A stand mixed optimization method and optimization system based on tree cluster units

Abstract

The present invention provides a method and system for optimizing stand mixed planting based on tree cluster units, which relates to the technical field of forest management. The method provided by the present invention includes: obtaining the information of tree planting points of arbors in the surveyed forest and selecting multiple tree cluster units, investigating the stand indexes of the tree cluster units and constructing a tree species database; constructing an association model between the stand indexes and each pair of relative traits, and obtaining the optimal mixed ratio of each pair of relative traits under the optimal stand indexes; obtaining the geographical information of the to-be-planted plot and randomly selecting to-be-planted points; based on the optimal mixed ratio of each pair of relative traits, performing trait assignment on the to-be-planted points and then matching arbor tree species for tree species mixed planting. The present invention can directly guide the construction of artificial mixed forests from scratch in the to-be-planted plots, which is beneficial to improving the efficiency and replicability of forest management.

Description

Technical Field

[0001] The present invention relates to the technical field of forest management, and particularly to a method and system for optimizing stand mixed planting based on tree group units. Background Art

[0002] A mixed forest is a composite forest ecosystem formed by two or more tree species through specific spatial configurations, which can break the homogeneous distribution of a single tree species. Compared with pure forests, mixed forests have significant advantages in ecological functions and economic values. They can not only enhance the stability of the forest ecosystem, but also improve the utilization efficiency of soil resources, light resources, etc. At the same time, mixed forests perform better in environmental benefits such as carbon sequestration capacity, soil and water conservation, and forest health. However, a mixed forest without tree species optimization, scientific proportioning, and layout planning is prone to interspecific competition imbalance and will also cause great waste of resources due to the unreasonable spatial configuration of growers. Therefore, efficient optimization of mixed forests has become an inevitable choice to improve forest quality and management benefits. Summary of the Invention

[0003] The purpose of the present invention is to provide a method and system for optimizing stand mixed planting based on tree group units, which can directly guide the construction of artificial mixed forests from scratch on the plot to be planted, and is beneficial to improving forest management efficiency and replicability.

[0004] In a first aspect, a method for optimizing stand mixed planting based on tree group units provided by the present invention includes: obtaining the information of tree planting points of arbors in the surveyed forest to construct a survey model and selecting multiple tree group units; investigating the stand indexes of the tree group units and extracting the species and traits of arbors to construct a tree species database; respectively constructing an association model between the stand indexes and each pair of relative traits within the tree group units; respectively obtaining the optimal mixing ratio of each pair of relative traits under the optimal stand indexes based on the association model; obtaining the geographical information of the plot to be planted to construct a planting map and randomly selecting planting points; after assigning trait values to the planting points based on the optimal mixing ratio of each pair of relative traits, matching arbor tree species in the tree species database to obtain a planting blueprint; and performing tree species mixed planting on the plot to be planted based on the planting blueprint.

[0005] Optionally, the surveyed forest and the plot to be planted are in the same spatio-temporal environment.

[0006] Optionally, the tree group unit is a combination of 3 to 7 non-collinear and adjacent arbors.

[0007] Optionally, the figure formed by the tree group units is a convex polygon.

[0008] Optionally, the information of tree planting points of arbors in the surveyed forest is obtained based on low-altitude technology.

[0009] Optionally, the surveyed forest includes artificial mixed forests or natural forests.

[0010] Optionally, the arbor planting point information includes arbor coordinates, leaf traits, and root shapes.

[0011] Optionally, the relative traits include broad-leaved traits and needle-leaved traits, deciduous traits and evergreen traits, deep-root traits and shallow-root traits, root endophyte-dominated traits and root ectophyte-dominated traits.

[0012] Optionally, the stand indicators include canopy density, mingling degree, volume, foliage biomass, near-surface air plant VOCs content, carbon sequestration amount, water conservation amount, nitrogen fixation amount, mixing ratio, and negative oxygen ion concentration.

[0013] Optionally, when constructing an investigation model and selecting multiple tree cluster units, the number of the tree cluster units is:

[0014]

[0015] where is the total number of tree cluster units in the investigation model, is the orthographic projection area of the investigation model, , are both adjustment coefficients, and is greater than 1.

