Multi-point remote synchronous geological disaster early warning method and system based on rainfall observation

Abstract

The invention discloses a multi-point different-place synchronous geological disaster early warning method and system based on rainfall observation, and belongs to the field of disaster early warning, and the method comprises the steps: dividing a target region into a plurality of monitoring nodes, arranging a rainfall detection station and a soil humidity sensor at each monitoring node, and installing a slope sensor in a landslide sensitive region; performing time coupling on the collected data, and then sending the collected data to a cloud platform; after receiving the sensor data, the cloud platform decompresses the data, and then inputs the collected sensor data into the comprehensive prediction model to dynamically update the soil humidity; the cloud platform comprises a geological condition database of each geological disaster-prone and high-incidence area, and the geological disaster risk is evaluated based on the change of the soil humidity in combination with the data of the geological condition database. According to the method, multi-source heterogeneous data can be fully utilized, so that the geological disaster risk is comprehensively evaluated.

Description

Technical Field

[0001] The present invention relates to the field of disaster prediction, and in particular to a multi-point remote synchronous geological disaster early warning method and system based on rainfall observation. Background Art

[0002] Geological disaster early warning technology aims to predict the potential threats of various natural disasters in advance and reduce the impact of disasters. Existing geological disaster early warning methods mainly rely on limited environmental monitoring and relatively simple data processing technology, which are obviously insufficient in terms of the accuracy of disaster prediction, the timeliness of early warning signals, and the effectiveness of emergency response. Traditional methods are often based on single parameter indicators, ignoring the comprehensive impact of multiple parameters, making it difficult to make full use of multi-source heterogeneous data, and unable to accurately and dynamically predict the probability of geological disasters, resulting in low accuracy and reliability of early warning information.

[0003] In addition, existing technologies are limited in considering the diversity and complexity of scenarios, and are difficult to adapt to different types of geological disasters and environmental conditions. At the same time, existing methods have limitations in prediction models, making it difficult to accurately predict the time and location of geological disasters. Emergency response measures are often insufficient, exacerbating the impact of disasters. Therefore, there is an urgent need for a new geological disaster early warning method that can integrate real-time rainfall data, geological condition data, and meteorological information from multiple locations, so as to dynamically predict the probability of geological disasters in different regions, improve the accuracy and timeliness of early warnings, and provide early warnings as early as possible before disasters occur. Summary of the invention

[0004] One of the purposes of the present invention is to provide a multi-point remote synchronous geological disaster early warning method based on rainfall observation to solve the shortcomings of the prior art that it is difficult to accurately predict the time and location of the occurrence of geological disasters.

[0005] The present invention is implemented through the following technical scheme, a multi-point remote synchronous geological disaster early warning method based on rainfall observation, comprising the following steps: S100, dividing the target area into multiple monitoring nodes, and arranging a rainfall detection station at each monitoring node, and arranging soil moisture sensors in soil layers of different depths at each monitoring node, and installing slope sensors in landslide-sensitive areas to monitor the slope changes of the area in real time; S200, correlating soil moisture data of different depths, rainfall detection station data and data collected by slope sensors, and uploading them uniformly, by combining the collected precipitation information, The soil moisture data and the slope sensor data are temporally coupled, and then the temporally coupled data are sent to the cloud platform; S300, after the cloud platform receives the sensor data, the data is first decompressed, and then the collected sensor data is input into the comprehensive prediction model to dynamically update the soil moisture; S400, the cloud platform includes a geological condition database of various high-incidence areas of geological disasters, and the soil moisture is dynamically updated according to the calculated data in step S300. Based on the change of soil moisture, combined with the data in the geological condition database, it is determined whether the predetermined risk threshold is reached, and the geological disaster risk is evaluated.

[0006] Furthermore, soil moisture sensors are arranged at 1 cm, 15 cm, 30 cm, 45 cm and 60 cm below the soil surface, respectively.

[0007] Furthermore, sending the collected data to the cloud platform includes:

[0008] S210, a corresponding wireless network node is provided in each rainfall detection station, multiple wireless network nodes in the target area are aggregated into a wireless network node set, and the received sensor data is integrated in the wireless network node set;

[0009] S220, adding a timestamp to the collected data, checking whether there is duplication in the data according to the timestamp, deleting the duplicate sensor data, and completing data cleaning;

[0010] S230, after cleaning is completed, the data is compressed and sent to the cloud platform via TCP / IP protocol.

