩P2=P1×P2= Where P1 is the probability that a slope unit will experience a rainfall pattern greater than the collapse warning threshold in the next year, and P2 is the probability that a slope unit will experience a collapse in the past 10 years when a rainfall pattern greater than the collapse warning threshold occurs. is a natural constant, t=1, which means the next year. It can be understood that when t is other values, P represents the probability of collapse of the slope unit in the next t years when a rainfall group greater than the collapse warning threshold occurs. is the number of collapse events that occurred in each slope unit in the past 10 years, is the number of rainfall patterns greater than the collapse warning threshold that occurred in the slope unit in the past 10 years, =M / N, with N=10, which represents the frequency of rainfall patterns greater than the collapse warning threshold occurring in the slope unit in the past 10 years.

[0068] The embodiment of the present invention first uses the exceedance probability model to calculate the probability P1 of a rain type group greater than the collapse warning threshold value occurring in the next year, and then divides the number of collapses that occurred in each slope unit in the past 10 years by the number of rain type groups exceeding the threshold value to calculate the probability P2 of collapse when a rain type group greater than the collapse warning threshold value occurs. P1 and P2 are multiplied to obtain the probability of collapse when a rain type group greater than the collapse warning threshold value occurs in the next year.

[0069] The embodiment of the present invention fully considers the randomness of landslide disasters caused by uncertainty of rainfall and the spatiotemporal laws of landslide disasters, and tracks the rainfall in different slope units in real time to reflect the difference in the probability of landslide disasters occurring in the control areas of different rain gauges, thereby calculating the probability of landslide occurring when the rainfall type group is greater than the landslide warning threshold, thereby improving the accuracy of landslide disaster warning.

[0070] A collapse disaster warning device provided by the present invention is described below. The collapse disaster warning device described below and the collapse disaster warning method described above can be referred to each other.

[0071] Based on any of the above embodiments, Figure 3 Schematic diagram of a collapse disaster warning device provided by the present invention. Figure 3 The embodiment of the present invention provides a collapse disaster warning device, including an acquisition module 301, a first calculation module 302, a second calculation module 303 and a warning module 304, wherein: The acquisition module 301 is used to acquire the cumulative rainfall of the slope unit within a predetermined historical period, the soil moisture content before rainfall, and the number of collapses of the slope unit within the predetermined historical period; The first calculation module 302 is used to obtain the number of rain type groups greater than the collapse warning threshold value that occur in the slope unit within a predetermined historical period based on the accumulated rainfall and the soil moisture content; the rain type group is a data pair consisting of the accumulated rainfall and the soil moisture content corresponding to the accumulated rainfall; the collapse warning threshold value is obtained using a collapse warning model; the collapse warning model is constructed based on the total number of collapses of multiple slope units, the accumulated rainfall within a predetermined historical period before the collapse occurs, and the soil moisture content before the rainfall; The second calculation module 303 is used to obtain a first probability based on the accumulated rainfall and the soil moisture content through an exceedance probability model, wherein the first probability is the probability that the slope unit will experience a rain type group greater than the collapse warning threshold within a predetermined future period; and, based on the number of collapses that occurred in the slope unit within a predetermined historical period and the number of rain type groups greater than the collapse warning threshold that occurred in the slope unit within a predetermined historical period, obtain a second probability, wherein the second probability is the probability that the slope unit will experience a collapse at the same time when a rain type group greater than the collapse warning threshold occurs within a predetermined historical period; the exceedance probability model is established based on Poisson distribution; The warning module 304 is used to obtain a third probability based on the first probability and the second probability; the third probability is the probability that the slope unit will collapse simultaneously within a predetermined future period of time when a rainfall pattern group greater than a collapse warning threshold occurs.

[0072] The present invention provides a landslide disaster warning device. The device obtains the number of rain type groups greater than the landslide warning threshold value in the slope unit within the predetermined historical period by acquiring the cumulative rainfall of the slope unit within the predetermined historical period, the soil moisture content before rainfall, and the number of times the slope unit has collapsed within the predetermined historical period; then, based on the cumulative rainfall and the soil moisture content, a first probability is obtained through a exceedance probability model; a second probability is obtained based on the number of times the slope unit has collapsed within the predetermined historical period and the number of times the slope unit has collapsed within the predetermined historical period. By comprehensively considering the cumulative rainfall of the slope unit within the predetermined historical period and the soil moisture content before rainfall, the randomness problem of the landslide disaster caused by the uncertainty of the rainfall time and rainfall and the spatial heterogeneity problem of the soil texture of different slope units are solved, so that in combination with the spatiotemporal law, the probability of the slope unit collapsing at the same time in the case of the rainfall type group greater than the landslide warning threshold value occurring within the predetermined future period is accurately predicted, thereby improving the accuracy and reliability of the landslide disaster warning.

