Water and soil conservation soil loss data monitoring system and method

Abstract

The invention relates to the technical field of water and soil conservation monitoring, and discloses a water and soil conservation soil loss data monitoring system and method. An area setting module of the system sets a geographic monitoring area by taking a monitoring station as a center, so that accurate delimiting of a monitoring range is realized; the data acquisition module is used for acquiring soil loss related data in a geographical monitoring area, wherein the related data covers multi-dimensional information such as rainfall, soil water content and vegetation coverage. The loss analysis module calculates a loss coefficient and a risk coefficient through the data, and quantitatively presents the soil loss condition; the early warning module generates an early warning instruction according to the loss coefficient and the risk coefficient, and accurately reflects the emergency degree and the risk level of soil loss; and the cloud interaction module establishes a communication channel with a cloud server when the early warning instruction is generated, so that real-time sharing and cooperative processing of data are realized. According to the system, the pertinence, comprehensiveness and timeliness of soil loss monitoring are improved, and powerful support is provided for water and soil conservation work.

Description

Technical Field

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

[0002] Soil and water conservation is an essential component of ecological and environmental protection. As a key issue affecting the ecological environment and agricultural production, effective monitoring of soil erosion is directly important for maintaining regional ecological balance, disaster prevention and mitigation, and the sustainable use of land resources. Driven by both natural factors and human activities, soil erosion exhibits regional, dynamic, and complex characteristics. Differences in topography, climate, vegetation cover, and land use patterns across regions lead to significant variations in the causes and extent of soil erosion.

[0003] Traditional soil loss monitoring methods rely heavily on manual field sampling and surveys, acquiring data through regular soil sampling, measuring slope erosion gully depth, and calculating vegetation cover. This approach not only consumes significant manpower, material resources, and time, but also suffers from long data acquisition cycles and poor timeliness, making it difficult to capture dynamic changes in soil loss in real time. Manual monitoring is particularly difficult in areas with complex terrain or inaccessible transportation, making it difficult to ensure data integrity and accuracy.

[0004] With the development of information technology, some regions have begun to introduce sensors and other equipment for automated data collection, but existing monitoring systems still have many limitations. Most systems lack a targeted regional setting mechanism for monitoring areas, often using fixed administrative regions or grid units as monitoring areas. These systems fail to accurately define monitoring areas based on the actual geographic environment of monitoring sites. This results in a mismatch between monitoring areas and actual soil loss-sensitive areas, leading to blind spots and wasted resources.

[0005] At the data collection level, existing systems often focus on acquiring a single type of data, such as monitoring rainfall or soil moisture. This ignores the combined impact of multiple factors on soil loss, such as changes in vegetation cover, slope aspect, and land use type conversion, making it difficult to fully reflect the driving mechanisms of soil loss. Furthermore, data collection and analysis are disconnected, and collected data is not promptly and effectively integrated and processed, resulting in a lack of sufficient data support for assessing soil loss risks.

[0006] In terms of early warning mechanisms, existing systems often rely on single thresholds, lacking quantitative analysis of soil erosion severity and risk levels. The generation of warning instructions is subjective and delayed. Furthermore, most systems lack real-time interaction with the cloud. When soil erosion risk reaches warning conditions, relevant data and warning information cannot be quickly transmitted to management platforms or relevant departments, hindering the timeliness and coordination of emergency responses. These issues make it difficult for existing soil erosion monitoring systems to meet the needs of refined and dynamic soil and water conservation management. Summary of the Invention

[0007] The purpose of the present invention is to provide a soil and water conservation soil loss data monitoring system to solve the problems raised in the above background technology.

[0008] To achieve the above object, the present invention provides a soil and water conservation soil loss data monitoring system, the system comprising: Region setting module, data collection module, churn analysis module, early warning module and cloud interaction module; The area setting module is used to set a geographical monitoring area with the monitoring site as the center; The data acquisition module is used to obtain soil loss related data in the geographical monitoring area; The loss analysis module is used to calculate the loss coefficient and risk coefficient based on the soil loss related data; The early warning module is used to generate an early warning instruction according to the loss coefficient and the risk coefficient; The cloud interaction module is used to establish a communication channel with the cloud server when the early warning instruction is generated.

[0009] Preferably, the data acquisition module is specifically used to: obtain rainfall data, soil moisture data and slope data in the geographical monitoring area; The rainfall data, soil moisture data and slope data are processed by a soil loss prediction model to output a soil loss rate.

[0010] Preferably, the loss analysis module is specifically used to: obtain the soil loss rate and the spatial range of the geographical monitoring area; Calculating a loss coefficient based on the soil loss rate and the spatial extent of the geographic monitoring area; Obtain soil type data and vegetation cover data; Calculating a risk factor using the soil type data and vegetation cover data; The churn coefficient and the risk coefficient are weighted and summed to obtain a churn risk value.

[0011] Preferably, the early warning module is specifically used to: set a loss risk threshold; comparing the churn risk value with the churn risk threshold; When the loss risk value is greater than or equal to the loss risk threshold, generating an early warning instruction; When the loss risk value is less than the loss risk threshold, a monitoring maintenance instruction is generated.

[0012] Preferably, the system further comprises a monitoring point generation module; The monitoring point generation module is used to: obtain the temperature range and humidity range of the geographical monitoring area; Dividing the temperature range and humidity range into a plurality of environmental intervals; The density of monitoring points is assigned to each environmental interval through the environmental sensitivity model; A distribution map of monitoring points is generated within the geographic monitoring area.

