Farmland water erosion monitoring management method and system based on multi-source data analysis

Abstract

The invention is suitable for the technical field of farmland water and soil conservation and remote sensing monitoring, and provides a farmland water erosion monitoring management method and system based on multi-source data analysis, and the method comprises the steps: obtaining an initial water erosion grade value of a target slope region after determining that the target slope region belongs to an agricultural machinery disturbance hot region, and a historical database of the affiliated farmland area. According to the invention, a multi-source data analysis system fusing remote sensing images, water erosion patch evolution and agricultural machinery disturbance information is constructed, and a water erosion monitoring method based on sudden change interval identification-sudden change degree value calculation-grade value dynamic correction is provided. The technical bottlenecks that traditional water erosion monitoring is insufficient in interference response to agricultural machinery, level evaluation is static and rigid and the like are broken through.

Description

Technical Field

[0001] This invention belongs to the field of farmland soil and water conservation and remote sensing monitoring technology, and in particular relates to a farmland water erosion monitoring and management method and system based on multi-source data analysis. Background Technology

[0002] In farmland production management, slope areas, due to their complex topography and susceptibility to soil and water loss, have long been a key area for farmland water erosion control. With the improvement of agricultural mechanization, frequent operation of agricultural machinery in slope areas brings high-intensity disturbance, making the soil structure in these areas more susceptible to damage, thus inducing nonlinear and sudden water erosion expansion phenomena. To achieve scientific management and resource allocation, it is urgent to build an efficient and accurate water erosion monitoring mechanism. In existing technologies, mainstream water erosion monitoring methods mostly rely on single data sources, such as remote sensing images, ground surveys, or watershed model outputs. They typically use fixed evaluation indicators and static classification rules, making it difficult to capture the complex evolution process induced by disturbance. Especially in areas like slopes where disturbance response behavior is highly coupled, there is a significant lack of adaptability.

[0003] Furthermore, although some technical solutions have attempted to incorporate time-series remote sensing images or perturbation factor analysis based on empirical models, three significant limitations remain in practical applications: First, they fail to effectively quantify the dynamic impact of agricultural machinery disturbance on water erosion response; second, they lack a systematic identification mechanism for abnormal expansion behavior, making it impossible to accurately extract key response signals from evolutionary trends; and third, the water erosion level assessment results are statically fixed, unable to be dynamically corrected according to different perturbation situations. These shortcomings result in low sensitivity and distorted diagnostic results in water erosion identification in agricultural machinery disturbance hotspots, hindering the scientific deployment of soil and water conservation measures and the accurate matching of response strategies. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for monitoring and managing farmland water erosion based on multi-source data analysis, in order to solve the problems mentioned in the background art.

[0005] This invention is implemented as follows: a method for monitoring and managing farmland water erosion based on multi-source data analysis, the method comprising: After determining that the target slope area belongs to the agricultural machinery disturbance hot zone, the initial water erosion level value of the target slope area and the historical database of the corresponding farmland area are obtained. Based on historical databases, the starting time of the appearance of specific water erosion patches in the target slope area is determined, and the trend of the expansion increment of specific water erosion patches is determined from the starting time. Identify abrupt change intervals in the trend of change, count the number of all abrupt change intervals and their corresponding expansion increments, and calculate the current degree of change in the target slope area. The abrupt change interval is a local abnormal interval in the trend of change that shows an abnormal rise and then falls back. Based on the historical database, reference slope areas with the same background characteristics as the target slope area and whose water erosion level has been consistently lower than the preset level for several historical monitoring periods are selected, and the reference abrupt change value of the reference slope area is obtained. The deviation between the current mutation level value and the reference mutation level value is compared and used as a correction factor. The initial water erosion level value is then corrected using the correction factor.

[0006] As a further limitation of the technical solution of the present invention, the agricultural machinery disturbance hot zone refers to the area in the slope area where there are traces of agricultural machinery operation and the disturbance heat exceeds a preset threshold.

[0007] As a further limitation of the technical solution of the present invention, the specific water erosion patch refers to a water erosion patch that matches at least one type in a preset water erosion patch type set. The preset water erosion patch type is used to characterize the typical water erosion morphology that is prone to occur in the slope area under agricultural machinery disturbance.

