Method and system for dynamically monitoring growth environment of ancient tree based on Internet of Things

Abstract

The invention relates to the technical field of environment monitoring management, in particular to an ancient tree growth environment dynamic monitoring method and system based on the Internet of Things. According to the method, the elevation monitoring node and the inclination angle sensor are introduced, and the spatial distribution of the terrain slope direction and the ancient tree number is combined, so that the division of the slope direction section and the classification and sorting of the inclination angle direction of the ancient tree are completed, and the deficiency of the micro-terrain difference recognition capability in the traditional ecological factor collection is effectively supplemented. Pairing calculation is carried out through the space linear distance between ancient trees and root system extension data, a root system distribution map with space extension characteristics is constructed in combination with a ratio fluctuation range, indirect quantification of underground structure information is formed on the data level, and environment response capability evaluation of the root system development condition is achieved. Time migration calculation between a humidity change node and an air temperature fluctuation point is introduced, and the absorption feedback rate of a root system to climatic factors is reflected through time delay characteristics, so that the sensitivity degree of an underground system to mutational environmental factors is revealed.

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 dynamic monitoring of the growth environment of ancient trees based on the Internet of Things.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a method for dynamic monitoring of the growth environment of ancient trees based on the Internet of Things, comprising the following steps: 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; 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.

[0007] As a further scheme of the present invention, 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 system 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.

[0008] As a further solution of the present invention, the step of obtaining the partition tilt angle direction list is specifically: 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 slope 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.

[0009] As a further solution of the present invention, the step of obtaining the root spatial distribution information is 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.

[0010] As a further solution of the present invention, the steps for obtaining the temperature and humidity correlation 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 rapid humidity rise 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 point of maximum temperature fluctuation.

[0011] As a further solution of the present invention, the steps of acquiring the gravity offset type annotation data set 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, 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.

[0012] As a further solution of the present invention, the step of acquiring the environment interaction monitoring set is specifically: 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 fields such as the point number, geographical affiliation, spatial distribution characteristics and type annotation are standardized and coded, uniformly organized into a single-line record data, and merged according to the field format to generate an ancient tree spatial point structure table; 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.

[0013] A dynamic monitoring system for the growth environment of ancient trees based on the Internet of Things, characterized in that, according to the dynamic monitoring method for the growth environment of ancient trees based on the Internet of Things, 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 list of partitioned tilt angle directions; 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.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by introducing elevation monitoring nodes and tilt angle sensors, combined with the spatial distribution of terrain slope and ancient tree numbers, the division of slope sections and the classification and integration of the tilt angle direction of ancient trees are completed, which effectively supplements the lack of micro-topography difference recognition ability in traditional ecological factor collection. By pairing the spatial straight-line distance between ancient trees with the root extension data, combined with the ratio fluctuation range, a root distribution map with spatial extension characteristics is constructed, and an indirect quantification of underground structure information is formed at the data level, so as to realize the environmental response ability assessment of root development. The time offset calculation between the humidity change node and the temperature fluctuation point is introduced to construct the response coupling sequence of the humidity main peak and the temperature change, and the absorption feedback rate of the root system to the climate factor is reflected by the time delay characteristics, thereby revealing the sensitivity of the underground system to the sudden change of environmental factors. Through the angle analysis of the tilt angle and the terrain slope, combined with the height difference between the top and the foot of the slope, the corresponding relationship between the force direction of the ancient tree and the environmental landform is extracted, and the type of attention offset is marked, which is helpful to judge whether the tilt of the ancient tree is caused by external force interference or terrain trend guidance. Ultimately, through standardized coding, multi-factor parameters such as spatial coordinates, elevation differences, response delays, root structure ratios, etc. are unified into the same data unit, realizing integrated monitoring of the environment, structure, and response behavior, and significantly improving the ability to identify and warn of the dynamic evolution of ancient tree growth stability and environmental adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the main steps of the present invention; Figure 2 This is a flow chart of step S1 of the present invention; Figure 3 This is a flow chart of step S2 of the present invention; Figure 4 This is a flow chart of step S3 of the present invention; Figure 5 This is a flow chart of step S4 of the present invention; Figure 6 This is a flow chart of step S5 of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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 intended to limit the present invention.

