Water and soil conservation monitoring system and method

Abstract

The invention discloses a water and soil conservation monitoring system and method, and belongs to the field of water and soil conservation monitoring, and the method comprises the following steps: determining a monitoring target and a measured core variable combination, and formulating a specific measurement technical scheme based on a multi-parameter measurement target; the method comprises the following steps: selecting monitoring points in a set monitoring range, evaluating the importance and potential interference degree of each point to a multivariable measurement result in combination with terrain, meteorological factors and human activity intensity, grading according to the importance and potential interference degree, and configuring different numbers of standby monitoring positions for different grades of point positions; rainfall, sediment and soil moisture sensors are deployed at selected points, initial data of all variables in a normal environment are collected, and initial reference values of all measurement channels are established. Through the combination of the steps, monitoring blindness and resource waste are avoided, the flexibility of a monitoring network is improved, data continuity and accuracy are guaranteed, the exception handling efficiency is improved, and efficient and accurate water and soil conservation monitoring is achieved.

Description

Technical Field

[0001] This invention relates to the field of soil and water conservation monitoring technology, and more specifically, to a soil and water conservation monitoring system and method. Background Technology

[0002] Soil and water conservation monitoring is a core means of assessing regional ecological conditions, supervising the effectiveness of prevention and control projects, and conducting scientific research related to soil erosion. It plays a vital supporting role in ecological protection, engineering management, and scientific research. However, current traditional soil and water conservation monitoring technologies still have many shortcomings and are unable to meet the needs for efficient, accurate, and continuous monitoring.

[0003] First, the current planning and redundancy of monitoring sites are unreasonable. Specifically, the selection of existing sites often fails to consider factors such as weather and topography, quantitatively assess the importance and potential anomaly risks of the sites, or pre-set backup sites. Second, the efficiency of data anomaly processing is low: traditional methods require secondary on-site testing to correct physical and signal drift caused by equipment inaccuracies; hardware failure necessitates re-surveying and selecting sites, which not only prolongs the processing cycle but also easily causes monitoring data interruption, seriously affecting data continuity and accuracy. These problems restrict the quality and efficiency of soil and water conservation monitoring, and a more scientific monitoring system and methods are urgently needed to overcome these bottlenecks.

[0004] No effective solutions have yet been proposed to address the problems in the relevant technologies. Summary of the Invention

[0005] To address the problems in related technologies, this invention proposes a soil and water conservation monitoring system and method to overcome the technical problems of unplanned backup monitoring points and the need for secondary testing to handle data anomalies in existing related technologies.

[0006] Therefore, the specific technical solution adopted by the present invention is as follows: A soil and water conservation monitoring system and method, the method comprising the following steps: S1. Clearly define the monitoring target and the core variable combination for measurement, and formulate a specific measurement technology solution based on this multi-parameter measurement target; S2. Within the established monitoring range, select monitoring points and assess the importance and potential interference of each point to the multivariate measurement results in combination with topography, meteorological factors and intensity of human activities. At the same time, classify the points into different levels and configure different numbers of backup monitoring points for different levels. S3. Deploy rainfall, sediment, and soil moisture sensors at selected locations, collect initial data of each variable under normal conditions, and establish initial reference values ​​for each measurement channel. S4. Continuously collect data from each sensor and compare the coordination of multiple variable data in real time through the built-in algorithm. When the measured value of a certain monitoring point continues to deviate from its benchmark, the data is automatically marked as abnormal and the diagnostic process is triggered. S5. Diagnose the potential causes of abnormal data based on its characteristics. If it is determined to be physical drift, the drift coefficient model is used to correct the abnormal data. If it is determined to be signal drift, the temperature compensation algorithm is called to correct the data. If it is determined to be hardware damage, the alternative point scheme is activated, and the best alternative point is selected from the preset alternative points. A new sensor is used for measurement. S6. After activating the backup point, the monitoring network structure is automatically updated, the measurement data of the new point is fused and calibrated with the original network data, and all calibration operations and equipment replacement information are recorded. Finally, the measurement dataset and analysis conclusions are output.

[0007] As a preferred embodiment, the process of defining the monitoring target and the combination of core variables to be measured, and formulating a specific measurement technology solution based on this multi-parameter measurement target, includes the following steps: S11. Define the monitoring objectives for this monitoring, which include: understanding the total amount of soil erosion in the region, assessing the actual benefits of soil and water conservation projects, and conducting scientific research on sediment transport patterns; based on these objectives, further determine the combination of core physical variables that need to be measured simultaneously, including the three key parameters of rainfall, surface runoff, and runoff sediment content. S12. Based on the identified objectives and variables, develop a detailed technical implementation plan, including the geographical boundaries of the monitoring area, the specific measurement indicators for each variable, the data acquisition frequency and total cycle, and the required sensor types, accuracy, and system configuration.

[0008] As a preferred implementation, the step of selecting monitoring points within a predetermined monitoring range, and assessing the importance and potential interference of each point to the multivariate measurement results in conjunction with topography, meteorological factors, and intensity of human activities, and classifying them into different levels and configuring different numbers of backup monitoring points for different levels, includes the following steps: S21. Within the established monitoring area, collect basic data such as topographic maps, soil type maps, land use status maps, historical meteorological data, and distribution of human activities. Based on this information, preliminarily delineate a batch of potential monitoring points on the map that can cover different slopes, aspects, vegetation conditions, and use types. S22. Quantitatively evaluate the monitoring points. The evaluation criteria include importance indicators and potential interference indicators. Standardize the scoring of each indicator. Then, weight and summarize the scores of each point on importance and potential interference to obtain a comprehensive risk score. Finally, rank all monitoring points according to the scores and clearly divide them into three risk levels: high, medium and low. S23. Based on the risk level of the monitoring points, implement a differentiated backup point configuration strategy. For high-risk monitoring points, pre-set 5 qualified backup points in their vicinity; for medium-risk points, pre-set 3 backup points; and for low-risk points, pre-set 1 backup point.

