Tree diameter dynamic monitoring device and method

Abstract

The invention discloses a tree diameter dynamic monitoring device and method, and relates to the technical field of plant growth monitoring, in particular to a method for constructing a tree trunk cross section model based on distance measurement of light emission and reflection time difference delta t, a radial feature vector V (t) is formed by obtaining multi-angle radius lengths r to rn, and the radial feature vector V (t) is obtained; the problem of elasticity weakening caused by long-term stretching of a traditional spring sensor is avoided; setting a monitoring period Gt, calculating a direction difference vector Iza, an average radial growth rate Izb and a radial growth variance Izc, normalizing to generate a comprehensive evaluation index Izh, and judging a growth state through thresholds I1 and I2; and if the Euclidean change delta V (t) is greater than a threshold value epsilon, a state correction and multi-source verification process is triggered, so that the precision and reliability of tree diameter monitoring are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of plant growth monitoring, and in particular to a device and method for dynamically monitoring tree diameters. Background Art

[0002] The background technology of tree trunk monitoring devices stems primarily from the urgent need for forest health management and ecological protection. Traditional manual inspection methods are inefficient and difficult to obtain real-time data. With the development of the Internet of Things and sensor technology, intelligent monitoring devices that integrate environmental sensors (such as temperature, humidity, and light), mechanical sensors (such as strain gauges), and wireless transmission modules have emerged. By collecting real-time data such as tree trunk growth parameters, tilt status, and signs of pests and diseases, and combining low-power wide area networks (such as LoRa and NB-IoT) to transmit to cloud platforms for analysis, they provide accurate and efficient solutions for forestry research and disaster warning, and promote the digitalization of smart forestry.

[0003] The basic principle of existing tree trunk monitoring devices is to use displacement sensors to monitor data such as the circumference and diameter of the tree trunk in real time. For example, the measurement module of CN 102494602A includes a linear displacement sensor with a spring, and the circumference of the tree trunk is measured based on the displacement of the linear displacement sensor with a spring.

[0004] If the spring in the above displacement sensor is in a stretched state for a long time, the elasticity will be weakened or lost, which will cause the measurement data error to gradually increase or even the measurement function to fail. Summary of the Invention

[0005] In view of the deficiencies in the prior art, the present invention provides a tree diameter dynamic monitoring device and method, which solves the technical shortcomings mentioned in the background art.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A tree diameter dynamic monitoring method, comprising:

[0007] S1. Use a light emitting sensor device to emit multi-angle light beams around the tree trunk, and obtain the spatial coordinates of the corresponding reflection points on the trunk surface based on the reflection return time of the light beams to generate initial cross-sectional dot matrix data of the trunk;

[0008] S2, based on the dot matrix data obtained in S1, construct a trunk cross-section model, extract representative reflection profiles, and record and generate the trunk radial feature vector at the monitoring time point t;

[0009] S3. Set a timed monitoring cycle to continuously obtain the radial feature vector sequence at each time t1, t2, ..., tn, and perform feature difference processing on the sequence to determine the tree diameter growth trend and change rate;

[0010] S4. Calculate any time ti The system automatically detects changes in the characteristic vector under the status quo, and presets the elastic offset threshold for evaluation, automatically generates a status correction mark and triggers the data credibility verification process. If the verification fails, an abnormal warning signal is issued, prompting the monitoring equipment to be recalibrated or replaced.

[0011] Preferably, S1.1, multi-angle light beam emission and time synchronization recording are performed, through the light emission sensor device set around the trunk, infrared light beams are emitted from different angles simultaneously or sequentially at a set frequency; and the timestamp and emission direction parameters of each beam of emitted light are synchronously recorded

[0012] Preferably, S1.2 collects the reflected signal and calculates the spatial coordinates. The signal of the light beam reflected by the tree trunk is received, and the precise time difference Δt between the emission and return of each light beam is measured. Combined with the pre-calibrated emission direction corresponding to each light-emitting hole, the return time Δt and the speed of light c are used to calculate the distance d between the reflection point and the emission point. The specific calculation formula is as follows:

[0013]

[0014] Then the distance d is converted into Cartesian three-dimensional coordinates (x, y, z) through polar coordinates;

[0015] Finally, all measured coordinate points are collected to form the trunk cross-section dot matrix data and uniformly projected to the local coordinate system.

