Landslide event determination method and related device

Abstract

The invention provides a landslide event determination method and a related device, and the method comprises the steps: determining the daily rainfall disaster-causing probability of each region through a rainfall disaster-causing probability model based on the simulated rainfall data in a target time period, and determining the landslide event based on the rainfall disaster-causing probability and the slope instability probability corresponding to each slope unit. And determining a first landslide probability corresponding to each slope unit every day, performing random extraction in an interval [0, 1] through a Monte Carlo method to obtain a second landslide probability corresponding to each slope unit every day, and when the second landslide probability is smaller than or equal to the first landslide probability, determining that a landslide event occurs in the slope unit. According to the method, random extraction is carried out through the Monte Carlo method, and the possibility that each slope unit has a landslide in an actual condition can be considered, so that whether the landslide occurs or not is determined without completely depending on the size of the first landslide probability, and a result of determining whether a certain slope unit has a landslide event is more in line with the actual condition; and the result is more accurate.

Description

Technical Field

[0001] The present application relates to the field of landslides, and in particular to a method for determining a landslide event and a related device. Background Art

[0002] At present, natural disasters such as landslides have caused great losses to people and seriously affected their daily lives. In order to ensure people's physical safety and reduce economic losses, it is necessary to prejudge landslide events. In addition, by accurately predicting landslides, insurance companies can have a reasonable basis when pricing premiums. Therefore, providing a suitable method for determining landslide events has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] In view of this, the purpose of this application is to provide a landslide event determination method and related device, which can take into account the possibility of landslide in each slope unit in actual situations, so as to determine whether a landslide occurs without completely relying on the size of the first landslide probability, and further determine whether a landslide event occurs in a certain slope unit. The result will be more in line with the actual situation and the result will be more accurate. The specific scheme is as follows:

[0004] In one aspect, the present application provides a method for determining a landslide event, comprising:

[0005] Based on the simulated rainfall data within the target time period, the probability of rainfall disasters in each region on a daily basis is determined by a rainfall disaster probability model; the simulated rainfall data includes the daily rainfall in each of the plurality of regions within the target time period; the region includes a plurality of slope units;

[0006] Based on the rainfall disaster probability and the slope instability probability corresponding to each of the slope units, determining a first landslide probability corresponding to each of the slope units every day; the first landslide probability is within the interval [0,1];

[0007] Performing random sampling in the interval [0,1] by using the Monte Carlo method, to obtain the second landslide probability corresponding to each slope unit every day;

[0008] The first landslide probability is compared with the second landslide probability, and when the second landslide probability is less than or equal to the first landslide probability, it is determined that a landslide event has occurred in the slope unit.

[0009] Optionally, the method further comprises:

[0010] For multiple target time periods in each of the N years, the simulated rainfall data based on the target time period is repeatedly executed, and the daily rainfall disaster probability in each area is determined by a rainfall disaster probability model; based on the rainfall disaster probability and the slope instability probability corresponding to each slope unit, a first landslide probability corresponding to each slope unit is determined daily; a second landslide probability corresponding to each slope unit is obtained by randomly selecting in the [0,1] interval by the Monte Carlo method; the first landslide probability is compared with the second landslide probability, and when the second landslide probability is less than or equal to the first landslide probability, it is determined that a landslide event has occurred in the slope unit;

[0011] For each of the said years, based on the number of landslide events occurring in each of the said slope units in each of the said regions and the number of slope units in the said regions, the total number of landslide events occurring in each of the said regions in each of the said years is determined to form a landslide random event set.

[0012] Optionally, the determining the first landslide probability corresponding to each slope unit on a daily basis based on the rainfall disaster probability and the slope instability probability corresponding to each slope unit comprises:

[0013] Determining whether the rainfall disaster probability is greater than an effective rainfall probability threshold;

[0014] If so, the step of determining the first landslide probability corresponding to each slope unit every day based on the rainfall disaster probability and the slope instability probability corresponding to each slope unit is performed.

