Trendedness prediction and early warning method applied to bay ecosystem health evaluation

Abstract

The invention discloses a trend prediction and early warning method applied to health evaluation of a bay ecological system, and relates to the technical field of bay ecological early warning, and the method comprises the following steps: collecting monitoring data of bay ecology, and dividing the monitoring data into historical data and real-time data, wherein the monitoring data comprises water quality data, geological data and hydro meteorological data, the collected monitoring data is preprocessed, bay ecosystem impact factors are screened, the bay ecosystem impact factors comprise physical impact factors and chemical impact factors, and the preprocessed historical data and the bay ecosystem impact factors are utilized. According to the method, trend prediction is carried out on the health condition of an ecological system in a period of time in the future by constructing the gulf ecological prediction model based on historical data and real-time monitoring information, so that related departments and the public can take actions before problems occur, and potential ecological risks are effectively prevented.

Description

Technical Field

[0001] The present invention relates to the technical field of bay ecological early warning, and particularly relates to a trend prediction and early warning method applied to the health assessment of bay ecosystems. Background Art

[0002] With the rapid development of the economic society, ecological environment problems have become increasingly prominent globally. Especially in coastal areas with prominent location characteristics such as bays and islands, the increasingly serious marine environmental pollution has led to a high degree of "pathologization" of bay ecosystems. The production function, service function, natural purification function, etc. have declined significantly. The health assessment of bay ecosystems is the basis for evaluating the overall status of bay ecosystems, identifying potential problems, and formulating protection strategies. Therefore, conducting a health assessment of bay ecosystems and making trend prediction and early warning based on the assessment results have become important means for protecting the marine ecological environment and achieving sustainable development.

[0003] Since there are multiple ecological factors in the health assessment of bay ecosystems, it is difficult to analyze the pollutant carrying capacity for different bay states, and then conduct targeted health assessments, which affects the matching degree of response measures. Therefore, how to ensure the matching degree between ecological factors and the health assessment of bay ecosystems to improve the matching degree of response measures is the problem we need to solve. For this reason, a trend prediction and early warning method applied to the health assessment of bay ecosystems is proposed. Summary of the Invention

[0004] The purpose of the present invention is to provide a trend prediction and early warning method applied to the health assessment of bay ecosystems to solve the problems raised in the above background art.

[0005] To solve the above technical problems, the technical solution adopted by the present invention is: A trend prediction and early warning method applied to the health assessment of bay ecosystems, comprising the following steps: Step 1, collect the monitoring data of bay ecology, and divide the monitoring data into historical data and real-time data, wherein the monitoring data includes water quality data, sediment data, and hydro-meteorological data. Among them, the water quality data: includes dissolved oxygen, pH value, temperature, salinity, nutrients, heavy metal content, etc.; the sediment data: includes the composition of bottom sediment, organic matter content, heavy metal deposition, etc.; the hydro-meteorological data: includes tides, water flow velocity, wind direction, wind speed, precipitation, etc.; Step 2, preprocess the collected monitoring data, and screen the influencing factors of the bay ecosystem. Among them, the influencing factors of the bay ecosystem include physical influencing factors and chemical influencing factors. The physical influencing factors include water temperature, salinity, tides, water flow velocity, etc.; the chemical influencing factors include dissolved oxygen, pH value, nutrient concentration, heavy metal content, etc.; Step 3: Using the preprocessed historical data and the influencing factors of the bay ecosystem, construct a bay ecological prediction model to obtain the physical impact index and the chemical impact index, and analyze the overall health status of the bay ecosystem. For example, when both the physical impact index and the chemical impact index are at relatively low levels, it indicates that the overall health status of the bay ecosystem is good; conversely, if the index is high, it means that the ecosystem may face greater pressure. Step 4: Based on the physical impact index and the chemical impact index, obtain the bay ecological health assessment coefficient, predict the development trend of the bay ecosystem health, including the change trend of the health status, the emergence time of potential problems, etc., and determine the warning level of the bay ecosystem health in combination with historical data, and set the corresponding warning assessment threshold at the same time. Step 5: Determine the corresponding warning level of the bay ecosystem health according to the bay ecological prediction model, and generate corresponding warning information. The warning information should include the warning level, the possible affected areas, the change situation of the main ecological factors, and the recommended countermeasures, etc., so that relevant departments and the public can take actions in a timely manner, and remind the corresponding departments and the public to take corresponding targeted countermeasures, such as strengthening pollution control, carrying out ecological restoration, strengthening monitoring and assessment, etc.

