Estuary bay early warning management method and system based on digital twinning

Abstract

The invention discloses an estuary and gulf early warning management method and system based on digital twinning, and relates to the technical field of early warning management, and the method comprises the following steps: collecting multi-source data of an estuary and gulf region, and integrating the multi-source data into a unified digital twinning system; building a digital twinborn model of the estuary and gulf by utilizing the collected multi-source data, and analyzing dynamic trends of a physical water quality environment, a hydrological environment and an ecological environment of the estuary and gulf; the estuary bay environment is monitored in real time by deploying a sensor and monitoring equipment. By scientifically identifying the potential ecological problems of the ecological system in the estuary and gulf area, evaluating and predicting the performance of the ecological system, optimizing land-sea overall planning, river-sea linkage comprehensive treatment and preventive maintenance schemes, the ecological function of the gulf is improved, the decision risk is reduced, the response effect and management efficiency are improved, and the economic benefit is increased. And a new technical approach is explored and technical support is provided for sustainable high-quality development of an estuary and gulf ecological system.

Description

Technical Field

[0001] The present invention relates to the technical field of early warning management, and in particular to an estuary and bay early warning management method and system based on digital twins. Background Art

[0002] At present, traditional estuary and bay early warning management methods have obvious deficiencies in real-time updating and dynamic simulation capabilities. They are often unable to quickly integrate and analyze new data, and it is difficult to analyze the carrying capacity of estuaries and bays for different pollutants according to different estuary and bay conditions, resulting in a lack of ability to respond to changes in environmental conditions. Therefore, when faced with complex and changeable environmental problems, traditional methods are often unable to meet the targeted needs of estuary and bay ecological environment protection and management. For this reason, an estuary and bay early warning management method and system based on digital twins are proposed. Summary of the invention

[0003] The purpose of the present invention is to provide an estuary and bay early warning management method and system based on digital twins to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is: In the first aspect, a digital twin-based estuary and bay early warning management method comprises the following steps: Step 1: Collect multi-source data in the estuary and bay area and integrate them into a unified digital twin system. The multi-source data includes water quality, meteorology, marine environment, and pollution source emissions; Step 2: Use the collected multi-source data to build a digital twin model of the estuary and bay to analyze the dynamic trends of the physical water quality environment, hydrological environment, and ecological environment of the estuary and bay; Step 3: Deploy sensors and monitoring equipment to monitor the estuary and bay environment in real time, and update the monitoring data to the digital twin model in real time for dynamic simulation; Step 4: Based on the digital twin model, simulate different pollution emission scenarios, evaluate the environmental capacity of the estuary and bay, assess the health status of the ecosystem, and analyze the comprehensive risk status of the estuary and bay environment; Step 5: Based on the risk status assessment results, combined with the environmental standards and protection objectives of estuaries and bays, formulate warning level classification standards and clarify the warning level of the estuary and bay environment in the assessment area; Step six: According to the warning level classification results, the warning system is automatically triggered to send warning information to management personnel, and corresponding warning management measures and emergency plans are initiated to implement intelligent warning management.

[0005] A further improvement of the technical solution of the present invention is that in step 1, the process of collecting multi-source data in the estuary bay area is as follows: Clarify the needs for early warning management in estuary and bay areas and determine the types of data to be collected, namely water quality, meteorology, hydrology, and pollution source emission data; Identify data sources and collect multi-source data for estuaries and bays of a certain type, including using online water quality monitoring technology to obtain water quality data through sensors and monitoring equipment, obtaining meteorological data through meteorological observation stations and satellite remote sensing, obtaining marine environmental data through marine observation equipment, buoys, and underwater robots, and obtaining pollution source emission data through sewage monitoring equipment and industrial emission records; Perform pre-processing operations such as cleaning, denoising, and format conversion on the collected multi-source data to ensure data quality and consistency. Use data fusion technology to integrate and associate data from different sources and formats to form a unified data view, establish the spatiotemporal relationship between data, and achieve spatiotemporal matching and synchronization of data. According to the actual situation of the estuary and bay area, a unified digital twin system is constructed, and multi-source data are integrated and stored in the digital twin system. A data indexing and query mechanism is established to improve the efficiency of data retrieval and access.