[0008] As a further limitation of the technical solution of this invention, the step of determining the starting time of the appearance of a specific water erosion patch in the target slope area based on a historical database, and determining the trend of the expansion increment of the specific water erosion patch from the starting time, includes: Historical remote sensing image data of the target slope area are extracted from the historical database, and the image data is processed to identify the starting time of the appearance of specific water erosion patches in the target slope area. The time range from the start time to the current time is divided into several time periods; Image processing techniques based on image segmentation, change detection, or patch tracking are used to calculate the area expansion increment of a specific water erosion patch in each time period compared to the previous time period. The trend of the expansion increment of a specific water erosion patch over time is constructed according to the time sequence.

[0009] As a further limitation of the technical solution of the present invention, the mutation interval is a local abnormal interval in the trend of expansion increment change where an abnormal rise occurs and then falls back. Specifically, it refers to the following: in the rising segment of the mutation interval, the slope of the expansion increment change over a number of consecutive time periods increases by more than a preset value compared to the average slope of the previous preset time period, and the expansion increment at the end of the falling segment of the mutation interval decreases to within the tolerance range of the average value of the expansion increment in the previous preset time period.

[0010] As a further limitation of the technical solution of the present invention, the same background features refer to the following: the reference slope area and the target slope area belong to the same agricultural machinery disturbance hot zone, and the disturbance frequencies of the two are within the same preset frequency range. The time when the specific water erosion patch first appears is within the preset time range, and they have consistent features in terms of slope area, slope grade, soil type and climate conditions.

[0011] As a further limitation of the technical solution of this embodiment of the invention, the step of comparing the deviation between the current mutation degree value and the reference mutation degree value as a correction factor, and using the correction factor to correct the initial water erosion level value includes: Quantify the deviation between the current mutation severity value and the reference mutation severity value, and use it as a correction factor; The preset correction function is retrieved and the initial water erosion level value is corrected by combining the correction factor to obtain the corrected water erosion level value. The revised water erosion level values ​​will be applied to the determination of farmland water erosion level, the prioritization of treatment, or the generation of soil and water conservation response strategies.

[0012] As a further limitation of the technical solution of this embodiment of the invention, the correction function is: ; in, This refers to the revised water erosion rating. This refers to the initial water erosion level value. This refers to the current mutation level value. This refers to the reference mutation level value. This refers to the correction factor, which is the deviation between the current mutation level value and the reference mutation level value. This refers to the control amplitude coefficient, and it satisfies... .

[0013] A farmland water erosion monitoring and management system based on multi-source data analysis, the system comprising: The data acquisition module is used to obtain the initial water erosion level value of the target slope area and the historical database of the corresponding farmland area after determining that the target slope area belongs to the agricultural machinery disturbance hot zone. The trend extraction module is used to determine the starting time of the appearance of a specific water erosion patch in the target slope area based on the historical database, and to determine the expansion increment trend of the specific water erosion patch from the starting time. The mutation identification module is used to identify mutation intervals that appear in the trend of change, count the number of all mutation intervals and their corresponding expansion increments, and calculate the current mutation degree value of the target slope area. The mutation interval is a local abnormal interval in the trend of change that shows an abnormal rise and then falls back. The reference selection module is used to select reference slope areas with the same background characteristics as the target slope area based on the historical database, and whose water erosion level has been continuously lower than the preset level for several historical monitoring periods, and to obtain the reference change degree value of the reference slope area. The grade correction module is used to compare the deviation between the current mutation degree value and the reference mutation degree value, and use the correction factor to correct the initial water erosion grade value.

[0014] As a further limitation of the technical solution of the present invention, the agricultural machinery disturbance hot zone refers to the area in the slope area where there are traces of agricultural machinery operation and the disturbance heat exceeds a preset threshold.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This invention proposes a water erosion monitoring method based on "abrupt change interval identification—abrupt change degree value calculation—dynamic correction of grade value" by constructing a multi-source data analysis system integrating remote sensing images, water erosion patch evolution, and agricultural machinery disturbance information. This method overcomes the technical bottlenecks of traditional water erosion monitoring, such as insufficient response to agricultural machinery disturbance and static rigidity in grade assessment. The proposed abrupt change degree value index can objectively quantify abnormal water erosion behavior on the side of slopes under different agricultural machinery disturbance conditions, and dynamically adjust the water erosion grade accordingly, significantly improving the sensitivity and accuracy of monitoring results in responding to actual disturbance differences. This method does not rely on manual on-site annotation, has strong adaptability and a high degree of automation, and is particularly suitable for high-frequency monitoring and soil and water conservation decision support in farmland slope areas, showing promising engineering application prospects. Attached Figure Description

[0016] Figure 1 A flowchart of the method provided in the embodiments of the present invention; Figure 2 This is a flowchart illustrating the extraction of the incremental trend of specific water erosion patches in the method provided in this embodiment of the invention. Figure 3 This is a flowchart illustrating the water erosion level correction process in the method provided in this embodiment of the invention. Figure 4 The application architecture diagram of the system provided in the embodiments of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] Figure 1 A flowchart of the method provided by an embodiment of the present invention is shown.

