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

Abstract

The application is suitable for the field of farmland water and soil conservation and remote sensing monitoring technology, and provides a farmland water erosion monitoring management method and system based on multi-source data analysis, which comprises the following steps: after determining that a target slope region belongs to a farmland disturbance hot zone, obtaining an initial water erosion grade value of the target slope region and a historical database of the farmland region to which the target slope region belongs. Through the construction of a multi-source data analysis system integrating remote sensing images, water erosion patch evolution and farmland disturbance information, the application proposes a water erosion monitoring method based on "abrupt change interval identification-mutation degree value calculation-grade value dynamic correction", which breaks through the technical bottlenecks of insufficient response to farmland disturbance and static rigidity of grade evaluation in traditional water erosion monitoring.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of farmland water and soil conservation and remote sensing monitoring, and particularly relates to a farmland water erosion monitoring management method and system based on multi-source data analysis. BACKGROUND

[0002] In the process of farmland production management, the side area of the slope region has been the key area of farmland water erosion prevention and control for a long time due to its complex terrain and easy loss of water and soil. With the improvement of the level of agricultural mechanization, the agricultural machinery frequently operates in the slope region, bringing high-intensity disturbance, which makes the soil structure of this kind of area more easily damaged, thereby inducing nonlinear and sudden water erosion expansion phenomenon. In order to realize scientific management and resource allocation, it is urgent to build an efficient and accurate water erosion monitoring mechanism. In the prior art, the mainstream water erosion monitoring method depends on a single data source, such as remote sensing image, ground investigation or basin model output, and usually adopts fixed evaluation indexes and static grade division rules, which is difficult to capture the complex evolution process induced by disturbance, especially in the slope region where the disturbance response behavior is highly coupled, and there is a significant problem of insufficient adaptability.

[0003] Further, although part of the technical solutions have tried to introduce time-series remote sensing images or disturbance factor analysis based on empirical models, there are still three significant limitations in actual application: first, the dynamic influence process of agricultural machinery disturbance on water erosion response cannot be effectively quantified; second, there is a lack of systematic identification mechanism for abnormal expansion behavior, and key response signals cannot be accurately extracted from the evolution trend; third, the water erosion grade evaluation result is statically solidified, and cannot be dynamically corrected according to different disturbance conditions. These deficiencies result in low sensitivity of the monitoring system in the water erosion identification of the agricultural machinery disturbance hot area, and the diagnostic results are distorted, which restricts the scientific deployment of soil and water conservation measures and the precise matching of response strategies. SUMMARY

[0004] The purpose of the present application is to provide a farmland water erosion monitoring management method and system based on multi-source data analysis, which aims to solve the problems raised in the background art.

[0005] The present application is implemented as follows: a farmland water erosion monitoring management method based on multi-source data analysis, the method comprising:

[0006] After determining that the target slope region belongs to the agricultural machinery disturbance hot area, an initial water erosion grade value of the target slope region and a historical database of the belonging farmland region are obtained;

[0007] Based on the historical database, a starting time of a specific water erosion patch appearing in the target slope region is determined, and an expansion increment change trend of the specific water erosion patch is determined from the starting time;

[0008] Identify the mutation interval appearing in the change trend, and count the number of all mutation intervals and their corresponding expansion increments, calculate the current mutation degree value of the target slope region, and the mutation interval is the local abnormal interval appearing after the abnormal rise and then falling in the change trend;

[0009] Select a reference slope region with the same background characteristics as the target slope region from the historical database, and the water erosion level of the reference slope region is continuously lower than the preset level in several historical monitoring periods, and obtain the reference mutation degree value of the reference slope region;

[0010] Compare the deviation amplitude between the current mutation degree value and the reference mutation degree value as a correction factor, and correct the initial water erosion level value using the correction factor.

[0011] As a further limitation of the technical scheme of the embodiment of the application, the agricultural machine disturbance hot area refers to a region in the slope region where there are traces of agricultural machine operation, and the disturbance heat exceeds the preset threshold.