[0006] A further improvement of the technical solution of the present invention is that in step 2, the construction process of the estuary bay digital twin model is: Extract the pre-processed multi-source data and fuse them to form a complete data set. According to the geographical characteristics and topographic information of the estuary, a three-dimensional geometric model of the estuary is constructed. Combined with the spatial distribution characteristics of meteorological, hydrological, water quality and pollution data, the corresponding physical field model is constructed. By using computer technology and numerical simulation methods, the structure, performance and operation status information of physical entities are digitally represented to form a visualized virtual system, and a multi-process coupling model system is constructed, including meteorological model, hydrological model, water quality model and pollution diffusion model, so as to simulate the dynamic changes of meteorological environment, hydrological and water quality environment and pollution interference in estuaries and bays; Establish real-time data connection, connect physical entities and digital models, realize real-time synchronization and update of data, and build a data interaction platform to realize data exchange and interaction between digital models and physical entities; Use meteorological models to simulate the meteorological changes in estuaries and bays, analyze the temporal and spatial distribution characteristics of meteorological elements such as wind speed, wind direction, temperature, humidity, and dew point temperature, and combine meteorological data to obtain the meteorological interference index to evaluate the impact of meteorological changes on the ecological environment and human activities in estuaries and bays; Use hydrological models to simulate the hydrological changes in estuaries and bays, analyze the spatiotemporal distribution characteristics of hydrological elements in estuaries and bays, use water quality models to simulate the water quality changes in estuaries and bays, analyze the spatiotemporal distribution characteristics of water temperature, dissolved oxygen, suspended solids concentration, and salinity indicators, and combine hydrological and water quality data to calculate the hydrological and water quality assessment index to assess the impact of hydrological and water quality changes on the ecological environment and human activities in estuaries and bays; The pollution diffusion model is used to simulate the diffusion process of pollution sources in estuaries and bays, analyze the spatiotemporal distribution characteristics of pollutant concentrations, suspended solids concentrations, and nutrient concentrations, and calculate the pollution interference assessment index based on pollution data to evaluate the pollution degree and impact range of pollution sources on the ecological environment of estuaries and bays.

[0007] A further improvement of the technical solution of the present invention is that the calculation expression of the meteorological interference index is: ; Among them, MI is the meteorological interference index, T is the actual temperature, As the reference temperature, the annual average temperature of the estuary bay area is selected. The highest temperature in history, is the lowest temperature in history, W is the actual wind speed, As the benchmark wind speed, the annual average wind speed in the estuary bay area is selected. The highest wind speed in history. The lowest wind speed in history. is the actual dew point temperature, is the reference dew point temperature, RH is the actual relative humidity, is the reference relative humidity. It should be noted that the value range of MI is from 0 to 1, where 0 indicates no meteorological interference and 1 indicates extreme meteorological interference; The calculation expression of the hydrological and water quality assessment index is: ; Among them, SI is the hydrological and water quality assessment index, WT is the actual water temperature, As the benchmark water temperature, the annual average water temperature in the estuary bay area is selected. is the range of water temperature, DO is the actual dissolved oxygen concentration, is the baseline dissolved oxygen concentration, is the highest dissolved oxygen concentration in history, TS is the actual total suspended solids concentration, is the baseline total suspended solids concentration, is the highest total suspended solids concentration in history, S is the actual salinity, As the benchmark salinity, the annual average salinity of the estuary bay area is selected. is the range of salinity. It should be noted that the SI value range is between 0 and 1, where 0 represents extremely poor hydrological and water quality conditions and 1 represents extremely good hydrological and water quality conditions; The calculation expression of the pollution interference assessment index is: ; Among them, PI is the pollution interference assessment index, C is the actual pollutant concentration, As the benchmark pollutant concentration, the annual average pollutant concentration in the estuary bay area is selected. is the range of pollutant concentration, TS is the actual total suspended solids concentration, is the baseline total suspended solids concentration, is the highest total suspended solids concentration in history, N is the actual nutrient concentration, is the baseline nutrient concentration, is the range of variation of nutrient concentration. It should be noted that the value range of PI is between 0 and 1, where 0 indicates no pollution interference and 1 indicates extreme pollution interference.

[0008] A further improvement of the technical solution of the present invention is that in step 3, the process of dynamic simulation is: According to the needs of estuary and bay environment monitoring, sensors and monitoring equipment are deployed at relevant locations of estuary and bay environment to ensure comprehensive coverage and monitoring of meteorological, hydrological, water quality and pollution data; Sensors and monitoring equipment transmit the real-time monitored data to the data acquisition system, and pre-process the collected data, including data cleaning, format conversion and data storage, to ensure the accuracy and availability of the data. The pre-processed data is updated in real time to the digital twin model to ensure the real-time and accuracy of the data, and dynamic simulation and analysis are performed in the digital twin model; Use the digital twin model to conduct dynamic simulation to simulate the changes in the estuary and bay environment under different conditions, and update the status of the digital twin model based on real-time monitoring data to reflect the real-time changes in the estuary and bay environment; The dynamic trends of the estuary and bay environment are analyzed through the simulation results of the digital twin model, and the impact of changes in meteorology, hydrology, water quality and pollution on the ecological environment and human activities is evaluated.

