Dynamic supervision method and system for marine ranching

Abstract

The invention discloses a dynamic supervision method and system for a marine ranch, and relates to the technical field of marine ranches, and the method comprises the following steps: determining the target, range and index of the dynamic supervision of the marine ranch, collecting the ecological environment data of the marine ranch, and transmitting the ecological environment data to a data management center; wherein the ecological environment data comprises historical data and real-time data, the collected ecological environment data is preprocessed, the preprocessed data is analyzed, environment influence factors are extracted, the environment influence factors comprise a water quality influence factor, a substrate influence factor and a biocenosis influence factor, and the water quality influence factor, the substrate influence factor and the biocenosis influence factor are analyzed; and analyzing the correlation among the water quality influence factor, the substrate influence factor and the biocenosis influence factor. According to the method, the water quality, the substrate and the biocenosis state of the marine ranch are monitored in real time, the ecological environment early warning evaluation coefficient is combined, and the potential ecological risk is quickly identified, so that prevention measures are taken in the first time, and the ecological environment is prevented from deteriorating to an irreversible degree.

Description

Technical Field

[0001] The present invention relates to the technical field of marine ranching, and in particular to a dynamic supervision method and system for marine ranching. Background Art

[0002] In the existing technology, proliferation and leisure marine ranches are often managed and maintained by the founding enterprises themselves, while the management and maintenance functions of conservation marine ranches are mostly undertaken by the fishery department. Due to the small number of management personnel in the fishery department, insufficient technical equipment, and lack of tracking, monitoring and effect evaluation capabilities of marine ranches, the prediction and effect evaluation capabilities of sudden ecological disasters are insufficient, which in turn affects the ecosystem of marine ranches. For this reason, a dynamic supervision method and system for marine ranches are proposed. Summary of the invention

[0003] The purpose of the present invention is to provide a dynamic supervision method and system for marine ranches to solve the problems raised in the above-mentioned background technology.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is: First, a dynamic supervision method and system for marine ranching includes the following steps: Step 1: Determine the goals, scope and indicators of dynamic supervision of marine ranches, such as biodiversity, water quality parameters, fishery output, etc., and collect ecological and environmental data of marine ranches and transmit them to the data management center. The ecological and environmental data include historical data and real-time data, and the scope includes aquaculture areas, leisure areas, protection areas, etc. The indicators include water quality indicators, bottom quality indicators and biological community indicators. Water quality indicators include: temperature, salinity, dissolved oxygen, pH value, nutrients, etc. Bottom quality indicators include: particle size, organic matter content, heavy metal content, etc., and biological community indicators include: species diversity, quantity distribution, biological health status, etc.; Step 2, preprocessing the collected ecological environment data, analyzing the preprocessed data, extracting environmental impact factors, where the environmental impact factors include water quality impact factors, bottom impact factors and biological community impact factors, and analyzing the correlation and impact mechanism among water quality impact factors, bottom impact factors and biological community impact factors; Step 3: Based on historical data and extracted environmental impact factors, an ecological analysis model is constructed to obtain water quality impact index, bottom quality impact index and biological community impact index to predict the deterioration of the ecological environment and the scope of impact; Step 4: Based on the water quality impact index, the bottom quality impact index and the biological community impact index, the ecological environment early warning assessment coefficient is obtained, and combined with the pre-processed historical data, different early warning levels of ecological environment deterioration are determined, and early warning thresholds are set for different early warning levels. At the same time, an early warning assessment sequence table is obtained to judge the current ecological environment status; Step 5: Input the preprocessed real-time data into the ecological environment analysis model to obtain the value of the ecological environment early warning assessment coefficient, determine the warning level of the current ecological environment deterioration, and generate corresponding warning information according to the corresponding warning level, initiate the emergency response procedure, and adopt emergency response plans to reduce the impact of disasters. According to the warning level and response results, evaluate the effectiveness of the supervision method, and continuously optimize the supervision process and technical means.

[0005] A further improvement of the technical solution of the present invention is that in step 1, the process of collecting the ecological environment data of the marine ranch is: Step 101, deploy various sensors and equipment in the marine ranch, including but not limited to temperature sensors, salinity sensors, dissolved oxygen sensors, pH sensors, underwater cameras, etc., to collect real-time data of the marine ranch's ecological environment data, and obtain historical data of the ecological environment data from an existing database, wherein the historical data includes historical ecological environment monitoring records and historical breeding records; Step 102, using wireless communication technology to transmit the collected historical data and real-time data to the data management center, and organize, archive and store the collected ecological environment data for later analysis and research; Step 103, using big data storage technology to build a database in the data management center to ensure the accessibility and persistence of the data. At the same time, a distributed storage architecture is used to improve the reliability and access speed of the data, and encryption measures are taken to ensure the security and privacy of the data. At the same time, the growth trends of the farmed organisms and the laws of environmental changes are extracted.