[0012] As a further limitation of the technical scheme of the embodiment of the application, the specific water erosion patch refers to a water erosion patch matching at least one type in a preset water erosion patch type set, and the preset water erosion patch type is used to represent the typical water erosion form prone to occur in the slope region under the disturbance of agricultural machines.

[0013] As a further limitation of the technical scheme of the embodiment of the application, based on the historical database, the starting time of the occurrence of the specific water erosion patch in the target slope region is determined, and the step of determining the expansion increment change trend of the specific water erosion patch from the starting time includes:

[0014] Extract the historical remote sensing image data of the target slope region from the historical database, and process the image data to identify the starting time of the occurrence of the specific water erosion patch in the target slope region;

[0015] Divide the time range from the starting time to the current time into several time periods;

[0016] Based on image segmentation, change detection or patch tracking image processing technology, calculate the area expansion increment of the specific water erosion patch in each time period compared with the last time period;

[0017] According to the time sequence, construct the change trend of the expansion increment of the specific water erosion patch with time.

[0018] As a further limitation of the technical scheme of the embodiment of the present application, the mutation interval is a local abnormal interval appearing after abnormal rise and then falling in the expansion increment change trend, specifically referring to that in the rising section of the mutation interval, the change slope of the expansion increment in the continuous time periods is increased by more than a preset value compared with the average change slope of the previous preset time period, and the expansion increment at the end of the falling section of the mutation interval falls within the tolerance range of the average expansion increment of the previous preset time period.

[0019] As a further limitation of the technical scheme of the embodiment of the present application, the same background feature refers to that the reference side slope region and the target side slope region belong to the agricultural disturbance hot area, and the disturbance frequencies of both are within the same preset frequency range, the time of first appearing of the specific water erosion patch is within the preset time range, and the side slope area, slope grade, soil type, and climate conditions have consistent characteristics.

[0020] As a further limitation of the technical scheme of the embodiment of the present application, the step of comparing the deviation amplitude between the current mutation degree value and the reference mutation degree value as a correction factor, and correcting the initial water erosion grade value by using the correction factor includes:

[0021] quantifying the deviation amplitude between the current mutation degree value and the reference mutation degree value, and taking it as a correction factor;

[0022] calling a preset correction function, and correcting the initial water erosion grade value by combining the correction factor to obtain a corrected water erosion grade value;

[0023] applying the corrected water erosion grade value to farmland water erosion grade judgment, management priority sorting, or water and soil conservation response strategy generation.

[0024] As a further limitation of the technical scheme of the embodiment of the present application, the correction function is:

[0025] ;

[0026] wherein, refers to the corrected water erosion grade value, refers to the initial water erosion grade value, refers to the current mutation degree value, refers to the reference mutation degree value, refers to the deviation amplitude between the current mutation degree value and the reference mutation degree value, refers to a control amplitude coefficient, and satisfies .

[0027] A farmland water erosion monitoring and management system based on multi-source data analysis, the system comprises:

[0028] The data acquisition module is configured to acquire an initial water erosion grade value of the target slope region and a historical database of the farmland region to which the target slope region belongs after determining that the target slope region belongs to the agricultural machinery disturbance hot zone.

[0029] The trend extraction module is configured to determine a starting time at which a specific water erosion patch appears in the target slope region based on the historical database, and determine an expansion increment variation trend of the specific water erosion patch since the starting time.

[0030] The mutation recognition module is configured to recognize a mutation interval appearing in the variation trend, and count a number of all mutation intervals and corresponding expansion increments, to calculate a current mutation degree value of the target slope region, the mutation interval being a local abnormal interval appearing after abnormal rising and falling in the variation trend.

[0031] The reference selection module is configured to select a reference slope region having the same background characteristics as the target slope region and having a water erosion grade continuously lower than a preset level in a plurality of historical monitoring periods based on the historical database, and acquire a reference mutation degree value of the reference slope region.

[0032] The grade correction module is configured to compare a deviation amplitude between the current mutation degree value and the reference mutation degree value as a correction factor, and correct the initial water erosion grade value by using the correction factor.

