A method and system for monitoring the growth environment of ancient trees

Abstract

The application provides a kind of ancient tree growth environment monitoring method and system, it is related to tree monitoring technical field, by obtaining the micro electromagnetic penetration data of ancient tree external sensing device, and constructs elastic medium coupling constitutive model, the stiffness decoupling operation is carried out to structural response frequency domain signal, accurately strips the mass reduction caused by physiological dehydration and the stiffness drop caused by internal decay;By solving the implicit damage comprehensive index and the implicit bending resistance bearing potential, and continuously comparing with dynamic structure health baseline and safety tolerance threshold, the intervention level is quantified;Even in the electromagnetic transmission appears normal but has hidden fungal cavity inside, still can acutely capture mechanical degradation, and generates intelligent early warning before structure instability, linkage issues maintenance tasks including damaged positioning and rejuvenation strategy, and cross-device control scheme including intelligent irrigation and physical support, realize the high confidence degree of ancient tree deep hidden structure damage and closed-loop collaborative management and control.

Description

Technical Field

[0001] This invention relates to the field of tree monitoring technology, specifically to a method and system for monitoring the growth environment of ancient trees. Background Technology

[0002] Due to their non-renewable nature, monitoring the structural safety and physiological health of ancient and famous trees has long been a core challenge in urban landscaping management. However, existing technologies often suffer from the following drawbacks: One drawback is the ambiguity of single-dimensional perception. Structural monitoring methods based on mechanical vibration characteristics rely on the tree's dominant vibration frequency, a core parameter determined by both the tree's equivalent stiffness and equivalent mass. When an ancient tree experiences a decrease in stiffness due to internal xylem decay, the direction of change in its dominant vibration frequency is the same as when the tree loses water, resulting in a decrease in mass (both manifest as an increase in dominant frequency or an abnormal damping ratio). Current technology cannot decouple these two physical mechanisms from a single vibration spectrum, leading to identical vibration characteristic outputs from two distinctly different damage states: "internal cavitation" and "physiological dehydration," resulting in a persistently high false alarm rate.

[0003] Second, there is a blind spot in the perception dimension. Physiological monitoring methods based on radio electromagnetic wave transmission loss can effectively quantify tree water content and biomass, but their output only reflects the physical properties of the tree medium and cannot perceive the mechanical integrity of the internal fiber structure of the trunk. An ancient tree with normal water content but large areas of fungal decay and cavities inside has electromagnetic transmission characteristics highly similar to those of a healthy tree, and this method is completely ineffective for this type of latent structural damage.

[0004] Meanwhile, the essence of the above-mentioned technical shortcomings lies in the fact that the perception of any single physical dimension cannot simultaneously constrain the two independent variables of "mass" and "stiffness", thus leading to an unavoidable ambiguity in the diagnosis of hidden damage inside ancient trees. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for monitoring the growth environment of ancient trees, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for monitoring the growth environment of ancient trees, comprising the following steps: S1. Obtain microscopic deformation trajectory data from external sensing devices in the ancient tree's growth environment as the first dataset, and based on the first dataset, determine the structural response frequency domain state signal used to characterize the modal response state of the ancient tree trunk structure. S2. Obtain the microscopic electromagnetic penetration data from the external sensing device of the ancient tree's growth environment as the second dataset, and based on the second dataset, determine the medium quality distribution signal used to characterize the medium quality characteristics of the ancient tree trunk. S3. Establish an elastic medium coupling constitutive relationship model between the medium mass distribution signal and the structural response frequency domain state signal. Based on the elastic medium coupling constitutive relationship model, and combined with the medium mass distribution signal acquired in real time as a constraint variable, perform stiffness decoupling operation on the structural response frequency domain state signal to obtain the latent damage comprehensive index characterizing the degree of latent damage inside the ancient tree trunk and evaluate it. S4. When the latent damage comprehensive index is detected to exceed the preset warning trigger threshold, an intelligent warning is generated before the ancient tree structure becomes physically unstable or the voids cascade and deteriorate. At the same time, a first control command for closed-loop task handling and a second control command for cross-device linkage control are generated in conjunction.

[0007] Furthermore, in the first dataset, structural vibration parameters of different dimensions are extracted for the target ancient tree microscopic observation area within a preset time window. The structural vibration parameters include the structural response power spectral density slope parameter, which reflects the structural stiffness response characteristics, and the vibration response damping ratio parameter, which reflects the structural energy dissipation trend. The structural response power spectral density slope parameter and the vibration response damping ratio parameter are respectively subjected to adaptive normalization processing based on their respective ancient tree historical benchmark values ​​or health model capacity, so that they are mapped to a unified numerical range; according to the preset nonlinear fusion model, the normalized structural response power spectral density slope parameter and the vibration response damping ratio parameter are dynamically weighted and fused to generate the structural response frequency domain state signal.

