Garden vegetation health monitoring system and method

Abstract

The invention relates to the technical field of vegetation monitoring, and particularly discloses a garden vegetation health monitoring system and method.The garden vegetation health monitoring system comprises a growth state coincidence analysis module, an environmental stress influence analysis module, a regional biological information acquisition module, a biological stress influence analysis module and a vegetation health state analysis module; by monitoring and analyzing the growth state, the regional environment and the regional organisms of the target garden vegetation, the problem of one-sidedness of garden vegetation monitoring in the prior art is solved, intelligent monitoring and analysis of garden vegetation health are realized, the growth condition of the vegetation and various stress factors faced by the vegetation can be mastered in an all-round manner, and the monitoring accuracy is improved. Rich data support is provided for accurately evaluating the health state, healthy growth of garden vegetation is improved to a certain extent, garden management is promoted to develop in the scientific and intelligent direction, and the stability and landscape quality of a garden ecological system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vegetation monitoring, and in particular to a garden vegetation health monitoring system and method. Background Art

[0002] With the acceleration of urbanization and rising demands for ecological and environmental quality, the health of garden vegetation, as a crucial component of urban ecosystems, has drawn considerable attention. Garden vegetation not only beautifies the environment and purifies the air, but also plays a key role in regulating urban microclimates and maintaining biodiversity. Therefore, effectively monitoring the health of garden vegetation has become a crucial task in urban garden management.

[0003] Current technology for monitoring the health of garden vegetation primarily focuses on simple manual observation of changes in vegetation appearance, such as leaf color and morphology. However, this monitoring method has numerous shortcomings: 1. Manual inspections are inefficient and have limited coverage, making it difficult to achieve real-time, comprehensive monitoring of large garden areas. Furthermore, they lack a deep understanding of the physiological state of vegetation, and monitoring results are significantly influenced by subjective experience. Different individuals have varying judgment criteria, resulting in insufficient data accuracy and reliability.

[0004] 2. Environmental and biotic stresses in garden vegetation affect its health. Current technologies for monitoring environmental stress are insufficiently comprehensive and continuous, relying primarily on discrete, single-point measurements. These measurements fail to adequately reflect the spatiotemporal variations in the regional environment and struggle to comprehensively analyze the combined stress effects of multiple environmental factors on vegetation. Furthermore, traditional methods for monitoring invasive alien species rely primarily on manual identification, making it difficult to quickly and accurately detect and identify emerging invasive species, or to promptly assess their spread and severity. When vegetation health issues arise, traditional monitoring methods, lacking comprehensive analysis of multi-source data, make it difficult to accurately identify the root cause, resulting in ineffective and untargeted response measures.

[0005] In order to solve the above-mentioned defects, a technical solution is now provided. Summary of the Invention

[0006] The purpose of the present invention is to provide a garden vegetation health monitoring system and method to solve the problems raised in the above background.

[0007] The purpose of the present invention can be achieved through the following technical solutions: In a first aspect, the present invention provides a garden vegetation health monitoring system, which includes:

[0008] The vegetation basic information collection module is used to collect basic information corresponding to various types of vegetation in the target garden, and obtain basic information corresponding to various types of vegetation in the target garden.

[0009] The growth state compliance analysis module is used to analyze the growth state corresponding to each type of vegetation in the target garden based on the basic information corresponding to each type of vegetation in the target garden, and obtain the growth state compliance coefficient corresponding to each type of vegetation in the target garden.

[0010] The regional environmental information collection module is used to collect basic regional environmental information corresponding to various types of vegetation in the target garden, and obtain basic regional environmental information corresponding to various types of vegetation in the target garden.

[0011] The environmental stress impact analysis module is used to analyze the environmental stress impact coefficient corresponding to each type of vegetation in the target garden based on the basic information of the regional environment corresponding to each type of vegetation in the target garden.

[0012] The regional biological information collection module is used to collect the basic regional biological information corresponding to each type of vegetation in the target garden, and obtain the basic regional biological information corresponding to each type of vegetation in the target garden.

[0013] The biological stress impact analysis module is used to analyze the biological stress impact coefficient corresponding to each type of vegetation in the target garden based on the basic regional biological information corresponding to each type of vegetation in the target garden.

[0014] The vegetation health status analysis module is used to analyze the health status safety index corresponding to the target garden vegetation based on the growth status compliance coefficient, environmental stress impact coefficient and biological stress impact coefficient corresponding to each type of vegetation in the target garden, and confirm the health status corresponding to the target garden vegetation.

[0015] The early warning terminal is used to judge the health status danger warning level of the target garden vegetation when the health status of the vegetation corresponding to the target garden vegetation is in a dangerous state, and then match the health status danger warning emergency plan corresponding to the target garden vegetation and implement it.

[0016] The garden database is used to store the normalized vegetation index interval, enhanced vegetation index interval, normalized moisture index interval, chlorophyll absorption ratio index interval, and photochemical reflectance index interval of the pixels in the regional remote sensing image corresponding to each type of vegetation, store each branch damage type, store each climate environment index interval corresponding to each type of vegetation, each regional soil physical property reference index, each soil chemical property reference index and each soil pollutant reference index, each soil physical property allowable index difference, each soil chemical property allowable index difference and each soil pollutant allowable index difference, regional area, and store the theoretical maximum domain Shannon-Wiener index corresponding to the regional area.

[0017] A second aspect of the present invention provides a method for monitoring the health of garden vegetation, the method comprising the following steps:

[0018] Step 1: Collect basic information of vegetation: Collect basic information corresponding to each type of vegetation in the target garden to obtain basic information corresponding to each type of vegetation in the target garden.

[0019] Step 2: Growth status safety analysis: Based on the basic information corresponding to each type of vegetation in the target garden, the growth status corresponding to each type of vegetation in the target garden is analyzed to obtain the growth status compliance coefficient corresponding to each type of vegetation in the target garden.

[0020] Step 3: Regional environmental information collection: Collect basic regional environmental information corresponding to various types of vegetation in the target garden to obtain basic regional environmental information corresponding to various types of vegetation in the target garden.

[0021] Step 4: Environmental stress impact analysis: Based on the basic information of the regional environment corresponding to each type of vegetation in the target garden, analyze the environmental stress impact coefficient corresponding to each type of vegetation in the target garden.

[0022] Step 5: Regional biological information collection: Collect basic regional biological information corresponding to various types of vegetation in the target garden to obtain basic regional biological information corresponding to various types of vegetation in the target garden.

[0023] Step 6. Biological stress impact analysis: Based on the basic regional biological information corresponding to each type of vegetation in the target garden, analyze the biological stress impact coefficient corresponding to each type of vegetation in the target garden.

[0024] Step 7. Vegetation health status analysis: Based on the growth status compliance coefficient, environmental stress impact coefficient and biological stress impact coefficient corresponding to each type of vegetation in the target garden, the health status safety index corresponding to the target garden vegetation is analyzed and the health status corresponding to the target garden vegetation is confirmed.