[0015] Optionally, before determining the probability of daily rainfall disasters in each region through a rainfall disaster probability model based on simulated rainfall data in the target time period, the method further includes:

[0016] Based on the historical landslide data and historical rainfall data within the historical time period, the rainfall disaster probability model is constructed by the logistic regression method; the historical landslide data include the number of landslides occurring in all the slope units in each of the multiple regions every day; the historical rainfall data include the daily rainfall in each of the multiple regions within the historical time period.

[0017] Optionally, the rainfall disaster probability model is specifically:

[0018]

[0019] p is the probability of rainfall causing disaster, x k is the rainfall in the region on the previous k-1 day, the β kis the effective rainfall coefficient of the region in the previous k-1 days, and the β 0 is the cut-off coefficient, and k is the number of effective rainfall days.

[0020] Optionally, the determining the first landslide probability corresponding to each slope unit on a daily basis based on the rainfall disaster probability and the slope instability probability corresponding to each slope unit comprises:

[0021] The rainfall disaster probability and the slope instability probability corresponding to each of the slope units are multiplied to determine the first landslide probability corresponding to each of the slope units every day.

[0022] Optionally, the slope instability probability is determined based on the geological conditions or steepness of the slope unit.

[0023] In another aspect, an embodiment of the present application further provides a landslide event determination device, comprising:

[0024] A first determination unit is used to determine the probability of rainfall disasters in each region on a daily basis through a rainfall disaster probability model based on simulated rainfall data in a target time period; the simulated rainfall data includes the daily rainfall in each of the plurality of regions in the target time period; the region includes a plurality of slope units;

[0025] A second determination unit is configured to determine a first landslide probability corresponding to each slope unit on a daily basis based on the rainfall disaster probability and the slope instability probability corresponding to each slope unit; the first landslide probability is within the interval [0,1];

[0026] An extraction unit, used for performing random extraction in the interval [0,1] by using the Monte Carlo method to obtain the second landslide probability corresponding to each slope unit every day;

[0027] The third determining unit is configured to compare the first landslide probability with the second landslide probability, and determine that a landslide event has occurred in the slope unit when the second landslide probability is less than or equal to the first landslide probability.

[0028] In another aspect, an embodiment of the present application provides a computer device, the computer device comprising a processor and a memory:

[0029] The memory is used to store program code and transmit the program code to the processor;

[0030] The processor is configured to execute the method described above according to the instructions in the program code.

[0031] On the other hand, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store a computer program, and the computer program is used to execute the method described in the above aspects.

[0032] The embodiment of the present application provides a method and related device for determining a landslide event. Based on the simulated rainfall data in a target time period, the daily rainfall disaster probability in each region is determined through a rainfall disaster probability model. The rainfall disaster probability can reflect the degree of disaster impact of rainfall on slope units. The simulated rainfall data includes the daily rainfall in each region in multiple regions in the target time period. The region includes multiple slope units. In addition, since each slope unit also has its own corresponding slope instability probability, based on the rainfall disaster probability and the slope instability probability corresponding to each slope unit, the first landslide probability corresponding to each slope unit is determined daily, so that the first landslide probability takes into account both the influence of rainfall and the influence of the soil conditions of the slope unit itself; wherein the first landslide probability is in the interval [0,1]. In order to determine whether a landslide event occurs in the slope unit based on the first landslide probability, a reference value needs to be determined for the first landslide probability. When determining the reference value, it is necessary to consider that each slope unit actually has the possibility of sliding. Then, a random sampling can be performed in the interval [0,1] by the Monte Carlo method to obtain the second landslide probability corresponding to each slope unit every day, and the second landslide probability is used as a reference value. The first landslide probability is compared with the second landslide probability. When the second landslide probability is less than or equal to the first landslide probability, it is determined that a landslide event occurs in the slope unit. In short, the present application determines the reference value of the first landslide probability by randomly sampling using the Monte Carlo method. It can be considered that each slope unit has the possibility of landslide in actual situations, so as not to completely rely on the size of the first landslide probability to determine whether a landslide occurs, and then the result of determining whether a landslide event occurs in a certain slope unit will be more in line with the actual situation, and the landslide event result will be more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0034] Figure 1 A schematic flow chart of a method for determining a landslide event provided in an embodiment of the present application is shown;

[0035] Figure 2 The simulated rainfall data of a certain area in one year provided by an embodiment of the present application is shown;

[0036] Figure 3 Another simulated rainfall data of a certain area in one year provided by an embodiment of the present application is shown;

[0037] Figure 4 A flow chart of another method for determining a landslide event provided in an embodiment of the present application is shown;

[0038] Figure 5 A structural block diagram of a landslide event determination device provided in an embodiment of the present application;

[0039] Figure 6 A structural diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0040] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below with reference to the accompanying drawings.

