Mangrove forest health assessment system based on tropical cyclone

Abstract

The invention relates to the technical field of data analysis, in particular to a mangrove forest health assessment system based on tropical cyclones. The structure risk coefficient, the geological risk coefficient and the ecological risk coefficient are combined to obtain the health risk index of the mangrove forest, so that accurate health assessment is realized, meteorological data and cyclonic state data are monitored to predict a cyclonic prediction path, and the prediction accuracy is improved. And then the cyclonic prediction path and the health risk index of the mangrove forest are combined to form risk response management and control on damaged and undamaged mangrove forests, so that accurate judgment and supervision on key areas are formed, and the situation of excessive damage caused by untimely management and control on the mangrove forest is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and particularly to a mangrove health assessment system based on tropical cyclones. Background Art

[0002] During the growth of mangroves, if they are in the moving path of a cyclone, they will be affected by the cyclone. Therefore, after the mangrove area is affected, in order to facilitate the analysis of the health status of mangroves, currently, image data of trees in the mangrove area can be collected through machine learning models and convolutional neural network models, and trained by means of feature annotation of the image data to achieve the analysis of the health degree of mangroves. However, when analyzing the health degree in the above manner, only analyzing the image data of the trees may lead to inaccurate assessment of the health degree of mangroves, because the health status of mangroves is not only shown in the image data on their own surface, but also affected by the environmental status.

[0003] Therefore, in order to improve the assessment and analysis of the health degree of mangroves after being affected by a cyclone, so as to effectively conduct risk control on both damaged and undamaged (in the moving path of the cyclone and the position where the cyclone has not reached) mangroves, the present application proposes a mangrove health assessment system based on tropical cyclones. Summary of the Invention

[0004] In view of the above-mentioned drawbacks of the prior art, the present invention provides a mangrove health assessment system based on tropical cyclones, which can effectively solve the problems of inaccurate assessment of the health degree of mangroves in the prior art and affecting the implementation of early warning control on damaged and undamaged mangroves.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions:

[0006] The present invention provides a mangrove health assessment system based on tropical cyclones, including a health status analysis module, which is used to obtain the status of the mangrove area currently affected by a cyclone, divide the mangrove area into contemporaneous mangrove areas, and obtain the corresponding health risk index H of the contemporaneous mangrove area sge , where:

[0007] The health risk index H sge is obtained by combining the structural risk coefficient Hs, the geological risk coefficient Hg, and the ecological risk coefficient He;

[0008] Based on the health risk index H sge judge the health degree of the contemporaneous mangrove area;

[0009] It further includes:

[0010] The associated risk monitoring module is used to obtain real-time meteorological data and cyclone status data, output the cyclone prediction path using the WRF model, record the mangrove areas in the same period under the cyclone prediction path as the risk mangrove areas, and predict the health risk index H of the risk mangrove areas based on the health status of the mangrove areas in the same period. sge , and thus determine whether to generate and execute an emergency plan.

[0011] The determination method of the structural risk coefficient Hs is as follows:

[0012] Obtain the structural risk coefficient Hs of the mangrove areas in the same period. The structural risk coefficient Hs is normalized according to the height difference value |fjh|, diameter difference value |fjr|, damaged area fjs, and damage rate fjm, and is calculated according to the following relational formula:

[0013] In the formula: |fjh| is the height difference value of the mangroves, |fjr| is the slope difference value of the mangroves, fjm is the damage rate, fjs is the damaged area of the mangroves, μ1, μ2, μ3, and μ4 are the corresponding weight coefficients, S h rms is the number of calculation items, and Hμ is the constant correction coefficient.

[0014] The determination method of the geological risk coefficient Hg is as follows:

[0015] Obtain the geological risk coefficient Hg of the mangrove areas in the same period. The geological risk coefficient Hg is normalized according to the soil erosion value fdq, soil moisture difference value |fds|, organic matter difference value |fdj|, soil pH difference value |fDpH|, and soil structure value fdp, and is calculated according to the following relational formula:

[0016]

[0017] In the formula: fdq is the soil erosion value, |fds| is the soil moisture difference value, |fdj| is the organic matter difference value, |fDpH| is the soil pH difference value, fdp is the soil structure value, β1, β2, β3, β4, and β5 are the corresponding weight coefficients, S qsj is the number of calculation items, and Hβ is the constant correction coefficient.