[0011] Furthermore, the integrated prediction model consists of a rainfall-soil saturation model and a state-space model. The rainfall-soil saturation model is used to describe the evolution of soil moisture in space and time, and then the rainfall-soil saturation model is discretized into the state-space model.

[0012] Furthermore, the rainfall-soil saturation model is constructed based on partial differential equations, including the following steps:

[0013] S311. Based on partial differential equations, the effect of rainfall on soil moisture at different locations is described. The change of soil moisture is affected by both external rainfall and internal soil moisture transmission. The diffusion equation is used to describe the spatial propagation of soil moisture:

[0014] ,

[0015] in, It is used to represent the rate of change of soil moisture S(x, t) over time; D is used to represent the diffusion coefficient; is the Laplace operator, which represents the second-order derivative of soil moisture in space and describes the diffusion behavior of moisture in space;

[0016] S312. At the same time, since soil moisture is also directly affected by external factors, the source term f(R(x, t), S(x, t)) is introduced to represent the influence of rainfall. The source term is added to the diffusion equation to obtain:

[0017] ,

[0018] Where, f(R(x,t), S(x,t)) represents the effect of rainfall R(x,t) on soil moisture S(x,t);

[0019] S313, the slope data θ(x, t) collected by the slope sensor is used to represent the angle of ground inclination. In places with larger slopes, water flow is more likely to concentrate, thereby causing geological disasters.

[0020] The influence of slope is reflected by modifying f(R(x,t), S(x,t)), using the slope data θ(x,t) as an adjustment factor.

[0021] Finally, the rainfall-soil saturation model based on partial differential equation is obtained:

[0022] .

[0023] Furthermore, the state space model is composed of state equations and observation equations. The state space model uses state variables to describe the dynamic behavior of the entire system and describes the change of state through state transition equations, where:

[0024] The state equation is used to describe the evolution of the system state, which is shown as follows:

[0025] ,

[0026] Where x(t) represents the state of the system, u(t) represents the input, w(t) is the noise or system uncertainty, and A and B are the parameters of the system;

[0027] The observation equation is used to describe the acquisition of observation data from the state of the system, which is shown as follows:

[0028] y(t)=Cx(t)+v(t)

[0029] y(t) is the observation data, C is the mapping matrix, and v(t) is the observation noise.

[0030] Furthermore, the rainfall-soil saturation model is discretized into a state space model, which includes the following steps:

[0031] S321, discretize the rainfall-soil saturation model by discretizing time t into discrete time points t k , t k = kΔt, where Δt is the time step, discretizing the space x into grid points x i The state equation is obtained in discrete form:

[0032] ,

[0033] Among them, soil moisture S(x i , t k ) becomes the state variable in the state space model,

[0034] The discretization of the partial differential equation is used to describe the state transition, which represents how the soil moisture changes from time t k Transfer to k+1 ;

[0035] S322, the observation equation describes the sensor measurement results obtained from the state of soil moisture. Multiple sensor measurement data are related to the state variables, so the observation equation can be written as:

[0036] ,

[0037] Among them, y(t k ) is the sensor observation data, C is the mapping matrix, v(t k ) is the observation noise.

[0038] Furthermore, judging whether the threshold for triggering a disaster has been reached includes: S410, soil saturation threshold judgment: setting a threshold for soil moisture according to the data in the geological conditions database to determine the conditions at the monitoring site; S420, high-risk area identification: constructing geological information such as the spatial distribution of soil moisture and slope through sensor data, and identifying high-risk areas in combination with the geological data in the geological conditions database; S430, geological disaster prediction and early warning triggering: based on the identified high-risk areas, when it is detected that the soil moisture in the high-risk area exceeds a predetermined threshold, an early warning is triggered.

