Environment parameter real-time monitoring and early warning system for ecological restoration

Abstract

The present application belongs to the technical field of environmental monitoring, and discloses an environmental parameter real-time monitoring and early warning system for ecological restoration; through correlation analysis of signal quality parameters and vegetation ecological parameters, a signal attenuation-vegetation growth correlation atlas is constructed to realize prediction of communication quality change. By using multipath scattering source identification and spatial positioning technology, equivalent attenuation and scattering characteristics of different vegetation density areas are accurately quantified, and a signal propagation model containing a vegetation medium layer is constructed. Based on the model, a multi-frequency band collaborative adaptive transmission strategy is generated, and abnormal attenuation events caused by sudden changes in vegetation water content can be identified in real time, and transmission parameters are dynamically adjusted. The present application provides continuous and reliable environmental data support for the ecological restoration process, improves the scientificity and efficiency of the restoration work, and reduces the equipment maintenance cost and energy consumption.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, and more specifically, to a real-time monitoring and early warning system for environmental parameters for ecological restoration. Background Technology

[0002] Existing environmental monitoring systems in ecological restoration areas face signal propagation problems caused by dynamic vegetation growth, resulting in severe and unpredictable fluctuations in system performance with the vegetation cycle. In dense forests and tall crop restoration areas (such as reed beds), initially deployed LoRa / NB-IoT network nodes exhibit good signal quality during sparse vegetation phases. However, as ecological restoration progresses, vegetation density, height, and biomass increase dramatically, leading to a significant deterioration in communication quality. Particularly during the vigorous summer growth period, high leaf water content causes severe absorption of electromagnetic waves in the 2.4GHz and higher frequency bands, with signal attenuation far exceeding design expectations. Simultaneously, the irregular three-dimensional structure of dense tree canopies leads to complex scattering, reflection, and diffraction phenomena, generating multipath propagation effects. This causes random superposition of signal phases at the receiver, forming deep fading zones, resulting in a sharp increase in packet loss rates and data transmission interruptions in these areas. Especially after rain or during the morning dew period, sudden changes in vegetation water content cause drastic fluctuations in signal attenuation characteristics, rendering the original static communication parameters completely inadequate. Current technologies lack precise modeling of vegetation-signal interaction mechanisms, making it impossible to predict signal propagation characteristics based on vegetation growth status. They also lack adaptive transmission strategies based on vegetation dynamics, leading to severe waste of signal resources after leaf fall in winter (excessive power) and insufficient communication reliability during the dense summer season (insufficient power). Furthermore, existing algorithms lack sufficient spatial positioning accuracy for multipath scattering sources, cannot identify the influence patterns of vegetation layer structure on signal propagation, and cannot resolve communication blind spots in complex vegetation environments through effective path planning. Ultimately, this results in drastic seasonal fluctuations in monitoring network data quality, affecting the continuity and reliability of ecological restoration monitoring.

[0003] In view of this, the present invention proposes a real-time monitoring and early warning system for environmental parameters for ecological restoration to solve the above problems. Summary of the Invention

[0004] To overcome the aforementioned shortcomings of the prior art and to achieve the above objectives, the present invention provides the following technical solution: a real-time environmental parameter monitoring and early warning system for ecological restoration, comprising:

[0005] The data acquisition module is used to acquire signal quality parameters and vegetation ecological parameters of each monitoring node in the ecological restoration area at different time periods;

[0006] The correlation map construction module is used to extract the seasonal variation curve of signal attenuation based on the periodic fluctuation characteristics of signal quality parameters in the time dimension, and to perform time-series correlation analysis with vegetation ecological parameter data to construct a correlation map of signal attenuation-vegetation growth.

[0007] The scattering source identification module is used to acquire the arrival time series of multipath signals received by each monitoring node, and to identify the spatial location of multipath scattering sources caused by vegetation layer by the statistical distribution characteristics of adjacent arrival time differences.

[0008] The attenuation coefficient calculation module is used to calculate the equivalent attenuation coefficient and angular scattering coefficient corresponding to different vegetation density areas based on the spatial location of multipath scattering sources and the correlation map between signal attenuation and vegetation growth.

[0009] The propagation model construction module is used to construct a spatial attenuation model for signal propagation that includes a vegetation medium layer, based on the equivalent attenuation coefficient and the angular scattering coefficient.

[0010] The transmission strategy generation module is used to generate a multi-band collaborative adaptive transmission strategy based on the signal propagation spatial attenuation model and the data acquisition priority of each monitoring area. The transmission strategy includes frequency switching sequence, transmit power allocation scheme and relay path planning scheme.

[0011] The abnormal attenuation identification module is used to identify abnormal attenuation events caused by sudden changes in vegetation water content by real-time monitoring of the signal envelope jitter characteristics and phase consistency index of the inter-node link.

[0012] The strategy adjustment module is used to dynamically adjust the frequency switching timing and power compensation gain in the transmission strategy based on the duration and attenuation depth of the abnormal attenuation event.

[0013] The control execution module is used to control the communication parameters of each monitoring node based on the adjusted transmission strategy, so as to achieve stable acquisition and reliable transmission of environmental data in the ecological restoration area.

[0014] The modules are connected via wired and / or wireless means to enable data transmission between them.

[0015] The technical effects and advantages of this invention's real-time environmental parameter monitoring and early warning system for ecological restoration are as follows:

[0016] This invention, through a deep understanding of the mechanisms of vegetation growth and signal interaction, can predictively adapt to seasonal changes, ensuring the monitoring network maintains optimal operation during both the dense summer growing season and the winter leaf-falling period. In the event of sudden rainfall or morning dew causing drastic changes in vegetation moisture content, this invention can rapidly sense environmental changes and intelligently adjust strategies, ensuring no critical environmental data is lost. For traditionally communication-blind areas such as dense forests and tall crop areas, this invention achieves full-coverage monitoring through intelligent path planning, providing comprehensive data support for the ecological restoration process. The adaptive characteristics of this invention significantly reduce maintenance costs and frequency, avoiding the interference of frequent entry by technicians into the restoration area to adjust equipment, as is common in traditional solutions. The energy-saving design extends equipment runtime, enabling long-term unattended operation, making it particularly suitable for ecological restoration projects in remote areas. By providing continuous and accurate environmental parameter data streams, it presents ecologists and managers with a complete picture of the restoration process, supporting precise intervention decisions, accelerating the reconstruction of ecosystem functions, and ultimately improving the scientific rigor and success rate of ecological restoration work. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the real-time monitoring and early warning system for environmental parameters for ecological restoration according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] This application provides a real-time monitoring and early warning system for environmental parameters for ecological restoration. The system's implementing entities include, but are not limited to, those mounted on the system: an ecological restoration monitoring platform, an environmental data acquisition network, a vegetation growth monitoring system, and a wireless sensor network, which can be considered general monitoring nodes in this application. The monitoring and early warning system includes, but is not limited to, at least one of: a distributed environmental data acquisition unit, a signal attenuation analysis engine, and a multipath propagation feature extraction system.

[0020] Please see Figure 1 In this embodiment of the invention, a real-time monitoring and early warning system for environmental parameters for ecological restoration includes:

[0021] The data acquisition module is used to acquire signal quality parameters and vegetation ecological parameters from various monitoring nodes within the ecological restoration area at different time periods. Signal quality parameters include key communication indicators such as signal strength, packet loss rate, bit error rate, and channel quality indices. Vegetation ecological parameters include indicators reflecting vegetation growth status such as normalized difference in vegetation index (NDPI), leaf area index (LAI), vegetation height, and vegetation density. These data are collected in real-time through distributed monitoring nodes. Signal quality parameters reflect the reliability and stability of the communication link, while vegetation ecological parameters reflect the seasonal variations and spatial distribution characteristics of vegetation growth. This data provides comprehensive raw material for subsequent analysis, ensuring the scientific validity and accuracy of attenuation characteristic analysis and early warning mechanisms.

[0022] The correlation map construction module is used to extract the seasonal variation curve of signal attenuation based on the periodic fluctuation characteristics of signal quality parameters over time, and to perform time-series correlation analysis with vegetation ecological parameter data to construct a signal attenuation-vegetation growth correlation map. This module first performs time-series analysis on signal quality parameters to identify periodic fluctuation characteristics, and then correlates these characteristics with the seasonal changes of vegetation ecological parameters to establish a quantitative relationship model between signal attenuation and vegetation growth, providing a data foundation for predicting the impact of vegetation growth on communication quality.

