Multi-basin water and soil conservation monitoring method and system

Abstract

The invention discloses a multi-basin water and soil conservation monitoring method and system, and belongs to the technical field of water and soil conservation monitoring, and the method comprises the following steps: S1, deploying high-precision sensors in a plurality of basins, and collecting water and soil conservation related parameters in real time; s2, transmitting the acquired data to a central server through a wireless communication network; s3, analyzing the collected data by using a data processing algorithm, and identifying water and soil conservation characteristics and change trends of the drainage basins; s4, specifying personalized evaluation standards according to specific characteristics of different drainage basins, and proposing targeted optimization suggestions according to evaluation results; and S5, establishing a continuous monitoring mechanism, regularly updating an evaluation result, and continuously adjusting and optimizing a monitoring scheme according to new data. According to the multi-basin water and soil conservation monitoring method and system, the water and soil conservation conditions of multiple basins can be comprehensively and accurately monitored, adjustment and optimization are carried out according to the evaluation result, and the method and system have important practical application value.

Description

Technical Field

[0001] The present invention relates to the technical field of soil and water conservation monitoring, and in particular to a multi-basin soil and water conservation monitoring method and system. Background Art

[0002] In existing technologies, traditional monitoring methods often only collect limited data points and cannot fully reflect the soil and water conservation status of the entire river basin. Due to untimely data collection and insufficient sensor accuracy, errors exist in data processing results, affecting the accuracy of monitoring. In addition, existing technologies often adopt unified evaluation standards, ignoring the differences between different river basins, resulting in inaccurate and targeted evaluation results. Existing technologies are often only able to conduct a single evaluation and cannot be adjusted and optimized based on the evaluation results, lacking a continuous monitoring and feedback mechanism. Summary of the Invention

[0003] The purpose of the present invention is to provide a multi-basin soil and water conservation monitoring method and system, which can comprehensively and accurately monitor the soil and water conservation status of multiple basins, and adjust and optimize according to the evaluation results, and has important practical application value.

[0004] To achieve the above object, the present invention provides a multi-basin soil and water conservation monitoring method, comprising the following steps:

[0005] Step S1: deploy high-precision sensors in multiple watersheds to collect various parameters related to soil and water conservation in real time;

[0006] Step S2: transmitting the collected data to a central server via a wireless communication network for storage and preliminary processing;

[0007] Step S3: Analyze the collected data using data processing algorithms to identify the soil and water conservation characteristics and change trends of each watershed;

[0008] Step S4: formulate personalized evaluation criteria according to the specific characteristics of different watersheds, and propose targeted optimization suggestions based on the evaluation results;

[0009] Step S5: Establish a continuous monitoring mechanism, regularly update the assessment results, and continuously adjust and optimize the monitoring plan based on new data.

[0010] Preferably, in step S1, the high-precision sensors include but are not limited to soil moisture sensors, vegetation coverage sensors, precipitation sensors, temperature sensors and wind speed sensors.

[0011] Preferably, in step S1, when collecting parameters, outliers or noises existing in the sensor collection process are removed, and different types of collected parameters are normalized for subsequent analysis.

[0012] Preferably, the wireless communication network includes one or more of a cellular network, a Wi-Fi network, a LoRa network and a NB-IoT network.

[0013] Preferably, in step S3, the data processing algorithm adopts a machine learning model to improve the accuracy and efficiency of data processing, specifically comprising the following steps:

[0014] Step S301: Divide the data set into a training set, a validation set, and a test set to ensure the generalization ability of the model;

[0015] Step S302: Use the training set to train the selected model, and adjust hyperparameters (such as learning rate, regularization parameters, etc.) during the training process to optimize model performance;

[0016] Step S303: Evaluate model performance through cross-validation, adjust model structure and parameters, and improve model accuracy and stability.

[0017] Preferably, in step S4, the personalized evaluation criteria are customized based on the historical data, geographical environment, climate conditions and land use types of each watershed.

[0018] Preferably, in step S5, the continuous monitoring mechanism includes automatically triggered data collection cycles, data transmission frequencies, data analysis processes, and feedback control logic.