[0009] The further improvement of the technical solution of the present invention is that in step 4, the analysis process of the comprehensive risk status of the estuary and bay environment is: According to the actual situation of the estuary and bay, different pollution emission scenarios are set, including industrial emissions, agricultural emissions, and urban runoff, and the emission sources, emission intensity, and emission time of each pollution emission scenario are determined; Use digital twin models to simulate the pollutant diffusion process of each emission source at different emission intensities and times, analyze the transmission, transformation and degradation process of pollutants in water bodies, and the impact of pollutants on the ecological environment; Based on the simulation results, the environmental capacity of estuaries and bays under different pollution emission scenarios is evaluated. Environmental capacity refers to the maximum amount of pollutant emissions that estuaries and bays can withstand without damaging the health of the ecosystem. Combining the meteorological interference index, hydrological and water quality assessment index and pollution interference assessment index, a comprehensive analysis is conducted to obtain the health risk assessment coefficient and assess the health status of the estuary and bay ecosystem. Based on the assessment results, the overall health status and changing trends of the estuarine and bay ecosystems are analyzed, and the comprehensive risk status of the estuarine and bay environment is evaluated.

[0010] A further improvement of the technical solution of the present invention is that the calculation expression of the health risk assessment coefficient is: ; Among them, HC is the health risk assessment coefficient, MI is the meteorological interference index, which reflects the degree of interference of meteorological conditions on the estuary and bay ecosystems, and SI is the hydrological and water quality assessment index. It is the benchmark value of the hydrological and water quality assessment index, reflecting the natural or undisturbed state of the hydrological and water quality of estuaries and bays. It is the maximum value of the hydrological and water quality assessment index, reflecting the optimal value of the hydrological and water quality of the estuary and bay under ideal conditions. PI is the pollution interference assessment index, reflecting the degree of interference of pollution sources on the estuary and bay ecosystem. It should be noted that the value range of HC is between 0 and 1, where 0 means that the estuary and bay ecosystem is in an extremely high-risk state, and 1 means that the estuary and bay ecosystem is in the best health.

[0011] A further improvement of the technical solution of the present invention is that in step 5, the process of determining the estuary and bay environmental warning level is as follows: Analyze the environmental standards and protection targets of the estuary and bay areas, and divide the warning levels into level 1, level 2, level 3 and level 4 based on the risk status assessment results and the severity of the risk level. The severity of the risk level increases gradually from level 1 to level 4. Combined with the health risk assessment coefficient, a corresponding risk assessment threshold is set for each warning level. When the risk assessment index exceeds the threshold of the corresponding level, the corresponding level of warning is triggered; Through environmental monitoring equipment and sensors, the environmental data of the estuaries and bays in the assessment area are collected in real time, the value of the health risk assessment coefficient is calculated and compared with the risk assessment threshold, and based on the comparison results, the warning level of the estuary and bay environment in the assessment area is determined; A plurality of the warning levels correspond to a plurality of the risk assessment thresholds, wherein the risk assessment thresholds include an upper threshold and a lower threshold; The multiple warning levels and the multiple risk assessment thresholds satisfy the following relationship: Level 1 warning ; Level 2 warning ; Level 3 warning ; Four warning levels ; Among them, HC is the health risk assessment coefficient, is the lower threshold corresponding to the first-level warning level and the upper threshold corresponding to the second-level warning level. is the lower threshold corresponding to the second-level warning level and the upper threshold corresponding to the third-level warning level. It is the lower threshold corresponding to the third warning level and the upper threshold corresponding to the fourth warning level.

[0012] A further improvement of the technical solution of the present invention is that in step 6, the process of sending warning information and implementing intelligent warning management is: According to the determined warning level, the warning system is automatically triggered to prepare to issue warning information, which includes the warning level, impact range, and recommended response measures; Send warning information through SMS, email, phone, and mobile applications to managers, environmental protection departments, emergency response teams, and relevant stakeholders in the estuary and bay areas; According to the warning level, initiate corresponding warning management measures, increase the frequency of environmental monitoring, limit pollutant emissions, and issue environmental health alerts. For level three and level four warning levels, immediately activate the emergency plan, which includes emergency response procedures, emergency resource allocation, personnel evacuation plans, and ecological restoration measures.

[0013] In the second aspect, an estuary and bay early warning management system based on digital twins is used to implement an estuary and bay early warning management method based on digital twins, including an early warning management center, wherein the early warning management center is communicatively connected with a data collection module, a digital twin model construction module, a data synchronization update module, a risk early warning module, an early warning level classification module, and an intelligent early warning management module, wherein the modules are connected by electrical signals; The data collection module is used to collect and integrate data from different sources, including meteorological, hydrological, water quality and pollution data; The digital twin model construction module combines the hydrological model and the meteorological model to construct a digital twin model of the estuary and bay; The data synchronization update module is used to update the collected data to the digital twin model in real time, analyze the latest status of the estuary and bay environment, and ensure the timeliness and accuracy of the data; The risk warning module is used to evaluate the health status of the ecosystem and analyze the comprehensive risk status of the estuary and bay environment; The warning level classification module divides different warning levels according to the risk status assessment results and environmental standards, and sets corresponding risk assessment thresholds. When the risk assessment index exceeds the threshold of the corresponding level, the corresponding level of warning is triggered; The intelligent early warning management module is used to automatically send early warning information to management personnel and initiate corresponding early warning management measures and emergency plans.