[0025] Step 8. Vegetation health status hazard warning: When the vegetation health status corresponding to the target garden is in a dangerous state, determine the health status hazard warning level corresponding to the target garden vegetation, and then match the health status hazard warning emergency plan corresponding to the target garden vegetation and implement it.

[0026] Beneficial effects of the present invention:

[0027] The present invention collects multiple aspects of data, including basic growth information, regional environmental information, and regional biological information of target garden vegetation. It not only collects regional remote sensing images of vegetation, the number of damaged branches and trunks, and other information itself, but also includes environmental data such as climate, soil, and human activities, as well as biological information such as invasive alien species. Compared with traditional one-sided monitoring methods, it realizes intelligent monitoring and analysis of garden vegetation health, can fully grasp the growth status of vegetation and the various stress factors it faces, and provide rich data support for accurate assessment of health status. To a certain extent, it improves the healthy growth of garden vegetation, promotes the development of garden management towards scientific and intelligent directions, and enhances the stability and landscape quality of garden ecosystems.

[0028] In the analysis of vegetation growth status, the present invention extracts the spectral characteristic index of vegetation remote sensing images and determines the type of branch damage. It reflects the physiological status of vegetation from multiple angles such as chlorophyll concentration, photosynthesis intensity, vegetation coverage, water content, light energy utilization efficiency, and branch damage. It can accurately quantify the vegetation growth status and promptly discover potential growth problems.

[0029] In the environmental stress impact analysis, the present invention comprehensively analyzes the climate environment assessment value, soil environment assessment value and human disturbance intensity by integrating multiple factors such as climate, soil and human disturbance. In the biotic stress impact analysis, it not only focuses on the change rate of the number of alien invasive species, coverage area, etc. (the first biotic stress impact index), but also considers the community species diversity (the second biotic stress impact index) in combination with the Shannon-Wiener index algorithm, comprehensively and meticulously assessing the environmental and biological stress effects on vegetation, ensuring the accuracy and reference value of the garden vegetation health monitoring analysis results, and to a certain extent avoiding the impact of environmental and biological stress on the healthy growth of garden vegetation.

[0030] By analyzing the health status of target garden vegetation, the present invention effectively ensures the scientificity and accuracy of the health status analysis results of garden vegetation, provides reliable data support for vegetation for garden management departments, and at the same time judges the health status hazard warning level corresponding to the target garden vegetation, and then matches the health status hazard warning emergency plan corresponding to the target garden vegetation, thereby realizing accurate warning and scientific response to vegetation health risks, and helping to take timely measures to protect vegetation health and reduce losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The present invention will be further described below with reference to the accompanying drawings.

[0032] Figure 1 It is a schematic diagram of the connection of various modules of the system of the present invention.

[0033] Figure 2 It is a flow chart of the implementation steps of the method of the present invention.

[0034] Figure 3 This is a flow chart of the vegetation health status analysis module of the present invention.

[0035] Figure 4 This is a diagram of the construction ideas of the soil environment impact factors of the present invention. DETAILED DESCRIPTION

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0037] See also Figure 1 、 Figure 3 、 Figure 4 As shown, the present invention is a garden vegetation health monitoring system, which includes:

[0038] The vegetation basic information collection module is used to collect basic information corresponding to various types of vegetation in the target garden, and obtain basic information corresponding to various types of vegetation in the target garden.

[0039] It should be noted that various types of vegetation include but are not limited to: trees, shrubs, and herbs.

[0040] Furthermore, the basic information corresponding to each type of vegetation in the target garden includes regional remote sensing images and the number of damaged branches and trunks.

[0041] It should be further explained that the basic information corresponding to each type of vegetation in the target garden is collected. The specific collection process is as follows:

[0042] The regional remote sensing images corresponding to various types of vegetation in the target garden are obtained by using UAV remote sensing equipment, and then preprocessing operations are performed on them to obtain the preprocessed regional remote sensing images corresponding to various types of vegetation in the target garden.

[0043] It should be noted that the preprocessing operations include but are not limited to: radiation correction, geometric correction and image enhancement; radiation correction is to eliminate the errors of the sensor itself and the influence of atmospheric factors on radiation measurement, so that the radiation value of the remote sensing image can truly reflect the reflective characteristics of the ground object; geometric correction is to correct the geometric deformation in the remote sensing image so that the image matches the position, shape and size of the actual ground object; image enhancement is to improve image clarity and contrast and further enhance vegetation feature information.

[0044] The branch and trunk images corresponding to each type of vegetation in the target garden are obtained by using a visual sensor equipped on a drone, and the number of branch and trunk damage corresponding to each type of vegetation in the target garden is obtained through image processing technology.

[0045] The growth state compliance analysis module is used to analyze the growth state corresponding to each type of vegetation in the target garden based on the basic information corresponding to each type of vegetation in the target garden, and obtain the growth state compliance coefficient corresponding to each type of vegetation in the target garden.

[0046] Furthermore, the growth status of each type of vegetation in the target garden is analyzed. The specific analysis process is as follows:

[0047] Select each band to extract spectral features of the regional remote sensing images corresponding to each type of vegetation in the preprocessed target garden, obtain the spectral feature information of each pixel in the regional remote sensing images corresponding to each type of vegetation in the target garden for each band, and extract the near-infrared band reflectivity, red light band reflectivity, blue light band reflectivity, green light band reflectivity, 700nm band reflectivity, 670nm band reflectivity, 550nm band reflectivity, 531nm band reflectivity and 570nm band reflectivity of each pixel in the regional remote sensing images corresponding to each type of vegetation, and thereby obtain the normalized vegetation index, enhanced vegetation index, normalized moisture index, chlorophyll absorption ratio index and photochemical reflectance index of each pixel in the regional remote sensing images corresponding to each type of vegetation in the target garden;

[0048] It should be noted that the specific statistical process of the normalized vegetation index, enhanced vegetation index, normalized moisture index, chlorophyll absorption ratio index, and photochemical reflectance index of each pixel in the regional remote sensing image corresponding to each type of vegetation in the target garden is as follows:

[0049] Normalized Difference Vegetation Index Calculate the normalized vegetation index of each pixel in the regional remote sensing image corresponding to each type of vegetation in the target garden i represents the number of each type of vegetation, i = 1, 2, ...., m, j represents the number of each pixel in the regional remote sensing image, j = 1, 2, ...., n, where They are respectively expressed as the near-infrared band reflectance and red light band reflectance of each pixel in the regional remote sensing image corresponding to each type of vegetation;

[0050] Enhanced Vegetation Index algorithm Calculate the enhanced vegetation index of each pixel in the regional remote sensing image corresponding to each type of vegetation in the target garden In the formula It is expressed as the blue light band reflectance of each pixel in the regional remote sensing image corresponding to each type of vegetation;

[0051] By using the normalized moisture index algorithm Calculate the normalized moisture index of each pixel in the regional remote sensing image corresponding to each type of vegetation in the target garden In the formula It is expressed as the green band reflectance of each pixel in the regional remote sensing image corresponding to each type of vegetation;