[0041] In the following description, many specific details are set forth to facilitate a full understanding of the present application, but the present application may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present application. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0042] For ease of understanding, a landslide event determination method and related devices provided in an embodiment of the present application are described in detail below with reference to the accompanying drawings.

[0043] refer to Figure 1 As shown, it is a flow chart of a method for determining a landslide event provided in an embodiment of the present application. The method may include the following steps.

[0044] S101, based on the simulated rainfall data in the target time period, determine the daily rainfall disaster probability in each region through a rainfall disaster probability model.

[0045] The target time period may be a period of time to be predicted in the future, such as a period of one week in the future or a period of one month in the future, and the target time period may not exceed one year. The server may simulate rainfall in the target time period, and may use an existing model to perform simulation calculations, thereby obtaining simulated rainfall data in the target time period.

[0046] The simulated rainfall data may include the daily rainfall in each of the multiple regions within the target time period, and the region includes multiple slope units. That is, each region may include multiple slope units, and the slope unit may be understood as the slope of a mountain. A mountain may be composed of multiple slopes. In addition, a region may also have multiple discontinuous mountains. The simulated rainfall data includes the daily rainfall in each region.

[0047] For example, the simulated rainfall data may include that the daily rainfall in area A in the next week is 20mm, 5.2mm, 4mm, 4.5mm, 3.0mm, 3.5mm and 2.3mm respectively. The daily rainfall in area B is 3.3mm, 3.8mm, 16mm, 6.0mm, 5.0mm, 5.5mm and 3.6mm respectively. Figure 2 As shown, it is a simulated rainfall data of a certain area in one year provided by an embodiment of the present application, the horizontal axis is day / 365, and the vertical axis is rainfall (mm).

[0048] The rainfall disaster probability model can be used to calculate the probability of disasters occurring under certain rainfall data, that is, to output the rainfall disaster probability. The rainfall disaster probability means the probability that the rainfall data will cause landslides, that is, it can reflect the degree of disaster impact of rainfall on slope units.

[0049] Specifically, the simulated rainfall data can be input into the rainfall disaster probability model to output the rainfall disaster probability. Since the simulated rainfall data is the rainfall situation in each region every day, the rainfall disaster probability is also the probability in each region every day. For example, in the next week, the rainfall disaster probability corresponding to region A every day is 0.18, 0.3, 0.27, 0.5, 0.65, 0.63 and 0.55.

[0050] In one possible implementation, in S101, before determining the daily rainfall disaster probability in each region through a rainfall disaster probability model based on simulated rainfall data in a target time period, a rainfall disaster probability model can also be constructed through a logistic regression method based on historical landslide data and historical rainfall data in a historical time period.

[0051] Specifically, the historical rainfall data may include the daily rainfall in each of the multiple regions within the historical time period. That is, the rainfall data is generally the rainfall in one region, and the historical rainfall data may include the daily rainfall in each of the multiple regions.

[0052] The historical landslide data may include the number of landslides that occurred in all slope units in each region of the multiple regions on a daily basis. Since the rainfall is measured in units of each region per day, the landslides caused by rainfall can also be measured in units of each region per day. The historical landslide data is the number of landslides that occurred in each region per day in many regions. In addition, each region usually has many slope units, that is, the number of landslides in a region on a certain day is the total number of landslides in all slope units in the region on that day.

[0053] Specifically, by using the logistic regression method, the data relationship between historical landslide data and historical rainfall data can be found, thereby generating a rainfall disaster probability model. By using the logistic regression algorithm, the rainfall disaster probability model can be quickly and accurately determined.

