Method and system for monitoring degraded restoration grassland based on wind-rolling plant supplemental sowing

Abstract

The application relates to the technical field of grassland restoration monitoring, and discloses a degraded restoration grassland monitoring method and system based on wind-rolling plant supplementary planting. The system comprises data acquisition, consistency determination, propagation influence evaluation, abnormality screening and monitoring output modules. The data acquisition module obtains degradation point coordinates, time, soil humidity and vegetation coverage, calculates a supplementary planting suitability value to generate a supplementary planting suitability set; the consistency determination module sorts adjacent point pairs according to spatial distance, marks consistent and conflict sections to obtain a direction consistency partition annotation set; the propagation influence evaluation module combines wind speed, precipitation time series and supplementary planting seed amount, evaluates propagation influence strength under environmental fluctuation to generate superposition analysis results; the abnormality screening module identifies abnormal points with propagation response values exceeding a reference value and located in conflict sections to form an abnormal point set; and the monitoring output module marks diffusion risk points to generate monitoring and risk early warning results, thereby helping to improve grassland degradation restoration monitoring accuracy and management efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of grassland restoration monitoring, in particular to a degraded restoration grassland monitoring method and system based on wind-rolling plant supplemental planting. BACKGROUND

[0002] In the field of ecological environment governance, grassland, as an important component of terrestrial ecosystems, undertakes multiple key ecological functions such as soil and water conservation, climate regulation, and biodiversity maintenance. However, due to the combined effects of global climate change, overgrazing, and unreasonable land development, the problem of grassland degradation is becoming increasingly serious worldwide. The area of degraded grassland is expanding, and the degree of degradation is deepening, which not only leads to a significant decline in the service function of the grassland ecosystem, but also poses a serious threat to regional ecological balance, food security, and sustainable social and economic development.

[0003] In response to the problem of grassland degradation, researchers and ecological governance workers have conducted extensive research and explored various grassland restoration techniques. Among them, the supplemental planting restoration technique based on wind-rolling plants has been widely applied in the restoration of degraded grasslands due to its low cost, strong adaptability, and high degree of compatibility with natural ecological processes. Wind-rolling plants rely on wind power to complete seed dispersal and diffusion, and the effectiveness of supplemental planting is closely related to environmental factors such as wind speed and precipitation. Meanwhile, there are significant differences in soil moisture and vegetation coverage in different areas of degraded grassland, which collectively determine the spatial heterogeneity and uncertainty of wind-rolling plant supplemental planting restoration.

[0004] During the restoration of degraded grassland based on wind-rolling plant supplemental planting, there are still obvious deficiencies in related monitoring work. Traditional monitoring methods rely heavily on manual field sampling and investigation, which not only consumes time and effort and has limited coverage, making it difficult to achieve continuous dynamic monitoring of large areas of degraded grassland, but also fails to accurately capture the spatial differences in supplemental planting suitability in different degraded areas, making it difficult to effectively identify abnormal seed dispersal caused by environmental fluctuations during the supplemental planting process. In addition, existing monitoring methods lack systematic analysis of the spatial correlation of supplemental planting areas, making it difficult to accurately determine the consistency and conflict of supplemental planting suitability between different degraded points, and assess the strength and range of the impact of environmental factors such as wind speed and precipitation on wind-rolling plant seed dispersal. This results in monitoring results that cannot provide precise support for the optimization and adjustment of supplemental planting schemes, often leading to uneven seed distribution in supplemental planting areas, poor restoration results in some areas, and even secondary degradation, which severely hinders the application effectiveness and promotion value of the degraded grassland restoration technique based on wind-rolling plant supplemental planting. SUMMARY

[0005] The present application aims to provide a degraded restoration grassland monitoring method and system based on wind-rolling plant supplemental planting to solve the problems raised in the background.

[0006] In order to achieve the above object, the present application provides a degraded repair grassland monitoring system based on wind rolling plant reseeding, which comprises:

[0007] The data acquisition module acquires the coordinates and time of the grassland degradation points, collects the soil moisture and vegetation coverage of the corresponding positions, correlates the degradation points and calculates the corresponding reseeding suitability values to generate a reseeding suitability set of the degradation points;

[0008] The consistency determination module extracts the reseeding suitability values and corresponding coordinates in the reseeding suitability set of the degradation points, sorts the adjacent point pairs according to the spatial distance, marks the consistent and conflict sections in the adjacent point pairs, and obtains a direction consistency partition annotation set;

[0009] The propagation influence evaluation module acquires the degradation points located in the direction consistent section in the direction consistency partition annotation set, extracts the wind speed and precipitation time series, compares the change amplitude combined with the reseeding seed amount, evaluates the propagation influence strength under environmental fluctuations, and generates a propagation influence superposition analysis result;

[0010] The anomaly screening module identifies the abnormal points in the degradation points in the propagation influence superposition analysis result which have a propagation response value greater than the average response reference value and are in the direction conflict section, and forms a reseeding abnormal point set;

[0011] The monitoring output module acquires all point positions and corresponding point information in the reseeding abnormal point set, marks the point positions with diffusion risk, and generates a grassland degradation repair monitoring and risk warning result.

[0012] Preferably, the reseeding suitability set of the degradation points comprises reseeding suitability values, degradation point spatial coordinates, and normalized environmental factors;

[0013] The direction consistency partition annotation set is specifically direction consistent section annotation, direction conflict section annotation, and adjacent degradation point suitability value difference rate;

[0014] The propagation influence superposition analysis result comprises the influence degree of wind speed rising rate on reseeding, the influence degree of precipitation falling rate on reseeding, and the seed propagation response comparison under each environmental fluctuation condition;

[0015] The reseeding abnormal point set comprises abnormal point spatial position, abnormal point wind direction humidity amplitude feature, and abnormal point seed amount and area fluctuation ratio;

[0016] The grassland degradation repair monitoring and risk warning result comprises a monitoring abnormal point position list and an abnormal point three-index joint determination label.

