Intelligent planning method for water and soil conservation measures of slope cropland

Abstract

The invention relates to the technical field of agricultural management, in particular to a slope cropland water and soil conservation measure intelligent planning method, which comprises the following steps: acquiring elevation, soil and rainstorm data to generate a drainage network, evaluating the scouring risk by combining the flood peak flow and the soil impact resistance, arranging a drainage opening and an access point, and then optimizing the network based on the risk and the cost to obtain a slope cropland water and soil conservation measure planning result. An access scheme is generated through a Hungary algorithm, then key nodes are identified to determine an operation threshold value, and finally the soil erosion state is judged by comparing collected turbidity and flow data with the threshold value. According to the method, multi-source data such as elevation, soil and rainstorm intensity are fused, the global drainage network topology can be accurately calculated, the scouring risk can be quantitatively evaluated, the reinforcement and reconstruction cost is weighed for global optimization, then the optimal connection scheme is solved through an algorithm, then key monitoring nodes are automatically identified, and through real-time analysis of turbidity and flow data, the real-time monitoring of the drainage network is realized. Dynamic monitoring and instant early warning of soil erosion are realized, and passive treatment is changed into active prevention and control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of agricultural management, and in particular to an intelligent planning method for water and soil conservation measures of slope farmland. BACKGROUND

[0002] The technical field of the intelligent planning method for water and soil conservation measures of slope farmland is an application field of computer science, geographic information system and soil and water conservation engineering, which includes obtaining multi-source heterogeneous data such as topography, soil properties and rainfall erosion of slope farmland, and constructing a water and soil loss prediction model and a measure benefit evaluation model to provide quantitative and intelligent planning schemes for regional water and soil loss control. The traditional agricultural management refers to the technical matters of determining the type and layout of the specific farmland prevention and control measures when planning water and soil conservation. The common method is to send technical personnel to the site to investigate, obtain information such as topographic slope and soil thickness, and manually layout engineering measures such as terraces and drainage ditches according to the general design specifications for water and soil conservation or personal work experience.

[0003] The traditional agricultural management method relies heavily on on-site investigation by technical personnel. This point sampling method cannot fully obtain the complex topography and geomorphology information of slope farmland, especially in large areas or areas with variable topography. It is easy to misjudge the risk of water and soil loss due to survey blind spots. At the same time, the planning and layout of the prevention and control measures highly depend on personal work experience or general specifications, and lack of quantitative evaluation and optimization process of the economic efficiency and benefit of the engineering layout, resulting in strong subjectivity of the scheme and difficulty to achieve the optimal effect. For example, an experienced drainage system may cause local erosion due to the failure to accurately identify the key convergence path. Finally, due to the lack of dynamic monitoring and feedback mechanism after construction, when encountering unexpected rainfall, the potential erosion risk cannot be warned in advance, and passive remediation is often needed after the disaster occurs. SUMMARY

[0004] The purpose of the present application is to solve the problems existing in the prior art and to provide an intelligent planning method for water and soil conservation measures of slope farmland.

[0005] In order to achieve the above purpose, the present application adopts the following technical scheme: an intelligent planning method for water and soil conservation measures of slope farmland, comprising the following steps: S1: obtaining elevation model data, soil distribution data and rainstorm intensity, calculating the elevation model data to generate a tree-shaped drainage network topology structure, calculating the flood peak flow, comparing the anti-erosion capacity of the soil distribution data to generate a quantitative value of erosion risk, obtaining the contour line data of terraces, and laying out discrete points to generate a set of drainage outlets and a set of access points; S2: comparing reinforcement and reconstruction costs based on the erosion risk quantification value, optimizing the tree-shaped drainage network topology to generate a drainage network topology, calling the set of drainage outlets and the set of access points to solve the connection cost by the Hungarian algorithm to generate an access scheme; S3: identifying key monitoring nodes using the drainage network topology, planning the deployment path of the monitoring equipment according to the access scheme, calculating the flood peak flow and sediment carrying capacity of the key monitoring nodes, and determining the operating reference threshold; S4: collecting turbidity data and flow data at the key monitoring nodes, and comparing them with the operating reference threshold, if the turbidity data exceeds the operating reference threshold, judging soil erosion and generating an erosion state identifier.

[0006] As a further scheme of the present application, the erosion risk quantification value includes a unique identifier of the channel segment, a risk level, and a difference value, the set of drainage outlets includes the geographic coordinates of the drainage outlets and the corresponding terrace numbers, the set of access points includes the geographic coordinates of the access points and the corresponding channel numbers, the drainage network topology specifically refers to the channel connection relationship, node type, and channel protection level, the access scheme specifically refers to the matching pairs of drainage outlets and access points and the connection channel engineering cost, the operating reference threshold includes a design flood peak flow threshold and a theoretical sediment carrying capacity threshold, and the erosion state identifier includes the location of the warning node, the turbidity exceeding amplitude, and the warning timestamp.

[0007] As a further scheme of the present application, the obtaining steps of the set of drainage outlets and the set of access points are specifically as follows: S101: obtaining elevation model data, soil distribution data, and rainstorm intensity, generating a tree-shaped drainage network topology structure based on the topographic information of the elevation model data, calculating the flood peak flow based on the tree-shaped drainage network topology structure and the rainstorm intensity, extracting the anti-erosion ability parameters of multiple soil types in the soil distribution data and performing numerical comparison with the flood peak flow to generate an erosion risk quantification value; S102: obtaining terrace contour data, setting an erosion risk judgment threshold for the erosion risk quantification value, retrieving a spatial region whose erosion risk quantification value exceeds the erosion risk judgment threshold, and performing overlay analysis on the spatial region vector boundary and the terrace contour data to obtain a set of discrete distribution points; S103: calling the tree-shaped drainage network topology structure and the set of discrete distribution points, calculating the spatial distances from multiple points in the set of discrete distribution points to the main stream line of the tree-shaped drainage network topology structure, classifying the multiple points according to a preset distance judgment value and point elevation information, and establishing a set of drainage outlets and a set of access points.

[0008] As a further scheme of the present application, the obtaining steps of the access scheme are specifically as follows: S201: Based on the erosion risk quantitative value, obtain the drainage network node reinforcement unit cost and the flow path reconstruction unit cost, traverse the plurality of flow path units in the tree-shaped drainage network topology, combine the erosion risk quantitative value and the drainage network node reinforcement unit cost to calculate the reinforcement cost, and retrieve the alternative flow path to calculate the reconstruction cost in combination with the flow path reconstruction unit cost. According to the numerical comparison result of the reinforcement cost and the reconstruction cost, a node optimization decision set is established; S202: Call the node optimization decision set, filter the flow path unit whose decision result is reconstruction, replace it with the alternative flow path in the tree-shaped drainage network topology, and update the connection relationship and weight parameter between nodes to generate a drainage network topology; S203: According to the drainage network topology, call the outlet set and the access point set, calculate the connection path cost between the plurality of outlets and the plurality of access points, construct a connection cost matrix, solve the connection cost matrix, obtain the connection pair with the minimum total cost, and generate an access scheme.