[0008] Furthermore, the generated structural response frequency domain state signal is subjected to time series smoothing processing to calculate the rate of change of the structural response frequency domain state signal within a preset time window, and the rate of change is used as the dynamic evolution trend feature of the structural response frequency domain state signal. The value of the frequency domain state signal of the structural response is equal to the product of the slope of the normalized structural response power spectral density and the first weighting coefficient, plus the product of the normalized vibration response damping ratio and the second weighting coefficient.

[0009] Furthermore, in the second dataset, for the target ancient tree microscopic spatial propagation path within a preset time window, multipath delay and energy attenuation information of the electromagnetic propagation path are extracted; based on the multipath delay and energy attenuation information of the electromagnetic propagation path, core medium measurement parameters, including the first medium measurement parameter and the second medium measurement parameter, are calculated in parallel. The first medium metric parameter is the broadband electromagnetic transmission attenuation coefficient, which characterizes the water content and biomass energy absorption of ancient trees; the second medium metric parameter is the channel state coherence time offset parameter, which characterizes the influence of the ancient tree canopy and trunk on multipath scattering.

[0010] Furthermore, based on a preset dynamic weighted aggregation model, the broadband electromagnetic transmission attenuation coefficient and the channel state coherence time offset parameter are fused to generate the medium quality distribution signal. The specific steps for generating the medium quality distribution signal are as follows: monitoring the data packet reception rate and average positioning delay of the sensing device, and generating a data confidence factor based on the monitoring results to calibrate the generated medium quality distribution signal in order to compensate for data uncertainty caused by external meteorological interference or communication quality degradation.

[0011] Furthermore, based on historical media mass distribution signals and structural response frequency domain state signal data, the elastic media coupling constitutive relation model is constructed; the elastic media coupling constitutive relation model is used to learn and quantify the mapping law of media mass characteristics transforming into structural equivalent mass and affecting the dominant frequency response under different meteorological and phenological conditions; By inputting the real-time acquired medium mass distribution signal and structural response frequency domain state signal into the elastic medium coupling constitutive relationship model for interference stripping calculation, the implicit bending bearing potential predicted by decoupling based on the structural response frequency domain state signal at the current moment is analyzed; the implicit bending bearing potential is used to reflect the physical safety margin of the target ancient tree's current mechanical limit without fracture.

[0012] Furthermore, a dynamic structural health baseline is set for dynamic adjustment based on the growth cycle and historical performance of micro-regions. The dynamic structural health baseline represents the minimum bending load capacity that the target ancient tree should possess. The bending load capacity potential corresponding to the calculated latent damage comprehensive index is continuously compared with the dynamic structural health baseline. When the bending load capacity potential is lower than the dynamic structural health baseline for multiple consecutive time steps, the intelligent early warning is generated. The value of the latent damage comprehensive index is equal to the pure stiffness degradation parameter obtained after removing the product compensation amount of the medium mass distribution signal from the current structural response frequency domain state signal, divided by a dynamic environment transfer coefficient.

[0013] Furthermore, the preset warning trigger threshold is specifically a structural safety tolerance threshold. Based on the difference between the currently acquired latent damage comprehensive index and the structural safety tolerance threshold, a unified intervention level that quantifies the current latent damage exposure is calculated and determined. Based on the intervention level and combined with the signal component characteristics of the specific medium mass distribution or pure stiffness degradation that leads to a reduction in flexural bearing capacity, the first control instruction is matched and generated from the preset knowledge base rules. The first control instruction is a structured maintenance task set that includes damage orientation prediction, rejuvenation and reinforcement strategies, and maintenance personnel assignment and scheduling. Simultaneously, after generating intelligent early warnings, the data on the distribution of green space assets, perception data, real-time location and status data of maintenance personnel and vehicles, and data on maintenance tasks to be processed within the target area are displayed in real time on the electronic map using at least one visualization layer.

[0014] Furthermore, the first control instruction is a structured maintenance task automatically generated based on early warning information and knowledge base rules and pushed to the mobile terminal of the target personnel; the second control instruction is used to call the application programming interface of the intelligent execution device corresponding to the target area to perform automatic control operations. Based on the intervention level and combined with the prediction results of the future evolution trend of the latent damage comprehensive index, the second control command is generated. The second control command is a cross-device linkage control scheme that includes intelligent irrigation water replenishment parameter adjustment or automatic triggering configuration of regional physical support equipment. Specifically, the first control command is sent as the structured maintenance task to the mobile terminal of the corresponding target personnel through the Internet of Things communication network, so as to receive the task execution process record and execution result feedback from the mobile terminal to achieve a closed loop, and the second control command is sent to the application program interface corresponding to the intelligent execution device in the target area through a unified device collaborative management and control bus to perform automatic control.