[0009] As a preferred implementation method, the monitoring points are quantitatively evaluated. The evaluation criteria include importance indicators and potential interference indicators. Each indicator is scored in a standardized manner. Then, the scores of each point on importance and potential interference are weighted and summed to obtain a comprehensive risk score. Finally, all points are ranked according to their scores and clearly divided into three risk levels: high, medium, and low. The steps include: S221. Importance indicators include the representativeness of the monitoring point to the overall soil and water loss in the region and the degree of its location in key terrain or sensitive areas; potential disturbance includes the slope of the monitoring point, the frequency of historical rainstorms, and the distance from human activity centers. S222. For each indicator, a clear grading range and corresponding score are set. The representativeness of soil erosion in the overall region is divided into high, medium, and low levels based on the typicality and scarcity of the soil type and land use patterns represented by the monitoring points, and assigned scores of 3, 2, and 1 respectively. The degree of soil erosion in key terrain or sensitive areas is determined and assigned a score based on whether the monitoring point is located at a key geomorphic location such as the top of a slope, the foot of a slope, or the head of a gully. For the potential interference index, slope is directly divided into intervals according to angle size: slope < 5°, 1 point; slope 5°-15°, 2 points; slope > 15°, 3 points. Historical rainstorm frequency is graded and scored based on the average number of rainstorm days per year according to meteorological data. Distance from human activity centers is graded according to actual meters: distance > 1000 meters, 1 point; distance 500-1000 meters, 2 points; distance < 500 meters, 3 points. S223. Determine the relative weight of each indicator within and between the two categories of importance and potential interference using the analytic hierarchy process. Then, multiply the scores of each point on all indicators by their corresponding weights and sum them up to calculate the importance score and potential interference score. Finally, combine these two scores according to the preset weights to obtain a comprehensive risk score of 0-100, and rank all points according to their risk level. S224. Based on the comprehensive risk score, the top 20% of the scores are classified as high risk, the middle 50% as medium risk, and the bottom 30% as low risk, clearly dividing all locations into three risk levels: high, medium, and low. This rating will be used directly to guide subsequent differentiated resource allocation, such as allocating more backup monitoring points for high-risk locations.

[0010] As a preferred implementation, the step of implementing a differentiated backup site configuration strategy based on the risk level of the monitoring sites, including setting up 5 qualified backup sites around high-risk monitoring sites, 3 backup sites for medium-risk sites, and 1 backup site for low-risk sites, includes the following steps: S231. Based on the completion of the risk level classification of all locations, confirm the configuration plan, mark the monitoring points as high-risk, and pre-set 5 qualified alternative points around them; pre-set 3 alternative points for each medium-risk point; and pre-set 1 alternative point for each low-risk point. S232. Taking each main monitoring point as the center, the search area for candidate points shall be delineated according to its representative spatial range, i.e., a 500-meter radius buffer zone, and the candidate points shall be required to meet the conditions of terrain, soil type and land use. S233. Collect the specific geographic coordinates of the candidate points, and record all the information of the determined candidate points, including coordinates, environmental parameters, and distances from the main point. Establish a clear association between each main point and all its candidate points to form a clear one-main-multiple-backup network topology.

[0011] As a preferred embodiment, the steps of deploying rainfall, sediment, and soil moisture sensors at selected locations, collecting initial data of each variable under normal conditions, and establishing initial reference values ​​for each measurement channel include the following: S31. Use GPS to mark and locate the selected monitoring points, and in accordance with the installation specifications of each sensor, firmly install the rain gauge, sediment sensor, and soil moisture sensor on the preset brackets. At the selected monitoring points, a detailed on-site survey must first be conducted to confirm that the actual environment of the points is consistent with the plan. At the same time, GPS and other equipment should be used to accurately mark the points to facilitate subsequent installation. S32. Continuously collect data of each variable, and perform statistical analysis on the collected normal period data. After removing obvious outliers, calculate the average value or characteristic range, and use it as the initial reference value for each measurement channel at that point.

[0012] Before formally collecting data, allow the system to run continuously for a period of time, such as 24-48 hours, to observe whether the output of each measurement channel is stable, eliminate short-term instability caused by transportation, installation vibration, etc., and ensure that the equipment is in normal working condition.

[0013] In a preferred embodiment, the continuous acquisition of data from various sensors and the real-time comparison of the consistency of multiple variable data using a built-in algorithm, automatically marking the data as abnormal and triggering the diagnostic process when the measured value of a certain monitoring point continuously deviates from its benchmark, includes the following steps: S41. Continuously receive raw data streams from rainfall, sediment, and soil moisture sensors at various locations, and perform preliminary cleaning and formatting of the data. S42. Using a built-in algorithm with a sliding time window as the unit, the algorithm analyzes the inherent logical relationship between multiple variable data at the same monitoring point in real time. While analyzing the coordination of multiple variables, the algorithm continuously compares the latest reading of each sensor with the initial reference value established under normal conditions, and calculates the deviation and duration between the current value and the reference value. S43. When the system detects that the measured value of a certain sensor deviates from its reference value, and this deviation continues for more than a preset time, and the change of this variable cannot be reasonably explained by the changes of other related variables, the system immediately determines that the data is abnormal.