[0019] Preferably, the continuous monitoring mechanism is determined by the calculated soil erosion index, water quality deterioration index, and vegetation cover change rate, and different warning levels are then derived therefrom, specifically:

[0020] Soil erosion index SEI calculation formula:

[0021] The soil erosion index takes into account factors such as soil moisture changes, wind speed and rainfall intensity. The specific formula is as follows:

[0022] SEI=α×(H max -H avg )+β×W+γ×R;

[0023] Among them, H max Indicates the maximum soil moisture value during the monitoring period; H avg represents the average soil moisture value during the monitoring period; W represents the average wind speed; R represents the cumulative rainfall; α, β, γ represent weight coefficients, which are adjusted according to the actual environment;

[0024] Water Quality Index WQI calculation formula:

[0025] The water quality deterioration index is evaluated based on parameters such as water turbidity, pH value, and dissolved oxygen concentration (DO). The specific formula is as follows:

[0026]

[0027] Among them, Turbidity min 、Turbidity max Respectively represent the minimum and maximum values ​​of water turbidity; pH ideal Indicates the ideal pH value; pH tolerance Indicates the tolerance range of pH value; DO ideal Indicates the ideal dissolved oxygen concentration;

[0028] The calculation formula of vegetation cover change rate VCR is:

[0029] The vegetation cover change rate is obtained by comparing the current vegetation cover rate with historical data. The specific formula is as follows:

[0030] VCR=(C current -C historical ) / C historical ×100%;

[0031] Among them, C current Indicates the current vegetation coverage; C historical represents the historical vegetation cover;

[0032] Input the above three indicators SEI, WQI, and VCR into the risk assessment model to calculate the comprehensive risk value RiskScore. The formula is as follows:

[0033] RiskScore=w1×SEI+w2×WQI+w3×|VCR|;

[0034] w1, w2, and w3 represent the weights of each indicator and need to be adjusted according to actual conditions; |VCR| represents the absolute value of the vegetation cover change rate.

[0035] The warning levels are as follows:

[0036] Low Risk: RiskScore <T1;

[0037] Medium risk: T1≤RiskScore <T2;

[0038] High risk: RiskScore ≥ T2;

[0039] Wherein, T1 and T2 represent preset thresholds.

[0040] The present invention also provides a multi-basin soil and water conservation monitoring system, comprising:

[0041] High-precision sensor module for real-time collection of soil and water conservation related data;

[0042] The data transmission module is responsible for transmitting the data collected by the sensor to the central server through the wireless communication network;

[0043] The data processing center module stores, processes and analyzes the collected data and generates evaluation reports;

[0044] Evaluation and optimization module, which formulates personalized evaluation criteria based on different watershed characteristics and provides optimization suggestions;

[0045] The feedback control module realizes continuous monitoring and feedback to ensure the effective operation of the monitoring system. Preferably, the high-precision sensor module can adapt to the data collection needs under complex terrain and extreme climate conditions.

[0046] Preferably, the data processing center module has distributed computing capabilities and large-scale data storage capabilities, and supports real-time data analysis and historical data backtracking.

[0047] Preferably, the evaluation and optimization module can automatically generate optimization suggestions and support users to manually adjust evaluation criteria and optimization strategies.

[0048] Preferably, the feedback control module can automatically detect abnormal situations and trigger alarms, while supporting remote monitoring and management functions.

[0049] Therefore, the present invention adopts the above-mentioned multi-basin soil and water conservation monitoring method and system, which can comprehensively and accurately monitor the soil and water conservation conditions of multiple basins, and adjust and optimize according to the evaluation results, and has important practical application value.

[0050] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flow chart of an embodiment of a multi-basin soil and water conservation monitoring method of the present invention;

[0052] Figure 2 It is a schematic diagram of an embodiment of a multi-basin soil and water conservation monitoring system of the present invention. DETAILED DESCRIPTION

[0053] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0054] Unless otherwise defined, technical or scientific terms used in the present invention shall have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

[0055] Example 1

[0056] like Figure 1 As shown, the present invention provides a multi-basin soil and water conservation monitoring method, comprising the following steps:

[0057] Step S1: Deploy high-precision sensors in multiple watersheds to collect multiple parameters related to soil and water conservation in real time; the high-precision sensors include but are not limited to soil moisture sensors, vegetation coverage sensors, precipitation sensors, temperature sensors, and wind speed sensors.

[0058] When collecting parameters, outliers or noise in the sensor collection process are removed, and different types of collected parameters are normalized for subsequent analysis.

[0059] Step S2: The collected data is transmitted to a central server via a wireless communication network for storage and preliminary processing; the wireless communication network includes one or more of a cellular network, a Wi-Fi network, a LoRa network, and a NB-IoT network.