[0009] As a further scheme of the present application, the obtaining step of the operation reference threshold value is specifically: S301: Calculate the network centrality parameter and the upstream confluence contribution degree parameter using the plurality of network nodes in the drainage network topology, perform weighted summation on the network centrality parameter and the upstream confluence contribution degree parameter, generate a node criticality score, set a node screening score threshold value, retrieve the network nodes whose node criticality scores exceed the node screening score threshold value, and generate a key monitoring node set; S302: According to the access scheme, call the key monitoring node set, obtain the spatial position coordinates of the plurality of key monitoring nodes and the terrain connectivity data between nodes, based on the spatial position coordinates and the terrain connectivity data, calculate the path total cost of the feasible deployment sequence traversing all key monitoring nodes, screen the deployment sequence with the minimum path total cost, and generate a monitoring device deployment path; S303: For each key monitoring node in the key monitoring node set, obtain the elevation model data and soil distribution data of the control watershed range, combine the rainstorm intensity to calculate the flood peak flow, and calculate the sediment carrying capacity. According to the calculation results of the flood peak flow and the sediment carrying capacity, set a plurality of response values, and establish an operation reference threshold value.

[0010] As a further scheme of the present application, the erosion state identification obtaining step is specifically: S401: Deploy a monitoring device based on the key monitoring node, and set data collection frequency parameters and data upload period. The monitoring device synchronously collects turbidity data and flow data according to the data collection frequency parameters, encodes the collected turbidity data and flow data into independent data frames, aggregates multiple groups of data frames at the end of the data upload period, adds node identifiers and timestamp information to each data frame, and obtains a monitoring node real-time data sequence; S402: Receive the monitoring node real-time data sequence, parse the data frames to extract the turbidity data, flow data, node identifier and timestamp information, call the multi-level turbidity response threshold value set for the differentiated flow data value interval in the running reference threshold value, sequentially and iteratively compare the current turbidity data value with the multi-level turbidity response threshold value corresponding to the interval of the current flow data, and obtain an erosion event determination result; S403: According to the erosion event determination result, if the determination result indicates that the turbidity data value exceeds any level of turbidity response threshold value, extract the node identifier, timestamp information and threshold level parameter of the exceeded threshold value, and structureally concatenate the three, wherein the turbidity exceeding amplitude is represented by the threshold level parameter of the exceeded threshold value, and establish an erosion state identifier.

[0011] As a further scheme of the present application, the generation step of the scouring risk quantitative value is specifically: Obtain the elevation model data, the soil distribution data, and the upstream catchment area of each channel segment in the tree-shaped drainage network topology, and call the flood peak flow; For the soil distribution data, extract the soil start-up flow parameter and the anti-scouring parameter corresponding to each soil type; Based on each channel segment in the tree-shaped drainage network topology, the scouring risk quantitative value of the channel segment is Through the formula: Calculate; Wherein, represents the scouring risk quantitative value of the channel segment, represents the channel morphology correction coefficient combining the channel curvature and the roughness, represents the design flood peak flow of the current channel segment, represents the soil start-up flow parameter of the location of the current channel segment, represents the upstream catchment area of the channel segment, represents the soil type anti-scouring parameter of the location of the channel segment; According to the calculated scouring risk quantitative value According to the preset risk level division standard, a unique identifier, a risk level and a difference value exceeding a threshold value are given to each channel section, and the erosion risk quantification value is generated.

[0012] As a further scheme of the present application, the node optimization decision set establishment step is specifically: The erosion risk quantification value, the drainage network node reinforcement unit cost and the flow path reconstruction unit cost are called, and all flow path units in the tree-shaped drainage network topology are traversed; For any flow path unit, according to the risk level in the erosion risk quantification value corresponding thereto, the required channel protection standard is determined, the length and terrain parameters of the flow path unit are combined, and the drainage network node reinforcement unit cost is called to calculate the reinforcement cost of the flow path unit; Based on the tree-shaped drainage network topology, an alternative flow path with the same starting and ending points and within a preset spatial search radius from the current flow path unit is searched, the length and expected engineering quantity of the alternative flow path are calculated, and the flow path reconstruction unit cost is called to calculate the reconstruction cost; The reinforcement cost and the reconstruction cost are compared, if the reinforcement cost is greater than the reconstruction cost, it is determined that the optimization strategy is reconstruction, otherwise it is reinforcement; The unique identifier of each flow path unit and the corresponding optimization strategy decision result are structurally paired to generate a node optimization decision set.

[0013] As a further scheme of the present application, the node criticality score generation step is specifically: The drainage network topology is called and all network nodes therein are traversed; For each network node, the betweenness centrality thereof in the drainage network topology is calculated to obtain an original betweenness centrality parameter, and the total confluence area of all channel sections upstream of the node is calculated to obtain an original upstream confluence contribution degree parameter; The original betweenness centrality parameter and the original upstream confluence contribution degree parameter of all network nodes are normalized to obtain the network centrality parameter and the upstream confluence contribution degree parameter; The node criticality score is calculated by the formula: ; wherein, the node criticality score of the network node, the weight coefficient of the network centrality for adjusting the importance influence of the network structure, the weight coefficient of the upstream confluence contribution degree for adjusting the importance influence of hydrology response, the original betweenness centrality parameter of the current node, respectively represent the maximum and minimum original betweenness centrality parameters in the whole drainage network topology, respectively represent the maximum and minimum original betweenness centrality parameters in the whole drainage network topology, represent the original upstream confluence contribution degree parameter of the current node, respectively represent the maximum and minimum original upstream confluence contribution degree parameter in the whole drainage network topology; respectively represent the maximum and minimum original upstream confluence contribution degree parameter in the whole drainage network topology; The node criticality score of each network node calculated is associated with the node unique identifier to generate a node criticality score. The node criticality score of each network node calculated is associated with the node unique identifier to generate a node criticality score.

[0014] As a further scheme of the present application, the erosion event determination result obtaining step is specifically: The running reference threshold is called and the monitoring node real-time data sequence is received to obtain the current turbidity data and current flow data at the target timestamp; According to the value of the current flow data, the flow data value interval to which the current flow data belongs is matched and found in the running reference threshold; The multi-level turbidity response threshold corresponding to the flow data value interval is obtained, and the multi-level turbidity response threshold includes multiple increasing thresholds from a first level to a last level; The current turbidity data is compared with a first level turbidity response threshold in the multi-level turbidity response threshold, and if the current turbidity data is greater than the first level turbidity response threshold, it is determined that an erosion event occurs, and the threshold level parameter that is exceeded is recorded as a first level; If it is less than or equal to, the current turbidity data is continuously compared with a second level turbidity response threshold, and iteration is performed in turn until the highest level threshold or the event is determined to occur, and the threshold level parameter that is exceeded is recorded; If the current turbidity data does not exceed the turbidity response threshold of all levels, it is determined that an erosion event does not occur, and an erosion event determination result is generated.