[0014] In a preferred embodiment, the step of diagnosing the potential causes of abnormal data based on its characteristics, and correcting the abnormal data using a drift coefficient model if it is determined to be physical drift, using a temperature compensation algorithm to correct the data if it is determined to be signal drift, and activating a backup point scheme if it is determined to be hardware damage, selecting the best backup point from the preset backup points and using a new sensor for measurement includes the following steps: S51. Retrieve historical data from multiple sensors at the abnormal location, analyze the drift pattern of the abnormal data, and diagnose it as physical drift caused by adsorption and scaling. Then, automatically calculate the cumulative immersion time of the sensor since the last cleaning, and establish a time-drift coefficient model to correct the abnormal data. The specific model formula is as follows: ; in, This is the corrected true concentration value. This refers to the actual measured reading of the sensor. Drift coefficient, This represents the cumulative immersion time of the sensor since its last cleaning and calibration. S52. If the signal drift is diagnosed as being caused by changes in ambient temperature, a temperature compensation algorithm is established, using the simultaneously recorded temperature data as input parameters to correct abnormal measurements in real time and eliminate temperature interference. The specific temperature compensation algorithm formula is as follows: ; in, The actual value after compensation. For temperature Sensor measurements below The actual temperature at the time of measurement. To calibrate the reference temperature for the sensor, This is the temperature compensation coefficient; S53. If the diagnosis is irreversible hardware damage, i.e. sensor wear and component aging, the list of alternative points preset for that point will be automatically retrieved, and the alternative point with the best conditions will be selected first. If the point is available, the sensor will be placed at the new point. In a preferred embodiment, after activating the backup monitoring point, the monitoring network structure is automatically updated, the measurement data from the new point is fused and calibrated with the original network data, and all calibration operations and equipment replacement information are recorded. The final output of the measurement dataset and analysis conclusions includes the following steps: S61. After the sensor at the backup location is installed and its stable operation is verified, the system administrator formally associates the physical coordinates of the new location with the logical identifier of the original fault location in the monitoring platform, ensuring that all subsequent data streams from the new device are recognized by the system as a continuation of the original location. S62. By performing spatial interpolation analysis using synchronous data from surrounding normal monitoring points, establish the correction relationship between the new point data and the original sequence, and perform smoothing calibration on the data from the connecting time periods; S63. The system automatically generates a data quality assessment report for this incident, recording the entire process from abnormal alarm, root cause diagnosis, activation of backup points to data fusion and correction, all operators and timestamps, forming an unalterable and traceable complete audit log; S64. The data processing workflow integrates all raw measurements and the final values ​​after calibration and fusion into a standardized dataset that can be directly analyzed. S65. Perform predetermined analysis and calculations on the dataset to generate a monitoring report on the status of soil and water loss.

[0015] A soil and water conservation monitoring system, which adopts a soil and water conservation monitoring method as described above, including a data acquisition module, a monitoring target and scheme formulation module, a point selection and backup configuration module, a benchmark establishment module, an anomaly monitoring and correction module, and a monitoring report output module; The data acquisition module is used to continuously collect raw data from the rainfall, sediment, and soil moisture sensors at each monitoring point. The monitoring target and scheme formulation module clarifies the monitoring target and core measurement variables, and formulates a technical scheme that includes the monitoring range, sampling frequency and sensor equipment configuration accordingly. The site selection and backup configuration module combines topographic and meteorological data to select potential monitoring sites, classifies risk levels through quantitative assessment, and configures backup sites according to the level. The reference establishment module receives initial data under normal conditions, calculates and establishes initial reference values ​​for each measurement channel; The anomaly monitoring and correction module compares the deviation of the data from the benchmark value and the multivariate consistency in real time, diagnoses the cause of the anomaly, and corrects the data or activates backup points through algorithms to ensure data reliability. The monitoring report output module integrates new and old monitoring data, records operation logs, outputs a standardized dataset, and generates a soil and water loss monitoring report.

[0016] The beneficial effects of this invention are as follows: 1. This invention first clarifies the monitoring objective, and then formulates a detailed plan that includes the monitoring scope, site planning, indicator selection, frequency cycle, and budget for human, financial, and material resources. This avoids the blindness of monitoring work and ensures that every monitoring action serves the core needs. On this basis, by combining factors such as weather and terrain, the importance and potential anomaly risks of monitoring points are assessed, and alternative sites are configured differently according to the scoring level. Five alternative sites are preset for high-level sites, three for medium-level sites, and one for low-level sites. This not only strengthens redundancy protection for key sites and effectively deals with possible site failures, but also avoids the waste of resources for low-risk sites. Finally, a monitoring network that is both targeted and flexible is constructed, laying the foundation for continuous and stable data acquisition in the future. 2. This invention addresses data anomalies caused by precision or reliability defects in monitoring equipment. By accurately distinguishing anomaly types and adopting corresponding strategies, it significantly improves processing efficiency and data quality. For physical drift, a model is built based on the sensor's immersion time to directly correct the data, eliminating the need for secondary on-site testing. For signal drift, a compensation algorithm is called based on the temperature difference to correct the data, similarly eliminating repeated testing steps and significantly simplifying the operation process. When irreversible hardware damage occurs, preset alternative locations are directly activated to quickly select and deploy new sensors without the need for replanning locations, effectively avoiding data interruption. This ensures both the continuity and accuracy of monitoring data and significantly improves anomaly handling efficiency.

