A slope collapse risk assessment method and system

Abstract

The present invention relates to the technical field of water and drought disaster data processing, and provides a slope collapse risk assessment method and system. The slope collapse risk assessment method includes: constructing a plurality of grid units and a plurality of data matrices based on the surface structure of the study area; obtaining the probability distribution of future rainfall; generating a future rainfall scenario library; constructing a soil hydrological mechanism model; obtaining a plurality of flood peak element results based on the future rainfall scenario library and the soil hydrological mechanism model to calculate uncertainty; obtaining a hydrological mechanism data group; establishing a fault tree model of the slope collapse event, evaluating the slope collapse risk expectation, and evaluating the accuracy of the slope collapse risk expectation based on the uncertainty. By establishing a data matrix related to the time scale, the model simulates the entire process of the hydrological cycle, and the prediction and understanding of the hydrological process are more in-depth, and the dynamic slope condition change process can be obtained, and the slope collapse risk expectation and the expected accuracy can be quantitatively evaluated.

Description

Technical Field

[0001] The present invention relates to the technical field of flood and drought disaster data processing, and in particular to a slope collapse risk assessment method and system. Background Art

[0002] With the acceleration of climate change and urbanization, slope collapse events have become a focus of disaster prevention and mitigation. Especially under heavy rain conditions, the slope state changes are complex, and it is urgent to establish a complete risk assessment mechanism for slope collapse during heavy rain.

[0003] Regarding the risk assessment of slope collapse, it can be roughly divided into two directions: mathematical statistics model and physical simulation model. Mathematical statistics model generally establishes an object-event-impact data set, takes parameters such as slope, soil hydrophilicity, rainfall intensity as indicators, and uses a large amount of historical case data to calculate the weight of each indicator using different models / algorithms, and finally obtains the expected risk of slope collapse in the area; physical simulation model generally calculates multi-dimensional values ​​such as rainfall infiltration changes soil weight, reduces soil shear strength, and reduces soil effective stress based on actual rainfall data, so as to judge the risk of slope collapse.

[0004] However, these two methods for assessing the risk of slope collapse during heavy rain still have shortcomings. Mathematical and statistical models are highly dependent on the quality and quantity of historical case data, usually ignoring the dynamic process and time effect of slope collapse, and it is difficult to accurately reflect the stability changes of slopes at different time scales. Physical simulation models require a large amount of computing resources and complex numerical algorithms. The calculation process is time-consuming and has high requirements for computing conditions. In addition, physical simulation models require reasonable boundary conditions to be set, and the setting of these conditions often depends on geological exploration or assumed / simplified engineering experience, which may deviate from the actual situation. The above two models basically lack in-depth understanding and quantitative analysis of hydrological processes, and it is difficult to accurately predict and assess the dynamic changes and potential risks of slopes under heavy rain conditions. Summary of the invention

[0005] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a slope collapse risk assessment method and system, which can realize dynamic monitoring and prediction of slope collapse risk under rainfall scenarios, so as to be applied to decision-making during extreme weather disasters. The present invention aims to solve the technical problems that the models in the prior art are difficult to reflect the changes in slope conditions on different time scales, lack an in-depth understanding of hydrological processes, and accurately and intuitively quantify the collapse risk.

[0006] In order to achieve the above object, the present invention is implemented by the following technical solutions:

[0007] A slope collapse risk assessment method comprises the following steps:

[0008] Based on the surface structure of the study area, a plurality of grid units are constructed, and a data group of the study area is obtained, so as to construct a plurality of data matrices including time nodes on each of the grid units;

[0009] Based on the meteorological forecast model of the study area and the plurality of data matrices, obtaining the probability distribution of future rainfall;

[0010] Generate a future rainfall scenario library based on the plurality of grid cells, the plurality of data matrices and the future rainfall probability distribution;

[0011] Acquire the soil hydrology data set of the study area and construct a soil hydrology mechanism model;

[0012] Based on the future rainfall scenario library and the soil hydrological mechanism model, a number of flood peak element results are obtained to calculate uncertainty;

[0013] Extracting hydrological data from a plurality of the data matrices and inputting the hydrological data into the soil hydrological mechanism model to obtain a hydrological mechanism data set;

[0014] According to the fault Bayesian network structure, a fault tree model of slope collapse events is established, and based on the fault tree model and the hydrological mechanism data group, the slope collapse risk expectation of the study area is evaluated, and the accuracy of the slope collapse risk expectation is evaluated according to the uncertainty.