[0018] The determination method of the soil structure value fdp is as follows:

[0019] Pre-obtain the porosity, permeability, stability, and bulk density of the soil in each mangrove area in the same period, normalize them to obtain the soil structure value of the soil in the current mangrove area in the same period, and obtain the soil structure values of all mangrove areas in the same period. The calculation formula of the soil structure value is as follows:

[0020]

[0021] Wherein, Tk is the porosity of the soil, Tw is the permeability of the soil, Tj is the stability of the soil, Tρ is the bulk density of the soil, and F1, F2, F3, and F4 are all corresponding weight coefficients.

[0022] The determination method of the ecological risk coefficient He is as follows:

[0023] Obtain the ecological risk coefficient He of the mangrove area in the same period. The ecological risk coefficient He is normalized according to the tidal difference value |ftc|, water level difference value fts, salinity difference value |fty|, and ecological pollution value ftw, and is calculated according to the following relational formula:

[0024]

[0025] Wherein: |ftc| is the tidal difference value, fts is the water level difference value, |fty| is the salinity difference value, ftw is the ecological pollution value, α1, α2, α3, and α4 are the corresponding weight coefficients respectively, S csyw is the number of calculation items, and Hα is the constant correction coefficient.

[0026] The health risk index H sge The calculation formula of is:

[0027]

[0028] Wherein: and are the weight coefficients respectively, and jdt is the number of calculation items.

[0029] The judgment formula for the health degree of the mangrove area in the same period is:

[0030]

[0031] If H sge is A, it is determined that the health status of the current mangrove area in the same period is good and no control and treatment are required;

[0032] On the contrary, if H sge is not A, it is determined that the health status of the current mangrove area in the same period is poor and control and treatment are required.

[0033] The meteorological data includes wind speed, wind direction, air pressure, temperature, and humidity;

[0034] The cyclone state data includes the moving position, intensity, and size of the cyclone.

[0035] When determining the predicted path of the cyclone:

[0036] Obtain the coordinate information of the risk mangrove area and the coordinate information of the current cyclone to obtain the travel time Xf for the cyclone to reach different risk mangrove areas T;

[0037] Combine the travel time Xf T with the current real-time time Nf T to obtain the predicted time Yf when the cyclone is in the risk mangrove area T ;

[0038] Obtain the environmental data and mangrove growth data of the risk mangrove area, determine the risk mangrove area that matches the environmental data, mangrove growth data, and cyclone risk data in the mangrove area during the same period affected by the cyclone, and predict the health risk index H of the risk mangrove area sge , where:

[0039] If the health risk index H of the risk mangrove area sge is in B, C, or D, then mark the risk mangrove area as the target mangrove area, and generate corresponding emergency plans according to the health level corresponding to the health risk index H sge ;

[0040] When determining the target mangrove area:

[0041] Clarify the predicted time Yf when the cyclone arrives in the target mangrove area T , obtain the evacuation time Cf of the personnel after the implementation of the emergency plan T , and thus generate the maximum completion time Wf of the corresponding emergency plan for different target mangrove areas T , and output the maximum completion time Wf T to the control terminal to respond to the execution time of the emergency plan

[0042] The technical solution provided by the present invention has the following beneficial effects compared with the known prior art:

[0043] By combining the structural risk coefficient, geological risk coefficient, and ecological risk coefficient to obtain the health risk index of the mangrove forest, accurate health assessment is realized. Thus, by monitoring meteorological data and cyclone state data to predict the movement path of the cyclone, and then combining the predicted path of the cyclone and the health risk index of the mangrove forest to realize risk response control for damaged and undamaged mangrove forests, so as to form precise judgment and supervision of key areas, thereby reducing the situation of excessive damage to the mangrove forest due to untimely control BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts

[0045] Figure 1 This is the overall process schematic diagram of the present invention. Specific implementation manners

[0046] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0047] The present invention will be further described below with reference to the embodiments.