[0015] A monitoring system for the growth environment of ancient trees, comprising: The structural modal extraction module is used to acquire microscopic deformation trajectory data from external sensing devices in the ancient tree's growth environment as the first dataset, and based on the first dataset, to determine the structural response frequency domain state signal used to characterize the structural modal response state of the ancient tree trunk. The medium quality sensing module is used to acquire microscopic electromagnetic penetration data from external sensing devices in the ancient tree's growth environment as a second dataset, and based on the second dataset, to determine a medium quality distribution signal that characterizes the medium quality properties of the ancient tree trunk. The stiffness decoupling evaluation module is used to establish an elastic medium coupling constitutive relationship model between the medium mass distribution signal and the structural response frequency domain state signal. Based on the elastic medium coupling constitutive relationship model, the module combines the real-time acquired medium mass distribution signal as a constraint variable to perform stiffness decoupling calculation on the structural response frequency domain state signal, solve for the hidden damage comprehensive index that characterizes the degree of hidden damage inside the ancient tree trunk, and evaluate it. The early warning linkage control module is used to generate an intelligent early warning when the latent damage comprehensive index exceeds the preset early warning trigger threshold, before the ancient tree structure becomes physically unstable or the voids cascade and deteriorate; at the same time, it generates a first control command for closed-loop task handling and a second control command for cross-device linkage control.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention acquires microscopic electromagnetic penetration data from external sensing devices in the ancient tree's growth environment, determines the medium mass distribution signal including parameters such as broadband electromagnetic transmission attenuation coefficient and channel state coherence time offset, and inputs it as an independent constraint variable of the mass dimension into the established elastic medium coupling constitutive relation model. It then performs stiffness decoupling operations on the structural response frequency domain state signal, which includes normalized structural response power spectral density slope parameters and vibration response damping ratio parameters. This accurately separates the equivalent mass reduction caused by physiological dehydration of the tree from the equivalent stiffness reduction caused by internal xylem decay, and calculates the pure stiffness degradation parameter after removing the product compensation amount of the medium mass distribution signal. This completely breaks through the technical bottleneck that the vibration dominant frequency parameter is jointly determined by equivalent stiffness and equivalent mass, and solves the single-dimensional perception ambiguity defect of existing technologies that cannot decouple two physical mechanisms from a single vibration spectrum, thus confusing "physiological dehydration" with "internal voids." It effectively eliminates the misleading nature of homologous features and significantly reduces the system's false alarm rate. This invention further divides the pure stiffness degradation parameter by the dynamic environment transfer coefficient to obtain a latent damage comprehensive index characterizing the degree of latent damage within the ancient tree trunk. Based on this index, it analyzes the latent bending bearing potential, reflecting the physical safety margin that the target ancient tree can currently withstand without fracture. By continuously comparing the latent bending bearing potential with the dynamic structural health baseline and comparing the latent damage comprehensive index with the preset structural safety tolerance threshold to quantify the intervention level, this invention successfully overcomes the limitations of relying solely on electromagnetic wave transmission loss, which only reflects the physical properties of the medium and cannot perceive the mechanical integrity of the internal fiber structure of the tree trunk. Even when an ancient tree appears healthy in terms of electromagnetic transmission characteristics and has normal moisture content, but has large areas of fungal decay and cavities hidden inside, it can still penetrate the surface and keenly capture the hidden degradation of its mechanical properties. This allows for the generation of intelligent early warnings before the ancient tree's structure becomes physically unstable or the cascading deterioration of cavities occurs. It also triggers the issuance of a first control command, which includes a structured maintenance task set containing damage location prediction and rejuvenation and reinforcement strategies, and a second control command, which includes a cross-device linkage control scheme containing intelligent irrigation and water replenishment parameter adjustment or automatic triggering configuration of regional physical support equipment. This enables high-confidence detection and comprehensive closed-loop collaborative management of deep hidden structural damage to ancient trees. Attached Figure Description

[0017] Figure 1 This is a schematic diagram illustrating the overall application of the method of the present invention; Figure 2 This is a schematic diagram of the overall method flow of the present invention; Figure 3 This is a schematic diagram of the overall system framework of the present invention. Detailed Implementation

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0019] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0020] Please see Figures 1 to 3 This invention provides a method for monitoring the growth environment of ancient trees, the specific steps of which include: S1. Obtain microscopic deformation trajectory data from external sensing devices in the ancient tree's growth environment as the first dataset, and based on the first dataset, determine the structural response frequency domain state signal used to characterize the modal response state of the ancient tree trunk structure. S2. Obtain the microscopic electromagnetic penetration data from the external sensing device of the ancient tree's growth environment as the second dataset, and based on the second dataset, determine the medium quality distribution signal used to characterize the medium quality characteristics of the ancient tree trunk. S3. Establish an elastic medium coupling constitutive relationship model between the medium mass distribution signal and the structural response frequency domain state signal. Based on the elastic medium coupling constitutive relationship model, and combined with the medium mass distribution signal acquired in real time as a constraint variable, perform stiffness decoupling operation on the structural response frequency domain state signal to obtain the latent damage comprehensive index characterizing the degree of latent damage inside the ancient tree trunk and evaluate it. S4. When the latent damage comprehensive index is detected to exceed the preset warning trigger threshold, an intelligent warning is generated before the ancient tree structure becomes physically unstable or the voids cascade and deteriorate. At the same time, a first control command for closed-loop task handling and a second control command for cross-device linkage control are generated in conjunction.