[0015] Compared with the prior art, the beneficial effects of the present invention are: the slope to be studied is accurately gridded and unitized, and the data matrix related to the time scale is established on the formed several grid units, and the data in the data matrix is ​​used as the input of the soil hydrological mechanism model. The soil hydrological mechanism model is established with real monitoring and research data as the background, and can simulate the whole process of the hydrological cycle. Compared with the traditional mathematical statistics model or physical simulation model, the evaluation method of the soil hydrological mechanism model is used to have a deeper understanding of the hydrological process, and the analysis is based on the data including the time nodes, so as to obtain the dynamic slope condition change process; by obtaining The future rainfall probability distribution is taken, and the uncertainty of rainfall prediction extracted from the weather forecast is incorporated into the data analysis. The future rainfall scenario library is generated by sampling, and the spatiotemporal distribution of soil hydrological elements under different scenarios is simulated and mathematically analyzed in combination with the soil hydrological mechanism model, so as to quantify the uncertainty transmitted between multiple models and support the accuracy and reliability of the assessed risk expectations. By constructing the fault tree model, the conditional probability distribution of each node can be calculated through the fault Bayesian network structure, and the collapse risk expectation of the slope to be studied can be quantified, so as to achieve accurate and intuitive quantification of the collapse risk, which is beneficial to make accurate and timely decisions on rainstorm disasters.

[0016] Further, the formula of the data matrix is:

[0017]

[0018] in, is a matrix, is the number of the grid cell, is the number of the data attribute, To Time node The value of the attribute, , , is the total number of grid cells, is the total number of time nodes.

[0019] Furthermore, the step of obtaining the probability distribution of future rainfall based on the meteorological forecast model of the study area and the plurality of data matrices includes:

[0020] Extracting a plurality of meteorological data from a plurality of the data matrices, and inputting the meteorological data into a meteorological forecast model of the study area to obtain a plurality of rainfall prediction data;

[0021] Statistical analysis is performed on the rainfall prediction data to generate a future rainfall probability distribution.

[0022] Furthermore, the step of generating a future rainfall scenario library based on the plurality of grid cells, the plurality of data matrices and the future rainfall probability distribution includes:

[0023] Extracting a plurality of historical rainfall data from a plurality of said data matrices;

[0024] The future rainfall probability distribution is sampled based on the historical rainfall data to generate a number of rainfall time scenarios and a number of rainfall space scenarios, and the number of rainfall space scenarios correspond one-to-one to a number of the grid units to form a future rainfall scenario library.

[0025] Furthermore, the soil hydrology data set includes upstream and downstream conditions of the river, rainfall data, topographic data, infiltration coefficient and soil moisture content.

[0026] Furthermore, the calculation formula of the uncertainty is:

[0027]

[0028] in, is the uncertainty, is the first sub-uncertainty, which is used to represent the uncertainty of the spatiotemporal distribution of soil hydrological elements. is the second sub-uncertainty, which is used to represent the uncertainty of spatiotemporal distribution coupling;

[0029] The calculation formula of the first sub-uncertainty is:

[0030]

[0031] in, represents the total number of rainfall spatial scenarios, represents the total number of rainfall time scenarios, Indicated in The results of simulated flood peak elements under different rainfall time scenarios are as follows: Indicated in The flood peak element results simulated under different rainfall spatial scenarios are shown in Figure 2. It represents the mean of several flood peak element results simulated under all rainfall time scenarios and rainfall space scenarios;

[0032] The calculation formula of the second sub-uncertainty is:

[0033]

[0034] in, Indicated in Rainfall time scenario and Results of simulated flood peak elements under different rainfall spatial scenarios.

[0035] Furthermore, the steps of establishing a fault tree model of slope collapse events according to the fault Bayesian network structure, and evaluating the expected risk of slope collapse in the study area based on the fault tree model and the hydrological mechanism data set include:

[0036] Extracting a number of causal events from the slope collapse events in the study area, and extracting a number of basic events from each of the causal events, each of the basic events corresponds to an impact indicator, and establishing a number of risk assessment levels based on the basic events;

[0037] Taking the slope collapse event as the target node, taking the several causal events as several intermediate nodes, and taking the several basic events as several evidence nodes, a fault tree model of the slope collapse event is established, and the value ranges of several influencing indicators under different risk assessment levels are determined;

[0038] Extracting a plurality of variables corresponding to a plurality of the influencing indicators from the hydrological mechanism data set, and standardizing the plurality of the variables into a plurality of dimensionless normalized indicators;

[0039] Based on the fault tree model, the probability importance and critical importance of several basic events are calculated to evaluate the expected risk of slope collapse in the study area.

