Method and system for regulating the structure of mixed coniferous and broad-leaved over-matured forest communities

Abstract

The application discloses a kind of needle broadleaf mixed over-bark forest community structure regulation method and system.The method comprises: the target forest area is divided into first area to N area;Establish the seasonal development model of coniferous and broad-leaved species in each area, calculate the mixed over-bark recovery trend sequence in different time periods in each area;Set the felling intensity and mixed proportion adjustment scheme of different regions, calculate the intergroup effect between different regions based on the recovery trend sequence, obtain the intergroup effect sequence;According to stand response parameter, update the seasonal development model and intergroup effect sequence applied to the current preset stage to the next preset stage, to achieve target condition.The application realizes the accurate prediction of community structure recovery trend and the dynamic balance regulation of interregional ecological effect through the closed-loop iteration of seasonal development modeling and intergroup effect sequence, significantly improves the near-natural succession efficiency and structure stability of over-bark forest ecosystem.

Description

Technical Field

[0001] This invention relates to the field of forest data processing technology, specifically to a method and system for regulating the community structure of mixed coniferous and broad-leaved over-cut forests. Background Technology

[0002] Mixed coniferous and broad-leaved forests, as an important forest ecosystem type in temperate and cold-temperate zones, possess advantages such as high biodiversity, strong soil and water conservation capacity, and excellent ecological resilience. However, due to the long-term impact of high-intensity logging, a large number of mixed coniferous and broad-leaved forests have evolved into over-logged forests, generally exhibiting problems such as forest fragmentation, monoculture of dominant species, degradation of vertical structure, and regeneration faults. Currently, the regulation of community structure in over-logged forests largely relies on traditional experience-based tending or static indicator management, which reveals the following technical bottlenecks in practical applications: First, the differences in seasonal phenological development characteristics between evergreen, slow-growing, and shade-tolerant coniferous species and deciduous, fast-growing, and light-demanding broadleaf species have not been fully incorporated into dynamic prediction models. Existing methods mostly rely on rough estimations based on average annual growth, which leads to deviations from natural phenological cycles in the adjustment of mixed planting ratios and the setting of logging intensity, resulting in a disconnect between seasonal development and an imbalance in regeneration competition.

[0003] Secondly, the spatial heterogeneity of forest areas is severely oversimplified. Due to differences in over-logging gradients, topographic relief, and the distribution of remaining trees, different regions exhibit drastically different restoration potentials and disturbance responses. Existing technologies often treat forest stands as homogeneous entities, lacking quantitative representations of inter-group effects such as light and heat resource interception, root water competition, and litter feedback between adjacent areas. This makes it difficult to accurately assess the cascading effects of logging interventions in one area on the restoration process of another, leading to inaccurate spatial configuration and hindered niche differentiation.

[0004] Third, there is a disconnect between monitoring and early warning systems and control directives. Traditional management relies heavily on manual inspections and lagging sampling indicators, lacking a dynamic evolution algorithm that integrates multiple core parameters such as basal area at breast height and vegetation coverage. Intervention thresholds are static and fixed, making it impossible to track recovery slopes and deviation rates in real time. Operational time windows are prone to conflict with phenologically sensitive periods or extreme weather windows, resulting in low conservation efficiency and even triggering secondary ecological fragility.

[0005] Therefore, there is an urgent need in this field for a method to regulate the community structure of mixed coniferous and broad-leaved over-cut forests that can couple the seasonal development patterns of coniferous and broad-leaved trees, quantify the inter-regional and inter-group effects, and achieve adaptive closed-loop regulation. Summary of the Invention

[0006] To address the aforementioned problems, this invention provides a method and system for regulating the community structure of mixed coniferous and broad-leaved over-cut forests, which can couple the seasonal development patterns of coniferous and broad-leaved trees, quantify inter-regional and inter-group effects, and achieve adaptive closed-loop regulation.