[0019] Specifically, a method for monitoring and managing farmland water erosion based on multi-source data analysis includes the following steps: Step S100: After determining that the target slope area belongs to the agricultural machinery disturbance hot zone, obtain the initial water erosion level value of the target slope area and the historical database of the farmland area to which it belongs.

[0020] The agricultural machinery disturbance hot zone refers to the area on the slope where there are traces of agricultural machinery operation and the disturbance heat exceeds a preset threshold.

[0021] In this embodiment of the invention, in farmland topography, slope areas typically refer to terrain transition zones with a certain slope located at or within farmland boundaries, characterized by undulating terrain. Due to the combined effects of gravity and water flow, these slopes are highly susceptible to surface runoff under natural rainfall or irrigation conditions, leading to water erosion patches on their sides. Especially during agricultural production, slope areas are frequently compacted by agricultural machinery or used as transit routes, disturbing their original soil structure and exacerbating the risk of water erosion expansion. Therefore, the sides of slope areas exhibit significant vulnerability and sensitivity in soil and water conservation and agricultural environmental protection, making them a key area of ​​focus and monitoring in this invention.

[0022] The agricultural machinery disturbance hotspot refers to a spatial area on a slope where there are obvious traces of agricultural machinery operation and where high-intensity agricultural activities have been ongoing for a long time. Agricultural machinery operation causes soil compaction, porosity destruction, and reduced water infiltration capacity, thereby exacerbating surface runoff formation and sediment erosion. This invention integrates multi-source data such as remote sensing images and agricultural machinery trajectory data to construct a disturbance heat evaluation model based on comprehensive factors such as disturbance frequency, heat index, and path density. When the disturbance heat of a region exceeds a preset threshold, it is determined to belong to an agricultural machinery disturbance hotspot. This disturbance heat evaluation model can be implemented by combining existing remote sensing identification algorithms and agricultural machinery trajectory monitoring technology, which is a relatively mature application method in the existing technology.

[0023] Water erosion severity ratings are used to quantify the degree of soil erosion on farmland surfaces, often expressed in a rating system (e.g., mild, moderate, severe), and are typically calculated based on factors such as the area, depth, and duration of water erosion. In existing technologies, water erosion severity ratings are widely used in ecological environment monitoring, agricultural protection zone delineation, and the formulation of soil and water conservation measures, and are considered mature application models. However, existing ratings are usually generated based on static models, making it difficult to dynamically reflect changes in soil structure caused by implicit variables such as agricultural machinery disturbance, leading to biases and delays in monitoring results.

[0024] Therefore, the core technical problem of this invention is that in farmland slope areas, the disturbance mode and intensity of agricultural machinery operations in disturbance hot zones are uncertain. For example, different types of agricultural machinery have different wheel pressures, traffic patterns, operating states, and load conditions, and the soil's response to disturbance also varies depending on factors such as topography, soil type, and moisture, making it difficult to predict the degree of damage to soil structure. Existing technologies often fail to accurately characterize the implicit impact of these disturbances on water erosion expansion, thus affecting the accuracy and timeliness of water erosion level assessment.

[0025] A historical database refers to a collection of multi-source time-series information accumulated at the regional farmland level, including remote sensing images, agricultural machinery operation records, water erosion monitoring data, and archives of soil and water conservation measures. This database can originate from the following sources: Multi-phase remote sensing image platforms (such as Sentinel, Landsat, etc.); agricultural machinery navigation / operation trajectory acquisition systems; ground monitoring stations or UAV patrol platforms; historical water erosion level assessment reports or land use data, etc.

[0026] Furthermore, the farmland water erosion monitoring and management method based on multi-source data analysis also includes the following steps: Step S200: Based on the historical database, determine the starting time of the appearance of specific water erosion patches in the target slope area, and determine the trend of the expansion increment of the specific water erosion patches from the starting time.

[0027] The specific water erosion patch refers to a water erosion patch that matches at least one type in a preset set of water erosion patch types. The preset water erosion patch types are used to characterize typical water erosion patterns that are prone to occur in slope areas under agricultural machinery disturbance.

[0028] Specifically, Figure 2 A flowchart illustrating the extraction process for the incremental trend of specific water erosion patches is shown.