[0006] A further improvement of the technical solution of the present invention is that in step 2, the extraction process of water quality influencing factors, bottom sediment influencing factors and biological community influencing factors is as follows: Step 201, cleaning the collected ecological environment data and removing abnormal data, including identifying and processing missing values, abnormal values ​​and noise data to ensure data quality, and performing data integration; Step 202, extracting features from the pre-processed historical data, obtaining water quality influencing factors, bottom quality influencing factors and biome influencing factors for water quality features, bottom quality features and biome features, and analyzing the correlations among the water quality influencing factors, bottom quality influencing factors and biome influencing factors; Step 203, based on the pre-processed historical data, analyze the change trend and distribution characteristics of water quality influencing factors and their associated data, including water temperature, salinity, dissolved oxygen and pH value, analyze the change law and spatial distribution of bottom sediment influencing factors and their associated data, explore the seasonal changes, interannual changes and spatial heterogeneity of water quality parameters, including sediment type, particle size distribution, heavy metal content and organic pollutant content, explore the source, migration and transformation process of bottom sediment pollutants, analyze the diversity and stability of biome influencing factors and their associated data, including plankton species, quantity, distribution, benthic species, quantity and biomass, explore the stability, resilience and response mechanism of biome structure to environmental changes; Step 204, analyzing the interaction between water quality influencing factors and bottom sediment influencing factors, such as turbidity may affect the stability of bottom sediment, the correlation between water quality influencing factors and biological community influencing factors, exploring the impact of water quality parameters on biological community structure, such as the impact of dissolved oxygen on fish survival, the correlation between bottom sediment influencing factors and biological community influencing factors, and studying how bottom sediment conditions affect the distribution and diversity of biological communities, such as the impact of bottom sediment particle size on benthic invertebrates.

[0007] A further improvement of the technical solution of the present invention is that in step 3, the process of obtaining the water quality impact index, the bottom quality impact index and the biological community impact index is as follows: Step 301, obtaining time series data of water quality influencing factors, bottom quality influencing factors and biological community influencing factors according to environmental influencing factors, and building an ecological analysis model in combination with historical data; Step 302, integrating the associated data of water quality influencing factors, bottom sediment influencing factors and biological community influencing factors in the pre-processed historical data to form a unified data set; Step 303, dividing the integrated data set into a training set, a validation set and a test set, using the training set data to train the ecological analysis model, and using the validation set and the test set data to validate the trained ecological analysis model, and evaluating the performance of the ecological analysis model; Step 304, using the trained ecological analysis model in combination with the pre-processed historical data, obtain the water quality impact index, bottom quality impact index and biological community impact index, analyze the impact scope of ecological environment deterioration, as well as the changing trends, deterioration degree and influencing factors of water quality, bottom quality and biological community, evaluate the degree and speed of water quality deterioration, evaluate the degree and scope of bottom quality deterioration and evaluate the changes in biological community diversity and stability.

[0008] A further improvement of the technical solution of the present invention is that the calculation formula of the water quality impact index is: ; Among them, WI is the water quality impact index, T is the comprehensive water quality parameter at the current time point, is the maximum value of water quality parameters in historical data, is the minimum value of the water quality parameter in the historical data, is the current measured value of the i-th water quality parameter, is the standard value of the i-th water quality parameter, is the weight of the i-th water quality parameter, n is the number of water quality parameters, is a very small positive number; ; Among them, SI is the bottom influence index, is the current measured value of the jth sediment parameter, such as heavy metal content, organic pollutant content, etc., It is the reference value of the bottom sediment parameter, usually indicating an unpolluted state. is the maximum allowable value of the bottom sediment parameter, is the influence coefficient of the jth sediment parameter, indicating the severity of the effect of this parameter on the sediment, and m is the number of sediment parameters; ; Among them, BI is the biome impact index, is the current measured value of the kth biome parameter, is the expected value of the biome parameter, is the maximum possible value of the biome parameter, is the weight of the kth biome parameter, and l is the number of biome parameters.