[0007] A first aspect of the present invention provides a method for regulating the community structure of a mixed coniferous and broad-leaved over-cut forest, comprising the following steps: S1. Based on the topographic gradient and historical logging density of the target forest area, the target forest area is divided into the first region to the Nth region; S2. Obtain the initial mixed coniferous and broad-leaved tree ratio, over-cut forest gap area ratio and forest quality index for each region, establish a seasonal development model of coniferous and broad-leaved tree species for each region, and calculate the mixed over-cutting recovery trend sequence for each region in different time periods based on the initial mixed coniferous and broad-leaved tree ratio and over-cut forest gap area for each region. S3. Set up different regional logging intensity and mixed planting ratio adjustment schemes, and calculate the intergroup effect between different regions based on the mixed planting over-logging recovery trend sequence. The intergroup effect represents the change in the basal area at breast height and the change in vegetation coverage after each region is affected by any region, and obtain the intergroup effect sequence of each region. S4. Based on the intergroup effect sequence and the mixed over-harvesting recovery trend sequence, calculate the difference between each region and the target threshold at each preset stage under different adjustment schemes, and generate community structure adjustment instructions to ensure that the target region at each preset stage reaches the target threshold. S5. Execute the community structure adjustment command, monitor and update the stand response parameters of each region, and update the seasonal development model and the intergroup effect sequence according to the stand response parameters, applying them from the current preset stage to the next preset stage until the target conditions are met.

[0008] As a preferred approach, S2 establishes a seasonal development model and calculates the recovery trend sequence of mixed over-harvesting, specifically including the following steps: Based on the evergreen and slow-growing characteristics of coniferous trees and the deciduous and fast-growing characteristics of broad-leaved trees, the basic growth function for coniferous trees and the photosynthetic phenological response function for broad-leaved trees were constructed respectively. The coniferous basic growth function and the broadleaf photosynthetic response function are coupled into the seasonal development model. The proportion of over-cut forest window area is used as the spatial decay factor, and the initial coniferous-broadleaf mixed ratio is used as the initial boundary condition. The natural regeneration rate and canopy closure rate of trees under the alternation of spring, summer, autumn and winter are simulated, and the mixed over-cutting recovery trend sequence of each region at different time periods is output.

[0009] As a preferred method, S3 calculates the between-group effects between different regions and obtains the between-group effect sequence, specifically including the following steps: Construct a spatial interaction influence matrix to quantify the resource radiation intensity and competition inhibition intensity of region i on adjacent region j under a specific logging intensity and mixed planting ratio adjustment scheme; Extract the change in the cross-sectional area of ​​the breast height of region j after it has been affected by any other region within the time period t. Changes in vegetation cover The inter-group effect sequence is generated by weighting and superimposing the resource radiation intensity and the competition inhibition intensity. The formula for calculating the between-group effect sequence is: , in, , These are the weighting coefficients for the change in basal area at breast height and the change in vegetation coverage, respectively. The adjustment coefficient for the hybrid-mixed coupling is given. To influence the intensity of logging in the region, This is the amount to adjust the mixing ratio. This is the time-period decay factor.

[0010] As a preferred approach, achieving the target condition in S5 is specifically accomplished by constructing dynamic equilibrium variables between groups and determining the cooperative convergence relationship between the two sets of sequences, including the following steps: Define intergroup dynamic equilibrium variables The dynamic coupling ratio between the spatial distribution dispersion of the between-group effect sequence and the overall evolution magnitude of the mixed over-harvesting recovery trend sequence is used to quantify the relationship between the spatial distribution dispersion of the between-group effect sequence and the overall evolution magnitude of the mixed over-harvesting recovery trend sequence. Its calculation formula is: ; in, Let be the inter-group effect sequence value for region j at time period t. The mean of the inter-group effect sequence for the entire region. Let N be the mixed over-harvest recovery trend sequence value of region j in time period t, and N be the total number of regions. The target condition is set as follows: the recovery trend sequence value of mixed over-harvesting in each region. Reach the corresponding regional target thresholds respectively And the inter-group dynamic equilibrium variable Converges to the preset equilibrium threshold range; During the iterative process of the preset phase, real-time calculation The evolutionary trajectory, when When the value exceeds a preset equilibrium threshold, the current community structure is determined to be in a state of imbalance between intergroup effects. An asymmetric community structure adjustment command is generated, which reduces the pruning intensity and mixing ratio adjustment for areas with high intergroup effect output values, and increases the light penetration pruning frequency or directional replanting density for areas with negative intergroup effect reception values, until all areas... Synchronous leap and If the temperature drops below the preset equilibrium threshold, the target condition is considered met.

[0011] As a preferred method, S5 monitors and updates the stand response parameters for each region, specifically including the following steps: The photosynthetically active radiation, soil moisture content, canopy height and root zone impedance data of each region were collected in real time by a multi-source sensor array. After spatiotemporal alignment and noise filtering, key forest stand response parameters were extracted. The key stand response parameters are input into the calculation logic of the seasonal development model and the intergroup effect sequence, and the growth function decay coefficient and the weight of the spatial interaction matrix are dynamically corrected to complete the iterative update of the model parameters and adjustment coefficients. The update results are then applied from the current preset stage to the next preset stage.

