A comprehensive monitoring system and method for forest-based health and wellness environments

Abstract

This invention discloses a comprehensive monitoring system and method for forest health and wellness environments, belonging to the field of environmental monitoring technology. The system includes acquiring data such as the area of ​​fallen leaves around the forest health and wellness activity area, the degree of shading above the fallen leaves, weather data, the number of trees, and the current date. The weather data includes wind speed and rainfall. Based on the area of ​​fallen leaves around the forest health and wellness activity area and the weather data, a probability model for leaf accumulation is established to generate a probability of leaf accumulation. Based on the number of trees and the current date, a probability model for leaf shedding and accumulation is established to generate a probability of leaf shedding and accumulation. Based on the probability of leaf accumulation and leaf shedding and accumulation, a leaf analysis index for the health and wellness environment is generated. The environment of the forest health and wellness activity area is marked based on the leaf analysis index. This invention achieves dynamic monitoring and prediction of leaf accumulation, improving the efficiency and accuracy of forest health and wellness environment maintenance.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of environmental monitoring, and particularly relates to a comprehensive monitoring system and method for forest health care environment. BACKGROUND

[0002] With the rapid development of forest health care industry, people's requirements for health care environment quality are increasing. At present, the monitoring of forest environment mainly focuses on macro indicators such as air quality and negative oxygen ion concentration, while ignoring the important environmental factor of fallen leaf accumulation. Although fallen leaves are a natural phenomenon, if they accumulate too much, they not only affect the landscape beauty, but also breed bacteria due to damp and rot, affect air quality, and even pose a potential threat to the health of health care personnel.

[0003] In the prior art, there is a lack of fine monitoring and prediction means for the dynamic change factor of fallen leaf accumulation. The traditional manual patrol method has the defects of low efficiency and limited coverage, and it is difficult to scientifically predict the future accumulation trend of fallen leaves, so it is impossible to realize active and preventive environmental maintenance and management. Especially in different seasons and weather conditions, the fallen leaf accumulation situation has significant differences, and the existing technology cannot accurately evaluate the comprehensive influence of these complex factors on fallen leaf accumulation, resulting in the lack of scientific basis and pertinence for health care environment maintenance. In addition, the existing monitoring system often cannot comprehensively analyze the fallen leaf accumulation and leaf shedding trend, and it is difficult to realize the comprehensive evaluation and early warning of the fallen leaf condition of forest health care environment. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a comprehensive monitoring system and method for forest health care environment, which solves the above problems.

[0005] To achieve the above purpose, the present application realizes the following technical scheme: a comprehensive monitoring method for forest health care environment, comprising:

[0006] acquiring fallen leaf area around the forest health care activity area, fallen leaf blocking degree above the fallen leaf, weather data, tree number and current date; wherein the weather data includes wind speed and rainfall;

[0007] establishing a fallen leaf accumulation probability model according to the fallen leaf area around the forest health care activity area and the weather data, and generating a fallen leaf accumulation probability;

[0008] establishing a leaf shedding accumulation probability model according to the tree number and the current date, and generating a leaf shedding accumulation probability;

[0009] generating a health care environment fallen leaf analysis index according to the fallen leaf accumulation probability and the leaf shedding accumulation probability;

[0010] labeling the environment of the forest health care activity area according to the health care environment fallen leaf analysis index.

[0011] Based on the above technical solutions, the present invention also provides the following optional technical solutions:

[0012] Further technical solution: The method for generating the probability of leaf accumulation specifically includes:

[0013] Rainfall inhibition factors are generated based on rainfall amount and the degree of shading above fallen leaves;

[0014] Based on the leaf fall area, wind speed, and rainfall inhibition factor around the forest health and wellness activity area, a leaf fall accumulation probability model is established to generate the leaf fall accumulation probability.

[0015] Further technical solution: The specific method for generating the rainfall inhibition factor includes:

[0016] Through the formula:

[0017] Generate rainfall inhibition factor ;

[0018] In the formula, R represents the rainfall amount. ref This represents the rainfall threshold. The attenuation coefficient of rainfall, This indicates the degree of shading above the fallen leaves.

[0019] A further technical solution: The expression for the leaf accumulation probability model is specifically as follows:

[0020] In the expression, This represents the probability of fallen leaves accumulating. This represents the area of ​​fallen leaves. This indicates the area of ​​the forest health and wellness activity zone. This indicates the wind speed in the forest health and wellness activity area. This represents the wind speed threshold. This represents the rainfall inhibition factor, and k is an empirical coefficient.

[0021] Further technical solution: The method for generating the probability of leaf fall and accumulation specifically includes:

[0022] Generate a correction value for the tree leaf fall period based on the current date;

[0023] Based on the correction value of the tree leaf fall period and the number of trees, a probability model of leaf fall and accumulation is established to generate the probability of leaf fall and accumulation.

[0024] Further technical solution: The method for generating the tree leaf fall period correction value specifically includes:

[0025] Through the formula:

[0026] ;

[0027] Generating tree leaf fall period correction values ;

[0028] In the formula, This represents the amplitude adjustment parameter, and t represents the current date. This indicates the date corresponding to the peak period of tree leaf shedding. This represents the duration of the tree's leaf-shedding period, where n is a constant.