[0013] Preferably, the cloud interaction module is specifically configured to: trigger a device verification request when generating the warning instruction; Lock local data write permissions; Upload the device context and verification question features to the cloud server through an encrypted channel; Receive the multi-level verification strategy issued by the cloud server.

[0014] Preferably, the system further comprises a data verification module; The data verification module is used to: load the multi-level verification strategy; Conduct risk validation on inserted devices at different levels; Access rights are granted based on the risk level validation results.

[0015] Preferably, the system further comprises a regulating module; The adjustment module is used to: obtain the monitoring point distribution map and the soil loss rate; adjusting the monitoring point positions in the monitoring point distribution map according to the soil loss rate; Output the updated monitoring point distribution map.

[0016] Preferably, the system further comprises a result generation module; The result generation module is used to: integrate the loss risk value, early warning instructions and the updated monitoring point distribution map; Generate soil loss monitoring reports.

[0017] Preferably, the present invention also includes a soil and water conservation soil loss data monitoring method, including all modules and method processes of the above-mentioned soil and water conservation soil loss data monitoring system.

[0018] Compared with the prior art, the present invention has the following beneficial effects: Through the collaborative operation of various modules, this soil and water conservation soil loss data monitoring system forms a complete monitoring closed loop, from regional demarcation and data collection to analysis and early warning, and cloud-based interaction. The regional setting module defines the geographic monitoring area centered on the monitoring site. It can accurately define the monitoring range by combining the actual geographical characteristics of the monitoring site, such as topography, vegetation distribution, and current land use. This avoids the resource waste caused by overly large scopes or incomplete monitoring caused by too small scopes in traditional fixed-area monitoring. It allows monitoring efforts to be concentrated on areas sensitive to soil loss, improving the relevance and effectiveness of monitoring.

[0019] The data collection module focuses on acquiring soil erosion data within the geographic monitoring area. This module transcends the limitations of traditional single-source data collection and integrates multi-dimensional information such as rainfall, soil moisture, vegetation cover, slope aspect, and tillage practices to comprehensively capture the various factors influencing soil erosion. This systematic collection forms a complete data chain, providing a rich foundation for subsequent soil erosion analysis. This allows for a comprehensive assessment of soil erosion conditions, rather than relying on isolated data.

[0020] The loss analysis module calculates loss coefficients and risk factors based on soil loss data, transforming the complex phenomenon of soil loss into quantifiable indicators, making the previously abstract extent of loss and risk conditions concrete and measurable. This quantitative analysis method breaks away from the subjectivity of traditional empirical judgments. By correlating data, it can more objectively reflect the actual situation of soil loss, including the intensity of loss, potential trends, and the scope of potential impact, providing a scientific basis for subsequent early warning.

[0021] The early warning module generates warning instructions based on the loss coefficient and risk factor, changing the traditional single-threshold warning model. It can now determine the urgency and risk level of soil loss based on different coefficient combinations, making warning instructions more tailored to actual conditions. When the soil loss risk reaches a certain level, the warning instruction is triggered immediately, prompting relevant personnel to take targeted prevention and control measures, such as strengthening vegetation restoration and implementing engineering protection, thus effectively intervening before soil loss escalates.

[0022] The cloud-based interactive module establishes a communication channel with the cloud server when warning instructions are generated, enabling real-time uploading and sharing of monitoring data and warning information. This function breaks the information silo of the monitoring system, enabling relevant management departments, scientific research institutions, and others to obtain detailed data on the warning area in a timely manner, facilitating collaborative analysis, assessment, and emergency response. Through the cloud platform, monitoring data from different regions can be aggregated and compared, providing a reference for regional soil and water conservation planning and policy formulation, promoting the optimal allocation of monitoring resources and enhancing overall prevention and control capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a time sequence diagram of the soil and water conservation soil loss data monitoring system of the present invention; Figure 2 This is the workflow diagram of the data acquisition module; Figure 3 This is the workflow diagram for the churn analysis module; Figure 4 Generate a workflow diagram of the module for monitoring points. DETAILED DESCRIPTION

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0025] See also Figure 1 The present invention provides a soil and water conservation soil loss data monitoring system and method. The system comprises: based on an integrated hardware and software architecture. The core of the system includes a region setting module, a data acquisition module, a loss analysis module, an early warning module, and a cloud interaction module.

[0026] The area setting module is responsible for defining the geographical scope of monitoring, which is centered on the selected monitoring site. Users can accurately define the boundaries of the monitoring area through a graphical interface or by entering geographic coordinate parameters (such as longitude and latitude, radius, or boundary point coordinates). The data acquisition module is deployed within the area and contains a variety of sensor networks and data interfaces for continuous or periodic collection of raw environmental parameters related to soil loss. The loss analysis module receives the collected data, processes it using built-in algorithm models, and calculates key indicators. The early warning module is set with threshold judgment logic. When the indicators output by the analysis module meet the preset conditions, the corresponding instructions are triggered. The cloud interaction module serves as a bridge between the system and remote services. When specific instructions (such as early warning instructions) are triggered, it actively establishes a secure network connection channel to achieve data exchange and instruction reception with the cloud server.