[0029] The process of determining the starting time of specific water erosion patches appearing in the target slope area based on historical databases, and then determining the trend of the expansion increment of specific water erosion patches from that starting time, specifically includes the following steps: Step S201: Extract historical remote sensing image data of the target slope area from the historical database, process the image data, and identify the starting time of the appearance of specific water erosion patches in the target slope area. Step S202: Divide the time range from the start time to the current time into several time periods; Step S203: Based on image processing techniques such as image segmentation, change detection, or patch tracking, calculate the area expansion increment of a specific water erosion patch in each time period compared to the previous time period; Step S204: Construct the trend of the expansion increment of a specific water erosion patch over time in chronological order.

[0030] In this embodiment of the invention, the "preset set of water erosion patch types" is constructed based on existing soil and water conservation research results and actual farmland observation experience. It covers a variety of typical water erosion morphologies commonly found in slope areas under agricultural machinery disturbance, such as shallow gully erosion, sheet erosion, and fine gully erosion. This set is derived from long-term remote sensing monitoring, soil erosion surveys, and field investigation data in different regions. It has clear classification standards and a rich practical foundation, and represents mature existing knowledge in the field of water erosion monitoring, possessing broad applicability and scientific validity.

[0031] The "starting moment" refers to the time point at which a specific water erosion patch conforming to a preset type first appears in the target slope area. This moment marks the explicit manifestation of water erosion activity in the monitoring area and serves as an important reference starting point for subsequent analysis of patch expansion dynamics, helping to accurately define the timeline of the occurrence and evolution of the water erosion process.

[0032] In step S201, remote sensing image data of the target slope area is extracted from the historical database and combined with image preprocessing techniques (such as radiometric correction, atmospheric correction, image registration, etc.) to improve image quality and comparability. Subsequently, image recognition algorithms (such as deep learning-based image segmentation models or traditional threshold segmentation and edge detection methods) are applied to identify specific types of water erosion patches and determine the time of their first appearance.

[0033] In step S202, the time range from the start time to the current time is divided into continuous time periods. These divisions can be based on the update frequency of the remote sensing images (e.g., monthly, weekly, or higher) to ensure sufficient temporal resolution. Since the remote sensing image update cycle is relatively short and the monitoring period typically spans many years, a large number of time periods are obtained, which can more meticulously depict the dynamic evolution of water erosion patches, thus providing more accurate basic data support for subsequent trend extraction and abrupt change identification.

[0034] In step S203, image processing techniques such as change detection, object tracking, or patch boundary extraction are used to match water erosion patches within consecutive time periods and calculate the area change to obtain the expansion increment for each time period. Commonly used methods, such as those based on temporal NDVI differences, object-oriented classification, and region growing, are all conventional techniques in existing remote sensing analysis and have practical application capabilities.

[0035] In step S204, a trend curve of the expansion increment changing with time is constructed according to the time series, forming a complete expansion dynamic trajectory.

[0036] The extended incremental change trend obtained by this invention can be used to characterize the spatiotemporal evolution of specific water erosion patches in the target slope area, indirectly reflecting the characteristics of soil structure stability, hydrodynamic disturbance and degree of human influence in the area. It is an important basic data for subsequent identification of abrupt change intervals and correction of water erosion level.

[0037] Furthermore, the farmland water erosion monitoring and management method based on multi-source data analysis also includes the following steps: Step S300: Identify abrupt change intervals in the trend of change, count the number of all abrupt change intervals and their corresponding expansion increments, and calculate the current degree of change in the target slope area. The abrupt change interval is a local abnormal interval in the trend of change that shows an abnormal rise followed by a fall.

[0038] The mutation interval is a local abnormal interval in the trend of expansion increment change where an abnormal rise occurs and then falls back. Specifically, it refers to the following: in the rising segment of the mutation interval, the slope of the expansion increment changes over a number of consecutive time periods increases by more than a preset value compared to the average slope of the previous preset time period, and the expansion increment at the end of the falling segment of the mutation interval decreases to within the tolerance range of the average value of the expansion increment in the previous preset time period.

[0039] In this invention, the core purpose of identifying "abrupt change intervals" is to address the problem of water erosion assessment bias caused by the uncertainty of disturbance intensity in agricultural machinery disturbance hotspots. In actual agricultural activities, the paths, methods, and loads of agricultural machinery operations on slopes vary significantly, leading to drastic changes in soil structure at different times due to sudden changes in disturbance intensity. These changes are difficult to accurately capture directly using existing remote sensing images or traditional water erosion models. Therefore, this invention introduces "abrupt change intervals" as a mediating variable to quantitatively reflect signs of soil damage by exploring the dynamic abnormal behavior during the expansion of water erosion patches, thereby more objectively correcting water erosion grade values.

