An Internet of Things-based dynamic monitoring method and system for the growth environment of ancient trees

Abstract

The present invention relates to the technical field of environmental monitoring and management, and specifically to a dynamic monitoring method and system for the growth environment of ancient trees based on the Internet of Things. In the present invention, by introducing elevation monitoring nodes and inclination angle sensors, and combining the spatial distribution of topographic aspect and ancient tree numbers, the division of aspect sections and the classification and integration of the inclination angle directions of ancient trees are completed, effectively supplementing the lack of the ability to identify micro-topographic differences in traditional ecological factor collection. By performing paired calculations on the spatial linear distances between ancient trees and the root system extension data, and combining the ratio fluctuation range, a root system distribution map with spatial extension characteristics is constructed, indirectly quantifying the underground structure information at the data level and realizing the assessment of the environmental response ability of the root system development. The calculation of the time offset between the humidity change node and the temperature fluctuation point is introduced, and the absorption feedback rate of the root system to climate factors is reflected through the time delay feature, thereby revealing the sensitivity of the underground system to sudden environmental factors.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring and management, and in particular to a method and system for dynamically monitoring the growth environment of ancient trees based on the Internet of Things. Background Art

[0002] The field of environmental monitoring and management technology includes continuous and systematic observation and recording of various ecological elements in the natural environment, and the collection of environmental data through various means to achieve the understanding and regulation of ecosystem changes. The core content of this technology includes the monitoring and information acquisition of environmental factors such as air, water quality, soil, and biology, and integrated analysis through collection equipment and data transmission methods, so as to achieve comprehensive management of environmental status.

[0003] Among them, the dynamic monitoring method of the growth environment of ancient trees based on the Internet of Things refers to the use of sensing devices with communication capabilities to collect real-time data on key ecological factors in the environment where the ancient trees are located, and upload them to the data platform through wireless transmission to achieve dynamic monitoring. The subject of this patent is mainly aimed at the long-term monitoring needs of factors such as soil temperature and humidity, air temperature and humidity, and light intensity in the small-scale ecological environment where the ancient trees are located. The information collection task is completed by deploying terminal devices with environmental sensor elements, and then the collected data is transmitted to the remote server platform through low-power wireless communication. The platform completes the centralized management and time series storage of the data, and presents the environmental changes around the ancient trees through a visual interface, which is convenient for managers to carry out targeted maintenance work.

[0004] Existing technologies in ancient tree environmental monitoring mostly focus on the fixed-point collection of basic ecological factors, such as soil temperature and humidity, air temperature and light intensity. Its collection mode lacks consideration of terrain differences and spatial structural correlations, and it is impossible to establish spatial interactive relationships between multiple points, resulting in problems such as limited root growth and tilt trend formation of ancient trees that are difficult to associate and identify. For example, in sloping areas, the slope and elevation differences of the area where the ancient trees are located are not analyzed, and it is impossible to determine whether the root development is subject to changes in terrain structure; in terms of monitoring response time, the existing system cannot distinguish whether the increase in humidity is caused by the actual absorption behavior of the roots of the ancient trees, nor can it correspond temperature fluctuations with the dynamics of underground water, resulting in insufficient guiding significance of data results in maintenance scheduling. In addition, the tilt angle, as a key structural risk indicator, is often monitored separately, and the cause of the tilt is not determined in combination with the terrain structure where the ancient trees are located. It is easy to misjudge the external force and terrain gravity effects, affecting the accuracy of subsequent disposal plans, and limiting the ability to fully identify the trend of changes in the stability of the ancient tree structure. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings existing in the prior art and to propose a method and system for dynamically monitoring the growth environment of ancient trees based on the Internet of Things.

[0006] To achieve the above object, the present invention adopts the following technical solutions: A method for dynamically monitoring the growth environment of ancient trees based on the Internet of Things, comprising the following steps:

[0007] S1: Obtain the topographic slope aspect, elevation continuous points, and ancient tree numbers recorded in the Internet of Things elevation monitoring nodes and inclination sensors deployed in the ancient tree community area, divide the single slope aspect section, and generate a list of partition inclination directions;

[0008] S2: Based on the ancient tree numbers in the list of partition inclination directions, calculate the spatial straight-line distance between adjacent ancient trees, statistically analyze the fluctuation range of the distance distribution by slope section, and generate root system spatial distribution information;

[0009] S3: Obtain the ancient tree numbers with a span range exceeding a preset threshold in the root system spatial distribution information, extract the humidity change information at a specified time, establish the relationship between the response delay node and the temperature fluctuation, and generate a temperature-humidity correlation response record sequence;

[0010] S4: According to the ancient tree numbers with a time offset interval greater than the set time difference threshold in the temperature-humidity correlation response record sequence, determine whether there is an opposite trend between the slope difference and the inclination angle and mark the trend type, and generate a gravity offset type marking data set;

[0011] S5: Based on all the marked points in the gravity offset type marking data set, output an environmental interaction monitoring set indexed by ancient tree numbers.

[0012] As a further solution of the present invention, the list of partition inclination directions specifically includes slope section number information, ancient tree inclination direction angle values, the correspondence between ancient tree coordinates and the corresponding slope sections. The root system spatial distribution information includes the root system horizontal extension distance value, ancient tree spacing ratio, and the ratio fluctuation range within the slope section. The temperature-humidity correlation response record sequence specifically refers to the humidity rising peak time point, the corresponding temperature fluctuation value, and the temperature offset time difference. The gravity offset type marking data set specifically includes the ancient tree inclination direction type, the comparison result of the slope difference and the inclination angle, and the offset type label. The environmental interaction monitoring set includes the ancient tree number index, slope section division information, root system structure ratio, climate response time value, and spatial offset type identifier.

[0013] As a further solution of the present invention, the steps for obtaining the list of partition inclination directions are specifically as follows:

[0014] S111: Obtain the topographic slope direction data, elevation continuous point data, and ancient tree numbers recorded in the Internet of Things elevation monitoring nodes and inclination sensors deployed in the ancient tree community area. Extract the coordinate sequences of the elevation points in sequence, calculate the elevation differences and coordinate differences between adjacent points, obtain the corresponding slope direction values, judge the slope directions of each group of three consecutive elevation points. If the slope directions of the three are the same, they are classified into the same section. Integrate all the monitoring point sequences to obtain a single slope direction section number group;

[0015] S112: Make a matching judgment based on the coordinate data of the single slope direction section number group and the ancient tree numbers, screen the target numbers whose ancient tree coordinate points fall within the corresponding slope section number group, and according to the elevation differences and horizontal distances between the monitoring points within the slope section, use the formula:

[0016] ;

[0017] Calculate the inclination angle direction offset of the th ancient tree in the slope section , and integrate to obtain a list of partition inclination angle directions;