[0021] In the first dataset, structural vibration parameters of different dimensions are extracted for the target ancient tree microscopic observation area within a preset time window. The structural vibration parameters include the structural response power spectral density slope parameter, which reflects the structural stiffness response characteristics, and the vibration response damping ratio parameter, which reflects the structural energy dissipation trend. The structural response power spectral density slope parameter and the vibration response damping ratio parameter are respectively subjected to adaptive normalization processing based on their respective ancient tree historical benchmark values ​​or health model capacity, so that they are mapped to a unified numerical range; according to the preset nonlinear fusion model, the normalized structural response power spectral density slope parameter and the vibration response damping ratio parameter are dynamically weighted and fused to generate the structural response frequency domain state signal.

[0022] Furthermore, the specific implementation process of the frequency domain feature extraction and dynamic fusion logic of the structural modal response parameters in S1 is as follows: The technical environment involved in this embodiment is as follows: the external sensing device must be a three-axis deformation sensing network mounted on the outside of the target ancient tree. The device must have high-frequency continuous sampling and micron-level displacement resolution capabilities, and output a time-series displacement matrix sequence through a standard serial data interface.

[0023] After obtaining the temporal deformation sequence within a preset time window in the first dataset, the mean is removed to eliminate static gravity offset interference, a discrete frequency domain transformation is performed, and the energy density corresponding to each frequency point is calculated to generate a preliminary power spectral density array.

[0024] Regarding the extraction of the power spectral density slope parameter of the structural response: In this step, a fundamental frequency cutoff boundary parameter is set to divide the frequency band, which is used to physically isolate the extremely low frequency band dominated by wind-induced loads from the high frequency band dominated by the inherent structural response. In this embodiment, the value of this parameter is set to 2.5 Hz. The technical consideration for setting this value is that it is a technical balance achieved between "avoiding strong interference from natural wind eddy shedding frequencies" and "fully preserving the first to third resonant modes of large trees".

[0025] The total energy of the frequency bands below and above the boundary parameter is summed separately, and the total energy of the low-frequency band is divided by the total energy of the high-frequency band. The quotient obtained is the extracted structural response power spectral density slope parameter. For the extraction of the vibration response damping ratio parameter: the center frequency value corresponding to the highest energy main resonance peak is retrieved in the power spectral density array, and the upper and lower limit frequency boundaries corresponding to the energy decaying to half of the highest energy of that resonance peak are searched downwards. The upper limit frequency and the lower limit frequency are subtracted to obtain the bandwidth, and then the bandwidth is divided by twice the center frequency value to obtain the vibration response damping ratio parameter.

[0026] In the adaptive normalization stage: the historical maximum and minimum values ​​of the current parameter over the past year are read from the local database. The absolute increment is obtained by subtracting the historical minimum value from the current value, and the extreme fluctuation range is obtained by subtracting the historical minimum value from the historical maximum value. Finally, the absolute increment is divided by the extreme fluctuation range to map it to a dimensionless floating-point number between 0 and 1. In the dynamic weight calculation and signal fusion stage: a variance analysis window parameter needs to be set for calculating local statistical characteristics. The function of this parameter is to extract a segment of historical data to evaluate the stability of the current signal. In this embodiment, this value is set to 600 discrete sampling periods, which can achieve a balance between "ensuring statistical significance" and "preventing a slow response to sudden damage". The local variance values ​​of the normalized slope parameter and the normalized damping ratio parameter within the window are calculated separately, and their reciprocals are calculated separately. The reciprocals of the two are added together to obtain a total reciprocal sum, and then the reciprocal of each is divided by the total reciprocal sum to obtain the first weight coefficient and the second weight coefficient respectively; the frequency domain state signal of the structural response is generated based on the sum of the products of their respective normalized values ​​and the corresponding weight coefficients.

[0027] Furthermore, to extract dynamic evolution trend features, a time span constant needs to be set to eliminate instantaneous sampling errors. In this embodiment, it is set to an equivalent duration of five minutes. All historical state signals within this time span are summed and their arithmetic average is calculated to obtain the current state baseline value. This is then subtracted from the old state baseline value of the previous window, and divided by the time interval value corresponding to the time span constant. The resulting rate of change of the difference is the evolution trend feature.

[0028] The generated structural response frequency domain state signal is subjected to time series smoothing processing to calculate the rate of change of the structural response frequency domain state signal within a preset time window, and the rate of change is used as the dynamic evolution trend feature of the structural response frequency domain state signal. The value of the frequency domain state signal of the structural response is equal to the product of the slope of the normalized structural response power spectral density and the first weighting coefficient, plus the product of the normalized vibration response damping ratio and the second weighting coefficient.