[0048] Embodiment 1 (refer to Figure 1 ): A mangrove health assessment system based on tropical cyclones includes at least:

[0049] A health status analysis module, which is used to obtain the mangrove area currently affected by the cyclone. Among them, the identification and acquisition of the mangrove area affected by the cyclone are as follows:

[0050] Step 1: Use satellite images to collect remote sensing image data of the mangrove area before and after the cyclone;

[0051] Step 2: Preprocess the remote sensing image data, such as performing standardization of the remote sensing image data. The calculation formula is:

[0052]

[0053] In the formula, X t is the processed remote sensing image data, X is the original remote sensing image data, r is the mean value, and ε is the standard deviation;

[0054] Step 2: Extract associated features from the remote sensing image data, including vegetation indices (NDVI, EVI), land cover classification (mangroves, mudflats), and spectral features, to reflect the growth status of the mangrove area;

[0055] Step 3: Select samples in the affected and unaffected mangrove areas, and label the samples to clarify the training samples and test samples. Among them, the training samples are used to train the machine learning model, and the test samples are used to evaluate the machine learning model to optimize the model parameters. Among them, the machine learning model adopted can be a convolutional neural network, a decision tree, etc., and the model can be tested and optimized through the training samples and test samples;

[0056] Among them, if training and recognition are performed through a convolutional neural network, the steps are as follows:

[0057] Perform a convolution operation:

[0058]

[0059] Where: is the convolution output of the i-th, j-th position in the l-th layer, is the convolution kernel weight at the mth and nth positions of the lth layer, is the activation value of the i+m-1, j+n-1th position in the previous layer, b (1) is the biased top of the lth layer;

[0060] Execute the activation function:

[0061] A (l) =f(Z (l) )=max(0,Z (l) )

[0062] Where: Z (l) is the output feature map of the lth layer;

[0063] Perform a max pooling operation:

[0064]

[0065] P i，j is the value of the i-th, j-th position in the output feature map after pooling, A is the input feature map, s·i+m is the row index of the current pooling window in the input feature map, and s·i+n is the column index of the current pooling window in the input feature map;

[0066] Execute the fully connected layer output:

[0067] Z (l) =W (l) ·A (l-1) +b (l)

[0068] Where: A (l-1) is the activation value of the l-1th layer, W (l) is the weight matrix of the lth layer;

[0069] Execute the Softmax layer output:

[0070]

[0071] Where: Z k is the linear output of the model in the kth class, Z j is the linear output of the model in the jth category, P(y=k丨X) is the predicted probability that sample X belongs to category k, k is the total number of categories, and the probability of each category is calculated based on this;

[0072] Implement the cross entropy loss function:

[0073]

[0074] Where: y i，k is the value of the true label of the i-th sample in the k-th class, k is the predicted probability of the i-th sample in the k-th class, N is the total number of samples, and the error between the model and the true label is calculated accordingly;

[0075] Parameter update formula to adjust the model parameters:

[0076]

[0077] Where: θ is the model parameter, including all weights and biases, η is the learning rate, is the gradient of the loss function with respect to the parameter θ, representing the rate of change of the loss function in the parameter space;

[0078] Step Four: Based on the above constructed machine learning model (i.e., convolutional neural network model), by applying the trained machine learning model (convolutional neural network model) to the identification area, the affected mangrove area is identified and output through machine learning.