[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 an estuary and bay early warning management method and system based on digital twins, which scientifically identifies potential ecological problems of ecosystems in estuary and bay areas, evaluates and predicts ecosystem performance, optimizes land-sea coordination, river-sea linkage comprehensive management and preventive maintenance plans, enhances the ecological functions of the bay, reduces decision-making risks, improves response effects and management efficiency, and explores new technical approaches and provides technical support for the sustainable and high-quality development of estuary and bay ecosystems.

[0015] The present invention provides an estuary and bay early warning management method and system based on digital twins. By evaluating the environmental capacity of estuaries and bays under different pollution emission scenarios, the transmission, transformation and degradation processes of pollutants are simulated, the impact of pollutants on the ecological environment is analyzed, and the health risk assessment coefficient is calculated. Then, the threshold of pollutant emissions is determined, providing a scientific basis for environmental management and policy formulation. According to the risk assessment results, a warning level classification standard is formulated, and the warning level of the estuary and bay environment in the assessment area is clearly defined, thereby achieving optimized management of environmental risks.

[0016] The present invention provides an estuary and bay early warning management method and system based on digital twins. By integrating multiple sensors and monitoring equipment, all-round and real-time monitoring of the estuary and bay environment can be achieved. Meteorological, hydrological, water quality and pollution data can be quickly collected and processed to provide accurate environmental status information. The digital twin model is used to simulate the diffusion and transformation process of pollutants, improve the response speed to environmental changes, and enable early warning information to be sent to management personnel in a timely manner, so that rapid and effective response measures can be taken. 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 figure is a flow chart for constructing the digital twin model of the estuary and bay of the present invention; Figure 3 The present invention is a flowchart for analyzing the comprehensive risk status of the estuary and bay environment; 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 2 As shown, the present invention provides an estuary and bay early warning management method based on digital twins, comprising the following steps: Step 1: Collect multi-source data in estuary and bay areas and integrate them into a unified digital twin system. Multi-source data include water quality, meteorology, marine environment, and pollution source emissions. Clarify the needs of early warning management in estuary and bay areas and determine the types of data to be collected, namely water quality, meteorology, hydrology, and pollution source emission data. Identify the source of data and collect multi-source data of the determined type in estuary and bay areas. Use online water quality monitoring technology to obtain water quality data through sensors and monitoring equipment, including oxygen, pH value, dissolved oxygen, total nitrogen, total phosphorus, chemical oxygen demand and other indicators in the water. Regularly maintain and calibrate the monitoring equipment to ensure the accuracy and reliability of the monitoring data. Obtain meteorological data through meteorological observation stations and satellite remote sensing methods, including temperature, humidity, air pressure, wind speed, wind direction, precipitation, etc. Use meteorological models to interpolate and predict the data to improve the temporal and spatial resolution and accuracy of the data. , use marine observation equipment, buoys, and underwater robots to obtain marine environmental data, including seawater temperature, salinity, flow velocity, flow direction, sea level, etc., and obtain pollution source emission data through pollution monitoring equipment and industrial emission records, including the emission volume and concentration of pollutants such as wastewater, waste gas, and solid waste. Conduct statistical analysis on emission data, identify major pollution sources and emission characteristics, and perform pre-processing operations such as cleaning, denoising, and format conversion on the collected multi-source data to ensure data quality and consistency. Use data fusion technology to integrate and associate data from different sources and formats to form a unified data view, establish spatiotemporal relationships between data, and achieve spatiotemporal matching and synchronization of data. According to the actual situation of the estuary and bay areas, build a unified digital twin system, integrate and store multi-source data into the digital twin system, establish a data index and query mechanism, and improve the efficiency of data retrieval and access; Step 2: Use the collected multi-source data to build a digital twin model of the estuary and bay, analyze the dynamic trends of the physical water quality environment, hydrological environment and ecological environment of the estuary and bay, extract the pre-processed multi-source data, and fuse them to form a complete data set. According to the geographical characteristics and topographic information of the estuary and bay, a three-dimensional geometric model of the estuary and bay is constructed. Combined with the spatial distribution characteristics of meteorological, hydrological, water quality and pollution data, a corresponding physical field model is constructed. Using computer technology and numerical simulation methods, the structure, performance and operating status information of the physical entity are digitally represented to form a visual virtual system. A multi-process coupling model system is constructed, including meteorological models, hydrological models, water quality models and pollution diffusion models, to simulate the dynamic changes of the meteorological environment, hydrological and water quality environment and pollution interference of the estuary and bay. A real-time data connection is established to connect the physical entity and the digital model to achieve real-time synchronization and update of data, and a data interaction platform is built to achieve interaction between the digital model and the physical entity. Data exchange and interaction, using meteorological models to simulate the meteorological changes in estuaries and bays, analyzing the spatiotemporal distribution characteristics of wind speed, wind direction, temperature, humidity, and dew point temperature meteorological elements, and combining meteorological data to obtain the meteorological interference index, and evaluate the impact of meteorological changes on the ecological environment and human activities of estuaries and bays; using hydrological models to simulate the hydrological changes in estuaries and bays, analyzing the spatiotemporal distribution characteristics of hydrological elements in estuaries