[0060] Step S3: Analyze the collected data using a data processing algorithm to identify the soil and water conservation characteristics and change trends of each watershed. The data processing algorithm uses a machine learning model to improve the accuracy and efficiency of data processing, and specifically includes the following steps:

[0061] Step S301: Divide the data set into a training set, a validation set, and a test set to ensure the generalization ability of the model;

[0062] Step S302: Use the training set to train the selected model, and adjust hyperparameters (such as learning rate, regularization parameters, etc.) during the training process to optimize model performance;

[0063] Step S303: Evaluate model performance through cross-validation, adjust model structure and parameters, and improve model accuracy and stability.

[0064] Step S4: formulate personalized evaluation criteria based on the specific characteristics of different river basins, and make targeted optimization suggestions based on the evaluation results; the personalized evaluation criteria are customized based on the historical data, geographical environment, climate conditions and land use types of each river basin.

[0065] Step S5: Establish a continuous monitoring mechanism, regularly update the evaluation results, and continuously adjust and optimize the monitoring plan based on new data. The continuous monitoring mechanism includes automatically triggered data collection cycles, data transmission frequency, data analysis process, and feedback control logic.

[0066] The continuous monitoring mechanism is determined by the calculated soil erosion index, water quality deterioration index and vegetation cover change rate, and then different warning levels are derived from them, specifically:

[0067] Soil erosion index SEI calculation formula:

[0068] The soil erosion index takes into account factors such as soil moisture changes, wind speed and rainfall intensity. The specific formula is as follows:

[0069] SEI=α×(H max -H avg )+β×W+γ×R;

[0070] Among them, H max Indicates the maximum soil moisture value during the monitoring period; H avg represents the average soil moisture value during the monitoring period; W represents the average wind speed; R represents the cumulative rainfall; α, β, γ represent weight coefficients, which are adjusted according to the actual environment;

[0071] Water Quality Index WQI calculation formula:

[0072] The water quality deterioration index is evaluated based on parameters such as water turbidity, pH value, and dissolved oxygen concentration (DO). The specific formula is as follows:

[0073]

[0074] Among them, Turbidity min 、Turbidity max Respectively represent the minimum and maximum values ​​of water turbidity; pH ideal Indicates the ideal pH value; pH tolerance Indicates the tolerance range of pH value; DO ideal Indicates the ideal dissolved oxygen concentration;

[0075] The calculation formula of vegetation cover change rate VCR is:

[0076] The vegetation cover change rate is obtained by comparing the current vegetation cover rate with historical data. The specific formula is as follows:

[0077] VCR=(C current -C historical ) / C historical ×100%;

[0078] Among them, C current Indicates the current vegetation coverage; C historical represents the historical vegetation cover;

[0079] Input the above three indicators SEI, WQI, and VCR into the risk assessment model to calculate the comprehensive risk value RiskScore. The formula is as follows:

[0080] RiskScore=w1×SEI+w2×WQI+w3×|VCR|;

[0081] w1, w2, and w3 represent the weights of each indicator and need to be adjusted according to actual conditions; |VCR| represents the absolute value of the vegetation cover change rate.

[0082] The warning levels are as follows:

[0083] Low Risk: RiskScore <T1;

[0084] Medium risk: T1≤RiskScore <T2;

[0085] High risk: RiskScore ≥ T2;

[0086] Wherein, T1 and T2 represent preset thresholds.

[0087] like Figure 2 As shown, the present invention also provides a multi-basin soil and water conservation monitoring system, comprising:

[0088] High-precision sensor modules are used to collect soil and water conservation-related data in real time; high-precision sensor modules can adapt to data collection needs in complex terrain and extreme climatic conditions.

[0089] The data transmission module is responsible for transmitting the data collected by the sensor to the central server through the wireless communication network;

[0090] The data processing center module stores, processes and analyzes the collected data and generates evaluation reports; the data processing center module has distributed computing capabilities and large-scale data storage capabilities, and supports real-time data analysis and historical data backtracking.

[0091] The evaluation and optimization module formulates personalized evaluation standards based on the characteristics of different watersheds and provides optimization suggestions. The evaluation and optimization module can automatically generate optimization suggestions and support users to manually adjust the evaluation standards and optimization strategies.

[0092] The feedback control module enables continuous monitoring and feedback to ensure the effective operation of the monitoring system. The feedback control module can automatically detect abnormal conditions and trigger alarms, while also supporting remote monitoring and management functions.

[0093] Therefore, the present invention adopts the above-mentioned multi-basin soil and water conservation monitoring method and system, which can comprehensively and accurately monitor the soil and water conservation conditions of multiple basins, and adjust and optimize according to the evaluation results, and has important practical application value.