[0015] Compared with the prior art, the present application has the advantages and positive effects that: In the present application, by fusing multiple source data such as elevation model, soil distribution and rainstorm intensity, the global tree-shaped drainage network topology structure can be accurately calculated and the erosion risk can be quantitatively evaluated to provide quantitative basis for water and soil conservation planning. On this basis, according to the erosion risk quantitative value, the reinforcement and reconstruction cost is weighed to globally optimize the network topology, and then the optimal connection scheme of each drainage outlet and access point is solved by algorithm to ensure the economy and rationality of the engineering layout, and then the key monitoring nodes in the drainage network are automatically identified, and the running reference threshold is preset, the turbidity and flow data are collected and analyzed in real time to realize dynamic monitoring and immediate warning of the soil erosion state, and passive governance is changed to active prevention and control. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 A slope farmland soil and water conservation measure intelligent planning method flow chart is provided in the present application; Figure 2 A scour risk quantification and distribution point flow chart is provided in the present application; Figure 3 A drainage network topology optimization and access scheme generation flow chart is provided in the present application; Figure 4 A key monitoring node identification and deployment path planning flow chart is provided in the present application; Figure 5 A soil erosion state monitoring and early warning flow chart is provided in the present application. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical scheme and advantages of the present application clearer, the technical scheme realized based on software will be described in detail below in combination with system architecture diagrams and embodiments. It should be understood that the specific embodiments described herein are only used to explain the technical scheme of the present application and do not constitute a limitation on the scope of protection.

[0018] In the description of the present application, the system architecture relationship or data processing flow indicated by the terms "level", "module", "interface", "data flow", "client", "server" and the like are defined based on the corresponding architecture diagram or flow chart of the embodiment. This way of expression is only used to clearly explain the logical relationship of each element in the technical scheme, and is not limited to the physical deployment form. The "multiple" includes two or more technical units, including but not limited to multiple data nodes, processing threads, service instances or functional components, and other expandable elements, and the specific number is determined according to the actual business scenario.

[0019] Please refer to Figure 1 and Figure 2 The present application provides a technical scheme: an intelligent planning method for slope farmland soil and water conservation measures, comprising the following steps: S1: Obtain elevation model data, soil distribution data and rainstorm intensity, calculate the elevation model data to generate a tree-shaped drainage network topology structure, calculate the flood peak flow, compare the anti-scour ability of the soil distribution data, generate a scour risk quantification value, obtain terrace contour data, and distribute discrete points to generate a set of drainage outlets and a set of access points; The scour risk quantification value includes a unique identifier of a channel segment, a risk level and a difference value; The set of drainage outlets includes the geographic coordinates of the drainage outlets and the terrace number to which they belong; The set of access points includes the geographic coordinates of the access points and the channel number to which they belong; The steps of obtaining the set of drainage outlets and the set of access points are as follows: S101: Obtain the elevation model data, soil distribution data and storm intensity, generate the tree drainage network topology structure based on the topographic information of the elevation model data, calculate the flood peak flow combining the storm intensity and the tree drainage network topology structure, extract the soil erosion resistance parameters of multiple soil types in the soil distribution data and compare the numerical values with the flood peak flow, and generate the erosion risk quantification value; S102: Obtain the terrace contour line data, set the erosion risk judgment threshold value for the erosion risk quantification value, retrieve the spatial region whose erosion risk quantification value exceeds the erosion risk judgment threshold value, and perform overlay analysis on the spatial region vector boundary and the terrace contour line data to obtain a set of discrete distribution points; S103: Call the tree drainage network topology structure and the set of discrete distribution points, calculate the spatial distance from multiple points in the set of discrete distribution points to the main stream line of the tree drainage network topology structure, classify the multiple points according to the preset distance judgment value and the elevation information of the points, and establish a set of drainage outlets and a set of access points; The generation step of the erosion risk quantification value is specifically: obtaining the upstream catchment area of multiple channel sections in the elevation model data, soil distribution data and tree drainage network topology structure, and calling the flood peak flow; For the soil distribution data, extract the soil start-up flow parameters and erosion resistance parameters corresponding to multiple soil types; Based on each channel section in the tree drainage network topology structure, the erosion risk quantification value of the channel section is Through the formula: Calculation; Wherein, represents the erosion risk quantification value of the channel section, represents the channel shape correction coefficient combining the channel curvature and the roughness, represents the design flood peak flow of the current channel section, represents the soil start-up flow parameter of the location of the current channel section, represents the upstream catchment area of the channel section, represents the soil type erosion resistance parameter of the location of the channel section; According to the calculated erosion risk quantification value , compare the preset risk level division standard, give each channel section a unique identifier, a risk level and a difference value representing exceeding the critical value, and generate the erosion risk quantification value.

[0020] A 1.5 square kilometer sloping farmland area located in the southern part of the Loess Plateau is taken as the implementation object. First, the digital elevation model (DEM) data with a resolution of 5 meters x 5 meters is obtained for the area. At the same time, the soil distribution data of the area is collected, and the data results are shown in Table 1. The design storm intensity is set, which is referred to Outdoor Drainage Design Standard (GB50014-2021), and the storm intensity formula with a return period of 20 years is selected for calculation, and the design storm intensity is 265.8 mm / hour; The above-mentioned calculation of the storm intensity formula with a return period of 20 years to obtain the design storm intensity of 265.8 mm / hour refers to the storm intensity formula provided by Outdoor Drainage Design Standard (GB50014-2021) for calculation; wherein, represents the storm intensity (liter / second·hectare), represents the rainfall return period (years), represents the rainfall duration (minutes), , , , is a parameter determined according to local storm data; for the area of this embodiment, by referring to the local storm statistical data, the parameter , , , is determined; the rainfall return period is set to 20 years , and the rainfall duration is set to 30 minutes According to the confluence time calculation of the research area, it is 30 minutes, and the design storm intensity is calculated by substituting the formula to be liter / second·hectare, converted to mm / hour unit, its value is about 265.8 mm / hour; Table 1 Soil type and anti-erosion parameter table of target area ; As shown in Table 1, the starting flow and anti-erosion characteristic parameters of the main soil types in the research area are listed.

[0021] Based on the 5-meter resolution DEM data, first, the depression filling processing is performed, and then the flow direction of each grid cell is calculated in turn, and finally the cumulative confluence network is generated. The confluence accumulation threshold is set to 500, that is, when the number of upstream water collection grids of a grid exceeds 500, the grid is defined as part of the channel network. By this method, a tree-shaped drainage network topology structure covering the whole area is generated, which consists of 25 main channels and 112 branches, and each channel segment is assigned a unique identifier, for example, the main channel 1 is recorded as M01, and the branch channels under it are recorded as S01-1, S01-2, etc.

[0022] Next, the design storm intensity and the drainage network topology are combined to calculate the peak flow. For the channel section S05-3, the upstream catchment area is 0.08 square kilometers according to the DEM data. The design peak flow is calculated by the Rational Method, with a runoff coefficient of 0.7 and a design storm intensity of 265.8 mm / h, and the calculated design peak flow of the channel section is 4.13 cubic meters per second. The design peak flow calculated by the Rational Method, with a runoff coefficient of 0.7 and a design storm intensity of 265.8 mm / h, is 4.13 cubic meters per second. The design peak flow calculated by the Rational Method, with a runoff coefficient of 0.7 and a design storm intensity of 265.8 mm / h, is 4.13 cubic meters per second. ; wherein, represents the design peak flow (cubic meters per second), represents the runoff coefficient (dimensionless), represents the design storm intensity (mm / h), represents the upstream catchment area (square kilometers); in this embodiment, , mm / h, square kilometers, and the formula calculation gives cubic meters per second; The soil distribution data is extracted, and the soil type at the location of the channel section S05-3 is loess, as shown in Table 1. The soil initiation flow parameter is 0.015 cubic meters per second, and the soil erosion resistance parameter is 0.85. The calculation formula of the erosion risk quantification value is:

[0023] The calculation formula of the erosion risk quantification value is: ; wherein, represents the erosion risk quantification value of the channel section, represents the channel morphology correction coefficient combined with the channel curvature and roughness, represents the design peak flow of the current channel section, represents the soil initiation flow parameter at the location of the current channel section, represents the upstream catchment area of the channel section, represents the soil type erosion resistance parameter at the location of the channel section. The parameters in the formula are assigned and calculated as follows: ​​​The actual morphology of the channel segment S05-3 is set as a reference. Through DEM data analysis, the curvature of this channel segment is 1.2 (the ratio of the length of the river channel to the straight-line distance between the two endpoints), and the roughness is determined to be 0.03 according to field survey. To determine , a series of scour simulation experiments were conducted in a controlled flume, different combinations of curvature and roughness were set, constant flow was applied, and the scour rate was measured. Through binary linear regression analysis of experimental data, the fitting relationship is obtained: . The curvature of the channel segment S05-3 is 1.2 and the roughness is 0.03, which is substituted into the formula to calculate .