[0073] Figure 4 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 4As shown, the electronic device may include: a processor 410, a communications interface 420, a memory 430 and a communication bus 440, wherein the processor 410, the communications interface 420 and the memory 430 communicate with each other via the communication bus 440. The processor 410 may call the logic instructions in the memory 430 to execute the collapse disaster early warning method, which includes: Obtaining the cumulative rainfall of the slope unit within a predetermined historical period, the soil moisture content before rainfall, and the number of collapses of the slope unit within the predetermined historical period; Based on the accumulated rainfall and the soil moisture content, the number of times the slope unit has a rainfall type group greater than a collapse warning threshold within a predetermined historical period is obtained; the rainfall type group is a data pair consisting of the accumulated rainfall and the soil moisture content corresponding to the accumulated rainfall; the collapse warning threshold is obtained using a collapse warning model; the collapse warning model is constructed based on the total number of collapses of multiple slope units, the accumulated rainfall within a predetermined historical period before the collapse occurs, and the soil moisture content before the rainfall; Based on the accumulated rainfall and the soil moisture content, a first probability is obtained through an exceedance probability model, the first probability being the probability that the slope unit will experience a rain type group greater than a collapse warning threshold within a predetermined future period; and based on the number of collapses that occurred in the slope unit within a predetermined historical period and the number of rain type groups greater than a collapse warning threshold that occurred in the slope unit within a predetermined historical period, a second probability is obtained, the second probability being the probability that the slope unit will experience a collapse at the same time when a rain type group greater than a collapse warning threshold occurs within a predetermined historical period; the exceedance probability model is established based on Poisson distribution; Based on the first probability and the second probability, a third probability is obtained; the third probability is the probability of the slope unit collapsing simultaneously within a predetermined future period of time when a rainfall pattern group greater than a collapse warning threshold occurs.

[0074] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0075] On the other hand, the present invention further provides a computer program product, the computer program product comprising a computer program, the computer program can be stored on a non-transitory computer-readable storage medium, when the computer program is executed by a processor, the computer can execute the collapse disaster early warning method provided by the above methods, the method comprising: Obtaining the cumulative rainfall of the slope unit within a predetermined historical period, the soil moisture content before rainfall, and the number of collapses of the slope unit within the predetermined historical period; Based on the accumulated rainfall and the soil moisture content, the number of times the slope unit has a rainfall type group greater than a collapse warning threshold within a predetermined historical period is obtained; the rainfall type group is a data pair consisting of the accumulated rainfall and the soil moisture content corresponding to the accumulated rainfall; the collapse warning threshold is obtained using a collapse warning model; the collapse warning model is constructed based on the total number of collapses of multiple slope units, the accumulated rainfall within a predetermined historical period before the collapse occurs, and the soil moisture content before the rainfall; Based on the accumulated rainfall and the soil moisture content, a first probability is obtained through an exceedance probability model, the first probability being the probability that the slope unit will experience a rain type group greater than a collapse warning threshold within a predetermined future period; and based on the number of collapses that occurred in the slope unit within a predetermined historical period and the number of rain type groups greater than a collapse warning threshold that occurred in the slope unit within a predetermined historical period, a second probability is obtained, the second probability being the probability that the slope unit will experience a collapse at the same time when a rain type group greater than a collapse warning threshold occurs within a predetermined historical period; the exceedance probability model is established based on Poisson distribution; Based on the first probability and the second probability, a third probability is obtained; the third probability is the probability of the slope unit collapsing simultaneously within a predetermined future period of time when a rainfall pattern group greater than a collapse warning threshold occurs.