[0029] Further technical solution: The expression for the probability model of leaf fall and accumulation is as follows:

[0030] ;

[0031] In the expression, This represents the probability of leaves falling and accumulating. This represents the daily amount of leaves falling from trees. It represents the area of ​​the tree's leaves. This indicates the area of ​​the forest health and wellness activity zone. This represents the correction value for the tree's leaf-fall period, and y is the empirical proportionality coefficient.

[0032] Further technical solution: The specific method for generating the leaf fall analysis index of the health and wellness environment includes:

[0033] Through the formula:

[0034] ;

[0035] Generate leaf fall analysis index for health and wellness environment ;

[0036] In the formula, This represents the probability of fallen leaves accumulating. This represents the threshold for the probability of fallen leaves accumulating. This represents the probability of leaves falling and accumulating. This represents the threshold for the probability of fallen leaves accumulating. , All are weighting coefficients.

[0037] A comprehensive monitoring system for forest health and wellness environments, the system being used to implement the aforementioned comprehensive monitoring method for forest health and wellness environments, specifically including:

[0038] The data acquisition unit is used to acquire information such as the area of ​​fallen leaves, the degree of shading above the fallen leaves, weather data, the number of trees, and the current date around the forest health and wellness activity area; among which, the weather data includes wind speed and rainfall.

[0039] The leaf fall analysis unit is used to establish a leaf fall accumulation probability model and generate leaf fall accumulation probability based on the leaf fall area and weather data around the forest health and wellness activity area.

[0040] The leaf fall analysis unit is used to build a probability model of leaf fall accumulation based on the number of trees and the current date, and generate the probability of leaf fall accumulation.

[0041] The environmental analysis unit is used to generate a leaf fall analysis index for the health and wellness environment based on the probability of leaf accumulation and the probability of leaf fall and accumulation.

[0042] The classification unit is used to classify the environment of forest health and wellness activity areas based on the leaf fall analysis index of the health and wellness environment.

[0043] Further technical solution: The leaf analysis unit specifically includes:

[0044] The rainfall analysis module is used to generate rainfall inhibition factors based on rainfall amount and the degree of shading above fallen leaves;

[0045] The leaf accumulation probability generation module is used to establish a leaf accumulation probability model based on the leaf area, wind speed, and rainfall inhibition factor around the forest health and wellness activity area, and generate the leaf accumulation probability.

[0046] The leaf shedding analysis unit specifically includes:

[0047] The date analysis module is used to generate a correction value for the tree leaf fall period based on the current date;

[0048] The leaf shedding and accumulation probability generation module is used to establish a leaf shedding and accumulation probability model based on the tree leaf shedding period correction value and the number of trees, and generate the leaf shedding and accumulation probability.

[0049] This invention provides a comprehensive monitoring system and method for forest health and wellness environments, which has the following advantages compared with existing technologies:

[0050] This invention obtains information on fallen leaf area, weather data, number of trees, and date, and combines this information with a leaf accumulation probability model and a leaf fall accumulation probability model to generate a leaf analysis index for the health and wellness environment. This enables dynamic monitoring and prediction of leaf accumulation, improving the efficiency and accuracy of maintaining the forest health and wellness environment. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating a comprehensive monitoring method for a forest health and wellness environment provided by the present invention.

[0052] Figure 2 This is a flowchart illustrating step S20 of the present invention.

[0053] Figure 3 This is a flowchart illustrating step S30 of the present invention.

[0054] Figure 4 This is a schematic diagram of the structure of a comprehensive monitoring system for forest health and wellness environment provided by the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0056] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0057] Please see Figure 1 The present invention provides a comprehensive monitoring method for a forest health and wellness environment, comprising the following steps:

[0058] Step S10: Obtain the area of ​​fallen leaves, the degree of shading above the fallen leaves, weather data, number of trees, and current date around the forest health and wellness activity area; where weather data includes wind speed and rainfall; fallen leaf area refers to the area covered by fallen leaves;

[0059] Step S20: Based on the area of ​​fallen leaves and weather data around the forest health and wellness activity area, establish a probability model for fallen leaf accumulation and generate the probability of fallen leaf accumulation;

[0060] Step S30: Based on the number of trees and the current date, establish a probability model for leaf fall and accumulation, and generate the probability of leaf fall and accumulation;

[0061] Step S40: Generate a leaf fall analysis index for the health and wellness environment based on the probability of leaf accumulation and the probability of leaf fall accumulation;

[0062] Step S50: Mark the environment of the forest health and wellness activity area according to the leaf fall analysis index of the health and wellness environment;

[0063] Among them, the fallen leaf area refers to the area covered by fallen leaves in a unit area. It can be measured by drone aerial image analysis technology to characterize the migration basis of existing fallen leaves.

[0064] The degree of shading above fallen leaves refers to the extent to which the tree canopy blocks rainfall. Specifically, the proportion of the tree canopy's projected area can be obtained by scanning with lidar to calculate the impact of rainfall on the migration of fallen leaves.