[0052] Chlorophyll absorption ratio index algorithm Calculate the chlorophyll absorption ratio index of each pixel in the regional remote sensing image corresponding to each type of vegetation in the target garden In the formula They are respectively expressed as the 700nm band reflectance, 670nm band reflectance and 550nm band reflectance of each pixel in the regional remote sensing image corresponding to each type of vegetation;

[0053] Photochemical reflectance index algorithm Calculate the photochemical reflectance index of each pixel in the regional remote sensing image corresponding to each type of vegetation in the target garden In the formula They are respectively expressed as the 531nm band reflectance and 570nm band reflectance of each pixel in the regional remote sensing image corresponding to each type of vegetation;

[0054] It should be further explained that the normalized vegetation index reflects the chlorophyll concentration and photosynthesis intensity of vegetation, the enhanced vegetation index reflects the vegetation coverage, the normalized moisture index reflects the vegetation water content and water stress, the chlorophyll absorption ratio index reflects the chlorophyll concentration, aging or stress degree of vegetation, and the photochemical reflectance index reflects the light energy utilization efficiency of vegetation.

[0055] Extract the normalized vegetation index interval, enhanced vegetation index interval, normalized moisture index interval, chlorophyll absorption ratio index interval, and photochemical reflectance index interval of the pixels in the regional remote sensing images corresponding to each type of vegetation stored in the garden database, and calculate and analyze the ideal reference index set and ideal deviation index set of the pixels in the regional remote sensing images corresponding to each type of vegetation;

[0056] It should be noted that the specific calculation process of the ideal reference index set and ideal deviation index set of pixels in the regional remote sensing image corresponding to each type of vegetation is as follows:

[0057] The upper and lower limits of the normalized vegetation index interval of the pixels in the regional remote sensing image corresponding to each type of vegetation are extracted and the mean is calculated to obtain the ideal reference normalized vegetation index, which is then subtracted from the lower limit of the normalized vegetation index interval to obtain the ideal deviation normalized vegetation index.

[0058] Similarly, the ideal reference enhanced vegetation index, ideal deviation enhanced vegetation index, ideal reference normalized moisture index, ideal deviation normalized moisture index, ideal reference chlorophyll absorption ratio index, ideal deviation chlorophyll absorption ratio index, ideal reference photochemical reflectance index, and ideal deviation photochemical reflectance index were obtained.

[0059] The ideal reference normalized vegetation index, the ideal reference enhanced vegetation index, the ideal reference normalized moisture index, the ideal reference chlorophyll absorption ratio index, and the ideal reference photochemical reflectance index constitute an ideal reference index set;

[0060] The ideal deviation index set is composed of the ideal deviation normalized vegetation index, the ideal deviation enhanced vegetation index, the ideal deviation normalized moisture index, the ideal deviation chlorophyll absorption ratio index, and the ideal deviation photochemical reflectance index.

[0061] The index indicators of each pixel in the regional remote sensing image corresponding to each type of vegetation in the target garden are respectively calculated by subtracting the absolute value from the corresponding ideal reference index indicator, and then calculating the ratio with the ideal deviation index indicator to obtain the relative deviation value of the index indicators of each pixel in the regional remote sensing image corresponding to each type of vegetation in the target garden;

[0062] The relative deviation values ​​of various index indicators are input into the graphics processor, which converts them into numerical values ​​according to a certain ratio and inputs them into the line graph to obtain the corresponding five points. The five points are sequentially connected by line segments to obtain a broken line. The two end points of the broken line are respectively perpendicular to the X-axis, so that the broken line and the two perpendicular lines form a closed figure with the X-axis. The numerical value of the closed figure area is identified and used as the characteristic difference of each pixel in the regional remote sensing image corresponding to each type of vegetation in the target garden, which is recorded as

[0063] The number of branch damage corresponding to each type of vegetation in the target garden is matched with each branch damage type stored in the garden database to obtain the damage type corresponding to each branch damage corresponding to each type of vegetation in the target garden. The corresponding number of each branch damage type corresponding to each type of vegetation in the target garden is obtained by integration, and each branch damage type corresponds to a damage value. The corresponding number of each branch damage type is multiplied by the damage value to obtain the total damage value of each branch damage type corresponding to each type of vegetation in the target garden, which is recorded as f represents the number of each branch damage type, f = 1, 2, ..., g;

[0064] It should be noted that the types of branch and trunk damage include but are not limited to: lesion damage, mildew damage, wormhole damage, scratch damage, and fracture damage.

[0065] Extract the total damage value of each pixel and each branch damage type in the regional remote sensing image corresponding to each type of vegetation in the target garden, and substitute it into the preset Sigmoid function model Calculate the growth state compliance coefficient α corresponding to each type of vegetation in the target garden i .

[0066] In a specific embodiment, the present invention extracts the spectral characteristic index of vegetation remote sensing images and determines the type of branch damage in the vegetation growth status compliance analysis, reflecting the physiological state of vegetation from multiple angles such as chlorophyll concentration, photosynthesis intensity, vegetation coverage, water content, light energy utilization efficiency, and branch damage. It can accurately quantify the vegetation growth state and promptly discover potential growth problems.

[0067] The regional environmental information collection module is used to collect basic regional environmental information corresponding to various types of vegetation in the target garden, and obtain basic regional environmental information corresponding to various types of vegetation in the target garden.

[0068] Furthermore, the basic regional environmental information corresponding to each type of vegetation in the target garden includes various climate environment indicators, various soil physical property indicators, various soil chemical property indicators, various soil pollutant indicators, and regional human flow, human crushing frequency, and garbage disposal amount.

[0069] It should be noted that the basic information of the regional environment corresponding to each type of vegetation in the target garden is collected. The specific collection process is as follows:

[0070] Deploy various climate and environmental monitoring devices in the areas corresponding to various types of vegetation in the target garden, obtain various climate and environmental indicators of various climate and environmental monitoring devices in the areas corresponding to various types of vegetation in the target garden, and select the maximum value of various climate and environmental indicators of various climate and environmental monitoring devices as various climate and environmental indicators of various areas corresponding to various types of vegetation in the target garden;

[0071] It should be further explained that the climate environment monitoring equipment integrates a temperature sensor, a humidity sensor, a light sensor, a wind speed sensor, a rain gauge, a snow gauge, and an air quality detector;

[0072] Climate and environmental indicators include: temperature, humidity, light intensity, wind speed, rainfall, snowfall, and concentrations of various pollutants (including but not limited to SO2, NO2, and O3);

[0073] Deploy soil monitoring equipment in areas corresponding to various types of vegetation in the target garden, and obtain soil physical property indicators, soil chemical property indicators, and soil pollutant indicators of each soil monitoring equipment in areas corresponding to various types of vegetation in the target garden, and then calculate the average of these indicators, and the obtained results are used as soil physical property indicators, soil chemical property indicators, and soil pollutant indicators in areas corresponding to various types of vegetation in the target garden;

[0074] It should be further explained that the soil monitoring equipment integrates a soil pH meter, a soil nutrient rapid tester, a soil conductivity meter, etc.

