A dynamic data processing system and method for land ecological protection

Abstract

The present invention discloses a dynamic data processing system and method for land ecological protection, including: dividing agricultural land into grids by using high-precision GPS; conducting low-altitude mapping with a drone to capture the topographic features of each crop area, constructing a characteristic influence value by recording the situation of each crop area; setting a basic evaluation period; constructing a set of node times, and collecting the rainfall areas of each crop area at the node times; establishing a risk assessment model to divide the flood risk levels of each crop area, and generating graded early warning information according to different risk levels. The present invention divides agricultural land and conducts risk assessment, constructs a characteristic influence value, sets a basic evaluation period and collects rainfall areas, and regularly collects the weather data of crop areas within the set evaluation period; it can conduct refined management of each crop area, achieve dynamic risk assessment and early warning, and realize the pre-prevention and real-time response to flood disasters.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a dynamic data processing system and method for land ecological protection. Background Art

[0002] Land ecological protection has always been a key issue in agricultural and environmental sciences. With global climate change and the intensification of human activities, agricultural ecosystems are facing more and more challenges, among which flood disasters are particularly significant. In the past few decades, agricultural production and land management technologies have made significant progress, especially in data collection and processing. The widespread application of high-precision GPS technology and drone technology has made it possible to carry out refined management of farmland, and the development of remote sensing technology has also provided reliable data support for real-time monitoring and evaluation. Through the combination of these advanced technologies, the protection measures of agricultural ecosystems have been continuously optimized, providing a solid foundation for responding to sudden environmental events.

[0003] Traditional land protection measures mostly rely on historical experience and static data, lacking real-time and dynamic features. Although some existing advanced technologies can provide relatively accurate data support, there are still many bottlenecks in data processing and application. For example, for risk assessment and early warning of flood disasters, existing methods often lack effective use of real-time weather data and cannot achieve dynamic prediction and management of risks. In addition, traditional flood control measures are mostly post-event remedial measures and lack the ability to prevent in advance and respond in real time. These shortcomings make existing technologies seem powerless in dealing with increasingly complex changes in the ecological environment. Summary of the invention

[0004] The purpose of the present invention is to provide a dynamic data processing system and method for land ecological protection to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a dynamic data processing method for land ecological protection, comprising: using high-precision GPS and drones to grid the use scope of agricultural land; constructing characteristic impact values ​​by recording the boundaries and terrain features of each crop area; setting a basic evaluation cycle; and constructing a node time set, and using remote sensing technology to collect rainfall areas; establishing a risk assessment model, and based on the risk assessment model, dividing each crop area into flood risk levels; and generating graded warning information according to the results of the flood risk level division.

[0006] As a preferred solution of the dynamic data processing method for land ecological protection described in the present invention, a high-precision GPS device is used to survey the usage range of agricultural land, and the usage range of the agricultural land is divided into a number of crop areas in a grid pattern, where one crop area corresponds to one type of crop; the crop areas are uniformly numbered, and the crop areas are classified based on the numbering information and the type of crop to generate a set of areas, denoted as AR r ={A i |i∈[1,n]}, where AR r represents the set of areas generated corresponding to the r-th type of crop, n represents the total number of crop areas, and A i represents the i-th crop area generated corresponding to the r-th type of crop.

[0007] Furthermore, a drone is used for low-altitude mapping to capture the topographic features of each crop area, record the topographic features of each crop area, and calculate the characteristic influence value of the crop area:

[0008]

[0009] where CF(A i ) is the characteristic influence value of the crop area A i , F ip is the characteristic value of the p-th topographic feature of the crop area A i , M represents the total number of topographic features, ω p is the weight of the p-th topographic feature, μ p is the mean of the p-th topographic feature, and σ p is the standard deviation of the p-th topographic feature.

[0010] By performing a standardization process on each topographic feature (i.e., subtracting the mean and then dividing by the standard deviation), the dimensional difference between the characteristic values of different topographic features can be eliminated. The weight ω p reflects the relative importance of each topographic feature to the risk factor and can be adjusted according to expert opinions or actual data.