[0018] Among them, represents the reciprocal of the number of point pairs used to calculate the average slope angle in the rd slope section, represents the sum of the th to the th to the th elevation point pairs in the rd slope section, is the slope angle value of the th point pair in the rd slope section, represents the elevation difference of the th point pair in the rd slope section, represents the horizontal distance of the th point pair in the rd slope section, represents the inclination direction angle recorded by the th ancient tree inclination sensor, represents the total number of elevation point pairs participating in the calculation within the

[0019] As a further solution of the present invention, the steps for obtaining the root system spatial distribution information are specifically as follows:

[0020] S211: Based on the ancient tree numbers in the list of partition tilt angle directions, retrieve the soil moisture sensor data deployed corresponding to each ancient tree number, identify the horizontal coordinate positions corresponding to the humidity change boundaries, calculate the maximum horizontal coordinate difference between the sensor points by locating all the sensor points at the boundary water content, and obtain the horizontal extension distance of the ancient tree roots;

[0021] S212: Based on the horizontal extension distance of the ancient tree roots and the spatial coordinate data corresponding to the ancient tree numbers, calculate the Euclidean space distance between the ancient trees using the horizontal and vertical coordinate differences, process each group of ancient tree number combinations, and use the formula:

[0022] ;

[0023] Calculate the spatial straight-line distance between the first ancient tree and the second ancient tree , and jointly collect the spatial straight-line distance between the first ancient tree and the second ancient tree and the value of the horizontal extension distance of the ancient tree roots to obtain a group of root system comparison spatial distance values between the ancient trees;

[0024] Among them, , are the horizontal and vertical coordinate values of the first ancient tree respectively, , are the horizontal and vertical coordinate values of the second ancient tree respectively;

[0025] S213: According to the group of root system comparison spatial distance values between the ancient trees, statistically analyze all the spatial distance values of the ancient trees within the same slope section number, calculate the maximum span value, the minimum span value and the span difference range respectively, organize the statistical results under each slope section number into independent entries, and establish the root system spatial distribution information.

[0026] As a further solution of the present invention, the steps for obtaining the temperature-humidity correlation response record sequence are specifically as follows:

[0027] S311: Obtain the span range in the root system spatial distribution information, compare each slope section with a set threshold, screen out the slope section numbers whose span ranges exceed the threshold, call the soil moisture sensor data bound to the ancient tree numbers, extract the humidity change sequence within a specified time period, identify the time point corresponding to the maximum increase in the humidity rapid rise section after rainfall, and obtain the set of response delay node time points;

[0028] S312: Based on the set of response delay node time points, extract the daily temperature data before and after each node, and use the formula:

[0029] ;

[0030] Calculate the time offset value between the response delay node of the soil moisture sensor bound to the th ancient tree number and the maximum air temperature fluctuation point where the soil moisture sensor bound to the th ancient tree number is located, and integrate to obtain a temperature-humidity correlation response record sequence; Among them,

[0031] wherein, represents the daily air temperature gradient value of the soil moisture sensor bound to the th ancient tree number on the day of the response delay node, represents the daily air temperature gradient value of the soil moisture sensor bound to the th ancient tree number on the day of the maximum air temperature fluctuation point, represents the date of the day after the maximum air temperature fluctuation point, represents the date of the day before the maximum air temperature fluctuation point.

[0032] As a further solution of the present invention, the acquisition steps of the gravity offset type annotation data set are specifically as follows:

[0033] S411: According to the temperature-humidity correlation response record sequence, screen the ancient tree numbers with a time offset interval value greater than the set time difference threshold, call the tilt direction data of the corresponding ancient trees, and extract the main direction data of the slope section to which they belong. Represent the tilt direction and the slope section main direction in angles respectively, and calculate the included angle value between the tilt direction of each ancient tree and the slope section main direction through the angle difference to obtain a tilt main direction included angle set;

[0034] S412: Obtain the ancient tree numbers in the tilt main direction included angle set, collect the elevation data of the slope foot and slope top around the point corresponding to each ancient tree number, and judge whether the tilt direction and the height difference change direction are distributed in the opposite direction. If the tilt direction of the ancient tree is consistent with the local slope direction, it is marked as the natural gravity consistent type. If there is a deviation, it is marked as the direction interference type. Summarize the annotation results according to the ancient tree numbers and establish a gravity offset type annotation data set.

[0035] As a further solution of the present invention, the acquisition steps of the environmental interaction monitoring set are specifically as follows:

[0036] S511: Based on all the ancient tree numbers in the gravity offset type annotation data set, sequentially extract the spatial coordinate information, the slope section number and slope section direction value corresponding to each number, the position relationship of the point in the slope section and the elevation section identifier corresponding to the slope section boundary. Standardize and encode the fields such as the number, geographical affiliation, spatial distribution characteristics and type annotation of the point, uniformly organize them into single-line record data, and merge them according to the field format to generate an ancient tree spatial point structure table;

[0037] S512: Call the structure records of each ancient tree number in the ancient tree spatial point structure table, respectively associate and extract the geometric parameters of the corresponding slope section, the horizontal diameter-depth ratio of the ancient tree root system, the response delay time value and the tilt type annotation field in the record, reorganize the information according to the ancient tree number, and organize it into a structured data group indexed by a single number. Each group of records completely covers the environmental interaction element content of this point, and establish an environmental interaction monitoring set.

[0038] An ancient tree growth environment dynamic monitoring system based on the Internet of Things, characterized in that, according to the described ancient tree growth environment dynamic monitoring method based on the Internet of Things, the system includes:

[0039] The elevation monitoring and tilt angle data processing module obtains the terrain slope direction, elevation continuous points and ancient tree numbers recorded in the Internet of Things elevation monitoring nodes and tilt angle sensors deployed in the ancient tree community area, divides the single slope direction section, and generates a list of partition tilt angle directions;

[0040] The root system spatial distribution calculation module calculates the spatial straight-line distance between adjacent ancient trees based on the ancient tree numbers in the list of partition tilt angle directions, statistically analyzes the fluctuation range of the distance distribution by slope section, and generates root system spatial distribution information;

[0041] The humidity change and temperature-humidity correlation response analysis module obtains the ancient tree numbers whose span range in the root system spatial distribution information exceeds the preset threshold, extracts the humidity change information at a specified time, establishes the relationship between the response delay node and the temperature fluctuation, and generates a sequence of temperature-humidity correlation response records;

[0042] The trend and offset analysis module determines whether there is an opposite trend between the slope difference and the tilt angle and annotates the trend type according to the ancient tree numbers whose time offset interval in the temperature-humidity correlation response record sequence is greater than the set time difference threshold, and generates a gravity offset type annotation data set;

