Mountain area detection point arrangement method and rainfall collection device

Abstract

The invention relates to the technical field of hydro meteorology, in particular to a mountainous area detection point arrangement method and a rainfall acquisition device, and the mountainous area detection point arrangement method integrates geographic space information by constructing a geographic space database of a target area. Key disaster prevention objects are identified and marked to construct a feature matrix; the comprehensive risk index of the disaster prevention object is calculated in combination with geographic data, the risk level is spatialized, and a priority basis is provided for site layout. And precise deployment and control are realized through geographic space data deep mining and a risk-oriented algorithm. The site density is dynamically allocated based on the comprehensive risk index, and the disaster early warning precision is remarkably improved; on the second aspect, the invention provides a rainfall acquisition device which can quickly arrange detection points and can upload soil saturation information and real-time risk level information in real time at the same time.

Description

Technical Field

[0001] The present invention and the field of hydrological and meteorological technology, in particular, relate to a detection point arrangement method and a rainfall collection device in a mountainous area. Background Art

[0002] Precipitation in mountainous areas is the core driving force for ecosystems and the hydrological cycle, and is a major contributor to natural disasters such as floods, landslides, and mudslides. Accurately understanding the spatial and temporal distribution of rainfall in mountainous areas is crucial for water resource management, disaster warning, ecological protection, and climate change research. However, the complex topography and climatic characteristics of mountainous areas make obtaining high-quality, high-resolution rainfall data a significant challenge, making the scientific and rational deployment of rainfall monitoring stations increasingly important.

[0003] Due to cost and management difficulties, most existing detection points are arranged at equal intervals. The number of existing detection points is far from meeting the needs of capturing the spatial variation of rainfall in complex terrain, and there are monitoring gaps in many key areas, which leads to the inability to provide timely disaster warnings. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, in a first aspect, the present invention proposes a method for arranging detection points in mountainous areas to ensure optimal coverage of detection points and improve disaster early warning capabilities. A method for arranging detection points in a mountainous area according to an embodiment of the present invention includes: S10: constructing a target area, collecting geographic spatial data within the target area, and establishing a geographic spatial database of the target area; S20: Based on the geographic spatial data, marking the disaster prevention objects in the target area and constructing the disaster prevention object feature matrix; S30: Calculating a comprehensive risk index of the disaster prevention object based on the geospatial data; S40: Calculating the number of monitoring sites within the target area based on the geospatial data; S50: performing simulated site selection for each monitoring site based on the number of the monitoring sites in the target area; S60: Based on the simulated site selection data, constraining and correcting the simulated site selection to determine the real address of the monitoring site; S70: Perform dynamic monitoring based on the real address of the monitoring site, wherein the dynamic monitoring includes coverage verification and real-time warning adjustment.

[0005] According to some embodiments of the present invention, the geospatial data includes a 25m resolution DEM digital elevation model and geological high-risk area data within the target area; the geological high-risk area data includes historical landslide areas and historical debris flow areas, and the steep slope of the historical landslide area is greater than 25°; step S10 includes the following steps: S11: Acquire river network data within the target area according to the digital elevation model data; S12: Acquire slope data within the target area according to the digital elevation model data; S13: Acquire slope aspect data within the target area according to the digital elevation model data.

[0006] According to some embodiments of the present invention, the number of disaster prevention objects in step S20 is M, and the characteristic vector of each disaster prevention object i is: Among them, (x i ,y i ) are the longitude and latitude coordinates, A i is the confluence area, S i is the soil saturation, G i It is a sign of high-risk area, i is the social attribute weight, R i The real-time risk level.

[0007] According to some embodiments of the present invention, the catchment area, real-time risk level and soil saturation of the disaster prevention object i are standardized.

[0008] According to some embodiments of the present invention, the calculation formula of the comprehensive risk index is: Among them, α is the catchment area weight; β is the high-risk area weight; γ is the risk level weight; δ is the soil saturation weight.

[0009] According to some embodiments of the present invention, S40 includes the following steps: S41: Counting the total number of disaster prevention objects located in high-risk areas within a region, and configuring basic monitoring stations based on the geological high-risk area data and the ratio of the total area of the statistical region; S42: Traverse all the disaster prevention objects and configure dynamic monitoring sites based on the real-time risk level.

[0010] According to some embodiments of the present invention, S50 includes the following steps: S51: Converting the characteristic data of all the disaster prevention objects in the target area into a standard format; S52: Calculating the distance from each disaster prevention object to the nearest monitoring site; S53: randomly selecting the next detection site according to the distance square weighted probability; S54: looping through steps S42 and S43 until all candidate monitoring site locations are determined; According to some embodiments of the present invention, S60 includes the following steps: S61: Screening the candidate monitoring sites located in geologically high-hazard areas; S62: Perform distance correction on the candidate monitoring site.