[0016] Optionally, multiple tree cluster units are randomly selected within the investigation model.

[0017] Optionally, the tree species database includes tree species and the corresponding arbor traits of the tree species. The arbor traits at least include the traits of the arbors within the tree cluster units, and the tree species at least include the tree species of the arbors within the tree cluster units.

[0018] Optionally, there are no other arbors in the figure formed by the tree cluster units.

[0019] Optionally, when constructing an association model between the stand indicators and each pair of relative traits within the tree cluster units, it includes: statistically sorting the mixing ratios of each relative trait within all tree cluster units to obtain a mixing ratio sequence; comprehensively obtaining the comprehensive stand indicator value of the tree cluster units under the same mixing ratio; constructing an association model based on the mixing ratio sequence and the comprehensive stand indicator value.

[0020] Optionally, the number of tree cluster units under each same mixing ratio is greater than or equal to the minimum threshold.

[0021] Optionally, when constructing an association model based on the mixing ratio sequence and the comprehensive stand indicator value, construct a mapping relationship of the comprehensive stand indicator value to the mixing ratio sequence and fit to obtain the association model.

[0022] Optionally, the deviation of the number of tree cluster units under each mixing ratio is less than or equal to 15%.

[0023] Optionally, when integrating the stand indexes of the tree group units at the same mixing ratio, it includes:

[0024]

[0025] Among them, is the comprehensive value of the stand indexes of the tree group units at the -th mixing ratio, is the total number of the tree group units at the -th mixing ratio, is the stand index of the -th tree group unit at the -th mixing ratio, is the forest age constraint coefficient of the -th tree group unit at the -th mixing ratio, is the orthographic projection area of the -th tree group unit at the -th mixing ratio.

[0026] In a second aspect, the present invention further provides a stand mixing optimization system based on tree group units, including:

[0027] A basic construction module, configured to obtain the information of the arbor planting points in the surveyed forest to construct a survey model, and obtain the geographical information of the plot to be planted to construct a planting map;

[0028] A selection and survey module, configured to select multiple tree group units in the survey model and survey the stand indexes of the tree group units, and randomly select the points to be planted in the planting map;

[0029] A database construction module, configured to extract the types and traits of the arbors in the tree group units to construct a tree species database;

[0030] An information association module, configured to construct an association model between the stand indexes and each pair of relative traits in the tree group units;

[0031] A mixing analysis module, configured to respectively obtain the optimal mixing ratios of each pair of relative traits under the optimal stand indexes based on the association model;

[0032] A trait assignment module, configured to assign traits to the points to be planted based on the optimal mixing ratios of each pair of relative traits;

[0033] A blueprint construction module, configured to match the arbor tree species for the points to be planted with assigned traits in the tree species database to obtain a planting blueprint;

[0034] A mixing planting module, configured to perform tree species mixing on the plot to be planted based on the planting blueprint. Description of the Drawings

[0035] Figure 1 Flow chart of a stand mixed planting optimization method based on tree cluster units provided by the present invention;

[0036] Figure 2 Schematic structural diagram of a stand mixed planting optimization system based on tree cluster units provided by the present invention. Specific embodiments

[0037] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention. Unless otherwise defined, the technical terms or scientific terms used herein shall have the ordinary meanings understood by those of ordinary skill in the art to which the present invention pertains.

[0038] See Figure 1 , the embodiments of the present invention provide a stand mixed planting optimization method based on tree cluster units, including the following steps:

[0039] S1. Obtain the information of tree planting points in the surveyed forest, construct a survey model and select multiple tree cluster units;

[0040] S2. Survey the stand indexes of the tree cluster units and extract the species and traits of the arbors to construct a tree species database;

[0041] S3. Respectively construct the association models between the stand indexes and each pair of relative traits within the tree cluster units;

[0042] S4. Respectively obtain the optimal mixing ratios of each pair of relative traits under the optimal stand indexes based on the association models;

[0043] S5. Obtain the geographical information of the plot to be planted, construct a planting map and randomly select the planting points to be planted;

[0044] S6. Assign trait values to the planting points to be planted based on the optimal mixing ratios of each pair of relative traits;

[0045] S7. Match the arbor tree species in the tree species database to obtain a planting blueprint;

[0046] S8. Conduct tree species mixed planting on the plot to be planted based on the planting blueprint.