[0023] The scattering source identification module is used to acquire the arrival time series of multipath signals received by each monitoring node. By analyzing the statistical distribution characteristics of adjacent arrival time differences, it identifies the spatial location of multipath scattering sources caused by vegetation. This module first performs time-domain analysis on the received signals to extract the temporal characteristics of the multipath components. Then, it calculates the spatial distribution of scattering sources using a geometric positioning algorithm, accurately identifying the scattering effect of vegetation on signal propagation and providing spatial scattering source information for constructing an accurate signal propagation model.

[0024] The attenuation coefficient calculation module is used to calculate the equivalent attenuation coefficient and angular scattering coefficient corresponding to different vegetation density regions based on the spatial location of multipath scattering sources and the signal attenuation-vegetation growth correlation map. This module divides vegetation density regions according to the distribution of scattering sources, and combines the signal attenuation-vegetation growth correlation map to calculate the equivalent attenuation characteristics of vegetation regions by comparing the measured attenuation with the predicted attenuation. This quantifies the attenuation and scattering effects of vegetation on wireless signals, providing key parameter inputs for the signal propagation model.

[0025] The propagation model construction module is used to construct a spatial attenuation model for signal propagation that includes a vegetation medium layer, based on the equivalent attenuation coefficient and angular scattering coefficient. This model integrates the three-dimensional spatial distribution and electromagnetic properties of vegetation, simulates the propagation behavior of signals in complex vegetation environments using a ray tracing algorithm, and considers multiple propagation mechanisms such as direct light, reflection, diffraction, and scattering. It accurately predicts the signal intensity distribution and phase characteristics, providing a theoretical basis for adaptive transmission strategies.

[0026] The transmission strategy generation module generates a multi-band collaborative adaptive transmission strategy based on the signal propagation spatial attenuation model and the data acquisition priority of each monitoring area. The transmission strategy includes a frequency switching sequence, a transmit power allocation scheme, and a relay path planning scheme. By optimizing the operating frequency, adjusting the transmit power, and planning the relay path, it maximizes communication reliability and ensures efficient data transmission in complex vegetation environments.

[0027] The abnormal attenuation identification module is used to identify abnormal attenuation events caused by sudden changes in vegetation water content by real-time monitoring of the signal envelope jitter characteristics and phase consistency index of the inter-node links. This module analyzes abnormal changes in the signal's time-domain characteristics and combines them with historical baseline data to accurately identify sudden changes in vegetation water content caused by rainfall, irrigation, etc., providing a triggering basis for dynamic adjustments to transmission strategies.

[0028] The strategy adjustment module dynamically adjusts the frequency switching timing and power compensation gain in the transmission strategy based on the duration and attenuation depth of abnormal attenuation events. This module uses an adaptive control algorithm based on the characteristic parameters of abnormal events to optimize transmission parameters in real time, ensuring the stability of the communication link under conditions of abrupt changes in vegetation status and improving the system's adaptability to environmental changes.

[0029] The control execution module controls the communication parameters of each monitoring node based on the adjusted transmission strategy, enabling stable acquisition and reliable transmission of environmental data in the ecological restoration area. This module translates the optimized transmission strategy into specific control commands, adjusting parameters such as the operating frequency, transmission power, and data routing of the monitoring nodes to ensure continuous acquisition and reliable transmission of environmental data, providing comprehensive data support for ecological restoration decisions.

[0030] The modules are connected via wired and / or wireless means to enable data transmission between them.

[0031] In this embodiment of the invention, the detailed implementation steps for constructing the signal attenuation-vegetation growth correlation map include:

[0032] A sliding window smoothing process is applied to the packet loss rate data in the signal quality parameters to obtain the trend of packet loss rate changes after removing random noise. Sliding window smoothing is a fundamental step in signal preprocessing, converting discrete sampling points into a continuous trend curve. The processing employs a weighted moving average algorithm, assigning different weights to data points within the window, with the center point having the highest weight and decreasing weights on either side, forming a Gaussian weight distribution. The size of the sliding window is dynamically adjusted according to the data sampling frequency, typically set to cover data points from 24 to 72 hours. Smoothing effectively eliminates short-term random fluctuations and measurement errors, preserving the long-term trend of packet loss rate changes, providing a clear signal foundation for subsequent periodic analysis.

[0033] Fourier decomposition was performed on the packet loss rate trend to extract seasonal fluctuation components within a preset period, denoted as the seasonal feature vector of signal attenuation. Fourier decomposition is an effective method for identifying signal periodicity, converting time-domain signals into frequency-domain representations to reveal implicit periodic patterns. The decomposition process employed the Fast Fourier Transform (FFT) algorithm to calculate the spectral distribution of the packet loss rate trend. Frequency components with periods within a preset range were selected from the spectrum, typically focusing on signal components with daily (24-hour), weekly (7-day), and seasonal (90-120-day) periods. The seasonal fluctuation components were reconstructed using inverse Fourier transform to form the seasonal feature vector of signal attenuation, which intuitively reflects the periodic variation pattern of signal quality and lays the foundation for correlation analysis of vegetation ecological parameters.

[0034] Obtain time-series data of the Normalized Difference Vegetation Index (NDV) from vegetation ecological parameter data, and calculate the cross-correlation coefficient between the NDC time-series data and the seasonal eigenvectors of signal attenuation. Cross-correlation analysis is a classic method for quantifying the correlation between two time-series data, and can identify the time dependence between signals. The analysis process first standardizes the two time-series data to eliminate differences in dimensions and amplitudes; then, it calculates the correlation coefficients at different time lags, identifying the maximum correlation and its corresponding time lag. The formula for calculating the cross-correlation coefficient is:

[0035] ;

[0036] in, For time lag cross-correlation coefficients, and These are the signal attenuation eigenvector and the normalized vegetation index, respectively. and For their respective mean, and For their respective standard deviations, The sequence length is given. Cross-correlation analysis revealed the temporal association pattern between vegetation growth changes and signal attenuation, including correlation strength and time delay characteristics, providing quantitative indicators for identifying strongly associated growth periods.

[0037] The time periods where the cross-correlation coefficient exceeds a preset correlation threshold are marked as strong correlation growth periods. The ratio of signal attenuation increment to vegetation index increment within these strong correlation growth periods is calculated and denoted as the unit vegetation growth attenuation factor. Strong correlation growth periods are critical periods where vegetation growth has the most significant impact on signal transmission; accurately identifying these periods is fundamental to constructing an effective correlation model. The identification process first sets a correlation threshold (typically 0.7-0.8) and filters continuous time periods with cross-correlation coefficients exceeding the threshold. Then, the quantitative relationship between vegetation growth and signal attenuation within these periods is analyzed. The calculation of the unit vegetation growth attenuation factor considers the rate of change of two indicators, reflecting the signal attenuation caused by unit vegetation growth, and is a key indicator for quantifying the impact of vegetation growth on communication quality. The calculation formula is:

[0038] ;

[0039] in, The vegetation growth attenuation factor is the unit vegetation growth attenuation factor. This represents the signal attenuation increment during the strong correlation growth phase. This represents the normalized difference in vegetation index increment for the same period. This factor provides a quantitative basis for subsequent signal attenuation prediction, enabling the system to predict changes in communication link quality based on vegetation growth.

[0040] Based on the temporal distribution of the unit vegetation growth attenuation factor and the strongly correlated growth period, a signal attenuation-vegetation growth correlation map is constructed. The horizontal axis of the correlation map represents the vegetation growth cycle, and the vertical axis represents the predicted signal attenuation. The correlation map is an intuitive and visual model of the relationship between signal attenuation and vegetation growth, comprehensively reflecting the influence of vegetation growth on signal transmission. The construction process first determines the coordinate system of the map, with the horizontal axis representing a complete vegetation growth cycle (usually one year) and the vertical axis representing the predicted signal attenuation for the corresponding period. Then, based on the unit vegetation growth attenuation factor and historical data, the predicted signal attenuation for each stage of the vegetation growth cycle is calculated. Finally, a continuous correlation curve is generated using a smoothing interpolation method, forming a complete correlation map. The correlation map not only demonstrates the seasonal variation of signal attenuation but also provides a quantitative model for predicting signal attenuation based on vegetation growth, providing a scientific basis for the system's adaptive transmission strategy. This allows communication parameters to be pre-adjusted according to the vegetation growth cycle, improving the stability and reliability of communication.