[0016] Preferably, S2.1, importing the tree trunk cross-section dot matrix data into a spatial modeling module, performing shape modeling on the tree trunk cross-section using a least squares fitting method, and reconstructing a complete two-dimensional cross-section profile model;

[0017] At the same time, density distribution analysis and noise elimination are performed on edge points to retain valid echo data points and construct a closed boundary curve reflecting the real tree trunk shape.

[0018] Preferably, S2.2 then uses the geometric center of the fitted circle of the cross-sectional model as the reference origin, emits rays outward in equal angles, intersects the boundary curve, and records the radius length in each direction;

[0019] The radial lengths measured in each direction are combined in sequence to form the radial feature vector corresponding to the monitoring time point t, which is used to represent the geometric state of the trunk cross section at that moment.

[0020] Preferably, S3.1 sets a fixed monitoring period Gt, and automatically calls the corresponding radial feature vector at each monitoring time t1, t2, ..., tn in represents the radial distance in the jth direction at the i-th time point;

[0021] After continuous acquisition, a time series vector set {V(t1), V(t2)..., V(ti)} is formed.

[0022] Preferably, S3.2, performing differential calculation on the radial feature vectors at adjacent time points, extracting the radial variation in each direction, obtaining the directional differential vector Iza, the average radial growth rate Izb, and the radial growth variance Izc, and performing dimensionless processing, and then obtaining the comprehensive radial growth evaluation index Izh by fitting calculation. The specific calculation formula is as follows:

[0023]

[0024] Preset comprehensive radial growth assessment thresholds, including I1 and I2, with I1 greater than I2, are compared with the comprehensive radial growth assessment index Izh to determine the growth rate and uniformity of the trunk. The specific assessment contents are as follows:

[0025] If the comprehensive radial growth assessment index Izh is less than I1, the growth status is determined to be normal and the data is recorded in the long-term trend database;

[0026] If I1≤Izh<I2: A Level 2 warning is triggered, marking potential anomalies, including insect infestation or water stress, and initiating a high-frequency review mode, including shortening the monitoring period.

[0027] If the comprehensive radial growth assessment index Izh ≥ I2: a level 1 alarm is triggered, which is judged to be a significant abnormality, including mechanical damage or disease outbreak, and an emergency notification is pushed and manual verification is recommended.

[0028] Preferably, S4.1, calculate the characteristic vector change ΔV(ti) at the current moment ti in real time, and the specific calculation formula is:

[0029] ΔV(ti)=||V(ti)-V(ti-1)||2;

[0030] Where V(ti) is the radial feature vector including diameter and circumference at time ti, and the Euclidean distance is used to quantify the change amplitude;

[0031] The preset elastic offset threshold ε is related to the characteristic vector change ΔV(t i ) for comparative evaluation. The specific evaluation contents are as follows:

[0032] If the characteristic vector change ΔV(ti) ≤ the elastic offset threshold ε: the data is considered credible and marked as normal;

[0033] If the characteristic vector change ΔV(ti)> the elastic offset threshold ε: the state correction flag is triggered and the verification process begins.

[0034] Preferably, S4.2, when the characteristic vector change ΔV(ti) exceeds the elastic offset threshold ε, the multi-source verification process is automatically triggered;

[0035] First, an environmental consistency check is performed, comparing real-time data such as light, temperature, and humidity with historical normal ranges. At the same time, a device self-test is performed to verify the stability of the sensor power supply and signal strength.

[0036] If the verification passes, indicating that the environment and equipment are normal, it is marked as a "false positive" and the baseline is reset;

[0037] If the verification fails, a graded alarm signal is generated based on the degree of ΔV(ti) exceeding the limit. After fusing multi-source data through the Bayesian network, the device ID, abnormal parameters and calibration suggestions are pushed to the operation and maintenance terminal.

[0038] A tree diameter dynamic monitoring device includes a data acquisition module, a modeling and analysis module, a dynamic monitoring module, and an abnormality warning module;

[0039] The data acquisition module emits multi-angle light beams around the tree trunk through a light-emitting sensor device, obtains the spatial coordinates of the reflection point based on the light beam reflection return time, and generates the initial cross-sectional dot matrix data of the tree trunk;

[0040] The modeling and analysis module builds a trunk cross-section model based on the dot matrix data, extracts representative reflection profiles, and records the trunk radial feature vector at the monitoring time point t0;

[0041] Dynamic monitoring module, set the timing monitoring cycle, continuously obtain the radial feature vector sequence at each time t1, t2, ..., tn, perform feature difference processing, and analyze the tree diameter growth trend and change rate;

[0042] The abnormal warning module automatically generates a status correction mark and triggers data credibility verification when the characteristic vector change exceeds the preset elastic offset threshold; if the verification fails, an abnormal warning signal is issued, prompting the device to be recalibrated or replaced.