[0027] Example 1: See Figure 2The data acquisition module, the core unit for acquiring environmental information, involves multi-layered hardware deployment, data transmission, and data processing. This module focuses on the continuous capture and initial transformation of key parameters directly related to soil loss within the geographic monitoring area. Implementation begins with the establishment of a comprehensive sensor network and data interface system within the geographic monitoring area defined by the user through the region setting module. Meteorological monitoring units are a crucial component of this network and typically consist of high-precision tipping bucket rain gauges or optical rain sensors. These devices are strategically deployed at representative locations within the monitoring area, such as the regional center, along varying elevation gradients, or along known microclimate variations, to capture potential spatial variations in rainfall distribution. Rain gauges record key rainfall event parameters in real time or at pre-set intervals (e.g., every 5 minutes or every hour), including but not limited to instantaneous rainfall intensity (mm / hour), duration of individual rainfall events (minutes or hours), and accumulated rainfall (mm). These raw rainfall data are transmitted to the local data aggregation node or directly sent to the central data processing unit via wired (such as RS485, Ethernet) or wireless (such as LoRaWAN, NB-IoT, 4G / 5G) communication protocols.

[0028] In parallel with rainfall monitoring, soil moisture data is collected. This relies on an array of sensors deployed on the ground surface and at varying depths. Sensor types can include frequency domain reflectometry (FDR) or time domain reflectometry (TDR), installed in pre-drilled soil profiles, typically at depths of 10 cm, 30 cm, 50 cm, or even deeper, to capture dynamic changes in soil moisture across the vertical profile. The sensor network also covers different geomorphic units (e.g., top, mid-slope, toe) and land use types (e.g., forest, grassland, cultivated land) within the monitoring area. Each sensor node periodically (e.g., every 15 minutes, every hour) measures the volumetric soil moisture content (%) at its location and transmits the data via a similar communication link. To ensure data representativeness and reliability, repeated measurements are typically performed at multiple points within a specific small area (e.g., a 10 m x 10 m grid), with the average calculated to mitigate the impact of local variation. Preliminary quality control is performed on the data before or during transmission, such as checking whether the sensor readings are within a reasonable range (such as between 0% and saturated moisture content) and marking outliers or missing data caused by communication interruptions.

[0029] Topographic data, particularly slope information, is a key factor in assessing soil loss potential. Slope data can be obtained through various methods. One approach utilizes high-precision digital elevation model (DEM) data, which may be derived from aerial photogrammetry, LiDAR scanning, or satellite stereo image processing. The system imports the DEM data for the area through a GIS interface and uses spatial analysis algorithms (such as the Slope tool in ArcGIS or the open-source GDAL library) to calculate the slope value (degrees or percentage slope) for each raster pixel or specific location within the monitoring area. Another approach, suitable for scenarios requiring higher real-time performance or sensitivity to local terrain changes, involves installing tilt sensors or inertial measurement units (IMUs) at key locations on site. These sensors directly measure the local slope angle at their installation point, providing real-time topographic information. Regardless of the method used, the slope data obtained must be strictly aligned with the geographic coordinate system of the monitoring area to ensure spatial consistency.

[0030] After rainfall, soil moisture, and slope data are collected and transmitted to the data processing unit, the data acquisition module's core processing function, the soil loss prediction model, begins operating. This model is a software algorithm running within the module. Essentially, it is a mathematical function or calculation process that maps input environmental parameters to predicted soil loss rates. The model's implementation can be a modified version of an empirical or semi-empirical formula, such as a locally calibrated version of the Universal Soil Loss Equation (USLE) or its revised version (RUSLE). The model's internal logic typically involves the calculation of several key factors: The rainfall erosivity factor (R factor) is calculated using rainfall intensity and duration, quantifying the potential for rainfall to strip and transport soil; soil moisture data is combined with soil texture information (such as known or indirectly estimated soil clay, silt, and sand content) to estimate or adjust the soil erodibility factor (K factor), as soil moisture directly affects its shear strength and dispersibility; and slope data is used to calculate the topographic factor (LS factor). Steeper slopes are associated with faster water velocities and greater erosion potential. Models may also incorporate mechanistic model components based on physical processes or machine learning models (e.g., random forests, neural networks) trained using historical monitoring data to improve prediction accuracy and adaptability.

[0031] Before running the model, the data processing unit will perform necessary preprocessing on the input data. This includes time alignment (ensuring that rainfall, humidity, and slope data are synchronized in timestamps), spatial interpolation (interpolating point sensor data or raster data to the unified grid required for model calculation), missing data processing (such as filling with nearby point data or time series interpolation), and data standardization (converting data of different dimensions to the input range required by the model). The preprocessed data is fed into the soil loss prediction model. The model executes its internally defined calculation steps, comprehensively considering the erosive power of rainfall, the soil's anti-erosion capacity, and the accelerating effect of terrain on runoff. After a series of calculations, the model finally outputs a quantitative result: the soil loss rate. This rate is usually expressed as the mass of soil lost per unit area per unit time, and the commonly used units are tons per hectare per year (t / ha / yr) or kilograms per square meter per year (kg / m 2 / yr). This output value is an estimate of the soil loss rate that may occur in the monitored area under current or predicted environmental conditions. After the data processing unit completes the calculation, it encapsulates the generated soil loss rate data, together with its corresponding timestamp, spatial location information (if the model output is a spatial distribution map) or regional representative value, into a data packet in a standard format. This data packet is then transmitted to the next link in the system, the loss analysis module, as the basic input for a more in-depth risk analysis. The entire process of data collection, transmission, preprocessing, model calculation and result output is cyclically executed within a set time period (such as every hour or every day) to achieve dynamic monitoring of soil loss conditions. The module also contains a status monitoring mechanism that continuously checks the sensor status, communication link status and model operation log, promptly detects and reports equipment failures or data anomalies, and maintains the continuity and reliability of monitoring activities.