[0047] In fact, by using the investigated forest as a sample to select tree group units for investigation to obtain stand indicators, and by constructing the correlation relationship between the stand indicators and the trait mixing ratio, the optimal mixing ratio of each relative trait under the optimal stand indicators is obtained, and these optimal mixing ratios are used to generate a planting blueprint, so that the overall planting blueprint conforms to the optimal mixing ratios of all relative traits, which is beneficial to improving the artificial mixed forest to reach the optimal management level. In addition, the planting blueprint can directly guide the construction of an artificial mixed forest from scratch on the plot to be planted, which is beneficial to improving the forest management efficiency and replicability.

[0048] In fact, the investigated forest used in step S1 is an artificial mixed forest or a natural forest. Among them, due to the long-term natural growth of the natural forest in the natural state, it can form a multi-layered and stable vegetation structure through the mutual cooperation of top-layer trees, middle-layer shrubs and bottom-layer herbs, while the artificial mixed forest is a mixed forest with a clear forest age obtained by artificial planting. In fact, at present, when constructing an artificial mixed forest, the artificial pure forest is usually mixed and transformed, so choosing the artificial mixed forest as the investigated forest can further extend and develop on the existing work basis. In addition, the artificial mixed forest constructed by the method of the present invention can also play a role in the stand mixing of another plot to be planted.

[0049] Specifically, the investigated forest used in step S1 and the plot to be planted in step S5 are in the same spatio-temporal environment. In fact, the investigated forest and the plot to be planted in the same spatio-temporal environment are in the same climate environment, and the stand structure in the investigated forest is the result of long-term adaptation to this climate environment. Therefore, based on the investigated forest for stand mixing can reduce the growth and death problems of the forest land caused by climate inadaptability after planting. At the same time, the soil matching and the ecological system succession process in the same spatio-temporal environment are similar, which can ensure the normal growth of the mixed forest on the mixed plot.

[0050] In some embodiments, the same spatio-temporal environment is specifically an area that is geographically in the same location or a nearby location, and has similar water systems and similar component soils within the same river basin range, and also needs to be in the same or similar climate zone. Further, an artificial mixed forest or a natural forest can be found as the investigated forest within a circular range with a radius of 300 km centered on the plot to be planted. Specifically, the difference between the forest land area of the investigated forest and the area of the plot to be planted is less than or equal to 20%.

[0051] In some embodiments, in step S1, the information on the tree planting points of the arbors in the surveyed forest can be obtained based on low-altitude technology. In fact, a low-altitude drone device can be used to fly inside and above the surveyed forest to capture image information for recognition, and the coordinates of the arbors can be directly obtained by combining positioning methods such as GPS and Beidou, and the leaf traits can be obtained through the image information. In addition, when obtaining the root traits of the arbors, the species of the arbors can be obtained through image recognition, and their root traits can be distinguished according to their species.

[0052] Specifically, the information on the tree planting points of the arbors obtained in step S1 includes the coordinates of the arbors, leaf traits, and root traits, and the coordinates of the arbors include the position of the arbor itself and the relative position between the current arbor and other arbors, so that the accurate position coordinates of all the arbors in the surveyed forest can be comprehensively known. More specifically, the leaf traits of the arbors include multiple pairs of relative traits, such as broad-leaf traits and needle-leaf traits, deciduous traits and evergreen traits, and the root traits of the arbors also include multiple pairs of relative traits, such as deep-root traits and shallow-root traits, root endophyte-dominated traits and root ectophyte-dominated traits.

[0053] In some embodiments, after obtaining the image information of the surveyed forest based on low-altitude technology in step S1, a mathematical model can be constructed using a neural network. In the initial stage, the tree species of the arbor pictures are manually labeled as the training set, and after training the mathematical model with the training set, the model is capable of distinguishing the tree species of the arbors from the arbor pictures, and the corresponding leaf traits and root trait information are matched using the distinguished tree species.