[0065] The wind speed parameter in the weather data can be obtained from real-time monitoring data from meteorological stations, which can be used to quantify the driving effect of wind on the migration of fallen leaves.

[0066] The exact number of trees can be statistically determined using satellite remote sensing data combined with ground surveys, which is used to correlate the total number of trees with the amount of fallen leaves. The current date can be obtained using the GPS time synchronization module, which is used to match the phenological cycle of tree leaf fall.

[0067] Specifically, by using drone aerial photography and real-time data from weather stations to acquire parameters such as leaf area, wind speed, and rainfall, a leaf accumulation probability model is established to calculate the risk of secondary migration of existing fallen leaves under wind conditions. Combining tree quantity statistics and date parameters, a leaf fall accumulation probability model is constructed to predict the increase in newly fallen leaves. The two probabilities are weighted and fused to generate a comprehensive index. When the index exceeds a set threshold, an early warning marker is automatically triggered. For example, during windy autumn weather, the system calculates a significant increase in the probability of leaf migration, and combined with the high probability of leaf fall during peak leaf-fall periods, generates a high-risk index and marks the area as a priority for cleanup.

[0068] Compared to existing technologies, traditional methods relying on manual observation cannot quantitatively assess the risk of leaf accumulation. This invention achieves dynamic prediction by establishing a dual-probability model. Existing technologies lack synergistic analysis of meteorological conditions and phenological cycles. This invention constructs a multi-factor coupled assessment system by integrating wind speed, rainfall, tree quantity, and date parameters. Traditional manual inspections can only reflect the current state, while this invention can predict leaf accumulation trends in advance for a period of time.

[0069] Through the above technical solution, this invention achieves automated monitoring and trend prediction of leaf accumulation in forest health and wellness areas, solving the problems of low efficiency and limited coverage of manual patrols. By quantitatively assessing the dual impacts of leaf migration and new leaf fall, it provides managers with accurate environmental grading and marking criteria, effectively reducing the risk of bacterial growth caused by leaf accumulation and improving the timeliness and scientific nature of health and wellness environment maintenance.

[0070] For preferred options, please refer to [link / reference]. Figure 2 The present invention further proposes a method for generating the probability of leaf accumulation, specifically including:

[0071] Step S21: Generate a rainfall inhibition factor based on rainfall amount and the degree of shading above fallen leaves;

[0072] Step S22: Based on the leaf litter area, wind speed, and rainfall inhibition factor around the forest health and wellness activity area, establish a leaf litter accumulation probability model and generate the leaf litter accumulation probability;

[0073] Among them, the rainfall inhibition factor refers to the parameter used to quantify the inhibitory effect of rainfall on leaf accumulation. Specifically, it can be achieved by using an exponential function combined with rainfall threshold and shading parameter. For example, the impact of rainfall on leaf migration can be reflected by an exponential decay model.

[0074] The leaf area refers to the area covered by fallen leaves in a unit area. Specifically, it can be obtained by remote sensing image analysis or ground sensor measurement to characterize the base amount of current leaf accumulation.

[0075] Wind speed refers to the rate of air movement, which can be specifically measured using real-time monitoring data from weather stations. It is used to reflect the driving effect of wind on the migration of fallen leaves.

[0076] Specifically, under rainfall conditions, rainwater erosion reduces leaf accumulation, but this effect is influenced by the degree of shading from vegetation above the leaves. By establishing a rainfall inhibition factor, rainfall amount and shading parameters can be coupled for calculation. For example, when the shading degree is low, the probability of rainwater directly impacting the leaf layer increases, and the inhibition factor value decreases accordingly. Subsequently, leaf area, wind speed, and this inhibition factor are input into a probability model, where leaf area serves as the basic accumulation parameter, wind speed as the dynamic migration driving force parameter, and the rainfall inhibition factor as the environmental correction parameter. The three work together to calculate the probability of leaf accumulation. For example, even with high wind speeds during rainfall, the rainfall inhibition factor will still reduce the final probability value, thus accurately reflecting the inhibitory effect of rainwater erosion on the accumulation trend.

[0077] Compared to existing technologies, traditional methods rely solely on static estimations based on leaf area and wind speed, neglecting the complex impact of rainfall on leaf migration. This invention, by introducing a rainfall inhibition factor, is the first to incorporate rainfall and vegetation shading into a dynamic evaluation system, solving the problem of quantifying rainwater runoff. For example, existing technologies do not distinguish between the differences in rainfall impact between shaded and unshaded areas, while this invention achieves spatial difference analysis through the shading parameter.

[0078] Through the above technical solution, this invention can dynamically quantify the inhibitory effect of rainfall conditions on leaf accumulation and accurately predict leaf accumulation trends under different weather scenarios. For example, during continuous rainy weather, it can identify areas with reduced risk of leaf accumulation in low-shade areas in advance, providing data support for environmental maintenance. This solution effectively solves the technical deficiency of traditional methods in assessing the impact of rainfall erosion, and provides a scientific basis for the dynamic management of leaf accumulation in forest health and wellness areas.