[0043] The present invention provides a tree diameter dynamic monitoring device and method, which has the following beneficial effects:

[0044] (1) A tree diameter dynamic monitoring device and method uses a light emitting sensor device to emit multi-angle infrared beams around the tree trunk, and calculates the spatial coordinates (x, y, z) of each reflection point based on the return time difference Δt, light speed c and emission direction parameters of each beam of light, and then constructs an initial cross-sectional dot matrix data model of the tree trunk, and reconstructs the two-dimensional contour boundary curve through the least squares fitting method, and then uses the fitting circle center as the geometric center to emit rays at equal angles outward to measure the radius lengths r1, r2...r in each direction. nThe combined radial eigenvector V(t) solves the mechanical failure problems of traditional spring-based displacement sensors due to long-term stress, such as elasticity loss, measurement value drift, and irreversible deformation, and improves the non-contact and data stability of the measurement process.

[0045] (2) A tree diameter dynamic monitoring device and method, by setting a fixed monitoring period Gt, continuously obtains radial feature vector sequences V(t1), V(t2)...V(tn) at each time t1, t2, ..., tn n ), and after dimensionless normalization based on the three parameters of the directional difference vector Iza, the average radial growth rate, and the radial growth variance Izc, a comprehensive radial growth assessment index Izh was constructed. Izh was then graded based on preset thresholds I1 and I2, triggering a second-level warning and a first-level alarm, respectively, indicating the need for emergency intervention. This method breaks through the limitation of traditional methods that cannot distinguish between "growth changes" and "error fluctuations", and realizes the quantitative, refined, and intelligent analysis of the dynamic growth of tree trunks.

[0046] (3) A tree diameter dynamic monitoring device and method, at any time ti, the system calculates the current feature vector change ΔV(ti), that is, the radial feature vectors of the two moments are compared using Euclidean distance, and the elastic offset threshold ε is combined for real-time evaluation, and the status correction mark is triggered according to the evaluation content and the data credibility verification process is started, including comparing the real-time environmental parameters such as light, temperature and humidity with the historical normal range, verifying the equipment operating status such as sensor signal strength and power supply stability, and if the verification fails, a graded alarm is generated based on the degree of ΔV(ti) exceeding the limit, and the device ID, abnormal parameters and calibration suggestions are automatically pushed through the Bayesian network by fusing multi-source information, realizing a comprehensive closed loop of abnormal identification and processing, eliminating the hidden false alarms and misjudgments caused by spring fatigue or displacement drift, and improving the stability, reliability and long-term operation capability of the system in actual forestry monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a schematic flow chart of the steps of a tree diameter dynamic monitoring method of the present invention;

[0048] Figure 2 The figure is a schematic diagram of the system framework structure of a tree diameter dynamic monitoring device of the present invention. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0050] Example 1

[0051] See also Figure 1 The present invention provides a tree diameter dynamic monitoring method, which is characterized by comprising:

[0052] S1. Use a light emitting sensor device to emit multi-angle light beams around the tree trunk, and obtain the spatial coordinates of the corresponding reflection points on the trunk surface based on the reflection return time of the light beams to generate initial cross-sectional dot matrix data of the trunk;

[0053] S2, based on the dot matrix data obtained in S1, construct a trunk cross-section model, extract representative reflection profiles, and record and generate the trunk radial feature vector at the monitoring time point t;

[0054] S3. Set a timed monitoring cycle to continuously obtain the radial feature vector sequence at each time t1, t2, ..., tn, and perform feature difference processing on the sequence to determine the tree diameter growth trend and change rate;

[0055] S4. Calculate any time t i The system automatically detects changes in the characteristic vector under the status quo, and presets the elastic offset threshold for evaluation, automatically generates a status correction mark and triggers the data credibility verification process. If the verification fails, an abnormal warning signal is issued, prompting the monitoring equipment to be recalibrated or replaced.