[0040] The aforementioned abrupt change interval refers to a local abnormal interval in the trend of expansion increment changes where an "abnormal rise followed by a fall" occurs. More specifically, in the trend curve, if the expansion increment continues to rise significantly within a certain period of time, and the slope of this rise is significantly higher than the average slope of the previous preset period (i.e., exceeding the preset slope threshold), it indicates that the area may be subject to strong disturbances (such as high-intensity agricultural machinery rolling) in a short period of time, leading to a rapid increase in water erosion; and when the expansion increment subsequently gradually decreases and eventually falls back to the tolerance range of the average expansion increment of the previous stage, it can be defined as a complete abrupt change interval.

[0041] The essence of this definition is to identify the nonlinear expansion pattern of "sudden exacerbation - phased mitigation". The rising phase reflects the rapid response process caused by the disturbance, while the falling phase reflects the ecological restoration trend of the regional soil structure gradually stabilizing the water erosion process under the conditions of weakening or termination of external disturbance, with the help of vegetation restoration, soil and water conservation intervention and other factors, and relying on its own certain self-repairing ability.

[0042] In practical applications, for example, in a specific target slope area, during remote sensing monitoring from period 36 to 42, the area expansion increment of a particular water erosion patch showed a continuous and rapid increase from period 36 to 38. The slope of the expansion increment was significantly higher than the average slope of the previous 30 periods, indicating that water erosion expansion entered an abnormally active period during this stage. Subsequently, starting from period 39, the expansion increment began to decline slowly, and by period 42, it had basically fallen back to within the tolerance range of the average value of period 30. According to the identification logic of this invention, period 36 to 42 can be defined as a typical abrupt change interval.

[0043] These abrupt change intervals are often associated with periodic human disturbances (such as concentrated agricultural machinery compaction) or extreme weather events. As the disturbance ceases or external conditions improve, the soil possesses a certain self-repairing capacity, such as structural remodeling, porosity restoration, or surface stabilization, thereby gradually reducing the expansion trend. Therefore, the abrupt change intervals not only reveal the response of water erosion to short-term external forces but also reflect the self-regulating capacity of the slope system.

[0044] The current mutation degree value is used to comprehensively reflect the frequency and intensity of abnormal expansion behavior in the target slope area. Its core function is to capture the unstable expansion trend of specific water erosion patches during the monitoring period, and then determine whether the area may be affected by sudden disturbances or structural damage.

[0045] The current mutation severity value can be calculated in several ways. For example, multiplying the number of mutations by the average intensity of each mutation can be equivalently represented as the total mutation impact of the entire area. This method can effectively characterize the total mutation intensity level experienced by the slope area under a certain time series, and thus more accurately reflect the significance of its water erosion response. Alternatively, the average expansion increment of all identified mutation intervals can be statistically analyzed and weighted and merged with the total number of mutation intervals to form an index representing the overall abnormal activity level of the area. Mutation intervals can also be sorted and assigned different weights according to their occurrence time to highlight the impact of recent mutation behavior.

[0046] The current mutation level value obtained through the above calculation method can objectively and dynamically depict the water erosion fluctuation characteristics of the slope area and provide a key diagnostic basis for subsequent water erosion level correction.

[0047] Furthermore, the farmland water erosion monitoring and management method based on multi-source data analysis also includes the following steps: Step S400: Select a reference slope area with the same background characteristics as the target slope area based on the historical database, and whose water erosion level has been continuously lower than the preset level for several historical monitoring periods, and obtain the reference change degree value of the reference slope area.

[0048] The same background characteristics refer to the following: the reference slope area and the target slope area belong to the same agricultural machinery disturbance hot zone, and the disturbance frequencies of both are within the same preset frequency range. The time when the specific water erosion patch first appears is within the preset time range, and they have consistent characteristics in terms of slope area, slope grade, soil type and climate conditions.

[0049] In this embodiment of the invention, to ensure the stability and representativeness of the obtained reference mutation degree values, a relatively strict screening condition is set in step S400 to identify reference slope areas with similar environmental and disturbance characteristics to the target slope area from the historical database. Specifically, only when the reference area and the target area are highly consistent or similar in several key background factors, such as whether they are in agricultural machinery disturbance hotspots, whether the disturbance frequency falls within the same preset frequency range, whether the time of the first appearance of specific water erosion patches (of the same or similar type) is similar, and whether the slope lateral area and slope grade, soil type, and climate conditions meet the requirements, can they be included in the subsequent comparative analysis. All of the above-mentioned data can be extracted and matched from multi-source data (such as remote sensing images, operation trajectories, meteorological records, soil surveys, etc.) in the historical database.