[0041] In this embodiment of the invention, the detailed implementation steps for identifying the spatial location of multipath scattering sources caused by vegetation layer include:

[0042] Time-domain peak detection is performed on the signal pulses received by each monitoring node to extract the arrival times of the first and delayed pulses. Time-domain peak detection is a fundamental step in multipath signal analysis, separating signal components from different propagation paths by identifying energy peaks in the received signal. The detection process employs an adaptive threshold detection algorithm, dynamically adjusting the detection threshold based on the background noise level to ensure detection stability under varying signal-to-noise ratios. The first pulse is typically from a direct or strongly reflected path, possessing the earliest arrival time and high energy; delayed pulses originate from propagation mechanisms such as scattering and diffraction, exhibiting significant time delays. The peak detection algorithm accurately records the arrival times of these pulses, forming time-series data that provides a time reference for locating multipath scattering sources.

[0043] The time difference between the arrival time of the delayed pulse and the arrival time of the first pulse is calculated and denoted as the multipath delay. Multipath delay is a key parameter for calculating the location of the scattering source, directly reflecting the geometric length differences of different propagation paths. The calculation process first determines the first pulse as a reference point, then calculates the time difference of each delayed pulse relative to the first pulse, forming a multipath delay sequence. The delay data undergoes calibration to eliminate the influence of equipment clock errors and system delays, ensuring measurement accuracy. Multipath delay is directly related to the propagation distance. By converting the propagation speed of electromagnetic waves in air (approximately the speed of light), the additional distance of the propagation path can be obtained, providing basic data for the spatial positioning of the scattering source.

[0044] The probability density distribution of multipath delays within a preset observation period is statistically analyzed, and delay clusters with peak values ​​exceeding a preset probability threshold are extracted. Probability density analysis is an effective method for discovering stable scattering sources, identifying major scattering regions by statistically analyzing the frequency of delay occurrences. The analysis process employs kernel density estimation to transform discrete delay samples into continuous probability density functions, clearly revealing delay clustering patterns. The preset observation period is typically 24-72 hours to ensure the capture of diurnal variations in the vegetation environment; the probability threshold is set to the upper quartile of the density distribution to filter out delay values ​​significantly higher than the background level. These high-probability delays form several delay clusters, each representing a stable scattering source, providing a candidate set of targets for subsequent spatial positioning.

[0045] Based on the center delay value of the delay cluster and the spatial coordinates of the transmitting and receiving nodes, a geometric triangulation algorithm is used to calculate the candidate spatial location of the scattering point. Geometric triangulation is the core algorithm for converting delay information into spatial location, based on the geometric principles of electromagnetic wave propagation to locate the scattering source. The positioning process first establishes the coordinate system of the transmitting and receiving nodes and determines the baseline distance of the direct path; then, based on the multipath delay, it calculates the additional propagation distance and constructs an ellipsoid equation with the transmitting node as the focus, the receiving node as the ellipsoidal direction point, and the total propagation path length as constant; for multiple receiving nodes, the intersection of the ellipsoids is solved to accurately locate the scattering source. For a single receiving point, terrain constraints and multiple measurement data are introduced to improve positioning accuracy. The positioning algorithm considers measurement errors and the influence of the propagation environment, and improves the accuracy of location estimation through iterative optimization, generating corresponding candidate spatial locations for each delay cluster.

[0046] Cluster analysis is performed on candidate spatial locations. Candidate locations whose cluster center distance is less than a preset distance threshold are merged into a unified scattering source, denoted as the multipath scattering source spatial location. Cluster analysis is an effective method for integrating scattered positioning results. By identifying spatially close candidate point groups, the actual scattering source region is determined. The analysis process uses a density-based spatial clustering algorithm (DBSCAN), which automatically identifies the number and shape of clusters based on the spatial distribution of candidate locations, adapting to different types of vegetation scatterers. The distance threshold is set according to the monitoring area scale and positioning accuracy, typically 3-5 meters, to ensure that different scattering sources can be distinguished at a reasonable resolution. After clustering, the center point of each cluster is determined as a multipath scattering source, and its three-dimensional spatial coordinates and scattering intensity parameters are recorded. These scattering sources typically correspond to vegetation structures such as tree canopies, shrubs, or high-density grass, intuitively reflecting the scattering influence of vegetation on signal propagation and providing key spatial scattering point distributions for subsequent attenuation models.

[0047] In this embodiment of the invention, the detailed implementation steps for calculating the equivalent attenuation coefficient and angular scattering coefficient corresponding to different vegetation density areas include:

[0048] Based on the spatial location of multipath scattering sources, the monitoring area was divided into multiple vegetation density zones. The standard deviation of the scattering source density within each zone was less than a preset zone threshold. Zone division is a crucial step in handling environmental heterogeneity. By segmenting the complex environment into relatively homogeneous sub-regions, model accuracy is improved. The division process employs a combination of spatial clustering and grid partitioning. Initial clustering is performed based on the spatial distribution of scattering sources, followed by further optimization of zone boundaries through grid subdivision. The scattering source density (number of scattering sources per unit area) within each zone remains relatively consistent, with the standard deviation controlled within a preset threshold (typically 20% of the average density), ensuring internal homogeneity within the zone. The partitioning results form a vegetation density map of the monitoring area, visually reflecting the non-uniformity of vegetation spatial distribution and providing a spatial reference framework for subsequent parameter calculations.

[0049] The mean Normalized Difference Vegetation Index (NDVI) for each vegetation density zone is obtained, and the predicted signal attenuation corresponding to the mean NDVI is queried based on the signal attenuation-vegetation growth correlation map. The NDVI is a standard parameter for quantifying vegetation growth status, calculated using reflectance in the near-infrared and visible light bands, directly reflecting vegetation biomass and vitality. The acquisition process combines satellite remote sensing data and ground measurement data to calculate the mean NDVI for each density zone. Then, based on the aforementioned signal attenuation-vegetation growth correlation map, the theoretically predicted attenuation corresponding to the NDVI value is queried, serving as a baseline attenuation estimate under that vegetation condition. This step correlates the optical and electromagnetic properties of vegetation, providing a comparative benchmark for actual attenuation measurements and forming the basis for assessing the impact of specific vegetation conditions.

[0050] The actual received power of the signal link passing through a vegetation density zone is measured, and the difference between the actual received power and the theoretical free-space propagation power is calculated, denoted as the actual attenuation. Actual attenuation measurement is a direct method to obtain the true propagation characteristics. By comparing signal strength under vegetated and non-vegetated conditions, the actual impact of vegetation is quantified. The measurement process first records the transmit power and antenna parameters of the transmitting node, then measures the received power of the receiving node, simultaneously recording the link distance and frequency parameters. Next, the theoretical received power is calculated using the free-space propagation formula, considering distance attenuation and antenna gain, but neglecting the influence of vegetation. Finally, the logarithmic difference between the theoretical power and the actual power is calculated to obtain the actual attenuation (in dB). This attenuation incorporates multiple effects such as vegetation scattering, absorption, and diffraction, and is a direct measurement result of the electromagnetic characteristics of vegetation, providing a measured basis for attenuation coefficient calculation.

[0051] The ratio of the actual attenuation to the predicted signal attenuation is used as the correction coefficient. Based on this correction coefficient and the equivalent propagation distance of the vegetation density zone, the equivalent attenuation coefficient is calculated. The equivalent attenuation coefficient is a core parameter for quantifying the electromagnetic properties of vegetation, representing the degree of signal attenuation caused by vegetation per unit distance. The calculation process first determines the correction coefficient, reflecting the deviation between the prediction model and actual measurements; then, combined with the equivalent propagation distance of the vegetation density zone (the actual propagation path length of the signal in the vegetation), the attenuation value per unit distance is calculated. The formula for calculating the equivalent attenuation coefficient is:

[0052] ;

[0053] in, The equivalent attenuation coefficient (dB / m) This represents the actual attenuation (dB). To predict the decay amount (dB). This represents the equivalent propagation distance (m) within the vegetation. This coefficient is directly used for path loss calculation and is a key parameter for constructing an accurate propagation model, reflecting the electromagnetic wave attenuation characteristics under specific vegetation types and densities.