[0075] Soil physical property indicators include: soil texture, soil bulk density, soil porosity;

[0076] Soil chemical property indicators include: soil pH value, soil nutrient content (including but not limited to nitrogen, phosphorus, potassium and trace elements), soil salt content, soil organic matter content;

[0077] Soil pollutant indicators include: various heavy metal contents in soil (including Pb, Cd, and Hg);

[0078] Through smart cameras, we can obtain videos of human activities in the areas corresponding to various types of vegetation in the target garden, and then obtain the regional human flow, human trampling frequency, and garbage discarding amount corresponding to various types of vegetation in the target garden.

[0079] The environmental stress impact analysis module is used to analyze the regional environmental stress impact coefficient corresponding to each type of vegetation in the target garden based on the basic information of the regional environment corresponding to each type of vegetation in the target garden.

[0080] Furthermore, the regional environmental stress impact coefficient corresponding to each type of vegetation in the target garden is analyzed, and the specific analysis process is as follows:

[0081] Extract the climate and environmental indicators of the area corresponding to each type of vegetation in the target garden on the preset time series, use each measurement time point on the preset time series as the horizontal coordinate and each climate and environmental indicator as the vertical coordinate, establish a two-dimensional coordinate system corresponding to each climate and environmental indicator, plot each climate and environmental indicator at each measurement time point and connect them with a curve to obtain a change curve of each climate and environmental indicator of the area corresponding to each type of vegetation in the target garden on the preset time series, obtain the absolute value of the maximum tangent slope on the change curve of each climate and environmental indicator, and record it as the change rate of each climate and environmental indicator of the area corresponding to each type of vegetation in the target garden on the preset time series;

[0082] Retrieve the intervals of various climate and environmental indicators corresponding to various types of vegetation in the garden database. If a certain climate and environmental indicator corresponding to a certain type of vegetation at a certain measurement time point in a preset time series does not fall within the standard interval of the climate and environmental indicator, then count the duration of the climate and environmental indicator not falling within the standard interval in the preset time series corresponding to the type of vegetation. In this way, integrate and obtain the duration of each climate and environmental indicator not falling within the corresponding standard interval in the preset time series for the area corresponding to each type of vegetation in the target garden.

[0083] The change rates of various climate and environmental indicators and the duration of non-standard intervals in the areas corresponding to various types of vegetation in the target garden are normalized in the preset time series and their values ​​are taken. The values ​​are then compared with the preset reference change rates of various climate and environmental indicators and the reference duration of non-standard intervals, and the values ​​are accumulated to obtain the regional climate and environmental impact factors corresponding to various types of vegetation in the target garden.

[0084] According to the soil physical property indicators, soil chemical property indicators and soil pollutant indicators of the regions corresponding to the various types of vegetation in the target garden, the reference indicators of soil physical properties, soil chemical property indicators and soil pollutant indicators of the regions corresponding to the various types of vegetation in the target garden stored in the garden database are retrieved, as well as the allowable index differences of the soil physical properties, the allowable index differences of the soil chemical properties and the allowable index differences of the soil pollutants;

[0085] According to the formula

[0086] Calculate the regional soil physical property index evaluation values ​​corresponding to each type of vegetation in the target garden, where π represents the summation symbol;

[0087] Similarly, the regional soil chemical property index evaluation values ​​and soil pollutant index evaluation values ​​corresponding to each type of vegetation in the target garden are obtained;

[0088] See also Figure 4 As shown, the soil physical property index evaluation value, the soil chemical property index evaluation value and the soil pollutant index evaluation value are converted into length according to a preset ratio, and the lengths of the soil physical property index evaluation value, the soil chemical property index evaluation value and the soil pollutant index evaluation value are used as the upper base circle radius, the lower base circle radius and the height of the frustum to construct a frustum, and the value of the frustum volume is extracted as the soil environmental impact factor, thereby statistically obtaining the regional soil environmental impact factor corresponding to each type of vegetation in the target garden;

[0089] Based on the regional human disturbance intensity Dis corresponding to each type of vegetation in the target garden, the human traffic volume, human trampling frequency and garbage disposal amount are analyzed and obtained. i ;

[0090] Substitute the climate environment influencing factors, soil environment influencing factors and human disturbance intensity corresponding to each type of vegetation in the target garden into the preset Softplus function model β i =ln[1+exp(Cli i +soil i +dis i )] to obtain the environmental stress impact coefficient β corresponding to each type of vegetation in the target garden i , where

[0091]

[0092] Furthermore, the analysis obtains the regional human disturbance intensity corresponding to each type of vegetation in the target garden. The specific analysis process is as follows:

[0093] Extract the regional pedestrian flow and garbage disposal amount corresponding to each type of vegetation in the target garden, obtain the regional area corresponding to each type of vegetation in the target garden from the garden database, and calculate the ratio of it to the regional area corresponding to each type of vegetation to obtain the unit area pedestrian flow and unit area garbage disposal amount corresponding to each type of vegetation in the target garden, which are recorded as

[0094] Compare the area corresponding to each type of vegetation in the target garden with the reference unit area pedestrian flow and reference garbage discard volume corresponding to each area stored in the garden database, and obtain the reference unit area pedestrian flow and reference garbage discard volume corresponding to each type of vegetation in the target garden, which are recorded as

[0095]

[0096] Extract the regional human trampling frequency corresponding to each type of vegetation in the target garden, and record it as At the same time, the average duration of human activities in the target garden corresponding to each type of vegetation is extracted, and the reference human trampling frequency corresponding to the preset average duration of each human activity is matched to obtain the regional reference human trampling frequency corresponding to each type of vegetation in the target garden, which is recorded as

[0097] Normalize the regional pedestrian flow, garbage disposal, and human crushing frequency corresponding to each type of vegetation in the target garden and take their values, and substitute them into the preset inverse tangent function model. Calculate the regional human disturbance intensity Dis corresponding to each type of vegetation in the target garden i ,π represents pi.

[0098] The regional biological information collection module is used to collect the basic regional biological information corresponding to each type of vegetation in the target garden, and obtain the basic regional biological information corresponding to each type of vegetation in the target garden.

[0099] Furthermore, the regional biological basic information corresponding to each type of vegetation in the target garden includes the total number of individuals of regional alien invasive species, the total number of individuals of regional alien invasive threat species, the number of individuals of each alien invasive threat species, the coverage area of ​​invasive plant threat species, the number of invasive animal threat species, the number of invasive insect threat species and the infection area of ​​invasive microbial threat species.