[0056] In this embodiment, through step S1, a light-emitting sensor device emits multi-angle light beams around the tree trunk. Based on the time difference Δt between the beams' reflections and the speed of light c, the spatial coordinates (x, y, z) of each reflection point are accurately calculated. This allows for the construction of high-density, high-precision initial cross-sectional dot matrix data for the tree trunk, effectively avoiding measurement errors caused by elastic attenuation in traditional spring sensors.

[0057] Through step S2, a cross-sectional profile model is established based on the dot matrix data, and multi-directional radii r1 to rn are obtained with the fitting circle center as the center, forming the radial feature vector V(t) at the monitoring time t, realizing a complete quantitative expression of the geometric state of the tree trunk;

[0058] Through step S3, the monitoring period Gt is set and the sequence of characteristic vectors at consecutive moments is obtained. The directional difference vector Iza, the average radial growth rate Izb, and the radial growth variance Izc are calculated and normalized to generate the comprehensive growth evaluation index Izh, which helps to dynamically grasp the growth trend and uniformity changes of the tree trunk.

[0059] Through step S4, the characteristic vector change ΔV(ti) is calculated in real time and compared with the elastic offset threshold ε. The state correction and multi-source environment and equipment verification process are triggered based on the evaluation results. If the verification fails, an abnormal warning signal is issued, which significantly improves the stability and reliability of the monitoring system in long-term operation.

[0060] Example 2

[0061] S1.1. Multi-angle light beam emission and time synchronization recording: The light emission sensor device set around the tree trunk emits infrared light beams from different angles simultaneously or sequentially at a set frequency; and the timestamp and emission direction parameters of each beam of light are synchronously recorded.

[0062] S1.2. Collect the reflected signal and calculate the spatial coordinates. Receive the signal after the light beam is reflected back from the tree trunk and measure the precise time difference Δt between each beam of light from emission to return. Combined with the pre-calibrated emission direction corresponding to each light hole, the return time Δt and the speed of light c are used to calculate the distance d between the reflection point and the emission point. The specific calculation formula is as follows:

[0063]

[0064] Then the distance d is converted into Cartesian three-dimensional coordinates (x, y, z) through polar coordinates;

[0065] Finally, all measured coordinate points are collected to form the trunk cross-section dot matrix data and uniformly projected to the local coordinate system.

[0066] S2.1. Import the tree trunk cross-section dot matrix data into the spatial modeling module, perform shape modeling on the tree trunk cross-section using the least squares fitting method, and reconstruct a complete two-dimensional cross-section contour model;

[0067] At the same time, density distribution analysis and noise elimination are performed on edge points to retain valid echo data points and construct a closed boundary curve reflecting the real tree trunk shape.

[0068] S2.2 Then, using the geometric center of the fitted circle of the cross-sectional model as the reference origin, send out rays in equiangular directions outward, intersecting the boundary curve, and record the radius length in each direction;

[0069] The radial lengths measured in each direction are combined in sequence to form the radial feature vector corresponding to the monitoring time point t, which is used to represent the geometric state of the trunk cross section at that moment.

[0070] In this embodiment, multi-angle infrared beam emission and time synchronization recording are achieved through step S1.1, and the timestamp and emission direction parameters of each beam of light are obtained, providing basic data for subsequent distance and spatial position calculations. In step S1.2, the reflected signal is received and the return time difference Δt of each beam of light is measured. The distance d of the reflection point is calculated by combining the speed of light c and the emission angle. The polar coordinates are converted into three-dimensional coordinates (x, y, z), thereby forming the dot matrix data of the tree trunk cross section and uniformly projecting it into the local coordinate system to ensure the uniformity and comparability of the data coordinates.

[0071] In step S2.1, the dot matrix data is imported for least squares fitting modeling. Edge point density analysis and noise removal are combined to generate true contour boundary curves, making the cross-sectional modeling closer to the actual structure. In step S2.2, equiangular rays are emitted from the center of the fitting circle as the geometric center. The radius lengths r1 to rn in each direction are recorded and combined into a feature vector V(t), allowing the trunk morphology to be digitally expressed and tracked.

[0072] Example 3

[0073] S3.1 sets a fixed monitoring period Gt, and automatically calls the corresponding radial feature vector at each monitoring time t1, t2, ..., tn in represents the radial distance in the jth direction at the i-th time point;

[0074] After continuous acquisition, a time series vector set {V(t1), V(t2)..., V(ti)} is formed.