[0040] A slope collapse risk assessment system is applied to the slope collapse risk assessment method as described in the above technical solution, and the system comprises:

[0041] A grid module, used to construct a plurality of grid cells based on the surface structure of the study area, and obtain a data group of the study area, so as to construct a plurality of data matrices including time nodes on each of the grid cells;

[0042] A probability module, used for obtaining the probability distribution of future rainfall based on the meteorological forecast model of the study area and the plurality of data matrices;

[0043] A generation module, used for generating a future rainfall scenario library based on a plurality of the grid cells, a plurality of the data matrices and the future rainfall probability distribution;

[0044] A construction module is used to obtain a soil hydrology data set of the study area and construct a soil hydrology mechanism model;

[0045] A calculation module, used to obtain a number of flood peak element results based on the future rainfall scenario library and the soil hydrological mechanism model to calculate uncertainty;

[0046] An acquisition module, used for extracting hydrological data from a plurality of said data matrices and inputting the hydrological data into said soil hydrological mechanism model to obtain a hydrological mechanism data set;

[0047] An evaluation module is used to establish a fault tree model of slope collapse events according to the fault Bayesian network structure, and to evaluate the expected risk of slope collapse in the study area based on the fault tree model and the hydrological mechanism data group, and to evaluate the accuracy of the expected risk of slope collapse according to the uncertainty. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A flowchart of a slope collapse risk assessment method according to an embodiment of the present invention;

[0049] Figure 2 It is a schematic diagram of the simulation process of the soil hydrological mechanism model in the slope collapse risk assessment method in an embodiment of the present invention;

[0050] Figure 3 It is a schematic structural diagram of a slope collapse risk assessment system in another embodiment of the present invention.

[0051] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0052] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0053] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0055] See also Figure 1 and Figure 2 The slope collapse risk assessment method in the embodiment of the present invention comprises the following steps:

[0056] Step S10: constructing a plurality of grid units based on the surface structure of the study area, and acquiring a data group of the study area, so as to construct a plurality of data matrices including time nodes on each of the grid units;

[0057] The data matrix constructed on each grid unit includes various types of geographic information of the location of the grid, preferably including elevation, slope, infiltration coefficient, friction coefficient, vegetation coverage, plasticity index, soil distribution, and rainfall and water depth that vary with time, wherein the data of the elevation and the slope are from the DEM data set scanned in the study area, the infiltration coefficient, the friction coefficient, and the plasticity index are obtained based on the land use data provided by relevant departments, the vegetation coverage and the soil distribution are from the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences, the rainfall is obtained by requesting the gas phase forecast interface, and the water depth is obtained by returning the monitoring value through the water level gauge designed in the river.

[0058] Specifically, the step S10 includes:

[0059] S110: The formula of the data matrix is:

[0060]

[0061] in, is a matrix, is the number of the grid cell, is the number of the data attribute, To Time node The value of the attribute, , , is the total number of grid cells, is the total number of time nodes.

[0062] Preferably, the total number of grid units is 1000, the total number of time nodes is 24, the hourly rainfall forecast data for the next 24 hours is obtained in real time, a rainfall data matrix is ​​constructed, the monitored water depth returned by water level gauges installed in rivers, lakes, etc. is obtained in real time, a water depth data matrix is ​​constructed, a raster file containing geographic elevation information data is read, the elevation of the corresponding coordinates is mapped to the data matrix and reflected as a fixed value, the soil distribution, the slope, the infiltration coefficient, the friction coefficient, the vegetation coverage rate, and the plasticity index are all reflected as fixed values.

[0063] Step S20: based on the meteorological forecast model of the study area and the plurality of data matrices, obtaining the probability distribution of future rainfall;

[0064] It can be understood that the future rainfall probability distribution is used to quantify the uncertainty of rainfall, and is beneficial to analyzing the propagation of uncertainty in the whole process of risk assessment, and finally evaluating the accuracy and reliability of the risk assessment results.

[0065] Specifically, the step S20 includes:

[0066] S210: extracting a plurality of meteorological data from a plurality of the data matrices, and inputting the meteorological data into a meteorological forecast model of the study area to obtain a plurality of rainfall prediction data;

[0067] Preferably, by accessing the weather forecast interface of the study area, the weather forecast result can be obtained by inputting data, and a plurality of the weather data related to rainfall are extracted from the plurality of the data matrices.