[0011] According to some embodiments of the present invention, S70 includes the following steps: S71: Verify coverage of candidate testing sites; S72: Performing real-time early warning adjustments on candidate detection sites based on the real-time risk level.

[0012] In a second aspect, the present invention proposes a rainfall collection device that can quickly deploy detection points and simultaneously upload soil saturation information and real-time risk level information in real time.

[0013] According to some embodiments of the present invention, a 5G communication module and a moisture content monitoring module are provided in the rain gauge, and the rain gauge can upload soil saturation information and real-time risk level information through the 5G communication module.

[0014] A detection point arrangement method and a rainfall collection device in a mountainous area according to an embodiment of the present invention have at least the following beneficial effects: According to the solution of the present invention, a geospatial database of the target area is constructed to integrate geospatial information. Key disaster prevention objects are identified and labeled to construct a feature matrix. The comprehensive risk index of the disaster prevention object is calculated in combination with geographic data, and the risk level is spatialized to provide a priority basis for site layout. Precise deployment is achieved through deep mining of geospatial data and risk-oriented algorithms. It dynamically allocates site density based on the comprehensive risk index, significantly improving the accuracy of disaster warning. It comprehensively optimizes construction costs and monitoring efficiency through a constraint correction mechanism, prioritizes coverage of key areas with limited sites, and maximizes resource utilization efficiency. The full-chain closed-loop design is highly systematic and scalable, and can flexibly adapt to various mountain scenarios. It ensures optimal coverage of detection points and improves disaster warning capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic flow chart of a method for arranging detection points in a mountainous area according to the present invention; Figure 2 The figure is a structural schematic diagram of the rainfall collection device of the present invention.

[0016] In the picture: 100-meter, 200-communication module, 300-water content monitoring module. DETAILED DESCRIPTION

[0017] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0018] In the description of the present invention, it should be understood that descriptions involving orientation, such as the orientation or positional relationship indicated by up, down, etc., are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0019] In the description of the present invention, "a plurality" refers to more than two. The use of "first" or "second" is solely for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of the indicated technical features, or implicitly indicating the order of the indicated technical features.

[0020] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, and connecting should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0021] In a first aspect, the present invention discloses a method for arranging detection points in a mountainous area and a rainfall collection device, comprising the following steps: S10: Construct the target area, collect geospatial data within the target area, and establish a geospatial database for the target area; the geospatial data includes a 25m resolution DEM digital elevation model and data on geologically high-risk areas within the target area; The geological high-risk area data includes historical landslide areas and historical debris flow areas, and the steep slope of the historical landslide area is greater than 25°; step S10 includes the following steps: S11: Obtain river network data within the target area based on the numbers; S12: Obtaining slope data within the target area based on the digital elevation model data; S13: Obtaining slope aspect data within the target area according to the digital elevation model data.

[0022] In this step, digital elevation models can be acquired using satellite or drone aerial surveys. By integrating local geological hazard survey databases and overlaying steep slopes (>25°) and historical debris flow areas, high-risk geological zones within the target area can be zoned, ensuring that the monitoring network covers areas where disasters are likely to occur.

[0023] S20: Based on the geographic spatial data, mark the disaster prevention objects in the target area and construct a disaster prevention object feature matrix; the number of disaster prevention objects is M, and the feature vector of each disaster prevention object i is: Among them, (x i ,y i ) are the longitude and latitude coordinates, A i is the confluence area, S i is the soil saturation, G i It is a sign of high-risk area, i is the social attribute weight, R i In this step, the disaster prevention objects are dynamically monitored through real-time rainfall intensity, soil saturation and risk level.

[0024] Specifically, in this step, each location and associated features are quantified, and the longitude and latitude coordinates can be parsed based on the longitude and latitude coordinates. The unit of the catchment area is km², and the catchment area is obtained based on the river network data. i is the soil saturation, which is obtained by real-time collection of the target area, 0≤S i ≤100%. G i It is a high-risk area sign. If the disaster prevention object is located in a high-risk area, G i =1, if the disaster prevention object is not located in the high-level area, then G i =0.ω i is the social attribute weight, 0≤ω i ≤1; According to quantitative calibration, wasteland ω i =0, village ω i =0.5, school ω i =0.9, hospital / power station ω i =1. R i is the real-time risk level, which is calculated based on the real-time rainfall and soil saturation and rounded to an integer, 0≤R i ≤5, the larger the number, the higher the risk. Among them, P current is the real-time rainfall intensity, P max The largest rainfall in history, S soil is the current soil saturation.