[0006] A further improvement of the technical solution of the present invention lies in: in the said Step 1, the process of obtaining the bay ecological monitoring data is as follows: Step 101: According to the geographical characteristics of the bay, the water flow direction, and the distribution of surrounding pollution sources, set up multiple monitoring stations, install sensors and monitoring equipment at the monitoring stations, collect water quality data, sediment data, and hydro-meteorological data in real time, and transmit them to the data center wirelessly for real-time processing and storage. The sensors include temperature sensors, salinity sensors, dissolved oxygen sensors, etc., and the monitoring equipment includes water quality analyzers, sediment samplers, hydro-meteorological monitors, etc. Step 102: Collect historical data from previous research reports, monitoring records, and public official databases, and sort out, verify, and file the historical data to ensure the integrity and reliability of the data. Step 103: Classify and store the obtained historical data and real-time data according to the time series and data types, and establish a data recovery mechanism to back up the database regularly to ensure timely recovery in case of data loss or damage.

[0007] A further improvement of the technical solution of the present invention lies in: in the said Step 2, the process of obtaining the influencing factors of the bay ecosystem is as follows: Step 201: Perform data cleaning, data standardization, data integration, and preprocessing on the collected historical data and real-time data Step 202: Screen and extract based on the water temperature, salinity, tides, water flow velocity, wind direction, and wind speed of the bay ecosystem to obtain physical impact factors. Step 203: Conduct characteristic screening and extraction based on the dissolved oxygen, pH value, nutrient concentrations such as nitrogen, phosphorus, and heavy metal content of the bay ecosystem to obtain chemical impact factors. Step 204: Analyze the correlation relationships among the obtained physical impact factors and chemical impact factors.

[0008] A further improvement of the technical solution of the present invention lies in that: in the said Step 3, the process of obtaining the physical impact index and the chemical impact index is as follows: Step 301: Integrate the correlation data of the physical impact factors and chemical impact factors in the preprocessed historical data to form a unified data set. Step 302: Conduct time series analysis on the physical impact factors and chemical impact factors, analyze the changing trends of the physical impact factors and chemical impact factors over time, and simultaneously construct a bay ecosystem prediction model. Step 303: Divide the preprocessed data into a training set and a test set, use the training set data to train the bay ecosystem prediction model, adjust the model parameters, optimize the model performance, and use the test set data to verify the bay ecosystem prediction model to evaluate the accuracy and reliability of the bay ecosystem prediction model. Step 304: Utilize the trained bay ecosystem prediction model and combine the obtained physical impact factors and chemical impact factors to calculate the physical impact index and the chemical impact index, and analyze the overall health status of the bay ecosystem.

[0009] A further improvement of the technical solution of the present invention lies in that: the calculation formula of the physical impact index is: ; where PII is the physical impact index, is the real-time measured value of the i-th physical impact factor, such as water temperature, salinity, tides, water flow velocity, etc., and is the standard value of the i-th physical impact factor, used to evaluate the deviation degree of this factor, is the standard deviation of the i-th physical impact factor, used to standardize the measured value of this factor to make it comparable under different dimensions, is the weight of the i-th physical impact factor, used to adjust the contribution of different factors to the total impact index, and n is the total number of physical impact factors; The calculation formula of the chemical impact index is: ; where CII is the chemical impact index, and is the real-time measured value of the j-th chemical impact factor, such as dissolved oxygen, pH value, nutrient concentration, heavy metal content, etc. is the standard value of the jth chemical impact factor, is the maximum allowable deviation degree of the jth chemical impact factor, which is used to evaluate whether the deviation degree of this factor is acceptable, is the weight of the jth chemical impact factor, which is used to adjust the contribution of different factors to the total impact index, and m is the total number of chemical impact factors.