[0068] S220: Performing statistical analysis on the rainfall prediction data to generate a future rainfall probability distribution.

[0069] Preferably, the future rainfall probability distribution is generated using Monte Carlo simulation.

[0070] Step S30: generating a future rainfall scenario library based on the plurality of grid cells, the plurality of data matrices and the future rainfall probability distribution;

[0071] The future rainfall scenario library covers different rainfall patterns, including extreme rainfall, continuous rainfall, etc., to improve the accuracy of the rainfall model. The rainfall scenarios established in the future rainfall scenario library are associated with the spatial scale of the grid unit and the time scale in the data matrix. It can be understood that as the monitoring process proceeds, the data in the data matrix is ​​updated, and the rainfall scenarios in the future rainfall scenario library are also updated accordingly, which is beneficial to provide a dynamic response for risk assessment.

[0072] Specifically, the step S30 includes:

[0073] S310: extracting a plurality of historical rainfall data from a plurality of the data matrices;

[0074] The data matrix includes data of multiple time nodes, and extracting the historical rainfall data is beneficial to sampling the future rainfall probability distribution in a targeted manner.

[0075] S320: Sampling the future rainfall probability distribution based on the historical rainfall data to generate a plurality of rainfall time scenarios and a plurality of rainfall space scenarios, wherein the plurality of rainfall space scenarios correspond one-to-one to a plurality of the grid units to form a future rainfall scenario library.

[0076] Preferably, the importance sampling method is adopted, each of the rainfall time scenarios represents the rainfall conditions within a certain time period, and each of the rainfall space scenarios represents the rainfall conditions within a certain grid unit. The rainfall time scenarios can be associated with the rainfall space scenarios to represent the rainfall conditions on a certain grid unit within a certain time period.

[0077] Step S40: obtaining a soil hydrological data set of the study area and constructing a soil hydrological mechanism model;

[0078] See also Figure 2 The soil hydrological mechanism model simulates the entire process of the hydrological cycle, sets the simulation duration and boundary conditions, inputs a number of the data matrices, and can calculate the data related to the soil hydrological mechanism through the hydrological model.

[0079] Specifically, the step S40 includes:

[0080] S410: The soil hydrological data set includes upstream and downstream conditions of the river, rainfall data, topographic data, infiltration coefficient and soil moisture content.

[0081] Preferably, by constructing the soil hydrological mechanism model, a virtual field in the future time period of the study area can be obtained, and a data group with a time series can be simulated and output for subsequent assessment of collapse risk. Furthermore, the soil hydrological mechanism model can calculate the cumulative evapotranspiration matrix to quantify the amount of water leaving the system, and can calculate the unsaturated zone attribute variable matrix and the saturated zone attribute variable matrix to analyze soil head pressure and groundwater recharge or absorption. It can be understood that by constructing the soil hydrological mechanism model, the slope collapse risk assessment method has a deeper understanding and analysis of the hydrological process than traditional mathematical and statistical models and physical simulation models.

[0082] Step S50: Based on the future rainfall scenario library and the soil hydrological mechanism model, a plurality of flood peak element results are obtained to calculate uncertainty;

[0083] There is uncertainty in predicting rainfall conditions based on meteorological factors, and there is also uncertainty in constructing the future rainfall scenario library based on the future rainfall probability distribution. In particular, when the time scale is correlated and coupled with the spatial scale, certain errors are likely to occur. When the future rainfall scenario library is coupled with the soil hydrological mechanism model, certain errors are also likely to occur. It is understandable that in the risk assessment process, uncertainty will be transmitted when multiple models are analyzed in sequence according to the link, and uncertainty affects the accuracy of risk assessment through the model. By calculating the uncertainty, the reliability and accuracy of the risk assessment results can be quantified, and coupling the rainfall scenario with the soil hydrological mechanism model for analysis is beneficial to deepen the understanding of the hydrological process and increase the accuracy of risk assessment.

[0084] Specifically, the step S50 includes:

[0085] S510: The calculation formula of the uncertainty is:

[0086]

[0087] in, is the uncertainty, is the first sub-uncertainty, which is used to represent the uncertainty of the spatiotemporal distribution of soil hydrological elements. is the second sub-uncertainty, which is used to represent the uncertainty of spatiotemporal distribution coupling;

[0088] The calculation formula of the first sub-uncertainty is:

[0089]

[0090] in, represents the total number of rainfall spatial scenarios, represents the total number of rainfall time scenarios, Indicated in The results of simulated flood peak elements under different rainfall time scenarios are as follows: Indicated in The flood peak element results simulated under different rainfall spatial scenarios are shown in Figure 2. It represents the mean of several flood peak element results simulated under all rainfall time scenarios and rainfall space scenarios;

[0091] The calculation formula of the second sub-uncertainty is:

[0092]

[0093] in, Indicated in Rainfall time scenario and Results of simulated flood peak elements under different rainfall spatial scenarios.