[0017] 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.

[0018] See also Figure 1 The present invention provides a technical solution: a method for dynamic monitoring of the growth environment of ancient trees based on the Internet of Things, comprising the following steps: 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; 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; 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; 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; S5: Based on the gravity offset type annotation data set, all the annotated points are annotated, and a point data structure table is constructed. The slope information, root ratio, response delay time, and tilt type combination records in the spatial segment are organized with a single point as a unit, and an environmental interaction monitoring set is output with the ancient tree number as the index; The partitioned inclination angle direction list specifically includes the slope section number information, the ancient tree inclination direction angle value, 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 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 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 structure ratio, climate response time value, and spatial offset type identification.

[0019] See also Figure 2 , the specific steps for obtaining the partition tilt angle direction list are: 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; After obtaining the 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, the monitoring node numbers are first sorted, and the coordinates and elevation information of each point are extracted. Suppose P1, P2, and P3 are three consecutive points with coordinates of P1 (10.0, 20.0, 102.4), P2 (13.0, 22.0, 103.2), and P3 (16.5, 25.0, 104.1). First, the elevation difference from P1 to P2 is calculated to be 0.8 meters, and the horizontal distance for: ; Get the slope for: ; Calculate the slope direction angle based on the coordinate difference Δx=3.0, Δy=2.0 : .

[0020] The elevation difference between P2 and P3 is calculated in the same way as above, which is 0.9 meters. for: ; Direction for: ; If P4 is (20.0, 28.0, 104.9), then the direction angle from P3 to P4 is Also for: ; To determine whether it is a uniform slope section, the direction consistency threshold is set to ±5° (this value is determined by the degree of slope fluctuation in the field survey area. The recommended threshold for the gentle slope area is 3°, and the recommended threshold for the hilly area is 5°~7°, which is set to 5° here). If the maximum and minimum difference of the three-segment direction angle is less than or equal to the threshold, it is determined to be a uniform segment. For example, the maximum and minimum difference of the direction angles of 33.7°, 40.6°, and 40.6° is 6.9°, which exceeds the threshold and is not classified as a uniform slope section. The difference of the three segments with direction angles of 40.1°, 40.6°, and 41.3° is 1.2°, which can be classified as the same slope section. The judgment is performed on all consecutive three-point groups in turn, and those that meet the conditions are numbered, such as A1, A2, B1, etc., and finally a single slope section number group for the entire domain is formed.

[0021] S112: Match and judge the coordinate data of the single slope section number group and the ancient tree number, select the target number whose coordinate point of the ancient tree falls in the corresponding slope section number group, and use the formula according to the elevation difference and horizontal distance between the monitoring points in the slope section: ; Calculate slope Middle The tilt angle direction deviation of the old tree , integrate to get the partition tilt angle direction list; in, Indicates slope section The inverse of the number of point pairs used to calculate the average slope angle in , Indicates the slope section Middle To Sum the height point pairs, For slope Middle The slope angle value of each point pair (in degrees), Indicates slope section Middle The elevation difference between the point pairs (in meters), Indicates slope section Middle The horizontal distance between the point pairs (in meters), Represents ancient trees The tilt direction angle recorded by the tilt sensor (in degrees), Indicates slope section The total number of elevation point pairs involved in the calculation.