[0079] Thus, a large number of mangrove areas are divided according to the growth cycle to determine the coeval mangrove areas with the same growth cycle (growth time) (the coeval mangrove areas are marked with the same growth cycle or growth time, which is convenient for further analysis of the situation of mangroves in the follow-up), and the health risk index H of this coeval mangrove area is calculated sge , where the health risk index H sge is obtained by combining the structural risk coefficient Hs, the geological risk coefficient Hg, and the ecological risk coefficient He. The specific calculation steps are as follows:

[0080] Step One: Obtain the structural risk coefficient Hs of the coeval mangrove area. The structural risk coefficient Hs is normalized according to the height difference value |fjh|, the diameter difference value ||fjr|, the damaged area fjs, and the damage rate fjm, and is calculated according to the following relational expression:

[0081]

[0082] Where: |fjh| is the height difference value of the mangroves. First, obtain the tree height difference between the average tree height of all mangroves in the current coeval mangrove area and the historical average tree height (obtain the corresponding historical coeval mangrove area (the historical coeval mangrove area with growth parameters meeting the preset standard values), and the same applies to the historical average diameter in the following text, including Steps Two, Three, etc., which will not be elaborated further), and obtain the average tree height difference (height difference value) of the mangroves based on the number of the same coeval mangrove areas;

[0083] Let |fjr| be the slope difference value of mangroves. The slope difference between the average inclination of all mangroves in the current contemporaneous mangrove area and the historical average inclination is obtained in advance, and the slope difference value (tilt difference value) of mangroves is obtained based on the number of the same contemporaneous mangrove areas.

[0084] Let fjm be the damage rate. The total damaged quantity of mangroves in all contemporaneous mangrove areas is obtained (damage includes the states of breakage, bending, scratches, inclination, and collapse of the mangrove diameter and branches), and the ratio of it to the total number of mangroves is obtained as the damage rate.

[0085] Let fjs be the damaged area of mangroves. The average damage area of all mangroves in the current contemporaneous mangrove area is obtained (including obtaining the sum of the fracture area, scratch area, and mangrove bark shedding area of mangroves in each contemporaneous mangrove area to obtain the average damage area of multiple contemporaneous mangrove areas); μ1, μ2, μ3, and μ4 are the corresponding weight coefficients, and S h rms is the number of calculation items, preset to 3, and Hμ is the constant correction coefficient.

[0086] Step 2: Obtain the geological risk coefficient Hg of the contemporaneous mangrove area. The geological risk coefficient Hg is normalized based on the soil erosion value fdq, soil humidity difference value |fds|, organic matter difference value |fdj|, soil pH difference value |fDpH|, and soil structure value fdp, and is calculated according to the following relational formula:

[0087]

[0088] In the formula: fdq is the soil erosion value, which is obtained by the ratio of the eroded soil volume (soil volume = soil area × soil depth) in all current contemporaneous mangrove areas to the total soil volume;

[0089] Let |fds| be the soil humidity difference value. The humidity difference between the soil humidity in the current contemporaneous mangrove area and the historical soil humidity is obtained in advance to obtain the average humidity difference (soil humidity difference value) of all contemporaneous mangrove areas. Herein, the soil humidity is obtained by the ratio of the soil water content to the soil volume;

[0090] Let |fdj| be the organic matter difference value. The content difference between the organic matter content in the soil of the current contemporaneous mangrove area and the historical organic matter content is obtained in advance to obtain the average content difference (organic matter difference value) of all contemporaneous mangrove areas;

[0091] Let |fDpH| be the soil pH difference value. The pH difference between the soil pH value in the current contemporaneous mangrove area and the historical soil pH value is obtained in advance to obtain the average pH difference (soil pH difference value) of all contemporaneous mangrove areas;

[0092] The fdp is the soil structure value. The porosity, permeability, stability, and bulk density of the soil in each mangrove area of the same period are obtained in advance and normalized to obtain the soil structure value of the mangrove area of the current period. The average soil structure value of all mangrove areas of the same period is obtained, which is the soil structure value. The calculation formula of the soil structure value is as follows:

[0093]

[0094] In the formula, Tk is the porosity of the soil, Tw is the permeability of the soil, Tj is the stability of the soil, which is obtained based on the product of the number of aggregates, the geometric mean diameter, and the mean weight diameter. Tρ is the bulk density of the soil. β1, β2, β3, β4, and β5 are the corresponding weight coefficients, and S qsH is the number of calculation items, preset to 4, Hβ is the constant correction coefficient, and F1, F2, F3, and F4 are all the corresponding weight coefficients.