[0029] In the second dataset, for the target ancient tree microscopic spatial propagation path within a preset time window, the multipath delay and energy attenuation information of the electromagnetic propagation path are extracted; based on the multipath delay and energy attenuation information of the electromagnetic propagation path, the core medium measurement parameters, including the first medium measurement parameter and the second medium measurement parameter, are calculated in parallel. The first medium metric parameter is the broadband electromagnetic transmission attenuation coefficient, which characterizes the water content and biomass energy absorption of ancient trees; the second medium metric parameter is the channel state coherence time offset parameter, which characterizes the influence of the ancient tree canopy and trunk on multipath scattering.

[0030] The specific implementation process of the parallel extraction logic for the microscopic electromagnetic features of the medium mass distribution signal in S2 is as follows: The technical environment of this embodiment is based on the following premise: the acquisition of the second dataset relies on an ultra-wideband radar transceiver antenna array symmetrically deployed on both sides of the tree trunk. This device must have picosecond-level time-gated transmission and full waveform amplitude acquisition attributes, and its interface output is a discrete-time waveform matrix containing pulse flight time and amplitude intensity. For the calculation of the first medium metric parameter (wideband electromagnetic transmission attenuation coefficient): the total energy calibration value of the transmitted signal and the integral energy value of the earliest arriving principal pulse at the receiving end are extracted, and the latter is divided by the former to obtain the energy residual rate; then, a logarithmic operation with the natural constant as the base is performed on this energy residual rate, and the absolute value is taken to characterize the degree of energy absorption by the water content inside the ancient tree.

[0031] For the calculation of the second medium metric parameter (channel state coherence time offset parameter): an effective multipath truncation determination parameter is set to define the boundary between effective multipath and spatial thermal noise. In this embodiment, it is set to an equivalent linear ratio of -25 dB relative to the main peak intensity. This setting strikes a balance between "not omitting weak scattering from hidden holes" and "avoiding interference from underlying thermal noise". Trailing pulses with intensities lower than this parameter are filtered out from the waveform matrix, retaining the effective multipath pulses; the absolute timestamps of the last effective pulse and the main path pulse are extracted and subtracted to obtain the maximum delay spread width; finally, the number is divided by this width, and the reciprocal value is the channel state coherence time offset parameter.

[0032] Based on a preset dynamic weighted aggregation model, the broadband electromagnetic transmission attenuation coefficient and the channel state coherence time offset parameter are fused to generate the medium quality distribution signal. The specific steps for generating the medium quality distribution signal are as follows: monitoring the data packet reception rate and average positioning delay of the sensing device, and generating a data confidence factor based on the monitoring results to calibrate the generated medium quality distribution signal in order to compensate for data uncertainty caused by external meteorological interference or communication quality degradation.

[0033] Based on historical media mass distribution signals and structural response frequency domain state signal data, a coupling constitutive relation model of the elastic medium is constructed. The coupling constitutive relation model of the elastic medium is used to learn and quantify the mapping law of the transformation of media mass characteristics into equivalent structural mass and its influence on the dominant frequency response under different meteorological and phenological conditions. By inputting the real-time acquired medium mass distribution signal and structural response frequency domain state signal into the elastic medium coupling constitutive relationship model for interference stripping calculation, the implicit bending bearing potential predicted by decoupling based on the structural response frequency domain state signal at the current moment is analyzed; the implicit bending bearing potential is used to reflect the physical safety margin of the target ancient tree's current mechanical limit without fracture.

[0034] A dynamic structural health baseline is set for dynamic adjustment based on the growth cycle and historical performance of micro-regions. The dynamic structural health baseline represents the minimum bending load capacity that the target ancient tree should possess. The bending load potential corresponding to the calculated latent damage comprehensive index is continuously compared with the dynamic structural health baseline. When the bending load potential is lower than the dynamic structural health baseline for multiple consecutive time steps, the intelligent early warning is generated. The value of the latent damage comprehensive index is equal to the pure stiffness degradation parameter obtained after removing the product compensation amount of the medium mass distribution signal from the current structural response frequency domain state signal, divided by a dynamic environment transfer coefficient.

[0035] The preset early warning trigger threshold is specifically a structural safety tolerance threshold. Based on the difference between the currently acquired latent damage comprehensive index and the structural safety tolerance threshold, a unified intervention level that quantifies the current latent damage exposure is calculated and determined. Based on the intervention level and combined with the signal component characteristics of the specific medium mass distribution or pure stiffness degradation that leads to a reduction in flexural bearing capacity, the first control instruction is matched and generated from the preset knowledge base rules. The first control instruction is a structured maintenance task set that includes damage orientation prediction, rejuvenation and reinforcement strategies, and maintenance personnel assignment and scheduling. Simultaneously, after generating intelligent early warnings, the data on the distribution of green space assets, perception data, real-time location and status data of maintenance personnel and vehicles, and data on maintenance tasks to be processed within the target area are displayed in real time on the electronic map using at least one visualization layer.