[0011] As a preferred solution of the dynamic data processing method for land ecological protection described in the present invention, a basic evaluation cycle length L is set, and a sliding window mechanism is introduced to define the evaluation cycle; let the k-th evaluation cycle be EP k =[Tstart(k),Tend(k)), where Tstart(k) is the start time of the evaluation cycle EP k , and the formula is Tstart(k)=Tstart(k - 1)+Δt, Tstart(k - 1) is the start time of the evaluation cycle EP k-1 , and Tend(k) is the end time of the evaluation cycle EPk The end time, with the formula Tend(k) = Tstart(k) + L, and Δt being the evaluation period EP k The sliding interval.

[0012] Within the evaluation period EP k Insert m evenly spaced node times to construct the node time set TN k ={T j | j ∈ [1, m]}, where T j is the j-th node time, and the calculation formula is T 1 represents the first inserted node time, and T 1 = Tstart(k); Weather data is collected for each time node.

[0013] Use satellite remote sensing technology to collect the rainfall area of each crop area A i at the node time T j , and set the rainfall distribution weight D(T j , A i ) according to the rainfall area. The calculation formula of the rainfall distribution weight D(T j , A i ) is as follows:

[0014]

[0015] where S(A i ) is the area of the rainfall area within the i-th crop area corresponding to the r-th crop type.

[0016] As a preferred solution of the dynamic data processing method for land ecological protection described in the present invention, design the historical flooding frequency, and the calculation formula is:

[0017]

[0018] where HF(A i ) represents the historical flooding frequency, N represents the number of historical years, and F i represents the number of times the crop area A i was flooded within the historical N years.

[0019] According to the node time T j , calculate the flooding risk value of each crop area at the node time T j , and the calculation formula is:

[0020] FR(A i | T j ) = α·HF(A i ) + β·CF(A i ) + D(Tj , A i );

[0021] Among them, FR(A i | T j ) represents the flooding risk value of crop area A i at node time T j , and α and β are respectively preset linear regression coefficients.

[0022] As a preferred solution of the dynamic data processing method for land ecological protection described in the present invention, where: the S3 includes the following steps:

[0023] Construct a risk assessment model to calculate the overall risk level of crop area A i in the k-th evaluation period:

[0024]

[0025] Among them, TR k (A i ) is the overall risk level of crop area A i in the evaluation period EP k .

[0026] Set a risk threshold RT. For each crop area A i , if TR k (A i ) < RT, then mark A i as a high-risk area. If TR k (A i ) < RT, then mark A i as a low-risk area.

[0027] Generate a high-risk area set HRA k = {A i | TR k (A i ) ≥ RT, A i ∈AR r} and a low-risk area set LRA k = {A i | TR k (A i ) < RT, A i ∈AR r}.

[0028] As a preferred solution of the dynamic data processing method for land ecological protection described in the present invention, calculate the overall early warning value of the evaluation period EP k :

[0029]

[0030] Among them, P(HRA k ) is the probability of the occurrence of the high-risk area set HRA k in the evaluation period EP k ; P(LRA k ) is the probability of the occurrence of the low-risk area set LRA k in the evaluation period EP k ; P(HRA k |LRA k ) is the probability of the occurrence of the high-risk area set HRA k when, in the given low-risk area set LRA k , the runoff generated by rainfall converges and causes TR i (A k ) ≥ RT.

[0031] As a preferred solution of the dynamic data processing method for land ecological protection described in the present invention, a preset overall warning value threshold AWT is set. If AWV k ≥ AWT, a warning is triggered, including the following steps:

[0032] Generate a warning message, including the high-risk area set HRA k and the corresponding overall risk degree TRk(A i );

[0033] Determine the warning level, where the warning level is the difference between AWV k and AWT, and send the warning message.

[0034] After the evaluation period EP k ends, add the current evaluation result to the historical record; adjust the risk threshold RT and the overall warning value threshold AWT according to the actual situation, and prepare to enter the next evaluation period EP k+1 .