[0010] A further improvement of the technical solution of the present invention lies in that: in the step 4, the process of obtaining the bay ecological health assessment coefficient is as follows: Step 401, according to the preprocessed historical data, combine the physical impact index and the chemical impact index, and analyze the correlation between the bay ecosystem health and the physical impact index and the chemical impact index; Step 402, according to the selected physical impact index and chemical impact index, calculate the standardized value of each factor, match different weights for the physical impact index and the chemical impact index, and perform weighted summation of these two indexes to obtain the bay ecological health assessment coefficient and predict the development trend of the bay ecosystem health; Step 403, according to the bay ecological health assessment coefficient and combined with historical data, divide the early warning levels of the bay ecosystem health, which are the blue early warning level, the yellow early warning level, the orange early warning level and the red early warning level respectively, where the early warning levels increase gradually from the blue early warning level to the red early warning level; Step 404, match the determined current bay ecosystem health early warning level with the bay ecological health assessment coefficient and set the corresponding early warning assessment threshold.

[0011] A further improvement of the technical solution of the present invention lies in that: the calculation formula of the bay ecological health assessment coefficient is: ; where EHAC is the bay ecological health assessment coefficient, PII is the physical impact index, CII is the chemical impact index, is the reference value of the physical impact index, which represents a reference value or average value of the physical impact index in the ideal state or historical data, is the reference value of the chemical impact index, which represents a reference value or average value of the chemical impact index in the ideal state or historical data, is a function about the physical impact index, which is used to adjust or standardize the value of the physical impact index to make it more in line with the evaluation requirements, is a function about the chemical impact index, which is used to adjust or standardize the value of the chemical impact index.

[0012] A further improvement of the technical solution of the present invention lies in that: multiple said warning levels correspond to multiple said warning evaluation thresholds, wherein, said warning evaluation thresholds include an upper threshold and a lower threshold; The multiple said warning levels and the multiple said warning evaluation thresholds satisfy the following relationship: Blue warning level ; Yellow warning level ; Orange warning level ; Red warning level ; Wherein, EHAC is the Gulf ecological health assessment coefficient, A is the upper threshold corresponding to the blue warning level and the lower threshold corresponding to the yellow warning level, B is the upper threshold corresponding to the yellow warning level and the lower threshold corresponding to the orange warning level, and C is the upper threshold corresponding to the orange warning level and the lower threshold corresponding to the red warning level.

[0013] A further improvement of the technical solution of the present invention lies in that: in the said step 5, the process of obtaining warning information is as follows: Step 501, input the preprocessed real-time data into the Gulf ecological prediction model, calculate the physical impact index and the chemical impact index respectively, and obtain the Gulf ecological health assessment coefficient; Step 502, based on the obtained Gulf ecological health assessment coefficient and the preset warning evaluation thresholds for matching, determine the corresponding warning level of the Gulf ecosystem health; Step 503, according to the predicted warning level, generate the corresponding warning information, formulate specific countermeasures, and at the same time transmit the warning information to the corresponding departments and the public, such as through the internal office systems of each department, the SMS platform, the telephone conference, etc., to convey the warning information to the relevant functional departments such as the environmental protection department, the ocean management department, and the fishery department in a timely manner.

[0014] Due to the adoption of the above technical solution, the technical progress achieved by the present invention compared with the prior art is: The present invention provides a trend prediction and warning method applied to the health assessment of the Gulf ecosystem. By constructing a Gulf ecological prediction model, based on historical data and real-time monitoring information, trend prediction of the health status of the ecosystem in the future for a period of time is carried out, enabling relevant departments and the public to take actions before problems occur and effectively preventing potential ecological risks.

[0015] The present invention provides a trend prediction and early warning method for the health assessment of a bay ecosystem. By constructing a comprehensive health assessment index system for the bay ecosystem and combining the physical impact index and the chemical impact index, the health status of the bay ecosystem can be more comprehensively evaluated. It not only considers the impact of single indicators but also integrates multiple indicators through the comprehensive index method, which helps the management department to more accurately understand the health status of the bay ecosystem, thereby formulating more effective protection and management measures.