[0100] It should be noted that the basic information of regional organisms corresponding to each type of vegetation in the target garden is collected. The specific collection process is as follows:

[0101] The intelligent camera carried by the drone is used to collect regional images corresponding to each type of vegetation in the target garden, and the trained model is used to identify the invasive alien species in the image. The total number of individuals of the regional invasive alien species corresponding to each type of vegetation in the target garden is obtained, and the total number of individuals of the regional invasive alien species corresponding to each type of vegetation in the target garden is matched with the threatened species corresponding to each type of vegetation stored in the garden database to obtain the regional invasive alien threatened species corresponding to each type of vegetation in the target garden, where the threatened species include plants, animals, insects, and microorganisms; and then the total number of individuals of the regional invasive alien threatened species corresponding to each type of vegetation in the target garden, the number of individuals of each invasive alien threatened species, the coverage area of ​​invasive plant threatened species, the number of invasive animal threatened species, the number of invasive insect threatened species, and the infection area of ​​invasive microbial threatened species are obtained;

[0102] The biological stress impact analysis module is used to analyze the regional biological stress impact coefficient corresponding to each type of vegetation in the target garden based on the basic regional biological information corresponding to each type of vegetation in the target garden.

[0103] Furthermore, the regional biotic stress impact coefficient corresponding to each type of vegetation in the target garden is analyzed, and the specific analysis process is as follows:

[0104] Extract the coverage area of ​​invasive plant species, the number of invasive animal species, the number of invasive insect species, and the infection area of ​​invasive microbial species at the initial monitoring time point and the end monitoring time point of the area corresponding to each type of vegetation in the target garden, and calculate the ratio with the area corresponding to each type of vegetation stored in the garden database, to obtain the occupation rate of invasive plant species, the density of invasive animal species, the density of invasive insect species, and the infection rate of invasive microbial species at the initial monitoring time point and the end monitoring time point corresponding to each type of vegetation in the target garden, retrieve the duration corresponding to the preset time series, and analyze to obtain the change rate of occupation rate of invasive plant species, the change rate of density of invasive animal species, the change rate of density of invasive insect species, and the change rate of infection rate of invasive microbial species in the area corresponding to each type of vegetation in the target garden;

[0105] The first biotic stress impact index of each type of vegetation in the target garden is obtained by accumulating the change rate of the regional invasive plant species occupation rate, the density change rate of the invasive animal species density change rate, the density change rate of the invasive insect species density change rate and the infection rate change rate of the invasive microbial species.

[0106] The total number of individuals of alien invasive species at the end of the monitoring time point in the preset time series in the area corresponding to each type of vegetation in the target garden was extracted, and the number of individuals of each alien invasive species was identified from it. The proportion of the number of individuals of each alien invasive species in the area corresponding to each type of vegetation in the target garden was calculated and recorded as s represents the number of each alien invasive species, s = 1, 2, ..., t, and the Shannon-Wiener index algorithm is used to calculate the Calculate the regional Shannon-Wiener index H corresponding to each type of vegetation in the target garden i ', match the area corresponding to each type of vegetation in the target garden with the theoretical maximum Shannon-Wiener index corresponding to each area stored in the garden database, and obtain the theoretical maximum Shannon-Wiener index corresponding to each type of vegetation in the target garden, which is recorded as

[0107] According to the formula Calculate the regional second biotic stress impact index corresponding to each type of vegetation in the target garden t represents the total number of individuals of alien invasive species, and t represents the total number of individuals of alien invasive species. The second biotic stress impact index comprehensively considers the proportion of threatened species and the species diversity of the community. The larger the value, the greater the impact of biotic stress on the vegetation community.

[0108] The regional first biotic stress impact index and the second biotic stress impact index corresponding to each type of vegetation in the target garden are analyzed to obtain the biotic stress impact coefficient corresponding to each type of vegetation in the target garden.

[0109] It should be noted that the biotic stress impact coefficient corresponding to each type of vegetation in the target garden is obtained by summing the regional first biotic stress impact index and the second biotic stress impact index corresponding to each type of vegetation in the target garden.

[0110] In a specific embodiment, the present invention comprehensively analyzes the climate environment assessment value, soil environment assessment value and human disturbance intensity by integrating multiple factors such as climate, soil and human disturbance in the environmental stress impact analysis. In the biological stress impact analysis, it not only focuses on the change rate of the number of alien invasive species, coverage area, etc. (the first biological stress impact index), but also considers the community species diversity (the second biological stress impact index) in combination with the Shannon-Wiener index algorithm, and comprehensively and meticulously evaluates the environmental and biological stress effects on vegetation, ensuring the accuracy and reference value of the garden vegetation health monitoring analysis results, and to a certain extent avoiding the impact of environmental and biological stress on the healthy growth of garden vegetation.

[0111] The vegetation health status analysis module is used to analyze the health status safety index corresponding to the target garden vegetation based on the physiological status safety compliance coefficient, corresponding environmental stress impact coefficient and corresponding biological stress impact coefficient of each type of vegetation in the target garden, and confirm the corresponding health status of the target garden vegetation.

[0112] See also Figure 3 As shown, the health status safety index corresponding to the target garden vegetation is analyzed and the health status corresponding to the target garden vegetation is confirmed. The specific execution process is as follows:

[0113] Extract the growth state compliance coefficient, environmental stress impact coefficient and biological stress impact coefficient corresponding to each type of vegetation in the target garden, and substitute them into the preset hyperbolic tangent function model The health safety index ψ corresponding to the target garden vegetation is calculated, where w1, w2, and w3 represent the weight factors of the set growth state compliance coefficient, environmental stress impact coefficient, and biological stress impact coefficient, respectively;

[0114] It should be noted that, in a specific embodiment, w1 can be set to 0.5, w2 can be set to 0.25, and w3 can be set to 0.25. In a garden ecosystem, the growth condition of the vegetation itself plays a dominant role in its survival, growth, and reproduction. Even if environmental stress and biological stress exist, as long as the vegetation is in good growth condition, it is more likely to resist these stresses. For example, healthy vegetation can more effectively absorb nutrients and water from the soil and synthesize the substances it needs, thereby enhancing its resistance to diseases and insect pests. In contrast, if the growth condition is not good, even if the degree of environmental and biological stress is low, vegetation is prone to health problems. Therefore, in order to more accurately assess the health of vegetation, a higher weight is given to the growth condition compliance coefficient, and a relatively low weight is given to the biological stress impact coefficient and the same environmental stress impact coefficient.

[0115] The health status safety index corresponding to the target garden vegetation is compared with the preset health status safety index threshold. If the health status safety index corresponding to the target garden vegetation is greater than or equal to the preset health status safety index threshold, the health status corresponding to the target garden vegetation is judged to be in a safe state; otherwise, the health status corresponding to the target garden vegetation is judged to be in a dangerous state.

[0116] The early warning terminal is used to judge the health status danger warning level of the target garden vegetation when the health status of the vegetation corresponding to the target garden vegetation is in a dangerous state, and then match the health status danger warning emergency plan corresponding to the target garden vegetation and implement it.

