Greening tree supporting device for garden design and stability control method thereof

Abstract

The invention discloses a greening tree supporting device for garden design and a stability control method thereof, and belongs to the technical field of garden design. The method comprises the following steps: (1) acquiring tree parameters and position coordinates of a target tree, and establishing a tree stability evaluation model; (2) determining a monitoring priority based on the stability evaluation model, and generating a regional monitoring topology network; (3) collecting stability monitoring data of the target tree through cooperation of multiple sensors; (4) establishing a multivariate data fusion algorithm through the stability monitoring data, and comprehensively evaluating the stability safety coefficient S of the target tree; and (5) executing self-adaptive adjustment control according to the stability safety coefficient S, and maintaining the stability of the tree supporting system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of landscaping, in particular to a landscaping tree supporting device for garden design and a stability control method thereof. BACKGROUND

[0002] With the rapid development of urban greening construction, large tree transplantation and slope greening projects are increasing. Traditional tree supporting systems mainly rely on manual experience for design and maintenance, which has problems such as single monitoring means, lagging response, high maintenance cost, etc. Especially in slope greening, harsh weather environment and complex urban environment, the tree supporting system faces greater stability challenges.

[0003] Traditional tree supporting methods mainly rely on manual experience and regular inspection, which have the following problems: lack of real-time monitoring capability, unable to timely discover potential stability risks; lack of scientific basis for supporting parameter adjustment, often using fixed supporting schemes; environmental factor changes are difficult to quantitatively evaluate the impact on tree stability; lack of regional collaborative protection mechanism, a single tree instability may trigger a chain reaction.

[0004] Therefore, an intelligent tree supporting stability control method is needed, which can realize real-time monitoring, adaptive adjustment and regional collaborative protection. SUMMARY

[0005] The purpose of the present application is to provide a landscaping tree supporting device for garden design and a stability control method thereof, which realizes intelligent stability management of tree supporting system through multi-sensor collaborative monitoring, data fusion analysis and adaptive control.

[0006] A stability control method for landscaping trees in garden design is applied to a landscaping tree supporting device for garden design, and the method comprises the following steps:

[0007] S1: Obtain tree parameters and position coordinates of a target tree, and establish a tree stability evaluation model;

[0008] S2: Determine the monitoring priority based on the stability evaluation model, and generate a regional monitoring topology network;

[0009] S3: Collect stability monitoring data of the target tree through multi-sensor collaboration;

[0010] S4: Establish a multi-element data fusion algorithm through the stability monitoring data, and comprehensively evaluate the stability safety factor S of the target tree;

[0011] S5: Perform adaptive adjustment control according to the stability safety factor S to maintain the stability of the tree supporting system.

[0012] Further, in step 1, the tree parameters include diameter at breast height, tree height, crown width, root depth, and wind resistance coefficient, and the tree stability evaluation model determines a basic stability index by weighted calculation of the tree parameters.

[0013] Further, in step 2, the regional monitoring topology network establishes a neighborhood influence relationship based on the distance d ij between trees and the basic stability index, and when the basic stability index of a certain tree is lower than a certain value, the linkage monitoring of trees with high influence weight is triggered.

[0014] Further, in step 3, the stability monitoring data includes: inclination θ collected by an inclination sensor; strain data ε collected by a strain sensor at a key node of the support structure; vibration frequency f collected by a vibration sensor; soil humidity h collected by a soil sensor; and real-time wind speed v collected by a wind speed sensor; wherein θ, ε, f, h, and v are normalized data.

[0015] Further, a GPS unified time reference is established, and the data collected by each sensor is aligned and corrected according to the time stamp, with a time synchronization accuracy controlled within 10 milliseconds; a sliding average filtering algorithm is used to smooth the original sensor data; and based on the 3σ criterion, abnormal data points are identified and repaired using cubic spline interpolation.