[0050] If a reference area with high consistency between the background features and the target slope area cannot be found, it is not recommended to use the method of the present invention for correction processing, so as to avoid judgment errors caused by inaccurate reference values, which in turn affect the objective assessment of the water erosion level on the side of the target area.

[0051] Furthermore, selecting areas where the water erosion level has consistently been below the preset level for multiple historical monitoring periods as reference slopes ensures that these areas still exhibit good erosion resistance under long-term disturbance, thus possessing the reference value of "ideal working conditions." This preset level can be set based on historical water erosion monitoring standards or empirical models, and is usually consistent with the evaluation system for the degree of abrupt change in step S300 to ensure the comparability of the reference value with the current degree of abrupt change.

[0052] The reason this invention selects this type of reference slope area is that, under the same background and similar disturbance frequency as the target area, it still maintains a stable and low level of water erosion response, indicating that its internal structure or surface features have strong resistance to disturbance and good water erosion toughness. Therefore, the abrupt change value exhibited by this type of area can serve as a reference benchmark for the target area, helping to determine the degree of deviation of the target area under actual disturbance conditions, and thereby making a more accurate correction to the initial water erosion level value.

[0053] Furthermore, the farmland water erosion monitoring and management method based on multi-source data analysis also includes the following steps: Step S500: Compare the deviation between the current mutation degree value and the reference mutation degree value, use it as a correction factor, and use the correction factor to correct the initial water erosion level value.

[0054] Specifically, Figure 3 A flowchart illustrating the process for correcting water erosion levels is shown.

[0055] The process of comparing the deviation between the current mutation level value and the reference mutation level value, using this deviation as a correction factor, and then using this correction factor to correct the initial water erosion level value specifically includes the following steps: Step S501: Quantify the deviation between the current mutation severity value and the reference mutation severity value, and use it as a correction factor; Step S502: Retrieve the preset correction function and combine it with the correction factor to correct the initial water erosion level value to obtain the corrected water erosion level value. Step S503: Apply the corrected water erosion level value to the determination of farmland water erosion level, the ranking of treatment priorities, or the generation of soil and water conservation response strategies.

[0056] The correction function is: ; in, This refers to the revised water erosion rating. This refers to the initial water erosion level value. This refers to the current mutation level value. This refers to the reference mutation level value. This refers to the correction factor, which is the deviation between the current mutation level value and the reference mutation level value. This refers to the control amplitude coefficient, and it satisfies... .

[0057] In this embodiment of the invention, in step S501, the deviation between the current mutation level value and the reference mutation level value is used as a correction factor to measure the significant difference in water erosion response between the target slope area and the ideal reference area. This deviation not only quantitatively reflects the abnormal water erosion behavior of the current area under similar background conditions due to potential soil structure damage or disturbance accumulation, but also serves as a dynamically adaptive adjustment parameter, making the original monitoring level value more consistent with the actual soil response in data interpretation.

[0058] In step S503, the corrected water erosion level value is further applied to the assessment of farmland water erosion levels. This not only improves the accuracy of identifying current regional water erosion risks but also assists in prioritizing remediation efforts. Specifically, in resource-constrained remediation practices, reinforcement or repair measures are prioritized for slopes with higher water erosion levels. Furthermore, this result can support local governments or agricultural management units in developing soil and water conservation response strategies, such as formulating reasonable agricultural machinery operation routes, load control measures, or slope protection engineering layouts. These aspects can be applied in areas such as precision agriculture management, ecological restoration planning, and soil and water conservation assessment.

[0059] The correction function used in this invention is a multiplicative adjustment expression based on a linear adjustment coefficient. This expression is intuitive and concise, effectively reflecting the relative deviation between the current state and the reference state, and adjusting the original water erosion level value by increasing or decreasing the magnitude. Alternatively, other calculation methods can be used, including but not limited to exponential correction functions, piecewise linear correction functions, or nonlinear fitting models based on machine learning, to adapt to the actual needs under different data backgrounds.

[0060] The following is a practical implementation example of the present invention: The initial water erosion level of the lateral portion of a target slope area was 3. Based on multi-source data analysis including remote sensing images and agricultural machinery trajectories, a total of 27 abrupt change intervals were identified in this area from period 5 to period 90. The average expansion increment of each interval was 0.8 square meters, resulting in a current abrupt change level of 21.6. Several reference slope areas with similar conditions were selected from the historical database. These areas met matching conditions such as the same disturbance heat zone attributes, similar times of first water erosion patch appearance, slope area, slope grade, and soil type. Furthermore, their water erosion levels remained consistently below the set threshold of 2.5 within the same historical monitoring period. Statistical analysis showed that the average abrupt change level of these reference slope areas was 13.6.