[0076] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for early warning of landslide disasters provided by the above methods is implemented, and the method comprises: Obtaining the cumulative rainfall of the slope unit within a predetermined historical period, the soil moisture content before rainfall, and the number of collapses of the slope unit within the predetermined historical period; Based on the accumulated rainfall and the soil moisture content, the number of times the slope unit has a rainfall type group greater than a collapse warning threshold within a predetermined historical period is obtained; the rainfall type group is a data pair consisting of the accumulated rainfall and the soil moisture content corresponding to the accumulated rainfall; the collapse warning threshold is obtained using a collapse warning model; the collapse warning model is constructed based on the total number of collapses of multiple slope units, the accumulated rainfall within a predetermined historical period before the collapse occurs, and the soil moisture content before the rainfall; Based on the accumulated rainfall and the soil moisture content, a first probability is obtained through an exceedance probability model, the first probability being the probability that the slope unit will experience a rain type group greater than a collapse warning threshold within a predetermined future period; and based on the number of collapses that occurred in the slope unit within a predetermined historical period and the number of rain type groups greater than a collapse warning threshold that occurred in the slope unit within a predetermined historical period, a second probability is obtained, the second probability being the probability that the slope unit will experience a collapse at the same time when a rain type group greater than a collapse warning threshold occurs within a predetermined historical period; the exceedance probability model is established based on Poisson distribution; Based on the first probability and the second probability, a third probability is obtained; the third probability is the probability of the slope unit collapsing simultaneously within a predetermined future period of time when a rainfall pattern group greater than a collapse warning threshold occurs.

[0077] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0078] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical 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 embodiment or some parts of the embodiments.

[0079] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0080] It should also be noted that the terms "first", "second", etc. in the present invention are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same type, and the number of objects is not limited. For example, the first object can be one or more.

[0081] "Determine B based on A" in the embodiments of the present application means that the factor A should be considered when determining B. It is not limited to "B can be determined based on A alone", but should also include: "Determine B based on A and C", "Determine B based on A, C and E", "Determine C based on A, and further determine B based on C", etc. In addition, it can also include taking A as a condition for determining B, for example, "When A meets the first condition, use the first method to determine B"; for another example, "When A meets the second condition, determine B", etc.; for another example, "When A meets the third condition, determine B based on the first parameter", etc. Of course, it can also be a condition that takes A as a factor for determining B, for example, "When A meets the first condition, use the first method to determine C, and further determine B based on C", etc.

[0082] In the present invention, the term "plurality" refers to two or more than two, and other quantifiers are similar to them.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A landslide disaster early warning method, characterized in that: include: Obtaining the cumulative rainfall of the slope unit within a predetermined historical period, the soil moisture content before rainfall, and the number of collapses of the slope unit within the predetermined historical period; Based on the accumulated rainfall and the soil moisture content, the number of times the slope unit has a rainfall type group greater than a collapse warning threshold within a predetermined historical period is obtained; the rainfall type group is a data pair consisting of the accumulated rainfall and the soil moisture content corresponding to the accumulated rainfall; the collapse warning threshold is obtained using a collapse warning model; the collapse warning model is constructed based on the total number of collapses of multiple slope units, the accumulated rainfall within a predetermined historical period before the collapse occurs, and the soil moisture content before the rainfall; Based on the accumulated rainfall and the soil moisture content, a first probability is obtained through an exceedance probability model, the first probability being the probability that the slope unit will experience a rain type group greater than a collapse warning threshold within a predetermined future period; and based on the number of collapses that occurred in the slope unit within a predetermined historical period and the number of rain type groups greater than a collapse warning threshold that occurred in the slope unit within a predetermined historical period, a second probability is obtained, the second probability being the probability that the slope unit will experience a collapse at the same time when a rain type group greater than a collapse warning threshold occurs within a predetermined historical period; the exceedance probability model is established based on Poisson distribution; Based on the first probability and the second probability, a third probability is obtained; the third probability is the probability of the slope unit collapsing simultaneously within a predetermined future period of time when a rainfall pattern group greater than a collapse warning threshold occurs.

2. The collapse disaster early warning method according to claim 1, characterized in that: The steps of constructing the collapse warning model include: Obtain the total number of collapses of multiple slope units, the cumulative rainfall in a predetermined historical period before the collapse, and the soil moisture content before the rainfall; Determine a collapse warning threshold corresponding to the accumulated rainfall and the soil moisture content according to the proportion of the number of collapses corresponding to the accumulated rainfall and the soil moisture content in the total number of collapses in the plurality of slope units; The collapse warning model is established based on the corresponding relationship between the accumulated rainfall, the soil moisture content and the collapse warning threshold.