[0016] Further, the correlation coefficient of inclination and strain data is calculated to identify the coupling relationship between inclination and strain data; when a strong coupling relationship is detected and the coupling strength exceeds a critical value, a tension adjustment response of the corresponding level is triggered in advance.

[0017] The frequency domain correlation between vibration frequency and real-time wind speed is analyzed to extract the characteristic frequency of wind-induced vibration and the resonance risk; when the comprehensive risk index R>0.7, a tension adjustment response of the corresponding level is triggered in advance.

[0018] The hysteresis correlation between soil humidity change and inclination change is evaluated to establish a dynamic response model of soil-root-stability.

[0019] Further, in step 4, a multivariate data fusion algorithm is implemented by constructing a data correlation matrix R, where the matrix element R ij represents the correlation strength of the i-th type of sensor data and the j-th type of sensor data; and based on the data correlation matrix, a stability comprehensive evaluation function is established:

[0020] Further, in step 5, the adaptive adjustment control performs hierarchical control according to different intervals of the stability safety factor S:

[0021] Further, the regional linkage protection mechanism includes:

[0022] When a tree enters a critical state, a warning signal is automatically sent to trees with high influence weights;

[0023] The neighboring tree support device that receives the warning signal automatically enters a warning state, with the monitoring frequency increased to 2 times and the support tension pre-adjusted to T0x1.1;

[0024] A regional stability situation map is established to display the stability distribution of each tree in the entire monitoring region in real time;

[0025] When more than 30% of the trees in the region are in a state above the warning state, regional protection measures are started, and all tree support systems enter a strengthened monitoring mode.

[0026] A green tree support device for garden design, comprising:

[0027] The support structure unit includes a main support cable, an auxiliary support cable, a root fixing device, and a tension adjusting mechanism;

[0028] The sensor network unit includes an inclination sensor, a strain sensor, a vibration sensor, a soil sensor, and a weather sensor, and each sensor realizes data transmission through a wireless communication module;

[0029] The data processing unit integrates a data analysis module and has data storage, processing, and analysis capabilities;

[0030] The execution control unit includes a tension adjusting driver, an emergency braking device, and a state feedback sensor;

[0031] Each unit realizes data interaction and collaborative control through a wireless communication network.

[0032] Compared with the prior art, the beneficial effects of the present application are:

[0033] The present application can obtain the stability state of the tree in real time and discover potential risks in time through multi-sensor collaborative monitoring. The influence of complex environmental factors on the stability of the tree can be intelligently analyzed by using a multi-element data fusion algorithm and data analysis technology. The support parameters are automatically adjusted according to the stability evaluation results, without manual intervention, which greatly reduces the maintenance cost. A regional monitoring topology network is established to realize collaborative monitoring and protection among multiple trees, which improves the overall protection effect. Through multi-dimensional data fusion and hierarchical control strategy, the reliability and robustness of the system are improved. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 A flowchart of a stability control method for a green tree in a garden design disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0035] For the purposes of making the objects, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be described in more detail below with reference to the drawings in the embodiments of the present application. Identical or similar numerals in the drawings represent identical or similar elements or elements with identical or similar functions throughout. The described embodiments are part of, but not all of, the embodiments of the present application.

[0036] All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative labor fall within the scope of the present application.

[0037] The embodiments described below with reference to the drawings and the directional words are exemplary and are intended to explain the present application and cannot be understood as limiting the present application.

[0038] Embodiment 1: A stability control method for green trees in landscape design, applied to a green tree supporting device for landscape design, the method comprising the following steps:

[0039] S1: Obtain tree parameters and position coordinates of a target tree, and establish a tree stability evaluation model;

[0040] S2: Determine a monitoring priority based on the stability evaluation model, and generate a regional monitoring topology network;

[0041] S3: Cooperatively collect stability monitoring data of the target tree through multiple sensors;

[0042] S4: Establish a multivariate data fusion algorithm through the stability monitoring data, and comprehensively evaluate a stability safety factor S of the target tree;

[0043] S5: Perform adaptive adjustment control according to the stability safety factor S to maintain stability of the tree supporting system.