[0017] Preferably, the data acquisition module comprises:

[0018] The grassland information acquisition submodule acquires the coordinate position and monitoring time of the grassland degradation point, acquires the soil humidity value corresponding to the coordinate position and the vegetation coverage data of the day, and records the acquisition results as two environmental factors of soil factors and vegetation factors, to obtain an environmental factor data group of the degradation point;

[0019] The environmental factor normalization submodule performs normalization processing on the soil factor and vegetation factor data in the environmental factor data group of the degradation point, respectively, establishes a corresponding relationship between the normalized results and the coordinate position of the degradation point, calculates the average value of the normalized soil humidity value and the normalized vegetation coverage as a suitable value for reseeding, and generates a suitable value set for reseeding of the degradation point.

[0020] Preferably, the consistency determination module comprises:

[0021] The suitable value extraction submodule acquires the suitable value for reseeding and the corresponding coordinate data in the suitable value set for reseeding of the degradation point, identifies the spatial positional relationship of all degradation points according to the coordinate information, calls the degradation point coordinate set, takes the adjacent distance threshold as the reference, calculates and sorts the distances of the degradation points in space, and generates a distance sorting sequence of adjacent degradation points;

[0022] The difference rate calculation submodule calculates the suitable value difference rate between two adjacent degradation points based on the distance sorting sequence of adjacent degradation points, and integrates to generate a suitable value difference rate sequence;

[0023] The direction consistency identification submodule extracts the change direction of soil humidity and the change direction of vegetation coverage between adjacent degradation points based on the suitable value difference rate sequence, classifies and labels according to whether the change trend of each pair of degradation points in the two directions is consistent, records and groups the sections with direction consistency and direction conflict respectively, and obtains a direction consistency partition labeling set.

[0024] Preferably, the propagation influence evaluation module comprises:

[0025] The environmental sequence extraction submodule screens the sections marked as direction consistent according to the direction consistency partition labeling set, detects the wind speed data and precipitation data in the monitoring time period of each degradation point, arranges them in time sequence to form a wind speed time sequence and a precipitation time sequence, and generates an environmental change time sequence set;

[0026] The propagation superposition calculation submodule calculates the wind speed rising rate and precipitation falling rate between consecutive time nodes in the time sequence of each degradation point based on the environmental change time sequence set, compares the wind speed rising rate and the precipitation falling rate under the same seed amount, identifies the numerical relationship of the change amplitude under the seed amount through joint analysis of the two types of rate indexes, integrates the influence value sequence of each degradation point, and establishes a propagation influence superposition analysis result.

[0027] Preferably, the anomaly screening module comprises:

[0028] The record extraction submodule screens the degradation points with a propagation response value greater than the average response reference value and the degradation points in the direction conflict section according to the propagation influence superposition analysis result, extracts the continuous monitoring records of the degradation points in time sequence, collects the supplemental seeding seed amount and supplemental seeding area data corresponding to each time node, and generates a continuous monitoring record set;

[0029] The seed area ratio calculation submodule extracts the seed amount and area of the degradation points at two continuous time nodes respectively, calculates the seed amount change ratio and supplemental seeding area change ratio respectively, integrates the seed amount change ratio sequence and supplemental seeding area change ratio sequence, and establishes a supplemental seeding fluctuation change data set by calling the continuous monitoring record set;

[0030] The anomaly recognition submodule extracts the wind direction amplitude data and humidity fluctuation data corresponding to the time period based on the supplemental seeding fluctuation change data set, judges whether the seed amount change ratio and area change ratio both exceed the set fluctuation recognition threshold, judges whether the wind direction amplitude and humidity fluctuation both exceed the anomaly judgment threshold, marks the time nodes that meet the conditions as anomaly points, and generates a supplemental seeding anomaly point set.

[0031] Preferably, the monitoring output module comprises:

[0032] The index joint determination submodule obtains all the point positions in the supplemental seeding anomaly point set and the corresponding coordinate and identification information, calculates a joint risk determination value, and establishes a joint risk determination value sequence;

[0033] The anomaly output arrangement submodule screens the point positions with a propagation response value greater than a propagation response risk threshold, a supplemental seeding suitability value lower than a suitability reference value, and a direction consistency label as a conflict section based on the joint risk determination value sequence, extracts the corresponding point position number, location identifier, and belonging partition, marks the point positions as monitoring anomalies and with a diffusion risk, and outputs the point positions meeting the joint conditions in a structured format according to the partition to generate a grassland degradation repair monitoring and risk early warning result.

[0034] Preferably, the system further comprises a remote verification module for triggering a preset verification rule for data validity verification in a fixed partition when the monitoring data is abnormal;

[0035] If the verification fails, the verification problem features are sent to a temporary cache area in the fixed partition, and the monitoring verification takeover of a remote partition is triggered;

[0036] After the remote partition takes over the monitoring verification, the write permission of the fixed partition is locked, and the monitoring context and the verification problem features are read from the temporary cache area;

[0037] The remote partition packs and uploads the monitoring context and the verification problem features to a cloud verification engine through a secure channel;

[0038] After the cloud verification engine receives and performs verification strategy divergence analysis according to the monitoring context and the verification problem features, the cloud verification engine encrypts and issues multi-level verification strategies to the remote partition;

[0039] The remote partition loads the multi-level verification strategies to perform hierarchical risk verification on the monitoring data, and issues binding interface permissions to the monitoring data according to the verification results.