[0043] The environmental interaction monitoring output module outputs an environmental interaction monitoring set indexed by ancient tree numbers based on all the annotated points in the gravity offset type annotation data set.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0045] In the present invention, by introducing elevation monitoring nodes and inclination angle sensors, and combining the spatial distribution of topographic aspect and ancient tree numbers, the division of aspect sections and the classification and regularization of the inclination angle directions of ancient trees are completed, effectively supplementing the lack of the ability to identify micro-topographic differences in traditional ecological factor collection. By performing paired calculations on the spatial straight-line distances between ancient trees and the root system extension data, and combining the ratio fluctuation range, a root system distribution map with spatial extension characteristics is constructed, indirectly quantifying the underground structure information at the data level and realizing the assessment of the environmental response ability of the root system development. By introducing the time offset calculation between the humidity change node and the temperature fluctuation point, a response coupling sequence of the humidity main peak and the temperature change is constructed, and the absorption feedback rate of the root system to climate factors is reflected through the time delay characteristics, thereby revealing the sensitivity of the underground system to sudden environmental factors. Through the analysis of the included angle between the inclination angle and the topographic aspect, and combining the height difference between the top and bottom of the slope, the corresponding relationship between the force direction of the ancient tree and the environmental landform is extracted, and the gravity offset type is marked, which helps to judge whether the inclination of the ancient tree belongs to external force interference or terrain trend guidance. Finally, through standardized coding, multi-factor parameters such as spatial coordinates, elevation differences, response delays, and root system structure ratios are unified into the same data unit, realizing the integrated monitoring of the environment, structure, and response behavior, and significantly improving the recognition and early warning ability of the dynamic evolution process of the growth stability and environmental adaptability of ancient trees. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a schematic diagram of the main steps of the present invention;

[0047] Figure 2 is a flowchart of step S1 of the present invention;

[0048] Figure 3 is a flowchart of step S2 of the present invention;

[0049] Figure 4 is a flowchart of step S3 of the present invention;

[0050] Figure 5 is a flowchart of step S4 of the present invention;

[0051] Figure 6 is a flowchart of step S5 of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0052] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0053] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0054] See also Figure 1 The present invention provides a technical solution: a method for dynamically monitoring the growth environment of ancient trees based on the Internet of Things, comprising the following steps:

[0055] S1: Obtain the terrain slope data, continuous elevation point data and ancient tree number recorded in the IoT elevation monitoring nodes and tilt angle sensors deployed in the ancient tree community area, compare the slope differences between elevation points, and if the slope directions between three consecutive elevation points are consistent, they are delineated as a single slope section. The slope sections of the single slope section are matched according to the ancient tree coordinate numbers, and the tilt direction angles recorded in the tilt sensors are correspondingly extracted and classified according to the slope sections to which they belong, generating a partitioned tilt angle direction list;

[0056] S2: Based on the ancient tree number in the partitioned inclination angle direction list, the boundary water content data recorded by the soil moisture sensor under the same number is obtained, and the farthest horizontal span of the boundary water content is used as the root extension distance. The linear distance between adjacent ancient trees is calculated, and the fluctuation range of the distance distribution is statistically analyzed according to the slope section to generate the root space distribution information;

[0057] S3: Obtain the number of ancient trees whose span range exceeds the preset threshold in the root spatial distribution information, extract the humidity change information of the specified time from the corresponding soil moisture sensor record, identify the time point of the main peak of humidity rise after rainfall as the response delay node, obtain the daily temperature data before and after the response delay node, calculate the time offset value between the response node and the maximum temperature fluctuation point, establish the relationship between the response delay node and the temperature fluctuation, and generate a temperature and humidity correlation response record sequence;

[0058] S4: According to the number of the ancient tree whose time offset interval in the temperature and humidity correlation response record sequence is greater than the set time difference threshold, the angle between the corresponding ancient tree's tilt direction and the main direction of the corresponding slope section is extracted, and the surrounding height difference data is collected to determine whether the slope difference and the tilt angle show an opposite trend. If the direction offset angle is consistent with the local slope direction, it is marked as a natural gravity consistent type, otherwise it is marked as a direction interference type, and a gravity offset type annotation dataset is generated;

[0059] S5: Based on the gravity offset type, annotate all the marked points in the dataset, construct a point data structure table, organize the combined records of slope segment information, root system ratio, response delay time, and tilt type within the spatial section where each single point is located, and output an environmental interaction monitoring set indexed by the ancient tree number;

[0060] The list of partition tilt angle directions specifically includes slope segment number information, the angle value of the ancient tree tilt direction, the correspondence between the ancient tree coordinates and the corresponding slope segment. The root system spatial distribution information includes the horizontal extension distance value of the root system, the ratio of the distance between ancient trees, and the range of ratio fluctuations within the slope segment. The temperature and humidity correlation response record sequence specifically refers to the main peak time point of humidity rise, the corresponding temperature fluctuation value, and the temperature offset time difference. The gravity offset type annotation dataset specifically includes the ancient tree tilt direction type, the comparison result of the slope difference and the tilt angle, and the offset type label. The environmental interaction monitoring set includes the ancient tree number index, slope segment division information, root system structure ratio, climate response time value, and spatial offset type identifier.

[0061] Please refer to Figure 2 , and the specific steps for obtaining the list of partition tilt angle directions are as follows:

[0062] S111: Obtain the topographic slope direction data, continuous elevation point data, and ancient tree numbers recorded in the Internet of Things elevation monitoring nodes and tilt angle sensors deployed in the ancient tree community area. Sequentially extract the coordinate sequences of the elevation points, calculate the elevation difference and coordinate difference between adjacent points, obtain the corresponding slope direction values, judge the slope directions of each group of three consecutive elevation points. If the slope directions of the three are the same, they are classified into the same section, and integrate all the monitoring point sequences to obtain a single slope direction section number group;

[0063] After obtaining the topographic slope direction data, continuous elevation point data, and ancient tree numbers recorded in the Internet of Things elevation monitoring nodes and tilt angle sensors deployed in the ancient tree community area, first sort the numbers of each monitoring node, and extract the coordinates and elevation information of each point. Let P1, P2, and P3 be three consecutive points, with coordinates P1(10.0, 20.0, 102.4), P2(13.0, 22.0, 103.2), and P3(16.5, 25.0, 104.1). First, calculate the elevation difference from P1 to P2 as 0.8 meters, and the horizontal distance is:

[0064] ;

[0065] Get the slope is: ;

[0066] Calculate the slope direction angle according to the coordinate difference Δx = 3.0, Δy = 2.0: .