[0054] In a possible implementation, the factors causing landslides include not only the rainfall on the day when the landslide occurs, but also the rainfall in the previous days, that is, the rainfall in a period of time before the landslide occurs can cause the landslide.

[0055] The specific probability model of rainfall disaster is as follows:

[0056]

[0057] Among them, p is the probability of rainfall causing disasters, p is between [0,1], x k is the rainfall in the region on the previous k-1 day, β k is the effective rainfall coefficient of the region in the previous k-1 days, β 0 is the cutoff coefficient, and k is the number of effective rainfall days. 1 is the rainfall in the area on that day, x 2 is the rainfall in the region on the first day before, β 1 is the effective rainfall coefficient of the region on that day, β 2 is the effective rainfall coefficient of the region on the first day before. The effective rainfall days are the rainfall days that will affect the landslide. As an example, k can be 7, that is, the rainfall in the 6 days before the landslide will affect the landslide.

[0058] In summary, using the above formula to express the rainfall-induced disaster probability model further improves the accuracy of the model.

[0059] S102, determining the first landslide probability corresponding to each slope unit every day based on the rainfall disaster probability and the slope instability probability corresponding to each slope unit.

[0060] Since each slope unit has its own structure and characteristics, each slope unit has a corresponding slope instability probability, which is the probability of landslide caused by the structure and characteristics of the slope unit itself. For example, the slope instability probability of slope unit X1 is 0.3, and the slope instability probability of slope unit X2 is 0.1.

[0061] Specifically, the probability of slope instability can be determined based on the geological conditions or steepness of the slope unit. For example, the looser the geological soil of the slope unit, the greater the probability of slope instability; the steeper the slope of the slope unit, the greater the probability of slope instability.

[0062] Specifically, the probability of rainfall causing disasters can reflect the impact of rainfall data on the occurrence of landslides in a certain area on a certain day, and the probability of slope instability can reflect the impact of the slope unit's own structure on the occurrence of landslides in a certain area on a certain day. The probability of rainfall causing disasters is combined with the probability of slope instability to determine the first landslide probability.

[0063] In addition, although the probability of rainfall disaster is the probability of a certain area on a certain day, it can be considered that the probability of landslides occurring in each slope unit in the area on that day is the probability of rainfall disaster. In other words, on the same day, the probability of rainfall disaster in a certain area is also the probability of rainfall disaster in each slope unit in the area.

[0064] Therefore, for a single slope unit on a certain day, the corresponding rainfall disaster probability can reflect the probability of landslide under the influence of rainfall, and the slope instability probability can reflect the probability of landslide due to the slope's own structure. Based on these two types of probabilities, the first landslide probability can be determined.

[0065] The first landslide probability is the probability of a landslide occurring in each slope unit every day. The first landslide probability takes into account the influence of both rainfall and the soil conditions of the slope unit itself. The first landslide probability can be in the interval [0,1].

[0066] In a possible implementation, S102, based on the rainfall disaster probability and the slope instability probability corresponding to each slope unit, determines the first landslide probability corresponding to each slope unit every day. Specifically, the rainfall disaster probability and the slope instability probability corresponding to each slope unit are multiplied to determine the first landslide probability corresponding to each slope unit every day.

[0067] In short, since the probability of rainfall disaster is the probability of landslide disaster in a certain area due to rainfall, the probability of rainfall disaster in which landslide may occur in each slope unit in the area is the same, which is equal to the corresponding rainfall disaster probability in the area. For a single slope unit, the probability of rainfall disaster can be multiplied by the probability of slope instability to obtain the first landslide probability corresponding to each slope unit, thereby improving the accuracy of the first landslide probability.

[0068] In a possible implementation, based on the rainfall disaster probability and the slope instability probability corresponding to each slope unit, the first landslide probability corresponding to each slope unit every day is determined. Specifically, it can be determined whether the rainfall disaster probability is greater than the effective rainfall probability threshold; if so, the step of determining the first landslide probability corresponding to each slope unit every day based on the rainfall disaster probability and the slope instability probability corresponding to each slope unit is executed.