[0016] The present invention provides a trend prediction and early warning method for the health assessment of a bay ecosystem. Through long-term monitoring and trend analysis of physical and chemical impacts, it can identify the changing trends of the health status of the bay ecosystem in advance, issue early warning signals in a timely manner, and by setting different early warning levels, preventive measures can be taken in advance to avoid further deterioration of the health status of the ecosystem. This helps to detect potential problems in a timely manner, take targeted countermeasures, and reduce the damage to the ecosystem. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0018] Figure 1 is a flowchart of the method of the present invention; Figure 2 is a flowchart for obtaining the physical impact factor and chemical impact factor of the present invention; Figure 3 is a flowchart for obtaining the bay ecological health assessment coefficient of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the 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.

[0020] Embodiment 1, as Figures 1 to 3 shown, the present invention provides a trend prediction and early warning method for the health assessment of a bay ecosystem, including the following steps: Step 1: Collect the monitoring data of the bay ecosystem and divide the monitoring data into historical data and real-time data. The monitoring data includes water quality data, sediment data, and hydro-meteorological data. Among them, water quality data includes dissolved oxygen, pH value, temperature, salinity, nutrient salts, heavy metal content, etc.; sediment data includes the composition of bottom sediment, organic matter content, heavy metal deposition, etc.; hydro-meteorological data includes tides, water flow velocity, wind direction, wind speed, precipitation, etc. The acquisition process of the bay ecosystem monitoring data is as follows: According to the geographical characteristics of the bay, the water flow direction, and the distribution of surrounding pollution sources, multiple monitoring stations are set up, and sensors and monitoring equipment are installed at the monitoring stations to collect water quality data, sediment data, and hydro-meteorological data in real time, and transmit them to the data center wirelessly for real-time processing and storage. The sensors include temperature sensors, salinity sensors, dissolved oxygen sensors, etc., and the monitoring equipment includes water quality analyzers, sediment samplers, hydro-meteorological monitors, etc. Collect historical data from previous research reports, monitoring records, and public official databases, and organize, verify, and archive the historical data to ensure the integrity and reliability of the data. Classify and store the obtained historical data and real-time data according to the time series and data type, and at the same time establish a data recovery mechanism to back up the database regularly to ensure timely recovery in case of data loss or damage; Step 2: Preprocess the collected monitoring data and screen the influencing factors of the bay ecosystem. Among them, the influencing factors of the bay ecosystem include physical influencing factors and chemical influencing factors. Physical influencing factors include water temperature, salinity, tides, water flow velocity, etc.; chemical influencing factors include dissolved oxygen, pH value, nutrient salt concentration, heavy metal content, etc. The acquisition process of the influencing factors of the bay ecosystem is as follows: Conduct data cleaning, data standardization, data integration, and preprocessing on the collected historical data and real-time data, and screen and extract according to the water temperature, salinity, tides, water flow velocity, wind direction, and wind speed of the bay ecosystem to obtain physical influencing factors. Screen and extract characteristics according to the dissolved oxygen, pH value, nutrient salt concentration such as nitrogen, phosphorus, and heavy metal content of the bay ecosystem to obtain chemical influencing factors. Based on the obtained physical influencing factors and chemical influencing factors, analyze the correlation between each influencing factor; Step 3: Using the preprocessed historical data and the impact factors of the bay ecosystem, construct a bay ecological prediction model to obtain the physical impact index and the chemical impact index, and analyze the overall health status of the bay ecosystem. For example, when both the physical impact index and the chemical impact index are at relatively low levels, it indicates that the overall health status of the bay ecosystem is good; conversely, if the indices are high, it means that the ecosystem may face greater pressure. The process of obtaining the physical impact index and the chemical impact index is as follows: Integrate the associated data of the physical impact factors and the chemical impact factors in the preprocessed historical data to form a unified data set, conduct time series analysis on the physical impact factors and the chemical impact factors, analyze the changing trends of the physical impact factors and the chemical impact factors over time, and at the same time construct a bay ecological prediction model. Divide the preprocessed data into a training set and a test set, use the training set data to train the bay ecological prediction model, adjust the model parameters, optimize the model performance, use the test set data to verify the bay ecological prediction model, evaluate the accuracy and reliability of the bay ecological prediction model, and use the trained bay ecological prediction model and combine the obtained physical impact factors and chemical impact factors to calculate the physical impact index and the chemical impact index, and analyze the overall health status of the bay ecosystem; Step 4: Based on the physical impact index and the chemical impact index, obtain the bay ecological health assessment coefficient, predict the development trend of the bay ecosystem health, including the changing trend of the health status, the emergence time of potential problems, etc., and determine the early warning level of the bay ecosystem health in combination with historical data, such as blue early warning, the ecosystem health status is good, but potential risks need to be concerned; yellow early warning, the ecosystem health status has declined to a certain extent, and preventive measures need to be taken; orange early warning, the ecosystem health status is poor, and repair measures need to be taken immediately; and red early warning, the ecosystem health status has deteriorated severely, and the emergency response mechanism needs to be urgently activated. At the same time, set the corresponding early warning assessment thresholds. The process of obtaining the bay ecological health assessment coefficient is as follows: According to the preprocessed historical data, combine the physical impact index and the chemical impact index, analyze the correlation between the bay ecosystem health and the physical impact index and the chemical impact index, calculate the standardized value of each factor according to the selected physical impact index and chemical impact index, match different weights for the physical impact index and the chemical impact index, and perform weighted summation of these two indices to obtain the bay ecological health assessment coefficient, predict the development trend of the bay ecosystem health, divide the early warning levels of the bay ecosystem health according to the bay ecological health assessment coefficient and in combination with historical data, which are the blue early warning level, the yellow early warning level, the orange early warning level and the red early warning level respectively. Among them, the early warning levels increase gradually from the blue early warning level to the red early warning level, match the determined current early warning level of the bay ecosystem health with the bay ecological health assessment coefficient, and set the corresponding early warning assessment thresholds; Step 5: Determine the corresponding early warning levels for the health of the bay ecosystem according to the bay ecological prediction model, and generate corresponding early warning information. The early warning information should include the early warning level, the areas that may be affected, the changes in the main ecological factors, and the recommended countermeasures, etc., so that relevant departments and the public can take actions in a timely manner, reminding the corresponding departments and the public to take corresponding targeted countermeasures, such as strengthening pollution control, carrying out ecological restoration, strengthening monitoring and assessment, etc.; the process of obtaining the early warning information is as follows: input the preprocessed real-time data into the bay ecological prediction model, calculate the physical impact index and the chemical impact index respectively, and obtain the bay ecological health assessment coefficient. Based on the obtained bay ecological health assessment coefficient and the preset early warning assessment threshold, determine the corresponding early warning level for the health of the bay ecosystem. According to the predicted early warning level, generate corresponding early warning information, formulate specific countermeasures, and at the same time transmit the early warning information to the corresponding departments and the public, such as through the internal office systems of various departments, SMS platforms, telephone conferences, etc., to convey the early warning information to relevant functional departments such as environmental protection departments, ocean management departments, and fishery departments in a timely manner.