[0117] Furthermore, the specific method for determining the health status danger level of the target garden vegetation includes:

[0118] When the health status of the vegetation corresponding to the target garden is in a dangerous state, the difference between the vegetation health status safety index corresponding to the target garden and the preset health status safety index threshold is calculated to obtain the difference between the vegetation health status corresponding to the target garden and the preset health status safety index, and the difference is matched with the preset health status safety index difference corresponding to each health status danger warning level to obtain the health status danger warning level corresponding to the vegetation of the target garden;

[0119] It should be noted that the health status danger warning levels include but are not limited to blue warning, yellow warning and orange warning.

[0120] Among them, the blue warning indicates that the current health status of vegetation is relatively stable, but there may be some minor health problems or stress risks, which require attention and appropriate preventive measures.

[0121] A yellow alert indicates that the current health status of vegetation has deteriorated to a certain extent. Growth status indicators, environmental stress indicators, and biological stress indicators are beyond the normal range. There are obvious stress effects and vegetation growth is hindered. Close attention should be paid and necessary response measures should be taken.

[0122] The orange alert means that the current health status of vegetation has seriously deteriorated. Growth status indicators, environmental stress indicators and biological stress indicators may reach or exceed the level of serious danger. Vegetation growth is seriously damaged, disease symptoms, growth stagnation and even death signs appear on a large scale, and ecological functions have declined significantly. It is necessary to immediately activate the emergency plan and take emergency measures to respond.

[0123] The health status risk warning level corresponding to the target garden vegetation is further matched with the health status hazard warning emergency plan corresponding to each health status risk warning level stored in the garden database to obtain the health status hazard warning emergency plan corresponding to the target garden vegetation and implement it.

[0124] It should be further explained that the specific contents of the health status danger warning emergency plan include:

[0125] (1) Blue warning: Provide necessary maintenance intervention and intensive monitoring. For example, if the soil is deficient in nutrients, use precision fertilization technology to supplement nitrogen, phosphorus, potassium and trace elements in proportion based on soil test results. If it is caused by drought, start the intelligent irrigation system to add water in appropriate amounts to maintain a reasonable soil humidity.

[0126] (2) Yellow Alert: High-frequency real-time monitoring, comprehensive prevention and control of environmental and biological stress indicators, comprehensive adjustment of maintenance strategies, and activation of emergency material reserves; provision of necessary maintenance interventions and intensified monitoring;

[0127] (3) Orange Alert: Provide necessary maintenance intervention and intensified monitoring; conduct high-frequency real-time monitoring, comprehensively control environmental and biological stress indicators, comprehensively adjust maintenance strategies, and activate emergency material reserves; physically isolate severely stressed garden areas, set up warning signs, restrict the entry of unauthorized personnel and animals, prevent the further spread of pests and diseases, and avoid human factors from aggravating vegetation damage. Contact professional garden pest control companies, forestry research institutions, and other external forces, invite expert teams for on-site guidance, and use their professional equipment and technology to carry out large-scale vegetation rescue work and activate the emergency resource allocation mechanism.

[0128] In a specific embodiment, the present invention effectively ensures the scientificity and accuracy of the health status analysis results of garden vegetation by analyzing the health status corresponding to the target garden vegetation, providing reliable data support for the garden management department for vegetation, and at the same time judging the health status hazard warning level corresponding to the target garden vegetation, and then matching the health status hazard warning emergency plan corresponding to the target garden vegetation, thereby achieving accurate warning and scientific response to vegetation health risks, and helping to take timely measures to protect vegetation health and reduce losses.

[0129] The garden database is used to store the normalized vegetation index interval, enhanced vegetation index interval, normalized moisture index interval, chlorophyll absorption ratio index interval, and photochemical reflectance index interval of the pixels in the regional remote sensing images corresponding to each type of vegetation, and to store each type of branch damage, each type of climate environment index interval, each type of regional soil physical property reference index, each soil chemical property reference index and each soil pollutant reference index, each soil physical property allowable index difference, each soil chemical property allowable index difference and each soil pollutant allowable index difference, regional area, and threatened species. It also stores the theoretical maximum domain Shannon-Wiener index corresponding to the regional area and the health status hazard warning emergency plan corresponding to each health status risk warning level.

[0130] See also Figure 2 As shown, the present invention is a garden vegetation health monitoring method, which includes the following steps:

[0131] Step 1: Collect basic information of vegetation: Collect basic information corresponding to each type of vegetation in the target garden to obtain basic information corresponding to each type of vegetation in the target garden.

[0132] Step 2: Growth status compliance analysis: Based on the basic information corresponding to each type of vegetation in the target garden, the growth status corresponding to each type of vegetation in the target garden is analyzed to obtain the growth status compliance coefficient corresponding to each type of vegetation in the target garden.

[0133] Step 3: Regional environmental information collection: Collect basic regional environmental information corresponding to various types of vegetation in the target garden to obtain basic regional environmental information corresponding to various types of vegetation in the target garden.

[0134] Step 4: Environmental stress impact analysis: Based on the basic information of the regional environment corresponding to each type of vegetation in the target garden, the regional environmental stress impact coefficient corresponding to each type of vegetation in the target garden is analyzed.

[0135] Step 5: Regional biological information collection: Collect basic regional biological information corresponding to various types of vegetation in the target garden to obtain basic regional biological information corresponding to various types of vegetation in the target garden.

[0136] Step 6. Biological stress impact analysis: Based on the basic information of regional organisms corresponding to each type of vegetation in the target garden, the regional biological stress impact coefficient corresponding to each type of vegetation in the target garden is analyzed.

[0137] Step 7. Vegetation health status analysis: Based on the physiological status safety compliance coefficient, environmental stress impact coefficient and biological stress impact coefficient corresponding to each type of vegetation in the target garden, the health status safety index corresponding to the target garden vegetation is analyzed and the health status corresponding to the target garden vegetation is confirmed.

[0138] Step 8. Vegetation health status hazard warning: When the vegetation health status corresponding to the target garden is in a dangerous state, determine the health status hazard warning level corresponding to the target garden vegetation, and then match the health status hazard warning emergency plan corresponding to the target garden vegetation and implement it.

[0139] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

Claims

1. A garden vegetation health monitoring system, including a garden database, characterized in that: Also includes: The vegetation basic information collection module collects the basic information corresponding to each type of vegetation in the target garden and obtains the basic information corresponding to each type of vegetation in the target garden; The growth state compliance analysis module analyzes the growth state of each type of vegetation in the target garden based on the basic information corresponding to each type of vegetation in the target garden, and obtains the growth state compliance coefficient corresponding to each type of vegetation in the target garden; The regional environmental information collection module collects basic regional environmental information corresponding to various types of vegetation in the target garden, and obtains basic regional environmental information corresponding to various types of vegetation in the target garden; The environmental stress impact analysis module analyzes the environmental stress impact coefficients corresponding to each type of vegetation in the target garden based on the basic regional environmental information corresponding to each type of vegetation in the target garden; The regional biological information collection module collects the basic regional biological information corresponding to each type of vegetation in the target garden, and obtains the basic regional biological information corresponding to each type of vegetation in the target garden; The biological stress impact analysis module analyzes the biological stress impact coefficient corresponding to each type of vegetation in the target garden based on the basic regional biological information corresponding to each type of vegetation in the target garden; The vegetation health status analysis module analyzes the health status safety index corresponding to the target garden vegetation based on the growth status compliance coefficient, environmental stress impact coefficient and biological stress impact coefficient corresponding to each type of vegetation in the target garden, and confirms the health status corresponding to the target garden vegetation.