[0067] Calculate the elevation difference between P2 and P3 in the same way, which is 0.9 meters, and the horizontal distance is: ;

[0068] Azimuth is: ;

[0069] If P4 is set as (20.0, 28.0, 104.9), then the azimuth from P3 to P4 is also: ;

[0070] To determine whether it is a unified slope direction section, set the azimuth consistency threshold to ±5° (this value is determined by the undulation degree of the slope surface in the field investigation area. It is recommended to take 3° for gentle slope areas, 5° - 7° for hilly areas, and 5° is set here). If the maximum and minimum difference of the three azimuths is less than or equal to this threshold, it is judged as a consistent section. For example, the maximum and minimum difference of the azimuths 33.7°, 40.6°, 40.6° is 6.9°, which exceeds the threshold and does not belong to the consistent slope direction section, while the difference of the three azimuths 40.1°, 40.6°, 41.3° is 1.2°, which can be classified into the same slope direction section. Execute this judgment for all consecutive three-point groups in turn, and mark those that meet the conditions, such as A1, A2, B1, etc., and finally form a global single slope direction section number group.

[0071] S112: According to the coordinate data of the single slope direction section number group and the ancient tree numbers, perform matching judgment, screen the target numbers of the ancient tree coordinate points falling in the corresponding slope section number group, and according to the elevation difference and horizontal distance between the monitoring points in the slope section, use the formula:

[0072] ;

[0073] Calculate the inclination angle direction offset of the th ancient tree in the slope section , and integrate to obtain the partition inclination angle direction list;

[0074] Among them, represents the reciprocal of the number of point pairs used to calculate the average slope angle in the slope section , represents the sum of the th to the th elevation point pairs in the slope section , is the slope angle value (in degrees) of the th point pair in the slope section , Indicates the slope section The elevation difference (in meters) of the Indicates the slope section The horizontal distance (in meters) of the Indicates the ancient tree tilt direction angle (in degrees) recorded by the tilt sensor, Indicates the slope section Total number of elevation point pairs participating in the calculation within the section.

[0075] According to the coordinate data of the single slope direction section number group and the ancient tree number, perform spatial matching. Compare the coordinates of each ancient tree, such as G1(35.2, 47.8), with the section boundary range. If its coordinates fall within the boundary defined by slope section A1, then classify it into slope section A1. Extract the tilt angle value β1 = 12.6° corresponding to G1. At this time, relevant monitoring point data within slope section A1 needs to be called for slope angle calculation. Suppose there are 3 groups of point pairs in the monitoring points of slope section A1, and the elevation differences are respectively 、 、 , and the horizontal distances are respectively 、 、 , then according to the formula: ;

[0076] The calculation result is: , , ;

[0077] The average value of the three is: ;

[0078] Direction offset is: ;

[0079] In order to determine whether this offset reflects an abnormal state, a direction offset judgment threshold needs to be set. According to the historical observation data of ancient trees, under the condition of no external force interference and wind erosion, the tilt change range of perennial ancient trees is basically controlled within ±3°. Therefore, the judgment threshold is set to 3°. If the θ value ≤ 3°, it is marked as a stable state, otherwise it is marked as a tilt offset risk. The current calculation result is 2.72°, which is lower than this threshold. Therefore, G1 is classified as a stable ancient tree. Calculate and classify all ancient tree numbers according to this process, and finally establish a partition tilt angle direction list.

[0080] Please refer to Figure 3 , the specific steps for obtaining the root system spatial distribution information are as follows:

[0081] S211: Based on the ancient tree numbers in the list of partition tilt angles, retrieve the soil moisture sensor data deployed corresponding to each ancient tree number, identify the horizontal coordinate positions corresponding to the humidity change boundaries, calculate the maximum horizontal coordinate difference between the sensor points by locating all the sensor points at the boundary water content, and obtain the horizontal extension distance of the ancient tree roots;

[0082] First, confirm the uniqueness and sort the numbered content. Retrieve one by one the identification of the soil moisture sensors bound to each ancient tree number in the deployment area. After confirming the unique association relationship of the humidity sensors under this number, extract the time series data recorded by the humidity sensors. This type of data record includes sampling time, measurement point coordinates, humidity values, depth levels, etc. It is necessary to first screen out invalid records and missing data, then select the record group within a specific time window from the valid humidity sequence, and extract the data points with water content change values lower than the critical threshold for positioning. For example, set the critical water content threshold to 20%. When the recorded value of a certain monitoring point continuously drops below this threshold for more than 3 consecutive measurement cycles, it is marked as a boundary water content point. Horizontally extract the coordinates of all such points to construct a two-dimensional projection coordinate set, and then calculate the paired combinations of the horizontal straight-line distances between all points in this projection coordinate set to identify the farthest horizontal span between all boundary points under this number. This span reflects the maximum boundary area of soil water conduction and is used to infer the lateral extension range of the roots corresponding to the ancient tree number. Assume the ancient tree number is G3, and its soil humidity boundary point projections are (12.1, 28.4), (15.2, 31.0), (18.6, 35.5), then the maximum distance is calculated as: ; This span is the horizontal extension distance value of the roots of ancient tree number G3. Repeat the above processing process for all ancient tree numbers, and finally summarize the maximum horizontal spans under all numbers to establish the horizontal extension distance value of the ancient tree roots.

[0083] S212: Based on the horizontal extension distance of the ancient tree roots and the spatial coordinate data corresponding to the ancient tree numbers, calculate the Euclidean space distance between ancient trees using the horizontal and vertical coordinate differences. Process each group of ancient tree number combinations using the formula:

[0084] ;

[0085] Calculate the spatial straight-line distance between ancient tree and ancient tree , and jointly collect the spatial straight-line distance between ancient tree and ancient tree and the horizontal extension distance value of the ancient tree roots to obtain a group of root comparison spatial distance values between ancient trees;

[0086] Among them, and are respectively the horizontal and vertical coordinate values of the ancient tree . and are respectively the horizontal and vertical coordinate values of the ancient tree .

[0087] Select the combination pairs between each group of ancient tree numbers, extract the horizontal and vertical coordinate values of the ancient trees respectively. According to the principles of spatial geometry, calculate the Euclidean space straight-line distance through the coordinate differences. On this basis, collate the straight-line distance and the root system horizontal extension distance value side by side for subsequent spatial distribution statistical processing. For example, assume the coordinates of ancient tree G1 are (10.0, 15.0), the coordinates of ancient tree G2 are (14.8, 19.3), the root system extension distance corresponding to G1 is 7.5 meters, and that of G2 is 6.8 meters. Then the spatial straight-line distance between the two is: . Calculate the distance for all combinations of ancient tree numbers in sequence and record the corresponding values, and at the same time bring in the root system horizontal extension distance values under each of the aforementioned numbers. For example, for G1 it is 7.5 meters and for G2 it is 6.8 meters. Incorporate these two values and the calculated 6.44-meter spatial straight-line distance into the same structured record table to form a complete distance relationship data set, and finally obtain the root system comparison spatial distance value group between ancient trees.