[0040] Preferably, the remote verification module comprises:

[0041] The preset rule loading submodule loads preset verification rules from a secure storage area, wherein the preset verification rules comprise a trusted data whitelist and an environment fingerprint hash library;

[0042] The identification information reading submodule reads identification information of the monitoring data through a data protocol stack, wherein the identification information comprises a data digital certificate, an environment serial number and a data protocol descriptor;

[0043] The first matching verification submodule drives a secure chip to traverse the trusted data whitelist to compare the data digital certificate after checking the issuing authority of the data digital certificate and the integrity of a signature chain, and outputs a first matching verification result;

[0044] The second matching verification submodule drives a secure chip to calculate a data feature hash value using the data protocol descriptor and the environment serial number, and outputs a second matching verification result by comparing the data feature hash value with the environment fingerprint hash library;

[0045] The result judging submodule verifies the data validity when the first matching verification result and the second matching verification result are both passed.

[0046] Preferably, the present application further comprises a degraded restoration grassland monitoring method based on wind-rolling plant supplementary planting, which comprises all the modules and method processes of the degraded restoration grassland monitoring system based on wind-rolling plant supplementary planting.

[0047] Compared with the prior art, the present application has the following beneficial effects:

[0048] The data acquisition module can accurately obtain the coordinates and time information of the grassland degradation points, synchronously collect key site parameters such as soil moisture and vegetation coverage at the corresponding positions, and calculate the reseeding suitability value through the correlation of the degradation points to generate a reseeding suitability set of the degradation points. This process realizes the quantification and systematization of the reseeding suitability evaluation index of the degraded grassland. Compared with the traditional manual sampling method which can only obtain scattered data, the overall and accuracy of the monitoring data are greatly improved, so that the staff can clearly understand the reseeding suitability of different degradation points and avoid the blindness of reseeding decision-making due to insufficient data.

[0049] The consistency determination module extracts the reseeding suitability value and the corresponding coordinates, sorts the adjacent point pairs according to the spatial distance, and marks the consistent and conflicting sections to form a directional consistency partition annotation set. This module breaks through the limitation of traditional monitoring which ignores the spatial correlation, can intuitively present the spatial distribution rule of the reseeding suitability of the degraded grassland in different regions, clearly identify the continuous region with consistent reseeding suitability and the transition section with conflict, help the staff accurately grasp the spatial heterogeneity of the reseeding suitability, and provide a clear spatial guide for formulating differentiated reseeding strategies, avoiding the use of the "one-size-fits-all" method in the reseeding process, so that the reseeding scheme is more suitable for the actual degradation condition of the grassland.

[0050] The propagation influence evaluation module focuses on the degradation points in the directional consistent section, combines the wind speed, precipitation time series and the amount of reseeding seeds, compares the change amplitude and evaluates the propagation influence strength under environmental fluctuations to generate propagation influence superposition analysis results. This module realizes the dynamic monitoring and quantitative evaluation of the correlation between environmental factors and wind-rolling plant seed propagation, can accurately capture the influence of environmental fluctuations such as wind speed and precipitation on the seed propagation process, and enables the staff to timely understand the change trend of seed propagation under different environmental conditions, so as to predict possible problems such as poor seed propagation or excessive diffusion in advance, provide a scientific basis for timely adjusting the reseeding time and optimizing the seed release amount, and ensure the stability and effectiveness of the wind-rolling plant seed propagation process.

[0051] The abnormality screening module accurately identifies abnormal degradation points whose propagation response value exceeds the average response reference value and is in the directional conflict section based on the propagation influence superposition analysis results to form a reseeding abnormal point set. This function solves the problem that traditional monitoring cannot find potential abnormalities in the reseeding process, can quickly locate the reseeding abnormal area caused by the combined action of environmental fluctuations and spatial suitability conflicts, and enables the staff to focus on the problem area in time, analyze the causes of the abnormality in depth, avoid the further spread of abnormal conditions affecting the overall repair effect, and effectively reduce the risk hidden danger in the reseeding repair process.

[0052] The monitoring output module integrates all point information of the abnormal point set of the reseeding, marks the point with the risk of spreading and generates the monitoring and risk warning results. The module converts the complex monitoring data into intuitive and easy-to-understand visual results, not only provides the staff with comprehensive information of the progress of the degraded grassland repair, but also clearly shows the areas that need to be focused on through the risk warning, so that the staff can quickly take targeted intervention measures, improves the fine level and emergency response capability of the repair management of the degraded grassland reseeding, and promotes the degraded grassland repair technology based on the wind-rolling plant reseeding to play a better ecological benefit. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 A timing diagram of the degraded grassland repair monitoring system based on the wind-rolling plant reseeding is shown in the figure.

[0054] Figure 2 A working principle diagram of the relationship between the sets of the degraded grassland repair monitoring system is shown in the figure.

[0055] Figure 3 A working flow principle diagram of the consistency determination module is shown in the figure. DETAILED DESCRIPTION

[0056] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0057] Please refer to Figure 1The application provides a degraded repair grassland monitoring method and system based on wind-rolling plant reseeding. The system includes multiple modules that work together to monitor and risk warn the grassland degradation points. The data collection module first acquires the coordinates and time information of the grassland degradation points, and collects the soil moisture and vegetation coverage data of the corresponding positions. These data are associated with the degradation points, and the reseeding suitability value is calculated to generate a reseeding suitability set of the degradation points. The consistency determination module then extracts the reseeding suitability value and coordinate data in the set, sorts the adjacent point pairs according to the spatial distance, marks the consistent and conflict sections, and forms a directional consistency partition annotation set. The propagation impact evaluation module acquires the degradation points in the directional consistent section, extracts the wind speed and precipitation time series, compares the change amplitude combined with the reseeding seed amount, evaluates the propagation impact strength under environmental fluctuations, and generates a propagation impact superposition analysis result. The anomaly screening module identifies the abnormal points in the degradation points in the analysis result whose propagation response value is greater than the average response reference value and is in the directional conflict section, and forms a reseeding abnormal point set. Finally, the monitoring output module acquires all the points and information in the abnormal point set, marks the points with diffusion risk, and generates the grassland degradation repair monitoring and risk warning result. The system realizes the comprehensive monitoring and risk warning of the grassland degradation repair process through modular design.