[0044] Further, in step 1, the tree parameters include a diameter at breast height D, a tree height H, a crown width C, a root depth Rd, and a wind resistance coefficient C w The tree stability evaluation model determines a basic stability index through weighted calculation of the tree parameters.

[0045] Basic stability index I b = α1×(D / D0) + α2×(H / H0) + α3×(C / C0) + α4×(Rd / Rd0) + α5×C w ; wherein α1, α2, α3, α4, and α5 are weight coefficients, satisfying Σα i = 1, D0, H0, C0, and Rd0 are corresponding standard reference values;

[0046] Further, in step 2, the regional monitoring topology network is established based on the distance d between trees ij and the base stability index, and when the base stability index of a certain tree is lower than a certain value, the linkage monitoring of the tree with high influence weight is triggered.

[0047] Further, in step 3, the stability monitoring data includes: the inclination θ collected by the inclination sensor; the strain data ε collected by the strain sensor at the key nodes of the support structure; the vibration frequency f of the tree trunk collected by the vibration sensor; the soil humidity h collected by the soil sensor; and the real-time wind speed v collected by the wind speed sensor; wherein θ, ε, f, h, and v are normalized data.

[0048] Further, a GPS unified time reference is established, and the data collected by each sensor is aligned and corrected according to the time stamp, with a time synchronization accuracy controlled within 10 milliseconds; a sliding average filtering algorithm is used to smooth the original sensor data; based on the 3σ criterion, abnormal data points are identified, and when there are more than 5 continuous abnormal values, sensor self-checking and data reacquisition are triggered.

[0049] Further, the correlation coefficient of the inclination and the strain data is calculated to identify the coupling relationship between the inclination and the strain data.

[0050] The inclination is decomposed into three-axis components to obtain the X-axis inclination component θ x , the Y-axis inclination component θ y , and the Z-axis rotation component θ z .

[0051] The strain data is classified according to the node positions of the support structure to obtain the main support point strain ε main , the auxiliary support point strain ε aux , and the root fixed point strain ε root .

[0052] The Pearson correlation coefficient is used to calculate the correlation between the inclination components and the strain components to establish a 9x3 correlation coefficient matrix C, where C ij = corr(θ i , ε j ).

[0053] When the correlation coefficient |C ij | is greater than 0.7, it is identified as a strong coupling relationship, and a corresponding deformation coupling model θ i = k i × ε j + b i is established, where k i and b i are coupling parameters.

[0054] When the strong coupling relationship is detected and the coupling strength exceeds the critical value, the corresponding level of tension adjustment response is triggered in advance.

[0055] The frequency domain correlation of vibration frequency and real-time wind speed is analyzed to extract the characteristic frequency of wind-induced vibration and resonance risk; when the comprehensive risk index R>0.7, the corresponding level of tension adjustment response is triggered in advance.

[0056] Wind-induced vibration analysis and resonance risk assessment steps:

[0057] (1) Perform Fast Fourier Transform (FFT) on the vibration data to obtain the vibration spectrum, and identify the main vibration frequency components f1, f2,..., fn. n ;

[0058] (2) Calculate the vortex shedding frequency according to the wind speed data: f s = St × v / D, where St is the Strouhal number, St is 0.2, v is the wind speed, and D is the tree diameter;

[0059] (3) Establish a matching judgment of vibration frequency and vortex shedding frequency: when |f i -f s | <0.1 Hz, it is determined that there is a resonance risk;

[0060] (4) Calculate the comprehensive risk index: R = ∑(A i × f i × M i ) / (Cw × ζ), where Ai is the vibration amplitude of the corresponding frequency, Mi is the frequency matching weight, C w is the wind resistance coefficient, and ζ is the damping ratio;

[0061] (5) When the comprehensive risk index R>0.7, the corresponding level of tension adjustment response is triggered in advance.

[0062] Evaluate the lag correlation of soil moisture change and inclination change, and establish a dynamic response model of soil-root-stability.