[0088] S213: According to the root system comparison spatial distance value group between ancient trees, statistically analyze all the spatial distance values of the ancient trees within the same slope section number, calculate the maximum span value, minimum span value and span difference range respectively, organize the statistical results under each slope section number into independent entries, and establish the root system spatial distribution information;

[0089] According to the root system comparison spatial distance value group between ancient trees, first identify the slope section number to which each group of ancient tree numbers belongs, classify and process all the distance value groups according to the slope section, incorporate the spatial distance values corresponding to all the combinations of ancient tree numbers included in each slope section number into their respective statistical sets, perform the operations of maximum value extraction, minimum value extraction and range calculation on each set. The range is the difference between the maximum value and the minimum value, and record the fluctuation range of the spatial distance values of the ancient trees within this slope section. For example, if the slope section number is S2, and the spatial distances of the included number combinations are 6.3 meters, 8.1 meters, 5.7 meters, and 7.4 meters respectively, then the maximum value is 8.1 meters, the minimum value is 5.7 meters, and the fluctuation range is 8.1 - 5.7 = 2.4 meters. Then bind this result with the slope section number for recording to form a structured information item. Perform the same processing process for all slope section numbers, correspond the statistically calculated range values with the original numbers, and finally summarize them in tabular form to establish the root system spatial distribution information

[0090] Please refer toFigure 4 , the steps for obtaining the temperature-humidity correlation response record sequence are specifically as follows:

[0091] S311: Obtain the span range in the root system spatial distribution information, compare each slope section with a set threshold, screen the slope section numbers with a span range exceeding the threshold, call the soil moisture sensor data bound to the ancient tree numbers, extract the humidity change sequence within a specified time period, identify the time point corresponding to the maximum increase in the rapidly rising section of humidity after rainfall, and obtain the set of response delay node time points;

[0092] First, sort the spatial distribution fluctuation ranges under all slope section numbers, and set the fluctuation range threshold to 2.0 meters. If the root system span range difference of a certain slope section is greater than this threshold, it is considered that there is an abnormal fluctuation in its root system spatial distribution. For example, if the maximum spatial span of the ancient trees in slope section S3 is 9.3 meters and the minimum is 6.4 meters, then the fluctuation range is 2.9 meters, exceeding the threshold, and S3 is determined as an abnormal slope section. Extract all the ancient tree numbers belonging to slope section S3, such as G12, G14, G16, obtain the soil moisture sensor identifiers corresponding to each ancient tree number, retrieve the humidity change record data of each sensor within the specified time range, select a continuous monitoring time period such as from July 10, 2024 to July 20, 2024, split the daily humidity change trend curve, calculate the difference for each group of humidity sequences, judge the humidity change rate by calculating the difference between the humidity value of the current day and the previous day. If the humidity rising rate is the largest at a certain time point, it is marked as the main peak time point of humidity rise. For example, the humidity of ancient tree G12 rises from 18.1% to 22.7% at 12:00 on July 13, and the rising rate is 4.6%, which is the highest increase rate during this period and is confirmed as the response delay node of this ancient tree. Record and collect this time point, and finally establish a set of response delay node time points containing multiple ancient tree numbers.

[0093] S312: Based on the set of response delay node time points, extract the daily temperature data before and after each node, and use the formula:

[0094] ;

[0095] Calculate the time offset value between the response delay node of the ancient tree number and the maximum temperature fluctuation point where the number is located, and integrate to obtain the temperature-humidity correlation response record sequence;

[0096] Among them, represents the date of the day after the maximum temperature fluctuation point, represents the date of the day before the maximum temperature fluctuation point, and the difference between the two is the time interval, with the unit of days; represents the ancient tree number The daily temperature gradient value on the day of the response delay node, with the unit of degrees Celsius per day; Represents the daily temperature gradient value on the day of the maximum temperature fluctuation point, with the unit of degrees Celsius per day; Is the sum of the squares of the temperature gradients for two days, Represents the result of its square root, forming the normalized denominator term.

[0097] This formula jointly calculates the temperature change amount and the temperature change rate, ensuring dimensional consistency and enhancing the regulation of the temperature fluctuation influence intensity in the offset calculation.

[0098] Call the set of response delay node time points, for each ancient tree number of the response time point , extract the average daily temperature data for 3 days before and after each, form a 7-day temperature sequence, calculate the temperature difference between any two adjacent days in the sequence, identify the pair of adjacent dates with the largest absolute value, and set the central day as the temperature fluctuation node time , thereby locating the point of maximum temperature change, and then calculate the temperature gradient corresponding to the response delay node of the ancient tree respectively, as well as the temperature gradient of the maximum temperature fluctuation point corresponding to this node within the slope section , assume the ancient tree number is G12, the response delay node is July 13, 2024, and the temperature data extracted from July 10 to July 16 is: 27.4°C, 28.2°C, 28.6°C, 29.1°C, 30.3°C, 29.8°C, 29.2°C. From this, the temperature difference from July 12 to July 13 is , the maximum temperature fluctuation occurs from July 14 to July 15, and the temperature difference is , the center of the maximum fluctuation point is set as July 14, and the temperature fluctuation node time is July 14, 2024, and the corresponding dates before and after it are July 13, 2024, July 15, 2024. Substitute into the formula:

[0099] ;

[0100] Substitute the known values, , , , and we get:

[0101] ;

[0102] Obtain the time offset value corresponding to the ancient tree G12 is 0.7071 days, or about 17 hours. The ancient tree is numbered G12 and the response node time is July 13, 2024, time of maximum temperature fluctuation July 14, 2024, and the offset value of 0.7071 were uniformly included in the record sequence, and finally the corresponding information of all ancient trees was sorted out to establish a temperature and humidity correlation response record sequence.