and bays; using water quality models to simulate the water quality changes in estuaries and bays, analyzing the spatiotemporal distribution characteristics of water temperature, dissolved oxygen, suspended solids concentration, and salinity indicators, and combining hydrological and water quality data to calculate the hydrological and water quality assessment index, and evaluate the impact of hydrological and water quality changes on the ecological environment and human activities of estuaries and bays; using pollution diffusion models to simulate the diffusion process of pollution sources in estuaries and bays, analyzing the spatiotemporal distribution characteristics of pollutant concentrations, suspended solids concentrations, and nutrient concentrations, and combining pollution data to calculate the pollution interference assessment index, and evaluate the pollution degree and impact range of pollution sources on the ecological environment of estuaries and bays; Furthermore, the calculation expression of the meteorological interference index is: ; Among them, MI is the meteorological interference index, T is the actual temperature, As the reference temperature, the annual average temperature of the estuary bay area is selected. The highest temperature in history, is the lowest temperature in history, W is the actual wind speed, As the benchmark wind speed, the annual average wind speed in the estuary bay area is selected. The highest wind speed in history. The lowest wind speed in history. is the actual dew point temperature, is the reference dew point temperature, RH is the actual relative humidity, It is the benchmark relative humidity. It should be noted that the value range of MI is 0 to 1, where 0 indicates no meteorological interference and 1 indicates extreme meteorological interference. When meteorological elements (such as temperature, wind speed, dew point temperature and relative humidity) deviate from the benchmark value, the MI value will increase, indicating that the meteorological interference has increased. When the meteorological elements are close to the historical extreme values, the MI value will increase significantly, indicating that the degree of meteorological interference is high. The calculation expression of the hydrological and water quality assessment index is: ; Among them, SI is the hydrological and water quality assessment index, WT is the actual water temperature, As the benchmark water temperature, the annual average water temperature in the estuary bay area is selected. is the range of water temperature, DO is the actual dissolved oxygen concentration, is the baseline dissolved oxygen concentration, is the highest dissolved oxygen concentration in history, TS is the actual total suspended solids concentration, is the baseline total suspended solids concentration, is the highest total suspended solids concentration in history, S is the actual salinity, As the benchmark salinity, the annual average salinity of the estuary bay area is selected. is the range of salinity. It should be noted that the SI value range is between 0 and 1, where 0 represents extremely poor hydrological and water quality conditions, and 1 represents extremely good hydrological and water quality conditions. When the actual hydrological and water quality parameters (such as water temperature, dissolved oxygen, total suspended solids and salinity) deviate from the baseline values, the SI value will decrease, indicating that the hydrological and water quality conditions have deteriorated. When the actual parameters are close to the historical extreme values, the SI value will decrease significantly, indicating that the hydrological and water quality conditions have seriously deteriorated. The calculation expression of pollution interference assessment index is: ; Among them, PI is the pollution interference assessment index, C is the actual pollutant concentration, As the benchmark pollutant concentration, the annual average pollutant concentration in the estuary bay area is selected. is the range of pollutant concentration, TS is the actual total suspended solids concentration, is the baseline total suspended solids concentration, is the highest total suspended solids concentration in history, N is the actual nutrient concentration, is the baseline nutrient concentration, is the range of variation of nutrient concentration. It should be noted that the value range of PI is between 0 and 1, where 0 indicates no pollution interference and 1 indicates extreme pollution interference. When the actual pollution parameters (such as pollutant concentration, total suspended solids and nutrient concentration) deviate from the baseline value, the PI value will increase, indicating an increase in pollution interference. When the actual parameters are close to the historical extreme values, the PI value will increase significantly, indicating a high degree of pollution interference. Step three: By deploying sensors and monitoring equipment, the estuary and bay environment is monitored in real time, and the monitoring data is updated in real time to the digital twin model for dynamic simulation. According to the needs of estuary and bay environment monitoring, sensors and monitoring equipment are deployed at relevant locations of the estuary and bay environment to ensure that they can fully cover and monitor and obtain meteorological, hydrological, water quality and pollution data. The sensors and monitoring equipment transmit the real-time monitored data to the data acquisition system, and pre-process the collected data, including data cleaning, format conversion and data storage to ensure the accuracy and availability of the data. The pre-processed data is updated in real time to the digital twin model to ensure the real-time and accuracy of the data. Dynamic simulation and analysis are performed in the digital twin model. The digital twin model is used for dynamic simulation to simulate the changes of the estuary and bay environment under different conditions, and the status of the digital twin model is updated according to the real-time monitoring data to reflect the real-time changes of the estuary and bay environment. The dynamic trend of the estuary and bay environment is analyzed through the simulation results of the digital twin model, and the impact of changes in meteorology, hydrology, water quality and pollution on the ecological environment and human activities is evaluated; Step 4: Based on the digital twin model, simulate different pollution emission scenarios, evaluate the environmental capacity of the estuary and bay, assess the health status of the ecosystem, and analyze the comprehensive risk status of the estuary and bay environment; Step 5: Based on the risk status assessment results, combined with the environmental standards and protection objectives of estuaries and bays, formulate warning level classification standards and clarify the warning level of the estuary and bay environment in the assessment area; Step six: According to the warning level classification results, the warning system is automatically triggered to send warning information to management personnel, and corresponding warning management measures and emergency plans are initiated to implement intelligent warning management.