[0032] Example 2: See Figure 3 The core function of the soil loss analysis module is to receive soil loss rate information from the data acquisition module and the spatial extent data of the geographical monitoring area defined by the region setting module. Based on this information and other environmental parameters, it calculates a comprehensive indicator reflecting the overall soil loss status of the region. The implementation of this module involves the coordinated work of multiple data processing steps and calculation logic.

[0033] Upon module startup, the first key input processed is soil erosion rate data. This data is typically received in the form of a spatial distribution map or as a collection of values ​​at specific monitoring points within the region. The erosion analysis module also obtains the precise spatial extent of the geographic monitoring area, including the region's boundary coordinates, total area, and possible subregions (such as subregions of different geomorphic units or land-use types). The module's primary task is to calculate the erosion coefficient. This calculation aims to integrate discrete or continuous soil erosion rate data into a single numerical value or graded indicator that represents the overall erosion intensity for the entire monitoring area. This implementation typically involves spatial analysis techniques. If soil erosion rate data is provided in the form of a spatial distribution map in raster or vector format, the module utilizes the spatial statistics capabilities of a geographic information system (GIS) to calculate statistics such as the average erosion rate for the entire region or a specified subregion, the maximum erosion rate, or the proportion of area where the erosion rate exceeds a certain threshold. These statistics, either on their own or after further transformation (such as normalization), can be used as erosion coefficients. If the input is the soil loss rate value of a discrete monitoring point, the module will use spatial interpolation methods (such as kriging interpolation and inverse distance weighting) to generate a continuous soil loss rate surface based on the spatial location and regional extent of the monitoring point. Regional statistics are then calculated. Alternatively, based on the representativeness and distribution density of the monitoring points, the module directly calculates the weighted average of the soil loss rates of all monitoring points (the weight can be set based on the area represented by the monitoring point or the environmental similarity) as the soil loss coefficient. The calculated soil loss coefficient is a quantitative value that reflects the overall rate of soil loss per unit area in the entire monitoring area under the current environmental conditions.

[0034] Simultaneously with or after calculating the erosion coefficient, the erosion analysis module initiates the risk factor calculation process. This requires obtaining additional environmental parameters: soil type data and vegetation cover data. Soil type data describes the physical and chemical properties of regional soils, which directly impact their ability to resist erosion. This data can be obtained from a variety of sources: a regional soil database established through a pre-existing soil survey, containing information such as soil texture (sand, silt, and clay content), organic matter content, and soil structure; or from specific soil sensors deployed in the region (such as near-infrared spectrometers) that measure key parameters in real time or periodically. Vegetation cover data characterizes the degree to which surface vegetation protects the soil. This data is typically derived from the interpretation of remote sensing imagery (such as the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI)) or from image analysis of vegetation cover percentages obtained through on-site cameras. The module must ensure that the spatial extent of this data is consistent with the geographic monitoring area and perform necessary spatial matching.

[0035] The risk factor calculation logic assesses the inherent erodibility of the soil and the mitigating effect of vegetation on erosion. The module includes a pre-set rule base or algorithmic model for mapping soil type attributes into an erodibility score. For example, a soil with a high clay content generally indicates strong interparticle cohesion and improved erosion resistance, resulting in a lower erodibility score (i.e., a lower risk contribution); whereas a soil with a high sand content or low organic matter content may receive a higher erodibility score. Similarly, vegetation cover data is mapped into a protection effectiveness score: high vegetation cover generally indicates that the canopy effectively intercepts rainfall and the root system consolidates the soil, resulting in a higher protection effectiveness score (i.e., a lower risk contribution); low cover or bare ground results in a lower protection effectiveness score (i.e., a higher risk contribution). Specific mapping rules may be based on empirical relationships, literature values, or local calibration. The module then combines the calculated erodibility and protection effectiveness scores. A common approach is to assign different weights to each (for example, soil erodibility with a weight of 0.7 and vegetation protection with a weight of 0.3, both of which are configurable). These are then weighted together or combined using other mathematical methods (such as a multiplication model) to produce a composite risk factor. This factor quantifies the potential for soil loss relative to other areas under given soil and vegetation cover conditions.

[0036] The final step of the churn analysis module is to combine the churn coefficient and risk coefficient calculated above into a more comprehensive evaluation indicator: churn risk value. The integration process is not a simple addition, but considers the relative importance of the two coefficients in reflecting churn risk. The module has preset weight coefficients, such as churn coefficient weight and risk factor weights ( , the specific value can be configured and adjusted). Before integration, it may be necessary to normalize the churn coefficient and risk factor (such as normalizing to the range of 0-1) to eliminate dimensional differences and facilitate comparison. The standardized churn coefficient ( ) and risk factor ( ) Perform weighted summation according to preset weights: Loss risk value = . The calculated loss risk value is a value between a certain range (such as 0 to 100 or 0 to 1). The higher the value, the greater the risk of severe soil loss currently faced by the geographical monitoring area. This loss risk value combines the actual monitored loss rate (loss coefficient) and the inherent environmental vulnerability of the region (risk coefficient), providing a core basis for subsequent early warning decisions. After the entire calculation process is completed, the loss risk value, together with its component factors (loss coefficient, risk coefficient) and related metadata (calculation time, area identification, weight setting, etc.), is encapsulated and output, and passed to the early warning module for threshold judgment. During operation, the module records detailed log information, including the source and quality of the input data, the intermediate results of the calculation steps, and the final output value, to facilitate subsequent traceability analysis and model tuning.