[0094] Preferably, the flood peak elements include flood peak flow, peak time, etc., and variance analysis is performed on the various flood peak element results generated by simulating the future rainfall scenario library and the soil hydrological mechanism model to examine and quantify the uncertainty of the model link.

[0095] Step S60: extracting hydrological data from a plurality of the data matrices and inputting the hydrological data into the soil hydrological mechanism model to obtain a hydrological mechanism data set;

[0096] Preferably, the hydrological mechanism data group includes groundwater level, soil moisture content, infiltration water, pore water pressure, soil cohesion, slope flow and surface interception. It can be understood that the data matrix includes time nodes, and the hydrological mechanism data is associated with the time scale, which is conducive to dynamically examining the changes in the situation in the study area. Especially under heavy rain conditions, the dynamic changes of the slope are very important. If the decision to make rescue, avoidance and other instructions is issued based on non-dynamic assessment, it is easy to cause misjudgment and has major safety hazards. The slope collapse risk assessment method can effectively improve the accuracy of collapse risk assessment, thereby effectively improving the accuracy of decision-making.

[0097] Step S70: Establish a fault tree model of slope collapse events according to the fault Bayesian network structure, and evaluate the expected risk of slope collapse in the study area based on the fault tree model and the hydrological mechanism data set, and evaluate the accuracy of the expected risk of slope collapse according to the uncertainty.

[0098] Preferably, a confidence level can be set up, and the confidence interval of the risk assessment result can be calculated according to the uncertainty transmitted by the model link, that is, it is used to evaluate the accuracy of the slope collapse risk expectation and provide data support for decision-making when facing the collapse risk. It can be understood that through the fault Bayesian network structure, combined with the Bayesian rule, the conditional probability and prior probability between each node can be calculated, and the collapse risk can be quantitatively evaluated based on the monitoring data.

[0099] Specifically, the step S70 includes:

[0100] S710: extracting a number of causal events from the slope collapse events in the study area, and extracting a number of basic events from each of the causal events, each of the basic events corresponding to an impact indicator, and establishing a number of risk assessment levels based on the basic events;

[0101] Preferably, three of the induced events are extracted, and 11 of the basic events are extracted. The 11 basic events represent that the slope collapse event contains 11 influencing indicators, and three risk assessment levels are established. Specifically, the slope collapse event is a top event. The three induced events are set according to the direct causes of the slope collapse, namely, the slope's own attributes, the decrease in slope resistance, and the increase in slope load. The slope's own attributes correspond to four of the influencing indicators, namely, the slope coefficient, the hydrophilicity of the soil, the vegetation cover, and the land capacity. The decrease in slope resistance corresponds to five of the influencing indicators, namely, groundwater precipitation, soil moisture content, infiltration water, pore water pressure, and soil cohesion. The increase in slope load corresponds to two of the influencing indicators, namely, overland flow and surface interception.

[0102] S720: taking the slope collapse event as a target node, taking the several causal events as several intermediate nodes, and taking the several basic events as several evidence nodes, so as to establish a fault tree model of the slope collapse event, and to determine the value ranges of several influencing indicators at different risk assessment levels;

[0103] The fault Bayesian network structure includes the target node, several intermediate nodes associated with the target node and several evidence nodes associated with the intermediate node. Preferably, the fault Bayesian network structure includes one target node, three intermediate nodes and eleven evidence nodes. The three intermediate nodes are all associated with the top-level target node and, as an intermediate level, will continue to be associated with the next level. The evidence nodes are associated with the intermediate nodes.

[0104] Preferably, taking the overland flow in the influencing index as an example, when the runoff depth of the overland flow is greater than 100 mm, it is a first-level risk, i.e., a high risk; when the runoff depth is between 30 mm and 100 mm, it is a second-level risk, i.e., a medium risk; and when the runoff depth is less than 30 mm, it is a third-level risk, i.e., a low risk. It can be understood that the risk level can be determined based on the hydrological mechanism data set obtained in the soil hydrological mechanism model, which is beneficial for intuitively displaying the risk trend.