[0021] Embodiment 2, as Figures 1 to 3 shown, on the basis of Embodiment 1, the present invention provides a technical solution: Preferably, the calculation formula for the physical impact index is: ; where, PII is the physical impact index, is the real-time measured value of the i-th physical impact factor, such as water temperature, salinity, tide, water flow velocity, etc., and is the standard value of the i-th physical impact factor, which is used to evaluate the deviation degree of this factor, is the standard deviation of the i-th physical impact factor, which is used to standardize the measured value of this factor so that it is comparable under different dimensions, is the weight of the i-th physical impact factor, which is used to adjust the contribution of different factors to the total impact index, and n is the total number of physical impact factors; The calculation formula for the chemical impact index is: ; where, CII is the chemical impact index, and is the real-time measured value of the j-th chemical impact factor such as dissolved oxygen, pH value, nutrient salt concentration, heavy metal content, etc., is the standard value of the j-th chemical impact factor, is the maximum allowable deviation degree of the j-th chemical impact factor, which is used to evaluate whether the deviation degree of this factor is acceptable, is the weight of the j-th chemical impact factor, which is used to adjust the contribution of different factors to the total impact index, and m is the total number of chemical impact factors; The calculation formula for the bay ecological health assessment coefficient is: ; wherein, EHAC is the Gulf ecological health assessment coefficient, PII is the physical impact index, CII is the chemical impact index, is the reference value of the physical impact index, representing a reference value or average value of the physical impact index under ideal conditions or in historical data, is the reference value of the chemical impact index, representing a reference value or average value of the chemical impact index under ideal conditions or in historical data, is a function of the physical impact index, used to adjust or standardize the value of the physical impact index to better meet the evaluation requirements, is a function of the chemical impact index, used to adjust or standardize the value of the chemical impact index; Multiple warning levels correspond to multiple warning evaluation thresholds. Among them, the warning evaluation thresholds include upper thresholds and lower thresholds; The multiple warning levels and the multiple warning evaluation thresholds satisfy the following relationship: Blue warning level ; Yellow warning level ; Orange warning level ; Red warning level ; wherein, EHAC is the Gulf ecological health assessment coefficient, A is the upper threshold corresponding to the blue warning level and the lower threshold corresponding to the yellow warning level, B is the upper threshold corresponding to the yellow warning level and the lower threshold corresponding to the orange warning level, and C is the upper threshold corresponding to the orange warning level and the lower threshold corresponding to the red warning level.