[0024] The above is to determine , a series of scour simulation experiments were conducted in a controlled flume, different combinations of curvature and roughness were set, constant flow was applied, and the scour rate was measured. Through binary linear regression analysis of experimental data, the fitting relationship is obtained: means that specific experimental data records and analysis processes are provided. The size of the controlled flume used in the experiment is 5 meters long, 0.5 meters wide, and 0.5 meters high, with adjustable slope. In the experiment, 20 groups of working conditions as shown in Table 2 were set for the loess sample. A constant flow of 0.05 cubic meters per second was applied under each working condition for 10 minutes, and the scour depth was measured by a high-precision laser scanner, which was converted into the scour rate per unit time. The scour rate after normalization is taken as the observed value, and binary linear regression is performed with the curvature and roughness to obtain the above fitting relationship, with a determination coefficient of 0.92.

[0025] Table 2 Coefficient fitting experimental data excerpt table ; has been calculated as 4.13 cubic meters per second; According to Table 1, the starting flow parameter of loess is 0.015 cubic meters per second.

[0026] has been calculated as 0.08 square kilometers.

[0027] According to Table 1, the anti-scour parameter of loess is 0.85.

[0028] Substitute the above parameter values into the formula: ; The result shows that the scour risk quantification value of the channel segment S05-3 is 63.54; The threshold value is set based on statistical analysis of the calculated values and the historical erosion data of 100 different channel segments in the study area Statistical analysis of the calculated values. It was found that when more than 95% of the channel segments did not experience significant erosion; when the channel segments experienced varying degrees of erosion; and when 98% of the channel segments experienced severe erosion. Therefore, the erosion risk determination threshold value is set to 15. The risk level classification criteria are: low risk; medium risk; high risk. The value of channel segment S05-3 is 63.54, which belongs to the high risk level, and the difference value that exceeds the threshold value is ; Finally, the erosion risk quantification value of channel segment S05-3 is recorded as {channel segment unique identifier: S05-3, risk level: high risk, difference value: 48.54}; The threshold value is set based on statistical analysis of the calculated values and the historical erosion data of 100 different channel segments in the study area Therefore, setting the erosion risk determination threshold value to 15 means providing a specific analysis process. Specifically, remote sensing images and ground survey data of 100 channel segments in the study area over the past five years (2020-2024) are collected, and the actual erosion conditions are divided into "no significant erosion" and "significant erosion" as true labels. The values of the 100 channel segments are calculated, and the receiver operating characteristic (ROC) curve analysis method is used with the value as the predictor and whether erosion occurs as the outcome variable. The analysis results show that when the value is 15, the area under the ROC curve (AUC) reaches a maximum value of 0.91, and Youden's index also reaches a maximum value at this point, indicating that this point is the best critical point to distinguish between erosion and non-erosion. Therefore, 15 is selected as the erosion risk determination threshold value.

[0029] Obtain the terrace contour data (vector format) of the area, perform GIS overlay analysis of the spatial region vector boundaries with medium and high risk levels and the terrace contour data, and identify the terraces located in these risk areas. At the lowest point or historical convergence point of these terraces, set up discrete points, and initially generate a discrete distribution point set containing 350 points.

[0030] The generated tree drainage network topology is called with the 350 discrete distribution point set. For each point, the shortest spatial distance to all main stream lines in the tree drainage network topology is calculated. The distance judgment value is set to 10 meters. If the spatial distance of a point to the main stream line is less than 10 meters, and the elevation is lower than the average elevation of the terrace where it is located, the point is classified as an access point; otherwise, if the spatial distance of the point to the main stream line is greater than or equal to 10 meters, and the elevation is at the lowest point of the terrace ridge where it is located, the point is classified as a drainage outlet. After this classification process, a drainage outlet set is finally established, containing 210 drainage outlets and their geographic coordinates and the terrace numbers to which they belong; at the same time, an access point set is established, containing 140 access points and their geographic coordinates and the channel numbers to which they belong.

[0031] See Figure 1 and Figure 3 , S2: comparing reinforcement and reconstruction costs based on the quantitative value of the erosion risk, optimizing the tree drainage network topology to generate a drainage network topology, calling the drainage outlet set and the access point set to solve the connection cost with the Hungarian algorithm, and generating an access scheme; The drainage network topology specifically refers to the channel connection relationship, node type and channel protection level. The access scheme specifically refers to the matching pair of the drainage outlet and the access point and the connection channel engineering cost. The access scheme obtaining step specifically includes: S201: based on the quantitative value of the erosion risk, obtaining the unit cost of drainage network node reinforcement and the unit cost of flow path reconstruction, traversing multiple flow path units in the tree drainage network topology, combining the quantitative value of the erosion risk and the unit cost of drainage network node reinforcement to calculate the reinforcement cost, and retrieving the alternative flow path to calculate the reconstruction cost, and establishing a node optimization judgment set according to the numerical comparison result of the reinforcement cost and the reconstruction cost; S202: calling the node optimization judgment set, screening the flow path units with the reconstruction judgment result, replacing the flow path with an alternative flow path in the tree drainage network topology, and updating the connection relationship and weight parameters between nodes to generate a drainage network topology; S203: according to the drainage network topology, calling the drainage outlet set and the access point set, calculating the connection path cost between multiple drainage outlets and multiple access points, constructing a connection cost matrix, solving the connection cost matrix, obtaining the connection pair with the minimum total cost, and generating an access scheme; The node optimization judgment set establishing step specifically includes: calling the quantitative value of the erosion risk, the unit cost of drainage network node reinforcement and the unit cost of flow path reconstruction, and traversing all flow path units in the tree drainage network topology; For any flow path unit, according to the risk level in its corresponding erosion risk quantification value, determine the required channel protection standard, combine the length of the flow path unit and the terrain parameters, and call the reinforcement unit cost of the drainage network node to calculate the reinforcement cost of the flow path unit; Based on the tree-shaped drainage network topology, search for alternative flow paths with the same starting and ending points as the current flow path unit within the preset spatial search radius, calculate the length and expected engineering quantity of the alternative flow path, and call the flow path reconstruction unit cost to calculate the reconstruction cost; Compare the reinforcement cost and the reconstruction cost. If the reinforcement cost is greater than the reconstruction cost, determine the optimization strategy as reconstruction, otherwise as reinforcement; Structurally pair the unique identifier of each flow path unit with its corresponding optimization strategy determination result to generate a node optimization determination set.