[0061] Therefore, the deviation between the current area and the reference area is calculated as (21.6 minus 13.6) divided by 13.6, resulting in approximately 0.588. Using this deviation as a correction factor, combined with a preset correction function and setting the control amplitude coefficient to 0.3, a correction operation is performed, yielding a corrected water erosion level value of 3.53. This corrected result is significantly higher than the initial level value, indicating a risk of underestimated water erosion potential in this area, and thus requiring increased intervention priority in subsequent remediation efforts.

[0062] Through the implementation of the complete scheme described above, the main effects of this invention are reflected in its ability to dynamically and objectively correct the original water erosion level assessment results, thereby improving the sensitivity and adaptability of water erosion monitoring, while considering multi-source interference and spatial background consistency. The core technical problem solved is that traditional water erosion monitoring methods struggle to identify soil microstructure damage caused by agricultural machinery disturbance and its nonlinear response to water erosion expansion. This invention addresses this monitoring blind spot by introducing a mutation behavior identification and reference comparison mechanism. This method has broad application prospects, particularly suitable for precision agriculture, water conservancy project layout, mountainous farming planning, and regional ecological assessment, providing strong data support and decision-making basis for the sustainable development of modern agriculture.

[0063] Furthermore, Figure 4 An application architecture diagram of the system provided in an embodiment of the present invention is shown.

[0064] In another preferred embodiment of the present invention, a farmland water erosion monitoring and management system based on multi-source data analysis includes: The data acquisition module 100 is used to acquire the initial water erosion level value of the target slope area and the historical database of the farmland area after determining that the target slope area belongs to the agricultural machinery disturbance hot zone. The agricultural machinery disturbance hot zone refers to the area on the slope where there are traces of agricultural machinery operation and the disturbance heat exceeds a preset threshold.

[0065] Furthermore, the farmland water erosion monitoring and management system based on multi-source data analysis also includes: The trend extraction module 200 is used to determine the starting time of the appearance of a specific water erosion patch in the target slope area based on the historical database, and to determine the expansion increment change trend of the specific water erosion patch from the starting time.

[0066] Furthermore, the farmland water erosion monitoring and management system based on multi-source data analysis also includes: The mutation identification module 300 is used to identify mutation intervals that appear in the trend of change, count the number of all mutation intervals and their corresponding expansion increments, and calculate the current mutation degree value of the target slope area. The mutation interval is a local abnormal interval in the trend of change that shows an abnormal rise and then falls back.

[0067] Furthermore, the farmland water erosion monitoring and management system based on multi-source data analysis also includes: The reference selection module 400 is used to select a reference slope area with the same background characteristics as the target slope area based on the historical database, and whose water erosion level has been continuously lower than the preset level for several historical monitoring periods, and to obtain the reference change degree value of the reference slope area.

[0068] Furthermore, the farmland water erosion monitoring and management system based on multi-source data analysis also includes: The grade correction module 500 is used to compare the deviation between the current mutation degree value and the reference mutation degree value, and use the correction factor to correct the initial water erosion grade value.

[0069] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0070] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0071] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0072] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0073] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for monitoring and managing farmland water erosion based on multi-source data analysis, characterized in that, The method includes: After determining that the target slope area belongs to the agricultural machinery disturbance hot zone, the initial water erosion level value of the target slope area and the historical database of the corresponding farmland area are obtained. Based on historical databases, the starting time of the appearance of specific water erosion patches in the target slope area is determined, and the trend of the expansion increment of specific water erosion patches is determined from the starting time. Identify abrupt change intervals in the trend of change, count the number of all abrupt change intervals and their corresponding expansion increments, and calculate the current degree of change in the target slope area. The abrupt change interval is a local abnormal interval in the trend of change that shows an abnormal rise followed by a fall. Based on the historical database, a reference slope area with the same background characteristics as the target slope area and whose water erosion level has been consistently lower than the preset level for several historical monitoring periods was selected, and the reference abrupt change value of the reference slope area was obtained. The deviation between the current mutation level value and the reference mutation level value is compared and used as a correction factor. The initial water erosion level value is then corrected using the correction factor.