[0017] In summary, this invention begins by clearly defining the monitoring objectives, and based on this, formulates a detailed plan covering the monitoring scope, site planning, indicator selection, frequency cycle, and budget for personnel, resources, and finances. This avoids blind monitoring from the outset and ensures that all actions are closely aligned with core needs. Next, based on the plan, monitoring sites are implemented. By considering weather and terrain factors, the importance and potential anomaly risks of each site are assessed. Then, sites are categorized into high, medium, and low levels according to their scores, and alternative sites are configured differently. This strengthens redundancy for critical sites to address failure issues while avoiding waste of resources at low-risk sites. Ultimately, this constructs a monitoring system that is both targeted and flexible. The network testing lays a solid foundation for continuous and stable data acquisition. The subsequent data collection and maintenance phase involves accurately identifying potential data anomalies and implementing efficient handling strategies. Physical drift is directly corrected based on a model built from the sensor's immersion time, while signal drift is corrected using a compensation algorithm based on temperature difference, eliminating the need for secondary on-site testing. For irreversible hardware damage, pre-selected equipment locations are directly activated without the need for replanning. This ensures both the continuity and accuracy of monitoring data and significantly improves anomaly handling efficiency, ultimately achieving efficient monitoring from target setting to data output. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of a soil and water conservation monitoring system and method according to an embodiment of the present invention. Detailed Implementation

[0020] To further illustrate the various embodiments, the present invention provides accompanying drawings, which are part of the disclosure of the present invention. These drawings are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these drawings, those skilled in the art should be able to understand other possible implementation methods and the advantages of the present invention. The components in the drawings are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0021] According to an embodiment of the present invention, a soil and water conservation monitoring system and method are provided.

[0022] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments, such as... Figure 1 As shown, a soil and water conservation monitoring method according to an embodiment of the present invention includes the following steps: S1. Clearly define the monitoring target and the core variable combination for measurement, and formulate a specific measurement technology solution based on this multi-parameter measurement target; Furthermore, the monitoring objectives and the core variable combinations for measurement are clearly defined, and based on these multi-parameter measurement objectives, a specific measurement technical solution is developed, including the following steps: S11. Define the monitoring objectives for this monitoring, which include: understanding the total amount of soil erosion in the region, assessing the actual benefits of soil and water conservation projects, and conducting scientific research on sediment transport patterns; based on these objectives, further determine the combination of core physical variables that need to be measured simultaneously, including the three key parameters of rainfall, surface runoff, and runoff sediment content. S12. Based on the identified objectives and variables, develop a detailed technical implementation plan, including the geographical boundaries of the monitoring area, the specific measurement indicators for each variable, the data acquisition frequency and total cycle, and the required sensor type, accuracy, and system configuration. S2. Within the established monitoring range, select monitoring points and assess the importance and potential interference of each point to the multivariate measurement results in combination with topography, meteorological factors and intensity of human activities. At the same time, classify the points into different levels and configure different numbers of backup monitoring points for different levels. Furthermore, within the established monitoring range, monitoring points are selected, and the importance and potential interference of each point to the multivariate measurement results are assessed in conjunction with topography, meteorological factors, and the intensity of human activities. Based on this, points are classified into different levels, and a different number of backup monitoring points are configured for each level. This includes the following steps: S21. Within the established monitoring area, collect basic data such as topographic maps, soil type maps, land use status maps, historical meteorological data, and distribution of human activities. Based on this information, preliminarily delineate a batch of potential monitoring points on the map that can cover different slopes, aspects, vegetation conditions, and use types. S22. Quantitatively evaluate the monitoring points. The evaluation criteria include importance indicators and potential interference indicators. Standardize the scoring of each indicator. Then, weight and summarize the scores of each point on importance and potential interference to obtain a comprehensive risk score. Finally, rank all monitoring points according to the scores and clearly divide them into three risk levels: high, medium and low. It should be noted that by clarifying the dual-dimensional indicators of importance and potential interference, standardizing scoring and weighted summarization, the risk of monitoring points can be accurately quantified and objectively ranked. After classifying them into high, medium and low risk levels, a scientific basis can be provided for subsequent differentiated resource allocation, which can not only ensure the monitoring stability of high-importance and high-interference-risk points, but also avoid resource redundancy of low-risk points, thereby improving the overall rationality of monitoring network planning. Furthermore, the monitoring sites are quantitatively evaluated using criteria including importance and potential interference indicators. Each indicator is scored using standardized methods. The scores for each site in terms of importance and potential interference are then weighted and summed to obtain a comprehensive risk score. Finally, all sites are ranked according to their scores and clearly divided into high, medium, and low risk levels. The process includes the following steps: S221. Importance indicators include the representativeness of the monitoring point to the overall soil and water loss in the region and the degree of its location in key terrain or sensitive areas; potential disturbance includes the slope of the monitoring point, the frequency of historical rainstorms, and the distance from human activity centers. S222. For each indicator, a clear grading range and corresponding score are set. The representativeness of soil erosion in the overall region is divided into high, medium, and low levels based on the typicality and scarcity of the soil type and land use patterns represented by the monitoring points, and assigned scores of 3, 2, and 1 respectively. The degree of soil erosion in key terrain or sensitive areas is determined and assigned a score based on whether the monitoring point is located at a key geomorphic location such as the top of a slope, the foot of a slope, or the head of a gully. For the potential interference index, slope is directly divided into intervals according to angle size: slope < 5°, 1 point; slope 5°-15°, 2 points; slope > 15°, 3 points. Historical rainstorm frequency is graded and scored based on the average number of rainstorm days per year according to meteorological data. Distance from human activity centers is graded according to actual meters: distance > 1000 meters, 1 point; distance 500-1000 meters, 2 points; distance < 500 meters, 3 points. S223. Determine the relative weight of each indicator within and between the two categories of importance and potential interference using the analytic hierarchy process. Then, multiply the scores of each point on all indicators by their corresponding weights and sum them up to calculate the importance score and potential interference score. Finally, combine these two scores according to the preset weights to obtain a comprehensive risk score of 0-100, and rank all points according to their risk level. S224. Based on the comprehensive risk score, the top 20% of the scores are classified as high risk, the middle 50% as medium risk, and the bottom 30% as low risk, clearly dividing all locations into three risk levels: high, medium, and low. S23. Based on the risk level of the monitoring points, implement a differentiated backup point configuration strategy. For high-risk monitoring points, set 5 qualified backup points around them; for medium-risk monitoring points, set 3 backup points; and for low-risk monitoring points, set 1 backup point. Furthermore, based on the risk level of the monitoring points, a differentiated backup point configuration strategy is implemented. For high-risk monitoring points, five qualified backup points are preset in their vicinity; for medium-risk points, three backup points are preset; and for low-risk points, one backup point is preset. This includes the following steps: S231. Based on the completion of the risk level classification of all locations, confirm the configuration plan, mark the monitoring points as high-risk, and pre-set 5 qualified alternative points around them; pre-set 3 alternative points for each medium-risk point; and pre-set 1 alternative point for each low-risk point. S232. Taking each main monitoring point as the center, the search area for candidate points shall be delineated according to its representative spatial range, i.e., a 500-meter radius buffer zone, and the candidate points shall be required to meet the conditions of terrain, soil type and land use. S233. Collect the specific geographic coordinates of the candidate points, and record all the information of the determined candidate points, including coordinates, environmental parameters, and distances from the main point. Establish a clear association between each main point and all candidate points to form a clear one-main-multiple-backup network topology.