[0022] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A trend prediction and early warning method applied to the health assessment of the bay ecosystem, characterized in that, It includes the following steps: Step 1: Collect the monitoring data of the bay ecosystem, and divide the monitoring data into historical data and real-time data, where the monitoring data includes water quality data, sediment data, and hydro-meteorological data; Step 2: Preprocess the collected monitoring data, and screen the influencing factors of the bay ecosystem. Among them, the influencing factors of the bay ecosystem include physical influencing factors and chemical influencing factors; Step 3: Use the preprocessed historical data and the influencing factors of the bay ecosystem to construct a bay ecological prediction model, obtain the physical influence index and the chemical influence index, and analyze the overall health status of the bay ecosystem; Step 4: Based on the physical influence index and the chemical influence index, obtain the bay ecological health assessment coefficient, predict the development trend of the bay ecosystem health, and determine the early warning level of the bay ecosystem health in combination with historical data. At the same time, set the corresponding early warning assessment threshold; Step 5: Determine the corresponding early warning level of the bay ecosystem health according to the bay ecological prediction model, generate corresponding early warning information, and remind the corresponding departments and the public to take corresponding targeted countermeasures.

2. The trend prediction and early warning method for the health assessment of the bay ecosystem according to claim 1, wherein: In the said Step 1, the process of obtaining the bay ecological monitoring data is as follows: Step 101: According to the geographical characteristics of the bay, the water flow direction, and the distribution of surrounding pollution sources, set up multiple monitoring stations, install sensors and monitoring equipment at the monitoring stations, collect water quality data, sediment data, and hydro-meteorological data in real time, and transmit them to the data center wirelessly for real-time processing and storage; Step 102: Collect historical data from previous research reports, monitoring records, and public official databases, and organize, verify, and file the historical data; Step 103: Classify and store the obtained historical data and real-time data according to the time series and data type. At the same time, establish a data recovery mechanism and back up the database regularly.

3. The trend prediction and early warning method for the health assessment of the bay ecosystem according to claim 1, wherein: In the said Step 2, the process of obtaining the influencing factors of the bay ecosystem is as follows: Step 201: Conduct data cleaning, data standardization, data integration, and preprocessing on the collected historical data and real-time data; Step 202: Screen and extract according to the water temperature, salinity, tide, water flow velocity, wind direction, and wind speed of the bay ecosystem to obtain physical influencing factors; Step 203: Screen and extract features according to the dissolved oxygen, pH value, nutrient salt concentration, and heavy metal content of the bay ecosystem to obtain chemical influencing factors; Step 204: Analyze the correlation relationship between the obtained physical influencing factors and chemical influencing factors.