[0012] As a preferred approach, before generating community structure adjustment instructions in S4, a dynamic translation step of the seasonal operation window is also included: Based on the delayed spring leaf unfolding of coniferous species and the advanced autumn leaf fall of broad-leaved species, and considering the impact of current pruning intensity on soil moisture content, the execution time window corresponding to the community structure adjustment command is shifted by 3-7 days to avoid phenological sensitive periods and extreme climate windows, and the optimal planting density and forest window overlap bandwidth are calculated in reverse.

[0013] As a preferred embodiment, the target threshold in S4 includes: The threshold for achieving the mixed planting ratio of coniferous and broad-leaved trees is used to set the target ratio range for coniferous and broad-leaved tree species in each region; Community structure steady-state thresholds are used to set structural stability boundaries for annual increase in basal area at breast height, vegetation cover, canopy closure, and interspecific niche differentiation index. The inter-group equilibrium critical threshold is used to limit the upper limit of convergence of the inter-group dynamic equilibrium variables; The target condition is a dynamic combination of the threshold for achieving the mixed coniferous and broadleaf planting ratio and the steady-state threshold for community structure, based on the differentiated mapping of site quality index and initial over-logging degree in each region.

[0014] A second aspect of the present invention provides a system for regulating the community structure of mixed coniferous and broad-leaved over-cut forests, comprising: The data acquisition module is configured to acquire the topographic gradient of the target forest area, the density of historical cleared forest land, the initial mixed coniferous and broad-leaved forest ratio of each region, the proportion of over-cut forest window area and forest quality index, and to collect forest stand response parameters of each region in real time. The data processing module communicates with the data acquisition module and is configured to establish seasonal development models of coniferous and broad-leaved tree species in each region and calculate the recovery trend sequence of mixed over-cutting; calculate the inter-group effect sequence based on the adjustment scheme of cutting intensity and mixed ratio; calculate the difference between the inter-group effect sequence and the recovery trend sequence and the target threshold of each preset stage to generate community structure adjustment instructions; construct inter-group dynamic equilibrium variables and determine target conditions; and iteratively update the seasonal development model and inter-group effect sequence based on the stand response parameters. The communication module is connected to both the data acquisition module and the data processing module. It is configured to transmit the parameters collected by the data acquisition module to the data processing module and send the community structure adjustment instructions generated by the data processing module to the execution terminal.

[0015] Compared with the prior art, the present invention has the following advantages: By constructing a seasonal development model for coniferous and broad-leaved tree species, the characteristics of evergreen slow-growing and deciduous fast-growing trees, the photosynthetic phenological response function, and the spatial decay factor of over-cut forest gaps are deeply integrated, ensuring that the regulation of mixed planting ratios and the prediction of recovery trends strictly conform to the natural phenological cycle. This mechanism significantly improves the spatiotemporal accuracy of forest gap closure rate and natural regeneration calculations, effectively avoiding the risks of seasonal development disconnect and phenological mismatch.

[0016] A computational mechanism based on the intergroup effect sequences of changes in basal area at breast height (BHH) and vegetation cover was developed, and combined with a spatial interaction matrix, to accurately quantify the resource radiation intensity and competitive inhibition intensity between adjacent areas under different logging intensities and mixed planting ratios. This quantitative representation breaks through the limitations of traditional assessments of overall stand homogeneity and provides direct data support for differentiated interventions, ecotone establishment, and interspecific niche maintenance in spatially heterogeneous forest areas.

[0017] The system tracks the coupling ratio between the spatial distribution dispersion of community structure and overall recovery potential. When deviations from a preset equilibrium threshold occur, the system automatically identifies imbalances in inter-group effects and generates asymmetric community structure adjustment commands. For areas with high effect output, it reduces pruning intensity and mixing ratios; for negatively receiving areas, it implements light-transmitting tending or targeted replanting to strengthen the effect, until the recovery trends of all areas synchronously cross the target threshold and inter-group effects tend to balance. This mechanism ensures precise matching between intervention actions and community dynamic evolution, significantly reducing reliance on human experience and regulatory lag. Attached Figure Description