[0054] In some embodiments, after obtaining the information on the tree planting points of the arbors in the surveyed forest in step S1, a digital map corresponding to the surveyed forest is constructed as a survey model, and through the survey model, tree cluster units can be quickly selected on a computer device, and at the same time, the selected tree cluster units together with their coordinate positions can be sent to the mobile terminal for precise positioning and identification by the survey personnel in the surveyed forest. Specifically, when constructing the survey model, the terrain changes in the surveyed forest can be ignored, and the focus is on constructing a simple model based on the coordinates of the arbors in the surveyed forest, which is beneficial to reducing the complexity of the model.

[0055] In fact, when selecting multiple tree cluster units after constructing the survey model in step S1, the selected tree cluster units are tree combinations composed of 3 to 7 non-collinear and adjacent arbors. In addition, the figure formed by the tree cluster units is a convex polygon, and there are no other arbors within the formed figure. Specifically, by selecting tree cluster units in the survey model, it is possible to avoid manually entering the surveyed forest to enclose survey plots, and at the same time, it is possible to improve the randomness of the tree cluster units in the survey model, which is beneficial to improving the credibility and accuracy of the survey results.

[0056] In some embodiments, in step S1, a plurality of tree cluster units are randomly selected, and the number of selected tree cluster units is positively correlated with the total area of the surveyed forest. In fact, the more tree cluster units are selected in the survey model, the higher the survey accuracy of the surveyed forest, but the corresponding survey workload will also increase. Therefore, to balance the survey accuracy and the survey workload, the number of selected tree cluster units in the survey model can be expressed as:

[0057]

[0058] Wherein, is the total number of tree cluster units in the survey model, is the orthographic projection area of the survey model, and are both adjustment coefficients, and is greater than 1.

[0059] In fact, when surveying the stand indicators of the tree cluster units in step S2, the stand indicators include at least one forest management indicator. Specifically, the stand indicators include canopy density, mixing degree, stock volume, branch and leaf biomass, near-surface air plant VOCs content, carbon sequestration volume, water conservation volume, nitrogen fixation volume, mixing ratio, negative oxygen ion concentration. In some embodiments, when calculating the stand indicators within the tree cluster units, common calculation or survey methods in the art can be used.

[0060] In fact, when extracting the species and traits of arbors in the tree cluster units in step S2 to construct a tree species database, it is beneficial to match arbor tree species in step S7. Specifically, since the surveyed forest and the plot to be planted are in the same spatio-temporal environment, it can be considered that the arbor tree species growing in the surveyed forest can also grow normally in the plot to be planted.

[0061] Furthermore, the tree species database includes arbor species and the corresponding arbor traits of the arbor species. For example, in the tree species database, there can be a mapping relationship of [arbor species, arbor traits], and the arbor species at least include the arbor species within the tree cluster units, and the arbor traits at least include the traits of the arbors within the tree cluster units. Specifically, when the arbor species within the tree cluster unit is A and the arbor traits include A, B, C, D, it can be expressed as [A, (A|B|C|D)] in the tree species database.

[0062] In fact, establishing the association model between stand indicators and relative traits within the tree group unit in step S3 can associate the size of the stand indicators with the mixing ratio of the relative traits within the tree group unit. In fact, different mixing ratios of arbor tree species within the tree group unit will significantly affect the size of the stand indicators. Taking the smallest tree group unit, the tree group unit composed of three arbor trees, as an example, if all three arbor trees are deep-rooted tree species, it is difficult to stratify the soil roots within the enclosed area, which will have an obvious impact on the water storage capacity. However, if two deep-rooted arbor trees and one shallow-rooted arbor tree are paired, since the roots can be well stratified in the soil, it is beneficial to improve the water storage capacity.

[0063] In some embodiments, when performing step S3, the following sub-steps are included:

[0064] S3.1. Statistically analyze the mixing ratio of each relative trait within all tree group units and sort them to obtain a mixing ratio sequence;

[0065] S3.2. Synthesize the stand indicators of the tree group units under the same mixing ratio to obtain a comprehensive value of the stand indicators;

[0066] S3.3. Construct an association model based on the mixing ratio sequence and the comprehensive value of the stand indicators.