[0035] The present invention also provides a dynamic data processing system for land ecological protection, including a land grid management module for grid-dividing agricultural land. Record the boundaries and topographic features of each crop area, construct a feature influence value, and ensure the precise management and risk analysis of the land. A remote sensing data acquisition and analysis module for using satellite remote sensing technology to collect data of each crop area A i at the node time T jRainfall areas at a certain time; set a basic evaluation period and a set of node times to provide data support for risk assessment. The risk assessment and early warning module is used to establish a risk assessment model and generate hierarchical early warning information. Divide the flood risk levels for each crop area, generate sets of high-risk and low-risk areas, provide real-time early warning information, and enhance the disaster response ability. The flood prevention measures module for crops is used to provide flood prevention suggestions for different risk levels. According to the early warning information and the characteristics of the crop area, generate specific flood prevention suggestions for each risk area to protect crops from flood damage. The data update and optimization module is used to update historical data and adjust risk thresholds after the evaluation period EP k ends. Add the results of this evaluation to the historical record, dynamically adjust the risk threshold and the overall early warning value threshold to ensure the accuracy and adaptability of the system evaluation. The comprehensive disaster prevention management module is used to integrate various data and information for comprehensive disaster prevention management. Based on the overall early warning value and the overall risk level, provide comprehensive disaster prevention strategies and measures to improve the efficiency and effectiveness of land ecological protection.

[0036] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: By using high-precision GPS and drones to grid-divide agricultural land and conduct risk assessment, ensure accurate recording of the boundaries and terrain features of each crop area, construct characteristic influence values, and provide accurate data for subsequent evaluations. Set a basic evaluation period and collect real-time weather data. Using remote sensing technology, regularly collect rainfall areas within the crop area during the set evaluation period to form a set of node times, ensuring the timeliness and dynamics of the data. Establish a risk assessment model and generate hierarchical early warning information. Based on the risk assessment model, divide the crop areas according to risk levels and generate corresponding early warning information to improve the timeliness and pertinence of flood prevention responses. Establish a database of flood prevention measures for crops. According to the early warning information, provide specific flood prevention suggestions for crop areas, combine crop characteristics and regional risks, optimize flood prevention measures, and enhance the disaster resistance ability of crops. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0038] Among them:

[0039] Figure 1 is a schematic flow chart of a dynamic data processing method for land ecological protection according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] Next, in combination with the accompanying drawings in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0041] Please refer to Figure 1 , the present invention provides a dynamic data processing method for land ecological protection, including,

[0042] S1: Use high-precision GPS and drones to divide the usage range of agricultural land into grids; by recording the boundaries and topographic features of each crop area, construct a characteristic influence value for evaluating the flood risk of each area.

[0043] Specifically, use high-precision GPS equipment to survey the usage range of agricultural land, divide the usage range of the agricultural land into grids to form several crop areas, where one crop area corresponds to one type of crop; uniformly number the crop areas, and classify the crop areas based on the numbering information and crop types to generate an area set, denoted as AR r ={A i |i∈[1,n]}, where AR r represents the area set generated corresponding to the rth type of crop, n represents the total number of crop areas, and A i represents the ith crop area generated corresponding to the rth type of crop.

[0044] Furthermore, use drones for low-altitude mapping to capture the topographic features of each crop area; at the same time, consult historical archives and record the flooding situation of each crop area in the past 10 years. Record the topographic features of each crop area and calculate the characteristic influence value of the crop area:

[0045]

[0046] Among them, CF(A i ) is the characteristic influence value of crop area A i , F ip is the characteristic value of the pth topographic feature of crop area A i . Since the units of different topographic features may be different, standardization processing is required. M represents the total number of topographic features, ω p is the weight of the pth topographic feature, μ p is the mean of the pth topographic feature, and σ p is the standard deviation of the pth topographic feature.

[0047] By standardizing each topographic feature (i.e., subtracting the mean and then dividing by the standard deviation), the dimensional differences between different topographic feature values can be eliminated. The weight ω p reflects the relative importance of each topographic feature to the risk factor and can be adjusted according to expert opinions or actual data.

[0048] If the characteristic impact value of a certain crop area is negative, it means that it performs better than the average level on the relevant topographic features, so the comprehensive risk is lower; if the characteristic impact value of a certain crop area is positive, it indicates that it performs lower than the average level on the relevant topographic features, so the comprehensive risk is higher.

[0049] For example, assume there are three characteristic values for the following five crop areas:

[0050]

[0051]

[0052] Among them, μ 高度 = 103, μ 坡度 = 4.2, μ 土壤酸碱度 = 5.88, σ 高度 = 4.2, σ 坡度 = 1.72, σ 土壤酸碱度＝0.23 , let Z = 1, ω 高度 = 0.5, ω 坡度 = 0.3, ω 土壤酸碱度 = 0.2, calculate the characteristic impact value of the first crop area A 1 :

[0053] The result shows that after considering three topographic features of terrain height, slope and soil pH, the comprehensive risk level of A 1 is lower than the overall mean.