[0009] A further improvement of the technical solution of the present invention is that in step 4, the process of obtaining the ecological environment early warning assessment coefficient is: Step 401, based on historical data, combining the water quality impact index, the bottom quality impact index and the biome impact index with historical ecological environment monitoring records, including historical records of water quality, bottom quality, biome and the overall status of the ecological environment, and analyzing the correlation between the water quality impact index, the bottom quality impact index and the biome impact index; Step 402, determining the weights of the water quality impact index, the bottom quality impact index and the biological community impact index, and performing weighted summation of the water quality impact index, the bottom quality impact index and the biological community impact index according to the determined weights to obtain an ecological environment early warning assessment coefficient; Step 403, according to the ecological environment early warning assessment coefficient and the pre-processed historical data, the ecological environment status is divided into different early warning levels, namely green warning level, yellow warning level, orange warning level and red warning level, wherein the ecological environment status of the early warning level increases step by step from the green warning level to the red warning level; Step 404, matching different warning levels with the results of the ecological environment warning assessment coefficient, and matching corresponding warning thresholds for different warning levels; Step 405, sort all the ecological environment early warning assessment coefficients, match the corresponding warning levels, list the corresponding relationship between the early warning assessment coefficients and the warning levels, generate an early warning assessment sequence table, analyze the changing trend of the ecological environment status, and the sequence table should contain information such as the assessment coefficient, warning level and corresponding warning threshold at each time point to analyze the changing trend of the ecological environment status.

[0010] A further improvement of the technical solution of the present invention is that the calculation formula of the ecological environment early warning assessment coefficient is: ; Among them, EI is the ecological environment early warning assessment coefficient, WI is the water quality impact index, SI is the bottom quality impact index, BI is the biological community impact index, is the standard value of water quality impact index, is the standard value of the bottom quality impact index, is the standard value of the biome impact index.

[0011] A further improvement of the technical solution of the present invention is that: the multiple warning levels correspond to multiple warning thresholds, wherein the warning thresholds include an upper threshold and a lower threshold; The multiple warning levels and the multiple warning thresholds satisfy the following relationship: Green warning level ; Yellow warning level ; Orange warning level ; Red alert level ; Among them, EI is the ecological environment early warning assessment coefficient, T1 is the upper threshold corresponding to the green warning level and the lower threshold corresponding to the yellow warning level, T2 is the upper threshold corresponding to the yellow warning level and the lower threshold corresponding to the orange warning level, and T3 is the upper threshold corresponding to the orange warning level and the lower threshold corresponding to the red warning level.

[0012] A further improvement of the technical solution of the present invention is that in step 5, the process of adopting the emergency response plan is: Step 501, inputting the water quality impact factor, bottom quality impact factor and biological community impact factor in the pre-processed real-time data into the ecological environment analysis model, and calculating the water quality impact index, bottom quality impact index and biological community impact index respectively; Step 502: outputting an ecological environment early warning assessment coefficient based on the calculated water quality impact index, bottom quality impact index and biological community impact index, dividing the value range of the ecological environment early warning assessment coefficient according to the preset early warning level, and determining the current early warning level; Step 503, generating corresponding warning information according to the current warning level, wherein the warning information includes the warning level, an overview of the ecological environment, the impact of the disaster, and recommended emergency measures; Step 504, according to the warning level, initiate the corresponding emergency response procedure, which may include emergency meeting, emergency resource deployment, personnel evacuation, pollution control and other measures. In accordance with the requirements of the emergency response procedure, various emergency measures are implemented, and the implementation of emergency measures is monitored, and timely adjustments and optimizations are made to evaluate the effectiveness of the emergency response procedure.

[0013] Second, a dynamic supervision system for marine ranches includes a data management center, wherein the data management center is communicatively connected to a data acquisition module, a monitoring module, a data analysis module and a processing module, a prediction module, a decision support module, and an emergency response module, wherein electrical signals are connected between the modules; The data acquisition module is used to collect ecological environment data in the marine ranch, including water quality data, bottom quality data, biological community data and equipment operation status data; The monitoring module is used to monitor the environmental status and biological activity status in the marine ranch, including the growth status, behavioral habits and abnormal conditions of marine organisms, to ensure the stability and biological safety of the breeding environment, including water quality changes, fish behavior, equipment operation status, etc., to promptly detect and handle abnormal conditions, and ensure the safe operation of the marine ranch; The data analysis module and processing module are used to process and analyze the data collected by the data acquisition module, extract useful information, and generate reports and alarms, such as water quality change trends, fish growth conditions, etc.; The prediction module is used to predict environmental changes and biological activity conditions in the marine ranch, and output prediction results to discover potential risks in advance, provide scientific basis for the decision support module, and help aquaculture companies adjust their aquaculture strategies in a timely manner; The decision support module is used to provide decision-making suggestions for aquaculture enterprises based on the prediction results of the data analysis module and the prediction module, including suggestions on aquaculture strategy adjustment, disease prevention, etc., while optimizing the aquaculture structure, improving production and quality, protecting the marine ecological environment, and achieving sustainable development; The emergency response module is used to activate the emergency plan and take corresponding measures to deal with emergencies and potential risks in the marine ranch, such as water quality deterioration and disease outbreaks, to ensure the safe operation of the marine ranch and improve the reliability and stability of the system.