[0037] Example 3: See Figure 4 The core responsibility of the early warning module is to make corresponding decisions and generate corresponding operation instructions based on the loss risk value output by the loss analysis module. The module maintains one or more loss risk thresholds internally. The setting of these thresholds is flexible. It can be a global default value preset by the system, or it can be manually configured by the user according to the characteristics of the specific monitoring area (such as the main soil type, historical erosion conditions), and can even be dynamically adjusted according to seasonal changes or real-time meteorological warning information, such as using stricter thresholds in the rainy season. The early warning module continuously receives the loss risk value data stream transmitted by the loss analysis module. The core processing logic of the module is to convert the real-time received loss risk value (denoted by the symbol ) and the currently effective churn risk threshold (denoted by The comparison operation follows a simple conditional judgment rule: if , it is determined that the soil loss risk in the current monitoring area has reached or exceeded the preset warning level. In this case, the early warning module will generate an early warning instruction. This early warning instruction is not a single signal, but a structured data package that usually contains the following key information: the precise timestamp that triggered the warning, the specific loss risk value that caused the warning, , the currently used threshold , and according to Beyond The warning level is automatically divided according to the degree of the warning (for example, different levels such as mild warning, moderate warning, and severe warning can be set, and each level corresponds to a different exceeding range). On the contrary, if the comparison result is , indicating that the current risk level is within an acceptable range and no immediate intervention is required. At this point, the early warning module generates a monitoring maintenance instruction. This instruction instructs the system to maintain the existing data collection, transmission, and analysis rhythm and continue normal monitoring. The generated instruction (whether it is an early warning instruction or a monitoring maintenance instruction) is immediately transmitted to other relevant modules of the system, such as the cloud interaction module (in the case of an early warning instruction) or the user interface module for status display.

[0038] The monitoring point generation module operates independently or in conjunction with the early warning module. Its core function is to optimize the spatial deployment strategy of monitoring sensors to improve monitoring efficiency and coverage of key areas. The module first needs to obtain environmental background data that defines the geographical monitoring area, especially temperature range data and humidity range data obtained from historical records or real-time monitoring. The temperature range usually refers to the lowest temperature (the lowest temperature observed in the area within a specific time period (such as the past year, a quarter, or estimated based on real-time data)). ) and the maximum temperature ( ), the humidity range refers to the minimum humidity observed ( ) and maximum humidity ( ). These range data define the upper and lower boundaries of the environmental conditions in the area. Based on the temperature range obtained and humidity range , the monitoring point generation module performs a partitioning operation. It divides the continuous temperature range into several discrete temperature intervals (for example, into low temperature intervals: ,Medium temperature range: ,High temperature range: ,in and is the dividing point). Similarly, the continuous humidity range is divided into several discrete humidity intervals (for example, the dry interval: ,Medium humidity range: ,Wet zone: ,in and is the dividing point). In this way, the entire environmental space is divided into multiple rectangular (or shapes determined by the division rules) environmental intervals, each of which is uniquely determined by a specific temperature sub-range and humidity sub-range.

[0039] The module needs to assign a monitoring point density value to each environmental interval divided. This relies on a built-in environmental sensitivity model. The model encapsulates the knowledge or empirical rules about the sensitivity of soil loss to different temperature and humidity combinations. The basic principle of the model is that some temperature and humidity combinations may significantly increase the risk of soil loss (for example, high temperature and high humidity conditions accelerate the decomposition of soil organic matter and microbial activity, which may reduce the stability of soil aggregates; freeze-thaw cycles or sudden heavy rains after droughts are also highly sensitive scenarios), while other combinations are relatively stable (for example, continuous low temperature and dry conditions). The model internally defines a sensitivity level (such as low, medium, high) or a continuous sensitivity score ( Then, according to this sensitivity level or score, a specific monitoring point density value is assigned to each environmental interval through a preset mapping relationship ( ). This mapping relationship can be expressed by a function: or ,in and The temperature and humidity conditions of the representative interval (or its representative value, such as the midpoint of the interval), represents the sensitivity score of the interval, or This is a function that defines the density allocation rule. For example, a high-sensitivity area might be assigned a higher density (e.g., 10 monitoring points per square kilometer), a medium-sensitivity area might be assigned a medium density (e.g., 5 points per square kilometer), and a low-sensitivity area might be assigned a lower density (e.g., 2 points per square kilometer). The specific function form or mapping table is set in the system configuration.