[0022] According to the coordinate data of the single slope segment number group and the ancient tree number, the coordinates of each ancient tree, such as G1 (35.2, 47.8), are compared with the segment boundary range. If its coordinates fall within the boundary defined by slope segment A1, it is classified into slope segment A1, and the inclination angle value β1 = 12.6° corresponding to G1 is extracted. At this time, the data of relevant monitoring points in slope segment A1 need to be called to calculate the slope angle. Suppose there are 3 groups of monitoring points in slope segment A1, and the elevation differences are , , The horizontal distances are , , , then according to the formula: ; Calculated: , , ; The average of the three is: ; Direction offset for: ; In order to determine whether the offset reflects an abnormal state, it is necessary to set a directional offset judgment threshold. According to the historical observation data of ancient trees, in the absence of external 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 is ≤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 the threshold. Therefore, G1 is classified as a stable ancient tree. According to this process, all ancient tree numbers are calculated and classified, and finally a partitioned tilt angle direction list is established.

[0023] See also Figure 3 ,The specific steps for obtaining root spatial distribution information are: 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 to identify the horizontal coordinate position corresponding to the moisture change boundary, and the maximum horizontal coordinate difference between the sensor points is calculated by locating all the sensor points at the boundary moisture content to obtain the horizontal extension distance of the ancient tree root system; First, the uniqueness of the number content is confirmed and sorted, and the soil moisture sensor identifier corresponding to each ancient tree number in the deployment area is retrieved one by one. After confirming the unique association of the humidity sensor under the number, the time series data recorded by the humidity sensor is extracted. This type of data record includes sampling time, measuring point coordinates, humidity value, depth layer, etc. It is necessary to first screen out invalid records and missing data, and then select the record group within a specific time window from the valid humidity sequence, and extract the data points where the moisture content change value is lower than the critical threshold for positioning. For example, the critical moisture threshold is set to 20%. When the record value of a certain monitoring point continues to be lower than this If the threshold exceeds three consecutive measurement cycles, it is marked as a boundary water point. The coordinates of all such points are extracted horizontally to construct a two-dimensional projection coordinate set. Then, the horizontal straight-line distances between all points in the projection coordinate set are calculated by pairing and combining to identify the farthest horizontal span between all boundary points under the number. This span reflects the maximum boundary area of ​​soil water conduction and is used to infer the lateral extension range of the root system under the corresponding ancient tree number. Assuming that the ancient tree number is G3, its soil moisture boundary point projections are (12.1, 28.4), (15.2, 31.0), and (18.6, 35.5). The maximum distance between the three points is Calculated as: ; This span is the horizontal extension distance value of the root system of the ancient tree number G3. Repeat the above processing flow for all ancient tree numbers, and finally summarize the maximum horizontal spans under all numbers to establish the horizontal extension distance value of the root system of the ancient tree.

[0024] 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. Each group of ancient tree number combinations is processed using the formula: ; Counting ancient trees With ancient trees The straight-line distance between and the old trees and ancient trees 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, , Ancient trees The horizontal and vertical coordinate values ​​of , Ancient trees The horizontal and vertical coordinate values ​​of .

[0025] Select the combination pairs between each group of ancient tree numbers, extract the horizontal coordinate values ​​and vertical coordinate values ​​of the ancient trees respectively, and calculate the Euclidean space straight-line distance through the coordinate difference according to the principle of spatial geometry. On this basis, the straight-line distance and the horizontal extension distance of the root system are grouped together for subsequent spatial distribution statistical processing. For example, if the coordinates of the ancient tree G1 are (10.0, 15.0) and the coordinates of the ancient tree G2 are (14.8, 19.3), the corresponding root extension distance of G1 is 7.5 meters and that of G2 is 6.8 meters, then the spatial straight-line distance between the two is for: ; Calculate the distances for all combinations of ancient tree numbers in turn and record the corresponding values, and at the same time bring in the horizontal extension distance value of the root system under each of the aforementioned numbers, such as G1 is 7.5 meters and G2 is 6.8 meters. These two values ​​​​are included in the same structural record table together with the calculated 6.44-meter spatial straight-line distance to form a complete distance relationship data set, and finally obtain a spatial distance value group for root system comparison between ancient trees.