[0054] Based on the spatial locations of multipath scattering sources and the locations of transmitting and receiving nodes, the angle between the incident and scattering directions is calculated, and the proportion of scattered energy within different angle ranges is statistically analyzed to construct an angle dependence function for the angle scattering coefficient. The angle scattering coefficient describes the influence of vegetation on the directionality of electromagnetic wave scattering and is an important component of the multipath propagation model. The calculation process first determines the incident angle of each scattering source relative to the transmitting node and the scattering angle relative to the receiving node, and calculates the three-dimensional angle between the two directions. Then, based on the intensity of the received scattered signal, the energy distribution within different angle ranges is calculated. Finally, through normalization, a functional relationship between the angle and the scattered energy is constructed. The angle dependence function is usually expressed as a piecewise function or a polynomial fitting curve, reflecting the directional characteristics of vegetation scattering. This function directly affects the spatial distribution prediction of multipath signals and is a key component in constructing an accurate spatial propagation model, enabling the system to accurately predict the signal propagation characteristics in different directions and optimize node deployment and transmission parameters.

[0055] In this embodiment of the invention, the detailed implementation steps for constructing a signal propagation spatial attenuation model including a vegetation medium layer include:

[0056] Based on the spatial distribution of vegetation density zones, a three-dimensional vegetation media topology structure is constructed for the monitoring area. This three-dimensional topology structure forms the foundational framework for spatial propagation modeling, extending two-dimensional planar information into a three-dimensional spatial representation. The construction process combines terrain data and a vegetation height model. First, a terrain reference surface is established, then vegetation vertical distribution information is overlaid to form a complete three-dimensional spatial model. Each vegetation density zone is represented as a spatial volume element with specific height and density parameters. The horizontal range of the volume element is determined by the zone boundary, and the vertical range is determined by vegetation height measurements. The topology structure is stored using an octree data structure, supporting multi-resolution representation and efficient spatial queries. This provides a spatial reference framework for subsequent ray tracing calculations, enabling the system to simulate signal propagation behavior under complex terrain and vegetation distribution conditions.

[0057] The equivalent attenuation coefficient and angular scattering coefficient of each vegetation density zone are labeled in the three-dimensional vegetation media topology. Parameter labeling is a key step in associating the measured electromagnetic properties with the spatial structure, assigning specific propagation characteristics to each spatial region. The labeling process maps the previously calculated equivalent attenuation coefficient and angular scattering coefficient to the corresponding vegetation density zones, forming a parameterized three-dimensional propagation environment model. For the equivalent attenuation coefficient, considering the vertical heterogeneity of vegetation, different attenuation values ​​are set at different height layers to reflect the differences between the canopy, branch layer, and ground surface layer; for the angular scattering coefficient, a complete angular dependence function is stored to support scattering calculations in any direction. The parameter labeling results form a three-dimensional distribution map of electromagnetic properties, intuitively reflecting the spatial variability of the propagation environment within the monitoring area, and providing complete parameter input for accurate propagation simulation.

[0058] Based on the ray tracing algorithm, this method calculates all possible propagation paths of a signal from the transmitting node to the receiving node, recording the sequence of vegetation media layers traversed by each propagation path. Ray tracing is the core algorithm for high-precision propagation simulation, predicting signal characteristics by tracking the propagation trajectory of electromagnetic waves. The calculation employs three-dimensional ray emission and tracing technology, emitting a large number of rays from the transmitting node in different directions, tracking the reflection, scattering, and diffraction behavior of each ray until it reaches the receiving node or its energy attenuates below a threshold. The tracing process accurately records the interaction points between the ray and the vegetation media, including the incident point, exit point, and scattering point, forming a complete set of propagation paths. For each effective path (the path through which energy reaches the receiving node), a detailed record of the sequence of vegetation media layers traversed is made, including the media type, penetration distance, and interaction mode, providing accurate path information for subsequent attenuation calculations. This step, through computer simulation, recreates the electromagnetic wave propagation process in complex environments and is the core algorithm of the spatial attenuation model.

[0059] For each propagation path, the total path attenuation and phase delay at each scattering point are cumulatively calculated based on the sequence of vegetation media layers traversed. Path attenuation calculation is a crucial step in converting media characteristics into signal loss, predicting the overall attenuation by accumulating the losses of each segment. The calculation process first obtains the physical thickness and equivalent attenuation coefficient of each vegetation media layer traversed by the path, calculating the propagation attenuation of a single layer; then, the attenuation of each layer is accumulated sequentially along the path to obtain the total path attenuation; for each scattering point, the scattering loss is looked up from the angle dependence function of the angle scattering coefficient based on the incident angle and scattering angle, and the phase delay caused by the additional propagation distance is calculated. The phase delay calculation formula is:

[0060] ;

[0061] in, The phase delay angle (in radians) is the phase delay angle. This represents the additional propagation distance (in meters) relative to the direct path. The operating wavelength is given in meters. Ultimately, the propagation characteristics of each path are represented by a complex transfer function, where amplitude represents attenuation and phase angle represents phase delay, comprehensively describing the propagation characteristics of the path and providing fundamental data for multipath superposition calculations.

[0062] By vector superimposing the complex amplitudes of all propagation paths at the receiving point, the intensity distribution and phase jitter distribution of the synthesized signal considering multipath interference are calculated, constructing a spatial attenuation model for signal propagation. Vector superposition is a key step in simulating multipath interference, predicting actual receiving characteristics by synthesizing signals from multiple paths. The superposition process employs complex number operations, representing the signal from each path as a complex vector with amplitude and phase, and superimposing them according to vector addition rules. The amplitude and phase of the synthesized signal vary with the receiving location, forming a spatial distribution pattern. By calculating at multiple locations within the monitoring area, a spatial distribution map of signal intensity and an evaluation index of phase stability are generated, forming a complete spatial attenuation model. This model not only predicts the average signal intensity but also evaluates the spatial variability of signal quality, including deep fading regions and phase instability regions, providing comprehensive environmental characteristic guidance for communication system design and node deployment, enabling the system to proactively adapt to complex vegetation environments and optimize communication performance.

[0063] In this embodiment of the invention, the detailed implementation steps for generating a multi-band cooperative adaptive transmission strategy include:

[0064] Based on the ecological restoration goals and environmental sensitivity of each monitoring area, a data collection priority weight matrix is ​​constructed. Priority weights serve as the foundation for resource allocation, reflecting the monitoring importance of different areas through differentiated weights. The construction process first clarifies the ecological restoration goals of each monitoring area, such as soil and water conservation, biodiversity restoration, or carbon sequestration enhancement, and assesses the environmental sensitivity indicators of the areas, such as soil erosion risk, species rarity, or ecosystem vulnerability. Then, based on the importance of the goals and sensitivity indicators, the comprehensive priority of each area is determined using the Analytic Hierarchy Process (AHP). Finally, the priorities are standardized into a weight matrix, ensuring that the sum of the weights is 1. The weight matrix, as a guiding principle for system resource allocation, directly affects the optimal allocation of communication resources, ensuring the reliability of data collection in key areas under limited resource conditions.

[0065] Based on the spatial attenuation model of signal propagation, the link reliability score of each monitoring node at different operating frequencies is calculated. The link reliability score comprehensively considers signal strength margin and multipath fading depth. The link reliability score is a comprehensive indicator for evaluating communication quality, predicting communication performance by simulating propagation characteristics at different frequencies. The calculation process first selects several operating frequencies supported by the system, typically including three commonly used frequency bands: low frequency (433MHz), medium frequency (915MHz), and high frequency (2.4GHz). Then, for each frequency, the signal strength and multipath characteristics of each link are calculated based on the aforementioned spatial attenuation model. Finally, a comprehensive scoring function is constructed to quantify the link reliability level. The scoring function comprehensively considers signal strength margin (the difference between received signal strength and received sensitivity) and multipath fading depth (the spatial variation of signal strength), evaluating both average performance and stability. The link reliability score provides a quantitative basis for the system to select the optimal operating frequency, enabling the transmission strategy to be optimized according to actual environmental conditions.