[0105] S730: extracting a plurality of variables corresponding to a plurality of the influencing indicators from the hydrological mechanism data set, and standardizing the plurality of the variables into a plurality of dimensionless normalized indicators;

[0106] It can be understood that standardizing the data and unifying the dimensions is beneficial to comparison in the fault Bayesian network structure and facilitates the implementation of visual early warnings of different risk levels.

[0107] S740: Based on the fault tree model, calculate the probability importance and critical importance of several basic events to evaluate the expected risk of slope collapse in the study area.

[0108] Preferably, a conditional probability distribution is extracted from the fault tree model, and the probability of occurrence of the slope collapse event and the probability of occurrence of the basic event are extracted from the conditional probability distribution. The probability of the basic event in the fault tree model corresponds to the prior probability of the evidence node in the fault Bayesian network structure, and the prior probability is obtained through the conditional probability distribution. The probability importance and the critical importance of several basic events are calculated according to the probability of occurrence of the slope collapse event and the probability of occurrence of the basic event. The probability importance can be used to determine the degree of influence of reducing the probability of occurrence of a certain basic event on the top event, that is, the reduction in the probability of occurrence of the slope collapse event. The critical importance further quantifies the sensitivity of the basic event to the slope collapse event on the basis of the probability importance, and is used to represent the rate of change of the probability of the slope collapse event caused by the probability change rate of the basic event. It can be understood that the fault tree model is helpful in providing data support for the importance of the influencing indicators, and is helpful in intuitively evaluating the risk of slope collapse under heavy rain conditions and making decisions. The probability importance and the critical importance both provide a basis for making more effective and accurate decisions to reduce the probability of the slope collapse event.

[0109] Preferably, the rainfall forecast data update interval is 1 hour, and the monitoring data update interval is 30 minutes. After obtaining the updated rainfall forecast data and monitoring data, the soil hydrological mechanism model and the uncertainty calculation time are 15 minutes, and the evaluation calculation time is 5 minutes. It can be understood that the calculated data can be connected to the visualization platform to display the slope collapse risk information and the importance of influencing indicators in a specific area in the form of charts, wherein the importance of influencing indicators is associated with the probability importance and the critical importance. Quantifying the importance of influencing indicators is helpful in judging the matters that need to be strengthened and remedied first.

[0110] See also Figure 3 In another embodiment of the present invention, a slope collapse risk assessment system is provided, which is applied to the slope collapse risk assessment method described in the above embodiment, and the system comprises:

[0111] A grid module 10 is used to construct a plurality of grid units based on the surface structure of the study area, and obtain a data group of the study area, so as to construct a plurality of data matrices including time nodes on each of the grid units;

[0112] The grid module 10 comprises:

[0113] In the first unit, the formula for the data matrix is:

[0114]

[0115] in, is a matrix, is the number of the grid cell, is the number of the data attribute, To Time node The value of the attribute, , , is the total number of grid cells, is the total number of time nodes.

[0116] A probability module 20 is used to obtain the probability distribution of future rainfall based on the meteorological forecast model of the study area and the plurality of data matrices;

[0117] The probability module 20 comprises:

[0118] The second unit is used for extracting a plurality of meteorological data from a plurality of the data matrices, and inputting the meteorological data into a meteorological forecast model of the study area to obtain a plurality of rainfall prediction data;

[0119] The third unit is used to perform statistical analysis on the rainfall prediction data to generate a future rainfall probability distribution.

[0120] A generating module 30, for generating a future rainfall scenario library based on a plurality of the grid cells, a plurality of the data matrices and the future rainfall probability distribution;

[0121] The generating module 30 comprises:

[0122] A fourth unit is used to extract a plurality of historical rainfall data from a plurality of said data matrices;

[0123] The fifth unit is used to sample the future rainfall probability distribution based on the historical rainfall data to generate a plurality of rainfall time scenarios and a plurality of rainfall space scenarios, wherein the plurality of rainfall space scenarios correspond one-to-one to a plurality of the grid units to form a future rainfall scenario library.

[0124] A construction module 40 is used to obtain a soil hydrology data set of the study area and construct a soil hydrology mechanism model;

[0125] The building block 40 comprises:

[0126] The sixth unit is used for the soil hydrological data set including upstream and downstream conditions of the river, rainfall data, topographic data, infiltration coefficient and soil moisture content.