[0014] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art: The present invention provides a dynamic supervision method and system for marine ranches, which can quickly identify potential ecological risks by real-time monitoring of the water quality, bottom quality and biological community status of the marine ranch, combined with the ecological environment early warning assessment coefficient, so as to take preventive measures in the first time to avoid the ecological environment from deteriorating to an irreversible level.

[0015] The present invention provides a dynamic supervision method and system for marine ranches. The method utilizes monitoring technology and early warning systems to monitor key ecological indicators of marine ranches in real time and accurately, and calculates ecological environment early warning assessment coefficients. Combined with preset early warning levels and thresholds, the early warning mechanism is triggered quickly to achieve immediate response, discover and deal with potential ecological problems as soon as possible, effectively avoid or reduce the negative impact of ecological environment deterioration, and provide solid ecological security for marine ranches.

[0016] The present invention provides a dynamic supervision method and system for marine ranches. By dividing the early warning levels, the ecological risks are quantified and concretized, and targeted measures are formulated and implemented according to different early warning levels to achieve refined management. This not only improves the management efficiency, but also ensures the pertinence and effectiveness of the management measures, laying a solid foundation for the sustainable development of marine ranches. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0018] Figure 1 is a flow chart of the method of the present invention; Figure 2 The flowchart of obtaining the impact index, substrate impact index and biological community impact index of the present invention; Figure 3 This is a flow chart for obtaining the ecological environment early warning assessment coefficient of the present invention; Figure 4 It is a module structure diagram of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 creative work are within the scope of protection of the present invention.