[0054] S2: Set the basic evaluation period and construct the set of node times; collect the rainfall area using remote sensing technology.

[0055] Set the length L of the basic evaluation period, usually 24 hours, and introduce a sliding window mechanism to define the evaluation period. Each new evaluation period starts at a fixed time interval after the end of the previous period (such as 6 hours). Let the kth evaluation period be EP k = [Tstart(k), Tend(k)], where Tstart(k) is the start time of the evaluation period EP k , and the formula is Tstart(k) = Tstart(k - 1)+Δt, Tstart(k - 1) is the start time of the evaluation period EP k-1The start time; Tend(k) is the evaluation period EP k The end time, and the formula is Tend(k) = Tstart(k) + L, where Δt is the evaluation period EP k The sliding interval.

[0056] During the evaluation period EP k Insert m evenly spaced node times to construct the node time set TN k ={T j | j ∈ [1, m]}, where T j is the j-th node time, and the calculation formula is T 1 represents the first inserted node time, and T 1 = Tstart(k).

[0057] For example, let the start time Tstart(k) be 00:00 on July 12, 2024, let the cycle length L be 2 hours, and m be 5. Substituting into the formula, the first node time T 1 is: 00:00 on July 12, 2024 + 0, that is, the first node time is 00:00 on July 12, 2024. Similarly, the second node time T 2 is 00:30 on July 12, 2024.

[0058] Furthermore, use satellite remote sensing technology to collect the rainfall area of each crop area A i at the node time T j , and set the rainfall distribution weight D(T j , A i ) according to the rainfall area. The calculation formula of the rainfall distribution weight D(T j , A i ) is:

[0059]

[0060] where S(A i ) is the area of the rainfall area in the i-th crop area corresponding to the r-th crop type.

[0061] S2.1: Design the historical inundation frequency, and the calculation formula is:

[0062]

[0063] where HF(A i ) represents the historical inundation frequency, N represents the number of historical years, and F i represents the number of times the crop area A i was inundated within the historical N years.

[0064] According to the node time T j , calculate the inundation risk value of each crop area at the node time T j . The calculation formula is as follows:

[0065] FR(A i |T j ) = α·HF(A i ) + β·CF(A i ) + D(T j , A i );

[0066] Among them, FR(A i |T j ) represents the inundation risk value of the crop area A i at the node time T j . α and β are respectively preset linear regression coefficients.

[0067] In this way, we can comprehensively consider different types of data (historical data, regional characteristics, weather forecasts) to obtain a comprehensive inundation risk value. This risk value is a numerical value between 0 and 1, and the closer it is to 1, the higher the risk.

[0068] S3: Establish a risk assessment model, and based on the risk assessment model, classify the flood risk levels of each crop area.

[0069] Construct a risk assessment model and calculate the overall risk level of the crop area A i during the evaluation period EP k :

[0070]

[0071] Among them, TR k (A i ) is the overall risk level of the crop area A i during the evaluation period EP k .

[0072] This formula can help comprehensively evaluate the inundation risk of the crop area within an evaluation period EP k and provide an important reference basis for decision-making

[0073] Set a risk threshold RT. For each crop area A i , if TR k (A i ) ≥ RT, then mark A i as a high-risk area; if TR k (A i ) < RT, then mark A i as a low-risk area.

[0074] Generate the high - risk area set HRA k ={A i |TR k (A i )≥RT, A i ∈AR r} and the low - risk area set LRA k ={A i |TR k (A i )<RT, A i ∈AR r}; Calculate the overall warning value of the evaluation period EP k :

[0075]

[0076] Where P(HRA k ) is the probability of the occurrence of the high - risk area set HRA k in the evaluation period EP k , P(LRA k ) is the probability of the occurrence of the low - risk area set LRA k in the evaluation period EP k , and P(HRA k |LRA k ) is the probability of the occurrence of the high - risk area set HRA k when, in the given low - risk area set LRA k , the runoff generated by rainfall converges and causes TR i (A k )≥RT.

[0077] It should be noted that the purpose of calculating this formula is to evaluate the overall warning value AWV k within the evaluation period EP k . This warning value comprehensively considers the occurrence probabilities of high - risk and low - risk areas and their mutual relationships, so as to help make more accurate and comprehensive risk assessment and warning decisions.

[0078] S4: Generate graded warning information according to the result of the flood risk level division.