[0018] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the method provided in an embodiment of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] In a first aspect, this embodiment provides a method for regulating the community structure of mixed coniferous and broad-leaved over-cut forests, such as... Figure 1 As shown, it includes the following steps: S1. Based on the topographic gradient and historical logging density of the target forest area, the target forest area is divided into the first region to the Nth region; S2. Obtain the initial mixed coniferous and broad-leaved tree ratio, over-cut forest gap area ratio and forest quality index for each region, establish a seasonal development model of coniferous and broad-leaved tree species for each region, and calculate the mixed over-cutting recovery trend sequence for each region in different time periods based on the initial mixed coniferous and broad-leaved tree ratio and over-cut forest gap area for each region. S3. Set up different regional logging intensity and mixed planting ratio adjustment schemes, and calculate the intergroup effect between different regions based on the mixed planting over-logging recovery trend sequence. The intergroup effect represents the change in the basal area at breast height and the change in vegetation coverage after each region is affected by any region, and obtain the intergroup effect sequence of each region. S4. Based on the intergroup effect sequence and the mixed over-harvesting recovery trend sequence, calculate the difference between each region and the target threshold at each preset stage under different adjustment schemes, and generate community structure adjustment instructions to ensure that the target region at each preset stage reaches the target threshold. S5. Execute the community structure adjustment command, monitor and update the stand response parameters of each region, and update the seasonal development model and the intergroup effect sequence according to the stand response parameters, applying them from the current preset stage to the next preset stage until the target conditions are met.

[0022] As a preferred approach, S2 establishes a seasonal development model and calculates the recovery trend sequence of mixed over-harvesting, specifically including the following steps: Based on the evergreen and slow-growing characteristics of coniferous trees and the deciduous and fast-growing characteristics of broad-leaved trees, the basic growth function for coniferous trees and the photosynthetic phenological response function for broad-leaved trees were constructed respectively. The coniferous basic growth function and the broadleaf photosynthetic response function are coupled into the seasonal development model. The proportion of over-cut forest window area is used as the spatial decay factor, and the initial coniferous-broadleaf mixed ratio is used as the initial boundary condition. The natural regeneration rate and canopy closure rate of trees under the alternation of spring, summer, autumn and winter are simulated, and the mixed over-cutting recovery trend sequence of each region at different time periods is output.

[0023] As a preferred method, S3 calculates the between-group effects between different regions and obtains the between-group effect sequence, specifically including the following steps: Construct a spatial interaction influence matrix to quantify the resource radiation intensity and competition inhibition intensity of region i on adjacent region j under a specific logging intensity and mixed planting ratio adjustment scheme; Extract the change in the cross-sectional area of ​​the breast height of region j after it has been affected by any other region within the time period t. Changes in vegetation cover The inter-group effect sequence is generated by weighting and superimposing the resource radiation intensity and the competition inhibition intensity. The formula for calculating the between-group effect sequence is: , in, , These are the weighting coefficients for the change in basal area at breast height and the change in vegetation coverage, respectively. The adjustment coefficient for the hybrid-mixed coupling is given. To influence the intensity of logging in the region, This is the amount to adjust the mixing ratio. This is the time-period decay factor.

[0024] As a preferred approach, achieving the target condition in S5 is specifically accomplished by constructing dynamic equilibrium variables between groups and determining the cooperative convergence relationship between the two sets of sequences, including the following steps: Define intergroup dynamic equilibrium variables The dynamic coupling ratio between the spatial distribution dispersion of the between-group effect sequence and the overall evolution magnitude of the mixed over-harvesting recovery trend sequence is used to quantify the relationship between the spatial distribution dispersion of the between-group effect sequence and the overall evolution magnitude of the mixed over-harvesting recovery trend sequence. Its calculation formula is: ; in, Let be the inter-group effect sequence value for region j at time period t. The mean of the inter-group effect sequence for the entire region. Let N be the mixed over-harvest recovery trend sequence value of region j in time period t, and N be the total number of regions. The target condition is set as follows: the recovery trend sequence value of mixed over-harvesting in each region. Reach the corresponding regional target thresholds respectively And the inter-group dynamic equilibrium variable Converges to the preset equilibrium threshold range; During the iterative process of the preset phase, real-time calculation The evolutionary trajectory, when When the value exceeds a preset equilibrium threshold, the current community structure is determined to be in a state of imbalance between intergroup effects. An asymmetric community structure adjustment command is generated, which reduces the pruning intensity and mixing ratio adjustment for areas with high intergroup effect output values, and increases the light penetration pruning frequency or directional replanting density for areas with negative intergroup effect reception values, until all areas... Synchronous leap and If the temperature drops below the preset equilibrium threshold, the target condition is considered met.

[0025] As a preferred method, S5 monitors and updates the stand response parameters for each region, specifically including the following steps: The photosynthetically active radiation, soil moisture content, canopy height and root zone impedance data of each region were collected in real time by a multi-source sensor array. After spatiotemporal alignment and noise filtering, key forest stand response parameters were extracted. The key stand response parameters are input into the calculation logic of the seasonal development model and the intergroup effect sequence, and the growth function decay coefficient and the weight of the spatial interaction matrix are dynamically corrected to complete the iterative update of the model parameters and adjustment coefficients. The update results are then applied from the current preset stage to the next preset stage.