[0020] Embodiment 1, as Figure 1-Figure 4 As shown, the present invention provides a dynamic supervision method for marine ranches, comprising the following steps: Step 1, determining the objectives, scope and indicators of dynamic supervision of marine ranches, such as biodiversity, water quality parameters, fishery output, etc., and collecting ecological environmental data of marine ranches, and transmitting them to a data management center, wherein the ecological environmental data include historical data and real-time data, wherein the scope includes aquaculture areas, leisure areas, protection areas, etc., and the indicators include water quality indicators, bottom quality indicators and biological community indicators, and the water quality indicators include: temperature, salinity, dissolved oxygen, pH value, nutrients, etc., and the bottom quality indicators include: particle size, organic matter content, heavy metal content, etc., and the biological community indicators include: species diversity, quantity distribution, biological health status, etc.; The collection process of marine ranch ecological environmental data is: deploying various sensors and equipment in the marine ranch, including but not limited to Limited to temperature sensors, salinity sensors, dissolved oxygen sensors, pH sensors, underwater cameras, etc., real-time data of marine ranch ecological environment data is collected, and historical data of ecological environment data is obtained from existing databases, where historical data includes historical ecological environment monitoring records and historical breeding records. Wireless communication technology is used to transmit the collected historical data and real-time data to the data management center, and the collected ecological environment data is sorted, archived and stored for later analysis and research. Big data storage technology is used to build a database in the data management center to ensure data accessibility and persistence. At the same time, a distributed storage architecture is used to improve data reliability and access speed, and encryption measures are taken to ensure data security and privacy, while extracting the growth trends of farmed organisms and the laws of environmental changes; Step 2, preprocess the collected ecological and environmental data, analyze the preprocessed data, extract environmental influencing factors, where the environmental influencing factors include water quality influencing factors, bottom sediment influencing factors and biological community influencing factors, and analyze the correlation and influencing mechanism between water quality influencing factors, bottom sediment influencing factors and biological community influencing factors; the extraction process of water quality influencing factors, bottom sediment influencing factors and biological community influencing factors is as follows: clean the collected ecological and environmental data and remove abnormal data, including identifying and processing missing values, abnormal values ​​and noise data to ensure the quality of the data, and integrate the data, extract features from the preprocessed historical data, obtain water quality influencing factors, bottom sediment influencing factors and biological community influencing factors for water quality characteristics, bottom sediment characteristics and biological community characteristics respectively, and analyze the correlation between water quality influencing factors, bottom sediment influencing factors and biological community influencing factors, and analyze the change trend and distribution characteristics of water quality influencing factors and their associated data based on the preprocessed historical data, including water temperature , salinity, dissolved oxygen and pH value, analyze the changing patterns and spatial distribution of bottom sediment influencing factors and their associated data, explore the seasonal changes, interannual changes and spatial heterogeneity of water quality parameters, including sediment type, particle size distribution, heavy metal content and organic pollutant content, explore the source, migration and transformation process of bottom sediment pollutants, analyze the diversity and stability of biome influencing factors and their associated data, including plankton species, quantity, distribution, benthic species, quantity and biomass, explore the stability, resilience and response mechanism of biome structure to environmental changes, analyze the interaction between water quality influencing factors and bottom sediment influencing factors, such as turbidity may affect the stability of bottom sediment, the correlation between water quality influencing factors and biome influencing factors: explore the impact of water quality parameters on biome structure, such as the impact of dissolved oxygen on fish survival, the correlation between bottom sediment influencing factors and biome influencing factors, and study how bottom sediment conditions affect the distribution and diversity of biomes, such as the impact of bottom sediment particle size on benthic invertebrates; Step 3, based on historical data and extracted environmental impact factors, construct an ecological analysis model to obtain water quality impact index, bottom quality impact index and biological community impact index to predict the deterioration of the ecological environment and the scope of impact; the process of obtaining water quality impact index, bottom quality impact index and biological community impact index is as follows: according to the environmental impact factors, the time series data of water quality impact factors, bottom quality impact factors and biological community impact factors are obtained, and the ecological analysis model is constructed in combination with historical data, and the associated data of water quality impact factors, bottom quality impact factors and biological community impact factors in the pre-processed historical data are integrated to form a unified data set, and the integrated data set is used as the input data set. The data set is divided into a training set, a validation set and a test set. The ecological analysis model is trained using the training set data, and the trained ecological analysis model is validated using the validation set and the test set data. The performance of the ecological analysis model is evaluated. The trained ecological analysis model is combined with the pre-processed historical data to obtain the water quality impact index, the bottom quality impact index and the biological community impact index. The impact scope of the ecological environment deterioration, as well as the changing trends, deterioration degree and influencing factors of water quality, bottom quality and biological community are analyzed. The degree and speed of water quality deterioration are evaluated, the degree and scope of bottom quality deterioration are evaluated, and the changes in the diversity and stability of biological communities are evaluated. Step 4, based on the water quality impact index, the bottom quality impact index and the biological community impact index, the ecological environment early warning assessment coefficient is obtained, and combined with the pre-processed historical data, different early warning levels of ecological environment deterioration are determined, and early warning thresholds are set for different early warning levels. At the same time, an early warning assessment sequence table is obtained to judge the current ecological environment status; the process of obtaining the ecological environment early warning assessment coefficient is as follows: according to historical data, the water quality impact index, the bottom quality impact index and the biological community impact index are combined with historical ecological environment monitoring records, including historical records of water quality, bottom quality, biological community and the overall status of the ecological environment, and the correlation between the water quality impact index, the bottom quality impact index and the biological community impact index is analyzed, and the weights of the water quality impact index, the bottom quality impact index and the biological community impact index are determined, and the water quality impact index, the bottom quality impact index and the biological community impact index are categorized according to the determined weights. The weighted sum of the numbers is performed to obtain the ecological environment early warning assessment coefficient. According to the ecological environment early warning assessment coefficient and the pre-processed historical data, the ecological environment status is divided into different warning levels, namely green warning level, yellow warning level, orange warning level and red warning level. Among them, the ecological environment status of the warning level increases step by step from the green warning level to the red warning level. The different warning levels are matched with the results of the ecological environment early warning assessment coefficient, and the corresponding warning thresholds are matched for different warning levels. All ecological environment early warning assessment coefficients are sorted and matched with the corresponding warning levels. The corresponding relationship between the early warning assessment coefficients and the warning levels is listed, and an early warning assessment sequence table is generated to analyze the changing trend of the ecological environment status. The sequence table should contain information such as the assessment coefficient, the warning level and the corresponding warning threshold at each time point to analyze the changing trend of the ecological environment status; Step 5: Input the preprocessed real-time data into the ecological environment analysis model to analyze and obtain the value of the ecological environment early warning assessment coefficient, determine the early warning level of the current ecological environment deterioration, and generate corresponding early warning information according to the corresponding early warning level, start the emergency response procedure, take emergency response plans to reduce the impact of disasters, and evaluate the effectiveness of the supervision method according to the early warning level and response results, and continuously optimize the supervision process and technical means; the process of taking emergency response plans is as follows: input the water quality influencing factors, bottom sediment influencing factors and biological community influencing factors in the preprocessed real-time data into the ecological environment analysis model, calculate the water quality impact index, bottom sediment impact index and biological community impact index respectively, and based on the calculated water quality impact The system calculates the impact index, bottom sediment impact index and biological community impact index, outputs the ecological environment early warning assessment coefficient, divides the value range of the ecological environment early warning assessment coefficient according to the preset warning level, determines the current warning level, and generates corresponding warning information according to the current warning level, where the warning information includes the warning level, an overview of the ecological environment status, the impact of the disaster and the recommended emergency measures. According to the warning level, the corresponding emergency response procedure is initiated. The emergency response procedure may include the convening of emergency meetings, the allocation of emergency resources, the evacuation of personnel, pollution control and other measures. In accordance with the requirements of the emergency response procedure, various emergency measures are implemented, and the implementation of emergency measures is monitored, and timely adjustments and optimizations are made to evaluate the effectiveness of the emergency response procedure.