[0058] Example 1: refer to Figure 2 The construction of the reseeding suitability set of the degradation points includes the core elements such as the reseeding suitability value, the spatial coordinates of the degradation points, and the normalized environmental factors. The reseeding suitability value is obtained by normalizing the collected soil moisture and vegetation coverage data and calculating the average value, and its value range is between zero and one, which can directly reflect whether a specific point is suitable for reseeding operation. The spatial coordinates are recorded in the latitude-longitude or plane coordinate system to ensure that each degradation point has a unique geographical identifier. The normalized environmental factors further standardize the soil and vegetation data, eliminate the dimensional differences, and make the environmental conditions of different points comparable. The generation of the directional consistency partition annotation set involves the trend consistency judgment of the spatial adjacent degradation point pairs. The set specifically includes directional consistent section annotation, directional conflict section annotation, and adjacent degradation point suitability value difference rate. The directional consistent section annotation is used to identify adjacent point pairs with the same trend of soil moisture and vegetation coverage change, such as both showing an upward or downward trend. This type of section usually indicates that the environmental conditions are relatively stable. The directional conflict section annotation is used to mark adjacent point pairs with opposite trends, such as soil moisture rising while vegetation coverage decreasing, which may indicate local environmental anomalies or human interference. The adjacent degradation point suitability value difference rate quantifies the change amplitude by calculating the relative difference between the reseeding suitability values of two points. A higher difference rate indicates a significant change in suitability between the points, which may need to be focused on.

[0059] The propagation impact superposition analysis result needs to comprehensively evaluate the potential impact of environmental fluctuations on seed propagation, which includes the wind speed increase rate impact on reseeding, the precipitation decrease rate impact on reseeding, and the seed propagation response comparison under each environmental fluctuation condition; the wind speed increase rate is calculated by analyzing the increase amount per unit time in the wind speed time series, reflecting the sharpness of wind power changes, and the precipitation decrease rate is similarly calculated based on the precipitation time series, representing the speed of water condition deterioration, under the condition of the same amount of seeds, comparing these rate indicators can identify which environmental fluctuations have a dominant impact on propagation behavior, and the seed propagation response comparison further analyzes the changes in seed dispersal distance and settlement success rate under different wind speed and precipitation change scenarios, thereby forming a comprehensive propagation impact evaluation.

[0060] The construction of the reseeding anomaly point set aims to identify points that respond sharply to environmental fluctuations and are in conflict sections, which includes anomaly point spatial location, anomaly point wind direction and humidity amplitude characteristics, and anomaly point seed amount and area fluctuation ratio; the anomaly point spatial location is directly extracted from the degradation point coordinates for geographic positioning and visualization, the anomaly point wind direction and humidity amplitude characteristics describe the wind direction change amplitude and humidity fluctuation range in a specific time period, which are often related to the uncertainty of seed propagation path, and the anomaly point seed amount and area fluctuation ratio is calculated by comparing the actual sowing seed amount and reseeding area change at consecutive time nodes, if the ratio is too high or too low, it indicates that the reseeding operation may be inconsistent or disturbed externally. The grassland degradation repair monitoring and risk warning result is the final product of the system output, which includes the monitoring anomaly point list and the anomaly point three-index joint determination label; the monitoring anomaly point list lists all the identified abnormal points in table or map form, including their number, coordinates, collection time, etc. basic information, and the anomaly point three-index joint determination label is based on the propagation response value, reseeding suitability value and direction consistency label for comprehensive assignment, for example, when the propagation response value of a point exceeds the threshold, the reseeding suitability value is lower than the benchmark and is in the direction conflict section, it is assigned a "high risk" label, otherwise it is assigned a "low risk" or "medium risk" label, thereby forming a structured warning output to guide subsequent repair decisions and action deployment.

[0061] Embodiment 2: refer to Figure 3 The data acquisition module starts the monitoring process through the grassland information acquisition submodule, which is responsible for obtaining the precise coordinate position of the grassland degradation point and the corresponding monitoring timestamp, using the sensor network and remote sensing equipment deployed in the field to collect the soil humidity value and the vegetation coverage data of each coordinate point on the same day. The original data collected are classified and recorded as soil factors and vegetation factors, two types of environmental indicators, which together constitute the degradation point environmental factor data set, which establishes complete data records for each degradation point including time, space and environmental attributes.

[0062] The environment factor normalization submodule receives the degradation point environment factor data set and performs standardization processing on the soil factor and vegetation factor data therein. The minimum-maximum normalization method is adopted to convert the soil humidity original value to the range of zero to one, and the vegetation coverage data is also subjected to the same processing to eliminate the dimensional difference. The normalized result establishes a stable mapping relationship with the coordinate position of the degradation point. The arithmetic mean of the normalized soil humidity value and the normalized vegetation coverage is calculated to obtain the suitable value of each point, and a suitable value set of the degradation point reseeding is finally generated, which contains coordinates, time and suitable value. The suitable value extraction submodule in the consistency determination module reads the reseeding suitable value and its corresponding coordinate data from the degradation point reseeding suitable value set, calculates the Euclidean distance between all degradation points based on the coordinate information and identifies the spatial proximity relationship. A fixed adjacent distance threshold is set to filter and sort the degradation points in space, and a distance sorting sequence of adjacent degradation points is generated in ascending order of distance.

[0063] The difference rate calculation submodule calculates the reseeding suitable value difference rate between two adjacent degradation points based on the adjacent degradation point distance sorting sequence, which quantifies the change amplitude through the ratio of the absolute difference value to the average value of the suitable values of the two points. The difference rates of all adjacent point pairs are integrated into an ordered suitable value difference rate sequence, which accurately reflects the suitability change gradient of the adjacent points in space. The direction consistency identification submodule further analyzes the environmental change trend of each adjacent point pair using the suitable value difference rate sequence, extracts the soil humidity change direction and vegetation coverage change direction of each pair of degradation points and compares them. When both directions increase or decrease simultaneously, it is marked as a direction consistent section, and when the change directions are opposite, it is marked as a direction conflict section. The classification and labeling results of all sections are recorded as a direction consistency partition labeling set, which provides a partition basis for environmental fluctuation analysis.

