Method, device and equipment for analyzing interspecific relationship of trees and storage medium

Abstract

The present application relates to the field of tree interspecific relationship analysis, and particularly relates to a tree interspecific relationship analysis method, which comprises the following steps: obtaining tree growth data and functional trait data of each tree in a target area; obtaining a target tree and a plurality of non-target trees from the trees, and obtaining tree type data of the target tree and the non-target trees; obtaining interspecific relationship parameters between the target tree and each non-target tree according to the tree type data of the target tree and the non-target trees, the tree growth data, the functional trait data and a preset interspecific relationship parameter calculation module; and obtaining interspecific relationship analysis results between the target tree and each non-target tree according to the interspecific relationship parameters, the functional trait data and a preset interspecific relationship analysis module. In combination with the tree type data, the method avoids the shortcoming of neglecting the influence difference caused by different tree types, and more accurately analyzes the interspecific relationship between trees of different tree types.

Description

Technical Field

[0001] This invention relates to the field of interspecific relationship analysis of trees, and particularly to a method, apparatus, device, and storage medium for analyzing interspecific relationships of trees. Background Technology

[0002] Interspecific relationship analysis, based on factors such as spatial occupancy, homospecific density, and the richness and size of neighboring species, analyzes the interactions between species. It is an essential factor in assessing species growth and survival and has long been valued by ecologists. Interspecific relationships mainly include competitive and mutually beneficial interactions between neighboring individuals and target individuals, also known as "neighborhood interactions."

[0003] Currently, a common method for analyzing interspecific relationships is to use competition index models to calculate competition indices. Taking trees in a forest ecosystem as an example, the competition index is calculated based on the size and number of trees in a given neighborhood to describe the neighbor effect within the neighborhood. However, the competition index model often treats trees of different types in the neighborhood as the same type, thus ignoring the differences in the influence of non-target trees of different types on the target tree, making it difficult to accurately analyze the interspecific relationships between different tree types. Summary of the Invention

[0004] Based on this, the purpose of the present invention is to provide a method, apparatus, device, and storage medium for analyzing interspecific relationships of trees. By combining tree type data of target trees and non-target trees, and based on tree growth data and functional trait data, the interspecific relationships of target trees and non-target trees are analyzed. This avoids the drawback of ignoring the differences in influence caused by different tree types and more accurately analyzes the interspecific relationships between trees of different tree types.

[0005] In a first aspect, embodiments of this application provide a method for analyzing interspecific relationships among trees, comprising the following steps:

[0006] Obtain tree growth data and functional trait data for each tree in the target area;

[0007] Obtain the target tree and several non-target trees from the trees, and obtain the tree type data of the target tree and the non-target trees; the target tree is a tree selected from the trees in the target area; the non-target tree is a tree selected outside the target tree within the sample circle area drawn with the target tree as the center and a preset radius data;

[0008] Based on the tree type data, tree growth data, functional trait data of the target tree and several non-target trees, as well as the preset interspecific relationship parameter calculation module, the interspecific relationship parameters between the target tree and each non-target tree are obtained.

[0009] Based on the interspecific relationship parameters, functional trait data, and the preset interspecific relationship analysis module, the interspecific relationship analysis results between the target tree and each non-target tree are obtained.

[0010] Secondly, embodiments of this application provide a tree interspecific relationship analysis device, comprising:

[0011] The first acquisition module is used to acquire tree growth data and functional trait data of each tree in the target area, wherein the tree growth data includes tree diameter at breast height (DBH) data and tree height at breast height (DBH) sectional area data.

[0012] The second acquisition module is used to acquire a target tree and several non-target trees from the trees, and to acquire tree type data of the target tree and the non-target trees; the target tree is a tree selected from the trees in the target area; the non-target tree is a tree selected outside the target tree within a sample circle area drawn with the target tree as the center and a preset radius data.

[0013] The interspecific relationship parameter calculation module is used to obtain the interspecific relationship parameters between the target tree and each non-target tree based on the tree type data, tree growth data, functional trait data of the target tree and several non-target trees, as well as the preset interspecific relationship parameter calculation module.

[0014] The analysis module is used to obtain the interspecific relationship analysis results between the target tree and each non-target tree based on the interspecific relationship parameters, functional trait data and the preset interspecific relationship analysis module.

[0015] Thirdly, embodiments of this application provide a computer device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor; when the computer program is executed by the processor, it implements the steps of the interspecific relationship analysis method for trees as described in the first aspect.