[0040] Obtain the density values ​​of monitoring points corresponding to all environmental intervals After that, the monitoring point generation module starts the spatial distribution calculation. It combines the digital map of the geographical monitoring area (usually in raster or vector format) to classify the entire area according to the temperature range and humidity range to which each location point (or grid unit) belongs. Since temperature and humidity usually change continuously in space, the module may need to use spatial interpolation technology (such as Kriging interpolation) to generate a continuous surface covering the entire area, representing the current or typical temperature and humidity conditions, and then classify each location point into a specific environmental interval according to the division rules. Once each location point in the area is associated with an environmental interval, the density of monitoring points corresponding to that interval is also associated. The point distribution algorithm (such as a spatial Poisson process or optimization algorithm) will be based on the density required for each location point. , while satisfying overall density constraints, a set of monitoring point location coordinates is calculated. These locations should be distributed as evenly as possible within their respective environmental zones, while also taking into account deployment feasibility (e.g., avoiding inaccessible areas such as buildings and water bodies). Finally, the module outputs a monitoring point distribution map. This map clearly identifies the specific geographic coordinates of the recommended monitoring sensors (e.g., rain gauges and soil moisture sensors) and may include information about the environmental zones and assigned density for each point. This map provides a direct, environmentally sensitive optimization solution for field deployment or adjustments to the monitoring network. The monitoring point generation module can be run periodically (e.g., quarterly updates based on seasonal changes) or triggered by specific events (e.g., when monitoring optimization is required after an early warning instruction is generated). The generated monitoring point distribution map can be used by the adjustment module for subsequent dynamic adjustments and can also be output as part of the results report.

[0041] Example 4: The cloud interaction module is activated when the warning module generates a warning instruction, starting a set of targeted secure communication and verification processes. The following uses a specific application scenario: a high-risk soil loss warning at a soil and water conservation monitoring station in a mountainous area to explain the implementation of this module in detail.

[0042] When the early warning module determines that the risk of loss in a monitored area exceeds the threshold and generates an early warning instruction, the instruction is immediately transmitted to the cloud interaction module. The module first performs two key operations: first, it triggers a device verification request process, which is marked as high priority; second, it immediately locks data write permissions on the local system, temporarily prohibiting any modification, deletion, or overwriting of locally stored monitoring data, configuration parameters, or analysis results. At this point, the local system is in a read-only state, preventing unauthorized data tampering during subsequent verification processes. The lock operation records a detailed log, including the lock time, operator (automatic by the system), and lock scope.

[0043] The cloud interaction module establishes a secure connection with the cloud server. It uses preconfigured encryption protocols (such as TLS 1.3) and authentication certificates to establish an encrypted communication channel between the local system and the designated cloud server. Through this secure channel, the module uploads a set of structured device context data and a verification question feature to the cloud server. The device context data includes: the device's unique hardware identifier (such as MAC address or serial number), the currently running software version number, the timestamp of the last successful verification, the current system status (such as CPU load, memory usage), and a summary of the warning instructions that triggered this interaction (such as warning level, risk value, and region ID). The verification question feature is designed to enhance the uniqueness of the verification. It may be a challenge code (ChallengeCode) dynamically generated based on the device's current state and a preset key, or an encrypted digest (Digest) of the device's pre-stored certificate. The uploaded data packet is encapsulated in a specific format (such as JSON or Protocol Buffers).

[0044] After receiving the uploaded data, the cloud server starts the verification and analysis process. The verification engine on the server side first verifies the validity of the device certificate, checks whether the hardware identifier is registered in the legal device library, and whether the software version is within the supported range. Then, the engine combines the received device context (such as whether the system status is abnormal, the time interval from the last verification) and the severity of the warning instruction (such as severe warning) to evaluate the potential risk level of this connection request (such as low risk, medium risk, high risk). Based on this risk assessment result, the verification engine generates a multi-level verification strategy. The strategy defines the number of levels that need to be verified, the specific verification method for each level, and the level of permission granted after passing. The strategy is encapsulated as an instruction and sent to the cloud interaction module of the local system.

[0045] The data verification module then loads the received multi-level verification policy. This module is responsible for performing security checks on external devices connected to the local system. The verification process is strictly carried out in the order of the layers defined in the policy.

[0046] Table 1: Example of multi-level verification strategy execution.

[0047]

[0048] Referring to Table 1, when a staff member attempts to insert a USB data exporter into a monitoring station host that is in the warning lock state, the data verification module starts the verification: Level 1 Verification: The module reads the digital certificate embedded in the USB device. This certificate is issued by the monitoring station management authority. The module verifies the certificate's cryptographic signature to confirm that it was issued by a trusted Certificate Authority (CA) and has not been revoked. It also checks the certificate's subject information to confirm that it was issued for a device such as a "field data exporter" and not another type of device (such as a debugging terminal). If the verification passes, the USB device is granted basic read-only access, allowing personnel to view the system's current real-time monitoring data, such as rainfall, soil moisture, and loss risk. However, the module cannot copy or modify any files.

[0049] Level 2 verification (when necessary): If the staff needs to export historical data reports for analysis, they need to apply for higher permissions. The system performs Level 2 verification according to policy requirements: the data verification module generates a 6-digit random number (such as "429517") and sends a text message through the mobile phone number bound to the staff account. The staff enters the received SMS password in the verification window that pops up in the system. The module verifies whether the input number is exactly the same as the sent number, and checks whether the time interval from sending to input is within the validity period of 2 minutes. If passed, the USB device permission is upgraded to restricted write permission, allowing the staff to export monitoring data reports for a specified time period (such as the past 24 hours) to the USB device, but still cannot access system configuration files or delete data.

[0050] Level 3 verification (when needed): In extreme cases, if critical configurations need to be modified on-site to deal with emergencies, Level 3 verification is required. The staff member chooses to apply for full access rights on the system interface. The system requires them to use a registered fingerprint for verification. The staff member presses his finger on the fingerprint reader connected to the host. The data verification module captures the fingerprint image, extracts feature points, and compares it with the fingerprint template pre-registered by the staff member and stored in the system's secure encryption area. If the calculated matching score exceeds the preset threshold (such as 95% similarity), the verification passes. The USB device (or the operation session) is granted full access rights, allowing all operations including parameter adjustment and data deletion.