[0079] Preferably, the present invention further proposes a method for generating the rainfall inhibition factor, specifically including:

[0080] Through the formula:

[0081] ;

[0082] Generate rainfall inhibition factor ;

[0083] In the formula, R represents the rainfall amount. ref This represents the rainfall threshold. The attenuation coefficient of rainfall, This indicates the degree of shading above the fallen leaves;

[0084] Among them, rainfall R refers to the amount of rainfall per unit time, which can be realized by real-time monitoring data from a rain gauge;

[0085] Rainfall threshold It refers to the critical rainfall at which a distinct water film begins to form on the forest surface. Specifically, it can be determined experimentally by measuring the water film formation threshold of different surface materials, and is used to determine whether rainfall has an inhibitory effect on leaf migration.

[0086] Attenuation coefficient The degree of influence of rainfall on the inhibitory factor is used to adjust the specific impact, which can be determined through regression analysis of historical data, and is used to reflect the sensitivity differences of rainfall inhibition effect under different surface conditions;

[0087] Shading above fallen leaves This refers to the degree to which the vegetation canopy blocks rainfall. Specifically, it can be achieved by measuring the proportion of the canopy's projected area using image analysis methods, which is then used to correct for the actual amount of rainfall reaching the leaf litter layer.

[0088] Specifically, by comparing the rainfall R with the shading above the fallen leaves... Coupled calculations were performed to obtain the effective rainfall amount actually acting on the leaf litter layer. A rainfall inhibition factor model was established using an exponential function, where the effective rainfall amount... When the threshold is approached or exceeded, the nonlinear characteristics of the exponential function cause the inhibition factor to decrease rapidly, accurately reflecting the physical inhibition effect of heavy rainfall on leaf movement. Attenuation coefficient. The introduction of this feature allows the model to adapt to the differences in frictional properties of different surface materials; for example, a larger value can be used in moss-covered areas to enhance the inhibition effect. (Opacity above fallen leaves) The corrected calculation of rainfall R effectively eliminates the interference of canopy shading on rainfall distribution, ensuring that the model only considers the impact of rainfall that actually reaches the leaf litter layer.

[0089] Compared to existing technologies, traditional methods only use linear relationships to estimate the impact of rainfall on leaf movement, failing to distinguish the differences in inhibition effects under different rainfall amounts. This invention accurately characterizes the nonlinear relationship between rainfall and inhibition effects using an exponential function model, and by combining shading correction and threshold determination, significantly improves the quantitative accuracy of rainfall inhibition effects. Existing technologies do not consider the uneven rainfall distribution caused by canopy shading; this invention effectively solves the technical deficiency of inaccurate rainfall measurement in canopy-covered areas by introducing a shading parameter.

[0090] Through the above technical solution, this invention can dynamically quantify the inhibitory effect of leaf accumulation under different rainfall conditions, accurately distinguishing between the weak inhibitory effect of light rain and the strong inhibitory effect of heavy rain. By correcting for shading, it effectively eliminates measurement errors caused by vegetation shading, ensuring that rainfall data truly reflects the actual rain-affected state of the leaf layer. This solution provides accurate dynamic correction parameters for the leaf accumulation probability model, significantly improving the reliability of leaf accumulation prediction.

[0091] Preferably, the present invention further proposes the following expression for the probability model of fallen leaf accumulation:

[0092] ;

[0093] In the expression, This represents the probability of fallen leaves accumulating. This represents the area of ​​fallen leaves. This indicates the area of ​​the forest health and wellness activity zone. This indicates the wind speed in the forest health and wellness activity area. This represents the wind speed threshold. This represents the rainfall inhibition factor, where k is an empirical coefficient;

[0094] Among them, the area of ​​fallen leaves It refers to the measured area of ​​fallen leaves currently covering the ground, which can be achieved through remote sensing image analysis or ground sensor measurement, and is used to characterize the initial state of fallen leaf accumulation.

[0095] Area of ​​forest health and wellness activity zone It refers to the overall area of ​​the monitoring area. Specifically, geographic information system data or field survey data can be used as the benchmark parameter to normalize the fallen leaf area into a proportional value.

[0096] Wind speed v refers to the real-time wind speed measurement value within the monitoring area, which can be collected by a meteorological station or a wind speed sensor, and is used to quantify the impact of wind on the migration of fallen leaves.

[0097] Wind speed threshold It refers to the critical wind speed that triggers significant migration of fallen leaves. For example, it can be an empirical value determined based on historical data or experiments, used to judge the intensity of wind action.

[0098] Rainfall Inhibition Factor This refers to the inhibitory effect of rainfall on leaf fixation, which can be calculated using a correlation model between rainfall and shading degree, and used to dynamically adjust the impact of precipitation on leaf migration.

[0099] The empirical coefficient k refers to the calibration parameter between the model and the actual scenario. It can be obtained, for example, by fitting historical data or optimizing through machine learning, and is used to improve the adaptability of the model.