[0064] Embodiment 3: The propagation influence evaluation module initiates the analysis process through the environmental sequence extraction submodule, which receives the directional consistency partition label set from the consistency judgment module, filters out all segments marked as directionally consistent, which represent relatively stable areas with the same trends of soil moisture and vegetation coverage changes, and then extracts the wind speed and precipitation original data within the complete monitoring period for each degradation point in these segments, arranges and cleanses them in chronological order to form wind speed time series and precipitation time series, which together constitute the environmental change time series set. The propagation superposition calculation submodule analyzes the environmental change time series set point by point, calculates the wind speed change and precipitation change between consecutive time nodes in each degradation point time series, and in order to quantify the intensity of environmental fluctuations, introduces two dynamic indicators: wind speed rise rate and precipitation drop rate. The wind speed rise rate reflects the speed of wind speed increase per unit time, and the precipitation drop rate represents the magnitude of precipitation reduction per unit time. In the calculation process, for points with the same supplemental seed amount, the two types of rate indicators are compared side by side to analyze their potential impact on seed propagation behavior.

[0065] In order to comprehensively evaluate the superimposed influence of environmental fluctuations on seed propagation, the following relationship is used for quantitative analysis:

[0066]

[0067] Where: represents the propagation influence intensity, represents the wind speed influence weight coefficient, is the wind speed rise rate, represents the precipitation influence weight coefficient, is the precipitation drop rate. and coefficients are obtained by training historical data, reflecting the relative importance of different environmental factors on seed propagation. is calculated by the ratio of the wind speed difference between adjacent time points to the time interval, is calculated by the ratio of the absolute value of the precipitation difference between adjacent time points to the time interval. Absolute value processing ensures that the drop rate is always positive to facilitate calculation.

[0068] Through the above calculation, each degradation point can obtain a series of time-ordered impact strength values, forming the propagation impact value sequence of the point, and the sequences of all points together constitute the propagation impact superposition analysis result. This result not only contains the original environmental fluctuation data, but more importantly, it provides a quantitatively processed impact strength index, which can intuitively reflect the potential impact degree of seed propagation under different environmental conditions. In the process of generating the final analysis result, the system also performs horizontal comparison on the points with the same seed amount but under different environmental fluctuation patterns, identifies different impact patterns such as wind speed dominant type, precipitation dominant type, and mixed impact type, and this comparison analysis helps to understand the differences in seed propagation mechanisms under different environmental conditions, providing more refined basis for subsequent anomaly point identification.

[0069] Taking the alpine grassland restoration project in the Three-River Source Region of Qinghai Province as an example, the system conducted a three-week continuous observation on five monitoring points numbered QH-08 to QH-12. The environmental sequence extraction submodule first selected all segments marked as direction consistent from the direction consistency partition annotation set. The soil moisture and vegetation coverage trends of all points in this area remain consistent, so they are all included in the analysis range. Extract the wind speed and precipitation original data of each point during the monitoring period: the wind speed sequence of QH-08 point shows that the daily average wind speed gradually increases from 2.1 m / s to 4.3 m / s, and the precipitation sequence records that the cumulative precipitation decreases from 28 mm in the first week to 12 mm in the third week; the wind speed of QH-09 point increases from 1.8 m / s to 3.9 m / s, and the precipitation decreases from 32 mm to 15 mm; the wind speed of QH-10 point increases from 2.3 m / s to 4.1 m / s, and the precipitation decreases from 30 mm to 13 mm; the wind speed of QH-11 point increases from 2.0 m / s to 4.4 m / s, and the precipitation decreases from 29 mm to 14 mm; the wind speed of QH-12 point increases from 2.2 m / s to 4.2 m / s, and the precipitation decreases from 31 mm to 16 mm. These time series data are cleaned and arranged to form a complete set of environmental change time series.

[0070] The propagation superposition calculation sub-module analyzes the environmental sequence of each point day by day, and calculates the wind speed change and precipitation change between the nodes in the continuous 24 hours. Taking QH-08 point as an example, the wind speed on the third day of the second week increased by 0.8 m / s compared with the previous day, the time interval was 24 hours, and the wind speed rising rate was 0.033 m / s / h; the precipitation on the same day decreased by 2.1 mm, and the precipitation falling rate was 0.087 mm / h. The same method is used to calculate the wind speed rising rate and precipitation falling rate of each point. Under the condition of the same amount of reseeding (5 kg of grass seed per hectare at each point), the two types of rate indicators are compared and analyzed: the maximum wind speed rising rate of QH-08 point reaches 0.042 m / s / h, and the corresponding precipitation falling rate is 0.091 mm / h; the maximum wind speed rising rate of QH-09 point is 0.038 m / s / h, and the corresponding precipitation falling rate is 0.083 mm / h; the maximum wind speed rising rate of QH-10 point is 0.039 m / s / h, and the corresponding precipitation falling rate is 0.085 mm / h; the maximum wind speed rising rate of QH-11 point is 0.043 m / s / h, and the corresponding precipitation falling rate is 0.089 mm / h; the maximum wind speed rising rate of QH-12 point is 0.040 m / s / h, and the corresponding precipitation falling rate is 0.088 mm / h.