[0039] On the other hand, the present invention provides a multi-point remote synchronous geological disaster early warning system based on rainfall observation, the early warning system includes: a data acquisition module, a data sending module and a cloud platform module, wherein the data acquisition module is configured to divide the target area into multiple monitoring nodes, and arrange a rainfall detection station at each monitoring node, and at the same time arrange soil moisture sensors in soil layers of different depths at each monitoring node, and at the same time install slope sensors in landslide-sensitive areas for real-time monitoring of slope changes in the area; the data sending module is connected to the data acquisition module and is configured to associate soil moisture data of different depths, rainfall detection station data and data collected by the slope sensor, and upload them uniformly, by collecting the precipitation data The quantity information, soil moisture data and slope sensor data are temporally coupled, and then the temporally coupled data are sent to the cloud platform; the cloud platform module is connected to the data sending module and configured as follows: the cloud platform module includes: a comprehensive prediction model submodule and an early warning prompt submodule, the comprehensive prediction model submodule is configured to receive the sensor data collected by the data acquisition module, and dynamically update the soil moisture through the comprehensive prediction model; the early warning prompt submodule is connected to the comprehensive prediction model submodule and configured to dynamically update the soil moisture obtained by the comprehensive prediction model submodule, based on the change of soil moisture, combined with the data in the geological conditions database, determine whether the predetermined risk threshold is reached, evaluate the geological disaster risk and issue an early warning.

[0040] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0041] 1. The present invention can make full use of multi-source heterogeneous data by integrating real-time rainfall data, geological condition data and meteorological information of multiple locations, so as to comprehensively assess the risk of geological disasters. At the same time, combined with the rainfall and soil saturation model constructed in the present invention and the state space model, it can dynamically predict the time, location and probability of geological disasters in different regions, thereby improving the accuracy of the prediction.

[0042] 2. The present invention can adapt to the diversity and complexity of scenarios of different types of geological disasters and environmental conditions by arranging rainfall detection stations, soil moisture sensors and slope sensors in the target area. At the same time, based on the calculated soil moisture changes and combined with the geological condition database, it is determined whether a predetermined threshold is reached, high-risk areas are identified, and warnings of different levels are triggered. The accuracy and reliability of warning information are improved, and emergency response measures are taken in a timely manner, which can effectively reduce the impact of disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0044] Figure 1 This is a flow chart of the method provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0046] Embodiment 1,

[0047] In this embodiment, a multi-point remote synchronous geological disaster early warning method based on rainfall observation is provided. The early warning method disclosed in this embodiment constructs a rainfall and soil saturation model by integrating real-time rainfall data, geological condition data and meteorological information at multiple locations, and combines it with the state space model to dynamically predict the probability of geological disasters (such as landslides, mudslides, etc.) in different regions, and provide early warning as early as possible before the disaster occurs. Figure 1 The flowchart of the early warning method in this embodiment is shown. It can be seen from the figure that this implementation includes the following steps:

[0048] Step 1: In the target area, that is, the area prone to geological disasters, it is divided into multiple monitoring nodes, and a rainfall detection station is arranged in each monitoring node to monitor the precipitation of each monitoring point in real time, and upload the data to the data transmission module through the wireless communication module. The layout of the rainfall detection station should cover the high-risk areas where disasters may occur, and the layout should be optimized by considering the terrain, climate and historical disaster data.

[0049] In addition, it is necessary to select appropriate soil moisture sensors according to the soil types of different monitoring nodes, and arrange soil moisture sensors in the soil at different depths to collect soil moisture data at different depths. For example, in this embodiment, soil moisture sensors can be arranged at 1 cm, 15 cm, 30 cm, 45 cm, and 60 cm on the soil surface, respectively. Through these soil moisture sensors arranged at different depths, the soil moisture data at different depths can be associated with the data collected by the rainfall detection station, and uploaded uniformly to facilitate subsequent data calculation.

[0050] Considering that there are landslide-sensitive areas, slope sensors can also be installed in landslide-sensitive areas to monitor the slope changes in the area in real time. Combining slope change information with rainfall and soil moisture data can help accurately predict the occurrence of landslides.

[0051] Step 2: Temporally couple the collected precipitation information with the soil moisture data and then send it to the cloud platform.

[0052] Specifically, sending the collected data to the cloud platform includes the following sub-steps:

[0053] First, a corresponding wireless network node is set in the rainfall detection station arranged at each monitoring node, and multiple wireless network nodes in the same geological disaster-prone area are aggregated into a wireless network node set, in which the received sensor data is integrated. Specifically, the collected data is timestamped, and the data is checked for duplication based on the timestamp, and the duplicate sensor data is deleted to complete the data cleaning. After the cleaning is completed, the data is compressed and sent to the cloud platform through the TCP / IP protocol.

[0054] Step 3: After the cloud platform receives the sensor data, it first decompresses the data and builds a comprehensive prediction model in the cloud platform. The collected sensor data is input into the comprehensive prediction model. The comprehensive prediction model consists of a rainfall-soil saturation model and a state space model. The rainfall-soil saturation model is used to describe the evolution of soil moisture in space and time. The rainfall-soil saturation model is then discretized into a state space model to dynamically update the soil moisture.