[0069] Specifically, the effective rainfall probability threshold may be a pre-set threshold, which may be understood as a probability value for determining whether the rainfall is effective rainfall. Effective rainfall refers to rainfall that will cause landslides. For example, the effective rainfall probability threshold may be 0.5. If the probability of rainfall causing disasters is greater than the effective rainfall probability threshold, it means that the probability of rainfall causing disasters is relatively high, and it is likely to cause landslides. The higher probability of rainfall causing disasters may be combined with the slope instability probability corresponding to each slope unit to determine the first landslide probability. For example, if the probability of rainfall causing disasters is 0.6, which is greater than the effective rainfall probability threshold of 0.5, it may be determined that there is an effective rainfall event on that day, and the next step may be entered to determine the first landslide probability.

[0070] refer to Figure 3 As shown, it is another simulated rainfall data of a certain area in one year provided by the embodiment of the present application, the horizontal axis is day / 365, and the vertical axis is rainfall (mm). Figure 2 In terms of Figure 3 The probability of rainfall disaster on the day corresponding to the red line is greater than the effective rainfall probability threshold and can be recorded as an effective rainfall event.

[0071] In short, by comparing the rainfall disaster probability with the effective rainfall probability threshold, the smaller rainfall disaster probability that is less likely to cause landslide events can be screened out, thereby reducing unnecessary calculation processes, reducing the amount of calculation, achieving the initial screening of landslide events, and improving the accuracy of landslide event determination.

[0072] S103, performing random sampling in the interval [0,1] by using the Monte Carlo method to obtain the second landslide probability corresponding to each slope unit every day.

[0073] In order to determine whether a landslide event occurs in the slope unit based on the first landslide probability, a reference value needs to be determined for the first landslide probability. When determining the reference value, it is necessary to consider that each slope unit actually has the possibility of sliding. Then, within the [0,1] interval where the first landslide probability is located, a random draw can be performed using the Monte Carlo method, and the drawn value can be used as the second landslide probability corresponding to each slope unit every day. For example, the second landslide probability is 0.3.

[0074] S104, comparing the first landslide probability with the second landslide probability, and when the second landslide probability is less than or equal to the first landslide probability, determining that a landslide event occurs in the slope unit.

[0075] Specifically, the second landslide probability can be used as a reference value of the first landslide probability, and the two can be compared. If the second landslide probability is less than the first landslide probability, it is determined that a landslide event has occurred in the slope unit, and a landslide event is recorded.

[0076] In addition, the Monte Carlo method converts the continuous probability distribution into a discrete type and determines the probability value by extraction. If the first landslide probability is 0.3, it means that there is a possibility of sliding 30 times out of 100 times of extraction. If the probability of extracting from the [1,100] interval is 0.3, so when the value extracted is 20, it means sliding. If the [1,100] interval is replaced by the [0,1] interval, when 0.2 is extracted, it means sliding, and when >0.3 is extracted, it means no sliding. Therefore, when the second landslide probability is less than the first landslide probability, it means that a landslide event has occurred.

[0077] In other words, if the first landslide probability is 0.3, it can correspond to sliding when the value drawn is 0-0.3, and no sliding when it is 0.3-1, which is consistent with the first landslide probability of 0.3. Therefore, if the second landslide probability drawn is in the range of 0-0.3, it means that a landslide has occurred, that is, when the second landslide probability is less than the first landslide probability, it means that a landslide event has occurred.

[0078] In summary, the present application determines the reference value of the first landslide probability, i.e., the second landslide probability, by using the Monte Carlo method for random sampling. It can take into account that each slope unit has the possibility of landslide in actual situations, so as not to completely rely on the size of the first landslide probability to determine whether a landslide occurs, and then the result of determining whether a landslide event occurs in a certain slope unit will be more in line with the actual situation, and the landslide event result will be more accurate. On the contrary, if the landslide event is directly determined based on the size of the first landslide probability, the landslide event will be fixed, that is, the slope unit with a high first landslide probability will experience a landslide, but in fact each slope unit has the possibility of sliding, so in the present application, the second landslide probability is combined to judge the landslide event, thereby improving the accuracy of the determination of the landslide event and avoiding the solidification of the landslide event.