2. The method for monitoring and managing farmland water erosion based on multi-source data analysis according to claim 1, characterized in that, The agricultural machinery disturbance hot zone refers to the area on the slope where there are traces of agricultural machinery operation and the disturbance heat exceeds a preset threshold.

3. The method for monitoring and managing farmland water erosion based on multi-source data analysis according to claim 1, characterized in that, The specific water erosion patch refers to a water erosion patch that matches at least one type in a preset set of water erosion patch types. The preset water erosion patch types are used to characterize typical water erosion patterns that are prone to occur in slope areas under agricultural machinery disturbance.

4. The method for monitoring and managing farmland water erosion based on multi-source data analysis according to claim 3, characterized in that, Based on historical databases, the steps for determining the initial time of the appearance of specific water erosion patches in the target slope area, and then determining the trend of the incremental expansion of these specific water erosion patches from that initial time, include: Historical remote sensing image data of the target slope area are extracted from the historical database, and the image data is processed to identify the starting time of the appearance of specific water erosion patches in the target slope area. The time range from the start time to the current time is divided into several time periods; Image processing techniques based on image segmentation, change detection, or patch tracking are used to calculate the area expansion increment of a specific water erosion patch in each time period compared to the previous time period. The trend of the expansion increment of a specific water erosion patch over time is constructed according to the time sequence.

5. The method for monitoring and managing farmland water erosion based on multi-source data analysis according to claim 1, characterized in that, The mutation interval is a local abnormal interval in the trend of expansion increment change where an abnormal rise occurs and then falls back. Specifically, it refers to the following: in the rising segment of the mutation interval, the slope of the expansion increment changes over a number of consecutive time periods increases by more than a preset value compared to the average slope of the previous preset time period, and the expansion increment at the end of the falling segment of the mutation interval decreases to within the tolerance range of the average value of the expansion increment in the previous preset time period.

6. The method for monitoring and managing farmland water erosion based on multi-source data analysis according to claim 1, characterized in that, The same background characteristics refer to the following: the reference slope area and the target slope area belong to the same agricultural machinery disturbance hot zone, and the disturbance frequencies of both are within the same preset frequency range. The time when the specific water erosion patch first appears is within the preset time range, and they have consistent characteristics in terms of slope area, slope grade, soil type and climate conditions.

7. The method for monitoring and managing farmland water erosion based on multi-source data analysis according to claim 1, characterized in that, The steps of comparing the deviation between the current mutation level value and the reference mutation level value, using this deviation as a correction factor, and then using this correction factor to correct the initial water erosion level value include: Quantify the deviation between the current mutation severity value and the reference mutation severity value, and use it as a correction factor; The preset correction function is retrieved and the initial water erosion level value is corrected by combining the correction factor to obtain the corrected water erosion level value. The revised water erosion level values ​​will be applied to the determination of farmland water erosion level, the prioritization of treatment, or the generation of soil and water conservation response strategies.

8. The method for monitoring and managing farmland water erosion based on multi-source data analysis according to claim 7, characterized in that, The correction function is: ； in, This refers to the revised water erosion rating. This refers to the initial water erosion level value. This refers to the current mutation level value. This refers to the reference mutation level value. This refers to the correction factor, which is the deviation between the current mutation level value and the reference mutation level value. This refers to the control amplitude coefficient, and it satisfies... .

9. A farmland water erosion monitoring and management system based on multi-source data analysis, characterized in that, The system includes: The data acquisition module is used to obtain the initial water erosion level value of the target slope area and the historical database of the corresponding farmland area after determining that the target slope area belongs to the agricultural machinery disturbance hot zone. The trend extraction module is used to determine the starting time of the appearance of a specific water erosion patch in the target slope area based on the historical database, and to determine the expansion increment trend of the specific water erosion patch from the starting time. The mutation identification module is used to identify mutation intervals that appear in the trend of change, count the number of all mutation intervals and their corresponding expansion increments, and calculate the current mutation degree value of the target slope area. The mutation interval is a local abnormal interval in the trend of change that shows an abnormal rise and then falls back. The reference selection module is used to select reference slope areas with the same background characteristics as the target slope area based on the historical database, and whose water erosion level has been continuously lower than the preset level for several historical monitoring periods, and to obtain the reference change degree value of the reference slope area. The grade correction module is used to compare the deviation between the current mutation degree value and the reference mutation degree value, and use the correction factor to correct the initial water erosion grade value.

10. The farmland water erosion monitoring and management system based on multi-source data analysis according to claim 9, characterized in that, The agricultural machinery disturbance hot zone refers to the area on the slope where there are traces of agricultural machinery operation and the disturbance heat exceeds a preset threshold.

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