[0036] The first control instruction is a structured maintenance task automatically generated based on early warning information and knowledge base rules and pushed to the mobile terminal of the target personnel. The second control instruction is used to call the application programming interface of the intelligent execution device corresponding to the target area to perform automatic control operations. Based on the intervention level and combined with the prediction results of the future evolution trend of the latent damage comprehensive index, the second control command is generated. The second control command is a cross-device linkage control scheme that includes intelligent irrigation water replenishment parameter adjustment or automatic triggering configuration of regional physical support equipment. Specifically, the first control command is sent as the structured maintenance task to the mobile terminal of the corresponding target personnel through the Internet of Things communication network, so as to receive the task execution process record and execution result feedback from the mobile terminal to achieve a closed loop, and the second control command is sent to the application program interface corresponding to the intelligent execution device in the target area through a unified device collaborative management and control bus to perform automatic control.

[0037] Furthermore, to address the data uncertainty caused by external weather and communication interference to sensing devices, the "data packet reception rate" and "average positioning delay" within the current sampling period are acquired in real time through the built-in IoT communication protocol stack module interface. In this step, a maximum baseline tolerance delay parameter is set, with a value of 500 milliseconds. This parameter aims to strike a balance between "compliance with the normal latency of low-power networks" and "strictly prohibiting the participation of outdated or invalid data in alarm calculations." The acquired average positioning delay is divided by this parameter to obtain a delay penalty term; subsequently, the delay penalty term is subtracted by a number one, and multiplied by the data packet reception rate to obtain a data confidence factor with a value limited to zero and one. A basic absorption contribution weight coefficient is set, with a value of 0.6, to reflect the physical law that the attenuation signal of healthy ancient trees is dominated by water content. The broadband electromagnetic transmission attenuation coefficient is multiplied by this 0.6, and the product of the channel state coherence time offset parameter and the complementary weight 0.4 is added to obtain a preliminary aggregated value. This preliminary aggregated value is multiplied by the data confidence factor to complete dynamic calibration and generate a medium quality distribution signal.

[0038] Stiffness decoupling is performed based on an elastic medium coupling constitutive relation model. Historical frequency domain data sequences of medium mass and structural response under undamaged conditions within a complete phenological year are extracted from the local database. The linear conversion rate between the two is extracted using the least squares method as the medium conversion coefficient. During real-time computation, the current medium mass distribution signal is multiplied by this medium conversion coefficient to obtain the medium compensation amount. This compensation amount is subtracted from the current structural response frequency domain state signal, thus purely extracting the pure stiffness degradation parameter. To eliminate non-pathological environmental interference, a dynamic environmental transfer coefficient is set, whose value is obtained by normalizing the current environmental temperature value and superimposing a basic constant. This avoids misjudging normal softening of wood caused by freeze-thaw cycles or high temperatures as damage. The pure stiffness degradation parameter is divided by this transfer coefficient, and the quotient is the implicit damage comprehensive index. The reciprocal mapping of this index is then performed to analyze the implicit bending bearing potential.

[0039] During the intelligent early warning triggering phase, the average value of the implicit bending bearing capacity under safe conditions over the past three months is extracted and multiplied by 90% to generate a rolling dynamic structural health baseline. A continuous anti-shake step size parameter is set here, with a value of six consecutive calculation cycles (equivalent to thirty minutes) to prevent false alarms caused by transient anomalies such as gusts. When the real-time bending bearing capacity falls below the baseline for a certain number of consecutive times, the alarm register is unlocked to generate an early warning. To quantify the intervention level, a structural safety tolerance threshold defining the physical yield limit is set, with a value of 0.85. The implicit damage comprehensive index is subtracted from this threshold, and the difference greater than zero is divided by the threshold itself to obtain the risk exposure ratio; then multiplied by a constant four and rounded up to generate an integer from one to four as a unified intervention level.

[0040] During the closed-loop signaling and linkage control phase, the deterioration rates of the pure stiffness degradation parameter and the medium quality signal over the past 24 hours are calculated. The former is divided by the latter to obtain the cause comparison factor. A cause identification threshold of 2.0 is set. When the comparison factor is greater than this threshold, it is determined that structural decay is the dominant factor; conversely, when the comparison factor is less than this threshold and the quality deterioration rate is negative, it is determined that water shortage is the dominant factor. The dominant cause and intervention level are used as a joint index to match the corresponding structured maintenance text from the in-memory knowledge base, package it to generate the first control command, and push it to the mobile terminal of the executor via the cellular network. Simultaneously, on the electronic map, the static bottom layer of green space assets, the red-yellow-green risk heat map based on implicit index mapping, and the dynamic top layer of personnel based on terminal GPS coordinates are rendered sequentially from bottom to top, and scheduling connections are drawn. A smoothing weight factor of 0.3 is set to perform first-order exponential smoothing calculation on the implicit damage comprehensive index to predict the evolution trend of the next window. When the intervention level is level four and the predicted trend shows an accelerating deterioration, a second control command for cross-device linkage control is generated: if water shortage is the main cause, the opening flag and water replenishment duration are written to the application interface of the regional intelligent solenoid valve through the industrial collaborative management and control bus; if structural decay is the main cause, the lifting height and locking command are written to the servo motor drive interface of the physical support equipment, thereby achieving automatic mechanical support instantly before physical failure.