[0016] Fourthly, embodiments of this application provide a storage medium storing a computer program that, when executed by a processor, implements the steps of the tree interspecific relationship analysis method as described in the first aspect.

[0017] In this application embodiment, a method, apparatus, device and storage medium for analyzing interspecific relationships of trees are provided. The method involves fusing thermal infrared images and visible light images, constructing a first sample training dataset based on the characteristics of each vegetation type, and classifying vegetation types using a vegetation classification model to obtain high-precision vegetation classification data, which is efficient and fast.

[0018] To better understand and implement this invention, the following detailed description is provided in conjunction with the accompanying drawings. Attached Figure Description

[0019] Figure 1 A flowchart illustrating a method for analyzing interspecific relationships of trees provided in one embodiment of this application;

[0020] Figure 2 This is a flowchart illustrating step S3 of a tree interspecific relationship analysis method provided in one embodiment of this application.

[0021] Figure 3 This is a flowchart illustrating step S4 of a tree interspecific relationship analysis method provided in one embodiment of this application.

[0022] Figure 4 This is a schematic diagram of the structure of a tree interspecific relationship analysis device provided in one embodiment of this application;

[0023] Figure 5 This is a schematic diagram of the structure of a computer device provided in one embodiment of this application. Detailed Implementation

[0024] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0025] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0026] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0027] Please see Figure 1 , Figure 1 The flowchart illustrates a method for analyzing interspecific relationships of trees according to an embodiment of this application. The method includes the following steps:

[0028] S1: Obtain tree growth data and functional trait data for each tree in the target area.

[0029] The execution subject of the interspecific relationship analysis method of trees is the analysis device for the interspecific relationship analysis method of trees (hereinafter referred to as the analysis device). In an optional embodiment, the analysis device can be a computer device, a server, or a server cluster composed of multiple computer devices.

[0030] In one optional embodiment, the target area is located in Jiaohe City, Jilin Province, belonging to the Zhangguangcai Mountains of the Changbai Mountain Range. The forest type is primary Korean pine broad-leaved forest, with an altitude of 689 meters and a geographical coordinate of E127°45′36.91″, 43°58′05.60″. The climate is a temperate continental climate significantly influenced by temperate monsoons, with an average annual temperature of 3.7℃. The highest annual temperature occurs in July at 21.7℃, and the lowest temperature occurs in January at -18.6℃. The average annual precipitation is 696.9 mm. The soil type in the study area is dark brown forest soil, with a thickness of 0.2-1 m and an average depth of 0.45 m. The forest vegetation belongs to the Northeast China Changbai Mountain Flora of the Sino-Japanese Forest Plant Subregion, with approximately 923 plant species belonging to 108 families. The vegetation type is mainly primary broad-leaved Korean pine forest and its secondary forest, including several different tree types, such as Korean pine, sand pine, walnut, Manchurian ash, split-leaved elm, Amur cork tree, purple linden, privet, maple, and birch, etc.

[0031] The tree growth data reflects the current growth status of the tree, and includes tree diameter at breast height (DBH) data and tree height at breast height sectional area data.

[0032] Functional traits refer to a series of plant attributes that have a potentially significant impact on plant establishment, survival, and growth. The functional trait data are data obtained by sampling various plant attributes of trees. The functional trait data include leaf area ratio data, leaf area data, leaf nitrogen content data, leaf carbon content data, carbon-nitrogen ratio data, and tree height data.

[0033] In this embodiment, the analysis device can acquire tree growth data and functional trait data of each tree in the target area input by the user, or it can download data from a preset database.

[0034] S2: Obtain the target tree and several non-target trees from the trees, and obtain the tree type data of the target tree and the non-target trees.

[0035] The target tree is a tree selected from the trees in the target area; the non-target tree is a tree selected outside the target tree within a sample circle drawn with the target tree as the center and a preset radius data.

[0036] In this embodiment, the analysis device selects healthy target species trees without obvious diseases or pests from the trees as target trees, and draws a sample circle area with the target tree as the center and a radius of 10m away from the target tree. Other trees within the sample circle area are regarded as non-target trees. At the same time, the tree type data of the target tree and the non-target trees are obtained.

[0037] S3: Based on the tree type data, tree growth data, functional trait data of the target tree and several non-target trees, and the preset interspecific relationship parameter calculation module, obtain the interspecific relationship parameters between the target tree and each non-target tree.

[0038] Interspecific relationships include competitive and mutually beneficial relationships, and the interspecific relationship parameters are used to reflect the competitive and mutually beneficial effects between the target tree and non-target trees.