[0075] S3.2. Perform differential calculation on the radial eigenvectors at adjacent time points, extract the radial variation in each direction, obtain the directional differential vector Iza, the average radial growth rate Izb, and the radial growth variance Izc, perform dimensionless processing, and then obtain the comprehensive radial growth evaluation index Izh through fitting calculation. The specific calculation formula is as follows:

[0076]

[0077] Preset comprehensive radial growth assessment thresholds, including I1 and I2, with I1 greater than I2, are compared with the comprehensive radial growth assessment index Izh to determine the growth rate and uniformity of the trunk. The specific assessment contents are as follows:

[0078] If the comprehensive radial growth assessment index Izh is less than I1, the growth status is determined to be normal and the data is recorded in the long-term trend database;

[0079] If I1≤Izh<I2: A Level 2 warning is triggered, marking potential anomalies, including insect infestation or water stress, and initiating a high-frequency review mode, including shortening the monitoring period.

[0080] If the comprehensive radial growth assessment index Izh ≥ I2: a level 1 alarm is triggered, which is judged to be a significant abnormality, including mechanical damage or disease outbreak, and an emergency notification is pushed and manual verification is recommended.

[0081] S4.1. Calculate the eigenvector change ΔV(ti) at the current moment ti in real time. The specific calculation formula is:

[0082] ΔV(ti)=||V(ti)-V(ti-1)||2;

[0083] Where V(ti) is the radial feature vector including diameter and circumference at time ti, and the Euclidean distance is used to quantify the change amplitude;

[0084] The preset elastic offset threshold ε is related to the characteristic vector change ΔV(t i ) for comparative evaluation. The specific evaluation contents are as follows:

[0085] If the characteristic vector change ΔV(ti) ≤ the elastic offset threshold ε: the data is considered credible and marked as normal;

[0086] If the characteristic vector change ΔV(ti)> the elastic offset threshold ε: the state correction flag is triggered and the verification process begins.

[0087] S4.2. When the feature vector change ΔV(ti) exceeds the elastic offset threshold ε, the multi-source verification process is automatically triggered;

[0088] First, an environmental consistency check is performed, comparing real-time data such as light, temperature, and humidity with historical normal ranges. At the same time, a device self-test is performed to verify the stability of the sensor power supply and signal strength.

[0089] If the verification passes, indicating that the environment and equipment are normal, it is marked as a "false positive" and the baseline is reset;

[0090] If the verification fails, a graded alarm signal is generated based on the degree of ΔV(ti) exceeding the limit. After fusing multi-source data through the Bayesian network, the device ID, abnormal parameters and calibration suggestions are pushed to the operation and maintenance terminal.

[0091] In this embodiment, a fixed monitoring period Gt is set in S3.1 to obtain radial eigenvectors V(t1), V(t2)…V(tn) at each moment. Each eigenvector contains radius values in multiple directions, forming a time series set that provides continuous data support for analyzing tree trunk growth changes. S3.2 calculates the difference between adjacent time points to obtain the directional difference vector Iza, the average radial growth rate Izb, and the radial growth variance Izc. After normalization, the three are calculated to calculate the comprehensive growth assessment index Izh, which is used to evaluate the overall growth rate and uniformity of the tree trunk. Thresholds I1 and I2 are set to determine whether the growth status is normal, potentially abnormal, or significantly abnormal.

[0092] In S4.1, the feature vector change ΔV(ti) is calculated based on the Euclidean distance and compared with the preset elastic offset threshold ε. If ΔV(t i) is greater than ε, a state correction is triggered; a multi-source verification process is started in S4.2, including real-time environmental parameter comparison and equipment operation status detection. If the verification fails, a graded alarm is generated based on the degree of ΔV(ti) exceeding the limit, and various types of information are integrated through the Bayesian network and pushed to the operation and maintenance terminal, realizing high-precision, low false alarm, and strong adaptability of intelligent tree diameter dynamic monitoring.

[0093] The calculation formula for the monitoring period setting parameters is as follows:

[0094] Gt=ti-ti -1 ;

[0095] It can be adjusted according to the growth rate of the tree species, and the recommended range is 1 hour to 24 hours.