[0033] As a further limitation of the technical scheme of the embodiment of the present application, the agricultural machinery disturbance hot zone refers to a region in which there is a trace of agricultural machinery operation in the slope region, and the disturbance heat exceeds a preset threshold.

[0034] Compared with the prior art, the present application has the following beneficial effects:

[0035] The present application breaks through the technical bottlenecks of insufficient response to agricultural machinery interference and static rigidity of grade evaluation in traditional water erosion monitoring by constructing a multi-source data analysis system integrating remote sensing images, water erosion patch evolution and agricultural machinery disturbance information, and proposing a water erosion monitoring method based on "mutation interval recognition-mutation degree value calculation-grade value dynamic correction". The proposed mutation degree value index can objectively quantify abnormal water erosion behavior of the side of the slope region under different agricultural machinery disturbance conditions, and dynamically adjust the water erosion grade accordingly, significantly improving the response sensitivity and discrimination accuracy of the monitoring result to the actual disturbance difference. The method does not need to rely on on-site human labeling, has strong adaptability and high automation degree, and is particularly suitable for high-frequency monitoring and soil and water conservation decision support of farmland slope regions, and has good engineering application prospect. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 The method flowchart provided for the embodiment of the present application;

[0037] Figure 2The flow chart for extracting the trend of the increment change of the specific water erosion patch expansion in the method provided by the embodiment of the present application is shown.

[0038] Figure 3 The flow chart for processing the water erosion grade correction in the method provided by the embodiment of the present application is shown.

[0039] Figure 4 The application architecture diagram of the system provided by the embodiment of the present application is shown. DETAILED DESCRIPTION

[0040] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0041] Figure 1 The flow chart of the method provided by the embodiment of the present application is shown.

[0042] Specifically, a farmland water erosion monitoring and management method based on multi-source data analysis, the method specifically comprises the following steps:

[0043] Step S100, after determining that the target slope region belongs to the farmland disturbance hot area, obtaining the initial water erosion grade value of the target slope region and the historical database of the farmland region to which the target slope region belongs.

[0044] The farmland disturbance hot area refers to a region in which there are traces of agricultural machinery operation in the slope region, and the disturbance heat exceeds a preset threshold.

[0045] In the embodiment of the present application, in the farmland topography structure, the slope region generally refers to a region located in the boundary or internal high and low land of the farmland, a topographic transition zone with a certain slope. Because the slope surface is jointly acted by gravity and water flow, it is easy to form surface runoff under the condition of natural rainfall or farmland irrigation, and then the side surface induces water erosion patches. Especially in the process of agricultural production, the slope region is often rolled by agricultural machinery or used as a transit route, so that the original soil structure is disturbed, and then the risk of water erosion expansion is aggravated. Therefore, the side surface of the slope region has significant vulnerability and sensitivity in soil and water conservation and agricultural environmental protection, and becomes the key region concerned and monitored by the present application.

[0046] The farmland disturbance hot area refers to a spatial area in a slope region where there are obvious traces of agricultural machinery operation and which is long-term affected by high-intensity agricultural activities. Agricultural machinery operation can cause soil compaction, pore destruction, and decrease of water infiltration capacity, thereby exacerbating surface runoff formation and sediment scouring. The present application fuses multi-source data such as remote sensing images and agricultural machinery track data, and constructs a disturbance heat evaluation model based on disturbance frequency, heat index, and path density, etc. When the regional disturbance heat exceeds a preset threshold, it is determined that it belongs to the farmland disturbance hot area. The disturbance heat evaluation model can be realized in combination with existing remote sensing recognition algorithms and agricultural machinery track monitoring technology, and belongs to a relatively mature application mode in the prior art.