[0066] For each monitoring node, the target transmission success rate is determined based on the data acquisition priority weight matrix. An iterative optimization algorithm is then used to find the optimal operating frequency and minimum transmit power that satisfy the target transmission success rate. Parameter optimization is the core step in generating the transmission strategy, finding the best parameter combination that balances performance and energy consumption through numerical calculations. The optimization process first determines the target transmission success rate based on the priority weight of the node's region; higher priority results in a higher required success rate. Then, an optimization objective function is constructed, aiming to minimize energy consumption, with achieving the target transmission success rate as a constraint. Finally, an iterative search algorithm is used to find the optimal solution in the frequency-power parameter space. The optimal operating frequency is typically selected from multiple discrete frequency points, while the minimum transmit power is a continuous variable, determined through gradual adjustment and testing. The optimization results provide personalized operating parameters for each node, satisfying communication reliability requirements while maximizing energy efficiency and extending system lifespan.

[0067] Communication blind spots with severe attenuation in the spatial attenuation model of signal propagation are identified, and intermediate nodes with high link reliability scores are selected as relay nodes between the communication blind spots and core nodes. Relay path planning is a key strategy to overcome communication blind spots, bypassing harsh propagation environments through multi-hop transmission. The planning process first identifies areas with signal strength below the reliable communication threshold based on the spatial attenuation model and marks them as communication blind spots; then, it analyzes the connectivity between blind spot nodes and core nodes (such as gateways or data aggregation points) to determine the links that need relaying; finally, it selects the optimal relay node from candidate nodes to construct a highly reliable multi-hop path. The selection of relay nodes considers multiple factors such as link reliability scores, energy status, and load balancing, and the best choice is determined through comprehensive evaluation. Relay path planning greatly expands the system's coverage, enabling the monitoring network to adapt to the spatial heterogeneity of complex vegetation environments and ensuring end-to-end reliability of data transmission.

[0068] By integrating optimal operating frequency, minimum transmit power, and relay node information, a multi-band collaborative adaptive transmission strategy is generated. The frequency switching times of each node in this strategy are preset based on the daily variation cycle of vegetation water content. Strategy integration is the final step in combining various optimization results into a unified execution plan, generating a complete network transmission strategy. The integration process first summarizes the optimal parameters of each node, including operating frequency, transmit power, and routing information. Then, based on the daily variation pattern of vegetation water content, the optimal timing for frequency switching is determined, typically adjusting parameters during periods of rapid water content change, such as early morning and evening. Finally, the overall strategy is organized into a structured set of control instructions, including frequency switching sequences, power adjustment schemes, and routing table update strategies. This transmission strategy not only considers the spatial heterogeneity of the environment but also incorporates the periodic changes in the temporal dimension, achieving spatiotemporal collaborative adaptive control. This allows the system to predictively adjust communication parameters, proactively adapt to dynamic changes in the vegetation environment, and maximize the continuity and reliability of data acquisition.

[0069] In this embodiment of the invention, the detailed implementation steps for identifying abnormal decline events caused by sudden changes in vegetation water content include:

[0070] The received signal is subjected to a Hilbert transform to extract the signal envelope sequence. The standard deviation of the signal envelope sequence within a sliding time window is calculated and denoted as the envelope jitter intensity. The Hilbert transform is an effective tool for extracting the time-domain characteristics of a signal, capable of separating amplitude and phase information from the original waveform. The transform process first digitally samples the received signal, then calculates the analytical representation of the signal using the discrete Hilbert transform, extracting the envelope sequence, which reflects the temporal variation of the signal strength. The envelope jitter intensity is obtained by calculating the standard deviation of the envelope sequence within the sliding window, intuitively reflecting the degree of signal strength fluctuation. The window size is adjusted according to the system sampling rate, typically set to cover 10-30 seconds of data, effectively capturing short-term fluctuation characteristics. Envelope jitter intensity is particularly sensitive to changes in vegetation moisture content. When rain or dew causes a sudden change in vegetation moisture content, the scattering and absorption characteristics of electromagnetic waves in the vegetation change significantly, causing severe fluctuations in the received signal envelope, making it a key indicator for detecting abnormal environmental events.

[0071] The carrier phase of the received signal is demodulated, and the phase difference sequence between adjacent data packets is calculated. The concentration parameter of the phase difference sequence is statistically analyzed and denoted as the phase consistency index. Phase demodulation is an important method for analyzing signal stability, assessing changes in the propagation path by extracting carrier phase information. The demodulation process uses digital phase-locked loop (PLL) technology to extract the carrier phase from the received signal, eliminating the influence of data modulation to obtain a pure phase sequence. Then, the phase difference between adjacent sampling points is calculated to form a phase difference sequence. Finally, the concentration parameter of the sequence is statistically analyzed to quantify the consistency of phase changes. The concentration calculation uses a cyclic statistical method, considering the periodicity of the phase. The consistency index is obtained by calculating the complex number of the phase difference to represent the degree of clustering on the unit circle. The index ranges from [0,1], with a value closer to 1 indicating more consistent phase changes and a more stable propagation environment. Phase consistency is highly sensitive to changes in vegetation microstructure and is an accurate indicator for detecting changes in the scattering environment caused by changes in water content, providing a complementary perspective to anomaly identification, different from signal strength.

[0072] A two-dimensional feature space is constructed using envelope jitter intensity and phase consistency index, and the distribution area of ​​historical normal working states is calibrated within this space. Feature space construction forms the fundamental framework for anomaly detection, improving detection accuracy through the combination of multi-dimensional features. The construction process first uses envelope jitter intensity and phase consistency index as coordinate axes in the two-dimensional space to form a feature plane; then, long-term historical data is collected, and the distribution of data points for normal working states is plotted on the feature plane; finally, density estimation or boundary learning algorithms are used to determine the distribution boundary of the normal state. Calibration employs kernel density estimation to convert discrete data points into a continuous probability density distribution, and a confidence interval (typically 95%) is set to determine the boundary of the normal state. This boundary visually distinguishes between normal fluctuations and abnormal changes, providing a reference benchmark for real-time monitoring, enabling the system to accurately identify abnormal patterns caused by sudden changes in vegetation water content amidst complex background fluctuations.

[0073] The system plots the current envelope jitter intensity and phase consistency index in a two-dimensional feature space in real time, and calculates the Mahalanobis distance from this point to the boundary of the distribution area. Real-time monitoring is the core process of anomaly detection, identifying abnormal events by continuously comparing the current state with the normal baseline. The monitoring process first calculates the envelope jitter intensity and phase consistency index of the real-time signal to determine the current point in the feature space; then, it calculates the Mahalanobis distance from this point to the boundary of the normal distribution, serving as a quantitative indicator of the degree of anomaly. The Mahalanobis distance considers the covariance structure of the features, is sensitive to the correlation between features, and reflects the true degree of anomaly better than the simple Euclidean distance. The distance calculation formula is:

[0074] ;

[0075] in, For point Mahalanobis distance, Let be the mean vector of a normal distribution. This is the covariance matrix. The larger the distance value, the further the current state deviates from the normal pattern, and the higher the probability of an anomaly, providing a quantitative criterion for triggering abnormal events.

[0076] When the Mahalanobis distance exceeds a preset anomaly threshold and the duration exceeds a preset duration threshold, an anomaly attenuation event is triggered, and the start time of the anomaly attenuation event and the corresponding peak envelope jitter intensity are recorded. Event triggering is the decision-making step in anomaly detection, determining the existence of an abnormal state through threshold judgment. The triggering process first compares the real-time calculated Mahalanobis distance with a preset anomaly threshold (usually set to 3-5, corresponding to 3-5 standard deviations of a normal distribution). When the distance exceeds the threshold, a potential anomaly is recorded. Then, the duration of the abnormal state is monitored. Only when the duration exceeds a preset threshold (usually 30-60 seconds) is it confirmed as a real anomaly event, effectively filtering out brief interference. Finally, key parameters of the event are recorded, including the start time, duration, and peak intensity. This dual judgment mechanism combining distance and time thresholds significantly improves the reliability of detection, reduces the false alarm rate, and ensures that the system can accurately identify real anomaly events caused by sudden changes in vegetation water content, providing a reliable triggering basis for subsequent transmission strategy adjustments.