[0067] In fact, when performing step S3.1 to statistically analyze the mixing ratio of each relative trait within all tree group units, assuming the relative traits in the tree group unit are A and a, the corresponding mixing ratio of trait A and the mixing ratio of trait a can be as shown in Table 1 below.

[0068] Table 1 Mixing ratios within the tree group unit

[0069]

[0070] In some embodiments, when sorting to obtain the mixing ratio sequence after statistically analyzing the mixing ratio of each relative trait, the sorting can be performed according to any one of each pair of relative traits. For example, sorting according to trait A in Table 1 is as shown in Table 2 below.

[0071] Table 2 Mixing ratio sequence of relative traits (A / a)

[0072]

[0073] In fact, it can be seen from Table 2 that when the mixing ratio of relative traits is (0, 1) or (1, 0), it is considered that there is no tree species mixing in the currently selected tree group unit, and the types of multiple arbor trees forming the tree group unit are the same or at least the traits are the same. Therefore, (0, 1) or (1, 0) can be excluded when constructing the mixing ratio sequence.

[0074] In some embodiments, the number of tree cluster units at each same mixing ratio in step S3.1 is greater than or equal to the minimum threshold. By setting the minimum threshold, it can be ensured that the stand index of the tree cluster units at the current mixing ratio has sufficient reference value, which is beneficial to improving the accuracy of the obtained optimal mixing ratio. Specifically, the number of tree cluster units at each mixing ratio needs to be approximately the same, and the deviation is less than or equal to 15%.

[0075] In fact, when selecting multiple tree cluster units in the survey model in step S1, it is possible to pre-calculate whether the mixing ratio of the relative traits in the tree cluster units satisfies the above constraints. If not, they can be re-selected within the survey model to meet the constraints and improve the accuracy of the optimal mixing ratio. Specifically, when pre-calculating whether the mixing ratio of the relative traits in the tree cluster units satisfies the constraints, 1 to 4 pairs of relative traits can be selected for calculation according to the optimization direction.

[0076] In some embodiments, when performing step S3.2 to synthesize the stand indices of the tree cluster units at the same mixing ratio, synthesizing the stand indices within multiple tree cluster units can effectively improve the credibility and accuracy of the stand indices. In fact, due to natural elimination and artificial selective logging operations during the growth process of natural forests and artificial mixed forests, as well as different numbers of arbors selected when choosing tree cluster units, the areas and sizes of the tree cluster unit areas enclosed are also different. At the same time, the tree cluster units are in different areas of the surveyed forest. Therefore, by synthesizing the stand indices, the influence brought by environmental differences and tree cluster unit size differences can be effectively overcome.

[0077] In some embodiments, when performing step S3.2 to synthesize the stand indices of the tree cluster units at the same mixing ratio, arbors of different tree ages will have an obvious impact on the stand indices. For example, when the arbors are young, their root systems and foliage systems are relatively tender and difficult to affect the stand indices, while the impact on the stand indices tends to be stable after the arbors grow into shape. Therefore, when comprehensively calculating the stand indices, adjustments can be made according to the forest age of the tree cluster units.

[0078] Specifically, the formula for comprehensively calculating the stand indices in step S3.2 can be:

[0079]

[0080] Among them, is the comprehensive value of the stand indices of the tree cluster units at the th mixing ratio, is the total number of tree cluster units at the th mixing ratio, is the stand index of the th tree cluster unit at the th mixing ratio, is the The forest age constraint coefficient of the th tree group unit under a certain mixing ratio, is the th tree group unit under a certain mixing ratio, and the orthographic projection area of the

[0081] In some embodiments, when retrieving the tree group unit in step S1, the forest age of the tree group unit can be used as a constraint condition. When the forest age within the selected tree group unit does not meet the constraint condition, it is excluded and reselected. At this time, the forest age constraint coefficient in the formula is fixed at 1.

[0082] In some embodiments, when performing step S3.3, when constructing the mapping relationship between the comprehensive value of the stand index and the mixing ratio sequence, it is specifically shown in Table 3.