[0032] Based on the erosion risk quantification value results generated by S1, obtain the drainage network node reinforcement unit cost and the flow path reconstruction unit cost. These two cost data are determined by investigating the local water and soil conservation engineering materials and labor market prices. The drainage network node reinforcement unit cost is set according to the protection level, for example, for medium-risk channels, use mortar stone protection, with a unit cost of 150 yuan / m; for high-risk channels, use concrete lining, with a unit cost of 300 yuan / m. The flow path reconstruction unit cost involves excavating a new channel and backfilling the old channel, with a comprehensive unit cost of 250 yuan / m.

[0033] The above two cost data are determined by investigating the local water and soil conservation engineering materials and labor market prices, which means that specific investigation and calculation processes are provided. For example, in October 2025, three different building material markets (A Building Material City, B Hardware Market, C Sandstone Factory) and two construction teams (Li's Engineering Team, Wang's Construction Team) in the project area were inquired, and the material and labor cost comprehensive quotation of mortar stone protection was 135 yuan / m, 155 yuan / m, 148 yuan / m, 158 yuan / m, 152 yuan / m, respectively. After arithmetic averaging, , rounding to the nearest integer, the unit cost is determined to be 150 yuan / m. Other cost data are also obtained through similar market investigation and calculation methods.

[0034] All 137 flow path units in the tree-shaped drainage network topology are traversed. Take the channel segment S05-3 calculated in S1 as an example, its risk level is high risk and needs to be reinforced; the length of this flow path unit is 80 meters, and the terrain parameters (such as average slope) are 8%. The required channel protection standard is concrete lining; call the drainage network node reinforcement unit cost 300 yuan / m, calculate the reinforcement cost of this flow path unit as: Yuan; Next, in the tree drainage network topology, search for an alternative flow path with the same origin and destination as S05-3 (the origin is the end of S05-2, and the destination is M05 main channel). Set the spatial search radius to 100 meters. Within the search radius, based on the DEM data, calculate a topographically feasible alternative flow path S05-3_alt. The length of this alternative flow path is 95 meters, and it is expected to increase the amount of work due to the need to cross a small rock area. Call the unit cost of flow path reconstruction 250 yuan / meter, and calculate the reconstruction cost to be:

[0035] Compare the reinforcement cost and the reconstruction cost: ; Since the reinforcement cost is greater than the reconstruction cost, it is determined that the optimization strategy for S05-3 is reconstruction; structure the unique identification "S05-3" of this flow path unit and its corresponding optimization strategy determination result "reconstruction"; repeat this process for all 137 flow path units, and finally generate a node optimization determination set. The specific example is shown in Table 3.

[0036] Table 3 Example of node optimization determination set ; As shown in Table 3, the table records the cost comparison and the final optimization decision of some flow path units.

[0037] Call the node optimization determination set, and filter out all flow path units with a determination result of "reconstruction", such as S08-1. In the original tree drainage network topology, delete the original flow path S08-1 and replace it with its alternative flow path S08-1_alt. At the same time, update the connection relationship between nodes related to this flow path, such as updating the pointing of its upstream and downstream nodes, and updating its weight parameters in the network model according to the length and slope of the new flow path. After completing all reconstruction operations, generate the final drainage network topology, which clearly defines the connection relationship of all channels, node types (ordinary nodes, convergence nodes, outlet nodes), and the protection level of each channel segment determined according to its final risk state.

[0038] According to the optimized drainage network topology, and call the set of drainage outlets (210) and the set of access points (140) generated in S1. Calculate the connection path cost between each drainage outlet and each access point. The connection path cost is determined by the length of the channel, the terrain slope, and the land use type. For example, the shortest path length from drainage outlet W_021 to access point A_058 is 55 meters, of which 30 meters passes through forest land and 25 meters passes through grassland. The unit construction cost of forest land is 80 yuan / meter, and that of grassland is 60 yuan / meter. Then the connection path cost is ​Yuan. In this way, the connection cost between all 210 outlets and 140 access points is calculated, and a 210x140 connection cost matrix is constructed.

[0039] To solve the connection cost matrix, the Hungarian algorithm is used to find the connection pairing scheme with the minimum total cost. The process first converts the cost matrix into a square matrix (by adding virtual nodes and high-cost values), and then finds an augmenting path and adjusts the matching by performing row reduction and column reduction on the matrix until an optimal matching is found. The optimal matching is the unique pairing of the 140 outlets and access points that minimizes the total connection channel engineering cost. The remaining 70 outlets do not have connection channels in this round of planning. Finally, an access scheme is generated, which specifies the matching of the 140 outlets and access points, and the total cost of the channel engineering required to connect these matching pairs is 856,300 yuan.

[0040] See Figure 1 and Figure 4 , S3: Identify key monitoring nodes using drainage network topology, plan deployment paths for monitoring equipment based on access scheme, calculate peak flow and sediment carrying capacity of key monitoring nodes, and determine operating reference thresholds; The operating reference thresholds include design peak flow thresholds and theoretical sediment carrying capacity thresholds; The steps for obtaining the operating reference thresholds are as follows: S301: Calculate the network centrality parameter and upstream convergence contribution parameter of multiple network nodes in the drainage network topology, perform weighted summation on the network centrality parameter and upstream convergence contribution parameter, generate node criticality score, set node screening score threshold, retrieve network nodes with node criticality score exceeding the node screening score threshold, and generate a set of key monitoring nodes; S302: Based on the access scheme, call the set of key monitoring nodes, obtain the spatial position coordinates of multiple key monitoring nodes and the terrain connectivity data between nodes, based on the spatial position coordinates and terrain connectivity data, calculate the total path cost of all feasible deployment sequences of all key monitoring nodes, select the deployment sequence with the minimum total path cost, and generate the monitoring equipment deployment path; S303: For each key monitoring node in the set of key monitoring nodes, obtain the elevation model data and soil distribution data of the control basin range, calculate the peak flow based on the rainstorm intensity, calculate the sediment carrying capacity, set multiple response values according to the calculation results of the peak flow and sediment carrying capacity, and establish the operating reference thresholds; The steps for generating the node criticality score are as follows: call the drainage network topology and traverse all network nodes in it; For each network node, calculate its betweenness centrality in the drainage network topology to obtain the original betweenness centrality parameter, and calculate the total confluence area of all channel segments upstream of the node to obtain the original upstream confluence contribution parameter; Perform normalization processing on the original betweenness centrality parameter and the original upstream confluence contribution parameter of all network nodes respectively to obtain the network centrality parameter and the upstream confluence contribution parameter; Node criticality score Through the formula: Calculate; Among them, represents the node criticality score of the network node, represents the weight coefficient of the network centrality for adjusting the importance influence of the network structure, represents the weight coefficient of the upstream confluence contribution for adjusting the importance influence of the hydrological response, represents the original betweenness centrality parameter of the current node, and respectively represent the maximum and minimum original betweenness centrality parameters in the entire drainage network topology, represents the original upstream confluence contribution parameter of the current node, and respectively represent the maximum and minimum original upstream confluence contribution parameters in the entire drainage network topology; The node criticality score of each network node calculated is associated with its node unique identifier to generate the node criticality score.