[0021] Embodiment 2, as Figure 3 As shown, based on Example 1, the present invention provides a technical solution: Preferably, in step 4, the analysis process of the comprehensive risk status of the estuary and bay environment is: According to the actual situation of the estuary and bay, different pollution emission scenarios are set, including industrial emissions, agricultural emissions, and urban runoff, and the emission sources, emission intensity, and emission time of each pollution emission scenario are determined. Among them, industrial emissions: the emission sources are industrial facilities such as chemical plants, oil refineries, and shipyards. The emission intensity is determined according to the production scale, process level, and wastewater treatment efficiency of the factory. It is usually high, and the emission time is mostly continuous, but it is affected by the factory production cycle and the operation status of wastewater treatment facilities; agricultural emissions: the emission sources are agricultural land such as farmland, orchards, and farms. The emission intensity is affected by the type of agricultural activities, the use of pesticides and fertilizers, and the intensity of rainwater erosion. It is usually seasonal, and the emission time is mostly concentrated in the rainy season or the peak period of agricultural activities, such as sowing, fertilizing, and harvesting. Urban runoff: the emission sources are urban roads, rainwater pipes, residential areas, etc., and the emission intensity is affected by rainfall, urban The city's topography, drainage facilities and human activity intensity affect the discharge time. Most of the discharge time is a short period of time after a rainstorm, but the urban sewage discharge is continuous. The digital twin model is used to simulate the pollutant diffusion process of each emission source under different emission intensities and times, and analyze the transmission, transformation and degradation process of pollutants in the water body and the impact of pollutants on the ecological environment. According to the simulation results, the environmental capacity of the estuary and bay under different pollution emission scenarios is evaluated. Among them, the environmental capacity refers to the maximum amount of pollutant emissions that the estuary and bay can withstand without damaging the health of the ecosystem. Combined with the meteorological interference index, the hydrological and water quality assessment index and the pollution interference assessment index, a comprehensive analysis is conducted to obtain the health risk assessment coefficient, and the health status of the estuary and bay ecosystem is evaluated. According to the assessment results, the overall health status and change trend of the estuary and bay ecosystem are analyzed, and the comprehensive risk status of the estuary and bay environment is evaluated; Furthermore, the calculation expression of the health risk assessment coefficient is: ; Among them, HC is the health risk assessment coefficient, MI is the meteorological interference index, which reflects the degree of interference of meteorological conditions on the estuary and bay ecosystems, and SI is the hydrological and water quality assessment index. It is the benchmark value of the hydrological and water quality assessment index, reflecting the natural or undisturbed state of the hydrological and water quality of estuaries and bays. HC is the maximum value of the hydrological and water quality assessment index, reflecting the optimal value of the hydrological and water quality of the estuary and bay under ideal conditions. PI is the pollution interference assessment index, reflecting the degree of interference of pollution sources on the estuary and bay ecosystem. It should be noted that the value range of HC is between 0 and 1, where 0 indicates that the estuary and bay ecosystem is in an extremely high risk state, and 1 indicates that the estuary and bay ecosystem is in the best health. When the values ​​of MI, SI, and PI increase, it means that the interference degree of meteorology, hydrology and water quality, and pollution increases. At this time, the value of HC will decrease, reflecting the deterioration of the health of the ecosystem. When the value of HC is close to 1, it means that the health of the ecosystem is good and various interference factors are small. In step 5, the process of determining the estuary and bay environmental warning level is as follows: Analyze the environmental standards and protection targets of the estuary and bay areas, and divide different warning levels into level one, level two, level three and level four in combination with the risk status assessment results and the severity of the risk level. The warning levels gradually increase from level one to level four in terms of the severity of the risk level. For each warning level, set a corresponding risk assessment threshold in combination with the health risk assessment coefficient. When the risk assessment index exceeds the threshold of the corresponding level, the corresponding level of warning is triggered. The environmental data of the estuary and bay in the assessment area are collected in real time through environmental monitoring equipment and sensors. The value of the health risk assessment coefficient is calculated and compared with the divided risk assessment threshold. Based on the comparison results, the warning level of the estuary and bay environment in the assessment area is determined. Multiple warning levels correspond to multiple risk assessment thresholds, and the risk assessment thresholds include an upper threshold and a lower threshold. Multiple warning levels and multiple risk assessment thresholds satisfy the following relationship: Level 1 warning ; Level 2 warning ; Level 3 warning ; Four warning levels ; Among them, HC is the health risk assessment coefficient, is the lower threshold corresponding to the first-level warning level and the upper threshold corresponding to the second-level warning level. is the lower threshold corresponding to the second-level warning level and the upper threshold corresponding to the third-level warning level. The lower threshold corresponding to the third warning level and the upper threshold corresponding to the fourth warning level; In step six, the process of sending warning information and implementing intelligent warning management is as follows: According to the determined warning level, the warning system is automatically triggered to prepare to issue warning information. The warning information includes the warning level, impact range, and recommended response measures. The warning information is sent through text messages, emails, phone calls, and mobile applications. The warning information is sent to managers, environmental protection departments, emergency response teams, and relevant stakeholders in the estuary and bay areas. According to the warning level, the corresponding warning management measures are initiated to strengthen the frequency of environmental monitoring, limit pollutant emissions, and issue environmental health alerts. For level 3 and 4 warning levels, the emergency plan is immediately activated. The emergency plan includes emergency response procedures, emergency resource allocation, personnel evacuation plans, and ecological restoration measures.