[0100] Specifically, the model uses the area of ​​fallen leaves... Area of ​​forest health and wellness activities The ratio of wind speed v to wind speed threshold is used as a basic parameter to reflect the potential impact of the current leaf cover on subsequent accumulation; The ratio represents the driving effect of wind on the migration of fallen leaves, for example, when the wind speed v exceeds the wind speed threshold. At times, increased wind force may lead to the dispersal of fallen leaves; rainfall inhibits the spread of these leaves. The model dynamically adjusts the fixation effect of precipitation on fallen leaves. For example, as rainfall R increases, leaves become more likely to adhere to the ground after being soaked by rainwater, thus reducing the probability of accumulation. The combined effect of various parameters in a product form dynamically reflects the coupling mechanism of leaf area, wind force, and rainfall. An empirical coefficient k further corrects for deviations between the model and the actual scenario; for example, in areas with high vegetation density, the coefficient can be adjusted to compensate for the attenuation of wind force.

[0101] Compared with existing technologies, traditional methods rely on manual inspections or single meteorological parameters to determine the trend of leaf accumulation, and cannot quantify the coupling effect of multiple factors. This invention establishes a mathematical model that includes leaf area, wind speed, rainfall inhibition factor and empirical coefficients, and combines dynamic environmental parameters with geographic information to achieve refined prediction of leaf accumulation probability, solving the problems of strong subjectivity and limited coverage in existing technologies.

[0102] Through the above technical solution, this invention can dynamically predict the trend of fallen leaf accumulation based on real-time environmental data and geographic information, providing a scientific basis for environmental maintenance in health and wellness areas. For example, when the model predicts a high probability of accumulation, it can trigger an early warning and plan cleanup operations, thereby reducing air quality degradation and health risks caused by decaying fallen leaves and improving the management efficiency of the health and wellness environment.

[0103] For preferred options, please refer to [link / reference]. Figure 3 The present invention further proposes a method for generating the probability of leaf fall and accumulation, specifically including:

[0104] Step S31: Generate a tree leaf fall period correction value based on the current date;

[0105] Step S32: Based on the tree leaf fall period correction value and the number of trees, establish a leaf fall and accumulation probability model and generate the leaf fall and accumulation probability;

[0106] Among them, the tree leaf fall period correction value refers to the correction value that reflects different stages in the natural leaf fall cycle of trees through date parameters. Specifically, it can be implemented by using a Gaussian function combined with the peak leaf fall period parameter, which is used to dynamically adjust the weight of the leaf fall probability on different dates.

[0107] The leaf fall and accumulation probability model is a mathematical model that correlates the number of trees with the leaf fall period correction value. Specifically, it can be implemented using linear weighting or multiplication relationships to quantify the combined impact of tree size and time factors on leaf fall accumulation.

[0108] Specifically, the tree leaf fall period correction value uses a Gaussian function to convert the deviation of the current date from the peak leaf fall period date into a correction value. For example, the correction value reaches its peak near the peak leaf fall period and gradually decreases as the date moves further away from the peak. The leaf fall accumulation probability model calculates the probability by multiplying the number of trees by the correction value and then combining it with the amount of leaves fallen per unit area. For example, when the number of trees increases or when the peak leaf fall period is approaching, the probability value output by the model increases accordingly. By dynamically associating date parameters and the number of trees, the model can reflect the objective laws governing leaf fall trends with seasonal changes and vegetation scale variations.

[0109] Compared to existing technologies, traditional methods rely on manual inspections or fixed-period statistics, failing to capture the dynamic fluctuations in leaf fall. For example, existing technologies do not consider the differences between peak and stable periods of leaf fall, nor do they input the number of trees as a variable into the model. This invention constructs a dynamic model by introducing date parameters and the number of trees, enabling the prediction results to adaptively adjust with time and vegetation scale, thus solving the problem of static data lag.

[0110] Through the above technical solution, this invention can automatically adjust the prediction weight of fallen leaves according to seasonal changes and accurately quantify the potential amount of fallen leaves by combining the number of trees in the area. For example, during the peak period of leaf fall, the correction value is automatically increased to make the model output a higher probability of accumulation; when the number of trees in the area increases, the model synchronously increases the prediction value. This enables dynamic tracking of the trend of fallen leaf accumulation, provides data support for environmental maintenance, and avoids the problems of low efficiency and insufficient coverage of manual inspections.

[0111] Preferably, the present invention further proposes a method for generating the tree leaf fall period correction value, specifically including:

[0112] Through the formula:

[0113] ;

[0114] Generating tree leaf fall period correction values ;

[0115] In the formula, This represents the amplitude adjustment parameter, and t represents the current date. This indicates the date corresponding to the peak period of tree leaf shedding. This represents the duration of the tree's leaf-shedding period, where n is a constant, typically taking the value of 2.

[0116] Among them, amplitude adjustment parameters This represents the adjustment parameter for the range of fluctuation of the correction value. It can be determined by empirical values ​​or by fitting experimental data to adapt to the differences in leaf fall amplitude among different tree species or regions.