[0071] By jointly analyzing the influence of the two types of rate indicators on seed propagation, it is found that when the wind speed rising rate exceeds 0.040 m / s / h, the seed propagation distance increases significantly; when the precipitation falling rate exceeds 0.085 mm / h, the seed colonization success rate decreases significantly. The system records the daily influence intensity value of each point, and the highest influence intensity value of QH-08 point is 0.87 on the second day of the third week, QH-09 point is 0.79, QH-10 point is 0.81, QH-11 point is 0.89, and QH-12 point is 0.83. The daily influence values of all points are arranged in chronological order to form a complete propagation influence value sequence. The final generated propagation influence superposition analysis result includes the original environmental data of each point, the calculated rate indicators, the daily influence intensity value, and the influence mode classification. The analysis shows that QH-11 point belongs to the typical wind speed dominant type of influence mode, its wind speed rising rate indicator is significantly higher than that of other points; QH-08 point shows mixed influence characteristics, both types of rate indicators are at a relatively high level; QH-09 point is relatively less affected, and all indicators are in the medium range.

[0072] Embodiment 4: The anomaly screening module initiates the analysis process by the record extraction submodule, which receives the propagation impact overlay analysis results from the propagation impact evaluation module, and screens out two types of key points: degradation points with a propagation response value higher than the average response benchmark value calculated by the system, and degradation points marked as directional conflict sections. Taking the area monitoring data as an example, the system identifies three points P-07, P-12, and P-19 that simultaneously meet both conditions, and then extracts the continuous records of these points within the complete monitoring period in chronological order, collects the actual supplemental seed amount and the implemented supplemental area data corresponding to each time node, and forms a structured continuous monitoring record set.

[0073] The seed area ratio calculation submodule performs time series change analysis on the above continuous monitoring record set. Taking point P-07 as an example, the seed amount value and supplemental area data at time nodes T1 to T5 are extracted, and the seed amount change ratio and supplemental area change ratio between adjacent time nodes are calculated. The seed amount change ratio is calculated by the ratio of the next node value to the previous node value, and the supplemental area change ratio is calculated using the same method. The calculation results of all points are integrated into a seed amount change ratio sequence and a supplemental area change ratio sequence, which together constitute a supplemental fluctuation change data set. This data set accurately records the detailed fluctuations of operation parameters over time. Refer to Table 1.

[0074] Table 1: Continuous monitoring records of degradation points

[0075]

[0076] The anomaly identification submodule performs multi-dimensional anomaly detection based on the supplemental fluctuation change data set, extracts environmental monitoring data including wind direction amplitude data and humidity fluctuation data for the corresponding time period. The wind direction amplitude is obtained by calculating the standard deviation of the wind direction angle change per unit time, and the humidity fluctuation is calculated by the range of relative humidity values. The system sets the fluctuation recognition threshold to 0.3 (both seed amount change ratio and area change ratio need to exceed this threshold), and the anomaly determination threshold to 15° (wind direction amplitude) and 20% (humidity fluctuation). When a time node simultaneously meets the threshold conditions of operation parameter fluctuation and environmental parameter fluctuation, it is marked as an anomaly point. Taking point P-07 as an example, at time node T4, the seed amount change ratio 1.393 and the area change ratio 0.933 both exceed 0.3, and the wind direction amplitude 18° and the humidity fluctuation 25% exceed the anomaly determination threshold, so this node is marked as an anomaly point. All anomaly points together constitute a supplemental anomaly point set.

[0077] The index joint determination submodule in the monitoring output module acquires all points in the reseeding abnormal point set and corresponding information, calculates a joint risk determination value for each point, the value is obtained by weighting the propagation response value, the reseeding suitability value and the direction consistency label, an ordered joint risk determination value sequence is established, taken point P-12 as an example, the propagation response value is 0.87 (standardized high response), the reseeding suitability value is 0.42 (lower than the reference value 0.5), the direction consistency label is "conflict", and a higher joint risk determination value 0.79 is obtained after weighting calculation. The abnormal output arrangement submodule performs final screening based on the joint risk determination value sequence, sets the propagation response risk threshold value as 0.7 and the suitability reference value as 0.5, screens out the points that meet the three conditions at the same time: the propagation response value is greater than 0.7, the reseeding suitability value is lower than 0.5 and the direction consistency label is the conflict section, extracts the number, position identifier and partition information of these points, marks them as monitoring abnormal points with diffusion risk, classifies all points meeting the conditions according to the partition and outputs them in a structured format, and finally generates the grassland degradation repair monitoring and risk early warning result containing detailed risk levels and spatial distribution.

[0078] Example 5: The remote verification module starts the verification process when the monitoring data is abnormal, when the system detects that the data index of a certain fixed partition exceeds the normal fluctuation range, it automatically triggers the preset verification rules, these rules are stored in the security storage area of the system including core elements such as trusted data whitelist and environment fingerprint hash library, taking a monitoring instance in the eastern region as an example, the system found that the soil moisture data of point P-15 appeared abnormal jump in continuous time nodes, and immediately activated the verification mechanism to enter the data validity verification stage. The preset rule loading submodule calls the pre-configured verification rule set from the security storage area, the trusted data whitelist contains the serial numbers of authenticated data acquisition devices and authorized transmission channel information, and the environment fingerprint hash library stores the characteristic hash values of legal environment data under different seasons and weather conditions, in the eastern region verification process, the system loads the verification rule set of this region, including the allowed device list and local environment parameter feature library, providing comparison reference for subsequent verification.

[0079] The identification information reading submodule parses the complete identification information of the abnormal monitoring data through the data protocol stack. The data protocol stack processes data packets according to the established communication protocol hierarchy, extracts key elements such as data digital certificate, environment serial number and data protocol descriptor, and in the verification instance of P-15 point, the submodule successfully reads the issuing agency information of the data digital certificate, the device environment serial number and the adopted data transmission protocol descriptor. These information constitutes the basis input for verification. The first matching verification submodule drives the built-in security chip to conduct in-depth verification on the data digital certificate. The security chip first checks whether the issuing agency of the certificate is in the trusted list, and then verifies the integrity and timeliness of the signature chain. After completing these basic checks, the module traverses the registered certificate information in the trusted data whitelist, and compares it with the current certificate item by item, and outputs the first matching verification result. In the instance verification, although the certificate of P-15 point comes from a legal agency, there is an interrupt node in its signature chain, resulting in the first matching verification result being marked as failed.