[0103] See also Figure 5 ,The specific steps for obtaining the gravity offset type annotation dataset are:

[0104] S411: According to the temperature and humidity correlation response record sequence, the old tree numbers whose time offset interval values ​​are greater than the set time difference threshold are screened, the tilt direction data of the corresponding old tree is called, and the main direction data of the slope section to which it belongs is extracted. The tilt direction and the main direction of the slope section are respectively expressed as angles, and the angle value between the tilt direction of each old tree and the main direction of the slope section is calculated by the angle difference, so as to obtain the tilt main direction angle set;

[0105] First, we screen out the set of ancient tree numbers whose time offset interval is greater than the set time difference threshold. The set time difference threshold is in days and is set to 1.2 days based on the sample data. The threshold is derived from the 75% quantile of the statistical value of the difference in temperature-humidity response data over the years. It is used as the identification boundary of abnormal response behavior. If the time offset of an ancient tree is 1.5 days, it will be selected and included in the analysis queue. For example, the offsets of ancient trees numbered G08, G12, and G17 are 1.5, 1.6, and 1.9 days, respectively, which all meet the threshold conditions. Then, we obtain the inclination direction data recorded for each number. The inclination direction is calculated by the projection of the top and base of the trunk in the line segment direction angle in degrees. For example, the inclination direction of G08 is 72.3°, and that of G12 is 148.5°. Then, we extract the slope section corresponding to the ancient tree. The main direction information is obtained by calculating the angle between the major axis of the slope section and the horizontal baseline. Assuming that the main direction of the slope section where G08 is located is 70.0° and that of G12 is 155.0°, each ancient tree corresponds to two direction angles. The angle is calculated by the difference between the two, and the minimum angle judgment strategy is adopted according to the actual geographical system. That is, if the difference between the angles of the two directions exceeds 180°, the difference between the complementary angles is taken to avoid confusion between positive and negative angles. For example, the angle value of G08 is 2.3°, and the angle value of G12 is 6.5°. All ancient tree numbers are arranged in correspondence with their main tilt angles. The angles between the tilt directions of each ancient tree and the main direction of the slope section are all numerical data, ranging from 0° to 180°. The smaller the angle, the more consistent the tilt is with the main direction of the terrain. This operation finally obtains the main tilt angle value set.

[0106] S412: Obtain the ancient tree numbers concentrated in the inclined main direction angle, collect the elevation data of the foot and top of the slope around the point corresponding to each ancient tree number, and determine whether the inclined direction and the height difference change direction are distributed in the opposite direction. If the inclined direction of the ancient tree is consistent with the local slope direction, it is marked as the natural gravity consistent type; if there is a deviation, it is marked as the direction interference type. Summarize the marking results according to the ancient tree numbers and establish a gravity offset type marking data set;

[0107] Call each ancient tree number in the inclined main direction angle value set, and collect the surrounding height difference information. The height difference data comes from the elevation difference between the surface elevation of the point where the ancient tree is located and the elevations of the two poles at the top and bottom of the slope on the nearest main slope. The slope direction is set as the elevation collection line by extending the straight line in the inclined direction. Set the sampling interval as the positions of the top and bottom of the slope 10 meters above and below the ancient tree point. The elevation data is measured using RTK or a laser rangefinder correspondingly. For example, in the measurement point of the ancient tree numbered G12, the elevation of the foot of the slope is 238.5 meters, and the elevation of the top of the slope is 243.1 meters. Then the height difference of the slope section is 4.6 meters. At the same time, the inclined direction is 148.5°, the main direction of the slope section is 155.0°, and the included angle is 6.5°. According to whether the inclined direction and the height difference change direction are opposite, a trend judgment is made. If the deviation direction of the ancient tree inclination angle is consistent with the rising direction of the height difference, it means that the ancient tree inclines towards the higher terrain. If the directions are consistent, it means that the gravity acts in the forward growth direction. At this time, it is judged as the natural gravity consistent type. If the included angle direction is opposite to the rising direction of the height difference, it is judged as the direction interference type. Further, the inclination direction, slope direction, and height difference difference of each ancient tree are combined for marking. For example, if the inclination direction of G12 is 148.5° and the rising direction of the slope section elevation is also close to 150°, it is judged as the natural gravity consistent type; if the slope direction of the ancient tree numbered G17 is 210°, but the inclination direction is 32°, which deviates from the slope direction trend, it is judged as the direction interference type. Finally, the judgment results are sorted out uniformly. The judgment basis used is whether the angle trend is consistent and the comparison with the height difference change direction. If the trend is consistent, that is, the offset is the same as the height difference direction, and the opposite is the action of interference factors. The marked records are used as the gravity offset type marking data set output by the system.

[0108] Please refer to Figure 6 , and the specific steps for obtaining the environmental interaction monitoring set are as follows:

[0109] S511: Based on all the ancient tree numbers in the gravity offset type marking data set, sequentially extract the corresponding spatial coordinate information, the slope section number and the slope section direction value of each number, the position relationship of the point in the slope section and the elevation section identifier corresponding to the slope section boundary. Through standardizing and coding fields such as the number, geographical affiliation, spatial distribution characteristics, and type marking of the point, they are uniformly sorted into single-line record data and merged according to the field format to generate an ancient tree spatial point structure table;

[0110] First, determine the unique identification number of the ancient tree. The number source is the number field corresponding to each ancient tree in the historical surveying and mapping data, and this field forms a one-to-one correspondence with the spatial coordinate information of the ancient trees in the database. The specific extraction process is to read the "number" field for each record in the dataset, and then call its spatial position field to obtain the two-dimensional coordinate values X and Y. Suppose an ancient tree numbered A001 has coordinates X equal to 372415.26 and Y equal to 4079428.58. After reading the coordinate information of all numbered data in this way, then perform a position matching judgment with the X and Y values and the DEM digital elevation model to obtain the slope section number and slope section direction value where this point is located. Among them, the slope section number is obtained by overlaying the point coordinates with the slope section vector layer and calculating the inclusion relationship between the point and the polygon in spatial vector analysis to confirm which slope section the point belongs to. The direction value is obtained by extracting the slope direction value of the central unit of the slope aspect raster data in the area where the slope section is located. If the slope section direction value matched by point A001 is 315 degrees, then this ancient tree is in the northwest slope section. On this basis, further judge the position relationship of the ancient tree in the slope section. Adopt the method of dividing the total length of the slope section into upper, middle, and lower three sections along the slope direction starting from the central axis of the slope section. Divide the total length of the slope section into three sections, and judge the relative position of the projection of the ancient tree point on the line segment. If the projection position is in the area above 75% of the segment length, it is judged as the "lower section". Then call the corresponding slope section boundary elevation section to read the upper and lower boundary elevations of the slope section. Suppose the upper and lower boundary elevations of the slope section are 327.6 meters and 312.2 meters respectively. Calculate the relative elevation interval of the ancient tree point as the percentage of the difference in the slope section elevation interval, and then divide it into three levels of "high", "medium", and "low", corresponding to the 33% interval in the section respectively. The elevation of point A001 is 314.0 meters, so it is in the "low" section. Subsequently, organize the information such as the point number, the corresponding slope section number, coordinate values, slope section direction, the section where it is located, and the elevation section into structured data, construct a single-row data structure, where the field format adopts a unified standardization method. For example, the number field is a 10-character type, the direction field is represented by an integer from 0 to 360, and the section relationship is represented by an enumerated value from 1 to 3. Complete the information integration through field merging operations, and finally generate the ancient tree spatial point position structure table. Each row in this structure table completely describes the spatial and affiliated information of an ancient tree.