[0063] Collect soil moisture time series data H(t) and inclination time series data θ(t); calculate the cross-correlation function R(τ) = ∫[H(t)-H][θ(t+τ)-θ]dt / √[∫(H(t)-H) 2 d t ×∫(θ(t+τ)-θ) 2 d t ] under different time delays τ; find the peak value of R(τ) to determine the optimal lag time τ max , usually 2-6 hours; establish a lag correlation model: θ(t) = k1 × H(t-τ max ) + k2 × dH / dt + k3 × d2 H / dt2+b where k1, k2, k3, b are model parameters fitted by least square method.

[0064] Further, in step 4, the multi-source data fusion algorithm is realized by constructing a data correlation matrix R, a 5x5 data correlation matrix R is constructed, and the matrix element Rij represents the correlation strength of the i-th type of sensor data and the j-th type of sensor data;

[0065]

[0066] Based on the data correlation matrix, a stability comprehensive evaluation function S = ∑ is established. i ＝1^5 α i ×D i ×(1+∑ j ＝1^ 5 R ij ×D j / 5) where α i is the basic weight of the i-th type of data, D i is the normalized i-th type of sensor data, R ij represents the correlation strength of the i-th type of sensor data and the j-th type of sensor data; the weight parameter is optimized by least square method to improve the evaluation precision.

[0067] Further, in step 5, the adaptive adjustment control executes hierarchical control according to different intervals of the stability safety factor S:

[0068] (1) Safe state (S≥0.8): maintain the standard monitoring frequency and the initial support tension T0;

[0069] (2) Warning state (0.6≤S<0.8): the monitoring frequency is increased to 1.5 times, and the main support cable tension is adjusted to T0x(1.1-1.2);

[0070] (3) Dangerous state (0.4≤S<0.6): the monitoring frequency is increased to 2 times, and the main support cable tension is adjusted to T0x(1.2-1.25);

[0071] (4) Critical state (S<0.4): the monitoring frequency is increased to 3 times, and all support cable tensions are increased to the maximum safety limit T max , and the regional linkage protection mechanism is triggered at the same time.

[0072] Further, the regional linkage protection mechanism comprises:

[0073] When a certain tree enters the critical state, a warning signal is automatically sent to the trees with high influence weight;

[0074] The adjacent tree support device receiving the early warning signal automatically enters the early warning state, the monitoring frequency is increased to 2 times, and the support tension is pre-adjusted to T0x1.1;

[0075] A regional stability situation map is established to display the stability distribution of each tree in the entire monitoring region in real time.

[0076] When more than 30% of the trees in the region are in a state of early warning or above, regional protection measures are started, and all tree support systems enter a strengthened monitoring mode.

[0077] Embodiment 2: A green tree support device for landscape design, comprising:

[0078] Support structure unit: including main support cable, auxiliary support cable, root fixing device and tension adjusting mechanism;

[0079] Sensor network unit: including inclination sensor, strain sensor, vibration sensor, soil sensor and weather sensor, each sensor realizes data transmission through wireless communication module;

[0080] Data processing unit: integrated data analysis module, with data storage, processing and analysis capability;

[0081] Execution control unit: including tension adjusting driver, emergency brake device and state feedback sensor;

[0082] Each unit realizes data interaction and cooperative control through wireless communication network.

[0083] Embodiment 3: Intelligent support system for large tree transplantation in urban park; this embodiment is applied to large tree transplantation project in a certain urban park, involving 20 ancient sophora trees with diameter at breast height of 80-120 cm.

[0084] System configuration: tree parameter collection: each tree is configured with an electronic tag to record basic parameters such as diameter at breast height, tree height, crown width and root depth. The wind resistance coefficient of sophora tree is set to 1.2, and the basic stability index is calculated by weighted calculation.