[0026] As a preferred approach, before generating community structure adjustment instructions in S4, a dynamic translation step of the seasonal operation window is also included: Based on the delayed spring leaf unfolding of coniferous species and the advanced autumn leaf fall of broad-leaved species, and considering the impact of current pruning intensity on soil moisture content, the execution time window corresponding to the community structure adjustment command is shifted by 3-7 days to avoid phenological sensitive periods and extreme climate windows, and the optimal planting density and forest window overlap bandwidth are calculated in reverse.

[0027] As a preferred embodiment, the target threshold in S4 includes: The threshold for achieving the mixed planting ratio of coniferous and broad-leaved trees is used to set the target ratio range for coniferous and broad-leaved tree species in each region; Community structure steady-state thresholds are used to set structural stability boundaries for annual increase in basal area at breast height, vegetation cover, canopy closure, and interspecific niche differentiation index. The inter-group equilibrium critical threshold is used to limit the upper limit of convergence of the inter-group dynamic equilibrium variables; The target condition is a dynamic combination of the threshold for achieving the mixed coniferous and broadleaf planting ratio and the steady-state threshold for community structure, based on the differentiated mapping of site quality index and initial over-logging degree in each region.

[0028] A second aspect of this embodiment provides a system for regulating the structure of a mixed coniferous and broad-leaved over-cut forest community, comprising: The data acquisition module is configured to acquire the topographic gradient of the target forest area, the density of historical cleared forest land, the initial mixed coniferous and broad-leaved forest ratio of each region, the proportion of over-cut forest window area and forest quality index, and to collect forest stand response parameters of each region in real time. The data processing module communicates with the data acquisition module and is configured to establish seasonal development models of coniferous and broad-leaved tree species in each region and calculate the recovery trend sequence of mixed over-cutting; calculate the inter-group effect sequence based on the adjustment scheme of cutting intensity and mixed ratio; calculate the difference between the inter-group effect sequence and the recovery trend sequence and the target threshold of each preset stage to generate community structure adjustment instructions; construct inter-group dynamic equilibrium variables and determine target conditions; and iteratively update the seasonal development model and inter-group effect sequence based on the stand response parameters. The communication module is connected to both the data acquisition module and the data processing module. It is configured to transmit the parameters collected by the data acquisition module to the data processing module and send the community structure adjustment instructions generated by the data processing module to the execution terminal.

[0029] Example 2 Taking a secondary mixed coniferous and broadleaf forest area in Changbai Mountain as the research subject, the system divided the area into three regions based on topography and logging density: Region 1 (severely over-logged area), Region 2 (moderately selective logging disturbance area), and Region 3 (natural restoration front). Initially, each region was mainly restored naturally by broadleaf pioneer species, and the system continuously collected stand parameters and ran the model.

[0030] By the third monitoring period, the system calculations revealed that the rapid expansion of broad-leaved forests in the second region suppressed conifer regeneration in the first region, resulting in a persistently negative change in the basal area at breast height in the first region. Intergroup effect sequence calculations showed that the intergroup dynamic equilibrium variable surged and exceeded the preset equilibrium threshold. The system determined that the community had entered a state of intergroup effect imbalance, abandoning traditional homogenization interventions and immediately triggering asymmetric adjustment commands: automatically reducing the pruning intensity in the second region and suspending the increase in the mixed planting ratio, while simultaneously replanting shade-tolerant coniferous seedlings and increasing the frequency of light-transmitting tending in the first region. Based on spring soil moisture predictions, the system shifted the replanting operation window backward to avoid phenologically sensitive periods.

[0031] After iteratively updating the model's decay coefficient through multi-source monitoring parameter feedback, the fifth-year monitoring showed that the excessive shading effect in the second region was significantly alleviated, and the natural regeneration rate of conifers in the first region rebounded. At this point, the inter-group dynamic equilibrium variables smoothly converged to within the preset equilibrium threshold range, and the recovery trend sequence values ​​of each region synchronously crossed their dynamically mapped target thresholds. The thresholds for the mixed coniferous and broadleaf forest ratio and the steady-state threshold of the community structure were both met. The system determined that the target conditions had been met and seamlessly imported the optimized seasonal development model and inter-group effect parameters into the next preset stage. Ultimately, the forest area achieved optimized coniferous and broadleaf forest structure of different ages, stable horizontal mosaic pattern, and adaptive reconstruction of ecosystem service functions.