[0117] The current date t represents the date of the monitoring, which can be expressed as a Gregorian calendar date or a segmented meteorological season, to reflect the dynamic characteristics of the leaf fall period over time;

[0118] Peak period of tree leaf fall The corresponding date represents the date when leaf fall is most concentrated for a specific tree species or region. It can be determined by statistical analysis of historical observation data or prediction by phenological models and is used as a time reference point for calculating correction values.

[0119] Duration of the tree's leaf-shedding period It represents the duration of leaf fall activity before and after the peak of leaf fall, which can be quantified by standard deviation or empirical days, and is used to adjust the distribution range of the correction value as a function of date.

[0120] Specifically, this technical solution uses the time difference between the current date and the peak leaf fall period as an input parameter, and utilizes a Gaussian function structure to generate a dynamically adjusted correction value over time. When the monitoring date is close to the peak leaf fall period, the square term in the function approaches zero, the exponential function value reaches its maximum, and the correction value increases significantly. When the date deviates from the peak period, the square term increases with the difference, causing the exponential function value to decay, and the correction value gradually returns to the baseline level. The amplitude adjustment parameter controls the maximum fluctuation range of the correction value, the duration adjusts the time range of the leaf fall period's influence, and the constant value optimizes the shape of the function curve. Through this dynamic correction mechanism, the impact of different seasons on leaf fall can be accurately quantified, overcoming the deficiency of traditional static models where fixed coefficients cannot reflect the temporal characteristics of the leaf fall period.

[0121] Compared to existing technologies, traditional methods typically rely on fixed seasonal coefficients or manual experience to determine the leaf-fall period, failing to accurately quantify the temporal correlation between dates and peak leaf fall. This invention establishes a Gaussian function model based on date differences, using the time location, duration, and correction magnitude of the peak leaf fall period as adjustable parameters to mathematically model the dynamic impact on the leaf-fall period. This time-series-based dynamic correction mechanism can more scientifically predict the changing trends in the probability of leaf fall and accumulation on different dates.

[0122] Through the above technical solution, this invention can automatically adjust the correction value according to the real-time changes of the monitoring date, accurately capturing the fluctuation characteristics of leaf drop before and after the peak leaf fall period. This dynamic correction method solves the problem that traditional static models cannot reflect seasonal changes, making the calculation results of the probability of leaf fall and accumulation more consistent with the actual leaf fall pattern, and providing accurate prediction of leaf accumulation trends for the maintenance of health and wellness environments.

[0123] Preferably, the present invention further proposes the following expression for the probability model of leaf shedding and accumulation:

[0124] ;

[0125] In the expression, This represents the probability of leaves falling and accumulating. This represents the daily amount of leaves falling from trees. It represents the area of ​​the tree's leaves. This indicates the area of ​​the forest health and wellness activity zone. This represents the correction value for the tree leaf fall period, where y is an empirical proportionality coefficient;

[0126] Among them, the daily amount of fallen leaves from trees It refers to the amount of leaves that fall naturally from a single tree per unit time. The specific amount can be determined by sensor measurement or empirical values ​​based on tree species databases. It is used to reflect the leaf drop characteristics of different tree species or the impact of tree health status on the amount of leaves falling.

[0127] Area of ​​tree leaves It refers to the average area of ​​tree leaves, which can be obtained through remote sensing image analysis or lidar scanning technology;

[0128] Tree leaf fall period correction value It refers to a coefficient that is dynamically adjusted based on the date. Specifically, it can be generated by fitting a time function to historical leaf fall data, and is used to capture the amplification effect of the peak leaf fall period on the amount of fallen leaves.

[0129] Specifically, the leaf fall and accumulation probability model calculates the potential leaf fall amount per tree by multiplying the daily leaf fall amount by the leaf area. This potential leaf fall amount is then used as a spatial distribution weight, proportional to the monitored area. Finally, the model is dynamically adjusted based on the impact of a leaf fall correction value on the time dimension. For example, during peak leaf fall periods, the correction value... This will significantly increase, leading to a higher probability value; when the number of trees increases, The increased product of these parameters further reflects the heightened risk of leaf accumulation in localized areas. This collaborative calculation of multidimensional parameters enables the model to quantify leaf accumulation trends in different seasons and tree species distribution areas, providing data support for predicting future risks.

[0130] Compared to existing technologies, traditional methods rely on manual inspections or fixed-period statistics, failing to dynamically reflect the correlation between leaf fall volume and temporal and spatial factors. This invention, however, integrates tree characteristics, regional distribution, and seasonal variations into probabilistic calculations through mathematical modeling, achieving continuous quantitative prediction of leaf accumulation trends. For example, existing technologies do not consider the nonlinear impact of peak leaf fall periods on the amount of fallen leaves, while this model uses correction values... Incorporating this factor into the calculation significantly improves prediction accuracy.

[0131] Through the above technical solution, this invention can dynamically predict the risk of leaf accumulation based on tree species, distribution density, and seasonal changes, solving the problems of data lag and limited coverage in traditional monitoring methods. For example, before the leaf fall season begins, the system can use correction values... By predicting areas of increased risk in advance, cleaning staff can be guided to prioritize high-probability areas, thereby avoiding landscape damage or sanitation problems caused by fallen leaves.