[0085] Multi-sensor deployment: each tree is equipped with a complete sensor suite, including: inclination sensor: installed at 1.5m from the ground, sampling frequency 10Hz; strain sensor: installed at 3 main support points and root fixing points, range ±2000με; vibration sensor: installed at the main branch point of the trunk, frequency range 0.1-50Hz; soil sensor: 1 each at depths of 30cm, 60cm and 90cm around the root system; wind speed sensor: 1 shared by every 5 trees;

[0086] Region monitoring topology network construction: based on the distance between trees (average 15 m), the neighborhood relationship is established, and a 4x5 monitoring grid is formed. When the stability safety factor of a tree is less than 0.8, the linkage monitoring of adjacent trees within a radius of 20 m is triggered, and the monitoring frequency is increased from every 10 minutes to every 2 minutes.

[0087] Data fusion algorithm implementation: a 5x5 data correlation matrix R is established, and the main correlation relationships include: the correlation coefficient between inclination and strain data is 0.82 on average; the correlation coefficient between vibration and wind speed data is 0.76; the lag correlation coefficient between soil moisture and inclination is 0.65; stability comprehensive evaluation function: S = 0.3xθx∑R1 j ×D j +0.25xεx∑R2 j ×D j +0.2xfx∑R3 j ×D j +0.15xhx∑R4 j ×D j +0.1xvx∑R5 j ×D j ; wherein θ, ε, f, h, v are normalized data.

[0088] Hierarchical response control: safe range (S>0.8): standard monitoring, main support cable pretension maintained at 80% of design value; warning state (0.6<S≤0.8): monitoring frequency increased to every 5 minutes, main support cable tension increased by 15%; dangerous state (0.4<S≤0.6): monitoring frequency increased to every 1 minute, main and auxiliary support cables adjusted simultaneously, tension increased by 30%; critical state (S≤0.4): continuous monitoring, all support cable tension increased to the upper limit of safety, emergency support equipment started.

[0089] During the 6-month operation of the system, 3 typhoon weather and 2 soil abnormal change events were successfully warned and disposed. Compared with the traditional support method, the survival rate of trees increased from 85% to 98%, and the maintenance cost decreased by 35%.

[0090] Example 4: Intelligent slope protection support system for slope greening;

[0091] This embodiment is applied to the greening project of a mountainous road slope, with a slope of 25-35 degrees and 150 green trees of different specifications planted. System configuration: Considering the particularity of the slope environment, the following configurations are added based on Embodiment 1: Geological stability monitoring: Soil pressure sensors and displacement sensors are arranged at key positions on the slope surface to monitor the stability of the slope body. Enhanced support structure: Anchor cable support system is used, with an anchoring depth of 2-3 m and high-strength steel wire used for support cable. Layered soil monitoring: According to the characteristics of complex soil layer structure on the slope, soil sensors are arranged at four depth layers of 20 cm, 50 cm, 80 cm, and 120 cm.

[0092] Optimization of regional linkage mechanism: Based on the slope terrain, a gradient monitoring network is established, and abnormal trees on the uphill will trigger high-precision monitoring of adjacent trees on the downhill first. The linkage trigger distance is adjusted according to the slope: <20 degrees, 15 m; 20-30 degrees, 20 m; >30 degrees, 25 m.

[0093] Special treatment for wind-induced vibration: The wind field on the slope is complex, and the wind-induced vibration analysis algorithm is optimized accordingly: considering the slope wind speed amplification effect, the correction coefficient is 1.2-1.5; a component analysis model of slope wind and transverse wind is established, focusing on monitoring transverse wind-induced vibration; add terrain factor in resonance risk assessment: R=∑(f i ×M i )×terrain coefficient / (wind resistance coefficient × damping ratio).

[0094] Adaptive control strategy: Adjust the hierarchical response threshold according to the characteristics of the slope: safety range: S>0.75 (more stringent than flat ground); warning state: 0.55<S≤0.75, tension increased by 20%; dangerous state: 0.35<S≤0.55, tension increased by 40%, slope reinforcement started; critical state: S≤0.35, overall reinforcement, temporary relocation measures if necessary.