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A multi-basin soil and water conservation monitoring method, characterized in that: The following steps are involved: Step S1: deploy high-precision sensors in multiple watersheds to collect soil and water conservation related parameters in real time; Step S2: transmitting the collected data to a central server via a wireless communication network for storage and preliminary processing; Step S3: Analyze the collected data using data processing algorithms to identify the soil and water conservation characteristics and change trends of each watershed; Step S4: formulate personalized evaluation criteria according to the specific characteristics of different watersheds, and propose targeted optimization suggestions based on the evaluation results; Step S5: Establish a continuous monitoring mechanism, regularly update the assessment results, and continuously adjust and optimize the monitoring plan based on new data.

2. The multi-basin soil and water conservation monitoring method according to claim 1, characterized in that: In step S1, the high-precision sensors include but are not limited to a soil moisture sensor, a vegetation coverage sensor, a precipitation sensor, a temperature sensor, and a wind speed sensor.

3. The multi-basin soil and water conservation monitoring method according to claim 2, characterized in that: In step S1, when collecting parameters, outliers or noises existing in the sensor collection process are removed, and different types of collected parameters are normalized.

4. The multi-basin soil and water conservation monitoring method according to claim 3, characterized in that: The wireless communication network includes one or more of a cellular network, a Wi-Fi network, a LoRa network, and an NB-IoT network.

5. The multi-basin soil and water conservation monitoring method according to claim 4, characterized in that: In step S3, the data processing algorithm adopts a machine learning model, which specifically includes the following steps: Step S301: Divide the data set into a training set, a validation set, and a test set to ensure the generalization ability of the model; Step S302: Use the training set to train the selected model, and adjust the hyperparameters during the training process to optimize the model performance; Step S303: Evaluate model performance through cross-validation and adjust model structure and parameters.

6. The multi-basin soil and water conservation monitoring method according to claim 5, characterized in that: In step S4, personalized evaluation criteria are customized based on the historical data, geographical environment, climate conditions and land use types of each watershed.

7. The multi-basin soil and water conservation monitoring method according to claim 6, characterized in that: In step S5, the continuous monitoring mechanism includes automatically triggered data collection cycles, data transmission frequency, data analysis process and feedback control logic.

8. The multi-basin soil and water conservation monitoring method according to claim 7, characterized in that: The continuous monitoring mechanism is determined by the calculated soil erosion index, water quality deterioration index, and vegetation cover change rate, and then different warning levels are obtained, specifically: The calculation formula of soil erosion index SEI is as follows: SIX=α×(H max -H avg )+β×W+γ×R; Among them, H max Indicates the maximum soil moisture value during the monitoring period; H avg represents the average soil moisture value during the monitoring period; W represents the average wind speed; R represents the cumulative rainfall; α, β, γ represent the weight coefficients; Water Quality Index WQI calculation formula: The water quality deterioration index is evaluated based on water turbidity, pH value and dissolved oxygen concentration (DO). The specific formula is as follows: Among them, Turbidity min 、Turbidity max Respectively represent the minimum and maximum values ​​of water turbidity; pH ideal Indicates the ideal pH value; pH tolerance Indicates the tolerance range of pH value; DO ideal Indicates the ideal dissolved oxygen concentration; The calculation formula of vegetation cover change rate VCR is: The vegetation cover change rate is obtained by comparing the current vegetation cover rate with historical data. The specific formula is as follows: VCR=(C current -C historical ) / C historical ×100%; Among them, C current Indicates the current vegetation coverage; C historical represents the historical vegetation cover; Input the three indicators SEI, WQI, and VCR into the risk assessment model to calculate the comprehensive risk value RiskScore. The formula is as follows: RiskScore=w1×SEI+w2×WQI+w3×|VCR|; w1, w2, and w3 represent the weights of each indicator and need to be adjusted according to actual conditions; |VCR| represents the absolute value of the vegetation cover change rate; The warning levels are as follows: Low Risk: RiskScore <T1; Medium risk: T1≤RiskScore <T2; High risk: RiskScore ≥ T2; Wherein, T1 and T2 represent preset thresholds.

9. A multi-basin soil and water conservation monitoring system, characterized in that: include: High-precision sensor module for real-time collection of soil and water conservation related data; The data transmission module is responsible for transmitting the data collected by the sensor to the central server through the wireless communication network; The data processing center module stores, processes and analyzes the collected data and generates evaluation reports; Evaluation and optimization module, which formulates personalized evaluation criteria based on different watershed characteristics and provides optimization suggestions; Feedback control module realizes continuous monitoring and feedback to ensure the effective operation of the monitoring system.

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