[0079] Preset the overall warning value threshold AWT. If AWV k ≥AWT, then trigger a warning:

[0080] Generate warning information, including the high - risk area set HRA k and the corresponding overall risk degree TR k (A i ).

[0081] Determine the warning level (such as low, medium, high), and the warning level is AWV k The difference from AWT, and send warning information to relevant personnel, including flood control measures. Specific measures and technologies can be formulated by the relevant person in charge according to the actual terrain characteristics.

[0082] During the evaluation period EP k After the end, add the evaluation results of this time to the historical record; adjust the risk threshold RT and the overall warning value threshold AWT according to the actual situation, and prepare to enter the next evaluation period EP k+1 。

[0083] Furthermore, according to the warning information, generate flood control suggestions for each risk area, and implement key monitoring for high-risk areas HRA k Increase the inspection frequency. In low-risk areas LRA k Establish conventional protection measures, such as drainage system maintenance; formulate area-specific emergency plans, clarify the responsible persons and response processes for each area; reinforce flood control dams and water conservancy facilities in high-risk areas, improve the drainage system, enhance the drainage capacity of low-lying areas, build facilities such as water storage ponds and flood detention areas to relieve the flood peak pressure.

[0084] This embodiment also provides a dynamic data processing system for land ecological protection, including: a land grid management module for grid division and risk assessment of agricultural land. Record the boundaries, terrain characteristics and historical inundation conditions of each crop area, construct characteristic risk factors to ensure precise land management and risk analysis. A remote sensing data collection and analysis module for collecting real-time weather data and historical inundation data using satellite remote sensing technology. Set the basic evaluation period and the set of time nodes, conduct dynamic monitoring and analysis of weather changes, and provide data support for risk assessment. A risk assessment and warning module for establishing a risk assessment model and generating hierarchical warning information. Divide the flood risk levels of each crop area, generate sets of high-risk and low-risk areas, provide real-time warning information, and enhance the disaster response ability. A crop flood control measure module for providing flood control suggestions for different risk levels. Establish a flood control measure database, and generate specific flood control suggestions for each risk area according to the warning information and crop area characteristics to protect crops from floods. A data update and optimization module for updating historical data and adjusting risk thresholds after the evaluation period. Add the evaluation results of this time to the historical record, dynamically adjust the risk threshold and the overall warning value threshold to ensure the accuracy and adaptability of system evaluation. A comprehensive disaster prevention management module for integrating various data and information for comprehensive disaster prevention management. Based on the overall warning value and the overall risk level, provide comprehensive disaster prevention strategies and measures to improve the efficiency and effectiveness of land ecological protection.

[0085] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0086] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A dynamic data processing method for land ecological protection, characterized in that: The method comprises the following steps: S1: Use high-precision GPS and drones to grid the use range of agricultural land; construct feature impact values ​​by recording the boundaries and terrain features of each crop area; S2: Set the basic evaluation cycle; construct a node time set, and use remote sensing technology to collect rainfall areas; S3: establishing a risk assessment model, and classifying each crop area into flood risk levels based on the risk assessment model; S4: generating graded warning information according to the result of the flood risk level classification; The S1 includes: Use high-precision GPS equipment to survey the use range of agricultural land, and divide the use range of the agricultural land into several crop areas in a grid-like manner, where one crop area corresponds to one crop type; uniformly number the crop areas, classify the crop areas based on the number information and crop type, and generate a region set, recorded as AR r ={A i |i∈[1,n]}, where AR r represents the set of regions generated corresponding to the rth crop type, n represents the total number of crop regions, A i Indicates the i-th crop area generated corresponding to the r-th crop type; Use drones for low-altitude mapping to capture the topographic features of each crop area, record the topographic features of each crop area, and calculate the characteristic impact value of the crop area: Among them, CF(A i ) is the crop area A i The characteristic influence value, F ip For crop area A i is the characteristic value of the pth terrain feature, M represents the total number of terrain features, ω p is the weight of the pth terrain feature, μ p is the mean of the pth terrain feature, σ p is the standard deviation of the pth terrain feature.