[0032] The specific steps for its execution are as follows: Step S1 is configured to divide the target mixed coniferous and broadleaf forest into three regions based on high-precision topographic gradient data and historical deforestation density distribution. Specifically, the system uses gridded sampling and spatial inversion techniques to obtain the initial mixed coniferous and broadleaf forest ratio, the proportion of deforestation gap area, and the forest quality index for each region. Based on the spatial heterogeneity of the site quality index and the initial degree of deforestation in each region, the system uses this as a benchmark for dynamically mapping differentiated target thresholds in subsequent stages and generates an initial community structure topology map and a set of zoning parameters.

[0033] Step S2 is configured to establish a seasonal development model for coniferous and broadleaf tree species and calculate the recovery trend sequence of mixed over-harvesting. Specifically, considering the biological phenological differences between evergreen, slow-growing, and shade-tolerant coniferous species and deciduous, fast-growing, and high-light-demand broadleaf species, the system constructs a basic growth function for coniferous trees and a photosynthetic phenological response function for broadleaf trees. These functions are coupled into the seasonal development model, using the proportion of over-harvested forest gaps as a spatial decay factor and the initial coniferous-broadleaf mixed ratio as the initial boundary condition, to simulate the natural regeneration rate and canopy closure rate of trees during the alternation of spring, summer, autumn, and winter. The model iterates periodically, outputting the recovery trend sequence of mixed over-harvesting in each region at different time periods, providing an evolutionary baseline for subsequent interventions.

[0034] Step S3 is configured to calculate the inter-group effects between different regions and generate an inter-group effect sequence. Specifically, the system sets differentiated logging intensity and mixed planting ratio adjustment schemes, constructs a spatial interaction influence matrix, and quantifies the resource radiation intensity and competition inhibition intensity of adjacent regions under specific intervention parameters. The system extracts the changes in basal area at breast height (BHH) and vegetation cover after the target region is affected by any neighboring region within a specified time period, and substitutes them into the inter-group effect calculation logic: First, the change in BHHH is multiplied by a first weighting coefficient, and then the change in vegetation cover is multiplied by a second weighting coefficient; second, the cumulative sum of the products of logging intensity, mixed planting ratio adjustment, and time period attenuation factor for each affected region is calculated, and then multiplied by the logging-mixing coupling adjustment coefficient; finally, the above two results are weighted and superimposed to obtain the inter-group effect sequence value for the region in the current time period. This sequence represents the intensity distribution of resource competition and ecological cascade response between regions in real time.

[0035] Step S4 is configured to calculate the difference, generate community structure adjustment instructions, and execute seasonal shifts. Specifically, the system calculates the difference between each region and the target threshold at each preset stage under different adjustment schemes. The target thresholds cover three categories: the threshold for achieving the standard ratio of coniferous and broadleaf mixed planting, the community structure steady-state threshold, and the critical threshold for inter-group equilibrium. The specific target threshold for each region is dynamically generated by the system based on its site quality index and initial over-logging degree. Based on the difference results, community structure adjustment instructions are generated and issued to the corresponding target regions. Before the instructions are generated, the system performs a dynamic shift of the seasonal operation window: combining the delayed spring leaf expansion of coniferous trees and the advanced autumn leaf fall of broadleaf trees, superimposed with the predicted disturbance of soil moisture content by the current logging intensity, the instruction execution time window is shifted by a specified number of days to avoid phenologically sensitive periods and extreme climate windows, and the optimal planting density and forest window overlap bandwidth are calculated simultaneously.