[0026] S213: according to the spatial distance value group of root system comparison between ancient trees, the spatial distance values ​​of all ancient trees in the same slope section number are counted, and the maximum span value, the minimum span value and the span difference range are calculated respectively, and the statistical results under each slope section number are sorted into independent items to establish the spatial distribution information of the root system; According to the spatial distance value group of root system comparison between ancient trees, first identify the slope section number to which each group of ancient tree numbers belongs, classify all distance value groups according to the slope sections, and classify the spatial distance values ​​corresponding to all ancient tree number combinations contained in each slope section number into their respective statistical sets. Perform maximum value extraction, minimum value extraction and range calculation operations on each set, where the range is the difference between the maximum and minimum values, and record the fluctuation range of the spatial distance values ​​of the ancient trees in the slope section. For example, if the slope section number is S2, and the spatial distances of the number combinations included are 6.3 meters, 8.1 meters, 5.7 meters, and 7.4 meters respectively, 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 the result with the slope section number and record it to form a structured information item. Perform the same processing on all slope section numbers, correspond the statistical range values ​​to the original numbers, and finally summarize them in a table form to establish the spatial distribution information of the root system. See also Figure 4 , the specific steps for obtaining the temperature and humidity correlation response record sequence are: S311: Obtain the span range in the root system spatial distribution information, compare each slope section with the 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 rapid humidity increase section after rainfall, and obtain the response delay node time point set; First, the spatial distribution fluctuation ranges under all slope section numbers are sorted, and the fluctuation range threshold is set to 2.0 meters. If the root span range of a slope section is greater than this threshold, it is determined that there is an abnormal fluctuation in the spatial distribution of its root system. 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, the fluctuation range is 2.9 meters, which exceeds the threshold. S3 is determined to be an abnormal slope section, and all ancient tree numbers belonging to the S3 slope section, such as G12, G14, and G16, are extracted. The soil moisture sensor identifier corresponding to each ancient tree number is obtained, and the humidity change record data of each sensor within the specified time range is retrieved. Continuous The monitoring period is from July 10, 2024 to July 20, 2024. The daily humidity change trend curve is split, and the difference calculation is performed on each group of humidity sequences. The humidity change rate is determined by calculating the difference between the humidity values ​​of the current day and the previous day. If the humidity rising rate is the largest at a certain point in time, it is marked as the main peak time point of the humidity rising. For example, the humidity of the ancient tree G12 increased from 18.1% to 22.7% at 12:00 on July 13, with an increase rate of 4.6%, which is the highest increase point in this period. It is confirmed as the response delay node of the ancient tree, and the time point is recorded and collected to finally establish a response delay node time point set containing multiple ancient tree numbers.

[0027] 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 number of ancient trees Response delay node and number The time offset between the points where the maximum temperature fluctuation occurs , integrate to get the temperature and humidity correlation response record sequence; in, 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. The difference between the two is the time interval in days. Indicates the number of ancient trees The daily temperature gradient value at the response delay node on the day, in degrees Celsius per day; It indicates the daily temperature gradient value on the day of the maximum temperature fluctuation point, in degrees Celsius per day; is the sum of the squares of the temperature gradients over two days, Represents its square root result, forming the normalized denominator term.

[0028] This formula jointly calculates the temperature change and the temperature change rate to ensure dimensional unity and enhance the regulation of the intensity of temperature fluctuations in the offset calculation.

[0029] Call response delay node time point set, for each ancient tree number Response time point , extract the daily average temperature data of 3 days forward and backward to form a 7-day temperature sequence, calculate the temperature difference between any two adjacent days in the sequence, identify a pair of adjacent dates with the largest absolute value, and set the central day as the temperature fluctuation node time , in order to locate the point of maximum temperature change, and then calculate the ancient trees The temperature gradient corresponding to the response delay node , and the temperature gradient of the maximum temperature fluctuation point corresponding to this node in the slope section , assuming that 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 are: 27.4℃, 28.2℃, 28.6℃, 29.1℃, 30.3℃, 29.8℃, 29.2℃, thus the temperature difference from July 12 to July 13 is obtained. for The maximum temperature fluctuation occurred from July 14 to 15. for The maximum fluctuation point is set at July 14, and the temperature fluctuation node time is for July 14, 2024, and the corresponding dates before and after for July 13, 2024 July 15, 2024, substitute into the formula: ; Substituting in the known values, , , ,have to: ; Get 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.