[0047] The water erosion grade value is used to quantify the severity of farmland surface suffering from water and soil loss, and is often represented in the form of a grade system (such as light, moderate, severe, etc.). It is usually calculated based on water erosion area, depth, duration, etc. In the prior art, the water erosion grade value has been widely used in application scenarios such as ecological environment monitoring, agricultural protection zoning, and soil and water conservation measure development, and belongs to a mature application model. However, the existing grade value is usually generated based on a static model, and it is difficult to dynamically reflect the changes in soil structure caused by implicit variables such as agricultural machinery disturbance, resulting in deviation and lag in the monitoring results.

[0048] Therefore, the core technical problem of the present application is that in the farmland slope region, due to the uncertainty of the disturbance mode and intensity of agricultural machinery operation in the disturbance hot area, for example, different types of agricultural machinery have different wheel pressure, passing mode, running state and load conditions, and the response ability of regional soil to disturbance also varies due to factors such as topography, soil quality, and moisture, making it difficult to estimate the degree of soil structure damage. The prior art is usually difficult to accurately depict the implicit influence of these disturbances on the expansion of water erosion, thereby affecting the accuracy and timeliness of water erosion grade evaluation.

[0049] The historical database refers to a multi-source time series information set accumulated for a regional farmland, including remote sensing images, agricultural operation records, water erosion monitoring data, and soil and water conservation measures archives, etc. The database can come from the following sources:

[0050] Multi-period remote sensing image platform (such as Sentinel, Landsat, etc.); agricultural machinery navigation / operation track collection system; ground monitoring station or unmanned aerial vehicle patrol platform; historical water erosion grade evaluation report or land use data, etc.

[0051] Further, the farmland water erosion monitoring and management method based on multi-source data analysis further comprises the following steps:

[0052] Step S200, based on the historical database, determining the starting time of the occurrence of a specific water erosion patch in the target slope region, and determining the expansion increment trend of the specific water erosion patch from the starting time.

[0053] 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, and the preset water erosion patch type is used to characterize typical water erosion forms prone to occur in a side slope region under agricultural machine disturbance.

[0054] Specifically, Figure 2 An extraction flowchart of an expansion increment change trend of a specific water erosion patch is shown.

[0055] The expansion increment change trend of the specific water erosion patch is determined based on a historical database, and the determination of the starting time at which the specific water erosion patch appears in the target side slope region comprises the following steps:

[0056] Step S201, historical remote sensing image data of a target side slope region is extracted from a historical database, and the image data is processed to identify a starting time at which a specific water erosion patch appears in the target side slope region;

[0057] Step S202, a time range from the starting time to a current time is divided into several time periods;

[0058] Step S203, based on image segmentation, change detection or patch tracking image processing technology, the area expansion increment of the specific water erosion patch in each time period compared with the last time period is calculated;

[0059] Step S204, a change trend of the expansion increment of the specific water erosion patch with time is constructed in time sequence.

[0060] In the embodiment of the present application, the "preset water erosion patch type set" is constructed based on existing water and soil conservation research results and actual observation experience of farmland, and covers various typical water erosion form types commonly seen in side slope regions under agricultural machine disturbance, such as shallow gully erosion, sheet erosion, fine gully erosion, etc. The set is derived from long-term remote sensing monitoring, soil erosion survey and field investigation data in different regions, has clear classification standards and rich practical basis, belongs to the mature cognitive achievements in the field of water erosion monitoring, and has wide applicability and scientific effectiveness.

[0061] The "starting time" refers to a time node at which the target side slope region first appears a specific water erosion patch of a preset type. This time node marks the explicit manifestation of water erosion activities in the monitoring region, and is an important reference starting point for subsequent analysis of patch expansion dynamics, which helps to accurately define the occurrence and evolution time axis of the water erosion process.

[0062] In step S201, remote sensing image data of the target slope region is extracted from a historical database, and image preprocessing techniques (such as radiation correction, atmospheric correction, image registration, etc.) are combined 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 their first occurrence time.

[0063] In step S202, the time range from the starting time to the current time is divided into consecutive time periods, which can be divided according to the update frequency of remote sensing images (such as monthly, weekly, or higher frequency) to ensure sufficient time resolution. Due to the short update period of remote sensing images and the long monitoring period usually spanning several years, the number of time periods obtained by division is large, which can more accurately depict the dynamic evolution process of water erosion patches, thereby providing more accurate basic data support for subsequent trend extraction and mutation recognition.