[0021] Embodiment 2, as Figure 1-Figure 3 As shown, based on Example 1, the present invention provides a technical solution: Preferably, the calculation formula of the water quality impact index is: ; Among them, WI is the water quality impact index, T is the comprehensive water quality parameter at the current time point, is the maximum value of water quality parameters in historical data, is the minimum value of the water quality parameter in the historical data, is the current measured value of the i-th water quality parameter, is the standard value of the i-th water quality parameter, is the weight of the i-th water quality parameter, n is the number of water quality parameters, is a very small positive number; ; Among them, SI is the bottom influence index, is the current measured value of the jth sediment parameter, such as heavy metal content, organic pollutant content, etc., It is the reference value of the bottom sediment parameter, usually indicating an unpolluted state. is the maximum allowable value of the bottom sediment parameter, is the influence coefficient of the jth sediment parameter, indicating the severity of the effect of this parameter on the sediment, and m is the number of sediment parameters; ; Among them, BI is the biome impact index, is the current measured value of the kth biome parameter, is the expected value of the biome parameter, is the maximum possible value of the biome parameter, is the weight of the kth biome parameter, l is the number of biome parameters; The calculation formula for the ecological environment early warning assessment coefficient is: ; Among them, EI is the ecological environment early warning assessment coefficient, WI is the water quality impact index, SI is the bottom quality impact index, BI is the biological community impact index, is the standard value of water quality impact index, is the standard value of the bottom quality impact index, is the standard value of the biome impact index; Multiple warning levels correspond to multiple warning thresholds, where the warning thresholds include an upper threshold and a lower threshold; Multiple warning levels and multiple warning thresholds satisfy the following relationship: Green warning level ; The corresponding early warning measures are: maintain normal monitoring and daily management, ensure that all equipment operates normally, and conduct regular inspections and maintenance; Yellow warning level The corresponding early warning measures are: strengthen the monitoring of water quality and biological behavior, prepare emergency supplies, and conduct safety education and emergency drills for staff; Orange warning level The corresponding early warning measures are: strengthen the monitoring of the marine ranch environment and biological activities, restrict non-essential marine activities, prepare emergency plans, and ensure that all personnel are aware of emergency procedures; Red alert level The corresponding early warning measures are: the emergency plan should be activated immediately, all non-essential personnel should be evacuated, the safety of all personnel should be ensured, and emergency management of the marine ranch should be carried out, including closing the breeding area to prevent further environmental damage and biological losses; Among them, EI is the ecological environment early warning assessment coefficient, T1 is the upper threshold corresponding to the green warning level and the lower threshold corresponding to the yellow warning level, T2 is the upper threshold corresponding to the yellow warning level and the lower threshold corresponding to the orange warning level, and T3 is the upper threshold corresponding to the orange warning level and the lower threshold corresponding to the red warning level.

[0022] Embodiment 3, as Figure 4 As shown, based on Example 1, the present invention provides a dynamic supervision system for marine ranches, including a data management center, the data management center is communicatively connected with a data acquisition module, a monitoring module, a data analysis module and a processing module, a prediction module, a decision support module and an emergency response module, wherein the modules are connected by electrical signals; Data collection module, used to collect ecological and environmental data in the marine ranch, including water quality data, bottom quality data, biological community data and equipment operation status data; The monitoring module is used to monitor the environmental status and biological activity status in the marine ranch, including the growth status, behavioral habits and abnormal conditions of marine organisms, to ensure the stability of the breeding environment and biological safety, including water quality changes, fish behavior, equipment operation status, etc., to promptly detect and handle abnormal conditions and ensure the safe operation of the marine ranch; The data analysis module and processing module are used to process and analyze the data collected by the data acquisition module, extract useful information, and generate reports and alarms, such as water quality change trends, fish growth status, etc.; The prediction module is used to predict environmental changes and biological activity in marine ranches, and output prediction results to detect potential risks in advance, provide scientific basis for the decision support module, and help aquaculture companies adjust their aquaculture strategies in a timely manner; The decision support module is used to provide decision-making suggestions for aquaculture enterprises based on the prediction results of the data analysis module and the prediction module, including suggestions on aquaculture strategy adjustment and disease prevention, while optimizing the aquaculture structure, improving production and quality, protecting the marine ecological environment, and achieving sustainable development; The emergency response module is used to activate emergency plans and take corresponding measures to deal with emergencies and potential risks when they occur in the marine ranch, such as water quality deterioration and disease outbreaks, to ensure the safe operation of the marine ranch and improve the reliability and stability of the system.