[0079] In a possible implementation, the steps of repeatedly executing the simulated rainfall data in the target time period for multiple target time periods in each of the N years, determining the daily rainfall disaster probability in each region through a rainfall disaster probability model; determining the first landslide probability corresponding to each slope unit daily based on the rainfall disaster probability and the slope instability probability corresponding to each slope unit; performing random sampling in the interval [0,1] through the Monte Carlo method to obtain the second landslide probability corresponding to each slope unit daily; comparing the first landslide probability with the second landslide probability, and determining that a landslide event has occurred in the slope unit when the second landslide probability is less than or equal to the first landslide probability; and for each year, determining the total number of landslide events in each region in each year based on the number of landslide events in each slope unit in each region and the number of slope units in the region, to form a landslide random event set.

[0080] Specifically, landslide events can be continuously monitored for a long time, thereby generating a landslide random event set. Continuous monitoring can be performed within N years, wherein N is an integer greater than or equal to 1, such as 100. In each year, multiple target time periods can be provided, that is, it can be judged whether a landslide event occurs multiple times within a year. For each target time period of each year, S101-S104 are performed to determine the landslide events that occur in each time period of each year, and since the landslide events correspond to slope units, the number of times that landslide events occur in each slope unit in each region in each year can be determined in units of years, and the number of times is multiplied by the number of slope units in the region, and the total number of events of landslide events that occur in each region in each year can be obtained, thereby forming a landslide random event set.

[0081] Refer to the table below, which is a set of random landslide events, including the year, the total number of events, the number of slope units, and the day corresponding to the landslide event occurring in the slope unit.

[0082]

[0083] In this way, since there is a lot of randomness and uncertainty in predicting the landslide situation in the area by only determining one landslide event, the accuracy is low. Therefore, by predicting and statistically analyzing landslide events over a long period of time to form a set of random landslide events and increase the sample size, it is possible to make more accurate predictions about landslide events occurring in various regions, thereby providing an accurate basis for premium pricing in the region and avoiding damage to the insurance company's property.

[0084] refer to Figure 4 As shown, a flow chart for determining another landslide event provided by an embodiment of the present application can be determined based on historical landslide data and historical rainfall data, and analyzed by a logistic regression method to determine the effective rainfall days and the effective rainfall coefficient, and thereby construct a rainfall disaster probability model. The simulated rainfall data within the target time period is obtained, and the daily rainfall disaster probability is determined by the rainfall disaster probability model. When the rainfall disaster probability of a certain day is greater than the effective rainfall probability threshold, it is recorded as an effective rainfall event. For an effective rainfall event, the first landslide probability is randomly extracted. If it is not greater than the second landslide probability (rainfall disaster probability x slope instability probability), a landslide event is recorded. The above calculations are performed in each target time period in a long time series to form a landslide random event set.

[0085] In summary, the present application determines the reference value of the first landslide probability by using the Monte Carlo method for random sampling. It can take into account that each slope unit has the possibility of landslide in actual situations, so as not to completely rely on the size of the first landslide probability to determine whether a landslide occurs. The result of determining whether a landslide event occurs in a certain slope unit will be more in line with the actual situation, and the landslide event result will be more accurate.

[0086] Based on the above landslide event determination method, the present application embodiment also provides a landslide event determination device, referring to Figure 5 As shown, it is a structural block diagram of a landslide event determination device provided in an embodiment of the present application, and the device may include:

[0087] The first determination unit 201 is used to determine the probability of rainfall disasters in each region on a daily basis through a rainfall disaster probability model based on simulated rainfall data in a target time period; the simulated rainfall data includes the daily rainfall in each of the plurality of regions in the target time period; the region includes a plurality of slope units;

[0088] A second determining unit 202 is used to determine a first landslide probability corresponding to each slope unit on a daily basis based on the rainfall disaster probability and the slope instability probability corresponding to each slope unit; the first landslide probability is within the interval [0,1];

[0089] An extraction unit 203 is used to perform random extraction in the interval [0, 1] by using a Monte Carlo method to obtain a second landslide probability corresponding to each slope unit on a daily basis;

[0090] The third determining unit 204 is configured to compare the first landslide probability with the second landslide probability, and determine that a landslide event has occurred in the slope unit when the second landslide probability is less than or equal to the first landslide probability.