[0132] Preferably, the present invention further proposes a method for generating the leaf fall analysis index of the health and wellness environment, specifically including:

[0133] Through the formula:

[0134] ;

[0135] Generate leaf fall analysis index for health and wellness environment ;

[0136] In the formula, This represents the probability of fallen leaves accumulating. This represents the threshold for the probability of fallen leaves accumulating. This represents the probability of leaves falling and accumulating. This represents the threshold for the probability of fallen leaves accumulating. , All are weighting coefficients;

[0137] Among them, the probability of leaf accumulation refers to the probability of current leaf accumulation in the forest health and wellness activity area. Specifically, it can be calculated by leaf area, wind speed and rainfall inhibition factor, and is used to reflect the prediction of leaf accumulation.

[0138] The probability of leaf fall and accumulation refers to the potential accumulation trend after leaves fall in the future. It can be calculated by the number of trees, the leaf fall period correction value, and the daily amount of leaves falling, and is used to predict the dynamic changes of leaf fall.

[0139] The probability threshold for leaf accumulation and the probability threshold for leaf fall accumulation are critical values ​​calibrated based on historical data or experiments, used to determine whether leaf accumulation exceeds the safe range.

[0140] The weighting coefficient is used to adjust the contribution ratio of the two types of probabilities to the final index. Specifically, it can be dynamically adjusted according to the actual needs of the scenario. For example, during the peak leaf fall period, the weight of the probability of leaf fall and accumulation can be increased.

[0141] Specifically, by subtracting the corresponding thresholds from the probability of leaf accumulation and the probability of leaf fall accumulation, and then normalizing by dividing by the thresholds, the influence of different dimensions is eliminated, unifying the numerical range of the two probabilities. The introduction of weighting coefficients allows for adjusting the priority of the two probabilities based on seasonal changes or management needs. For example, during the non-leaf-falling season, the weight of the probability of leaf accumulation can be set to a higher value to strengthen the assessment of the current accumulation status; during peak leaf-falling season, the weight of the probability of leaf fall accumulation can be increased to enhance the predictive ability for future risks. The resulting health and wellness environment leaf fall analysis index comprehensively reflects the current accumulation status and future trends. When the index exceeds a preset threshold, an environmental risk warning is triggered, guiding managers to prioritize the cleanup of high-risk areas or take protective measures.

[0142] Compared to existing technologies, which rely on manual inspections or single-indicator assessments, these methods cannot quantify the dynamic changes in leaf accumulation. For example, traditional methods only monitor leaf area but do not consider the impact of wind speed and rainfall on accumulation, nor can they predict future leaf drop trends. This invention achieves a dynamic and comprehensive assessment of leaf drop risk by integrating real-time accumulation probability and future drop probability, combined with threshold normalization and weight adjustment.

[0143] Through the above technical solution, this invention solves the problems of traditional methods, such as a single assessment dimension and a lack of scientific prediction, and can accurately identify high-risk areas for leaf accumulation. For example, when the probability of leaf accumulation exceeds a threshold but the probability of leaf falling is low, the index suggests that existing accumulation should be cleared first; when both probabilities exceed the threshold, the index suggests that preventive measures should be taken immediately. This enables hierarchical and classified management of forest health and wellness activity areas, providing data support for environmental maintenance.

[0144] For preferred options, please refer to [link / reference]. Figure 4 The present invention further proposes a comprehensive monitoring system for forest health and wellness environments. This system is used to execute the aforementioned comprehensive monitoring method for forest health and wellness environments, specifically including:

[0145] The data acquisition unit 10 is used to acquire information such as the area of ​​fallen leaves, the degree of shading above the fallen leaves, weather data, the number of trees, and the current date around the forest health and wellness activity area; among which, the weather data includes wind speed and rainfall.

[0146] The leaf analysis unit 20 is used to establish a leaf accumulation probability model and generate leaf accumulation probability based on the leaf area and weather data around the forest health and wellness activity area.

[0147] The leaf fall analysis unit 30 is used to establish a leaf fall accumulation probability model based on the number of trees and the current date, and generate the leaf fall accumulation probability.

[0148] Environmental analysis unit 40 is used to generate a leaf fall analysis index for the health and wellness environment based on the probability of leaf accumulation and the probability of leaf fall and accumulation.

[0149] Classification unit 50 is used to classify the environment of forest health and wellness activity areas based on the leaf fall analysis index of the health and wellness environment.

[0150] Preferably, the present invention further proposes that the leaf analysis unit 20 specifically includes:

[0151] The rainfall analysis module is used to generate rainfall inhibition factors based on rainfall amount and the degree of shading above fallen leaves;

[0152] The leaf accumulation probability generation module is used to establish a leaf accumulation probability model based on the leaf area, wind speed, and rainfall inhibition factor around the forest health and wellness activity area, and generate the leaf accumulation probability.