[0080] The second matching verification submodule generates a data feature hash value using the data protocol descriptor and the environment serial number. The security chip calculates the data content and environment parameters according to the hash algorithm to generate a unique feature identification value. The module then performs feature value matching search in the environment fingerprint hash library, and outputs the second matching verification result. In the example, although the environment serial number of P-15 point shows that it is a registered device, the data feature hash value does not match the normal mode stored in the library, and the second verification also fails. The result judgment submodule makes a final judgment by combining the output results of the two verification submodules. When the first matching verification result and the second matching verification result both show pass, the data validity verification is successful, otherwise the verification fails. In the case of P-15 point, since both verifications fail, the system determines that the data validity verification fails, and triggers the subsequent abnormal processing procedure. After the verification fails, the system automatically generates a verification problem feature record in the fixed partition, including detailed information such as abnormal data identification, verification timestamp, failure reason code, etc. These feature data are sent to the temporary buffer area for temporary storage, and a monitoring verification takeover request is sent to the remote partition. The remote partition responds immediately after receiving the request, and starts the standby verification mechanism to take over the monitoring verification work of the fixed partition.

[0081] After the formal takeover of monitoring verification by the remote partition, the system immediately locks the data write permission of the fixed partition to prevent the possible abnormal data from continuing to flow in. The remote verification service reads the complete monitoring context information and verification problem characteristics from the temporary cache area. The monitoring context includes the system state, environmental parameters and historical data records at the time of abnormal occurrence, providing background support for remote analysis. The remote partition encrypts and packages the monitoring context and verification problem characteristics through the established secure channel, uses an asymmetric encryption algorithm to ensure the security of the transmission process, and uploads the encrypted data packet to the cloud verification engine through a special network link. After receiving the data packet, the cloud service first performs decryption processing, and then performs verification strategy divergence analysis according to the content characteristics. The cloud verification engine performs deep analysis on the monitoring context and verification problem characteristics based on machine learning algorithms, generates multi-level verification strategy schemes, and these strategies are divided into multiple levels according to verification strength and coverage. Each level contains different verification rules and check items. After the analysis is completed, the cloud engine encrypts the multi-level verification strategy and sends it to the remote partition through the secure channel.

[0082] After receiving and decrypting the multi-level verification strategy, the remote partition performs risk verification of the monitoring data according to the requirements of the strategy. The primary verification mainly checks the data format and source credibility, the intermediate verification involves data logic consistency and environmental relevance, and the advanced verification contains deep pattern recognition and abnormal behavior detection. According to the final verification result, the system issues a binding interface permission to the monitoring data that passes the verification, allowing it to enter the subsequent processing flow. The data that does not pass the verification is isolated and marked as untrusted data.

[0083] 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 such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device.

[0084] Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A monitoring system for degraded grassland restoration based on tumbleweed reseeding, characterized in that, The system includes: The data acquisition module obtains the coordinates and time of grassland degradation points, collects soil moisture and vegetation cover at the corresponding locations, associates degradation points and calculates the corresponding reseeding suitability values, and generates a set of suitable reseeding values ​​for degradation points. The consistency determination module extracts the suitable reseeding value and corresponding coordinates from the suitable reseeding set of the degraded points, sorts adjacent point pairs according to spatial distance, marks the consistent and conflicting segments in the adjacent point pairs, and obtains the directional consistency partition label set. The propagation impact assessment module obtains the degradation points located in the directional consistency section of the directional consistency partition annotation set, extracts the wind speed and precipitation time series, compares the change range with the reseeding seed amount, assesses the intensity of propagation impact under environmental fluctuations, and generates propagation impact superposition analysis results. The anomaly screening module identifies anomalies in the propagation impact superposition analysis results where the propagation response value is greater than the average response benchmark value and is located in the degradation point of the directional conflict section, forming a re-re-seeding anomaly point set; The monitoring output module acquires all locations and corresponding location information in the set of abnormal reseeding points, marks locations with a risk of spread, and generates grassland degradation restoration monitoring and risk warning results.

2. The monitoring system for degraded grassland restoration based on wind-blown vegetation reseeding as described in claim 1, characterized in that, The set of suitable reseeding options for degradation points includes suitable reseeding values, spatial coordinates of degradation points, and normalized environmental factors; The directional consistency partition label set specifically includes directional consistency segment labels, directional conflict segment labels, and the difference rate of suitable values ​​between adjacent degradation points. The results of the superimposed analysis of the propagation effects include the degree of influence of the rate of increase in wind speed on reseeding, the degree of influence of the rate of decrease in precipitation on reseeding, and a comparison of the seed propagation response under each environmental fluctuation condition; The set of anomalous points for reseeding includes the spatial location of the anomalous points, the wind direction and humidity variation characteristics of the anomalous points, and the ratio of seed quantity to area fluctuation of the anomalous points. The grassland degradation restoration monitoring and risk warning results include a list of abnormal monitoring points and a joint judgment label for three indicators of abnormal points.

3. The monitoring system for degraded grassland restoration based on tumbleweed reseeding according to claim 1, characterized in that, The data acquisition module includes: The grassland information collection submodule obtains the coordinates and monitoring time of grassland degradation points, collects the soil moisture value and vegetation coverage data corresponding to the coordinates, and records the collection results as two environmental factors: soil factors and vegetation factors, and obtains the environmental factor data set of degradation points. The environmental factor normalization submodule performs normalization processing on the soil factor and vegetation factor data in the environmental factor data group of the degradation point, establishes a correspondence between the normalized results and the coordinate position of the degradation point, calculates the average value of the normalized soil moisture value and the normalized vegetation coverage as the suitable value for reseeding, and generates a suitable set for reseeding of degradation points.