[0064] In step S203, image processing techniques such as image change detection, object tracking, or patch boundary extraction are used to match water erosion patches in consecutive time periods and calculate area changes to obtain the expansion increment of each time period. Common methods such as time-series NDVI difference, object-oriented classification, and region growing are all conventional means in existing remote sensing analysis and have practical implementation capabilities.

[0065] In step S204, a trend curve of the expansion increment over time is constructed according to the time series to form a complete expansion dynamic trajectory.

[0066] The obtained expansion increment change trend can be used to characterize the spatio-temporal evolution process of specific water erosion patches in the target slope region, indirectly reflecting the soil structure stability, water dynamic disturbance conditions, and human influence degree of the region, and is an important basis for subsequent mutation interval identification and water erosion grade correction.

[0067] Further, the farmland water erosion monitoring and management method based on multi-source data analysis further includes the following steps:

[0068] Step S300, identify the mutation interval appearing in the change trend, and count the number of all mutation intervals and their corresponding expansion increments to calculate the current mutation degree value of the target slope region. The mutation interval is a local abnormal interval that appears abnormally rising and then falling in the change trend.

[0069] The mutation interval is a local abnormal interval in which the abnormal rise is followed by a fall in the expansion increment change trend, and specifically refers to that in the rising section of the mutation interval, the change slope of the expansion increment in a plurality of continuous time periods is increased by more than a preset value compared with the average change slope of a previous preset time period, and the expansion increment at the end of the falling section of the mutation interval falls within the tolerance range of the average expansion increment of the previous preset time period.

[0070] In the embodiments of the present application, the core purpose of identifying the "mutation interval" is to solve the water erosion evaluation deviation problem caused by the uncertainty of disturbance intensity in the disturbed hot area of agricultural machinery. In actual agricultural activities, the path, method, load and other factors of agricultural operation in the slope area are significantly different, resulting in that the soil structure in the same area may change dramatically due to the mutation of disturbance intensity at different times. This change is difficult to accurately capture directly through existing remote sensing images or traditional water erosion models, therefore, the present application mines the dynamic abnormal behavior in the expansion process of water erosion patches, introduces the "mutation interval" as an intermediate variable to quantitatively reflect the signs of soil damage, so as to more objectively correct the water erosion grade value.

[0071] The mutation interval refers to a local abnormal interval in which the abnormal rise is followed by a fall in the expansion increment change trend. More specifically, in the trend curve, if the expansion increment continuously and significantly rises in a time period, and the change slope of the rising section is higher than the average change slope of a previous preset time period by a significant amplitude (i.e. more than a preset slope threshold), it indicates that the region may be subjected to strong disturbance (such as high-intensity agricultural machinery rolling) in a short time, resulting in rapid intensification of water erosion; and when the expansion increment gradually decreases and finally falls within the tolerance range of the average expansion increment of the previous stage, it can be defined as a complete mutation interval.

[0072] The essence of the definition is to identify the nonlinear expansion mode of "sudden intensification-phase relief". The rising section reflects the rapid response process caused by disturbance, and the falling section reflects the ecological restoration trend that the regional soil structure gradually tends to be stable under the action of factors such as vegetation restoration, soil and water conservation intervention and relying on its own self-repairing ability, under the condition that the external disturbance is weakened or terminated.

[0073] In actual application, for example, in the remote sensing monitoring of a target slope area from the 36th to the 42nd period, the area expansion increment of a specific water erosion patch continuously and rapidly rises from the 36th to the 38th period, and the change slope of the expansion increment is significantly higher than the average change slope of the previous 30 periods, indicating that the water erosion expansion enters an abnormal active period at this stage; then, the expansion increment begins to slowly decrease from the 39th period, and basically falls within the tolerance range of the average of the 30th period at the 42nd period. According to the identification logic of the present application, the 36th to the 42nd period can be defined as a typical mutation interval.