[0023] It should be noted that the differentiated backup site configuration strategy can balance monitoring reliability and resource utilization efficiency: by configuring more backup sites for high-risk sites, the problems of susceptibility to interference and hardware damage can be effectively addressed, ensuring that critical monitoring data is not interrupted; by reasonably reducing the number of backup sites for medium and low-risk sites, the waste of resources can be avoided; at the same time, by clarifying the planning and topological association of backup sites, backup sites can be quickly and optimally activated when the main site is abnormal, reducing data loss, improving the resilience and stability of the monitoring network, and providing continuous and reliable data support for subsequent data fusion calibration and soil erosion assessment; S3. Deploy rainfall, sediment, and soil moisture sensors at selected locations, collect initial data of each variable under normal conditions, and establish initial reference values ​​for each measurement channel. Deploying rainfall, sediment, and soil moisture sensors at selected locations and collecting initial data for each variable under normal conditions to establish initial baseline values ​​for each measurement channel includes the following steps: S31. Use GPS to mark and locate the selected monitoring points, and in accordance with the installation specifications of each sensor, firmly install the rain gauge, sediment sensor, and soil moisture sensor on the preset brackets. S32. Continuously collect data of each variable, and perform statistical analysis on the collected normal period data. After removing obvious outliers, calculate the average value or characteristic range, and use it as the initial reference value for each measurement channel at that point.