[0023] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A dynamic supervision method for marine ranches, characterized by: The following steps are involved: Step 1: Determine the goals, scope and indicators of dynamic supervision of marine ranches, collect ecological and environmental data of marine ranches, and transmit them to the data management center, where the ecological and environmental data includes historical data and real-time data; Step 2, preprocessing the collected ecological environment data, analyzing the preprocessed data, extracting environmental impact factors, wherein the environmental impact factors include water quality impact factors, bottom impact factors and biological community impact factors, and analyzing the correlation between the water quality impact factors, bottom impact factors and biological community impact factors; Step 3: Based on historical data and extracted environmental impact factors, an ecological analysis model is constructed to obtain water quality impact index, bottom quality impact index and biological community impact index to predict the deterioration of the ecological environment and the scope of impact; Step 4: Based on the water quality impact index, the bottom quality impact index and the biological community impact index, the ecological environment early warning assessment coefficient is obtained, and combined with the pre-processed historical data, different early warning levels of ecological environment deterioration are determined, and early warning thresholds are set for different early warning levels. At the same time, an early warning assessment sequence table is obtained to judge the current ecological environment status; Step 5: Input the preprocessed real-time data into the ecological environment analysis model to analyze and obtain the value of the ecological environment early warning assessment coefficient, determine the early warning level of the current ecological environment deterioration, and generate corresponding early warning information according to the corresponding early warning level, initiate the emergency response procedure, and adopt emergency response plans to reduce the impact of disasters, and evaluate the effectiveness of the supervision method based on the early warning level and response results.

2. A dynamic supervision method for marine ranching according to claim 1, characterized in that: In step 1, the process of collecting marine ranch ecological environment data is as follows: Step 101, deploy various sensors and equipment in the marine ranch to collect real-time data of the marine ranch's ecological environment data, and obtain historical data of the ecological environment data from an existing database, wherein the historical data includes historical ecological environment monitoring records and historical breeding records; Step 102, using wireless communication technology to transmit the collected historical data and real-time data to the data management center, and organize, archive and store the collected ecological environment data; Step 103, using big data storage technology, building a database in the data management center, and taking encryption measures, while extracting the growth trends of the farmed organisms and the laws of environmental changes.

3. A dynamic supervision method for marine ranching according to claim 2, characterized in that: In step 2, the extraction process of water quality influencing factors, bottom sediment influencing factors and biological community influencing factors is as follows: Step 201, cleaning the collected ecological environment data and removing abnormal data, including identifying and processing missing values, abnormal values ​​and noise data, and integrating the data; Step 202, extracting features from the pre-processed historical data, obtaining water quality influencing factors, bottom quality influencing factors and biome influencing factors for water quality features, bottom quality features and biome features, and analyzing the correlations among the water quality influencing factors, bottom quality influencing factors and biome influencing factors; Step 203, based on the pre-processed historical data, analyzing the change trend and distribution characteristics of water quality influencing factors and their associated data, including water temperature, salinity, dissolved oxygen and pH value, analyzing the change law and spatial distribution of bottom sediment influencing factors and their associated data; Step 204, analyzing the interaction between water quality influencing factors and bottom sediment influencing factors, and the correlation between water quality influencing factors and biological community influencing factors.

4. A dynamic supervision method for marine ranching according to claim 3, characterized in that: In step 3, the process of obtaining the water quality impact index, the bottom quality impact index and the biological community impact index is as follows: Step 301, obtaining time series data of water quality influencing factors, bottom quality influencing factors and biological community influencing factors according to environmental influencing factors, and building an ecological analysis model in combination with historical data; Step 302, integrating the associated data of water quality influencing factors, bottom sediment influencing factors and biological community influencing factors in the pre-processed historical data to form a unified data set; Step 303, dividing the integrated data set into a training set, a validation set and a test set, using the training set data to train the ecological analysis model, and using the validation set and the test set data to validate the trained ecological analysis model, and evaluating the performance of the ecological analysis model; Step 304, using the trained ecological analysis model in combination with the pre-processed historical data, obtain the water quality impact index, bottom quality impact index and biological community impact index, analyze the impact range of ecological environment deterioration, as well as the changing trends, deterioration degree and influencing factors of water quality, bottom quality and biological community.

5. A dynamic supervision method for marine ranching according to claim 4, characterized in that: The calculation formula of the water quality impact index is: ； Among them, WI is the water quality impact index, T is the comprehensive water quality parameter at the current time point, is the maximum value of water quality parameters in historical data, is the minimum value of the water quality parameter in the historical data, is the current measured value of the i-th water quality parameter, is the standard value of the i-th water quality parameter, is the weight of the i-th water quality parameter, n is the number of water quality parameters, is a very small positive number; ； Among them, SI is the bottom influence index, is the current measured value of the jth sediment parameter, is the reference value of the bottom sediment parameter, is the maximum allowable value of the bottom sediment parameter, is the influence coefficient of the jth sediment parameter, and m is the number of sediment parameters; ； Among them, BI is the biome impact index, is the current measured value of the kth biome parameter, is the expected value of the biome parameter, is the maximum possible value of the biome parameter, and l is the number of biome parameters.