4. The monitoring system for degraded grassland restoration based on wind-blown vegetation reseeding as described in claim 3, characterized in that, The consistency determination module includes: The suitable value extraction submodule obtains the suitable reseeding value and corresponding coordinate data in the suitable reseeding set of the degraded points, identifies the spatial positional relationship of all degraded points based on the coordinate information, calls the set of coordinates of degraded points, calculates and sorts the distance of degraded points in space based on the adjacent distance threshold, and generates a distance sorting sequence of adjacent degraded points. The difference rate calculation submodule calculates the appropriate value difference rate between two adjacent degradation points based on the adjacent degradation point distance sorting sequence, and integrates them to generate an appropriate value difference rate sequence. The directional consistency identification submodule extracts the direction of soil moisture change and vegetation cover change between adjacent degradation points based on the suitability value difference rate sequence. It classifies and labels each pair of degradation points according to whether the change trends in the two directions are consistent. It records and groups the segments with consistent and conflicting directions respectively to obtain the directional consistency partition label set.

5. The monitoring system for degraded grassland restoration based on tumbleweed reseeding according to claim 4, characterized in that, The propagation impact assessment module includes: The environmental sequence extraction submodule filters out segments marked with consistent directions based on the directional consistency partition label set, detects wind speed and precipitation data within the monitoring time period of each degradation point, arranges them in chronological order to form wind speed time series and precipitation time series, and generates an environmental change time series set. The propagation superposition calculation submodule calculates the wind speed increase rate and precipitation decrease rate between consecutive time nodes in the time series of each degradation point based on the environmental change time series. It compares the wind speed increase rate and precipitation decrease rate side by side under the same seed quantity condition, identifies the numerical relationship of the change magnitude under the seed quantity by jointly analyzing the two types of rate indicators, integrates the influence value sequence of each degradation point, and establishes the propagation influence superposition analysis results.

6. The monitoring system for degraded grassland restoration based on wind-blown vegetation reseeding according to claim 5, characterized in that, The anomaly filtering module includes: The record extraction submodule, based on the propagation impact superposition analysis results, filters out degradation points whose propagation response values ​​are greater than the average response benchmark value, as well as degradation points located in directional conflict zones. It extracts continuous monitoring records of degradation points in chronological order, collects reseeding seed quantity and reseeding area data corresponding to each time node, and generates a continuous monitoring record set. The seed area ratio calculation submodule calls the continuous monitoring record set, extracts the seed quantity and area of ​​the degradation point at two consecutive time nodes, calculates the seed quantity change ratio and the reseeding area change ratio, integrates them into the seed quantity change ratio sequence and the reseeding area change ratio sequence, and establishes the reseeding fluctuation change dataset. The anomaly identification submodule extracts wind direction variation data and humidity fluctuation data for the corresponding time period based on the reseeding fluctuation change dataset, determines whether the seed quantity change ratio and area change ratio both exceed the set fluctuation identification threshold, determines whether the wind direction variation and humidity fluctuation both exceed the anomaly judgment threshold, marks the time nodes that meet the conditions as anomaly points, and generates a reseeding anomaly point set.

7. The monitoring system for degraded grassland restoration based on tumbleweed reseeding according to claim 6, characterized in that, The monitoring output module includes: The joint risk judgment submodule obtains all locations and corresponding coordinates and identification information of the reseeding anomaly point set, calculates the joint risk judgment value, and establishes a joint risk judgment value sequence. The abnormal output processing submodule, based on the joint risk judgment value sequence, filters out points whose propagation response value is greater than the propagation response risk threshold, whose reseeding suitability value is lower than the suitability benchmark value, and whose directional consistency label is a conflict segment. It extracts the corresponding point number, location identifier, and the partition to which it belongs, marks them as monitoring anomalies with a risk of spread, and outputs the points that meet the joint conditions in a structured format to generate grassland degradation restoration monitoring and risk warning results.

8. The monitoring system for degraded grassland restoration based on wind-blown vegetation reseeding as described in claim 1, characterized in that, The system also includes a remote verification module, which is used to trigger preset verification rules in a fixed partition to verify the validity of the data when the monitored data is abnormal. If verification fails, the verification problem characteristics are sent from the fixed partition to the temporary cache area, and the monitoring and verification takeover of the remote partition is triggered. After the remote partition takeover monitoring and verification is completed, the write permission of the fixed partition is locked, and the monitoring context and the verification problem characteristics are read from the temporary cache. The monitoring context and verification question features are packaged and uploaded to the cloud verification engine via a secure channel in the remote partition. After receiving the verification strategy and performing divergent analysis based on the monitoring context and verification issue characteristics, the cloud verification engine encrypts and distributes the multi-level verification strategy to the remote partition. The multi-level verification strategy is loaded in the remote partition to perform hierarchical risk verification of the monitoring data, and binding interface permissions are granted to the monitoring data based on the verification results.

9. The monitoring system for degraded grassland restoration based on wind-blown vegetation reseeding according to claim 8, characterized in that, The remote verification module includes: The preset rule loading submodule loads preset verification rules from the secure storage area, wherein the preset verification rules include a trusted data whitelist and an environment fingerprint hash library; The identification information reading submodule reads the identification information of the monitoring data through the data protocol stack. The identification information includes the data digital certificate, environment serial number and data protocol descriptor. After verifying the issuing authority and signature chain integrity of the data digital certificate, the first matching verification submodule drives the security chip to traverse the trusted data whitelist to compare the data digital certificate and output the first matching verification result. The second matching and verification submodule uses the data protocol descriptor and environment serial number to drive the security chip to calculate the data feature hash value, and then compares the data feature hash value with the environment fingerprint hash library to output the second matching and verification result. The data validity verification is successful when both the first and second matching verification results pass in the result judgment submodule.

10. A method for monitoring degraded grassland restoration based on tumbleweed reseeding, characterized in that, It includes all modules and method flows of the degraded grassland monitoring system based on windblown plant reseeding as described in any one of claims 1 to 9.

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