[0024] S4. Continuously collect data from each sensor and compare the coordination of multiple variable data in real time through the built-in algorithm. When the measured value of a certain monitoring point continues to deviate from its benchmark, the data is automatically marked as abnormal and the diagnostic process is triggered. Furthermore, data from various sensors is continuously collected, and the consistency of multiple variable data is compared in real time through a built-in algorithm. When the measured value of a certain monitoring point continuously deviates from its benchmark, the data is automatically marked as abnormal and a diagnostic process is triggered, including the following steps: S41. Continuously receive raw data streams from rainfall, sediment, and soil moisture sensors at various locations, and perform preliminary cleaning and formatting of the data. S42. Using a built-in algorithm with a sliding time window as the unit, the algorithm analyzes the inherent logical relationship between multiple variable data at the same monitoring point in real time. While analyzing the coordination of multiple variables, the algorithm continuously compares the latest reading of each sensor with the initial reference value established under normal conditions, and calculates the deviation and duration between the current value and the reference value. S43. When the system detects that the measured value of a certain sensor deviates from its reference value, and this deviation continues for more than a preset time, and the change in this variable cannot be reasonably explained by the changes in other related variables, the system immediately determines that the data is abnormal. S5. Diagnose the potential causes of abnormal data based on its characteristics. If it is determined to be physical drift, the drift coefficient model is used to correct the abnormal data. If it is determined to be signal drift, the temperature compensation algorithm is called to correct the data. If it is determined to be hardware damage, the alternative point scheme is activated, and the best alternative point is selected from the preset alternative points. A new sensor is used for measurement. Furthermore, based on the characteristics of the abnormal data, the potential causes are diagnosed. If it is determined to be physical drift, the drift coefficient model corrects the abnormal data; if it is determined to be signal drift, a temperature compensation algorithm is invoked to correct the data; if it is determined to be hardware damage, an alternative location scheme is activated, selecting the best option from the preset alternative locations and using a new sensor for measurement, including the following steps: S51. Retrieve historical data from multiple sensors at the abnormal location, analyze the drift pattern of the abnormal data, and diagnose it as physical drift caused by adsorption and scaling. Then, automatically calculate the cumulative immersion time of the sensor since the last cleaning, and establish a time-drift coefficient model to correct the abnormal data. The specific model formula is as follows: ; in, This is the corrected true concentration value. This refers to the actual measured reading of the sensor. Drift coefficient, This represents the cumulative immersion time of the sensor since its last cleaning and calibration. It should be noted that the drift coefficient This is achieved by analyzing data changes during the maintenance cycle of field equipment. After each cleaning and maintenance of the sensor, its reading is considered a relatively accurate benchmark. The system analyzes the slow, unidirectional drift trend of the sensor reading during the stable phase with minimal external disturbances such as no rainfall or low flow velocity from the end of the current maintenance to the beginning of the next maintenance. The average slope of this trend (i.e., the amount by which the reading deviates from the benchmark value per unit time) is calculated as the empirical drift coefficient of the sensor under the current actual environment. ; S52. If the signal drift is diagnosed as being caused by changes in ambient temperature, a temperature compensation algorithm is established, using the simultaneously recorded temperature data as input parameters to correct abnormal measurements in real time and eliminate temperature interference. The specific temperature compensation algorithm formula is as follows: ; in, The actual value after compensation. For temperature Sensor measurements below The actual temperature at the time of measurement. To calibrate the reference temperature for the sensor, This is the temperature compensation coefficient; It should be noted that the temperature compensation coefficient By analyzing historical field data, during periods of significant seasonal temperature variation or large diurnal temperature range, the system synchronously records sensor readings and temperature data. During this period, manual sampling measurements are periodically performed to obtain a true baseline value unaffected by temperature. Subsequently, using all synchronous data, regression analysis is used to fit a functional relationship between sensor reading deviation and temperature change. The coefficient of the linear term in this relationship is... The coefficient of the quadratic term is ; S53. If the diagnosis is irreversible hardware damage, i.e. sensor wear and component aging, the list of alternative points preset for that point will be automatically retrieved, and the alternative point with the best conditions will be selected first. If the point is available, the sensor will be placed at the new point. It should be noted that when it shows a slow, unidirectional linear deviation, it indicates physical drift caused by adsorption and scaling. When the temperature sensor reading shows obvious synchronous fluctuations, it indicates signal drift caused by temperature changes. When it shows complete data disorder or loss, it indicates hardware damage. S6. After activating the backup point, the monitoring network structure is automatically updated, the measurement data of the new point is fused and calibrated with the original network data, and all calibration operations and equipment replacement information are recorded. Finally, the measurement dataset and analysis conclusions are output.

[0025] Furthermore, after activating the backup monitoring points, the monitoring network structure is automatically updated, the measurement data from the new points are fused and calibrated with the original network data, and all calibration operations and equipment replacement information are recorded. The final output of the measurement dataset and analysis conclusions includes the following steps: S61. After the sensor at the backup location is installed and its stable operation is verified, the system administrator formally associates the physical coordinates of the new location with the logical identifier of the original fault location in the monitoring platform, ensuring that all subsequent data streams from the new device are recognized by the system as a continuation of the original location. S62. By performing spatial interpolation analysis using synchronous data from surrounding normal monitoring points, establish the correction relationship between the new point data and the original sequence, and perform smoothing calibration on the data from the connecting time periods; S63. The system automatically generates a data quality assessment report for this incident, recording the entire process from abnormal alarm, root cause diagnosis, activation of backup points to data fusion and correction, all operators and timestamps, forming an unalterable and traceable complete audit log; S64. The data processing workflow integrates all raw measurements and the final values ​​after calibration and fusion into a standardized dataset that can be directly analyzed. S65. Perform predetermined analysis and calculations on the dataset to generate a monitoring report on the status of soil and water loss; A soil and water conservation monitoring system, which adopts any one of the soil and water conservation monitoring methods mentioned above, including a data acquisition module, a monitoring target and scheme formulation module, a point selection and backup configuration module, a benchmark establishment module, an anomaly monitoring and correction module, and a monitoring report output module; The data acquisition module is used to continuously collect raw data from the sensors for rainfall, sediment, and soil moisture at each monitoring point; The monitoring target and scheme formulation module clarifies the monitoring targets and core measurement variables, and formulates a technical scheme that includes the monitoring range, sampling frequency, and sensor equipment configuration accordingly. The site selection and backup configuration module combines topographic and meteorological data to select potential monitoring sites, classifies risk levels through quantitative assessment, and configures backup sites according to the level. The baseline establishment module receives initial data under normal conditions, calculates and establishes the initial baseline values ​​for each measurement channel; The anomaly monitoring and correction module compares the deviation of data from the baseline value and the consistency of multiple variables in real time, diagnoses the cause of anomalies, and corrects the data or activates backup points through algorithms to ensure data reliability. The monitoring report output module integrates new and old monitoring data from calibration, records operation logs, outputs standardized datasets, and generates a monitoring report on soil and water loss.