[0091] Optionally, the device further comprises:

[0092] An execution unit is used for repeatedly executing the simulated rainfall data based on the target time period for multiple target time periods of each of the N years, determining the daily rainfall disaster probability in each area through a rainfall disaster probability model; determining the first landslide probability corresponding to each slope unit daily based on the rainfall disaster probability and the slope instability probability corresponding to each slope unit; performing random sampling in the [0,1] interval through the Monte Carlo method to obtain the second landslide probability corresponding to each slope unit daily; comparing the first landslide probability with the second landslide probability, and when the second landslide probability is less than or equal to the first landslide probability, determining that a landslide event has occurred in the slope unit;

[0093] The fourth determination unit is used to determine the total number of landslide events occurring in each of the regions in each of the years based on the number of landslide events occurring in each of the slope units in each of the regions and the number of slope units in the regions, so as to form a landslide random event set.

[0094] Optionally, the second determining unit is configured to:

[0095] Determining whether the rainfall disaster probability is greater than an effective rainfall probability threshold;

[0096] If so, the step of determining the first landslide probability corresponding to each slope unit every day based on the rainfall disaster probability and the slope instability probability corresponding to each slope unit is performed.

[0097] Optionally, the device further comprises:

[0098] A construction unit is used to construct the rainfall disaster probability model through a logistic regression method based on historical landslide data and historical rainfall data within a historical time period; the historical landslide data includes the number of landslides occurring daily in all the slope units in each of the multiple regions; the historical rainfall data includes the daily rainfall in each of the multiple regions within the historical time period.

[0099] Optionally, the rainfall disaster probability model is specifically:

[0100]

[0101] p is the probability of rainfall causing disaster, x k is the rainfall in the region on the previous k-1 day, the β k is the effective rainfall coefficient of the region in the previous k-1 days, and the β 0 is the cut-off coefficient, and k is the number of effective rainfall days.

[0102] Optionally, the second determining unit is used to:

[0103] The rainfall disaster probability and the slope instability probability corresponding to each of the slope units are multiplied to determine the first landslide probability corresponding to each of the slope units every day.

[0104] Optionally, the slope instability probability is determined based on the geological conditions or steepness of the slope unit.

[0105] In another aspect, the present application provides a computer device, referring to Figure 6 , which is a structural diagram of a computer device provided in an embodiment of the present application, wherein the computer device includes a processor 310 and a memory 320:

[0106] The memory 320 is used to store program codes and transmit the program codes to the processor 310;

[0107] The processor 310 is used to execute the method provided in the above embodiment according to the instructions in the program code.

[0108] The computer device may include a terminal device or a server, and the aforementioned apparatus may be configured in the computer device.

[0109] On the other hand, an embodiment of the present application further provides a storage medium, wherein the storage medium is used to store a computer program, and the computer program is used to execute the method provided by the above embodiment.

[0110] A person skilled in the art can understand that all or part of the steps of implementing the above method embodiment can be completed by program instruction hardware, and the above program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the above storage medium can be at least one of the following media: read-only memory (English: Read-only Memory, abbreviated: ROM), RAM, magnetic disk or optical disk, etc. Various media that can store program codes.

[0111] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0112] The above is only a preferred implementation of the present application. Although the present application has been disclosed as a preferred embodiment, it is not intended to limit the present application. Any technician familiar with the art can use the above disclosed methods and technical contents to make many possible changes and modifications to the technical solution of the present application without departing from the scope of the technical solution of the present application, or modify it into an equivalent embodiment of equivalent changes. Therefore, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present application without departing from the content of the technical solution of the present application still falls within the scope of protection of the technical solution of the present application.