[0039] In this embodiment, the analysis device obtains the interspecific relationship parameters between the target tree and each non-target tree based on the tree type data, tree growth data, functional trait data of the target tree and several non-target trees, as well as the preset interspecific relationship parameter calculation module.

[0040] Please see Figure 2 , Figure 2 The flowchart of step S3 in the tree interspecific relationship analysis method provided in one embodiment of this application includes steps S301 to S302, as follows:

[0041] S301: Obtain environmental factors associated with the tree type data of the target tree.

[0042] In this embodiment, the analysis device obtains the environmental factors associated with the tree type data of the target tree from a preset mapping table of tree types and environmental factors, based on the tree type data of the target tree.

[0043] S302: Based on the environmental factors, the tree growth data and functional trait data of the target tree and non-target trees are respectively input into the interspecific relationship parameter calculation module, and the interspecific relationship parameters between the target tree and each non-target tree are obtained according to the preset interspecific relationship parameter calculation algorithm.

[0044] The algorithm for calculating the interspecific relationship parameters is as follows:

[0045]

[0046] In the formula, Gi E represents the growth of the vertical area at breast height (BH5) in the tree growth data of the i-th target tree. i For the environmental factors corresponding to the tree type data of the preset i-th target tree, G max D represents the maximum basal area growth at breast height of the target tree. i C represents the diameter at breast height (DBH) of the i-th target tree; i，j Let x be the interspecific relationship parameter. i Let x be the functional trait data of the i-th target tree. j Let sgn() be the functional trait data of the j-th non-target tree, and sgn() be the symbolic function.

[0047] In this embodiment, the analysis device inputs the tree growth data and functional trait data of the target tree and non-target trees into the interspecific relationship parameter calculation module based on the environmental factors to obtain the interspecific relationship parameters between the target tree and each non-target tree.

[0048] Specifically, the interspecific relationship parameters include a first relationship parameter, a second relationship parameter, a third relationship parameter, a fourth relationship parameter, and a tolerance parameter, and the expressions for the interspecific relationship parameters are as follows:

[0049] C i，j =C1+C2-C x +C3+C4

[0050] In the formula, C1 is the first relation parameter, C2 is the second relation parameter, C3 is the third relation parameter, C4 is the fourth relation parameter, and C... x This is a tolerance parameter. It avoids the drawback of ignoring the differences in the impact of different tree types, and more accurately obtains the interspecific relationship parameters between the target tree and various non-target trees.

[0051] S4: Based on the interspecific relationship parameters, functional trait data, and the preset interspecific relationship analysis module, obtain the interspecific relationship analysis results between the target tree and each non-target tree.

[0052] The interspecific relationship analysis results include intraspecific and extraspecific types of interspecific relationship analysis results. In order to better analyze the interspecific relationship analysis results between the target tree and each non-target tree, in this embodiment, the analysis device obtains the interspecific relationship analysis results between the target tree and each non-target tree based on the interspecific relationship parameters, functional trait data and preset interspecific relationship analysis modules.

[0053] Please see Figure 3 , Figure 3 The flowchart of S4 in the tree interspecific relationship analysis method provided in one embodiment of this application is as follows: S401 to S402 are detailed below:

[0054] S401: Input the interspecific relationship parameters and functional trait data into the interspecific relationship analysis module, and obtain the corresponding competition parameters for each interspecific relationship parameter according to the preset competition parameter calculation algorithm.

[0055] The algorithm for calculating the competition parameters is as follows:

[0056] C1 = C intra，i α

[0057] C2 = C inter，i (1-α)

[0058] C x =C t x i

[0059] C3 = C e x j，tot

[0060] C4 = C d |x j -x i |

[0061] In the formula, C intra，i The first competition parameter represents the intra-neighbor effect experienced by the i-th target tree relative to non-target trees; C inter，i The second competition parameter represents the extraspecific neighbor effect experienced by the i-th target tree relative to the non-target tree. α is the tree type identifier; when the target tree and the non-target tree have the same tree type, α is 0; when the target tree and the non-target tree have different tree types, α is 1. t The third competition parameter, x, represents the target tree's resilience to competition relative to non-target trees. i C represents the functional trait data corresponding to the i-th target tree; e The fourth competition parameter, x, represents the trait effect of the target tree relative to the non-target trees. j，tot This is the accumulated data for the functional traits of all non-target trees surrounding the i-th target tree; C d The fifth competition parameter represents the effect of trait differences between the target tree and non-target trees, |x j -x i | represents the absolute distance data of traits between the i-th target tree and the j-th non-target tree;