[0083] Table 3 Mapping between the comprehensive value of the stand index and the mixing ratio sequence

[0084]

[0085] In fact, when constructing the mapping relationship in step S3.3, the relative trait mixing ratio can be used as the horizontal axis and the comprehensive value of the stand index as the vertical axis for mapping. After linearly fitting the scatter points obtained from the mapping, an association model can be obtained, and from the association model, the change in the stand index brought about by the change in the trait mixing ratio can be intuitively known.

[0086] Specifically, in step S4, when obtaining the optimal mixing ratio of each pair of relative traits based on the association model, the change trend of the comprehensive value of the stand index with the mixing ratio in the association model can be calculated by mathematical methods, and the mixing ratio value when the optimal stand index is reached can be known. In fact, the calculated optimal mixing ratio may not necessarily be the actual mixing ratio in the tree group unit, and it can be a new mixing ratio value obtained through trend calculation.

[0087] In some embodiments, after calculating the optimal mixing ratio of each pair of relative traits in step S4, it can be expressed as [relative trait 1, X1, 1 - X1], [relative trait 2, X2, 1 - X2], [relative trait 3, X3, 1 - X3], etc. In fact, after obtaining the above set of optimal mixing ratios, it can be considered that under the current explored stand index, these mixing ratios can well improve the efficiency of forest management.

[0088] In fact, when obtaining the geographical information of the plot to be planted in step S5, the low-altitude technology can be used in the same way as in step S1 to obtain the geographical information of the plot to be planted. Specifically, when the surveyed forest is close to the location of the plot to be planted, the two types of information can be directly obtained through low-altitude equipment, and the survey model and planting map can be constructed respectively, which can greatly improve the convenience and efficiency.

[0089] In some embodiments, the plot to be planted in step S5 is a plot without arbors on the surface or with arbors on the surface that do not need to be retained. Through the plot to be planted, the positions of the arbors during the planting process can be directly planned, avoiding mutual influence among the arbors during the growth process. Specifically, when selecting the points to be planted in the planting map, they are selected based on a preset planting density, and the points to be planted can be relatively evenly distributed on the planting map.

[0090] In some embodiments, the geographical information of the plot to be planted, such as factors that can affect the growth and distribution of arbors, such as slope, height, water area, and watershed, is recorded in the planting map in step S5. In fact, the preset planting density used when selecting the points to be planted can be set conventionally according to common knowledge in the art and combined with the planting density of the surveyed forest. In addition, the spacing between adjacent points to be planted can also be set when selecting the points to be planted.

[0091] In fact, the points to be planted generated in step S5 are blank nodes, which only record the coordinate information in the planting map for precise positioning by forestry personnel. In addition, by assigning traits to the points to be planted in step S6, the points to be planted can record the corresponding traits and then match the corresponding arbor tree species, so that forestry personnel can plant the required arbor tree species at the points to be planted.

[0092] Specifically, by assigning traits to all the points to be planted in the planting map, the overall mixing ratio of the arbor tree species in the planting map can meet the optimal mixing ratio obtained in step S4, and further, the mixing ratio of the relative traits of the overall arbors in the planting map can be optimal, which is beneficial to improving the stability of forestry indicators during the operation process of the planting map and improving forestry indicators.

[0093] In some embodiments, when performing step S6, assuming that the relative traits for which trait assignment is required are: relative trait 1 (A / a), relative trait 2 (B / b), relative trait 3 (C / c), relative trait 4 (D / d), then after assignment, [relative trait 1, relative trait 2, relative trait 3, relative trait 4] is recorded in the point to be planted.

[0094] Specifically, assume that the optimal mixing ratios of relative trait 1 (A / a) are (0.46, 0.54), those of relative trait 2 (B / b) are (0.53, 0.47), those of relative trait 3 (C / c) are (0.61, 0.39), and those of relative trait 4 (D / d) are (0.66, 0.34), and the number of points to be planted in the planting map is 10,000. Then, in the planting map, the overall arbors need to have 4,600 A traits and 5,400 a traits, 5,300 B traits and 4,700 b traits, 6,100 C traits and 3,900 c traits, 6,600 D traits and 3,400 d traits. When assigning values, the above traits are evenly distributed to the points to be planted, so that the overall planting map meets the above optimal mixing ratios.