[0095] In the 18 months of system deployment, it successfully coped with 5 heavy rains and 3 strong winds, and no tree toppling accidents occurred. Compared with similar projects, soil loss is reduced by 60%, and vegetation survival rate reaches 96%.

[0096] Embodiment 5: Intelligent support management system for urban street trees: This embodiment is applied to the management of street trees on a main urban road, with a total length of 8 kilometers and involving 500 street trees of different species.

[0097] System Configuration Features: Considering the complexity of urban environment, the system has the following special configurations: Anti-interference design: sensors use industrial-grade packaging with EMC electromagnetic compatibility and anti-vehicle vibration interference. Intelligent degree improvement: set an edge computing node for every 50 trees to realize near data processing and fast response. Integration with city management system: through the Internet of Things platform, data sharing is realized with city greening management system, meteorological system, and traffic management system.

[0098] Diversified tree species processing: For different tree species such as sycamore, ginkgo, and locust tree, establish differentiated monitoring strategies: Sycamore: Focus on monitoring wind-induced vibration, wind resistance coefficient 0.8; Ginkgo: Focus on monitoring root stability, wind resistance coefficient 1.1; Locust tree: Comprehensive monitoring, wind resistance coefficient 1.0; Each species independently trains a stability evaluation model to improve evaluation accuracy.

[0099] Urban environment adaptability optimization: Traffic vibration impact filtering: distinguish vehicle vibration (main frequency 2-8Hz) and wind-induced vibration (main frequency 0.2-2Hz) through frequency domain analysis to avoid false alarms. Heat island effect compensation: combined with urban temperature distribution data, correct soil temperature sensor readings. Air quality correlation analysis: include PM2.5, SO2, and other air quality indicators in tree health assessment to optimize support strategies.

[0100] Regional collaborative management: Establish a road-level linkage network: Single abnormality: affects adjacent trees within a range of 10m before and after; Road section abnormality: affects the monitoring accuracy of upstream and downstream 100m road sections;

[0101] Regional anomaly: triggers emergency monitoring mode for the entire road section; Traffic management linkage: when multiple trees simultaneously show stability problems, automatically send an early warning to the traffic management department and implement traffic control if necessary.

[0102] Predictive maintenance in-depth application:

[0103] Intelligent response strategy: Four-level response mechanism: Green state: S>0.85, standard maintenance period; Yellow state: 0.7<S≤0.85, enhanced monitoring, tension adjustment; Orange state: 0.5<S≤0.7, start emergency plan, manual intervention; Red state: S≤0.5, immediate disposal, set safety warning if necessary.

[0104] System performance excellent in 2 years of operation: Monitoring accuracy: stability prediction accuracy reaches 94%; Response efficiency: average response time is shortened from 24 hours to 15 minutes; Maintenance efficiency: maintenance cost is reduced by 45%, manual inspection frequency is reduced by 70%.

[0105] Through detailed descriptions of three typical embodiments, the technical advantages and practical value of the stability control method for landscaping trees in landscape design of this invention are fully demonstrated. This method can adapt to different application scenarios and significantly improves the reliability and management efficiency of tree support systems through intelligent means.

[0106] The stability control method for green trees used in landscape design provided by this invention has good industrial applicability and can be widely applied in urban greening, landscape architecture, ecological restoration, and other fields, yielding significant economic and social benefits. With the continuous development of IoT and artificial intelligence technologies, the application prospects of this method will be even broader.

Claims

1. A method for controlling the stability of green trees used in landscape design, applied to support devices for green trees used in landscape design, characterized in that, The method includes the following steps: S1 obtains the tree parameters and location coordinates of the target tree and establishes a tree stability assessment model; S2 determines monitoring priorities based on a stability assessment model and generates a regional monitoring topology network. S3 collects stability monitoring data of the target tree through multiple sensors in a coordinated manner; S4 establishes a multi-source data fusion algorithm based on stability monitoring data to comprehensively evaluate the stability safety factor S of the target tree; S5 performs adaptive adjustment control based on the stability safety factor S to maintain the stability of the tree support system.