[0030] See also Figure 5 ,The specific steps for obtaining the gravity offset type annotation dataset are: 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; 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.

[0031] S412: Obtain the numbers of ancient trees with concentrated tilt main direction angles, collect the elevation data of the slope foot and slope top around the corresponding points of each ancient tree number, and determine whether the tilt direction and the height difference change direction are inversely distributed. If the tilt 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; The main tilt angle value is called to collect each ancient tree number, and the surrounding elevation difference information is collected. The elevation difference data comes from the surface elevation of the ancient tree point and the elevation difference between the top and foot of the nearest main slope. The slope direction is extended according to the tilt direction to set the elevation collection line, and the sampling interval is set to the top and foot of the slope 10 meters up and down from the ancient tree point. The corresponding elevation data is measured using RTK or laser rangefinder. For example, in the measuring point numbered G12, the foot of the slope is 238.5 meters, the top of the slope is 243.1 meters, and the height difference of the slope section is 4.6 meters. At the same time, the tilt direction is 148.5°, the main direction of the slope section is 155.0°, and the angle is 6.5°. The trend is judged based on whether the tilt direction is opposite to the direction of the height difference change. If the deviation direction of the ancient tree tilt angle is consistent with the direction of the height difference increase, it means that the ancient tree tilt is consistent with the height difference increase. Towards the topography, if the direction is consistent, it means that the gravity is growing in the forward direction. At this time, it is judged as the natural gravity consistent type. If the angle direction is opposite to the direction of the elevation difference, it is judged as the direction interference type. The inclination direction, slope direction and elevation difference of each ancient tree are further marked. For example, if the inclination direction of G12 is 148.5° and the elevation rise direction of the slope section is also close to 150°, it is judged as the natural gravity consistent type; if the slope direction of number G17 is 210°, but the inclination direction is 32°, it deviates from the slope trend and is judged as the direction interference type. Finally, the judgment results are unified and sorted. The judgment basis is whether the angle trend is consistent with the direction of elevation difference change. If the trend is consistent, the offset is similar to the elevation difference direction, and the opposite is the effect of interference factors. The marked records are used as the gravity offset type annotation data set output by the system.

[0032] See also Figure 6 ,The specific steps for obtaining the environmental interaction monitoring set are: S511: Based on the gravity offset type annotation of all the ancient tree numbers in the dataset, 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 turn, and the fields such as the point number, geographical affiliation, spatial distribution characteristics and type annotation are standardized and coded, uniformly organized into a single-line record data, and merged according to the field format to generate the ancient tree spatial point structure table; First, determine the unique identification number of the ancient tree. The source of the number is the number field corresponding to each ancient tree in the historical surveying and mapping data. This field forms a one-to-one correspondence with the spatial coordinate information of the ancient tree in the database. The specific extraction process is to read the "number" field of each record in the data set, and then call its spatial position field to obtain the two-dimensional coordinate values ​​X and Y. Assume that the coordinates of an ancient tree numbered A001 are X equal to 372415.26 and Y equal to 4079428.58. After completing the coordinate information reading of all numbered data in this way, the X and Y values ​​are compared with D The EM digital elevation model is used for position matching judgment to obtain the slope section number and slope section direction value of the point. The slope section number is obtained by superimposing the point coordinates with the slope section vector layer, and calculating the inclusion relationship between the point and the polygon in the spatial vector analysis to confirm which slope section the point belongs to. The direction value is obtained by extracting the slope aspect value of the data center unit of the slope aspect grid in the area where the slope section is located. If the slope section direction value matched by point A001 is 315 degrees, the ancient tree is located in the northwest slope section. On this basis, the position relationship of the ancient tree in the slope section is further judged. The slope direction is calculated from the central axis of the slope section along the slope. The total length of the slope is divided into three sections, and the relative position of the ancient tree point projection on the line segment is determined. If the projection position is located in an area above 75% of the segment length, it is determined to be the "lower section". Then the corresponding slope boundary elevation section is called to read the elevation of the upper and lower boundaries of the slope. Assuming that the elevations of the upper and lower boundaries of the slope are 327.6 meters and 312.2 meters respectively, the relative elevation interval of the ancient tree point is calculated as the percentage of the slope elevation interval difference, and then it is divided into three levels of "high", "medium" and "low", corresponding to 33% of the interval in the section, A001 The point elevation is 314.0 meters, which means it is in the "low" section. Then, the point number, corresponding slope section number, coordinate value, slope section direction, section and elevation section are organized into structured data to construct a single-line data structure. The field format adopts a unified standardized method. For example, the number field is a 10-bit character type, the direction field is represented by an integer from 0 to 360, and the section relationship is represented by an enumeration value of 1 to 3. Information integration is completed through field merging operations, and finally a spatial point structure table of ancient trees is generated. Each row in the structure table fully describes the space and affiliation information of an ancient tree.