2. A dynamic data processing method for land ecological protection according to claim 1, characterized in that: The S2 includes: Set the length of the basic evaluation cycle L, introduce a sliding window mechanism to define the evaluation cycle; let the kth evaluation cycle be EP k =[Tstart(k),Tend(k)], where Tstart(k) is the evaluation period EP k The start time is Tstart(k)=Tstart(k-1)+Δt, where Tstart(k-1) is the evaluation period EP. k-1 The start time of Tend(k) is the evaluation period EP k The end time of the evaluation period is: Tend(k) = Tstart(k) + L, Δt is the evaluation period EP k The sliding interval; During the evaluation period EP k Insert m uniform node times and construct the node time set TN k ={T j [j∈[1,m]}, where T j is the jth node time, and the calculation formula is T1 represents the time of the first node inserted, and T1 = Tstart(k); Satellite remote sensing technology is used to collect A i At node time T j The rainfall area at that time, and the rainfall distribution weight D(T j , A i ), the rainfall distribution weight D(T j , A i ) is calculated as: Among them, S(A i ) is the area of ​​the rainfall region within the i-th crop region generated corresponding to the r-th crop type.

3. A dynamic data processing method for land ecological protection according to claim 1, characterized in that: The S2 further includes: Design historical flooding frequency, calculated as: Among them, HF(A i ) represents the historical flooding frequency, N represents the historical years, F i Indicates the crop area A in the history N years i Number of flooding events; According to the node time T j , calculate each crop area at node time T j The flooding risk value is calculated as follows: FR(A i |T j )=α·HF(A i )+β·CF(A i )+D(T j ,A i ); Among them, FR(A i |T j ) represents the crop area A i At node time T j is the flooding risk value, α and β are the preset linear regression coefficients.

4. A dynamic data processing method for land ecological protection according to claim 1, characterized in that: The S3 includes: Construct a risk assessment model to calculate the risk of crop area Ai in the assessment period EP k Overall risk level: Among them, TR k (A i ) is the crop area A i During the evaluation period EP k the overall risk level; Set the risk threshold RT for each crop area A i , if TR k (A i )≥RT, then A i Marked as a high-risk area, if TR k (A i )<RT, then A i Marked as low risk area; Generate high risk area set HRA k ={A i |TR k (A i )≥RT,A i ∈AR r } and Low Risk Area Aggregate (LRA) k ={A i |TR k (A i )<RT,A i ∈AR r }.

5. The dynamic data processing method for land ecological protection according to claim 1 is characterized in that: The S3 further includes: Calculate the evaluation period EP k Overall warning value: Among them, P(HRA k ) is the period of evaluation EP k Medium and high risk areas HRA k Probability of occurrence, P(LRA k ) is the period of evaluation EP k Medium and low risk areas LRA k The probability of occurrence, P(HRA k |LRA k ) is the LRA set in a given low risk area k In the case of convergence of runoff generated by rainfall, TR k (A i )≥RT, high risk area set HRA k Probability of occurrence.

6. A dynamic data processing method for land ecological protection according to claim 1, characterized in that: The S4 includes: Preset overall warning value threshold AWT, if AWV k ≥AWT, an early warning is triggered, including the following steps: Generate early warning information, including high-risk area collection HRA k and the corresponding overall risk level TR k (A i ); Determine the warning level, the warning level is AWV k The difference with AWT and send warning information; During the evaluation period EP k After completion, the evaluation results will be added to the historical records.

7. A dynamic data processing system for land ecological protection, executing a dynamic data processing method for land ecological protection as claimed in any one of claims 1 to 6, characterized in that: The system includes: The land grid management module is used to grid the agricultural land; record the boundaries and terrain features of each crop area, and construct the feature impact value; Remote sensing data collection and analysis module, used to collect A data of each crop area using satellite remote sensing technology i At node time T j The rainfall area at that time; set the basic evaluation period and node time set; The risk assessment and early warning module is used to establish a risk assessment model and generate graded early warning information; classify the flood risk level of each crop area and generate a set of high-risk and low-risk areas; The crop flood prevention measures module is used to provide flood prevention suggestions for different risk levels; based on the warning information and crop area characteristics, specific flood prevention suggestions are generated for each risk area; Data update and optimization module, used in the evaluation cycle EP k After completion, update historical data and adjust risk thresholds; add the assessment results to historical records, and dynamically adjust risk thresholds and overall warning thresholds; The comprehensive disaster prevention management module is used to integrate various data and information for comprehensive disaster prevention management; it provides comprehensive disaster prevention strategies and measures based on the overall warning value and overall risk level.

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