[0074] The mutation interval is often related to stage disturbance (such as concentrated agricultural machine rolling) or extreme weather events. With the stop of disturbance or improvement of external conditions, the soil has certain self-repairing ability, such as structure remodeling, pore recovery or surface layer stabilization, so that the expansion trend gradually falls. Therefore, the mutation interval not only reveals the response of water erosion to short-term external force, but also reflects the self-regulating ability of the slope system.

[0075] The current mutation degree value is used to comprehensively reflect the frequency and intensity of abnormal expansion behavior in the target slope area, and its core function is to capture the unstable expansion trend of a specific water erosion patch in the monitoring period, and then judge whether the area is likely to be affected by sudden disturbance or structural damage.

[0076] The calculation of the current mutation degree value can be done in various ways, for example: multiplying the number of mutations by the average intensity of each mutation, which can be equivalent to the total amount of mutation influence of the region as a whole. This way can effectively depict the total level of mutation intensity experienced by the slope area under certain time sequence, and then more accurately reflect the significance of its water erosion response; or the average expansion increment of all mutation intervals identified is counted and weighted with the total number of mutation intervals to form an index representing the overall abnormal activity of the region; or the mutation intervals can be sorted, and different weights are assigned according to their occurrence time to highlight the impact of recent mutation behavior.

[0077] The current mutation degree value obtained by the above calculation method can objectively and dynamically depict the water erosion fluctuation characteristics of the slope area, and provide key diagnostic basis for subsequent water erosion grade correction.

[0078] Further, the farmland water erosion monitoring and management method based on multi-source data analysis further comprises the following steps:

[0079] Step S400, based on the historical database, selecting a reference slope area with the same background characteristics as the target slope area, and the water erosion grade of the reference slope area being continuously lower than the preset level in several historical monitoring periods, and obtaining the reference mutation degree value of the reference slope area.

[0080] The same background characteristics refer to: the reference slope area and the target slope area belong to the same agricultural machine disturbance hot area, and the disturbance frequency of both is within the same preset frequency range, the time of first appearance of a specific water erosion patch is within the preset time range, and they have consistent characteristics in terms of slope area, slope grade, soil type and climate conditions.

[0081] In the embodiments of the present application, in order to ensure that the obtained reference mutation degree value has stability and representativeness, the present application sets a relatively strict screening condition in step S400, which is used to identify the reference slope region with similar environment and disturbance characteristics from the historical database. Specifically, only when the reference region and the target region are highly consistent or close in multiple key background factors, such as whether they are in the hot area of agricultural disturbance, whether the disturbance frequency falls within the same preset frequency range, whether the time of first occurrence of a specific water erosion patch (consistent or close in type) is similar, the slope side area and slope grade, soil type, climate condition and the like meet the requirements, can be included in the subsequent comparison analysis. The above various data can be extracted and matched by the multi-source data (such as remote sensing image, operation track, weather record, soil survey and the like) in the historical database.

[0082] If a reference region with high consistency in background characteristics and the target slope region cannot be found, it is not recommended to use the method of the present application for correction processing, so as to avoid the judgment error caused by the inaccurate reference value, and further affect the objective evaluation of the water erosion grade of the side surface of the target region.

[0083] In addition, the region with water erosion grade continuously lower than the preset level in multiple historical monitoring periods is selected as the reference slope, in order to ensure that the region still shows good erosion resistance under long-term disturbance, thereby having the reference value of "ideal working condition". The preset level can be set according to the historical water erosion monitoring standard or the experience model, and is usually consistent with the evaluation system of the mutation degree value in step S300, so as to ensure the comparability of the reference value and the current mutation degree value.

[0084] The reason why the present application selects such a reference slope region is that it can still maintain stable and low level of water erosion response under the same background and similar disturbance frequency as the target region, which indicates that its internal structure or surface feature has strong anti-disturbance ability and good water erosion toughness. Therefore, the mutation degree value embodied by such a region can be used as a reference benchmark for the target region, which is helpful to determine the deviation degree of the target region under actual disturbance conditions, and to correct the initial water erosion grade value more accurately.