3. The collapse disaster early warning method according to claim 1, characterized in that: The obtaining a first probability based on the accumulated rainfall and the soil moisture content includes: Based on the accumulated rainfall of the slope unit within a predetermined historical period and the soil moisture content before rainfall, determining the number of times the accumulated rainfall and the soil moisture content are greater than a collapse warning threshold value corresponding to the accumulated rainfall and the soil moisture content; Based on the number of times the cumulative rainfall and the soil moisture content are greater than the collapse warning threshold corresponding to the cumulative rainfall and the soil moisture content and the number of years of the predetermined historical period, the frequency of the occurrence of a rainfall type group greater than the collapse warning threshold in the slope unit within the predetermined historical period is obtained; The first probability is calculated based on the frequency of the occurrence of rainfall pattern groups greater than the collapse warning threshold value in the slope unit within a predetermined historical period of time, using an exceedance probability model.

4. The collapse disaster early warning method according to claim 3, characterized in that: The obtaining, based on the number of times that the cumulative rainfall and the soil moisture content are greater than the collapse warning threshold corresponding to the cumulative rainfall and the soil moisture content and the number of years of the predetermined historical period, of a frequency of a rainfall type group greater than the collapse warning threshold occurring in the slope unit within the predetermined historical period, comprises: The result of dividing the number of times the cumulative rainfall and the soil moisture content are greater than the collapse warning threshold corresponding to the cumulative rainfall and the soil moisture content by the number of years of the predetermined historical period is taken as the frequency of the occurrence of a rainfall type group greater than the collapse warning threshold in the slope unit within the predetermined historical period.

5. The collapse disaster early warning method according to claim 1, characterized in that: The obtaining of the second probability based on the number of collapses of the slope unit within a predetermined historical period and the number of rain pattern groups greater than the collapse warning threshold value occurring in the slope unit within a predetermined historical period comprises: The second probability is obtained by dividing the number of times the slope unit collapses within a predetermined historical period by the number of times the slope unit experiences a rainfall pattern greater than a collapse warning threshold within a predetermined historical period.

6. The collapse disaster early warning method according to claim 1, characterized in that: The obtaining a third probability based on the first probability and the second probability includes: The result of multiplying the first probability by the second probability is used as the third probability.

7. A collapse disaster warning device, characterized in that: include: An acquisition module, used to acquire the accumulated rainfall of the slope unit within a predetermined historical period, the soil moisture content before rainfall, and the number of collapses of the slope unit within the predetermined historical period; A first calculation module is used to obtain the number of times that the slope unit has a rainfall type group greater than a collapse warning threshold value within a predetermined historical period based on the accumulated rainfall and the soil moisture content; the rainfall type group is a data pair consisting of the accumulated rainfall and the soil moisture content corresponding to the accumulated rainfall; the collapse warning threshold value is obtained using a collapse warning model; the collapse warning model is constructed based on the total number of times that a collapse occurs in multiple slope units, the accumulated rainfall within a predetermined historical period before the collapse occurs, and the soil moisture content before the rainfall; The second calculation module is used to obtain a first probability based on the accumulated rainfall and the soil moisture content through a exceedance probability model, wherein the first probability is the probability that the slope unit will experience a rain type group greater than a collapse warning threshold within a predetermined future period; and, based on the number of collapses that occurred in the slope unit within a predetermined historical period and the number of rain type groups greater than a collapse warning threshold that occurred in the slope unit within a predetermined historical period, obtain a second probability, wherein the second probability is the probability that the slope unit will experience a collapse at the same time when a rain type group greater than a collapse warning threshold occurs within a predetermined historical period; the exceedance probability model is established based on Poisson distribution; The early warning module is used to obtain a third probability based on the first probability and the second probability; the third probability is the probability that the slope unit will collapse simultaneously within a predetermined future period of time when a rainfall type group greater than a collapse early warning threshold occurs.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the collapse disaster warning method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the collapse disaster warning method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the collapse disaster warning method according to any one of claims 1 to 6 is implemented.

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