[0077] In this embodiment of the invention, the detailed implementation steps for dynamically adjusting the frequency switching timing and power compensation gain in the transmission strategy include:

[0078] The attenuation depth is defined as the difference between the average received signal strength during an abnormal attenuation event and the baseline received signal strength under normal operating conditions. Attenuation depth is a key indicator for quantifying the impact of anomalies, directly reflecting the signal attenuation effect of vegetation water content changes. The calculation process first determines the baseline signal strength under normal operating conditions from historical data, typically using the average level of the same period in the recent 24-48 hours, considering the influence of daily cycles and workload. Then, the actual signal strength during the abnormal event is measured, and the average received power of consecutive data packets is calculated. Finally, the difference between the two values ​​is calculated to obtain the attenuation depth (in dB). Attenuation depth is a key reference value for adjusting compensation strategies; a larger depth indicates more drastic environmental changes, requiring stronger compensation measures, and serves as a quantitative basis for the system's adaptive response strength.

[0079] The duration is the time span from the triggering to the end of a statistically anomalous decay event. Duration is a crucial parameter for assessing the nature of an event, reflecting the timescale of environmental changes. The statistical process records the start time (the moment the Mahalanobis distance first exceeds the threshold) and the end time (the moment the Mahalanobis distance continuously falls back below the threshold) of the anomalous event, calculating the time difference between these two times to obtain the duration. Duration directly impacts the system's response strategy; short-term events may be weathered through temporary adjustments, while long-term events require more stable adaptation measures, making it a key basis for determining the event type and adjustment plan.

[0080] The type of abnormal decay event is determined by the ratio of its duration to the preset vegetation moisture recovery period. Events with a ratio less than a first-type threshold are marked as transient decay, while those with a ratio greater than a second-type threshold are marked as persistent decay. Event classification is the foundation for developing differentiated response strategies, distinguishing different types of environmental changes based on temporal characteristics. The classification process first determines the typical vegetation moisture recovery period based on historical observations and vegetation physiological characteristics; for example, dew evaporation typically takes 2-4 hours, while recovery after light rain takes 6-12 hours. Then, the ratio of the actual duration to the recovery period is calculated. Finally, the event type is determined based on the ratio range. The first-type threshold is typically set at 0.3; events with a ratio less than this value are considered transient decays, such as short-term fog or dew. The second-type threshold is typically set at 0.8; events with a ratio greater than this value are considered persistent decays, such as continuous rainfall or irrigation. This time-scale-based classification method enables the system to adopt corresponding adjustment strategies based on the nature of the event, improving the targeting and effectiveness of the response.

[0081] For transient attenuation events, the frequency switching timing is advanced to the next data packet transmission time after the attenuation event is triggered. For persistent attenuation events, the current operating frequency is maintained and the power compensation gain is increased. Differentiated response is the core principle of strategy adjustment, taking the most suitable response measures for different types of events. For transient attenuation events, the system adopts a frequency adjustment strategy, utilizing the differences in sensitivity of different frequencies to changes in vegetation water content, switching to a less affected frequency band to quickly restore communication quality. The switching timing is arranged before the next data packet transmission to minimize response latency and improve system agility. For persistent attenuation events, considering the high energy consumption and limited benefits of frequent frequency switching, the system chooses to maintain the current frequency but increase the transmission power, using power compensation to offset the additional attenuation caused by environmental changes. This differentiated response strategy fully considers the characteristics of different events and system resource constraints, achieving efficient adaptive adjustment and maximizing the balance between communication reliability and energy efficiency.

[0082] Based on the attenuation depth and the power margin in the transmission strategy, the adjustment step size of the power compensation gain is calculated. The adjustment step size is the smaller of a preset multiple of the attenuation depth and the power margin. Power compensation is a key measure to cope with continuous attenuation, offsetting environmental attenuation by precisely controlling the power gain. The calculation process first determines the compensation target, usually set as completely offsetting the attenuation depth or restoring to the lowest acceptable signal quality level; then, the system's power margin constraint is considered to ensure that the adjustment does not exceed the hardware capability limit; finally, the specific adjustment step size is calculated. The preset multiple is usually set to 1.2-1.5 to provide a certain safety margin to ensure that the compensation is sufficiently effective; the power margin is the difference between the system's maximum transmit power and the current power, representing the available adjustment space. By taking the smaller of the two, the system ensures effective compensation while avoiding energy waste or hardware risks caused by over-adjustment. This balanced power adjustment method ensures the stability and sustainability of the system in the face of continuous environmental changes and is a key component of the adaptive transmission strategy.

[0083] In this embodiment of the invention, the detailed implementation steps for extracting seasonal fluctuation components within a preset period range include:

[0084] The packet loss rate trend was analyzed using Discrete Fourier Transform (DFT) to obtain the frequency domain spectral distribution. DFT is a classic method for signal periodicity analysis, capable of decomposing time series into periodic components of different frequencies. The Fast Fourier Transform (FFT) algorithm was employed to improve computational efficiency and support real-time analysis of long-sequence data. First, the packet loss rate trend was preprocessed, including detrending and window function smoothing to reduce edge effects. Then, FFT calculations were performed to obtain the frequency domain representation. Finally, power spectral density (PSD) analysis was used to determine the distribution of significant frequency components. The frequency domain spectral lines visually represent the energy distribution of each periodic component in the signal, forming the basis for identifying major periodic patterns. This enables the system to separate meaningful periodic changes from complex time series, providing a frequency perspective for identifying vegetation growth cycles.

[0085] In the frequency domain spectral distribution, spectral lines whose corresponding frequencies fall within a preset period range are screened. The ratio of the sum of amplitudes of the screened spectral lines to the sum of amplitudes of the total spectral lines is calculated and denoted as the seasonal contribution. Period screening is a key step focusing on the target time scale, separating vegetation-related periodic components through frequency screening. The screening process first determines the preset period range based on the system analysis objectives, typically including daily (24-hour), weekly (7-day), and seasonal (90-120-day) periods. Then, spectral line components within the corresponding frequency range are identified in the frequency domain spectral lines. Finally, the energy contribution ratio of these components is calculated to obtain the seasonal contribution. The contribution calculation formula is:

[0086] ;

[0087] in, As for seasonal contribution, For frequency Power spectral density at that point This refers to the set of frequencies within a predefined periodic range. Contribution is an important indicator for assessing the significance of periodicity; a higher value indicates a more significant seasonal component and is a key reference value for determining the effectiveness of seasonal fluctuation components.

[0088] When the seasonal contribution exceeds a preset contribution threshold, the frequency components corresponding to the selected spectral lines are extracted, and an inverse Fourier transform is performed to reconstruct the seasonal fluctuation component. Signal reconstruction is a necessary step from the frequency domain back to the time domain. By selectively retaining specific frequency components, the signal pattern of interest is separated. The reconstruction process first sets a preset contribution threshold (usually 0.3-0.5) to ensure that the seasonal component has sufficient significance; then, frequency components within a preset period range are retained, while other frequency components are set to zero; finally, the frequency domain representation is converted back to the time domain signal through an inverse Fourier transform (IFFT) to obtain the purified seasonal fluctuation component. This component eliminates random noise and interference from non-target periods, intuitively reflecting the seasonal change pattern of the signal. It is the core input for constructing the signal attenuation-vegetation growth correlation map, enabling the system to accurately capture the influence of vegetation periodic changes on signal transmission.

[0089] The peak times of the seasonal fluctuation components are calibrated, and the interval between adjacent peak times is taken as the actual vegetation growth cycle. The seasonal fluctuation component is considered valid when the coefficient of variation of the actual vegetation growth cycle is less than a preset variation threshold. Periodic verification is the final step to ensure the reliability of the extraction results. The validity of the seasonal components is verified by evaluating the stability of the period. The verification process first performs peak detection on the reconstructed seasonal fluctuation components, marking the time positions of all local maxima; then, the time interval between adjacent peaks is calculated to form a periodic sequence; finally, the coefficient of variation (the ratio of standard deviation to mean) of the periodic sequence is calculated to evaluate the stability of the period. The preset variation threshold is usually set to 0.15-0.25. When the coefficient of variation is below this value, the period is considered stable and reliable, and the seasonal fluctuation component is valid; otherwise, it is judged as unstable or a pseudo-period, requiring readjustment of analysis parameters or collection of more data. This rigorous verification mechanism ensures the scientific rigor and reliability of the seasonal analysis, avoids prediction bias caused by erroneous periods, and provides high-quality time-series feature input for subsequent correlation analysis.