[0085] Further, the farmland water erosion monitoring and management method based on multi-source data analysis further comprises the following steps:

[0086] Step S500, comparing the deviation amplitude between the current mutation degree value and the reference mutation degree value as a correction factor, and correcting the initial water erosion grade value by using the correction factor.

[0087] Specifically, Figure 3 A processing flowchart of water erosion grade correction is shown.

[0088] The deviation range between the current mutation degree value and the reference mutation degree value is compared as a correction factor, and the initial water erosion grade value is corrected by using the correction factor, specifically including the following steps:

[0089] In step S501, the deviation range between the current mutation degree value and the reference mutation degree value is quantified and taken as a correction factor.

[0090] In step S502, a preset correction function is called, and the initial water erosion grade value is corrected by combining the correction factor to obtain a corrected water erosion grade value.

[0091] In step S503, the corrected water erosion grade value is applied to farmland water erosion grade judgment, management priority sorting, or water and soil conservation response strategy generation.

[0092] The correction function is:

[0093]

[0094] wherein, denotes the corrected water erosion grade value, denotes the initial water erosion grade value, denotes the current mutation degree value, denotes the reference mutation degree value, denotes the correction factor, that is, the deviation range between the current mutation degree value and the reference mutation degree value, denotes a control range coefficient, and satisfies .

[0095] In the embodiment of the present application, in step S501, the deviation range between the current mutation degree value and the reference mutation degree value is taken as a correction factor, in order to measure the significant difference in water erosion response between the target slope area and the ideal reference area. This deviation range not only quantitatively reflects the abnormal water erosion behavior of the current area due to potential soil structure damage or disturbance accumulation under similar background conditions, but also serves as a dynamic adaptive adjustment parameter, making the original monitoring grade value more consistent with the real soil response in data interpretation.

[0096] In step S503, the corrected water erosion grade value is further applied to farmland water erosion grade judgment, which not only improves the recognition accuracy of the current regional water erosion risk, but also assists in realizing management priority sorting, that is, in the limited management practice, reinforcement or repair measures are preferentially carried out on the slope area with higher water erosion degree. At the same time, this result can also provide support for local governments or agricultural management units to develop water and soil conservation response strategies, such as developing reasonable agricultural machinery operation routes, load control measures, or slope protection engineering layout, etc. These contents can be applied in the fields of precision agriculture management, ecological restoration planning, water and soil conservation assessment, etc.​

[0097] The correction function adopted in the present application is a multiplicative adjustment expression constructed based on a linear adjustment coefficient, which is intuitive in structure, concise in form, and can effectively reflect the relative deviation between the current and the reference state, and amplify or reduce the original water erosion grade value. In addition, other calculation methods can also be selected, including but not limited to exponential correction function, piecewise linear correction function or nonlinear fitting model based on machine learning, etc., to adapt to the actual needs under different data backgrounds.

[0098] The following is an actual implementation example of the present application scheme:

[0099] The initial water erosion grade value of the side part of a certain target slope region is 3. Based on remote sensing images and multi-source data analysis such as agricultural machinery tracks, it is found that there are a total of 27 mutation intervals in this region during the 5th to 90th period, and the average expansion increment of each interval is 0.8 square meters. Therefore, the current mutation degree value is calculated to be 21.6. A plurality of reference slope regions with similar conditions are selected from the historical database, which meet the matching conditions of the same disturbance hot area attribute, similar first water erosion patch appearance time, slope area, slope grade and soil type, and the water erosion grade is continuously lower than the set threshold value 2.5 in the same historical monitoring period. Through statistical analysis, the average mutation degree value of these reference slope regions is 13.6.

[0100] Therefore, the deviation amplitude between the current region and the reference region is (21.6 minus 13.6) divided by 13.6, which is about 0.588. Taking this deviation amplitude as a correction factor, combining the preset correction function and setting the control amplitude coefficient to 0.3, the corrected water erosion grade value is 3.53. This correction result is obviously higher than the initial grade value, indicating that there is a risk of underestimating the water erosion potential of the region, and the intervention priority should be improved in the subsequent management.