[0096] The directional difference vector Iza is used to measure the consistency of the changing trend of the radial eigenvector in each direction, that is, whether most directions are growing; the acquisition formula is:

[0097]

[0098] Where, represents the radial value of the j-th direction at the current time ti, represents the radial value of the jth direction at the previous time ti-1; sign(x) represents the sign function, which returns 1 for positive values, -1 for negative values, and 0 for 0; n represents the dimension of the feature vector, such as sampling every 5° in 360°, then n = 72;

[0099] The average radial growth rate Izb is used to reflect the growth rate of the overall outer diameter of the trunk, and the formula is:

[0100]

[0101] Where Gt represents the fixed monitoring period; the unit is the unit time

[0102] The radial growth variance Izc is used to measure the degree of fluctuation of growth in each direction, that is, the unevenness, and the formula is:

[0103]

[0104] Example 4

[0105] See also Figure 2 , a tree diameter dynamic monitoring device, including a data acquisition module, a modeling and analysis module, a dynamic monitoring module and an abnormality warning module;

[0106] The data acquisition module emits multi-angle light beams around the tree trunk through a light-emitting sensor device, obtains the spatial coordinates of the reflection point based on the light beam reflection return time, and generates the initial cross-sectional dot matrix data of the tree trunk;

[0107] The modeling and analysis module builds a trunk cross-section model based on the dot matrix data, extracts representative reflection profiles, and records the trunk radial feature vector at the monitoring time point t0;

[0108] Dynamic monitoring module, set the timing monitoring cycle, continuously obtain the radial feature vector sequence at each time t1, t2, ..., tn, perform feature difference processing, and analyze the tree diameter growth trend and change rate;

[0109] The abnormal warning module automatically generates a status correction mark and triggers data credibility verification when the characteristic vector change exceeds the preset elastic offset threshold; if the verification fails, an abnormal warning signal is issued, prompting the device to be recalibrated or replaced.

Claims

1. A method for dynamic tree diameter monitoring, characterized by: include: S1. Use a light emitting sensor device to emit multi-angle light beams around the tree trunk, and obtain the spatial coordinates of the corresponding reflection points on the trunk surface based on the reflection return time of the light beams to generate initial cross-sectional dot matrix data of the trunk; S2, based on the dot matrix data obtained in S1, construct a trunk cross-section model, extract representative reflection profiles, and record and generate the trunk radial feature vector at the monitoring time point t; S3. Set a timed monitoring cycle to continuously obtain the radial feature vector sequence at each time t1, t2, ..., tn, and perform feature difference processing on the sequence to determine the tree diameter growth trend and change rate; S4. Calculate any time t i The system automatically detects changes in the characteristic vector under the status quo, and presets the elastic offset threshold for evaluation, automatically generates a status correction mark and triggers the data credibility verification process. If the verification fails, an abnormal warning signal is issued, prompting the monitoring equipment to be recalibrated or replaced.

2. A tree diameter dynamic monitoring method according to claim 1, characterized in that: S1.

1. Multi-angle light beam emission and time synchronization recording are performed. The light emission sensor device set around the tree trunk emits infrared light beams simultaneously or sequentially from different angles at a set frequency; and the timestamp and emission direction parameters of each beam of emitted light are synchronously recorded.

3. A tree diameter dynamic monitoring method according to claim 1, characterized in that: S1.

2. Collect the reflected signal and calculate the spatial coordinates. Receive the signal after the light beam is reflected back from the tree trunk and measure the precise time difference Δt between each beam of light from emission to return. Combined with the pre-calibrated emission direction corresponding to each light hole, the return time Δt and the speed of light c are used to calculate the distance d between the reflection point and the emission point. The specific calculation formula is as follows: Then the distance d is converted into Cartesian three-dimensional coordinates (x, y, z) through polar coordinates; Finally, all measured coordinate points are collected to form the trunk cross-section dot matrix data and uniformly projected to the local coordinate system.

4. A tree diameter dynamic monitoring method according to claim 1, characterized in that: S2.

1. Import the tree trunk cross-section dot matrix data into the spatial modeling module, perform shape modeling on the tree trunk cross-section using the least squares fitting method, and reconstruct a complete two-dimensional cross-section contour model; At the same time, density distribution analysis and noise elimination are performed on edge points to retain valid echo data points and construct a closed boundary curve reflecting the real tree trunk shape.