[0095] Actually, when performing step S6, assume that the number of relative traits that need trait assignment is t. Then, the number of trait combinations generated after trait assignment is 2 t ones. In fact, when performing trait assignment, special situations may occur, such as the arbor tree species corresponding to the trait combination does not exist or it is difficult to survive in the environment of the plot to be planted. Then, some trait combinations can be pre-marked before trait assignment to avoid them. For example, if the trait combination [A, b, C, D] does not exist, this combination should be avoided when performing trait assignment.

[0096] Actually, after performing trait assignment in step S6, not only the geographical location information is recorded in the points to be planted, but also the trait combination of the arbors required at this location is recorded. Therefore, when performing step S7, the corresponding arbor tree species are matched through the trait combination in the tree species database for forestry planting. Specifically, the tree species database used for matching tree species can also rely on the existing arbor database, and after screening the environment where the plot to be planted is located, it is supplemented into the tree species database for tree species matching.

[0097] See Figure 2 , the present invention also provides a stand mixing optimization and coordination system based on tree group units, including:

[0098] A basic construction module 100, which is used to obtain the information of arbor planting points in the surveyed forest to construct a survey model, and obtain the geographical information of the plot to be planted to construct a planting map;

[0099] A selection and survey module 200, which is used to select multiple tree group units in the survey model and survey the stand indexes of the tree group units, and randomly select points to be planted in the planting map;

[0100] A database construction module 300, which is used to extract the types and traits of arbors in the tree group units to construct a tree species database;

[0101] An information association module 400 is used to construct an association model between stand indicators and each pair of relative traits within the tree group unit;

[0102] A mixed planting analysis module 500 respectively obtains the optimal mixed planting ratio of each pair of relative traits under the optimal stand indicators based on the association model;

[0103] A trait assignment module 600 assigns traits to the to-be-planted points based on the optimal mixed planting ratio of each pair of relative traits;

[0104] A blueprint construction module 700 matches tree species for the to-be-planted points with assigned traits in the tree species database to obtain a planting blueprint;

[0105] A mixed planting module 800 performs tree species mixed planting on the to-be-planted plot based on the planting blueprint.

[0106] Although the embodiments of the present invention have been described in detail above, it is obvious to those skilled in the art that various modifications and changes can be made to these embodiments. However, it should be understood that such modifications and changes are all within the scope and spirit of the present invention described in the claims. Moreover, the present invention described herein can have other embodiments and can be implemented or realized in various ways.

Claims

1. A stand mixed optimization method based on tree cluster units, characterized in that, Including: Obtain the information of the tree planting points of arbors in the surveyed forest to construct a survey model, and select multiple tree cluster units. The tree cluster unit is a combination of 3 to 7 adjacent arbors that are not in a straight line, and the figure formed by the tree cluster units is a convex polygon; survey the stand indicators of the tree cluster units and extract the species and traits of the arbors to construct a tree species database; respectively count the mixed ratios of each relative trait within all tree cluster units and sort them to obtain a mixed ratio sequence. The relative traits include broad-leaved traits and needle-leaved traits, deciduous traits and evergreen traits, deep-root traits and shallow-root traits, root endophyte-dominated traits and root ectophyte-dominated traits, and the number of tree cluster units with each same mixed ratio is greater than or equal to the minimum threshold, and the deviation of the number of tree cluster units under each mixed ratio is less than or equal to 15%; comprehensively obtain the comprehensive value of the stand indicators of the tree cluster units with the same mixed ratio; construct the mapping relationship between the comprehensive value of the stand indicators and the mixed ratio sequence and fit to obtain an association model; respectively obtain the optimal mixed ratio of each pair of relative traits under the optimal stand indicators based on the association model; obtain the geographical information of the plot to be planted to construct a planting map and randomly select the points to be planted; after assigning trait values to the points to be planted based on the optimal mixed ratio of each pair of relative traits, match the arbor species in the tree species database to obtain a planting blueprint; Carry out tree species mixing on the plot to be planted based on the planting blueprint; When comprehensively obtaining the stand indicators of the tree cluster units with the same mixed ratio, it includes: ； Among them, is the comprehensive value of the stand indicators of the tree group unit at the th mixing ratio, is the total number of tree group units at the th mixing ratio, is the stand indicator of the th tree group unit at the th mixing ratio, is the forest age constraint coefficient of the th tree group unit at the th mixing ratio, is the orthographic projection area of the th tree group unit at the th mixing ratio.