[0036] Step S5 is configured to execute instructions, monitor stand response parameters, and iteratively update them until the target conditions are met. Specifically, the system uses a multi-source sensor array to collect real-time data on photosynthetically active radiation, soil moisture content, canopy height, and root zone impedance in each region. After spatiotemporal alignment and noise filtering at edge computing nodes, key stand response parameters are extracted and fed back to the model layer to dynamically correct the growth decay coefficient and spatial interaction matrix weights. This completes the parameter iteration of the seasonal development model and the inter-group effect sequence, and the updated results are applied from the current preset stage to the next preset stage. The core decision logic of this step introduces an inter-group dynamic equilibrium variable to quantify the dynamic coupling ratio between the spatial distribution dispersion of the inter-group effect sequence and the overall evolution magnitude of the recovery trend sequence. This variable is calculated as follows: first, the standard deviation of the inter-group effect sequence values ​​for the entire region is calculated to represent the spatial distribution dispersion; then, the arithmetic mean of the recovery trend sequence values ​​for mixed over-harvesting in the entire region is calculated to represent the overall evolution magnitude; finally, the standard deviation is divided by the arithmetic mean to obtain the inter-group dynamic equilibrium variable. The system sets dual-threshold collaborative convergence as the target condition: the recovery trend sequence value of each region must reach its corresponding dynamic target threshold, and the aforementioned inter-group dynamic equilibrium variable must converge to within the preset equilibrium threshold range. During the iteration process, the evolution trajectory of this variable is tracked in real time. When it exceeds the preset equilibrium threshold, the community is determined to be in an inter-group effect imbalance state, and the system immediately generates an asymmetric community structure adjustment command: for regions with high inter-group effect output values, the pruning intensity and mixed planting ratio adjustment amount are automatically reduced; for regions with negative inter-group effect reception values, the light penetration pruning frequency or directional replanting density is increased. This closed-loop control continues until the recovery trend sequence value of each region synchronously crosses the dynamic target threshold and the inter-group dynamic equilibrium variable drops below the preset equilibrium threshold, at which point the target condition is determined to have been met.

[0037] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent variations only. Individual components and functions are optional unless explicitly required, and the order of operations may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or” as used in this application means including one or more of the associated listed combinations. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0038] 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 using electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods for each specific application to achieve the described functions, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the described devices, apparatuses, and units can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0039] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, function, and operation of implementations of apparatus, methods, and computer program products according to embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions indicated in the blocks may occur in a different order than indicated in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description; sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based device that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A method for regulating the community structure of mixed coniferous and broad-leaved over-cut forests, characterized in that, Includes the following steps: S1. Based on the topographic gradient and historical logging density of the target forest area, the target forest area is divided into the first region to the Nth region; S2. Obtain the initial mixed coniferous and broad-leaved tree ratio, over-cut forest gap area ratio and forest quality index for each region, establish a seasonal development model of coniferous and broad-leaved tree species for each region, and calculate the mixed over-cutting recovery trend sequence for each region in different time periods based on the initial mixed coniferous and broad-leaved tree ratio and over-cut forest gap area for each region. S3. Set up different regional logging intensity and mixed planting ratio adjustment schemes, and calculate the intergroup effect between different regions based on the mixed planting over-logging recovery trend sequence. The intergroup effect represents the change in the basal area at breast height and the change in vegetation coverage after each region is affected by any region, and obtain the intergroup effect sequence of each region. S4. Based on the intergroup effect sequence and the mixed over-harvesting recovery trend sequence, calculate the difference between each region and the target threshold at each preset stage under different adjustment schemes, and generate community structure adjustment instructions to ensure that the target region at each preset stage reaches the target threshold. S5. Execute the community structure adjustment command, monitor and update the stand response parameters of each region, and update the seasonal development model and the intergroup effect sequence according to the stand response parameters, applying them from the current preset stage to the next preset stage until the target conditions are met.

2. The method according to claim 1, characterized in that, In S2, a seasonal development model is established and the recovery trend sequence of mixed over-harvesting is calculated, which includes the following steps: Based on the evergreen and slow-growing characteristics of coniferous trees and the deciduous and fast-growing characteristics of broad-leaved trees, the basic growth function for coniferous trees and the photosynthetic phenological response function for broad-leaved trees were constructed respectively. The coniferous basic growth function and the broadleaf photosynthetic response function are coupled into the seasonal development model. The proportion of over-cut forest window area is used as the spatial decay factor, and the initial coniferous-broadleaf mixed ratio is used as the initial boundary condition. The natural regeneration rate and canopy closure rate of trees under the alternation of spring, summer, autumn and winter are simulated, and the mixed over-cutting recovery trend sequence of each region at different time periods is output.

3. The method according to claim 1, characterized in that, S3 calculates the between-group effects between different regions and obtains the between-group effect sequence, specifically including the following steps: Construct a spatial interaction influence matrix to quantify the resource radiation intensity and competition inhibition intensity of region i on adjacent region j under a specific logging intensity and mixed planting ratio adjustment scheme; Extract the change in the cross-sectional area of ​​the breast height of region j after it has been affected by any other region within the time period t. Changes in vegetation cover The inter-group effect sequence is generated by weighting and superimposing the resource radiation intensity and the competition inhibition intensity. The formula for calculating the between-group effect sequence is: ， in, , These are the weighting coefficients for the change in basal area at breast height and the change in vegetation coverage, respectively. The adjustment coefficient for the hybrid-mixed coupling is given. To influence the intensity of logging in the region, This is the amount to adjust the mixing ratio. This is the time-period decay factor.