6. A dynamic supervision method for marine ranching according to claim 5, characterized in that: In step 4, the process of obtaining the ecological environment early warning assessment coefficient is as follows: Step 401, combining the water quality impact index, the bottom quality impact index and the biological community impact index with the historical ecological environment monitoring records according to the historical data, and analyzing the correlation degree among the water quality impact index, the bottom quality impact index and the biological community impact index; Step 402, determining the weights of the water quality impact index, the bottom quality impact index and the biological community impact index, and performing weighted summation of the water quality impact index, the bottom quality impact index and the biological community impact index according to the determined weights to obtain an ecological environment early warning assessment coefficient; Step 403, according to the ecological environment early warning assessment coefficient and the pre-processed historical data, the ecological environment status is divided into different early warning levels, namely green warning level, yellow warning level, orange warning level and red warning level, wherein the ecological environment status of the early warning level increases step by step from the green warning level to the red warning level; Step 404, matching different warning levels with the results of the ecological environment warning assessment coefficient, and matching corresponding warning thresholds for different warning levels; Step 405, sort all ecological environment early warning assessment coefficients, match the corresponding early warning levels, list the corresponding relationship between the early warning assessment coefficients and the early warning levels, generate an early warning assessment sequence table, and analyze the changing trend of the ecological environment status.

7. A dynamic supervision method for marine ranching according to claim 6, characterized in that: The calculation formula of the ecological environment early warning assessment coefficient is: ； Among them, EI is the ecological environment early warning assessment coefficient, WI is the water quality impact index, SI is the bottom quality impact index, BI is the biological community impact index, is the standard value of water quality impact index, is the standard value of the bottom quality impact index, is the standard value of the biome impact index.

8. A dynamic supervision method for marine ranching according to claim 7, characterized in that: A plurality of the warning levels correspond to a plurality of the warning thresholds, wherein the warning thresholds include an upper threshold and a lower threshold; The multiple warning levels and the multiple warning thresholds satisfy the following relationship: Green warning level ; Yellow warning level ; Orange warning level ; Red alert level ; Among them, EI is the ecological environment early warning assessment coefficient, T1 is the upper threshold corresponding to the green warning level and the lower threshold corresponding to the yellow warning level, T2 is the upper threshold corresponding to the yellow warning level and the lower threshold corresponding to the orange warning level, and T3 is the upper threshold corresponding to the orange warning level and the lower threshold corresponding to the red warning level.

9. A dynamic supervision method for marine ranching according to claim 8, characterized in that: In step 5, the process of adopting the emergency response plan is as follows: Step 501, inputting the water quality impact factor, bottom quality impact factor and biological community impact factor in the pre-processed real-time data into the ecological environment analysis model, and calculating the water quality impact index, bottom quality impact index and biological community impact index respectively; Step 502: outputting an ecological environment early warning assessment coefficient based on the calculated water quality impact index, bottom quality impact index and biological community impact index, dividing the value range of the ecological environment early warning assessment coefficient according to the preset early warning level, and determining the current early warning level; Step 503, generating corresponding warning information according to the current warning level, wherein the warning information includes the warning level, an overview of the ecological environment, the impact of the disaster, and recommended emergency measures; Step 504, according to the warning level, start the corresponding emergency response procedure, implement various emergency measures according to the requirements of the emergency response procedure, and monitor the implementation of the emergency measures.

10. A dynamic supervision system for marine ranches, used to implement the dynamic supervision method for marine ranches as described in any one of claims 1 to 9, comprising a data management center, characterized in that: The data management center is communicatively connected with a data acquisition module, a monitoring module, a data analysis module and a processing module, a prediction module, a decision support module and an emergency response module, wherein the modules are connected by electrical signals; The data acquisition module is used to collect ecological environment data in the marine ranch, including water quality data, bottom quality data, biological community data and equipment operation status data; The monitoring module is used to monitor the environmental status and biological activity status in the marine ranch, including the growth status, behavioral habits and abnormal conditions of marine organisms; The data analysis module and processing module are used to process and analyze the data collected by the data acquisition module, extract useful information, and generate reports and alarms; The prediction module is used to predict environmental changes and biological activity conditions in the marine ranch, and output prediction results to detect potential risks in advance; The decision support module is used to provide decision suggestions for the breeding enterprises based on the prediction results of the data analysis module and the prediction module; The emergency response module is used to activate the emergency plan and take corresponding measures to deal with emergencies and potential risks in the marine ranch to ensure the safe operation of the marine ranch.

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