2. The garden vegetation health monitoring system according to claim 1, characterized in that: The basic information corresponding to each type of vegetation in the target garden includes pre-processed regional remote sensing images and the number of damaged branches and trunks; The basic regional environmental information corresponding to each type of vegetation in the target garden includes various climate and environmental indicators, various soil physical property indicators, various soil chemical property indicators, various soil pollutant indicators, and regional human flow, human compaction frequency, and garbage disposal volume; The regional biological basic information corresponding to each type of vegetation in the target garden includes the total number of individuals of regional alien invasive species, the total number of individuals of regional alien invasive threatening species, the number of individuals of each alien invasive threatening species, the coverage area of ​​invasive plant threatening species, the number of invasive animal threatening species, the number of invasive insect threatening species and the infection area of ​​invasive microbial threatening species.

3. The garden vegetation health monitoring system according to claim 1, characterized in that: Analyze the growth status of various types of vegetation in the target garden. The specific analysis process includes: Select each band to extract spectral features of the regional remote sensing images corresponding to each type of vegetation in the preprocessed target garden, obtain the spectral feature information of each pixel in the regional remote sensing images corresponding to each type of vegetation in the target garden for each band, and extract the near-infrared band reflectivity, red light band reflectivity, blue light band reflectivity, green light band reflectivity, 700nm band reflectivity, 670nm band reflectivity, 550nm band reflectivity, 531nm band reflectivity and 570nm band reflectivity of each pixel in the regional remote sensing images corresponding to each type of vegetation, and thereby obtain the normalized vegetation index, enhanced vegetation index, normalized moisture index, chlorophyll absorption ratio index and photochemical reflectance index of each pixel in the regional remote sensing images corresponding to each type of vegetation in the target garden; Extract the normalized vegetation index interval, enhanced vegetation index interval, normalized moisture index interval, chlorophyll absorption ratio index interval, and photochemical reflectance index interval of the pixels in the regional remote sensing images corresponding to each type of vegetation stored in the garden database, and calculate and analyze the ideal reference index set and ideal deviation index set of the pixels in the regional remote sensing images corresponding to each type of vegetation; The index indicators of each pixel in the regional remote sensing image corresponding to each type of vegetation in the target garden are respectively calculated by subtracting the absolute value from the corresponding ideal reference index indicator, and then calculating the ratio with the ideal deviation index indicator to obtain the relative deviation value of the index indicators of each pixel in the regional remote sensing image corresponding to each type of vegetation in the target garden; The relative deviation values ​​of various index indicators are input into the graphics processor, which converts them into numerical values ​​according to a certain ratio and inputs them into the corresponding five points in the line graph. The five points are connected in sequence by line segments to obtain a broken line. The two endpoints of the broken line are made perpendicular to the X-axis, so that the broken line and the two perpendicular lines form a closed figure with the X-axis. The numerical value of the closed figure area is identified and used as the characteristic difference of each pixel in the regional remote sensing image corresponding to each type of vegetation in the target garden.

4. The garden vegetation health monitoring system according to claim 1, characterized in that: The growth status of each type of vegetation in the target garden is analyzed, and the specific analysis process also includes: Match the number of branch and trunk damage corresponding to each type of vegetation in the target garden with the branch and trunk damage types stored in the garden database to obtain the damage type corresponding to each branch and trunk damage corresponding to each type of vegetation in the target garden. From this, the corresponding number of each branch and trunk damage type corresponding to each type of vegetation in the target garden is obtained by integration, and each branch and trunk damage type corresponds to a damage value. The corresponding number of each branch and trunk damage type is multiplied by the damage value to obtain the total damage value of each branch and trunk damage type corresponding to each type of vegetation in the target garden; The total damage value of each pixel and each branch damage type in the regional remote sensing image corresponding to each type of vegetation in the target garden is extracted, and substituted into the preset Sigmoid function model to calculate the growth state compliance coefficient corresponding to each type of vegetation in the target garden.

5. The garden vegetation health monitoring system according to claim 1, characterized in that: The analysis of the regional environmental stress impact coefficient corresponding to each type of vegetation in the target garden includes the following specific analysis process: Extract the climate and environmental indicators of the area corresponding to each type of vegetation in the target garden on the preset time series, use each measurement time point on the preset time series as the horizontal coordinate and each climate and environmental indicator as the vertical coordinate, establish a two-dimensional coordinate system corresponding to each climate and environmental indicator, plot each climate and environmental indicator at each measurement time point and connect them with a curve to obtain a change curve of each climate and environmental indicator of the area corresponding to each type of vegetation in the target garden on the preset time series, obtain the absolute value of the maximum tangent slope on the change curve of each climate and environmental indicator, and record it as the change rate of each climate and environmental indicator of the area corresponding to each type of vegetation in the target garden on the preset time series; Retrieve the intervals of various climate and environmental indicators corresponding to various types of vegetation in the garden database. If a certain climate and environmental indicator corresponding to a certain type of vegetation at a certain measurement time point in a preset time series does not fall within the standard interval of the climate and environmental indicator, then count the duration of the climate and environmental indicator not falling within the standard interval in the preset time series corresponding to the type of vegetation. In this way, integrate and obtain the duration of each climate and environmental indicator not falling within the corresponding standard interval in the preset time series for the area corresponding to each type of vegetation in the target garden. The change rates of various climate and environmental indicators and the duration that does not belong to the corresponding standard interval in the areas corresponding to each type of vegetation in the target garden are normalized in the preset time series and their values ​​are taken. They are then compared with the preset reference change rates of various climate and environmental indicators and the reference duration that does not belong to the corresponding standard interval and accumulated and calculated to obtain the regional climate and environmental impact factors corresponding to each type of vegetation in the target garden.