[0055] Specifically, the rainfall-soil saturation model describes the propagation characteristics of rainfall in time and space by using partial differential equations. Its core is to establish the relationship between rainfall and geological conditions such as soil moisture and slope. In order to simulate the interaction between rainfall and soil moisture, slope and other factors, the rainfall-soil saturation model can be constructed using the following steps:

[0056] 1) First, partial differential equations are used to describe the effect of rainfall on soil moisture at different locations (i.e., data monitored by sensors). To simplify the analysis, it is assumed that soil moisture S(x, t) depends on the spatial location x and time t, and it is affected by rainfall R(x, t), soil moisture itself S(x, t), and other factors (such as physical properties of the soil, slope, etc.).

[0057] Then it is necessary to identify the main factors affecting soil moisture:

[0058] Impact of rainfall: Rainfall directly increases soil moisture, causing soil moisture to rise.

[0059] Diffusion of soil moisture: Soil moisture is not evenly distributed and will spread in space due to the influence of soil permeability, soil structure, etc.

[0060] External factors: such as soil permeability, slope, vegetation cover, etc., which will affect the distribution of water after rainfall.

[0061] Based on these influencing factors, a partial differential equation suitable for describing the variation of soil moisture over time and space can be constructed.

[0062] 2) The change of soil moisture is affected by both external rainfall and internal soil moisture transmission (diffusion). Therefore, the diffusion equation can be used to describe the spatial propagation of soil moisture:

[0063] ,

[0064] in, It is used to represent the rate of change of soil moisture S(x, t) over time, that is, the change of soil moisture at time t; D is used to represent the diffusion coefficient, that is, the speed of water diffusion in the soil, which depends on the properties of the soil (such as permeability, etc.); is the Laplace operator, which represents the second-order derivative of soil moisture in space and describes the diffusion behavior of moisture in space. If the soil moisture is higher at a certain point and lower around it, moisture will diffuse from the high-humidity area to the low-humidity area.

[0065] The diffusion equation can be used to express that the change in water in a small area is equal to the water flow into the area minus the water flow out. For soil, the process of water flow is mainly affected by soil permeability, so we can use the diffusion equation to approximate it.

[0066] 3) However, in reality, soil moisture is not only the result of spatial diffusion, but also directly affected by external factors (such as rainfall). The rainfall R(x, t) will increase the soil moisture in a certain area. In this embodiment, we use a source term f(R(x, t), S(x, t)) to represent the influence of rainfall. Adding this source term to the diffusion equation yields:

[0067] ,

[0068] Among them, f(R(x,t), S(x,t)) represents the effect of rainfall R(x,t) on soil moisture S(x,t). It should be noted that the specific form of this function is usually determined based on the data of the actual sensor, and can be modeled based on the relationship between rainfall and soil moisture. Generally speaking, the greater the rainfall, the higher the soil moisture, and this effect has nonlinear characteristics. In order to make the function more accurate, the initial moisture of the soil and the permeability characteristics of the soil can also be obtained.

[0069] 4) In this embodiment, it is considered that the change of soil moisture is also affected by factors such as terrain slope and soil type. The slope data θ(x, t) collected by the slope sensor represents the angle of ground inclination. The places with larger slopes are more likely to have concentrated water flow, which may cause disasters such as landslides or mudslides.

[0070] The effect of slope can be reflected by modifying f(R(x,t), S(x,t)). For example, in areas with larger slopes, rainfall may lead to faster water flow and stronger soil moisture changes. So the slope data θ(x,t) can be used as an adjustment factor.

[0071] Therefore, the final partial differential equation model is:

[0072] .

[0073] In this model, sensor data serves the following purpose:

[0074] The data R(x, t) collected by the rainfall monitoring station provides real-time rainfall data and is one of the external driving factors in the model.

[0075] Soil moisture sensor data S(x,t) is used to monitor soil moisture at different locations and depths and help the model update the soil moisture status.

[0076] The slope sensor data θ(x, t) is used to provide terrain slope information to help adjust the intensity of rainfall impact on soil moisture.