[0090] In this embodiment of the invention, the detailed implementation steps for calculating the total path attenuation and the phase delay at each scattering point include:

[0091] The physical thickness and equivalent attenuation coefficient of each vegetation layer traversed by the propagation path are obtained. Parameter acquisition is the data preparation stage for path loss calculation, and the characteristics of the medium through which the signal penetrates are determined through spatial analysis. The acquisition process is based on the previously constructed three-dimensional vegetation topology. First, the intersection points of the ray and the medium layer are determined, and the propagation distance of the ray in each layer is calculated to obtain the physical thickness. Then, the equivalent attenuation coefficient at the corresponding location is queried from the labeled parameter map, and spatial interpolation is considered to improve accuracy. The physical thickness directly reflects the distance the signal propagates in the vegetation, and the equivalent attenuation coefficient quantifies the signal loss per unit distance. The combination of the two provides the basic parameters for path attenuation calculation, enabling the system to accurately simulate the signal propagation characteristics in complex heterogeneous environments.

[0092] The propagation attenuation of the signal in each vegetation layer is calculated, and the attenuation values ​​are accumulated sequentially along the path to obtain the total path attenuation. Path attenuation calculation is the core step in propagation loss assessment, predicting the overall attenuation by accumulating the loss of each segment. The calculation process first applies an attenuation model, multiplying the physical thickness by an equivalent attenuation coefficient to obtain the attenuation value of a single layer (in dB); then, the attenuation values ​​of each layer are linearly accumulated in the order of ray penetration to obtain the total path attenuation. For frequency-dependent attenuation characteristics, the equivalent attenuation coefficient is adjusted according to the operating frequency to ensure that the model is applicable to different frequency bands. The total path attenuation directly reflects the weakening effect of the vegetation environment on signal strength and is a key indicator for evaluating link quality, providing a quantitative basis for transmission parameter optimization and node deployment.

[0093] For each scattering point on the path, the scattering loss is looked up from the angle dependence function of the angular scattering coefficient based on the incident angle and the scattering angle, and the additional propagation distance of the scattering path relative to the direct path is calculated. Scattering analysis is a key component of multipath propagation simulation, improving model accuracy by considering directional scattering characteristics. The analysis process first determines the spatial location of the scattering point, calculates the three-dimensional direction vectors of the incident and scattered rays, and obtains the incident and scattering angles; then, the scattering coefficient for the corresponding angle combination is looked up from the previously constructed angle dependence function to quantify the scattering intensity; finally, based on the path lengths before and after scattering, the additional propagation distance relative to the direct path is calculated. Scattering loss reflects the energy loss of the signal during scattering, while the additional propagation distance affects the phase characteristics of the signal. Together, they determine the complex amplitude characteristics of the scattering component and are key parameters for accurately simulating multipath interference.

[0094] The extra propagation distance is converted into phase delay, which is related to both the extra propagation distance and the operating wavelength. Phase calculation is a necessary step in evaluating signal coherence, converting the geometric path difference into electromagnetic wave phase difference. Phase delay is a periodic parameter, ranging from [0, 2π), and directly affects the coherence and interference effects of multipath signals. Wavelength is inversely proportional to the operating frequency; therefore, high-frequency signals are more sensitive to path differences, resulting in faster phase changes. This explains why high-frequency communication often faces more severe multipath fading problems in complex environments. Phase delay calculation provides phase information for multipath superposition analysis, enabling the system to accurately predict the coherent superposition effects of signals.

[0095] The phase delays of all scattering points along the propagation path are vector-summed to obtain the total phase delay of the propagation path. This total phase delay is then combined with the total path attenuation to construct the complex transfer function of the path. Transfer function construction is the concluding step in path characteristic modeling, comprehensively describing the amplitude and phase characteristics of the path using complex numbers. The construction process first expresses the phase delay of the scattering points in complex form (using Euler's formula). Then, complex multiplication is performed according to the signal propagation order to accumulate the phase effect of all scattering points; finally, the total path attenuation is converted into a linear amplitude scaling factor. , (The value is in dB), which, combined with the phase factor, forms a complete complex transfer function. The transfer function uses... In the form of, This is the amplitude attenuation coefficient. The phase delay angle comprehensively describes the electromagnetic characteristics of this propagation path. This complex representation provides the mathematical basis for subsequent signal vector superposition, enabling the system to accurately simulate interference effects in multipath environments, including destructive and constructive phase transitions and deep fading phenomena, providing a comprehensive theoretical basis for link quality assessment and communication parameter optimization.

[0096] This invention achieves stable data acquisition and transmission in complex vegetated environments through correlation analysis of signal quality parameters and vegetation ecological parameters, identification of multipath scattering sources, construction of a propagation attenuation model, and adjustment of adaptive transmission strategies. The adaptive transmission method of this invention can dynamically adjust communication parameters based on changes in vegetation growth and sudden changes in water content, effectively overcoming the attenuation effect of vegetation on wireless signals and providing reliable environmental monitoring data support for ecological restoration areas.

[0097] The above description is merely a preferred embodiment of the present invention and is 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 make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0098] It should be noted that all formulas in this manual are calculated by removing dimensions and taking their numerical values. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0099] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A real-time monitoring and early warning system for environmental parameters for ecological restoration, characterized in that, include: The data acquisition module is used to acquire signal quality parameters and vegetation ecological parameters of each monitoring node in the ecological restoration area at different time periods; The correlation map construction module is used to extract the seasonal variation curve of signal attenuation based on the periodic fluctuation characteristics of the signal quality parameters in the time dimension, and to perform time-series correlation analysis with the vegetation ecological parameter data to construct a correlation map of signal attenuation-vegetation growth. The scattering source identification module is used to acquire the arrival time series of multipath signals received by each monitoring node, and to identify the spatial location of multipath scattering sources caused by vegetation layer by the statistical distribution characteristics of adjacent arrival time differences. The attenuation coefficient calculation module is used to calculate the equivalent attenuation coefficient and angular scattering coefficient corresponding to different vegetation density areas based on the spatial location of the multipath scattering source and the signal attenuation-vegetation growth correlation map. The propagation model construction module is used to construct a signal propagation spatial attenuation model including a vegetation medium layer based on the equivalent attenuation coefficient and the angular scattering coefficient. The transmission strategy generation module is used to generate a multi-band collaborative adaptive transmission strategy based on the signal propagation spatial attenuation model and the data acquisition priority of each monitoring area. The abnormal attenuation identification module is used to identify abnormal attenuation events caused by sudden changes in vegetation water content by real-time monitoring of the signal envelope jitter characteristics and phase consistency index of the inter-node link. The strategy adjustment module is used to dynamically adjust the frequency switching timing and power compensation gain in the transmission strategy based on the duration and attenuation depth of the abnormal attenuation event. The control execution module is used to control the communication parameters of each monitoring node based on the adjusted transmission strategy.

2. The real-time monitoring and early warning system for environmental parameters for ecological restoration according to claim 1, characterized in that, The step involves extracting the seasonal variation curve of signal attenuation based on the periodic fluctuation characteristics of the signal quality parameters over time, and performing a time-series correlation analysis with the vegetation ecological parameter data to construct a signal attenuation-vegetation growth correlation map, including: The packet loss rate data in the signal quality parameters is smoothed by a sliding window to obtain the trend of packet loss rate after removing random noise; The packet loss rate trend is subjected to Fourier decomposition to extract the seasonal fluctuation component within a preset period range, which is denoted as the seasonal feature vector of signal attenuation. Obtain the time series data of the normalized vegetation index from the vegetation ecological parameter data, and calculate the cross-correlation coefficient between the time series data of the normalized vegetation index and the seasonal feature vector of signal attenuation. The time period in which the cross-correlation coefficient is greater than a preset correlation threshold is marked as a strong correlation growth period. The ratio of the signal attenuation increment to the vegetation index increment during the strong correlation growth period is calculated and recorded as the unit vegetation growth attenuation factor. Based on the temporal distribution of the unit vegetation growth attenuation factor and the strongly correlated growth period, the signal attenuation-vegetation growth correlation map is constructed.