6. The garden vegetation health monitoring system according to claim 1, characterized in that: The analysis of the regional environmental stress impact coefficient corresponding to each type of vegetation in the target garden includes the following specific analysis process: According to the soil physical property indicators, soil chemical property indicators and soil pollutant indicators of the regions corresponding to the various types of vegetation in the target garden, the reference indicators of soil physical properties, soil chemical property indicators and soil pollutant indicators of the regions corresponding to the various types of vegetation in the target garden stored in the garden database are retrieved, as well as the allowable index differences of the soil physical properties, the allowable index differences of the soil chemical properties and the allowable index differences of the soil pollutants; Through analysis, we can obtain the regional soil physical property index evaluation value, soil chemical property index evaluation value and soil pollutant index evaluation value corresponding to each type of vegetation in the target garden; The soil physical property index evaluation value, soil chemical property index evaluation value and soil pollutant index evaluation value are converted into length according to a preset ratio, and the length of the soil physical property index evaluation value, soil chemical property index evaluation value and soil pollutant index evaluation value is used as the upper base circle radius, lower base circle radius and height of the frustum to construct the frustum, and the value of the frustum volume is extracted as the soil environmental impact factor, thereby statistically obtaining the regional soil environmental impact factor corresponding to each type of vegetation in the target garden; Based on the regional human disturbance intensity corresponding to each type of vegetation in the target garden, the human traffic volume, human trampling frequency, and garbage disposal volume were analyzed. The climate environment influencing factors, soil environment influencing factors and human disturbance intensity corresponding to each type of vegetation in the target garden are substituted into the preset Softplus function model to obtain the environmental stress impact coefficient corresponding to each type of vegetation in the target garden.

7. The garden vegetation health monitoring system according to claim 1, characterized in that: The regional biotic stress impact coefficient corresponding to each type of vegetation in the target garden is analyzed, and the specific analysis process is as follows: Extract the coverage area of ​​invasive plant species, the number of invasive animal species, the number of invasive insect species, and the infection area of ​​invasive microbial species at the initial monitoring time point and the end monitoring time point of the area corresponding to each type of vegetation in the target garden, and calculate the ratio with the area corresponding to each type of vegetation stored in the garden database, to obtain the occupation rate of invasive plant species, the density of invasive animal species, the density of invasive insect species, and the infection rate of invasive microbial species at the initial monitoring time point and the end monitoring time point corresponding to each type of vegetation in the target garden, retrieve the duration corresponding to the preset time series, and analyze to obtain the change rate of occupation rate of invasive plant species, the change rate of density of invasive animal species, the change rate of density of invasive insect species, and the change rate of infection rate of invasive microbial species in the area corresponding to each type of vegetation in the target garden; The first biotic stress impact index for each type of vegetation in the target garden was obtained by cumulatively calculating the change rate of occupation rate of invasive plant species, density change rate of invasive animal species, density change rate of invasive insect species, and infection rate change rate of invasive microbial species corresponding to each type of vegetation in the target garden. The total number of individuals of alien invasive species at the end of the monitoring time point in the preset time series in the area corresponding to each type of vegetation in the target garden was extracted, and the number of individuals of each alien invasive species was identified from it. The proportion of the number of individuals of each alien invasive species in the area corresponding to each type of vegetation in the target garden was calculated and recorded as i represents the number of each type of vegetation, i = 1, 2, ...., m, s represents the number of each alien invasive species, s = 1, 2, ...., t, through the Shannon-Wiener index algorithm Calculate the regional Shannon-Wiener index H corresponding to each type of vegetation in the target garden i ' , the area corresponding to each type of vegetation in the target garden is matched with the theoretical maximum Shannon-Wiener index corresponding to each area stored in the garden database, and the theoretical maximum Shannon-Wiener index corresponding to each type of vegetation in the target garden is obtained, which is recorded as According to the formula Calculate the regional second biotic stress impact index corresponding to each type of vegetation in the target garden It is expressed as the total number of individuals of alien invasive species, and n is expressed as the total number of individuals of alien invasive species; The regional first biotic stress impact index and the second biotic stress impact index corresponding to each type of vegetation in the target garden are analyzed to obtain the biotic stress impact coefficient corresponding to each type of vegetation in the target garden.

8. The garden vegetation health monitoring system according to claim 1, characterized in that: The health status safety index corresponding to the target garden vegetation is analyzed and the health status corresponding to the target garden vegetation is confirmed. The specific execution process is as follows: Extract the growth state compliance coefficient, environmental stress impact coefficient, and biological stress impact coefficient corresponding to each type of vegetation in the target garden, and substitute them into the preset hyperbolic tangent function model to calculate the health status safety index corresponding to the target garden vegetation; The health status safety index corresponding to the target garden vegetation is compared with the preset health status safety index threshold. If the health status safety index corresponding to the target garden vegetation is greater than or equal to the preset health status safety index threshold, the health status corresponding to the target garden vegetation is judged to be in a safe state; otherwise, the health status corresponding to the target garden vegetation is judged to be in a dangerous state.

9. The garden vegetation health monitoring system according to claim 1, characterized in that: Also includes: The early warning terminal is used to determine the health status danger warning level of the vegetation in the target garden when the health status of the vegetation in the target garden is in a dangerous state, and then match the health status danger warning emergency plan of the vegetation in the target garden and implement it; The specific method for determining the health status danger level of the target garden vegetation includes: When the health status of the vegetation corresponding to the target garden is in a dangerous state, the difference between the vegetation health status safety index corresponding to the target garden and the preset health status safety index threshold is calculated to obtain the difference between the vegetation health status corresponding to the target garden and the preset health status safety index, and the difference is matched with the preset health status safety index difference corresponding to each health status danger warning level to obtain the health status danger warning level corresponding to the vegetation of the target garden; The health status risk warning level corresponding to the target garden vegetation is matched with the health status hazard warning emergency plan corresponding to each health status risk warning level stored in the garden database to obtain the health status hazard warning emergency plan corresponding to the target garden vegetation and implement it.

10. A garden vegetation health monitoring method, used to implement the garden vegetation health monitoring system according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Collect basic information of vegetation: Collect basic information corresponding to each type of vegetation in the target garden to obtain basic information corresponding to each type of vegetation in the target garden; Step 2: Growth status safety analysis: Based on the basic information corresponding to each type of vegetation in the target garden, the growth status corresponding to each type of vegetation in the target garden is analyzed to obtain the growth status compliance coefficient corresponding to each type of vegetation in the target garden; Step 3: Regional environmental information collection: Collect basic regional environmental information corresponding to various types of vegetation in the target garden to obtain basic regional environmental information corresponding to various types of vegetation in the target garden; Step 4: Environmental stress impact analysis: Based on the basic regional environmental information corresponding to each type of vegetation in the target garden, analyze the environmental stress impact coefficient corresponding to each type of vegetation in the target garden; Step 5: Regional biological information collection: Collect basic regional biological information corresponding to each type of vegetation in the target garden to obtain basic regional biological information corresponding to each type of vegetation in the target garden; Step 6: Biological stress impact analysis: Based on the basic regional biological information corresponding to each type of vegetation in the target garden, analyze the biological stress impact coefficient corresponding to each type of vegetation in the target garden; Step 7: Vegetation health status analysis: Based on the growth status compliance coefficient, environmental stress impact coefficient, and biotic stress impact coefficient corresponding to each type of vegetation in the target garden, analyze the health status safety index corresponding to the target garden vegetation and confirm the health status of the target garden vegetation; Step 8. Vegetation health status hazard warning: When the vegetation health status corresponding to the target garden is in a dangerous state, determine the health status hazard warning level corresponding to the target garden vegetation, and then match the health status hazard warning emergency plan corresponding to the target garden vegetation and implement it.

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