[0127] A calculation module 50 is used to obtain a plurality of flood peak element results based on the future rainfall scenario library and the soil hydrological mechanism model to calculate uncertainty;

[0128] Unit 7, the calculation formula for the uncertainty is:

[0129]

[0130] in, is the uncertainty, is the first sub-uncertainty, which is used to represent the uncertainty of the spatiotemporal distribution of soil hydrological elements. is the second sub-uncertainty, which is used to represent the uncertainty of spatiotemporal distribution coupling;

[0131] The calculation formula of the first sub-uncertainty is:

[0132]

[0133] in, represents the total number of rainfall spatial scenarios, represents the total number of rainfall time scenarios, Indicated in The results of simulated flood peak elements under different rainfall time scenarios are as follows: Indicated in The flood peak element results simulated under different rainfall spatial scenarios are shown in Figure 2. It represents the mean of several flood peak element results simulated under all rainfall time scenarios and rainfall space scenarios;

[0134] The calculation formula of the second sub-uncertainty is:

[0135]

[0136] in, Indicated in Rainfall time scenario and Results of simulated flood peak elements under different rainfall spatial scenarios.

[0137] An acquisition module 60, for extracting hydrological data from a plurality of the data matrices and inputting the hydrological data into the soil hydrological mechanism model to obtain a hydrological mechanism data set;

[0138] The evaluation module 70 is used to establish a fault tree model of the slope collapse event according to the fault Bayesian network structure, and to evaluate the expected risk of slope collapse in the study area based on the fault tree model and the hydrological mechanism data set, and to evaluate the accuracy of the expected risk of slope collapse according to the uncertainty.

[0139] The evaluation module 70 includes:

[0140] The eighth unit is used to extract a number of causal events from the slope collapse events in the study area, and to extract a number of basic events from each of the causal events, each of the basic events corresponds to an impact indicator, and to establish a number of risk assessment levels based on the basic events;

[0141] The ninth unit is used to establish a fault tree model of the slope collapse event by taking the slope collapse event as a target node, taking a number of the causal events as a number of intermediate nodes, and taking a number of the basic events as a number of evidence nodes, and to establish the value ranges of a number of the influencing indicators at different risk assessment levels;

[0142] The tenth unit is used to extract a plurality of variables corresponding to a plurality of the influencing indicators from the hydrological mechanism data set, and standardize the plurality of the variables into a plurality of dimensionless normalized indicators;

[0143] The eleventh unit is used to calculate the probability importance and critical importance of several basic events based on the fault tree model to evaluate the expected risk of slope collapse in the study area.

[0144] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0145] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A slope collapse risk assessment method, characterized in that: The steps include: Based on the surface structure of the study area, a plurality of grid units are constructed, and a data group of the study area is obtained, so as to construct a plurality of data matrices including time nodes on each of the grid units; Based on the meteorological forecast model of the study area and the plurality of data matrices, obtaining the probability distribution of future rainfall; The step of obtaining the future rainfall probability distribution based on the meteorological forecast model of the study area and the plurality of data matrices comprises: Extracting a plurality of meteorological data from a plurality of the data matrices, and inputting the meteorological data into a meteorological forecast model of the study area to obtain a plurality of rainfall prediction data; Performing statistical analysis on the rainfall prediction data to generate a future rainfall probability distribution; Generate a future rainfall scenario library based on the plurality of grid cells, the plurality of data matrices and the future rainfall probability distribution; The step of generating a future rainfall scenario library based on the plurality of grid cells, the plurality of data matrices and the future rainfall probability distribution comprises: Extracting a plurality of historical rainfall data from a plurality of said data matrices; Sampling the future rainfall probability distribution based on the historical rainfall data to generate a plurality of rainfall time scenarios and a plurality of rainfall space scenarios, wherein the plurality of rainfall space scenarios correspond one-to-one to a plurality of the grid cells to form a future rainfall scenario library; Acquire the soil hydrology data set of the study area and construct a soil hydrology mechanism model; The soil hydrology data set includes upstream and downstream conditions of the river, rainfall data, topographic data, infiltration coefficient and soil moisture content; Based on the future rainfall scenario library and the soil hydrological mechanism model, a number of flood peak element results are obtained to calculate uncertainty; Extracting hydrological data from a plurality of the data matrices and inputting the data into the soil hydrological mechanism model to obtain a hydrological mechanism data set, wherein the hydrological mechanism data set includes groundwater level, soil moisture content, infiltration water volume, pore water pressure, soil cohesion, slope flow and surface interception volume; According to the fault Bayesian network structure, a fault tree model of slope collapse events is established, and based on the fault tree model and the hydrological mechanism data group, the slope collapse risk expectation of the study area is evaluated, and the accuracy of the slope collapse risk expectation is evaluated according to the uncertainty.