[0041] A monitoring system for the growth environment of ancient trees, comprising: The structural modal extraction module is used to acquire microscopic deformation trajectory data from external sensing devices in the ancient tree's growth environment as the first dataset, and based on the first dataset, to determine the structural response frequency domain state signal used to characterize the structural modal response state of the ancient tree trunk. The medium quality sensing module is used to acquire microscopic electromagnetic penetration data from external sensing devices in the ancient tree's growth environment as a second dataset, and based on the second dataset, to determine a medium quality distribution signal that characterizes the medium quality properties of the ancient tree trunk. The stiffness decoupling evaluation module is used to establish an elastic medium coupling constitutive relationship model between the medium mass distribution signal and the structural response frequency domain state signal. Based on the elastic medium coupling constitutive relationship model, the module combines the real-time acquired medium mass distribution signal as a constraint variable to perform stiffness decoupling calculation on the structural response frequency domain state signal, solve for the hidden damage comprehensive index that characterizes the degree of hidden damage inside the ancient tree trunk, and evaluate it. The early warning linkage control module is used to generate an intelligent early warning when the latent damage comprehensive index exceeds the preset early warning trigger threshold, before the ancient tree structure becomes physically unstable or the voids cascade and deteriorate; at the same time, it generates a first control command for closed-loop task handling and a second control command for cross-device linkage control.

[0042] It should be noted that all calculation formulas in this application employ regression analysis, including but not limited to machine learning algorithms, to deeply analyze the collected parameters and identify their natural trends and interrelationships. Specialized software, such as Python's Scikit-learn library or the R language, is used to automatically generate mathematical models that match the data. Cross-validation and other methods are used to objectively evaluate model performance, and continuous feedback and optimization are combined to ensure that the created formulas truly reflect the inherent laws of the data, thereby guaranteeing their effectiveness and accuracy. In all calculation formulas in this application, the parameters in each formula undergo dimensionless processing within a consistent range to ensure that different physical quantities are compared on the same scale; dimensionless processing techniques include, but are not limited to, min-max-normalization and Z-score standardization. The technical solution of this invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random-access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of this invention.

[0043] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0044] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for monitoring the growth environment of ancient trees, characterized in that, The specific steps include: S1. Obtain microscopic deformation trajectory data from external sensing devices in the ancient tree's growth environment as the first dataset, and based on the first dataset, determine the structural response frequency domain state signal used to characterize the modal response state of the ancient tree trunk structure. S2. Obtain the microscopic electromagnetic penetration data from the external sensing device of the ancient tree's growth environment as the second dataset, and based on the second dataset, determine the medium quality distribution signal used to characterize the medium quality characteristics of the ancient tree trunk. S3. Establish an elastic medium coupling constitutive relationship model between the medium mass distribution signal and the structural response frequency domain state signal. Based on the elastic medium coupling constitutive relationship model, and using the real-time acquired medium mass distribution signal as a constraint variable, perform stiffness decoupling calculations on the structural response frequency domain state signal to obtain a comprehensive latent damage index characterizing the degree of latent damage within the ancient tree trunk and evaluate it. Based on historical medium mass distribution signal and structural response frequency domain state signal data, construct the elastic medium coupling constitutive relationship model. The elastic medium coupling constitutive relationship model is used to learn and quantify the transformation of medium mass characteristics into structural equivalent mass under different meteorological and phenological conditions. The mapping law that influences the dominant frequency response is transformed; by inputting the real-time acquired medium mass distribution signal and structural response frequency domain state signal into the elastic medium coupling constitutive relation model for interference stripping operation, the implicit bending bearing potential predicted by decoupling the structural response frequency domain state signal at the current moment is analyzed; the implicit bending bearing potential is used to reflect the physical safety margin of the target ancient tree's current mechanical limit without fracture; wherein, the value of the implicit damage comprehensive index is equal to the pure stiffness degradation parameter obtained after stripping the medium mass distribution signal from the current structural response frequency domain state signal, divided by a dynamic environment transfer coefficient; S4. When the latent damage comprehensive index is detected to exceed the preset warning trigger threshold, an intelligent warning is generated before the ancient tree structure experiences physical instability or cascading deterioration of voids. Simultaneously, a first control command for closed-loop task handling and a second control command for cross-device linkage control are generated. The preset warning trigger threshold is specifically a structural safety tolerance threshold. Based on the difference between the currently acquired latent damage comprehensive index and the structural safety tolerance threshold, a unified intervention level that quantifies the current latent damage exposure is calculated and determined. According to the intervention level, and combined with the signal component characteristics that lead to a reduction in latent bending bearing potential, specifically the distribution of medium quality or degradation of pure stiffness, the first control command is matched and generated from the preset knowledge base rules. The first control command is a structured maintenance task set that includes damage orientation prediction, rejuvenation and reinforcement strategies, and maintenance personnel assignment and scheduling. Simultaneously, after generating the intelligent warning, the distribution data of green space assets, perception data, real-time location and status data of maintenance personnel and vehicles, and maintenance task data to be processed within the target area are displayed in real time on the electronic map using at least one visualization layer.