[0051] The data verification module records and manages the status based on the verification results (pass or fail) of each level. If a level fails to verify, the module will record the reason for failure (such as invalid certificate, wrong password, fingerprint mismatch) and decide whether to allow a retry or directly terminate the verification process based on the policy. Once all required levels are verified, the module will ultimately grant the device (or operation session) the corresponding access permission level (such as read-only, restricted write, full access) based on the policy. At the same time, if the verification is successful and the granted permission level allows write operations, the data verification module will notify the cloud interaction module to release the previously locked data write permission and restore the normal read and write functions of the system. Detailed logs of the entire verification process, including the triggered policy, the execution level, verification results, granted permissions, and operation timestamps, are all recorded for audit purposes. This hierarchical verification mechanism effectively balances data security protection and necessary operational flexibility in an early warning state.

[0052] Example 5: The core function of the adjustment module is to dynamically optimize the spatial layout of the monitoring network. Its operation depends on the initial monitoring point distribution map provided by the monitoring point generation module and the soil loss rate data continuously updated by the data acquisition module. After the module is started, it first receives and parses the input monitoring point distribution map. The distribution map is usually in a vector or raster format supported by the geographic information system, and clearly marks the set of geographical location coordinates of the currently recommended monitoring sensors (such as rain gauges, soil moisture probes). Each location point may be associated with additional information, such as the recommended sensor type or the environmental sensitivity range to which it belongs. At the same time, the adjustment module accesses the real-time or near real-time soil loss rate data stream from the data acquisition module. The data stream contains spatial information, which may be expressed as a loss rate contour map covering the monitoring area, a raster surface map, or rate measurements at discrete monitoring point locations and their timestamps.

[0053] The module's core processing logic is to compare and analyze the spatial pattern of soil erosion rates with the distribution of monitoring points. Its goal is to identify any spatial mismatches between the current monitoring network deployment and actual soil erosion hotspots. The conditioning module applies spatial analysis algorithms to the soil erosion rate data. If the input is a continuous erosion rate surface (raster or contour), the module calculates statistical characteristics of the surface, such as the spatial mean and standard deviation, and identifies local areas with erosion rates significantly above the regional mean (hotspots) and significantly below the mean (coldspots). This identification method may involve setting relative thresholds (e.g., above the mean plus one standard deviation for hotspots, below the mean minus one standard deviation for coldspots) or using clustering algorithms to automatically detect spatially clustered high-value areas. If the input is erosion rate values ​​from discrete monitoring points, the module first performs spatial interpolation to generate a continuous erosion rate estimate surface before performing the aforementioned analysis. The spatial extent, intensity (the degree to which the mean erosion rate exceeds the background value), and boundaries of hotspots and coldspots are accurately extracted.

[0054] After identifying hotspots and coldspots, the adjustment module overlays these with the monitoring point distribution map for analysis. The module calculates the density of existing monitoring points within these hotspots and coldspots and compares it to the regional average density or a preset desired density. For example, the module may find that a newly emerging hotspot with high soil loss rates has a sparse distribution of monitoring points, while a coldspot with a long-term stable low soil loss rate has a relatively high density of monitoring points. This spatial mismatch suggests room for optimization and adjustment of monitoring resources. Based on this analysis, the adjustment module initiates the calculation of monitoring point relocations. The adjustment strategy follows the principle of strengthening monitoring capacity in hotspots where soil loss is active or the risk is suddenly increasing, and appropriately reducing monitoring density in coldspots where loss is stable or the risk is lower. The specific adjustment algorithm may be based on spatial optimization theory. One approach is to calculate the distance between existing monitoring points and the geometric center of the hotspot and relocate monitoring points that are too far from the center of the hotspot (especially if they are currently located in a coldspot) to locations closer to the hotspot, while also considering the feasibility and cost of relocation (for example, the relocation distance should not be too far). Another approach involves calculating the number of monitoring points a hotspot "deserves" based on its area and the intensity of its erosion rate. This is then compared to the actual number. If the number is insufficient, new virtual monitoring points are added to the area or relocated from neighboring areas. Conversely, for cold spots, if the number of monitoring points is too dense, some are selectively removed or relocated to areas where they are more needed. This adjustment process also needs to consider geographic constraints, such as avoiding placing monitoring points in areas where they cannot be deployed (e.g., near water bodies, cliffs, buildings) or in locations that are difficult to reach.

[0055] After completing the position adjustment calculations, the adjustment module generates an updated monitoring point distribution map. This map retains the basic format and attribute information of the original distribution map, but the set of monitoring point location coordinates has been modified according to the optimization logic described above: some points have been moved to new locations, a small number of points may be added (if the algorithm allows and sufficient resources are assumed), and individual points may be removed (if the algorithm allows). The new distribution map reflects the recommended monitoring network layout optimized based on the latest spatial characteristics of soil loss rates. This map is output and passed to the result generation module.

[0056] The Results Generation Module, the final step in the system's information output, integrates key results from multiple modules to produce a structured monitoring report. This module receives and integrates three core inputs: the current churn risk value (a quantitative, comprehensive risk indicator) calculated by the Churn Analysis Module; the instructions generated by the Early Warning Module based on this risk value (including information such as levels and trigger values, or maintenance instructions); and the updated monitoring point distribution map output by the Adjustment Module. The module includes a pre-configured report template that defines the report's structure, sections, data presentation, and visual element styles.