[0111] S512: Call the structure records of each ancient tree number in the ancient tree spatial point position structure table, respectively associate and extract the geometric parameters of the corresponding slope section, the horizontal diameter-depth ratio of the ancient tree roots, the response delay time value in the record, and the tilt type annotation field. Reorganize the information according to the ancient tree number, and organize it into a structured data group indexed by a single number. Each group of records completely covers the environmental interaction element content of this point, and establish an environmental interaction monitoring set;

[0112] Call each record in the aforementioned ancient tree spatial point structure table, read the ancient tree structure information item by item with the number as the index item, and extract the geometric parameter information of the slope segment corresponding to this number. The geometric parameters include slope, slope length, and slope segment width. Specifically, call the slope segment attribute information recorded in the slope segment vector layer, where the slope is the angle between the center line of the slope segment and the horizontal plane, and the unit is degree. Assume that the slope of a slope segment numbered A001 is 17 degrees, the slope length is 96.3 meters, and the slope width is 32.5 meters. After reading, write it into the structure group of the ancient tree number through dictionary matching. Continue to extract the horizontal diameter-depth ratio of the ancient tree roots. This value is collected through root system survey data. Assume that the horizontal expansion radius of the roots at point A001 is 2.1 meters and the downward depth of the roots is 1.2 meters. Then the diameter-depth ratio is 2.1 divided by 1.2, and the result is one to one point seven five. This value is recorded in this structure record. Then call the ancient tree response delay time value, which comes from the maximum lag duration of the tree body response in the tilt sensor, and the unit is second. Assume that the delay time of A001 is 27 seconds. Further extract the tilt type annotation of this point. This field is represented in encoded form. For example, "0" represents no tilt, "1" represents mild tilt, "2" represents moderate tilt, and "3" represents severe tilt. Assume that A001 is moderately tilted, then assign a value of 2. Organize the above information in the order of fields to form a structured data group. The field format includes number, slope, slope length, slope width, diameter-depth ratio, delay time, tilt level, etc. After completing the data combination, use the number as the only index mark to realize the attribution of the data group. Finally, each group of records completely covers the environmental interaction parameter content of this point, and constructs an environmental interaction monitoring set.

[0113] An Internet of Things-based dynamic monitoring system for the growth environment of ancient trees, characterized in that, according to the Internet of Things-based dynamic monitoring method for the growth environment of ancient trees, the system includes:

[0114] The elevation monitoring and tilt angle data processing module obtains the topographic slope direction, elevation continuous points, and ancient tree numbers recorded in the Internet of Things elevation monitoring nodes and tilt angle sensors deployed in the ancient tree community area, divides the single slope direction section, and generates a list of partition tilt angle directions;

[0115] The root system spatial distribution calculation module calculates the spatial straight-line distance between adjacent ancient trees based on the ancient tree numbers in the list of partition tilt angle directions, statistically analyzes the fluctuation range of the distance distribution by slope segment, and generates root system spatial distribution information;

[0116] The humidity change and temperature-humidity correlation response analysis module obtains the ancient tree numbers with a span range exceeding the preset threshold in the root system spatial distribution information, extracts the humidity change information at a specified time, establishes the relationship between the response delay node and the temperature fluctuation, and generates a sequence of temperature-humidity correlation response records;

[0117] The trend and offset analysis module determines whether there is an opposite trend between the slope difference and the inclination angle and labels the trend type according to the ancient tree numbers with a time offset interval greater than the set time difference threshold in the temperature-humidity correlation response record sequence, and generates a gravity offset type annotation dataset;

[0118] Based on all the annotated points in the gravity offset type annotation dataset, the environmental interaction monitoring output module outputs an environmental interaction monitoring set indexed by the ancient tree numbers.

[0119] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution content of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A dynamic monitoring method for the growth environment of ancient trees based on the Internet of Things, characterized in that: The following steps are involved: S1: Obtain the terrain slope direction, continuous elevation points and ancient tree numbers recorded in the IoT elevation monitoring nodes and tilt angle sensors deployed in the ancient tree community area, divide the single slope direction section, and generate a partitioned tilt angle direction list; S2: Based on the ancient tree numbers in the partitioned inclination angle direction list, the spatial straight-line distance between adjacent ancient trees is calculated, and the fluctuation range of the distance distribution is statistically analyzed according to the slope section to generate the root system spatial distribution information; S3: Obtain the numbers of ancient trees whose span range exceeds a preset threshold in the root system spatial distribution information, extract humidity change information at a specified time, establish a relationship between a response delay node and temperature fluctuation, and generate a temperature-humidity correlation response record sequence; The steps for obtaining the temperature and humidity associated response record sequence are specifically as follows: S311: Obtain the span range in the root system spatial distribution information, compare each slope section with a set threshold, filter the slope section numbers whose span range exceeds the threshold, call the soil moisture sensor data bound to the ancient tree number, extract the humidity change sequence within the specified time period, identify the time point corresponding to the maximum increase in the humidity rising section after rainfall, and obtain the response delay node time point set; S312: Based on the response delay node time point set, extract the daily temperature data before and after each node, using the formula: ； Calculate the The response delay node of the soil moisture sensor bound to the ancient tree number is The time offset between the maximum temperature fluctuation points of the soil moisture sensors bound to the ancient tree numbers , integrate to get the temperature and humidity correlation response record sequence; in, Indicates The soil moisture sensor bound to the ancient tree number responds to the daily temperature gradient value of the delayed node on the day of the day. Indicates The daily temperature gradient value of the maximum temperature fluctuation point of the soil moisture sensor bound to the ancient tree number, Indicates the date of the day after the point of maximum temperature fluctuation. Indicates the date of the day before the maximum temperature fluctuation point; S4: according to the numbers of the ancient trees whose time offset interval in the temperature and humidity correlation response record sequence is greater than the set time difference threshold, determine whether the slope difference and the inclination angle show an opposite trend and mark the trend type, and generate a gravity offset type marked data set; S5: Based on the gravity offset type, all the marked points in the data set are marked, and an environmental interaction monitoring set indexed by the ancient tree number is output.

2. The method for dynamic monitoring of ancient tree growth environment based on the Internet of Things according to claim 1 is characterized in that: The partitioned inclination angle direction list specifically includes slope section number information, ancient tree inclination direction angle value, and the correspondence between the ancient tree coordinates and the slope section to which they belong. The root spatial distribution information includes the horizontal extension distance value of the root system, the ancient tree spacing ratio, and the ratio fluctuation range within the slope section. The temperature and humidity associated response record sequence specifically refers to the main peak time point of humidity rise, the corresponding temperature fluctuation value, and the temperature offset time difference. The gravity offset type annotation data set specifically includes the ancient tree inclination direction type, the slope difference and inclination angle comparison results, and the offset type label. The environmental interaction monitoring set includes the ancient tree number index, slope section division information, root system structure ratio, climate response time value, and spatial offset type identification.