[0095] Step 3: Obtain the ecological risk coefficient He of the mangrove area of the same period. The ecological risk coefficient He is normalized based on the tidal difference value |ftc|, water level difference value fts, salinity difference value |fty|, and ecological pollution value ftw, and is calculated according to the following relationship:

[0096]

[0097] In the formula: |ftc| is the tidal difference value. The tidal difference between the tidal height in the mangrove area of the current period and the historical tidal height is obtained in advance, and the average tidal difference (tidal difference value) of all mangrove areas of the same period is obtained;

[0098] fts is the water level difference value. It is assigned a value by obtaining whether the average value of the water level change in all mangrove areas of the current period is within the historical water level change range. If it is within, it is assigned a value of 0.1, otherwise it is 0.5;

[0099] |fty| is the salinity difference value. The salinity difference between the salinity in the mangrove area of the current period and the historical salinity is obtained in advance, and the average salinity difference (salinity difference value) of all mangrove areas of the same period is obtained;

[0100] ftw is the ecological pollution value. The product of the heavy metal content and the organic pollutant concentration in each mangrove area of the current period is obtained in advance to clarify the pollution value of each mangrove area of the same period, and thus the average pollution value of all mangrove areas of the same period is obtained, which is the ecological pollution value; α1, α2, α3, and α4 are the corresponding weight coefficients, and S csyw is the number of calculation items, preset to 4, and Hα is the constant correction coefficient.

[0101] Step 4: Calculate the health risk index H sge :

[0102]

[0103] Wherein: and are weight coefficients respectively, and jdt is the number of calculation items, which is preset to 3.

[0104] It should be noted that in this solution, the set weight coefficients can be determined by the analytic hierarchy process or the principal component analysis method;

[0105] Principal Component Analysis (PCA): Find the main influencing factors by dimensionality reduction and assign weights according to the contribution degree;

[0106] Analytic Hierarchy Process (AHP): Construct a judgment matrix and calculate the weights by comparing the importance of each parameter. The above-mentioned analytic hierarchy process or principal component analysis method are both well-known technologies at present, so this case will not elaborate on them further.

[0107] Thus, according to the above steps, the health risk index H of the current mangrove area in the same period is obtained sge , and further, the preset health risk level is obtained to judge the health degree of the mangrove area in the same period affected by the cyclone. The judgment formula is as follows:

[0108]

[0109] In the above, if it is A, it means that the health status of the current mangrove area in the same period is good, and the impact of the cyclone on the mangrove area in the same period can be ignored;

[0110] On the contrary, if it is not A, it means that the health status of the current mangrove area in the same period is not good, and the cyclone has a certain impact on the mangrove area in the same period, and control and disposal are required.

[0111] The associated risk monitoring module is used to obtain real-time meteorological data, including wind speed, wind direction, air pressure, temperature, humidity, etc., and obtain cyclone state data, including the moving position, intensity and size of the cyclone, so as to input the cyclone state data and meteorological data into the WRF model to output the predicted paths of the cyclone at different future times. Furthermore, it is obtained whether there is a mangrove area under the predicted path of the future cyclone. If so, the mangrove area is marked as a risk mangrove area. Thus, the coordinate information of the risk mangrove area and the coordinate information of the current cyclone are obtained. According to the travel time Xf for the cyclone to reach different risk mangrove areas is obtained T , and thus the travel time Xf T is combined with the current real-time time Nf T to obtain the predicted time Yf when the cyclone is in the risk mangrove area T, obtain the environmental data and mangrove growth data of the risk mangrove area. Based on the environmental data, mangrove growth data (the growth cycle time of the mangroves), and cyclone risk data (the intensity and size of the cyclone at different positions output by the WRF model), further, in the mangrove areas affected by the cyclone in the same period before, determine the risk mangrove areas that match (are the same as) the environmental data, mangrove growth data, and cyclone risk data. Thus, referring to the above, directly predict the health risk index H of the risk mangrove area sge , where:

[0112] If the health risk index H of the risk mangrove area sge is at A, then do not execute the emergency plan for this risk mangrove area (generally speaking, the emergency plan is preset in the database according to the damage degree of the risk mangrove area, the intensity and size of the cyclone);

[0113] If the health risk index H of the risk mangrove area sge is at B, C, or D, then mark this risk mangrove area as the target mangrove area. Generate the corresponding emergency plan according to the corresponding health degree of the health risk index H sge to reduce the damage degree of the target mangrove area and improve the health degree of the target mangrove area.

[0114] It should be noted that the emergency plan includes:

[0115] Tree reinforcement: For important trees or trees located in dangerous areas, take reinforcement measures, such as adding supports, tying or pruning branches, to improve their wind resistance ability;

[0116] Preventive pruning: Conduct preventive pruning on the trees that may be affected, remove the dead branches, diseased branches and overlong branches that may be broken by strong winds, and reduce the damage of the trees caused by strong winds;

[0117] Temporary wind shelter measures: Before the cyclone arrives, set up temporary wind shelter facilities, such as wind barriers or shielding nets, in the tree areas that are vulnerable to be affected to reduce the direct action of the wind force;

[0118] Remove vulnerable trees: For vulnerable trees or trees located in high-risk areas, they can be removed in advance to prevent further damage caused by the collapse of the trees.

[0119] It should be noted that when determining the target mangrove area, clarify the predicted arrival time Yf of the cyclone in the target mangrove area T , obtain the evacuation time Cf of the personnel after the implementation of the emergency plan T (the time generated by evacuating the target mangrove area), so as to consider the evacuation time Cf T at the predicted time Yf T (the predicted time Yf T to reduce the evacuation time Cf T) to generate the maximum completion time Wf of the emergency response plans corresponding to different target mangrove areas T , with the maximum completion time Wf T output to the control terminal to indicate the execution time of the emergency response plans corresponding to different target mangrove areas, so as to facilitate the safe execution of the emergency response plans.

[0120] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the protection scope of the technical solutions of the embodiments of the present invention.

Claims

1. A mangrove health assessment system based on tropical cyclones, including a health status analysis module, for obtaining the current status of mangrove areas affected by cyclones and dividing the mangrove areas into corresponding mangrove areas, characterized in that: Get the health risk index H corresponding to the mangrove area in the same period sge ,in: Health risk index H sge It is obtained by combining the structural risk factor Hs, the geological risk factor Hg and the ecological risk factor He; According to the health risk index H sge Determine the health of the mangrove area during the same period; Also includes: The associated risk monitoring module is used to obtain real-time meteorological data and cyclone status data, use the WRF model to output the cyclone prediction path, record the mangrove areas in the same period under the cyclone prediction path as risk mangrove areas, and predict the health risk index H of the risk mangrove areas based on the health level of the mangrove areas in the same period. sge , thereby determining whether to generate an emergency plan and execute it.

2. The tropical cyclone-based mangrove health assessment system according to claim 1, characterized in that: The method for determining the structural risk coefficient Hs is as follows: The structural risk coefficient Hs of the mangrove area in the same period is obtained. The structural risk coefficient Hs is normalized based on the height difference value |fjh|, the diameter difference value |fjr|, the damaged area fjs and the damaged rate fjm, and is calculated according to the following relationship: Where: |fjh| is the height difference of mangroves, |fjr| is the slope difference of mangroves, fjm is the damage rate, fjs is the damaged area of ​​mangroves, μ1, μ2, μ3 and μ4 are the corresponding weight coefficients, S h rms is the number of calculation items, and Hμ is the constant correction coefficient.