[0153] Preferably, the present invention further proposes that the leaf shedding analysis unit 30 specifically includes:

[0154] The date analysis module is used to generate a correction value for the tree leaf fall period based on the current date;

[0155] The leaf shedding and accumulation probability generation module is used to establish a leaf shedding and accumulation probability model based on the tree leaf shedding period correction value and the number of trees, and generate the leaf shedding and accumulation probability.

[0156] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A comprehensive monitoring method of a forest health environment, characterized in that, Specifically comprising the following steps: Obtaining the leaf area around the forest health care activity area, the shielding degree above the fallen leaves, weather data, the number of trees and the current date; wherein the weather data includes wind speed and rainfall; According to the leaf area around the forest health care activity area and the weather data, a fallen leaf accumulation probability model is established to generate a fallen leaf accumulation probability; According to the number of trees and the current date, a leaf shedding accumulation probability model is established to generate a leaf shedding accumulation probability; According to the fallen leaf accumulation probability and the leaf shedding accumulation probability, a health care environment fallen leaf analysis index is generated; According to the health care environment fallen leaf analysis index, the environment of the forest health care activity area is marked; The fallen leaf accumulation probability is generated in the following manner: According to the rainfall and the shielding degree above the fallen leaves, a rainfall inhibition factor is generated; According to the leaf area around the forest health care activity area, the wind speed and the rainfall inhibition factor, a fallen leaf accumulation probability model is established to generate a fallen leaf accumulation probability; The generation manner of the rainfall inhibition factor specifically includes: Through the formula: ； Generating a rain suppression factor ; In the formula, R represents rainfall, R ref represents a rainfall threshold, is a decay coefficient of rainfall, represents the degree of obstruction above fallen leaves; The expression of the fallen leaf accumulation probability model is specifically: ； In the expression, represents the probability of leaf fall accumulation, represents the daily leaf fall amount of the tree, represents the area of the tree leaves, represents the area of the forest health and wellness activity area, represents the tree leaf fall period correction value, and y is an empirical proportionality coefficient. The generation manner of the health care environment fallen leaf analysis index specifically includes: Through the formula: ； Generating a health and wellness environment leaf fall analysis index ; In the formula, represents the fallen leaf accumulation probability, represents the fallen leaf accumulation probability threshold, represents the leaf shedding accumulation probability, represents the leaf shedding accumulation probability threshold, , are weight coefficients; The expression of the fallen leaf accumulation probability model is specifically: ； In the expression, represents the fallen leaf accumulation probability, represents the fallen leaf area, represents the area of the forest health and wellness activity area, represents the wind speed of the forest health and wellness activity area, represents the wind speed threshold value, represents the rainfall suppression factor, and k is an empirical coefficient. 2.The comprehensive monitoring method of the forest health environment according to claim 1, characterized in that, The generation manner of the leaf shedding accumulation probability specifically includes: According to the current date, a tree leaf shedding period correction value is generated; According to the tree leaf shedding period correction value and the number of trees, a leaf shedding accumulation probability model is established to generate a leaf shedding accumulation probability. 3.The comprehensive monitoring method of the forest health environment according to claim 2, characterized in that, The generation manner of the tree leaf shedding period correction value specifically includes: Through the formula: ； Generation of tree leaf fall correction values ; In the formula, represents the amplitude adjustment parameter, t represents the current date, represents the date corresponding to the peak of tree leaf fall, represents the duration length of the tree leaf fall period, and n is a constant.

4. A comprehensive monitoring system for forest health environment, characterized in that, The system is used to execute the comprehensive monitoring method of the forest health care environment according to any one of claims 1-3, and specifically comprises: A data acquisition unit is configured to obtain the leaf area around the forest health care activity area, the shielding degree above the fallen leaves, weather data, the number of trees and the current date; wherein the weather data includes wind speed and rainfall; A fallen leaf analysis unit is configured to establish a fallen leaf accumulation probability model according to the leaf area around the forest health care activity area and the weather data, and generate a fallen leaf accumulation probability; A leaf shedding analysis unit is configured to establish a leaf shedding accumulation probability model according to the number of trees and the current date, and generate a leaf shedding accumulation probability; An environment analysis unit is configured to generate a health care environment fallen leaf analysis index according to the fallen leaf accumulation probability and the leaf shedding accumulation probability; A classification unit is configured to classify the environment of the forest health care activity area according to the health care environment fallen leaf analysis index; The fallen leaf analysis unit specifically includes: A rainfall analysis module is configured to generate a rainfall inhibition factor according to the rainfall and the shielding degree above the fallen leaves; A fallen leaf accumulation probability generation module is configured to establish a fallen leaf accumulation probability model according to the leaf area around the forest health care activity area, the wind speed and the rainfall inhibition factor, and generate a fallen leaf accumulation probability. 5.The comprehensive monitoring system of forest health environment according to claim 4, characterized in that, The leaf shedding analysis unit specifically includes: A date analysis module is configured to generate a tree leaf shedding period correction value according to the current date; A leaf shedding accumulation probability generation module is configured to establish a leaf shedding accumulation probability model according to the tree leaf shedding period correction value and the number of trees, and generate a leaf shedding accumulation probability.

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