[0022] Embodiment 3, as Figure 4 As shown, on the basis of Embodiment 1-2, the present invention further provides an estuary and bay early warning management system based on digital twins, which is used to implement an estuary and bay early warning management method based on digital twins, including an early warning management center, the early warning management center is communicatively connected with a data collection module, a digital twin model construction module, a data synchronization update module, a risk early warning module, an early warning level classification module and an intelligent early warning management module, wherein the modules are connected by electrical signals; A data collection module to collect and integrate data from different sources, including meteorological, hydrological, water quality and pollution data; The digital twin model construction module combines the hydrological model and the meteorological model to construct a digital twin model of the estuary and bay; The data synchronization update module is used to update the collected data to the digital twin model in real time, analyze the latest status of the estuary and bay environment, and ensure the timeliness and accuracy of the data; Risk early warning module, used to assess the health status of ecosystems and analyze the comprehensive risk status of estuary and bay environments; The warning level classification module divides different warning levels according to the risk status assessment results and environmental standards, and sets corresponding risk assessment thresholds. When the risk assessment index exceeds the threshold of the corresponding level, the corresponding level of warning is triggered; The intelligent early warning management module is used to automatically send early warning information to management personnel and initiate corresponding early warning management measures and emergency plans.

[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. An estuary and bay early warning management method based on digital twins, characterized by: The following steps are involved: Step 1: Collect multi-source data from the estuary and bay area and integrate them into a unified digital twin system; Step 2: Use the collected multi-source data to build a digital twin model of the estuary and bay to analyze the dynamic trends of the physical water quality environment, hydrological environment, and ecological environment of the estuary and bay; Step 3: Deploy sensors and monitoring equipment to monitor the estuary and bay environment in real time, and update the monitoring data to the digital twin model in real time for dynamic simulation; Step 4: Based on the digital twin model, simulate different pollution emission scenarios, evaluate the environmental capacity of the estuary and bay, assess the health status of the ecosystem, and analyze the comprehensive risk status of the estuary and bay environment; Step 5: Based on the risk status assessment results, combined with the environmental standards and protection objectives of estuaries and bays, formulate warning level classification standards and clarify the warning level of the estuary and bay environment in the assessment area; Step six: According to the warning level classification results, the warning system is automatically triggered to send warning information to management personnel, and corresponding warning management measures and emergency plans are initiated to implement intelligent warning management.

2. According to the digital twin-based estuary and bay early warning management method of claim 1, it is characterized by: In step 1, the process of collecting multi-source data in the estuary bay area is as follows: Clarify the needs for early warning management in estuary and bay areas and determine the types of data to be collected, namely water quality, meteorology, hydrology, and pollution source emission data; Identify data sources and collect multi-source data for estuaries and bays of a certain type, including using online water quality monitoring technology to obtain water quality data through sensors and monitoring equipment, obtaining meteorological data through meteorological observation stations and satellite remote sensing, obtaining marine environmental data through marine observation equipment, buoys, and underwater robots, and obtaining pollution source emission data through sewage monitoring equipment and industrial emission records; Perform pre-processing operations such as cleaning, denoising, and format conversion on the collected multi-source data. Use data fusion technology to integrate and associate data from different sources and formats to form a unified data view, establish the spatiotemporal relationship between data, and achieve spatiotemporal matching and synchronization of data. According to the actual situation of the estuary and bay area, a unified digital twin system is constructed, and multi-source data are integrated and stored in the digital twin system.