[0026] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A water and soil conservation monitoring method characterized by, The method comprises the following steps: S1, clearly define the monitoring target and the core variable combination of measurement, and develop a specific measurement technical scheme based on the multi-parameter measurement target; S2, select monitoring points within the established monitoring range, and evaluate the importance and potential interference degree of each point to the multi-variable measurement results in combination with topography, meteorological factors, and human activity intensity, and simultaneously divide the levels and configure different numbers of backup monitoring sites for different level points; S3, deploy rain, sediment, and soil moisture sensors at the selected points, and collect initial data of each variable under normal environment to establish the initial baseline value of each measurement channel; S4, continuously collect sensor data, and compare the coordination of multiple variable data in real time through the built-in algorithm, and when the measurement value of a monitoring point continuously deviates from the baseline, mark the data as abnormal and trigger the diagnosis process; S5, diagnose the potential causes of abnormal data according to the characteristics of the abnormal data, if it is judged as physical drift, a time-drift coefficient model is constructed according to the water immersion time of the sensor to correct the abnormal data, if it is judged as signal drift, a temperature compensation algorithm is called to correct the data, and if it is judged as hardware damage, the backup point scheme is started, the new sensor is selected for measurement from the preset backup point; S6, after the backup point is enabled, the monitoring network structure is automatically updated, the measurement data of the new point is fused and calibrated with the original network data, and all correction operations and equipment replacement information are recorded, and finally the measurement data set and analysis conclusion are output.

2. The water and soil conservation monitoring method according to claim 1, characterized in that, The specific steps of clearly defining the monitoring target and the core variable combination of measurement, and developing a specific measurement technical scheme based on the multi-parameter measurement target comprise the following steps: S11, clearly define the monitoring target, the monitoring target includes mastering the total amount of regional soil erosion, evaluating the actual benefit of soil and water conservation engineering, and scientifically researching the law of sediment transport; based on the target, further determine the core physical variable combination that needs to be measured simultaneously, including three key parameters of rainfall, surface runoff, and runoff sediment content; S12, according to the determined target and variable, develop a detailed technical implementation scheme, which specifically includes the geographical boundary of monitoring, the specific measurement index of each variable, the data collection frequency and total cycle, and the type, accuracy and system configuration of the required sensor.

3. The water and soil conservation monitoring method of claim 1, wherein The specific steps of selecting monitoring points within the established monitoring range, and evaluating the importance and potential interference degree of each point to the multi-variable measurement results in combination with topography, meteorological factors, and human activity intensity, and simultaneously dividing the levels and configuring different numbers of backup monitoring sites for different level points comprise the following steps: S21, within the determined monitoring range, collect topographic map, soil type map, land use status map, historical meteorological data, and human activity distribution, based on these information, preliminarily enclose a batch of potential monitoring points on the map which can cover different slope, slope direction, vegetation condition and utilization type; S22, the evaluation standard of the quantitative evaluation of the monitoring point includes importance index and potential interference degree index, and each index is scored by standardization, then the scores of each point in importance and potential interference degree are weighted and summarized to obtain a comprehensive risk score, finally all monitoring points are sorted according to the score, and are clearly divided into three risk levels of high, medium and low; S23, according to the risk level of the monitoring point, a differentiated backup point configuration strategy is implemented, 5 qualified alternative points are preset around the monitoring point of high risk level, 3 alternative points are preset for the medium risk point, and 1 alternative point is preset for the low risk point.

4. The water and soil conservation monitoring method according to claim 3, characterized by, The quantitative evaluation of the monitoring point, the evaluation standard of which includes importance index and potential interference degree index, and each index is scored by standardization, then the scores of each point in importance and potential interference degree are weighted and summarized to obtain a comprehensive risk score, finally all points are sorted according to the score, and are clearly divided into three risk levels of high, medium and low, including the following steps: S221, the importance index includes the representativeness of the monitoring point to the overall regional soil erosion, and the degree of being located in the key terrain or sensitive area; the potential interference degree includes the slope of the monitoring point, the historical rainstorm frequency and the distance from the human activity center; S222, for each index, a clear grading interval and corresponding score are set, the representativeness of the overall regional soil erosion is divided into three levels of high, medium and low according to the typicality and scarcity of soil type and land use mode, and is correspondingly given 3, 2 and 1 points; the degree of being located in the key terrain or sensitive area is determined and valued according to whether the monitoring point is located at the top of the slope, the foot of the slope or the head of the ditch; for the slope in the potential interference degree index, the interval is directly divided according to the angle size, specifically, the slope < 5° is given 1 point, the slope 5°-15° is given 2 points, and the slope > 15° is given 3 points, the historical rainstorm frequency is graded and scored according to the annual average rainstorm days of meteorological data, and the distance from the human activity center is set according to the actual distance gradient, the distance > 1000 meters is given 1 point, the distance 500-1000 meters is given 2 points, and the distance < 500 meters is given 3 points; S223, the relative weight of each index in the two categories of importance and potential interference degree and between the two categories is determined by the analytic hierarchy process, then the scores of each point in all indexes are multiplied by the corresponding weight and added up to calculate the importance score and the potential interference degree score; then the two scores are combined according to the preset weight to obtain a comprehensive risk score of 0-100, and the risk of all points is sorted according to the score; S224, according to the comprehensive risk score, the top 20% of the score is high risk, the middle 50% is medium risk, and the last 30% is low risk, and all points are clearly divided into three risk levels of high, medium and low.

5. The water and soil conservation monitoring method according to claim 3, characterized in that, The method comprises the following steps of: S231, on the basis of having completed all point risk level division, confirming the configuration scheme, marking the monitoring point with high risk, and presetting five eligible alternative points in the periphery of the monitoring point; each medium risk point is preset with three alternative points; and each low risk point is preset with one alternative point; S232, taking each main monitoring point as the center, dividing the search area of the alternative point according to the representative spatial range, that is, the 500-meter radius buffer zone, and stipulating that the alternative point must meet the terrain, soil type and land use mode conditions; S233, collecting the specific geographic coordinates of the alternative point, and recording all the determined alternative point information, including the coordinates, environmental parameters and distance from the main point, and establishing clear association between each main point and all alternative points to form a clear one-main-multiple-backup network topology diagram.