[0077] By coupling these data, a soil moisture change model based on partial differential equations was obtained. This model describes how rainfall affects the evolution of soil moisture through spatial diffusion and precipitation input, and takes into account the influence of factors such as slope on moisture propagation. This model can be used to predict whether the soil moisture in a certain area reaches the conditions that trigger geological disasters (such as landslides, mudslides, etc.) under specific rainfall and terrain conditions.

[0078] In the above content, the sensor data describes the change process of soil moisture S(x, t) in space and time through partial differential equations, which helps us understand and predict the evolution of soil moisture under different conditions. In this embodiment, after obtaining the rainfall-soil saturation model based on partial differential equations, a state space model is constructed to describe the influence of rainfall on the state evolution of the dynamic system of geological disasters. The core idea of ​​the state space model is to describe the state of the system as a vector in a high-dimensional space, and to describe the change of the system state over time through a state transfer equation.

[0079] Specifically, in this embodiment, the state space model is constructed by the following steps:

[0080] In the state space model, state variables are used to describe the dynamic behavior of the entire system, and the state change is described by the state transition equation. In this embodiment, the state space model is composed of the state equation and the observation equation.

[0081] Among them, the state equation is used to describe the evolution of the system state, which is shown as follows:

[0082] ,

[0083] x(t) represents the state of the system, u(t) represents the input (external drive), w(t) is the noise or system uncertainty, and A and B are the parameters of the system.

[0084] The observation equation is used to describe how to obtain observation data from the state of the system, which is shown as follows:

[0085] y(t)=Cx(t)+v(t)

[0086] y(t) is the observation data, C is the mapping matrix, and v(t) is the observation noise.

[0087] In this embodiment, the state x(t) in the state space model represents variables such as soil moisture or disaster risk, the input u(t) is rainfall, and the observation data y(t) is real-time data obtained through sensors, such as the measurement results of soil moisture sensors and rainfall stations.

[0088] It should be noted that the rainfall-soil saturation model based on partial differential equations and the state-space model are two different modeling frameworks used to solve different types of problems. The rainfall-soil saturation model based on partial differential equations describes the evolution of a continuous system, especially the spatial and temporal evolution of the system. It can well capture the propagation and diffusion of soil moisture, rainfall, etc. in space. The state-space model focuses more on the description of discrete-time systems, emphasizing how to reason and predict through the state and observation data of the system, and is particularly suitable for time series analysis and dynamic prediction.

[0089] In the geological disaster risk prediction provided in this embodiment, the partial differential equation model is used as a component of the state space model to help describe the state transition and spatial evolution of the system. Specifically, the partial differential equation is used to provide the dynamics of state transition in the state space model, and the state space model provides a framework for how to estimate and update these states based on observation data for the final geological disaster risk prediction.

[0090] Specifically, combining the two different models of rainfall-soil saturation model of partial differential equations and state space model includes the following steps:

[0091] First, the rainfall-soil saturation model describes the evolution of soil moisture S(x,t) in space x and time t. The model is regarded as a state equation of a continuous system, which means that the change of soil moisture is a continuous process affected by factors such as spatial diffusion, rainfall and topography.

[0092] In order to incorporate this equation into the state space model, we first need to discretize this equation to adapt it to the requirements of discrete time steps. For example, we can discretize time t into discrete time points t k (t k = kΔt, where Δt is the time step), the space x is discretized into grid points x i (For example, grid division based on sensor locations). The state equation can be obtained in discrete form:

[0093] At this time, soil moisture S(x i , t k ) becomes the state variable in the state space model. The state transition here is described by the discretization of the partial differential equation, which shows how the soil moisture changes from time t k Transfer to k+1 .

[0094] In the state space model, the observation equation describes how to get the sensor measurement results from the state of soil moisture. Multiple sensor measurements are related to the state variable (i.e., soil moisture). For example, the measurement value of the soil moisture sensor can be directly related to the state variable S(x i , t k ), and the reading of the rainfall sensor affects the rainfall R(x i , t k ), thus affecting the dynamics of the system, so the observation equation can be written as:

[0095] , where y(t k ) is the sensor observation data (e.g., the soil moisture sensor reading), C is the mapping matrix (usually a parameter related to the sensor location and measurement accuracy, such as the sensor to soil moisture mapping matrix), v(t k ) is the observation noise.

[0096] By combining the rainfall-soil saturation model and the state-space model, the spatial propagation and temporal evolution of soil moisture, i.e. the dynamic behavior of the system, is described through the rainfall-soil saturation model. The state-space model estimates and predicts the system state mainly through observation data, and adjusts the model parameters according to real-time data, thereby providing more accurate disaster risk prediction.