[0041] The final drainage network topology generated by S2 contains 358 network nodes. To identify the key monitoring nodes, the network centrality parameter and the upstream confluence contribution parameter of each node need to be calculated. The network centrality parameter selects the betweenness centrality, which measures the degree of a node as a "bridge" in the network. The upstream confluence contribution parameter directly uses the upstream confluence area of the node.

[0042] For all 358 network nodes, calculate their original betweenness centrality parameters. For example, node N128 is located at the confluence of two main branch ditches, and a large number of shortest paths from upstream to downstream outlets in the network pass through it, and the original betweenness centrality parameter is calculated as 2540.5. At the same time, the total confluence area of all channel segments upstream of the node is calculated to obtain the original upstream confluence contribution parameter 0.45 square kilometers. After traversing all nodes, the maximum original betweenness centrality parameter in the entire network topology is 3100.0, and the minimum is 0; the maximum original upstream confluence contribution degree parameter is 1.5 square kilometers, the minimum is 0.0025 square kilometers (single grid area).

[0043] Node criticality score The calculation formula is: ; Among them, represents the node criticality score of the network node, represents the weight coefficient of the network centrality for adjusting the importance of the network structure, represents the weight coefficient of the upstream confluence contribution degree for adjusting the importance of the hydrological response, and , represents the original betweenness centrality parameter of the current node, and represent the maximum and minimum original betweenness centrality parameters in the entire drainage network topology, represents the original upstream confluence contribution degree parameter of the current node, and represent the maximum and minimum original upstream confluence contribution degree parameters in the entire drainage network topology; The parameters in the formula are assigned and calculated: The weight coefficients and are set based on the analysis of historical soil and water loss events in the region. Analysis shows that 60% of the total number of erosion events are caused by network structure key points (such as confluence), and 40% of the total number of runoff erosion events are caused by large catchment areas. To reflect this historical law, set , .

[0044] The above weight coefficients and are set based on the analysis of historical soil and water loss events in the region, and to reflect this historical law, set , means that a specific analysis process is provided. Specifically, 10 serious erosion events that caused large economic losses from 2020 to 2024 recorded by the soil and water conservation station in the project area were analyzed. Through on-site investigation and cause analysis report of the event occurrence point, it is determined that the causes of 6 of the events are mainly due to the concentrated erosion of water flow at the junction of the main channels (corresponding to high betweenness centrality nodes), and the causes of the other 4 events are mainly due to the strong runoff formed by the confluence of broad slopes (corresponding to large upstream confluence area nodes). Based on the 6:4 attribution ratio, the weight is set to , So that the scoring model can reflect the main cause characteristics of local scour disasters.

[0045] For node N128, it , square kilometers. , . square kilometers, square kilometers.

[0046] Substitute the above parameter values into the formula: ; ; ; ; The results show that the node criticality score of node N128 is 0.6112.

[0047] Set the node screening score threshold to 0.5. The determination of this threshold is by sorting the scores of all 358 nodes, and selecting the top 10% of nodes, it is found that the score of the 36th node is 0.508, so the threshold is set to 0.5. Retrieve the criticality scores of all nodes, and extract the 38 network nodes with scores exceeding 0.5 to generate a set of critical monitoring nodes.

[0048] According to the access scheme generated by S2 and the set of 38 critical monitoring nodes, obtain their spatial position coordinates and topographic connectivity data between nodes. To plan the deployment path of the monitoring equipment, this problem is converted into a traveling salesman problem (TSP). Calculate the total path cost of all feasible deployment sequences of the 38 critical monitoring nodes, considering both distance and elevation gain. Solve by simulated annealing algorithm, and select the deployment sequence with the minimum total path cost. The total length of this sequence is 5.8 kilometers, which is the final monitoring equipment deployment path.

[0049] The above-mentioned solving by simulated annealing algorithm to select the deployment sequence with the minimum total path cost refers to the specific parameters set when solving by simulated annealing algorithm: the initial temperature is set to 1000, the temperature decay coefficient (cooling rate) is set to 0.99, the internal loop iteration number (Markov chain length) at each temperature is set to 100 times, and the termination condition of the algorithm is set to when the optimal solution value does not improve in 50 consecutive external loop iterations.

[0050] For each node in the set of key monitoring nodes, such as N128, obtain the elevation model data and soil distribution data of the 0.45 square kilometer watershed range controlled by it. Combined with the storm intensity of 20-year return period (120 mm / hour), calculate its design flood peak flow again, and get the value of 3.5 cubic meters / second. At the same time, based on the soil type and slope information of the watershed, calculate its theoretical sediment carrying capacity, and get the value of 8.8 kg / second.

[0051] The above-mentioned calculation of the theoretical sediment carrying capacity of the watershed based on the soil type and slope information of the watershed is to use the empirical formula suitable for the local For calculation. Among them, represents the sediment carrying capacity (unit: kg / second), represents the flood peak flow (unit: cubic meters / second), represents the average slope of the channel (dimensionless), represents the watershed sediment yield coefficient related to soil type. For the watershed controlled by node N128 mainly composed of loess and meadow soil, according to the historical measured data, the value of is rated as 15. The flood peak flow of this node is cubic meters / second, the average slope of the channel is , and the formula is substituted to calculate kg / second.

[0052] According to the calculation results, set multi-level response values and establish operation reference thresholds. The specific example is shown in Table 4.

[0053] Table 4 Key monitoring node N128 operation reference threshold table ; As shown in Table 4, the table establishes a hierarchical turbidity early warning threshold for node N128 under different flow levels, constituting its operation reference threshold system.

[0054] Please refer to Figure 1 and Figure 5 , S4: Collect turbidity data and flow data at key monitoring nodes, and compare them with operation reference thresholds. If the turbidity data exceeds the operation reference thresholds, it is judged that soil erosion occurs, and an erosion state identifier is generated; The erosion state identifier includes the location of the early warning node, the turbidity exceeding standard amplitude, and the early warning timestamp; The steps for obtaining the erosion state identifier are as follows: S401: Deploy monitoring equipment based on key monitoring nodes, and set data collection frequency parameters and data upload period. The monitoring equipment synchronously collects turbidity data and flow data according to the data collection frequency parameters, encodes the collected turbidity data and flow data into independent data frames, aggregates multiple data frames at the end of the data upload period, adds node identifiers and timestamp information to each data frame, and obtains the monitoring node real-time data sequence; S402: Receive the monitoring node real-time data sequence, parse the data frames to extract turbidity data, flow data, node identifiers, and timestamp information, call the multi-level turbidity response threshold set for the differentiated flow data value interval in the running reference threshold, sequentially and iteratively compare the current turbidity data value with the multi-level turbidity response threshold of the interval corresponding to the current flow data, and obtain the erosion event determination result; S403: According to the erosion event determination result, if the determination result indicates that the turbidity data value exceeds any level of turbidity response threshold, extract the node identifier, timestamp information, and threshold level parameter of the exceeded threshold, and structureally concatenate the three, wherein the turbidity exceeding amplitude is represented by the threshold level parameter of the exceeded threshold, and establish the erosion state identifier; The erosion event determination result obtaining step specifically comprises: calling the running reference threshold, and receiving the monitoring node real-time data sequence to obtain the current turbidity data and current flow data under the target timestamp; According to the value of the current flow data, match and find the flow data value interval to which it belongs in the running reference threshold; Obtain the multi-level turbidity response threshold corresponding to the flow data value interval, which includes multiple increasing thresholds from the first level to the last level; Compare the current turbidity data with the first level turbidity response threshold in the multi-level turbidity response threshold, if the current turbidity data is greater than the first level turbidity response threshold, it is determined that the erosion event occurs, and the threshold level parameter of the exceeded threshold is recorded as the first level; If it is less than or equal to, continue to compare the current turbidity data with the second level turbidity response threshold, and iteratively compare in turn until the highest level threshold or the event is determined to occur, and record the threshold level parameter of the exceeded threshold; If the current turbidity data does not exceed the turbidity response threshold of all levels, it is determined that the erosion event does not occur, and the erosion event determination result is generated.