2. The method for controlling the stability of green trees used in landscape design according to claim 1, characterized in that, In step 1, tree parameters include diameter at breast height (DBH), tree height, crown width, root depth, and wind resistance coefficient. The tree stability assessment model determines the basic stability index by weighting the tree parameters.

3. The intelligent support and stability control method for garden trees according to claim 1, characterized in that, In step 2, the regional monitoring topology network is based on the distance d between trees. ij Establish a neighborhood influence relationship with the basic stability index. When the basic stability index of a certain tree is lower than a certain value, trigger the linkage monitoring of trees with high influence weight.

4. The method for controlling the stability of green trees used in landscape design according to claim 1, characterized in that, In step 3, the stability monitoring data includes: tilt angle θ collected by tilt sensor; strain data ε collected by strain sensor of key nodes of support structure; vibration frequency f of tree trunk collected by vibration sensor; soil moisture h collected by soil sensor; and real-time wind speed v collected by wind speed sensor; where θ, ε, f, h, and v are normalized data.

5. The method for controlling the stability of green trees used in landscape design according to claim 1, characterized in that, A unified GPS time reference is established, and the data collected by each sensor are aligned and corrected according to the timestamp, with the time synchronization accuracy controlled within 10 milliseconds. The moving average filtering algorithm is used to smooth the raw sensor data. Abnormal data points are identified based on the 3σ criterion, and cubic spline interpolation is used to repair the outliers.

6. The method for controlling the stability of green trees used in landscape design according to claim 4, characterized in that, Calculate the correlation coefficient between tilt angle and strain data to identify the coupling relationship between them; when a strong coupling relationship is detected and the coupling strength exceeds the critical value, trigger the corresponding level of tension adjustment response in advance. The frequency domain correlation between vibration frequency and real-time wind speed was analyzed to extract the characteristic frequency and resonance risk of wind-induced vibration; when the comprehensive risk index R>0.7, the corresponding level of tension adjustment response was triggered in advance. To assess the lag correlation between changes in soil moisture and changes in slope, a dynamic response model of soil-root system-stability was established.

7. The method for controlling the stability of green trees used in landscape design according to claim 1, characterized in that, In step 4, the multivariate data fusion algorithm is implemented by constructing a data association matrix R, where the matrix elements R... ij This represents the correlation strength between the data from sensor type i and the data from sensor type j; A comprehensive stability evaluation function is established based on the data association matrix R.

8. The method for controlling the stability of green trees used in landscape design according to claim 6, characterized in that, In step 5, the adaptive adjustment control performs hierarchical control according to different ranges of the stability safety factor S.

9. The method for controlling the stability of green trees used in landscape design according to claim 8, characterized in that, The regional joint protection mechanism includes: When a tree enters a critical state, an early warning signal is automatically sent to trees with high influence weight. Upon receiving the warning signal, the support devices of adjacent trees automatically enter the warning state, the monitoring frequency is increased to twice, and the support tension is pre-adjusted to T0×1.1; Establish a regional stability status map to display the stability distribution of each tree in the entire monitoring area in real time; When more than 30% of the trees in the area are in a state of alert or above, regional protection measures are activated, and all tree support systems enter enhanced monitoring mode.

10. A tree support device for landscape design, used to implement the control method according to any one of claims 1-9, characterized in that, include: Support structure unit: includes main support cable, auxiliary support cable, root fixing device and tension adjustment mechanism; Sensor network unit: includes tilt sensor, strain sensor, vibration sensor, soil sensor and weather sensor, and each sensor realizes data transmission through wireless communication module; Data processing unit: integrates a data analysis module, possessing data storage, processing, and analysis capabilities; The execution control unit includes a tension adjustment actuator, an emergency braking device, and a status feedback sensor. Each unit achieves data interaction and collaborative control through a wireless communication network.

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