[0097] The combination of these two models makes it possible to consider simultaneously when making disaster predictions: the spatial diffusion process, that is, how soil moisture diffuses between different locations; the temporal changes, that is, how soil moisture is affected by external factors such as rainfall over time; real-time observation data, that is, dynamically adjusting the model through data obtained from sensors to provide accurate soil moisture estimates, thereby comprehensively assessing disaster risks.

[0098] Step 4: The cloud platform contains a geological condition database for areas prone to geological disasters. The soil moisture calculated in step 3 is dynamically updated. Based on the change in soil moisture and the data in the geological condition database, it is determined whether the predetermined risk threshold is reached to assess the geological disaster risk.

[0099] Specifically, the determination of whether the disaster triggering threshold has been reached includes:

[0100] 1) Soil saturation threshold judgment: The soil moisture threshold is set based on the data in the geological condition database to determine the specific conditions of the monitoring site. When the threshold is exceeded, a warning can be issued (for example, when the soil moisture exceeds a certain critical value, it may cause a landslide or flood).

[0101] 2) Identification of high-risk areas: The spatial distribution of soil moisture and geological information such as slope are constructed through sensor data, and high-risk areas are identified by combining geological data in the geological condition database. For example, areas with larger slopes may be more prone to landslides after rainfall.

[0102] 3) Geological disaster prediction and early warning triggering: Based on the identified high-risk areas, when the soil moisture in the high-risk areas is detected to exceed the predetermined threshold, an early warning is triggered. Different levels of early warnings (such as green, yellow, and red warnings) can also be set to notify local residents or relevant departments based on the size of the risk.

[0103] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A multi-point remote synchronous geological disaster early warning method based on rainfall observation, characterized in that: The early warning method comprises: S100, dividing the target area into multiple monitoring nodes, and arranging a rainfall detection station at each monitoring node, and arranging soil moisture sensors in soil layers of different depths at each monitoring node, and installing slope sensors in landslide-sensitive areas to monitor slope changes in the area in real time; S200, associating soil moisture data at different depths, rainfall detection station data, and data collected by the slope sensor, and uploading them uniformly, by temporally coupling the collected precipitation information, soil moisture data, and slope sensor data, and then sending the temporally coupled data to the cloud platform; S300, after receiving the sensor data on the cloud platform, the data is first decompressed, and then the collected sensor data is input into the comprehensive prediction model to dynamically update the soil moisture; S400. The cloud platform includes a geological condition database for areas prone to geological disasters. The soil moisture calculated in step S300 is dynamically updated. Based on the change in soil moisture and combined with the data in the geological condition database, it is determined whether a predetermined risk threshold is reached to assess the geological disaster risk.

2. The multi-point remote synchronous geological disaster early warning method based on rainfall observation according to claim 1 is characterized in that: The soil moisture sensors are arranged at 1 cm, 15 cm, 30 cm, 45 cm and 60 cm below the soil surface, respectively.

3. The multi-point remote synchronous geological disaster early warning method based on rainfall observation according to claim 1 is characterized in that: The sending of the collected data to the cloud platform includes: S210, a corresponding wireless network node is provided in each rainfall detection station, multiple wireless network nodes in the target area are aggregated into a wireless network node set, and the received sensor data is integrated in the wireless network node set; S220, adding a timestamp to the collected data, checking whether there is duplication in the data according to the timestamp, deleting the duplicate sensor data, and completing data cleaning; S230, after cleaning is completed, the data is compressed and sent to the cloud platform via TCP / IP protocol.

4. The multi-point remote synchronous geological disaster early warning method based on rainfall observation according to claim 1 is characterized in that: The comprehensive prediction model consists of a rainfall-soil saturation model and a state space model. The rainfall-soil saturation model is used to describe the evolution of soil moisture in space and time, and then the rainfall-soil saturation model is discretized into the state space model.