[0055] Based on the key monitoring node set and deployment path generated by S3, monitoring equipment integrating flow meter and turbidity meter is deployed at 38 key monitoring nodes such as N128. The data acquisition frequency parameter is set to once every 5 minutes, and the data upload period is once every 30 minutes. The monitoring equipment synchronously acquires turbidity data of 1350 NTU and flow data of 2.8 cubic meters per second at 3:00:00 on October 11, 2025 in accordance with the set frequency. The equipment encodes the two sets of data into independent data frames respectively, and appends the node identifier "N128" and the time stamp "2025-10-11 15:00:00". At the end of the data upload period at 3:30:00 in the afternoon, the 6 sets of data frames collected once every 5 minutes are aggregated, uploaded to the data center through the wireless network, and form the real-time data sequence of the monitoring node.

[0056] The data center receives the real-time data sequence from node N128, and parses the data frame with the time stamp "15:00:00" to extract the turbidity data of 1350 NTU, the flow data of 2.8 cubic meters per second, the node identifier "N128", and the time stamp information "2025-10-11 15:00:00".

[0057] The running reference threshold value established for node N128 in S3 is called (see Table 4). According to the value of the current flow data 2.8 cubic meters per second, the flow data value interval to which it belongs is matched and searched in the running reference threshold value. 2.8 is greater than 2.5, so it is matched to the interval ">2.5". The multi-level turbidity response threshold value corresponding to this interval is obtained, which is 1200 NTU for the first level, 2000 NTU for the second level, and 3500 NTU for the third level.

[0058] The current turbidity data value 1350 NTU is sequentially iteratively compared with the multi-level turbidity response threshold value of the interval. First, 1350 NTU is compared with the first level turbidity response threshold value 1200 NTU. Because 1350>1200, it is determined that an erosion event occurs, and the threshold level parameter that is exceeded is recorded as the first level. Since the first level threshold value has been triggered, the comparison stops and does not continue with the second and third level threshold values. Finally, the erosion event determination result is obtained as: a first level erosion event occurs.

[0059] According to the erosion event determination result "a first level erosion event occurs", the node identifier "N128", the time stamp information "2025-10-11 15:00:00" and the threshold level parameter "first level" that is exceeded triggering the determination are extracted. These three are structurally concatenated to generate an erosion state identifier. Among them, the turbidity exceeding standard amplitude is represented by the threshold level parameter "first level" that is exceeded. The finally established erosion state identifier is {warning node location: N128, turbidity exceeding standard amplitude: first level, warning time stamp: 2025-10-11 15:00:00}.

[0060] The above embodiments demonstrate the preferred implementation of the present application, and any equivalent adjustment to the technical solutions based on software engineering methods shall fall within the protection scope, including but not limited to: implementing algorithm logic in different programming languages, service reconstruction of functional modules, adjustment of data interaction protocols, optimization of resource scheduling strategies, etc. Any implementation derived through reasonable modification of the data processing flow, service calling link or system architecture level without deviating from the technical core of the present application shall be considered within the protection scope defined by the claims of the present application.

Claims

1. A method for intelligent planning of water and soil conservation measures for sloping farmland, characterized in that, The method comprises the following steps: S1: obtaining elevation model data, soil distribution data and storm intensity, calculating the elevation model data to generate a tree-shaped drainage network topology, calculating the flood peak flow, comparing the soil distribution data with the anti-impact ability to generate a quantitative value of erosion risk, obtaining terrace contour data, and arranging discrete points to generate a set of drainage outlets and a set of access points; S2: comparing reinforcement and reconstruction costs based on the quantitative value of erosion risk, optimizing the tree-shaped drainage network topology to generate a drainage network topology, calling the set of drainage outlets and the set of access points, and using the Hungarian algorithm to solve the connection cost to generate an access scheme; S3: identifying key monitoring nodes using the drainage network topology, planning the deployment path of the monitoring equipment according to the access scheme, calculating the flood peak flow and sediment carrying capacity of the key monitoring nodes, and determining the operating reference threshold; S4: collecting turbidity data and flow data at the key monitoring nodes, and comparing them with the operating reference threshold, if the turbidity data exceeds the operating reference threshold, judging soil erosion, and generating an erosion state identifier.

2. The method of claim 1, wherein, The quantitative value of erosion risk includes a unique identifier of a channel segment, a risk level and a difference value, the set of drainage outlets includes the geographic coordinates of the drainage outlets and the terrace number to which they belong, the set of access points includes the geographic coordinates of the access points and the channel number to which they belong, the drainage network topology specifically refers to the connection relationship of the channels, the node type and the channel protection level, the access scheme specifically refers to the matching pair of the drainage outlets and the access points and the connection channel engineering cost, the operating reference threshold includes a design flood peak flow threshold and a theoretical sediment carrying capacity threshold, and the erosion state identifier includes the location of the warning node, the turbidity exceeding the standard and the warning timestamp. 3.The intelligent planning method for slope farmland soil and water conservation measure according to claim 2, characterized in that, The obtaining step of the set of drainage outlets and the set of access points specifically comprises: S101: obtaining elevation model data, soil distribution data and storm intensity, calculating the tree-shaped drainage network topology based on the topographic information of the elevation model data, calculating the flood peak flow based on the storm intensity and the tree-shaped drainage network topology, extracting the anti-impact ability parameters of multiple soil types in the soil distribution data and comparing them with the flood peak flow to generate a quantitative value of erosion risk; S102: obtaining terrace contour data, setting a erosion risk determination threshold for the quantitative value of erosion risk, retrieving a spatial region whose quantitative value of erosion risk exceeds the erosion risk determination threshold, and performing overlay analysis on the spatial region vector boundary and the terrace contour data to obtain a set of discrete distribution points; S103: calling the tree-shaped drainage network topology and the set of discrete distribution points, calculating the spatial distance from multiple points in the set of discrete distribution points to the main stream line of the tree-shaped drainage network topology, classifying the multiple points according to a preset distance determination value and point elevation information, and establishing a set of drainage outlets and a set of access points.