[0057] The result generation module organizes data according to the template. The loss risk value is usually placed in a prominent position in the report and may be displayed in numerical form, supplemented by risk level labels (such as "low", "medium", "high", "extremely high") and trend indicators (such as whether it has increased, decreased or remained the same compared to the last report). After the early warning instructions or monitoring maintenance instructions are parsed, their core information (such as "red warning has been triggered", "the system maintains routine monitoring") is extracted and presented in a striking manner. The updated monitoring point distribution map is converted into a format suitable for embedding in the report (such as a PNG image or SVG vector image). The map clearly shows the optimized and adjusted monitoring point locations, and different symbols or colors may be used to distinguish between the original unchanged points, migrated points and newly added points (if any). The map is usually accompanied by a legend and a brief description.

[0058] In addition to these three core inputs, the report template may also include interfaces for integrating other relevant information, such as average values ​​of key parameters reported by the data collection module (e.g., average rainfall, average soil moisture), basic information about the monitoring area (name, area, geographic location), and the report generation time. The result generation module populates this information into the corresponding fields in the template. For key indicators such as soil loss risk, the module may generate simple trend charts (e.g., a 24-hour or 7-day risk change curve) if historical data is available. After all text descriptions, numerical values, and charts are combined in the template, the module performs final formatting and rendering to generate a complete soil loss monitoring report document. This document can be output in common file formats such as PDF, DOCX, or HTML, making it easy for users to view and print it or upload it to a server for archiving or further distribution via the cloud-based interactive module. After the report is generated, the system may notify relevant personnel of the report's availability through pre-defined communication channels (e.g., email or push notification). The entire report generation process is logged, including information such as the data source, generation time, and template version used. This report provides on-site managers or remote decision makers with a comprehensive summary of the current soil loss risk status, system response actions (early warnings), and recommendations for optimizing the monitoring network.

[0059] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0060] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A soil and water conservation soil loss data monitoring system, characterized in that: It includes area setting module, data collection module, loss analysis module, early warning module and cloud interaction module; The area setting module is used to set a geographical monitoring area with the monitoring site as the center; The data acquisition module is used to obtain soil loss related data in the geographical monitoring area; The loss analysis module is used to calculate the loss coefficient and risk coefficient based on the soil loss related data; The early warning module is used to generate an early warning instruction according to the loss coefficient and the risk coefficient; The cloud interaction module is used to establish a communication channel with the cloud server when the early warning instruction is generated.

2. A soil and water conservation soil loss data monitoring system according to claim 1, characterized in that: The data acquisition module is specifically used to: obtain rainfall data, soil moisture data and slope data in the geographical monitoring area; The rainfall data, soil moisture data and slope data are processed by a soil loss prediction model to output a soil loss rate.

3. A soil and water conservation soil loss data monitoring system according to claim 2, characterized in that: The loss analysis module is specifically used to: obtain the soil loss rate and the spatial range of the geographical monitoring area; Calculating a loss coefficient based on the soil loss rate and the spatial extent of the geographic monitoring area; Obtain soil type data and vegetation cover data; Calculating a risk factor using the soil type data and vegetation cover data; The churn coefficient and the risk coefficient are weighted and summed to obtain a churn risk value.

4. A soil and water conservation soil loss data monitoring system according to claim 3, characterized in that: The early warning module is specifically used to: set a loss risk threshold; comparing the churn risk value with the churn risk threshold; When the loss risk value is greater than or equal to the loss risk threshold, generating an early warning instruction; When the loss risk value is less than the loss risk threshold, a monitoring maintenance instruction is generated.

5. A soil and water conservation soil loss data monitoring system according to claim 4, characterized in that: It also includes a monitoring point generation module; The monitoring point generation module is used to: obtain the temperature range and humidity range of the geographical monitoring area; Dividing the temperature range and humidity range into a plurality of environmental intervals; The density of monitoring points is assigned to each environmental interval through the environmental sensitivity model; A distribution map of monitoring points is generated within the geographic monitoring area.

6. A soil and water conservation soil loss data monitoring system according to claim 5, characterized in that: The cloud interaction module is specifically configured to: trigger a device verification request when generating the warning instruction; Lock local data write permissions; Upload the device context and verification question features to the cloud server through an encrypted channel; Receive the multi-level verification strategy issued by the cloud server.

7. A soil and water conservation soil loss data monitoring system according to claim 6, characterized in that: Also includes data validation module; The data verification module is used to: load the multi-level verification strategy; Conduct risk validation on inserted devices at different levels; Access rights are granted based on the risk level validation results.

8. The soil and water conservation soil loss data monitoring system according to claim 7, characterized in that: Also included is a regulation module; The adjustment module is used to: obtain the monitoring point distribution map and the soil loss rate; adjusting the monitoring point positions in the monitoring point distribution map according to the soil loss rate; Output the updated monitoring point distribution map.

9. The soil and water conservation soil loss data monitoring system according to claim 8, characterized in that: Also included is a result generation module; The result generation module is used to: integrate the loss risk value, early warning instructions and the updated monitoring point distribution map; Generate soil loss monitoring reports.

10. A soil and water conservation soil loss data monitoring method, characterized in that: The invention comprises all modules and method processes of the soil and water conservation and soil loss data monitoring system as described in any one of claims 1 to 9.

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