5. The tree diameter dynamic monitoring method according to claim 1, characterized in that: S2.2 Then, using the geometric center of the fitted circle of the cross-sectional model as the reference origin, send out rays in equiangular directions outward, intersecting the boundary curve, and record the radius length in each direction; The radial lengths measured in each direction are combined in sequence to form the radial feature vector corresponding to the monitoring time point t, which is used to represent the geometric state of the trunk cross section at that moment.

6. A tree diameter dynamic monitoring method according to claim 1, characterized in that: S3.1 sets a fixed monitoring period Gt, and automatically calls the corresponding radial feature vector at each monitoring time t1, t2, ..., tn in represents the radial distance in the jth direction at the i-th time point; After continuous acquisition, a time series vector set {V(t1), V(t2)..., V(ti)} is formed.

7. A tree diameter dynamic monitoring method according to claim 1, characterized in that: S3.

2. Perform differential calculation on the radial eigenvectors at adjacent time points, extract the radial variation in each direction, obtain the directional differential vector Iza, the average radial growth rate Izb, and the radial growth variance Izc, perform dimensionless processing, and then obtain the comprehensive radial growth evaluation index Izh through fitting calculation. The specific calculation formula is as follows: Preset comprehensive radial growth assessment thresholds, including I1 and I2, with I1 greater than I2, are compared with the comprehensive radial growth assessment index Izh to determine the growth rate and uniformity of the trunk. The specific assessment contents are as follows: If the comprehensive radial growth assessment index Izh is less than I1, the growth status is determined to be normal and the data is recorded in the long-term trend database; If I1≤Izh<I2: A Level 2 warning is triggered, marking potential anomalies, including insect infestation or water stress, and initiating a high-frequency review mode, including shortening the monitoring period. If the comprehensive radial growth assessment index Izh ≥ I2: a level 1 alarm is triggered, which is judged to be a significant abnormality, including mechanical damage or disease outbreak, and an emergency notification is pushed and manual verification is recommended.

8. The method for dynamic tree diameter monitoring according to claim 1, characterized in that: S4.

1. Calculate the eigenvector change ΔV(ti) at the current moment ti in real time. The specific calculation formula is: ΔV(ti)=||V(ti)-V(ti-1)||2; Where V(ti) is the radial feature vector including diameter and circumference at time ti, and the Euclidean distance is used to quantify the change amplitude; The preset elastic offset threshold ε is related to the characteristic vector change ΔV(t i ) for comparative evaluation. The specific evaluation contents are as follows: If the characteristic vector change ΔV(ti) ≤ the elastic offset threshold ε: the data is considered credible and marked as normal; If the characteristic vector change ΔV(ti)> the elastic offset threshold ε: the state correction flag is triggered and the verification process begins.

9. The method for dynamic tree diameter monitoring according to claim 1, characterized in that: S4.

2. When the feature vector change ΔV(ti) exceeds the elastic offset threshold ε, the multi-source verification process is automatically triggered; First, an environmental consistency check is performed, comparing real-time data such as light, temperature, and humidity with historical normal ranges. At the same time, a device self-test is performed to verify the stability of the sensor power supply and signal strength. If the verification passes, indicating that the environment and equipment are normal, it is marked as a "false positive" and the baseline is reset; If the verification fails, a graded alarm signal is generated based on the degree of ΔV(ti) exceeding the limit. After fusing multi-source data through the Bayesian network, the device ID, abnormal parameters and calibration suggestions are pushed to the operation and maintenance terminal.

10. A tree diameter dynamic monitoring device, according to the tree diameter dynamic monitoring method according to any one of claims 1 to 9, characterized in that: It includes data acquisition module, modeling and analysis module, dynamic monitoring module and abnormal warning module; The data acquisition module emits multi-angle light beams around the tree trunk through a light-emitting sensor device, obtains the spatial coordinates of the reflection point based on the light beam reflection return time, and generates the initial cross-sectional dot matrix data of the tree trunk; The modeling and analysis module builds a trunk cross-section model based on the dot matrix data, extracts representative reflection profiles, and records the trunk radial feature vector at the monitoring time point t0; Dynamic monitoring module, set the timing monitoring cycle, continuously obtain the radial feature vector sequence at each time t1, t2, ..., tn, perform feature difference processing, and analyze the tree diameter growth trend and change rate; Abnormal warning module: When the feature vector change exceeds the preset elastic offset threshold, it automatically generates a state correction mark and triggers data credibility verification; If the calibration fails, an abnormal warning signal will be issued, indicating that the equipment needs to be recalibrated or replaced.

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