6. The water and soil conservation monitoring method of claim 1, wherein The method comprises the following steps of: S31, using GPS to mark and position the selected monitoring point, and according to the installation specification of each sensor, firmly installing the rain gauge, sediment sensor and soil moisture sensor on the preset support; S32, continuously collecting variable data, and statistically analyzing the collected normal period data, calculating the average value or characteristic range after removing the obvious abnormal values, and taking the average value or characteristic range as the initial reference value of each measurement channel of the point.

7. The water and soil conservation monitoring method of claim 1, wherein The method comprises the following steps of: S41, continuously receiving the original data stream from the rain, sediment and soil moisture sensors of each point, and performing preliminary cleaning and formatting processing on the data; S42, through the built-in algorithm, the internal logical relationship between multiple variable data of the same monitoring point is analyzed in real time in a sliding time window unit, while analyzing the coordination of multiple variables, the algorithm continuously compares the latest reading of each sensor with the initial reference value established under normal environment, and calculates the deviation amplitude and duration between the current value and the reference value; S43, when the system detects that the measurement value of a sensor deviates from the reference value, and the deviation state lasts for more than a preset time, and the change of the variable cannot be reasonably explained by the change of other related variables, it is immediately determined that the data is abnormal.

8. The water and soil conservation monitoring method of claim 1, wherein, The characteristics of the abnormal data are diagnosed to find the potential causes, if it is judged as physical drift, the drift coefficient model is used to correct the abnormal data, if it is judged as signal drift, the temperature compensation algorithm is called to correct the data, if it is judged as hardware damage, the alternative point scheme is started, the new sensor is selected to measure including the following steps: S51, the multi-sensor historical data of the abnormal point is called, the drift mode of the abnormal data is analyzed, it is diagnosed that the physical drift is caused by adsorption and scaling, the cumulative water immersion time of the sensor since the last cleaning is automatically calculated, and a time-length-drift coefficient model is established to correct the abnormal data, the specific model formula is: ； wherein, is the corrected true concentration value, is the sensor actual measurement reading, is the drift coefficient, is the cumulative submersion time since the last cleaning calibration of the sensor; S52, if it is diagnosed that the signal drift is caused by environmental temperature change, a temperature compensation algorithm is established, and the temperature data recorded at the same time are used as input parameters to correct the abnormal measurement value in real time, and the temperature interference is eliminated, the specific temperature compensation algorithm formula is: ； wherein is the compensated true value, is the temperature at which the sensor measured, is the actual temperature at the time of the current measurement, is the sensor calibration reference temperature, is the temperature compensation coefficient; S53, if it is diagnosed that the hardware damage is irreversible, that is, the wear and tear of the sensor and the aging of the element, the alternative point list preset for the point is automatically called out, the alternative point with the optimal condition is selected preferentially, if the point is available, the sensor is arranged at the new point.

9. The water and soil conservation monitoring method of claim 8, wherein, After the standby point is enabled, the monitoring network structure is automatically updated, the measurement data of the new point are fused and calibrated with the original network data, and all correction operations and equipment replacement information are recorded, finally the measurement data set and analysis conclusion are output including the following steps S61, after the sensor of the standby point is installed and verified to be stable, the system administrator formally associates the physical coordinates of the new point to the logical identification of the original fault point in the monitoring platform, ensuring that all subsequent data streams from the new equipment are recognized as the continuation of the original point; S62, the spatial interpolation analysis is carried out through the synchronous data of the surrounding normal monitoring points, the correction relationship between the new point data and the original sequence is established, and the data of the connection period is smoothed and calibrated; S63, the system automatically generates a data quality evaluation report of this event, records the whole process from abnormal alarm, root cause diagnosis, standby point enablement to data fusion correction, all operators and time stamps, and forms an unalterable and traceable complete audit log; S64, the data processing flow integrates all original measurement values and the final values after correction and fusion processing into a standardized data set which can be directly analyzed and used; S65, the data set is analyzed and calculated according to the established analysis, and the monitoring report of water and soil loss is obtained.

10. A water and soil conservation monitoring system, characterized by, The system adopts a water and soil conservation monitoring method according to any one of claims 1-9, including a data acquisition module, a monitoring target and scheme development module, a point selection and standby configuration module, a reference establishment module, an abnormal monitoring and correction module, and a monitoring report output module; The data acquisition module is used for continuously acquiring the original data of the rain, sediment and soil moisture sensors of each monitoring point; The monitoring target and scheme development module clearly defines the monitoring target and core measurement variable, and develops a technical scheme including the monitoring range, sampling frequency and sensor configuration. The point position screening and backup configuration module screens potential monitoring point positions in combination with topographic and meteorological data, divides risk levels through quantitative evaluation, and differentially configures backup point positions according to the levels; The reference establishing module receives initial data in a normal environment, calculates and establishes initial reference values of each measurement channel; The abnormality monitoring and correction module compares the deviation of data and reference values and the variability of multiple variables in real time, diagnoses abnormal reasons, and corrects data or enables backup point positions through an algorithm to ensure data reliability; The monitoring report output module fuses new and old monitoring data, records operation logs, outputs standardized data sets, and simultaneously generates a soil and water loss condition monitoring report.

Images