4. A trend prediction and early warning method for the health assessment of a bay ecosystem according to claim 1, characterized in that: In the said Step 3, the process of obtaining the physical influence index and the chemical influence index is as follows: Step 301: Integrate the correlation data of the physical influencing factors and chemical influencing factors in the preprocessed historical data to form a unified data set; Step 302: Conduct time series analysis on the physical influencing factors and chemical influencing factors, analyze the changing trends of the physical influencing factors and chemical influencing factors over time, and at the same time construct a bay ecological prediction model; Step 303: Divide the preprocessed data into a training set and a test set. Use the training set data to train the bay ecological prediction model, adjust the model parameters, and optimize the model performance. Use the test set data to verify the bay ecological prediction model and evaluate the accuracy and reliability of the bay ecological prediction model; Step 304: Utilize the trained bay ecological prediction model and combine it with the obtained physical impact factors and chemical impact factors to calculate the physical impact index and the chemical impact index, and analyze the overall health status of the bay ecosystem.

5. The trend prediction and early warning method for the health assessment of the bay ecosystem according to claim 4, wherein: The calculation formula for the physical impact index is: ； Among them, PII is the Physical Impact Index, is the i-th physical impact factor, and is the standard value of the i-th physical impact factor, is the standard deviation of the i-th physical impact factor, is the weight of the i-th physical impact factor, and n is the total number of physical impact factors; The calculation formula for the chemical impact index is: ； Among them, CII is the chemical influence index, which is the real-time measurement value of the j-th chemical influence factor, is the standard value of the j-th chemical influence factor, is the maximum allowable deviation degree of the j-th chemical influence factor, is the weight of the j-th chemical influence factor, and m is the total number of chemical influence factors.

6. The trend prediction and early warning method for the health assessment of the bay ecosystem according to claim 5, wherein: In step 4, the process of obtaining the bay ecological health assessment coefficient is as follows: Step 401: Based on the preprocessed historical data, combine the physical impact index and the chemical impact index to analyze the correlation between the bay ecosystem health and the physical impact index and the chemical impact index; Step 402: According to the selected physical impact index and chemical impact index, calculate the standardized value of each factor, match different weights for the physical impact index and the chemical impact index, and perform weighted summation of these two indices to obtain the bay ecological health assessment coefficient and predict the development trend of the bay ecosystem health; Step 403: Based on the bay ecological health assessment coefficient and combined with historical data, divide the warning levels of the bay ecosystem health into blue warning level, yellow warning level, orange warning level, and red warning level. Among them, the warning levels increase gradually from the blue warning level to the red warning level; Step 404: Match the determined current bay ecosystem health warning level with the bay ecological health assessment coefficient and set the corresponding warning assessment threshold.

7. The trend prediction and early warning method for the health assessment of the bay ecosystem according to claim 6, wherein: The calculation formula for the bay ecological health assessment coefficient is: ； Among them, EHAC is the Gulf ecological health assessment coefficient, PII is the physical impact index, CII is the chemical impact index, is the reference value of the physical impact index, is the reference value of the chemical impact index, is a function of the physical impact index, is a function of the chemical impact index.

8. A trend prediction and early warning method for the health assessment of a bay ecosystem according to claim 7, characterized in that: Multiple warning levels correspond to multiple warning assessment thresholds. Among them, the warning assessment thresholds include upper threshold and lower threshold; The multiple warning levels and the multiple warning assessment thresholds satisfy the following relationship: Blue warning level ; Yellow warning level ; Orange warning level ; Red warning level ; Where EHAC is the bay ecological health assessment coefficient, A is the upper threshold corresponding to the blue warning level and the lower threshold corresponding to the yellow warning level, B is the upper threshold corresponding to the yellow warning level and the lower threshold corresponding to the orange warning level, and C is the upper threshold corresponding to the orange warning level and the lower threshold corresponding to the red warning level.

9. The trend prediction and early warning method for the health assessment of the bay ecosystem according to claim 8, characterized in that: In step 5, the process of obtaining the warning information is as follows: Step 501: Input the preprocessed real-time data into the bay ecological prediction model, calculate the physical impact index and the chemical impact index respectively, and obtain the bay ecological health assessment coefficient; Step 502: Based on the obtained bay ecological health assessment coefficient and the preset warning assessment threshold, determine the corresponding warning level of the bay ecosystem health; Step 503: Generate the corresponding warning information according to the predicted warning level, formulate specific countermeasures, and at the same time transmit the warning information to the corresponding departments and the public.

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