[0033] S512: call the structure record 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-to-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, 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 establish an environmental interaction monitoring set; Call each record in the aforementioned ancient tree spatial point structure table, use the number as the index item to read the ancient tree structure information one by one, and extract the slope section geometric parameter information corresponding to the number. The geometric parameters include slope, slope length and slope width. Specifically, call the slope section attribute information recorded in the slope section vector layer, where the slope is the angle between the center line of the slope section and the horizontal plane, and the unit is degree. Assume that a slope section numbered A001 has a slope of 17 degrees, a slope length of 96.3 meters, and a slope width of 32.5 meters. After reading, write it into the structure group of the ancient tree number through dictionary matching, and continue to extract the horizontal diameter-to-depth ratio of the ancient tree root system. This value is collected through root survey data. Assuming that the horizontal extension radius of the root system of point A001 is 2.1 meters and the downward depth of the root system is 1.2 meters, the diameter-to-depth ratio is 2.1 divided by 1.2, and the result is 1:1.75. This value is recorded. The data is recorded in the structural record, and then the response delay time value of the ancient tree is called. This value comes from the maximum lag time of the tree response in the tilt sensor, in seconds. Assume that the delay time of A001 is twenty-seven seconds, and further extract the tilt type label of the point. This field is represented in coded form, such as "0" for no tilt, "1" for mild tilt, "2" for moderate tilt, and "3" for severe tilt. Assuming that A001 is moderate tilt, the value is assigned to 2. The above information is sorted in field order to form a structured data group. The field format includes number, slope, slope length, slope width, diameter-to-depth ratio, delay time, tilt level, etc. After the data is combined, the number is used as the unique index mark to realize the data group attribution. Finally, each group of records completely covers the environmental interaction parameter content of the point to construct an environmental interaction monitoring set.

[0034] A dynamic monitoring system for the growth environment of ancient trees based on the Internet of Things, characterized in that, according to the dynamic monitoring method for the growth environment of ancient trees based on the Internet of Things, the system includes: 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 list of partitioned tilt angle directions; The root 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 by slope section, and generates root spatial distribution information; The humidity change and temperature-humidity correlation response analysis module obtains the number of ancient trees whose span range exceeds 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 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 trees 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 annotated points in the dataset based on the gravity offset type, and outputs an environmental interaction monitoring set indexed by the ancient tree number.

[0035] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them 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 of the present invention still falls 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; 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 slope The slope angle value of the point pair, Indicates The first slope The elevation difference of a pair of points, Indicates The first slope 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 temperature and humidity correlation 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 point of maximum temperature fluctuation.

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 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.

7. 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.

8. 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 7, 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.

Images