[0062] In this embodiment, the analysis device inputs the interspecific relationship parameters and functional trait data into the interspecific relationship analysis module. Based on a preset competition parameter calculation algorithm, it obtains the corresponding competition parameters for each interspecific relationship parameter. Specifically, based on leaf nitrogen content data, x... iLet x be the leaf nitrogen content data corresponding to the i-th target tree. j When the leaf area ratio data corresponding to the j-th non-target tree is given, C is calculated. d，氮 Based on tree height data, x j，tot When accumulating the tree height data of all non-target trees surrounding the i-th target tree, C is calculated. e，树高 .

[0063] S402: Based on the competition parameters corresponding to each interspecific relationship parameter and the preset competition parameter threshold, obtain the interspecific relationship analysis results between the target tree and each non-target tree.

[0064] The analysis device compares the corresponding competition parameters of each interspecific relationship parameter with the corresponding competition parameter threshold. When the competition parameter is greater than the competition parameter threshold, the interspecific relationship between the target tree and the non-target tree is set as a competitive relationship. When the competition parameter is less than the competition parameter threshold, the interspecific relationship between the target tree and the non-target tree is set as a mutually beneficial relationship, and the interspecific relationship analysis results are obtained.

[0065] In an optional embodiment, all the competition parameter thresholds are set to 0. Specifically, when the first competition parameter C... intra,i >0, the interspecific relationship analysis result between the target tree and the non-target tree is the result of the competition relationship analysis of the extraspecific type, when the second competition parameter C inter,i >0, the interspecific relationship analysis results between the target tree and non-target trees are the intraspecific type competition relationship analysis results;

[0066] When the third competition parameter C e,树高 >0 indicates a competitive relationship between the target tree and non-target trees based on tree height data;

[0067] When the fourth competition parameter C d,氮 If the result is >0, the interspecific relationship analysis between the target tree and non-target trees based on the leaf nitrogen content data is a competition relationship analysis result.

[0068] Please refer to Figure 4 , Figure 4 This is a schematic diagram of the structure of a tree interspecific relationship analysis device according to an embodiment of this application. The device can be implemented entirely or partially through software, hardware, or a combination of both. The device 4 includes:

[0069] The first acquisition module 41 is used to acquire tree growth data and functional trait data of each tree in the target area, wherein the tree growth data includes tree diameter at breast height (DBH) data and tree height at breast height (DBH) sectional area data.

[0070] The second acquisition module 42 is used to acquire a target tree and several non-target trees from the trees, and to acquire tree type data of the target tree and the non-target trees; the target tree is a tree selected from the trees in the target area; the non-target tree is a tree selected outside the target tree within the sample circle area drawn with the target tree as the center and a preset radius data.

[0071] The interspecific relationship parameter calculation module 43 is used to obtain the interspecific relationship parameters between the target tree and each non-target tree based on the tree type data, tree growth data, functional trait data of the target tree and several non-target trees, as well as the preset interspecific relationship parameter calculation module.

[0072] The analysis module is used to obtain the interspecific relationship analysis results between the target tree and each non-target tree based on the interspecific relationship parameters, functional trait data and the preset interspecific relationship analysis module.

[0073] In this embodiment, a first acquisition module acquires tree growth data and functional trait data of each tree in the target area, wherein the tree growth data includes tree diameter at breast height (DBH) data and tree height at breast height sectional area data; a second acquisition module acquires a target tree and several non-target trees from the trees, and acquires tree type data of the target tree and the non-target trees; the target tree is a tree selected from the trees in the target area; the non-target trees are trees selected outside the target tree within a sample circle drawn with the target tree as the center and a preset radius data; an interspecific relationship parameter calculation module acquires interspecific relationship parameters between the target tree and each non-target tree based on the tree type data, tree growth data, functional trait data of the target tree and several non-target trees, and a preset interspecific relationship parameter calculation module; an analysis module acquires the interspecific relationship analysis results between the target tree and each non-target tree based on the interspecific relationship parameters, functional trait data, and a preset interspecific relationship analysis module. By combining tree type data of target trees and non-target trees, and based on tree growth data and functional trait data, the interspecific relationships of target trees and non-target trees are analyzed. This avoids the shortcoming of ignoring the differences in the impact of different tree types and provides a more accurate analysis of the interspecific relationships between trees of different types.