3. The tropical cyclone-based mangrove health assessment system according to claim 1, characterized in that: The method for determining the geological risk factor Hg is as follows: The geological risk coefficient Hg of the mangrove area in the same period was obtained. The geological risk coefficient Hg was normalized based on the soil erosion value fdq, soil moisture difference value |fds|, organic matter difference value |fdj|, soil pH difference value |fDpH| and soil structure value fdp, and calculated according to the following relationship: Where: fdq is the soil erosion value, |fds| is the soil moisture difference value, |fdj| is the organic matter difference value, |fDpH| is the soil pH difference value, fdp is the soil structure value, β1, β2, β3, β4 and β5 are the corresponding weight coefficients, S qsj is the number of calculation items, and Hβ is the constant correction coefficient.

4. The tropical cyclone-based mangrove health assessment system according to claim 3, characterized in that: The soil structure value fdp is determined as follows: The porosity, permeability, stability and volume density of the soil in each mangrove area of ​​the same period are obtained in advance and normalized to obtain the soil structure value of the soil in the current mangrove area of ​​the same period, and the soil structure values ​​of all mangrove areas of the same period are obtained. The calculation formula of the soil structure value is as follows: Where Tk is the porosity of soil, Tw is the permeability of soil, Tj is the stability of soil, Tρ is the bulk density of soil, and F1, F2, F3 and F4 are the corresponding weight coefficients.

5. The tropical cyclone-based mangrove health assessment system according to claim 1, characterized in that: The ecological risk coefficient He is determined as follows: The ecological risk coefficient He of the mangrove area in the same period is obtained. The ecological risk coefficient He is normalized based on the tidal difference value |ftc|, the water level difference value fts, the salinity difference value |fty| and the ecological pollution value ftw, and is calculated according to the following relationship: Where: |ftc| is the tidal difference value, fts is the water level difference value, |fty| is the salinity difference value, ftw is the ecological pollution value, α1, α2, α3 and α4 are the corresponding weight coefficients, S csyw is the number of calculation items, and Hα is the constant correction coefficient.

6. The tropical cyclone-based mangrove health assessment system according to claim 1, characterized in that: The health risk index H sge The calculation formula is: Where: and are weight coefficients respectively, and jdt is the number of calculation items.

7. The tropical cyclone-based mangrove health assessment system according to claim 1, characterized in that: The formula for judging the health of the mangrove area during the same period is: If H sge When it is A, it is judged that the health status of the current mangrove area is good and no control or disposal is required; On the contrary, if H sge When it is not A, it is determined that the health status of the current mangrove area is poor and control and disposal are required.

8. The tropical cyclone-based mangrove health assessment system according to claim 1, characterized in that: The meteorological data include wind speed, wind direction, air pressure, temperature, and humidity; Cyclone status data includes the movement, intensity and size of the cyclone.

9. The tropical cyclone-based mangrove health assessment system according to claim 7, characterized in that: When the cyclone forecast path is determined: Obtain the coordinate information of the risk mangrove area and the coordinate information of the current cyclone, and obtain the travel time Xf of the cyclone to different risk mangrove areas T ; Set the travel time to Xf T With the current real time Nf T Combined with the predicted time Yf, the cyclone is in the risk mangrove area T ; Obtain environmental data and mangrove growth data of risk mangrove areas, determine the risk mangrove areas that match the environmental data, mangrove growth data, and cyclone risk data in the mangrove areas affected by the cyclone during the same period, and predict the health risk index H of the risk mangrove areas sge ,in: If there is a health risk index H in the risk mangrove area sge If it is in B, C or D, the risk mangrove area is marked as a target mangrove area, and the health risk index H sge The corresponding emergency plan is generated according to the corresponding health level.

10. The tropical cyclone-based mangrove health assessment system according to claim 9, characterized in that: When the target mangrove area is determined: Clearly predict the arrival time of cyclones in the target mangrove area Yf T , obtain the evacuation time Cf of personnel after the emergency plan is implemented T , thereby generating the maximum completion time Wf of the emergency plan corresponding to different target mangrove areas T , with the maximum completion time Wf T Output to the control terminal to respond to the execution time of the emergency plan.

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