Claims

1. A method for determining a landslide event, characterized in that: include: Based on the simulated rainfall data within the target time period, the probability of rainfall causing disasters in each area on a daily basis is determined through the rainfall causing disaster probability model; The simulated rainfall data includes daily rainfall in each of the plurality of regions within the target time period; the region includes a plurality of slope units; Based on the rainfall disaster probability and the slope instability probability corresponding to each of the slope units, determining a first landslide probability corresponding to each of the slope units every day; the first landslide probability is within the interval [0,1]; Performing random sampling in the interval [0,1] by using the Monte Carlo method, to obtain the second landslide probability corresponding to each slope unit every day; The first landslide probability is compared with the second landslide probability, and when the second landslide probability is less than or equal to the first landslide probability, it is determined that a landslide event has occurred in the slope unit.

2. The method according to claim 1, characterized in that The method further comprises: For multiple target time periods in each of the N years, the simulated rainfall data based on the target time period is repeatedly executed, and the daily rainfall disaster probability in each area is determined by a rainfall disaster probability model; based on the rainfall disaster probability and the slope instability probability corresponding to each slope unit, a first landslide probability corresponding to each slope unit is determined daily; a second landslide probability corresponding to each slope unit is obtained by randomly selecting in the [0,1] interval by the Monte Carlo method; the first landslide probability is compared with the second landslide probability, and when the second landslide probability is less than or equal to the first landslide probability, it is determined that a landslide event has occurred in the slope unit; For each of the said years, based on the number of landslide events occurring in each of the said slope units in each of the said regions and the number of slope units in the said regions, the total number of landslide events occurring in each of the said regions in each of the said years is determined to form a landslide random event set.

3. The method according to claim 1, characterized in that The determining, based on the rainfall disaster probability and the slope instability probability corresponding to each slope unit, a first landslide probability corresponding to each slope unit on a daily basis comprises: Determining whether the rainfall disaster probability is greater than an effective rainfall probability threshold; If so, the step of determining the first landslide probability corresponding to each slope unit every day based on the rainfall disaster probability and the slope instability probability corresponding to each slope unit is performed.

4. The method according to any one of claims 1 to 3, characterized in that: Before determining the probability of daily rainfall disasters in each region through a rainfall disaster probability model based on simulated rainfall data in the target time period, the method further includes: Based on the historical landslide data and historical rainfall data within the historical time period, the rainfall disaster probability model is constructed by the logistic regression method; the historical landslide data include the number of landslides occurring in all the slope units in each of the multiple regions every day; the historical rainfall data include the daily rainfall in each of the multiple regions within the historical time period.

5. The method according to claim 4, characterized in that The rainfall disaster probability model is specifically as follows: p is the probability of rainfall causing disaster, x k is the rainfall in the region on the previous k-1 day, the β k is the effective rainfall coefficient of the region in the previous k-1 days, β0 is the cutoff coefficient, and k is the number of effective rainfall days.

6. The method according to claim 1, characterized in that The determining, based on the rainfall disaster probability and the slope instability probability corresponding to each slope unit, a first landslide probability corresponding to each slope unit on a daily basis comprises: The rainfall disaster probability and the slope instability probability corresponding to each of the slope units are multiplied to determine the first landslide probability corresponding to each of the slope units every day.

7. The method according to claim 1, characterized in that The slope instability probability is determined based on the geological conditions or steepness of the slope unit.

8. A landslide event determination device, characterized in that: include: A first determination unit is used to determine the probability of rainfall disasters in each area on a daily basis through a rainfall disaster probability model based on simulated rainfall data in a target time period; The simulated rainfall data includes daily rainfall in each of the plurality of regions within the target time period; the region includes a plurality of slope units; A second determination unit is configured to determine a first landslide probability corresponding to each slope unit on a daily basis based on the rainfall disaster probability and the slope instability probability corresponding to each slope unit; the first landslide probability is within the interval [0,1]; An extraction unit, used for performing random extraction in the interval [0,1] by using the Monte Carlo method to obtain the second landslide probability corresponding to each slope unit every day; The third determining unit is configured to compare the first landslide probability with the second landslide probability, and determine that a landslide event has occurred in the slope unit when the second landslide probability is less than or equal to the first landslide probability.

9. A computer device, characterized in that: The computer device comprises a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is configured to execute the method according to any one of claims 1 to 7 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store a computer program, and the computer program is used to execute the method according to any one of claims 1 to 6.

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