3. The method for dynamic monitoring of ancient tree growth environment based on the Internet of Things according to claim 1 is characterized in that: The steps for obtaining the partition tilt angle direction list are specifically as follows: S111: obtaining terrain slope data, continuous elevation point data and ancient tree numbers recorded in the IoT elevation monitoring nodes and tilt angle sensors deployed in the ancient tree community area, extracting the coordinate sequence of the elevation points in turn, calculating the elevation difference and coordinate difference between adjacent points, obtaining the corresponding slope direction value, judging the slope direction of each group of three continuous elevation points, and if the slope directions of the three are consistent, they are classified into the same section, integrating all monitoring point sequences, and obtaining a single slope section number group; S112: Matching and judging the coordinate data of the single slope section number group and the ancient tree number, selecting the target number whose coordinate point of the ancient tree falls in the corresponding slope section number group, and using the formula: ； Calculate slope Middle The tilt angle direction deviation of the old tree , integrate to get the partition tilt angle direction list; in, Indicates The reciprocal of the number of point pairs used to calculate the average slope angle in a slope segment, Indicates Slope To Sum the height point pairs, For the The first The slope angle value of the point pair, Indicates The first The elevation difference of a pair of points, Indicates The first The horizontal distance between the point pairs, Indicates The tilt direction angle recorded by the tilt sensor of the ancient tree, Indicates The total number of elevation point pairs involved in the calculation within a slope section.

4. The method for dynamic monitoring of ancient tree growth environment based on the Internet of Things according to claim 1 is characterized in that: The steps for obtaining the root system spatial distribution information are specifically as follows: S211: Based on the ancient tree numbers in the partitioned tilt angle direction list, the soil moisture sensor data deployed corresponding to each ancient tree number is retrieved, the horizontal coordinate position corresponding to the moisture change boundary is identified, and the maximum horizontal coordinate difference between the sensor points is calculated by locating all the sensor points at the boundary moisture content, so as to obtain the horizontal extension distance of the ancient tree root system; S212: Based on the spatial coordinate data corresponding to the horizontal extension distance of the ancient tree root system and the ancient tree number, the Euclidean spatial distance between the ancient trees is calculated using the horizontal and vertical coordinate differences, and each group of ancient tree number combinations is processed using the formula: ； Calculating the first ancient tree The second oldest tree The straight-line distance between and the first ancient tree The second oldest tree The spatial straight-line distance of the ancient trees and the horizontal extension distance of the ancient tree roots are combined to obtain the spatial distance value group of the ancient tree root system comparison; in, , The first ancient tree The horizontal and vertical coordinate values ​​of , The second oldest tree The horizontal and vertical coordinate values ​​of S213: According to the spatial distance value group for comparing the root systems of the ancient trees, the spatial distance values ​​of all the ancient trees within the same slope section number are counted, and the maximum span value, the minimum span value and the span difference range are calculated respectively. The statistical results under each slope section number are organized into independent entries to establish the spatial distribution information of the root system.

5. The method for dynamic monitoring of ancient tree growth environment based on the Internet of Things according to claim 1 is characterized in that: The steps for obtaining the gravity offset type annotation dataset are specifically as follows: S411: According to the temperature and humidity correlation response record sequence, the old tree numbers whose time offset interval values ​​are greater than the set time difference threshold are screened, the inclination direction data of the corresponding old tree is called, and the main direction data of the slope section to which it belongs is extracted, and the inclination direction and the main direction of the slope section are respectively expressed as angles, and the angle value between the inclination direction of each old tree and the main direction of the slope section is calculated by the angle difference, so as to obtain the inclination main direction angle set; S412: Obtain the numbers of the ancient trees in the main inclination angle concentration, collect the elevation data of the slope foot and slope top around the corresponding points of each ancient tree number, and determine whether the inclination direction and the elevation difference change direction are inversely distributed. If the inclination direction of the ancient tree is consistent with the local slope direction, it is marked as a natural gravity consistent type. If there is a deviation, it is marked as a direction interference type. The annotation results are summarized according to the ancient tree number, and a gravity offset type annotation data set is established.

6. The method for dynamic monitoring of ancient tree growth environment based on the Internet of Things according to claim 1 is characterized in that: The steps for obtaining the environment interaction monitoring set are specifically as follows: S511: Based on all the ancient tree numbers in the gravity offset type annotation data set, the spatial coordinate information corresponding to each number, the slope section number and slope section direction value, the position relationship of the point in the slope section and the elevation section identifier corresponding to the slope section boundary are extracted in sequence, and the number, geographical affiliation, spatial distribution characteristics and type annotation fields of the point are standardized and coded, unified into a single-line record data, merged according to the field format, and the ancient tree spatial point structure table is generated; S512: Call the structural record of each ancient tree number in the ancient tree spatial point structure table, and respectively associate and extract the geometric parameters of the corresponding slope section, the horizontal diameter-to-depth ratio of the ancient tree root system, the response delay time value in the record and the tilt type annotation field, reorganize the information according to the ancient tree number, and organize it into a structured data group indexed by a single number. Each group of records completely covers the environmental interaction element content of the point, and establishes an environmental interaction monitoring set.

7. A dynamic monitoring system for the growth environment of ancient trees based on the Internet of Things, characterized in that: According to the method for dynamic monitoring of the growth environment of ancient trees based on the Internet of Things according to any one of claims 1 to 6, the system comprises: The elevation monitoring and tilt angle data processing module obtains the terrain slope direction, continuous elevation points and ancient tree numbers recorded in the IoT elevation monitoring nodes and tilt angle sensors deployed in the ancient tree community area, divides the single slope direction section, and generates a partitioned tilt angle direction list; The root system spatial distribution calculation module calculates the spatial straight-line distance between adjacent ancient trees based on the ancient tree numbers in the partitioned inclination angle direction list, calculates the fluctuation range of the distance distribution according to the slope section, and generates the root system spatial distribution information; The humidity change and temperature-humidity correlation response analysis module obtains the number of ancient trees whose span range exceeds a preset threshold in the root system spatial distribution information, extracts the humidity change information at a specified time, establishes the relationship between the response delay node and the temperature fluctuation, and generates a temperature-humidity correlation response record sequence; The trend and offset analysis module determines whether the slope difference and the inclination angle show an opposite trend and marks the trend type according to the number of the ancient tree with a time offset interval greater than the set time difference threshold in the temperature and humidity correlation response record sequence, and generates a gravity offset type annotation data set; The environmental interaction monitoring output module labels all the marked points in the data set based on the gravity offset type, and outputs an environmental interaction monitoring set indexed by the ancient tree number.

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