3. The real-time monitoring and early warning system for environmental parameters for ecological restoration according to claim 1, characterized in that, The method of identifying the spatial location of multipath scattering sources caused by vegetation layer through the statistical distribution characteristics of adjacent arrival time differences includes: Time-domain peak detection is performed on the signal pulses received by each monitoring node to extract the arrival time sequence of the first pulse and the arrival time sequence of the delayed pulse; Calculate the time difference between the arrival time of the delayed pulse and the arrival time of the first pulse, and denot it as the multipath delay; The probability density distribution of the multipath delay within a preset observation period is statistically analyzed, and delay clusters with peak values ​​greater than a preset probability threshold are extracted from the probability density distribution. Based on the center delay value of the delay cluster, and combined with the spatial coordinates of the transmitting and receiving nodes, the candidate spatial location of the scattering point is calculated using a geometric triangulation positioning algorithm. Cluster analysis is performed on the candidate spatial locations, and candidate locations whose cluster center distance is less than a preset distance threshold are merged into a unified scattering source, which is denoted as the multipath scattering source spatial location.

4. The real-time monitoring and early warning system for environmental parameters for ecological restoration according to claim 1, characterized in that, The calculation of the equivalent attenuation coefficient and angular scattering coefficient corresponding to different vegetation density regions based on the spatial location of the multipath scattering source and the signal attenuation-vegetation growth correlation map includes: Based on the spatial location of the multipath scattering source, the monitoring area is divided into multiple vegetation density zones, and the standard deviation of the scattering source density in each vegetation density zone is less than a preset zone threshold. Obtain the mean normalized vegetation index corresponding to each vegetation density partition, and query the predicted signal attenuation corresponding to the mean normalized vegetation index based on the signal attenuation-vegetation growth correlation map. The actual received power of the signal link passing through the vegetation density zone is measured, and the difference between the actual received power and the theoretical free space propagation power is calculated and recorded as the actual attenuation. The ratio of the actual attenuation to the predicted signal attenuation is used as a correction coefficient. Based on the correction coefficient and the equivalent propagation distance of the vegetation density zone, the equivalent attenuation coefficient is calculated. Based on the spatial location of the multipath scattering source and the location of the transmitting and receiving nodes, the angle between the incident direction and the scattering direction is calculated, the proportion of scattered energy in different angle ranges is statistically analyzed, and the angle dependence function of the angle scattering coefficient is constructed.

5. The real-time monitoring and early warning system for environmental parameters for ecological restoration according to claim 1, characterized in that, The step of constructing a signal propagation spatial attenuation model including a vegetation medium layer based on the equivalent attenuation coefficient and the angular scattering coefficient includes: Based on the spatial distribution of the vegetation density zones, a three-dimensional vegetation media topology of the monitoring area is constructed. The equivalent attenuation coefficient and the angular scattering coefficient of each vegetation density zone are marked in the three-dimensional vegetation media topology; Based on the ray tracing algorithm, all possible propagation paths of the signal from the transmitting node to the receiving node are calculated, and the sequence of vegetation media layers traversed by each propagation path is recorded. For each propagation path, the total path attenuation and the phase delay at each scattering point are cumulatively calculated based on the sequence of vegetation media layers traversed. The complex amplitudes of all propagation paths at the receiving point are vector-superimposed, and the intensity distribution and phase jitter distribution of the synthesized signal after considering multipath interference are calculated to construct the signal propagation spatial attenuation model.

6. The real-time monitoring and early warning system for environmental parameters for ecological restoration according to claim 1, characterized in that, The generation of a multi-band collaborative adaptive transmission strategy based on the signal propagation spatial attenuation model and the data acquisition priority of each monitoring area includes: Based on the ecological restoration goals and environmental sensitivity of each monitoring area, a data collection priority weight matrix is ​​constructed; Based on the signal propagation spatial attenuation model, the link reliability score of each monitoring node is calculated at different operating frequencies. The link reliability score comprehensively considers the signal strength margin and the multipath fading depth. For each monitoring node, the target transmission success rate is determined based on the data acquisition priority weight matrix, and the optimal operating frequency and minimum transmission power that satisfy the target transmission success rate are solved by an iterative optimization algorithm. A communication blind zone with severe attenuation is identified in the signal propagation spatial attenuation model. An intermediate node with a high link reliability score is selected as a relay node between the communication blind zone and the core node. The optimal operating frequency, the minimum transmit power, and the relay node information are integrated to generate the multi-band cooperative adaptive transmission strategy.

7. The real-time monitoring and early warning system for environmental parameters for ecological restoration according to claim 1, characterized in that, The method of identifying abnormal attenuation events caused by sudden changes in vegetation water content by real-time monitoring of signal envelope jitter characteristics and phase consistency indicators of inter-node links includes: Perform Hilbert transform on the received signal to extract the signal envelope sequence, and calculate the standard deviation of the signal envelope sequence within the sliding time window, which is denoted as the envelope jitter intensity. The carrier phase of the received signal is demodulated, the phase difference sequence between adjacent data packets is calculated, and the concentration parameter of the phase difference sequence is statistically analyzed and denoted as the phase consistency index. Construct a two-dimensional feature space for the envelope jitter intensity and the phase consistency index, and mark the distribution area of ​​historical normal working state in the two-dimensional feature space; The position points of the current envelope jitter intensity and the phase consistency index in the two-dimensional feature space are plotted in real time, and the Mahalanobis distance from the position point to the boundary of the distribution area is calculated. When the Mahalanobis distance is greater than a preset abnormal threshold and the duration exceeds a preset duration threshold, an abnormal decay event is triggered, and the start time of the abnormal decay event and the corresponding peak value of the envelope jitter intensity are recorded.

8. The real-time monitoring and early warning system for environmental parameters for ecological restoration according to claim 1, characterized in that, The step of dynamically adjusting the frequency switching timing and power compensation gain in the transmission strategy based on the duration and attenuation depth of the abnormal attenuation event includes: The difference between the average received signal strength during the abnormal attenuation event and the reference value of the received signal strength under normal operating conditions is calculated and denoted as the attenuation depth. The time span from the triggering to the end of the abnormal decay event is recorded as the duration. The type of abnormal decay event is determined based on the ratio of the duration to the preset vegetation moisture recovery cycle. When the ratio is less than the first type threshold, it is marked as transient decay; when the ratio is greater than the second type threshold, it is marked as continuous decay. For abnormal attenuation events of the transient attenuation type, the frequency switching timing is advanced to the next data packet transmission time after the abnormal attenuation event is triggered; for abnormal attenuation events of the continuous attenuation type, the current operating frequency is maintained and the power compensation gain is increased. The adjustment step size of the power compensation gain is calculated based on the attenuation depth and the power margin in the transmission strategy.

9. The real-time monitoring and early warning system for environmental parameters for ecological restoration according to claim 2, characterized in that, The step of performing Fourier decomposition on the packet loss rate trend to extract seasonal fluctuation components within a preset period range includes: The frequency domain spectral distribution is obtained by performing a discrete Fourier transform on the packet loss rate trend. In the frequency domain spectral line distribution, spectral lines whose corresponding frequency period is within a preset period range are selected, and the ratio of the sum of amplitudes of the selected spectral lines to the sum of amplitudes of the total spectral lines is calculated and recorded as the seasonal contribution. When the seasonal contribution is greater than the preset contribution threshold, the frequency component corresponding to the selected spectral line is extracted, and an inverse Fourier transform is performed to reconstruct the seasonal fluctuation component. The peak time of the seasonal fluctuation component is calibrated, and the interval between adjacent peak times is taken as the actual vegetation growth cycle. When the coefficient of variation of the actual vegetation growth cycle is less than a preset variation threshold, the seasonal fluctuation component is confirmed to be valid.

10. The real-time monitoring and early warning system for environmental parameters for ecological restoration according to claim 5, characterized in that, For each propagation path, based on the sequence of vegetation media layers traversed, the total path attenuation and the phase delay at each scattering point are cumulatively calculated, including: Obtain the physical thickness of each of the vegetation media layers traversed by the propagation path and the equivalent attenuation coefficient; Calculate the propagation attenuation of the signal in each of the vegetation media layers, and accumulate the propagation attenuation values ​​in the path order to obtain the total path attenuation. For each scattering point on the path, the scattering loss is queried from the angle dependence function of the angular scattering coefficient based on the incident angle and the scattering angle, and the additional propagation distance of the scattering path relative to the direct path is calculated. The additional propagation distance is converted into a phase delay, and the phase delays of all scattering points on the path are vector-accumulated to obtain the total phase delay of the propagation path. The complex transfer function of the path is then constructed by combining the total attenuation of the path.

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