4. The method of claim 3, wherein, The obtaining step of the access scheme specifically comprises: S201: Based on the erosion risk quantification value, obtain the drainage network node reinforcement unit cost and the flow path reconstruction unit cost, traverse the multiple flow path units in the tree-shaped drainage network topology, combine the erosion risk quantification value and the drainage network node reinforcement unit cost to calculate the reinforcement cost, and retrieve the alternative flow path to calculate the reconstruction cost. According to the numerical comparison result of the reinforcement cost and the reconstruction cost, a node optimization decision set is established; S202: Call the node optimization decision set, filter the flow path units with reconstruction as the decision result, replace the tree-shaped drainage network topology with the alternative flow path, update the connection relationship and weight parameter between nodes, and generate a drainage network topology; S203: According to the drainage network topology, call the outlet set and the access point set, calculate the connection path cost between multiple outlets and multiple access points, construct a connection cost matrix, solve the connection cost matrix, obtain the connection pair with the minimum total cost, and generate an access scheme. 5.The intelligent planning method for slope farmland soil and water conservation measure according to claim 4, characterized in that, The operation reference threshold value is obtained by the following steps: S301: Calculate the network centrality parameter and the upstream convergence contribution degree parameter in the drainage network topology using multiple network nodes, perform weighted summation on the network centrality parameter and the upstream convergence contribution degree parameter, generate a node criticality score, set a node screening score threshold, retrieve the network nodes whose node criticality score exceeds the node screening score threshold, and generate a key monitoring node set; S302: According to the access scheme, call the key monitoring node set, obtain the spatial position coordinates of multiple key monitoring nodes and the terrain connectivity data between nodes, based on the spatial position coordinates and the terrain connectivity data, calculate the path total cost of the feasible deployment sequence traversing all key monitoring nodes, filter the deployment sequence with the minimum path total cost, and generate a monitoring device deployment path; S303: For each key monitoring node in the key monitoring node set, obtain the elevation model data and soil distribution data of the control basin range, combine the rainstorm intensity to calculate the flood peak flow, and calculate the sediment carrying capacity. According to the calculation results of the flood peak flow and the sediment carrying capacity, set multiple response values, and establish an operation reference threshold value. 6.The intelligent planning method for slope farmland soil and water conservation measure according to claim 5, characterized in that, The erosion state identification is obtained by the following steps: S401: Based on the key monitoring node deployment monitoring device, set the data acquisition frequency parameter and the data upload period, the monitoring device synchronously acquires turbidity data and flow data according to the data acquisition frequency parameter, encodes the acquired turbidity data and flow data into independent data frames, aggregates multiple data frames at the end of the data upload period, adds node identifiers and time stamp information to each data frame, and obtains a monitoring node real-time data sequence; S402: receiving the monitoring node real-time data sequence, parsing the data frame to extract the turbidity data, the flow data, the node identifier and the timestamp information, calling the multi-level turbidity response threshold value set for the differentiated flow data value interval in the running reference threshold value, sequentially iterating the current turbidity data value and the multi-level turbidity response threshold value of the interval corresponding to the current flow data, and obtaining the erosion event judgment result; S403: according to the erosion event judgment result, if the judgment result indicates that the turbidity data value exceeds any level of turbidity response threshold value, the node identifier, the timestamp information and the threshold value grade parameter of the exceeding are extracted, and the three are structured in series, wherein the turbidity exceeding amplitude is represented by the threshold value grade parameter of the exceeding, and the erosion state identifier is established.

7. The method of claim 3, wherein the method further comprises: The generation step of the erosion risk quantitative value is specifically: obtaining the elevation model data, the soil distribution data and the upstream catchment area of the plurality of channel segments in the tree-shaped drainage network topology, and calling the flood peak flow; for the soil distribution data, extracting the soil starting flow parameter and the erosion resistance parameter corresponding to a plurality of soil types; a quantitative value of a risk of erosion for each channel segment in the tree-like drainage network topology by the formula: computing; wherein, a scour risk quantification value representative of the channel segment, a channel morphology correction factor representative of the combination of the channel sinuosity and the roughness, a design peak flow representative of the current channel segment, a soil initiation flow parameter representative of the location where the current channel segment is located, an upstream catchment cross-sectional area representative of the channel segment, a soil type anti-scour parameter representative of the location where the channel segment is located; According to the calculated erosion risk quantification value According to the preset risk level division standard, a unique identifier, a risk level, and a difference value representing the value exceeding the critical value are assigned to each channel segment to generate the erosion risk quantification value. 8.The intelligent planning method of slope farmland soil and water conservation measure according to claim 4, characterized in that, The establishment step of the node optimization judgment set is specifically: calling the erosion risk quantitative value, the drainage network node reinforcement unit cost and the flow path reconstruction unit cost, and traversing all flow path units in the tree-shaped drainage network topology; for any flow path unit, according to the risk level in the erosion risk quantitative value corresponding thereto, determining the required channel protection standard, combining the length and terrain parameters of the flow path unit, and calling the drainage network node reinforcement unit cost to calculate the reinforcement cost of the flow path unit; based on the tree-shaped drainage network topology, searching for an alternative flow path with the same starting and ending points as the current flow path unit within a preset spatial search radius, calculating the length and expected engineering quantity of the alternative flow path, and calling the flow path reconstruction unit cost to calculate the reconstruction cost; comparing the reinforcement cost and the reconstruction cost, if the reinforcement cost is greater than the reconstruction cost, determining that the optimization strategy is reconstruction, otherwise, it is reinforcement; structurally pairing the unique identifier of each flow path unit and the corresponding optimization strategy judgment result to generate a node optimization judgment set. 9.The intelligent planning method of slope farmland soil and water conservation measure according to claim 5, characterized in that, The generation step of the node criticality score is specifically: calling the drainage network topology and traversing all network nodes therein; for each network node, calculating its betweenness centrality in the drainage network topology to obtain an original betweenness centrality parameter, and calculating the total flow convergence area of all channel segments upstream of the node to obtain an original upstream flow convergence contribution degree parameter; normalizing the original betweenness centrality parameter and the original upstream flow convergence contribution degree parameter of all network nodes respectively to obtain the network centrality parameter and the upstream flow convergence contribution degree parameter; The node criticality score By the formula: computing; wherein, a node criticality score representing a network node, a weight coefficient representing a network centrality for adjusting the importance influence of the network structure, a weight coefficient representing an upstream catchment contribution for adjusting the importance influence of the hydrological response, a raw betweenness centrality parameter of a current node, a maximum and a minimum raw betweenness centrality parameter, respectively, a maximum and a minimum raw upstream catchment contribution parameter, respectively, a raw upstream catchment contribution parameter of a current node, a maximum and a minimum raw upstream catchment contribution parameter, respectively, a maximum and a minimum raw upstream catchment contribution parameter, respectively, The node criticality score of each network node is calculated The node criticality score is generated in association with its node unique identification. 10.The intelligent planning method of slope farmland soil and water conservation measure according to claim 6, characterized in that, The erosion event judgment result obtaining step is specifically: calling the running reference threshold value and receiving the monitoring node real-time data sequence, parsing the current turbidity data and the current flow data under the target timestamp to obtain the erosion event judgment result; According to the value of the current flow data, a flow data value interval to which the current flow data belongs is found in the running reference threshold value; A multi-level turbidity response threshold value corresponding to the flow data value interval is obtained, the multi-level turbidity response threshold value including multiple increasing threshold values from a first level to a last level; The current turbidity data is compared with a first-level turbidity response threshold value in the multi-level turbidity response threshold value, if the current turbidity data is greater than the first-level turbidity response threshold value, it is determined that an erosion event occurs, and a threshold level parameter that is exceeded is recorded as a first level; If it is less than or equal to, the current turbidity data is continuously compared with a second-level turbidity response threshold value, and iteration is performed in sequence until a highest-level threshold value or an event is determined to occur, and a threshold level parameter that is exceeded is recorded; If the current turbidity data does not exceed the turbidity response threshold value of all levels, it is determined that an erosion event does not occur, and an erosion event determination result is generated.

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