[0074] Please refer to Figure 5 , Figure 5 This is a schematic diagram of the structure of a computer device provided in one embodiment of this application. The computer device 5 includes: a processor 51, a memory 52, and a computer program 53 stored in the memory 52 and executable on the processor 51; the computer device can store multiple instructions, which are adapted to be loaded and executed by the processor 51. Figures 1 to 3The method steps of the illustrated embodiment can be found in the following documentation for detailed execution. Figures 1 to 3 The specific details of the illustrated embodiments will not be elaborated here.

[0075] The processor 51 may include one or more processing cores. The processor 51 connects to various parts of the server using various interfaces and lines. It executes various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 52, and by calling data from the memory 52. ​​Optionally, the processor 51 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 51 may integrate one or more of the following: a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), and a modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content displayed on the touch screen; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the processor 51.

[0076] The memory 52 may include random access memory (RAM) or read-only memory. Optionally, the memory 52 may include a non-transitory computer-readable storage medium. The memory 52 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 52 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch instructions), instructions for implementing the various method embodiments described above, etc.; the data storage area may store data involved in the various method embodiments described above, etc. Optionally, the memory 52 may also be at least one storage device located remotely from the aforementioned processor 51.

[0077] This application embodiment also provides a storage medium that can store multiple instructions, which are adapted to be loaded and executed by a processor as described above. Figures 1 to 3The method steps of the illustrated embodiment can be found in the following documentation for detailed execution. Figures 1 to 3 The specific details of the illustrated embodiments will not be elaborated here.

[0078] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0079] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0080] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the algorithm. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0081] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0082] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0083] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0084] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms.

[0085] This invention is not limited to the above-described embodiments. If any modifications or variations to this invention do not depart from the spirit and scope of this invention, and if such modifications and variations fall within the scope of the claims and equivalent technologies of this invention, then this invention also intends to include such modifications and variations.

Claims

1. A method for analyzing interspecific relationships in trees, characterized in that, Includes the following steps: Obtain tree growth data and functional trait data for each tree in the target area; Obtain the target tree and several non-target trees from the trees, and obtain the tree type data of the target tree and the non-target trees; the target tree is a tree selected from the trees in the target area; the non-target tree is a tree selected outside the target tree within the sample circle area drawn with the target tree as the center and a preset radius data; Obtain environmental factors associated with the tree type data of the target tree; Based on the environmental factors, the tree growth data and functional trait data of the target tree and non-target trees are respectively input into a preset interspecific relationship parameter calculation module. According to a preset interspecific relationship parameter calculation algorithm, the interspecific relationship parameters between the target tree and each non-target tree are obtained. The interspecific relationship parameter calculation algorithm is as follows: In the formula, For the first i The growth of the vertical area at breast height in the tree growth data of each target tree. For the preset first i The tree type data for each target tree corresponds to environmental factors. The maximum basal area growth at breast height of the target tree. For the first i Tree diameter at breast height (DBH) data for each target tree; These are the parameters representing the interspecific relationships. For the first i Functional trait data of a target tree For the first j Functional trait data of non-target trees, where sgn() is the sign function, and the interspecific relationship parameters are... Including a first relation parameter, a second relation parameter, a third relation parameter, a fourth relation parameter, and a tolerance parameter, the expression for the interspecific relation parameter is: In the formula, As the first relation parameter, For the second relation parameter, For the third relation parameter, The fourth relation parameter, For endurance parameters; The interspecific relationship parameters and functional trait data are input into a preset interspecific relationship analysis module. Based on a preset competition parameter calculation algorithm, the corresponding competition parameters for each interspecific relationship parameter are obtained. The competition parameter calculation algorithm is as follows: In the formula, As the first competing parameter, it is expressed as the... i The intra-neighbor effect of a target tree relative to a non-target tree; The second competing parameter is represented by the first. i The effects of exo-neighbors on a target tree relative to non-target trees. For tree type identification, when the target tree and a non-target tree have the same tree type, then... The value is 0 when the target tree and the non-target tree are of different tree types. The value is 1; This is the third competition parameter, representing the target tree's resistance to competition relative to non-target trees. This represents the functional trait data corresponding to the i-th target tree; This is the fourth competitive parameter, representing the trait effect of the target tree relative to the non-target trees. For the first i The data is the sum of the functional trait data corresponding to all non-target trees surrounding the target tree; C d This is the fifth competition parameter, representing the effect of trait differences between the target tree and non-target trees. For the i-th target tree and the i-th target tree j Absolute distance data of traits between non-target trees; Based on the competition parameters corresponding to each interspecific relationship parameter and the preset competition parameter threshold, the interspecific relationship analysis results between the target tree and each non-target tree are obtained. The interspecific relationship analysis results include intraspecific analysis results, extraspecific analysis results, and neighboring tree analysis results. The neighboring tree analysis results include neighboring tree individual trait analysis results and neighboring tree trait difference analysis results.