[0025] Furthermore, the catchment area A of the disaster prevention target i To standardize: Real-time risk level R for disaster prevention objects i To standardize: Soil saturation S for disaster prevention targets i To standardize: S30: Calculate the comprehensive risk index of the disaster prevention object based on geospatial data; the calculation formula for the comprehensive risk index is: Among them, α is the weight of the catchment area; β is the weight of the high-risk area; γ is the weight of the risk level; and δ is the weight of the soil saturation. In this step, α+β+γ+δ=1. Under the current constraints, α=0.4, β=0.3, γ=0.2, and δ=0.1. The weight coefficients α, β, γ, and δ are key parameters in the formula, which determine the contribution ratio of each risk factor to the comprehensive risk. By setting the weights in this step, it is possible to ensure that the risk index is not distorted, thereby improving the rationality of site selection. It can be understood that the larger the comprehensive risk index, the greater the risk of the disaster prevention object, and it needs to be covered first.

[0026] S40: Based on the geospatial data, calculate the number of monitoring sites within the target area; in this step, the number of monitoring sites is K. S40 includes the following steps: S41: Count the total number of disaster prevention objects located in high-risk areas within the statistical area, and configure basic monitoring stations based on the geological high-risk area data and the total area ratio of the statistical area; In this step, at least one monitoring station is required for every five high-risk areas or every 50 km², and the basic monitoring station K base The calculation formula is: S42: Traverse all disaster prevention objects and configure dynamic monitoring sites based on real-time risk levels. Add a monitoring site for every three high-risk points. Dynamic monitoring site K dynamic The calculation formula is: The final total number of monitoring stations is K: In this step, during normal operations, basic monitoring stations are used for operation; during the rainy season, dynamic monitoring stations are added for operation. Through the design of this step, the linkage arrangement of static basic coverage and dynamic emergency enhancement is realized, ensuring high-density monitoring of geological risk areas, real-time enhancement of rainstorm hotspots, and economic coverage of general areas in the three major monitoring station step modes.

[0027] S50: Based on the number of monitoring stations in the target area, simulate site selection for each monitoring station. S50 includes the following steps: In this embodiment, the K-means algorithm is used for clustering site selection. The specific steps are: S51: Convert the characteristic data of all disaster prevention objects in the target area into a standard format; establish a characteristic matrix of the disaster prevention objects: Disaster prevention objects are clustered using three-dimensional space including longitude, latitude and risk index. K-means is used to select the initial site locations of K monitoring sites, and a location is uniformly randomly selected from all disaster prevention objects as the initial site μ k .

[0028] S52: Calculate the distance from each disaster prevention object to the nearest monitoring site; calculate the Euclidean distance D to the nearest existing site i : Generate a probability distribution: S53: Randomly select the next detection site according to the distance square weighted probability; S54: Repeat steps S52 and S53 until all candidate monitoring site locations are determined; select K initial sites and calculate the weighted distance to each site K: It is understandable that the risk index ρ i The larger the value, the greater the weighted distance. Furthermore, the cluster of sites corresponding to the minimum weighted distance is assigned, and high-risk disaster prevention objects are forcibly assigned to the nearest monitoring site.

[0029] For each cluster k, calculate the new site location as a risk-weighted average: Ensure that monitoring stations are offset toward high-risk disaster prevention targets. Check convergence. If the movement distance of all monitoring stations is less than ε, stop; otherwise, continue iterating.

[0030] S60: Based on the simulated site selection data, constraining and correcting the simulated site selection to determine the real address of the monitoring site; S60 includes the following steps: S61: Screen candidate monitoring sites located in geologically high-risk areas; specifically, for each candidate site location μ k , μk=(μ x , μy) extracts slope information θ from geospatial data k If, θ k Slope>25°; all candidate monitoring sites in the cluster with a slope ≤25° were screened.

[0031] S62: Correct the distance of the candidate monitoring site. The specific correction formula is: Where λ is the slope penalty factor. In this step, λ=0.1.

[0032] S70: Dynamic monitoring is performed based on the real address of the monitoring site. Dynamic monitoring includes coverage verification and real-time warning adjustment. S70 includes the following steps: S71: Verify the coverage of candidate detection sites; use the risk coverage index to verify the coverage, where the risk coverage index Γ is used. k To verify, the calculation formula of the insurance coverage index is: It can be understood that in this formula, the numerator is the total risk within the cluster, and the denominator is the average distance from the point within the cluster to the site. k >2.5 S72: Based on the real-time risk level, adjust the candidate detection sites in real-time. Specifically, when: R i ≥4 and S i When the rainfall intensity is greater than 85% and the rainfall threshold is greater than all other conditions, additional monitoring stations need to be added. Through the design of this method, it is possible to ensure that monitoring stations focus on high-risk areas, respond quickly to real-time risks, and strictly meet terrain constraints.