4. The method according to claim 3, characterized in that, The target condition in S5 is achieved by constructing dynamic equilibrium variables between groups and determining the cooperative convergence relationship between the two sets of sequences, including the following steps: Define intergroup dynamic equilibrium variables The dynamic coupling ratio between the spatial distribution dispersion of the between-group effect sequence and the overall evolution magnitude of the mixed over-harvesting recovery trend sequence is used to quantify the relationship between the spatial distribution dispersion of the between-group effect sequence and the overall evolution magnitude of the mixed over-harvesting recovery trend sequence. Its calculation formula is: ； in, Let be the inter-group effect sequence value for region j at time period t. The mean of the inter-group effect sequence for the entire region. Let N be the mixed over-harvest recovery trend sequence value of region j in time period t, and N be the total number of regions. The target condition is set as follows: the recovery trend sequence value of mixed over-harvesting in each region. Reach the corresponding regional target thresholds respectively And the inter-group dynamic equilibrium variable Converges to the preset equilibrium threshold range; During the iterative process of the preset phase, real-time calculation The evolutionary trajectory, when When the value exceeds a preset equilibrium threshold, the current community structure is determined to be in a state of imbalance between intergroup effects. An asymmetric community structure adjustment command is generated, which reduces the pruning intensity and mixing ratio adjustment for areas with high intergroup effect output values, and increases the light penetration pruning frequency or directional replanting density for areas with negative intergroup effect reception values, until all areas... Synchronous leap and If the temperature drops below the preset equilibrium threshold, the target condition is considered met.

5. The method according to claim 4, characterized in that, The S5 process monitors and updates forest stand response parameters for each region, specifically including the following steps: The photosynthetically active radiation, soil moisture content, canopy height and root zone impedance data of each region were collected in real time by a multi-source sensor array. After spatiotemporal alignment and noise filtering, key forest stand response parameters were extracted. The key stand response parameters are input into the calculation logic of the seasonal development model and the intergroup effect sequence, and the growth function decay coefficient and the weight of the spatial interaction matrix are dynamically corrected to complete the iterative update of the model parameters and adjustment coefficients. The update results are then applied from the current preset stage to the next preset stage.

6. The method according to claim 5, characterized in that, Before generating community structure adjustment instructions in S4, a dynamic translation step for seasonal operation windows is also included: Based on the delayed spring leaf unfolding of coniferous species and the advanced autumn leaf fall of broad-leaved species, and considering the impact of current pruning intensity on soil moisture content, the execution time window corresponding to the community structure adjustment command is shifted by 3-7 days to avoid phenological sensitive periods and extreme climate windows, and the optimal planting density and forest window overlap bandwidth are calculated in reverse.

7. The method according to claim 6, characterized in that, The target threshold in S4 includes: The threshold for achieving the mixed planting ratio of coniferous and broad-leaved trees is used to set the target ratio range for coniferous and broad-leaved tree species in each region; Community structure steady-state thresholds are used to set structural stability boundaries for annual increase in basal area at breast height, vegetation cover, canopy closure, and interspecific niche differentiation index. The inter-group equilibrium critical threshold is used to limit the upper limit of convergence of the inter-group dynamic equilibrium variables; The target condition is a dynamic combination of the threshold for achieving the mixed coniferous and broadleaf planting ratio and the steady-state threshold for community structure, based on the differentiated mapping of site quality index and initial over-logging degree in each region.

8. A system for regulating the community structure of mixed coniferous and broad-leaved over-cut forests, suitable for implementing the method of any one of claims 1-7, characterized in that, include: The data acquisition module is configured to acquire the topographic gradient of the target forest area, the density of historical cleared forest land, the initial mixed coniferous and broad-leaved forest ratio of each region, the proportion of over-cut forest window area and forest quality index, and to collect forest stand response parameters of each region in real time. The data processing module communicates with the data acquisition module and is configured to establish seasonal development models of coniferous and broad-leaved tree species in each region and calculate the recovery trend sequence of mixed over-cutting; calculate the inter-group effect sequence based on the adjustment scheme of cutting intensity and mixed ratio; calculate the difference between the inter-group effect sequence and the recovery trend sequence and the target threshold of each preset stage to generate community structure adjustment instructions; construct inter-group dynamic equilibrium variables and determine target conditions; and iteratively update the seasonal development model and inter-group effect sequence based on the stand response parameters. The communication module is connected to both the data acquisition module and the data processing module. It is configured to transmit the parameters collected by the data acquisition module to the data processing module and send the community structure adjustment instructions generated by the data processing module to the execution terminal.

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