[0101] Through the above complete scheme implementation process, the main effect of the present application is that it can dynamically and objectively correct the original water erosion grade evaluation result on the basis of considering multi-source interference and spatial background consistency, and improve the sensitivity and adaptability of water erosion monitoring. The core technical problem solved is that the traditional water erosion monitoring method cannot identify the soil microstructure damage caused by agricultural machinery disturbance and its nonlinear response to water erosion expansion, and the present application remedies this monitoring blind spot by introducing the mutation behavior identification and reference comparison mechanism. This method has wide application prospects, especially suitable for precision agriculture, water conservancy engineering layout, mountain farming planning and regional ecological assessment, etc., and can provide strong data support and decision basis for the sustainable development of modern agriculture.

[0102] Further, Figure 4 The application architecture diagram of the system provided by the embodiment of the present application is shown.

[0103] In a further preferred embodiment provided by the present application, a farmland water erosion monitoring management system based on multi-source data analysis comprises:

[0104] The data acquisition module 100 is configured to acquire an initial water erosion grade value of the target slope region and a historical database of the farmland region to which the target slope region belongs after determining that the target slope region belongs to a farmland disturbance hot zone.

[0105] The farmland disturbance hot zone refers to a region in which there are traces of agricultural machinery operation in the slope region and the disturbance degree exceeds a preset threshold.

[0106] Further, the farmland water erosion monitoring management system based on multi-source data analysis further comprises:

[0107] The trend extraction module 200 is configured to determine a starting time at which a specific water erosion patch appears in the target slope region based on the historical database, and determine an expansion increment change trend of the specific water erosion patch from the starting time.

[0108] Further, the farmland water erosion monitoring management system based on multi-source data analysis further comprises:

[0109] The mutation recognition module 300 is configured to recognize a mutation interval appearing in the change trend, and count the number of all mutation intervals and their corresponding expansion increments, to calculate a current mutation degree value of the target slope region. The mutation interval is a local abnormal interval in which an abnormal rise is followed by a fall.

[0110] Further, the farmland water erosion monitoring management system based on multi-source data analysis further comprises:

[0111] The reference selection module 400 is configured to select a reference slope region having the same background characteristics as the target slope region and having a water erosion grade continuously lower than a preset level in a plurality of historical monitoring periods based on the historical database, and acquire a reference mutation degree value of the reference slope region.

[0112] Further, the farmland water erosion monitoring management system based on multi-source data analysis further comprises:

[0113] The grade correction module 500 is configured to compare a deviation amplitude between the current mutation degree value and the reference mutation degree value as a correction factor, and correct the initial water erosion grade value by using the correction factor.

[0114] It should be understood that, although the steps in the flowcharts of the embodiments of the present application are shown in a certain order according to the arrows, the steps are not necessarily executed in the order of the arrows. Unless otherwise specified herein, the execution of the steps is not strictly limited in order, and the steps can be executed in other orders. Moreover, at least some of the steps in the embodiments can include a plurality of sub-steps or a plurality of stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of the sub-steps or stages is not necessarily sequential, but can be round-robin or alternately executed with at least some of the other steps or sub-steps or stages of the other steps.

[0115] It can be understood by those skilled in the art that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware, and the program can be stored in a non-volatile computer readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments of the methods. Any reference to memory, storage, database or other medium used in the embodiments provided in the present 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. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0116] The technical features of the above-mentioned embodiments can be combined in any way. In order to make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not described, but as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0117] The above embodiments only express several implementation manners of the present application, which are described in a more specific and detailed manner, but should not be understood as a limitation on the patent scope of the present application. It should be noted that, for ordinary skilled persons in the art, several modifications and improvements can be made without departing from the concept of the present application, which are all within the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

[0118] The above merely describes the preferred embodiments of the present application and should not be used to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

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.

Images