[0033] In the second aspect, the present invention discloses a rainfall collection device, including a rain gauge, wherein a 5G communication module and a moisture content monitoring module are provided in the rain gauge, and the rain gauge can upload soil saturation information and the real-time risk level information through the 5G communication module. Specifically, in this embodiment, the built-in moisture content monitoring module of the device measures the soil moisture content in real time and converts it into soil saturation. The rain gauge records rainfall every minute and calculates hourly rainfall intensity. The built-in processor of the device generates a real-time risk level according to preset rules based on soil saturation and rainfall intensity. The soil saturation, rainfall intensity, and real-time risk level are uploaded to the central decision-making system (levels 1 to 5) in real time through the 5G communication module. The 5G module is used for real-time communication, and the transmission delay is <100ms, ensuring the timeliness of the rainstorm response. When the device monitors that R i ≥4 and Si When rainfall intensity exceeds all rainfall thresholds, an alert is immediately sent to the command center, triggering the deployment of mobile stations. The rain gauge automatically marks its coordinates, and the command system deploys a mobile monitoring station 500 meters upstream based on the information provided by the rain gauge. This rain gauge, through its three-in-one monitoring and 5G second-level transmission, provides an accurate information source for the dynamic monitoring network.

[0034] The embodiments of the present invention are described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Various changes can be made within the knowledge of ordinary technicians in the relevant technical field without departing from the scope of the present invention.

Claims

1. A method for arranging rainfall collection points in mountainous areas, characterized in that: The following steps are involved: S10: constructing a target area, collecting geographic spatial data within the target area, and establishing a geographic spatial database of the target area; S20: Based on the geographic spatial data, marking the disaster prevention objects in the target area and constructing the disaster prevention object feature matrix; S30: Calculating a comprehensive risk index of the disaster prevention object based on the geospatial data; S40: Calculating the number of monitoring sites within the target area based on the geospatial data; S50: performing simulated site selection for each monitoring site based on the number of the monitoring sites in the target area; S60: Based on the simulated site selection data, constraining and correcting the simulated site selection to determine the real address of the monitoring site; S70: Perform dynamic monitoring based on the real address of the monitoring site, wherein the dynamic monitoring includes coverage verification and real-time warning adjustment.

2. The method for arranging detection points in mountainous areas according to claim 1, characterized in that: The geospatial data includes a 25m resolution DEM digital elevation model and geological high-risk area data within the target area; the geological high-risk area data includes historical landslide areas and historical debris flow areas, and the steep slope of the historical landslide area is greater than 25°; step S10 includes the following steps: S11: Acquire river network data within the target area according to the digital elevation model data; S12: Acquire slope data within the target area according to the digital elevation model data; S13: Acquire slope aspect data within the target area according to the digital elevation model data.

3. The method for arranging detection points in mountainous areas according to claim 2, characterized in that: The number of disaster prevention objects in step S20 is M, and the characteristic vector of each disaster prevention object i is: Among them, (x i ,y i ) are the longitude and latitude coordinates, A i is the confluence area, S i is the soil saturation, G i It is a sign of high-risk area, i is the social attribute weight, R i The real-time risk level.

4. The method for arranging detection points in mountainous areas according to claim 3, characterized in that: The catchment area, real-time risk level and soil saturation of the disaster prevention object i are standardized.

5. The method for arranging detection points in mountainous areas according to claim 4, characterized in that: The calculation formula of the comprehensive risk index is: Among them, α is the catchment area weight; β is the high-risk area weight; γ is the risk level weight; δ is the soil saturation weight.

6. The method for arranging detection points in mountainous areas according to claim 5, characterized in that: S40 includes the following steps: S41: Counting the total number of disaster prevention objects located in high-risk areas within a region, and configuring basic monitoring stations based on the geological high-risk area data and the ratio of the total area of the statistical region; S42: Traverse all the disaster prevention objects and configure dynamic monitoring sites based on the real-time risk level.

7. The method for arranging detection points in mountainous areas according to claim 6, characterized in that: S50 includes the following steps: S51: Converting the characteristic data of all the disaster prevention objects in the target area into a standard format; S52: Calculating the distance from each disaster prevention object to the nearest monitoring site; S53: randomly selecting the next detection site according to the distance square weighted probability; S54: loop through steps S52 and S53 until all candidate monitoring site locations are determined.

8. The method for arranging detection points in mountainous areas according to claim 1, characterized in that: S60 includes the following steps: S61: Screening the candidate monitoring sites located in geologically high-hazard areas; S62: Perform distance correction on the candidate monitoring site.

9. The method for arranging detection points in mountainous areas according to claim 1, characterized in that: S70 includes the following steps: S71: Verify coverage of candidate testing sites; S72: Performing real-time early warning adjustments on candidate detection sites based on the real-time risk level.

10. A rainfall collection device according to any one of claims 1 to 9, characterized in that: The invention comprises a rain gauge (100), wherein a communication module (200) and a water content monitoring module (300) are provided in the rain gauge (100), and the rain gauge (100) can upload soil saturation information and the real-time risk level information via the communication module (100).

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