2. The slope collapse risk assessment method according to claim 1, characterized in that: The formula of the data matrix is: in, is a matrix, is the number of the grid cell, is the number of the data attribute, To Time node The value of the attribute, , , is the total number of grid cells, is the total number of time nodes.

3. The slope collapse risk assessment method according to claim 1, characterized in that: The calculation formula for the uncertainty is: in, is the uncertainty, is the first sub-uncertainty, which is used to represent the uncertainty of the spatiotemporal distribution of soil hydrological elements. is the second sub-uncertainty, which is used to represent the uncertainty of spatiotemporal distribution coupling; The calculation formula of the first sub-uncertainty is: in, represents the total number of rainfall spatial scenarios, represents the total number of rainfall time scenarios, Expressed in The results of simulated flood peak elements under different rainfall time scenarios are as follows: Expressed in The flood peak element results simulated under different rainfall spatial scenarios are shown in Figure 2. It represents the mean of several flood peak element results simulated under all rainfall time scenarios and rainfall space scenarios; The calculation formula of the second sub-uncertainty is: in, Expressed in Rainfall time scenario and Results of simulated flood peak elements under different rainfall spatial scenarios.

4. The slope collapse risk assessment method according to claim 1, characterized in that: The steps of establishing a fault tree model of slope collapse events according to the fault Bayesian network structure, and evaluating the expected risk of slope collapse in the study area based on the fault tree model and the hydrological mechanism data set include: Extracting a number of causal events from the slope collapse events in the study area, and extracting a number of basic events from each of the causal events, each of the basic events corresponds to an impact indicator, and establishing a number of risk assessment levels based on the basic events; Taking the slope collapse event as the target node, taking the several causal events as several intermediate nodes, and taking the several basic events as several evidence nodes, a fault tree model of the slope collapse event is established, and the value ranges of several influencing indicators under different risk assessment levels are determined; Extracting a plurality of variables corresponding to a plurality of the influencing indicators from the hydrological mechanism data set, and standardizing the plurality of the variables into a plurality of dimensionless normalized indicators; Based on the fault tree model, the probability importance and critical importance of several basic events are calculated to evaluate the expected risk of slope collapse in the study area.

5. A slope collapse risk assessment system, applied to the slope collapse risk assessment method as described in any one of claims 1 to 4 above, characterized in that: The system comprises: A grid module, used to construct a plurality of grid cells based on the surface structure of the study area, and obtain a data group of the study area, so as to construct a plurality of data matrices including time nodes on each of the grid cells; A probability module, used for obtaining the probability distribution of future rainfall based on the meteorological forecast model of the study area and the plurality of data matrices; The probability module includes: The second unit is used for extracting a plurality of meteorological data from a plurality of the data matrices, and inputting the meteorological data into a meteorological forecast model of the study area to obtain a plurality of rainfall prediction data; The third unit is used to perform statistical analysis on the rainfall prediction data to generate a future rainfall probability distribution; A generation module, used for generating a future rainfall scenario library based on a plurality of the grid cells, a plurality of the data matrices and the future rainfall probability distribution; The generation module comprises: A fourth unit is used to extract a plurality of historical rainfall data from a plurality of said data matrices; A fifth unit is used to sample the future rainfall probability distribution based on the historical rainfall data to generate a plurality of rainfall time scenarios and a plurality of rainfall space scenarios, wherein the plurality of rainfall space scenarios correspond one-to-one to the plurality of grid units to form a future rainfall scenario library; A construction module is used to obtain a soil hydrology data set of the study area and construct a soil hydrology mechanism model; The building blocks include: The sixth unit is used for the soil hydrological data set including upstream and downstream conditions of the river, rainfall data, topographic data, infiltration coefficient and soil moisture content; A calculation module, used to obtain a number of flood peak element results based on the future rainfall scenario library and the soil hydrological mechanism model to calculate uncertainty; An acquisition module is used to extract hydrological data from a plurality of the data matrices and input the hydrological data into the soil hydrological mechanism model to obtain a hydrological mechanism data set, wherein the hydrological mechanism data set includes groundwater level, soil moisture content, infiltration water volume, pore water pressure, soil cohesion, slope flow and surface interception volume; An evaluation module is used to establish a fault tree model of slope collapse events according to the fault Bayesian network structure, and to evaluate the expected risk of slope collapse in the study area based on the fault tree model and the hydrological mechanism data group, and to evaluate the accuracy of the expected risk of slope collapse according to the uncertainty.

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