2. The method for analyzing interspecific relationships of trees according to claim 1, characterized in that: The functional trait data include leaf area ratio data, leaf area data, leaf nitrogen content data, leaf carbon content data, carbon-nitrogen ratio data, and tree height data.

3. The method for analyzing interspecific relationships of trees according to claim 2, characterized in that: The tree growth data includes tree diameter at breast height (DBH) data and tree height at breast height sectional area data.

4. A device for analyzing interspecific relationships in trees, characterized in that, include: The first acquisition module is used to acquire tree growth data and functional trait data of each tree in the target area, wherein the tree growth data includes tree diameter at breast height (DBH) data and tree height at breast height (DBH) sectional area data. The second acquisition module is used to acquire a target tree and several non-target trees from the trees, and to acquire tree type data of the target tree and the non-target trees; the target tree is a tree selected from the trees in the target area; the non-target tree is a tree selected outside the target tree within a sample circle area drawn with the target tree as the center and a preset radius data; An interspecific relationship parameter calculation module is used to obtain environmental factors associated with the tree type data of the target tree; Based on the environmental factors, the tree growth data and functional trait data of the target tree and non-target trees are respectively input into a preset interspecific relationship parameter calculation module. According to a preset interspecific relationship parameter calculation algorithm, the interspecific relationship parameters between the target tree and each non-target tree are obtained. The interspecific relationship parameter calculation algorithm is as follows: In the formula, For the first i The growth of the vertical area at breast height in the tree growth data of each target tree. For the preset first i The tree type data for each target tree corresponds to environmental factors. The maximum basal area growth at breast height of the target tree. For the first i Tree diameter at breast height (DBH) data for each target tree; These are the parameters representing the interspecific relationships. For the first i Functional trait data of a target tree For the first j Functional trait data of non-target trees, where sgn() is the sign function, and the interspecific relationship parameters are... Including a first relation parameter, a second relation parameter, a third relation parameter, a fourth relation parameter, and a tolerance parameter, the expression for the interspecific relation parameter is: In the formula, As the first relation parameter, For the second relation parameter, For the third relation parameter, The fourth relation parameter, For endurance parameters; The analysis module is used to input the interspecific relationship parameters and functional trait data into a preset interspecific relationship analysis module, and to obtain the corresponding competition parameters for each interspecific relationship parameter according to a preset competition parameter calculation algorithm. The competition parameter calculation algorithm is as follows: In the formula, As the first competing parameter, it is expressed as the... i The intra-neighbor effect of a target tree relative to a non-target tree; The second competing parameter is represented by the first. i The effects of exo-neighbors on a target tree relative to non-target trees. For tree type identification, when the target tree and a non-target tree have the same tree type, then... The value is 0 when the target tree and the non-target tree are of different tree types. The value is 1; This is the third competition parameter, representing the target tree's resistance to competition relative to non-target trees. This represents the functional trait data corresponding to the i-th target tree; This is the fourth competitive parameter, representing the trait effect of the target tree relative to the non-target trees. For the first i The data is the sum of the functional trait data corresponding to all non-target trees surrounding the target tree; C d This is the fifth competition parameter, representing the effect of trait differences between the target tree and non-target trees. For the i-th target tree and the i-th target tree j Absolute distance data of traits between non-target trees; Based on the competition parameters corresponding to each interspecific relationship parameter and the preset competition parameter threshold, the interspecific relationship analysis results between the target tree and each non-target tree are obtained. The interspecific relationship analysis results include intraspecific analysis results, extraspecific analysis results, and neighboring tree analysis results. The neighboring tree analysis results include neighboring tree individual trait analysis results and neighboring tree trait difference analysis results.

5. A computer device, characterized in that, include: A processor, a memory, and a computer program stored in the memory and executable on the processor; the computer program, when executed by the processor, implements the steps of the method for analyzing interspecific relationships of trees as described in any one of claims 1 to 3.

6. A storage medium, characterized in that: The storage medium stores a computer program that, when executed by a processor, implements the steps of the tree interspecific relationship analysis method as described in any one of claims 1 to 3.

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