5. The multi-point remote synchronous geological disaster early warning method based on rainfall observation according to claim 4 is characterized in that: The rainfall-soil saturation model is constructed based on partial differential equations and includes the following steps: S311. Based on partial differential equations, the effect of rainfall on soil moisture at different locations is described. The change of soil moisture is affected by both external rainfall and internal moisture transmission in the soil. The diffusion equation is used to describe the spatial propagation of soil moisture: ， in, It is used to represent the rate of change of soil moisture S(x, t) over time; D is used to represent the diffusion coefficient; is the Laplace operator, which represents the second-order derivative of soil moisture in space and describes the diffusion behavior of moisture in space; S312. At the same time, since soil moisture is also directly affected by external factors, the source term f(R(x, t), S(x, t)) is introduced to represent the influence of rainfall. The source term is added to the diffusion equation to obtain: ， Where, f(R(x,t), S(x,t)) represents the effect of rainfall R(x,t) on soil moisture S(x,t); S313, the slope data θ(x, t) collected by the slope sensor is used to represent the angle of ground inclination. In places with larger slopes, water flow is more likely to concentrate, thereby causing geological disasters. The influence of slope is reflected by modifying f(R(x,t), S(x,t)), using the slope data θ(x,t) as an adjustment factor. Finally, the rainfall-soil saturation model based on partial differential equation is obtained: 。 6. The multi-point remote synchronous geological disaster early warning method based on rainfall observation according to claim 4 is characterized in that: The state space model is composed of state equations and observation equations. The state space model uses state variables to describe the dynamic behavior of the entire system and describes the change of state through state transition equations, where: The state equation is used to describe the evolution of the system state, which is shown as follows: ， Where x(t) represents the state of the system, u(t) represents the input, w(t) is the noise or system uncertainty, and A and B are the parameters of the system; The observation equation is used to describe the acquisition of observation data from the state of the system, which is shown as follows: y(t)=Cx(t)+v(t), y(t) is the observation data, C is the mapping matrix, and v(t) is the observation noise.

7. The multi-point remote synchronous geological disaster early warning method based on rainfall observation according to claim 4 is characterized in that: The step of discretizing the rainfall-soil saturation model into a state space model comprises the following steps: S321, discretize the rainfall-soil saturation model by discretizing time t into discrete time points t k , t k = kΔt, where Δt is the time step, discretizing the space x into grid points x i The state equation is obtained in discrete form: , Among them, soil moisture S(x i , t k ) becomes the state variable in the state space model, The discretization of the partial differential equation is used to describe the state transition, which represents how the soil moisture changes from time t k Transfer to k+1 ; S322, the observation equation describes the sensor measurement results obtained from the state of soil moisture. Multiple sensor measurement data are related to the state variables, so the observation equation is written as: ， Among them, y(t k ) is the sensor observation data, C is the mapping matrix, v(t k ) is the observation noise.

8. The multi-point remote synchronous geological disaster early warning method based on rainfall observation according to claim 1 is characterized in that: The determination of whether a predetermined risk threshold is reached by combining the data in the geological condition database includes: S410, soil saturation threshold determination: determining the soil moisture threshold of the monitoring site according to the data in the geological condition database; S420, high-risk area identification: construct the spatial distribution of soil moisture and slope geological information through sensor data, and identify high-risk areas in combination with geological data in the geological condition database; S430, geological disaster prediction and warning triggering: based on the identified high-risk areas, when it is detected that the soil moisture in the high-risk areas exceeds a predetermined threshold, a warning is triggered.

9. A multi-point remote synchronous geological disaster early warning system based on rainfall observation, characterized in that: The early warning system includes: Data collection module, data transmission module and cloud platform module, among which, The data acquisition module is configured to divide the target area into multiple monitoring nodes, and arrange a rainfall detection station at each monitoring node, and arrange soil moisture sensors in soil layers of different depths at each monitoring node, and install slope sensors in landslide-sensitive areas to monitor the slope changes of the area in real time; The data transmission module is connected to the data acquisition module and is configured to associate soil moisture data at different depths, rainfall detection station data, and data collected by the slope sensor, and upload them uniformly, by temporally coupling the collected precipitation information, soil moisture data, and slope sensor data, and then sending the temporally coupled data to the cloud platform; The cloud platform module is connected to the data sending module and is configured as follows: Comprehensive prediction model submodule and early warning prompt submodule, The comprehensive prediction model submodule is configured to receive the sensor data collected by the data acquisition module and dynamically update the soil moisture through the comprehensive prediction model; The early warning submodule is connected to the comprehensive prediction model submodule and is configured to dynamically update the soil moisture obtained by the comprehensive prediction model submodule, based on the changes in soil moisture, combined with the data in the geological conditions database, determine whether the predetermined risk threshold is reached, assess the geological disaster risk and issue an early warning.

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