2. The method for monitoring the growth environment of ancient trees according to claim 1, characterized in that: In the first dataset, structural vibration parameters of different dimensions are extracted for the target ancient tree microscopic observation area within a preset time window. The structural vibration parameters include the structural response power spectral density slope parameter, which reflects the structural stiffness response characteristics, and the vibration response damping ratio parameter, which reflects the structural energy dissipation trend. The structural response power spectral density slope parameter and the vibration response damping ratio parameter are respectively subjected to adaptive normalization processing based on their respective ancient tree historical benchmark values ​​or health model capacity, so that they are mapped to a unified numerical range; according to the preset nonlinear fusion model, the normalized structural response power spectral density slope parameter and the vibration response damping ratio parameter are dynamically weighted and fused to generate the structural response frequency domain state signal.

3. The method for monitoring the growth environment of ancient trees according to claim 2, characterized in that: The generated structural response frequency domain state signal is subjected to time series smoothing processing to calculate the rate of change of the structural response frequency domain state signal within a preset time window, and the rate of change is used as the dynamic evolution trend feature of the structural response frequency domain state signal. The value of the frequency domain state signal of the structural response is equal to the product of the slope of the normalized structural response power spectral density and the first weighting coefficient, plus the product of the normalized vibration response damping ratio and the second weighting coefficient.

4. The method for monitoring the growth environment of ancient trees according to claim 3, characterized in that: In the second dataset, for the target ancient tree microscopic spatial propagation path within a preset time window, the multipath delay and energy attenuation information of the electromagnetic propagation path are extracted; Based on the multipath delay and energy attenuation information of electromagnetic propagation paths, core medium metric parameters are calculated in parallel, including the first medium metric parameter and the second medium metric parameter. The first medium measurement parameter is the broadband electromagnetic transmission attenuation coefficient, which characterizes the water content and biomass energy absorption of ancient trees. The second medium metric parameter is the channel state coherence time offset parameter, which characterizes the influence of the ancient tree canopy and trunk on multipath scattering.

5. The method for monitoring the growth environment of ancient trees according to claim 4, characterized in that: Based on a preset dynamic weighted aggregation model, the broadband electromagnetic transmission attenuation coefficient and the channel state coherence time offset parameter are fused to generate the medium quality distribution signal. The specific steps for generating the medium quality distribution signal are as follows: monitoring the data packet reception rate and average positioning delay of the sensing device, and generating a data confidence factor based on the monitoring results to calibrate the generated medium quality distribution signal in order to compensate for data uncertainty caused by external meteorological interference or communication quality degradation.

6. The method for monitoring the growth environment of ancient trees according to claim 5, characterized in that: The first control instruction is a structured maintenance task automatically generated based on early warning information and knowledge base rules and pushed to the mobile terminal of the target personnel. The second control instruction is used to call the application programming interface of the intelligent execution device corresponding to the target area to perform automatic control operations. Based on the intervention level and combined with the prediction results of the future evolution trend of the latent damage comprehensive index, the second control command is generated. The second control command is a cross-device linkage control scheme that includes intelligent irrigation water replenishment parameter adjustment or automatic triggering configuration of regional physical support equipment. Specifically, the first control command is sent as the structured maintenance task to the mobile terminal of the corresponding target personnel through the Internet of Things communication network, so as to receive the task execution process record and execution result feedback from the mobile terminal to achieve a closed loop, and the second control command is sent to the application program interface corresponding to the intelligent execution device in the target area through a unified device collaborative management and control bus to perform automatic control.

7. A monitoring system for the growth environment of ancient trees, characterized in that: The system is used to execute a method for monitoring the growth environment of ancient trees as described in any one of claims 1-6, including: The structural modal extraction module is used to acquire microscopic deformation trajectory data from external sensing devices in the ancient tree's growth environment as the first dataset, and based on the first dataset, to determine the structural response frequency domain state signal used to characterize the structural modal response state of the ancient tree trunk. The medium quality sensing module is used to acquire microscopic electromagnetic penetration data from external sensing devices in the ancient tree's growth environment as a second dataset, and based on the second dataset, to determine a medium quality distribution signal that characterizes the medium quality properties of the ancient tree trunk. The stiffness decoupling evaluation module is used to establish an elastic medium coupling constitutive relationship model between the medium mass distribution signal and the structural response frequency domain state signal. Based on the elastic medium coupling constitutive relationship model, the module combines the real-time acquired medium mass distribution signal as a constraint variable to perform stiffness decoupling calculation on the structural response frequency domain state signal, solve for the hidden damage comprehensive index that characterizes the degree of hidden damage inside the ancient tree trunk, and evaluate it. The early warning linkage control module is used to generate an intelligent early warning when the latent damage comprehensive index exceeds the preset early warning trigger threshold, before the ancient tree structure becomes physically unstable or the voids cascade and deteriorate; at the same time, it generates a first control command for closed-loop task handling and a second control command for cross-device linkage control.

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