2. The stand mixed optimization method according to claim 1, characterized in that The surveyed forest and the plot to be planted are in the same spatio-temporal environment; obtain the information of the tree planting points of arbors in the surveyed forest based on low-altitude technology; and / or, the surveyed forest includes an artificial mixed forest or a natural forest.

3. The stand mixed optimization method according to claim 1, characterized in that The information of the tree planting points of arbors includes arbor coordinates, leaf traits, and root shapes; the stand indicators include canopy density, mixing degree, volume, branch and leaf biomass, near-surface air plant VOCs content, carbon sink amount, water conservation amount, nitrogen fixation amount, mixed ratio, and negative oxygen ion concentration.

4. The stand mixed optimization method according to claim 1, characterized in that When constructing a survey model and selecting multiple tree cluster units, the number of the tree cluster units is: ； Among them, is the total number of tree cluster units in the investigation model, is the orthographic projection area of the investigation model, , are both adjustment coefficients, and is greater than 1; and / or, randomly select multiple tree cluster units in the survey model; and / or, the tree species database includes arbor species and the arbor traits corresponding to the arbor species, the arbor traits at least include the traits of the arbors within the tree cluster units, and the arbor species at least include the species of the arbors within the tree cluster units; and / or, there are no other arbors in the figure formed by the tree cluster units.

5. An optimization system for implementing the stand mixed optimization method according to any one of claims 1 to 4, characterized in that, Including: A basic construction module for obtaining the information of the tree planting points of arbors in the surveyed forest to construct a survey model, and obtaining the geographical information of the plot to be planted to construct a planting map; A selection and survey module for selecting multiple tree cluster units in the survey model, surveying the stand indicators of the tree cluster units, and randomly selecting the points to be planted in the planting map; the tree cluster unit is a combination of 3 to 7 adjacent arbors that are not in a straight line, and the figure formed by the tree cluster units is a convex polygon; A database construction module for extracting the species and traits of the arbors in the tree cluster units to construct a tree species database; An information association module, which is used to count the mixed ratios of each relative trait within all tree group units and sort them to obtain a mixed ratio sequence. The relative traits include broad-leaved traits and coniferous traits, deciduous traits and evergreen traits, deep-root traits and shallow-root traits, root endophyte-dominated traits and root ectophyte-dominated traits, and the number of tree group units with each same mixed ratio is greater than or equal to the minimum threshold, and the deviation of the number of tree group units under each mixed ratio is less than or equal to 15%; obtain the comprehensive value of stand indicators by synthesizing the stand indicators of tree group units with the same mixed ratio; Construct the mapping relationship between the comprehensive value of stand indicators and the mixed ratio sequence and fit to obtain an association model; When synthesizing the stand indicators of tree group units with the same mixed ratio, it includes: ； Among them, is the comprehensive value of the stand indicators of the tree group unit under the th mixing ratio, is the total number of tree group units under the th mixing ratio, is the stand indicator of the th tree group unit under the th mixing ratio, is the stand age constraint coefficient of the th tree group unit under the th mixing ratio, is the orthographic projection area of the th tree group unit under the th mixing ratio; A mixed analysis module, which respectively obtains the optimal mixed ratio of each pair of relative traits under the optimal stand indicators based on the association model; A trait assignment module, which assigns traits to the to-be-planted points based on the optimal mixed ratio of each pair of relative traits; A blueprint construction module, which matches arbor tree species for the to-be-planted points with assigned traits in the tree species database to obtain a planting blueprint; A mixed planting module, which conducts tree species mixing on the to-be-planted plot based on the planting blueprint.

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