3. The estuary and bay early warning management method based on digital twin according to claim 2 is characterized by: In step 2, the construction process of the estuary bay digital twin model is as follows: Extract the pre-processed multi-source data and fuse them to form a complete data set. According to the geographical characteristics and topographic information of the estuary, a three-dimensional geometric model of the estuary is constructed. Combined with the spatial distribution characteristics of meteorological, hydrological, water quality and pollution data, the corresponding physical field model is constructed. By using computer technology and numerical simulation methods, the structure, performance and operation status information of physical entities are digitally represented to form a visualized virtual system, and a multi-process coupling model system is constructed, including meteorological model, hydrological model, water quality model and pollution diffusion model, so as to simulate the dynamic changes of meteorological environment, hydrological and water quality environment and pollution interference in estuaries and bays; Establish real-time data connection, connect physical entities and digital models, realize real-time synchronization and update of data, and build a data interaction platform to realize data exchange and interaction between digital models and physical entities; Use meteorological models to simulate the meteorological changes in estuaries and bays, analyze the temporal and spatial distribution characteristics of meteorological elements such as wind speed, wind direction, temperature, humidity, and dew point temperature, and combine meteorological data to obtain the meteorological interference index to evaluate the impact of meteorological changes on the ecological environment and human activities in estuaries and bays; Use hydrological models to simulate the hydrological changes in estuaries and bays, analyze the spatiotemporal distribution characteristics of hydrological elements in estuaries and bays, use water quality models to simulate the water quality changes in estuaries and bays, analyze the spatiotemporal distribution characteristics of water temperature, dissolved oxygen, suspended solids concentration, and salinity indicators, and combine hydrological and water quality data to calculate the hydrological and water quality assessment index to assess the impact of hydrological and water quality changes on the ecological environment and human activities in estuaries and bays; The pollution diffusion model is used to simulate the diffusion process of pollution sources in estuaries and bays, analyze the spatiotemporal distribution characteristics of pollutant concentrations, suspended solids concentrations, and nutrient concentrations, and calculate the pollution interference assessment index based on pollution data to evaluate the pollution degree and impact range of pollution sources on the ecological environment of estuaries and bays.

4. The estuary and bay early warning management method based on digital twin according to claim 3 is characterized by: The calculation expression of the meteorological interference index is: ； Among them, MI is the meteorological interference index, T is the actual temperature, is the reference temperature, The highest temperature in history, is the lowest temperature in history, W is the actual wind speed, is the reference wind speed, The highest wind speed in history. The lowest wind speed in history. is the actual dew point temperature, is the reference dew point temperature, RH is the actual relative humidity, is the base relative humidity; The calculation expression of the hydrological and water quality assessment index is: ； Among them, SI is the hydrological and water quality assessment index, WT is the actual water temperature, is the base water temperature, is the range of water temperature, DO is the actual dissolved oxygen concentration, is the baseline dissolved oxygen concentration, is the highest dissolved oxygen concentration in history, TS is the actual total suspended solids concentration, is the baseline total suspended solids concentration, is the highest total suspended solids concentration in history, S is the actual salinity, is the base salinity, is the range of salinity; The calculation expression of the pollution interference assessment index is: ； Among them, PI is the pollution interference assessment index, C is the actual pollutant concentration, is the baseline pollutant concentration, is the range of pollutant concentration, TS is the actual total suspended solids concentration, is the baseline total suspended solids concentration, is the highest total suspended solids concentration in history, N is the actual nutrient concentration, is the baseline nutrient concentration, is the range of nutrient concentration.

5. The estuary and bay early warning management method based on digital twin according to claim 4 is characterized by: In step 3, the process of dynamic simulation is: Deploy sensors and monitoring equipment at relevant locations of estuary and bay environments according to the needs of estuary and bay environment monitoring; Sensors and monitoring equipment transmit the real-time monitored data to the data acquisition system, pre-process the collected data, and update the pre-processed data to the digital twin model in real time, and perform dynamic simulation and analysis in the digital twin model; Use the digital twin model to conduct dynamic simulation to simulate the changes in the estuary and bay environment under different conditions, and update the status of the digital twin model based on real-time monitoring data to reflect the real-time changes in the estuary and bay environment; The dynamic trends of the estuary and bay environment are analyzed through the simulation results of the digital twin model, and the impact of changes in meteorology, hydrology, water quality and pollution on the ecological environment and human activities is evaluated.

6. The estuary and bay early warning management method based on digital twin according to claim 5 is characterized by: In step 4, the analysis process of the comprehensive risk status of the estuary and bay environment is as follows: According to the actual situation of the estuary and bay, different pollution emission scenarios are set, including industrial emissions, agricultural emissions, and urban runoff, and the emission sources, emission intensity, and emission time of each pollution emission scenario are determined; Use digital twin models to simulate the pollutant diffusion process of each emission source at different emission intensities and times, analyze the transmission, transformation and degradation process of pollutants in water bodies, and the impact of pollutants on the ecological environment; Based on the simulation results, the environmental capacity of estuaries and bays under different pollution emission scenarios is evaluated; Combining the meteorological interference index, hydrological and water quality assessment index and pollution interference assessment index, a comprehensive analysis is conducted to obtain the health risk assessment coefficient and assess the health status of the estuary and bay ecosystem. Based on the assessment results, the overall health status and changing trends of the estuarine and bay ecosystems are analyzed, and the comprehensive risk status of the estuarine and bay environment is evaluated.

7. The estuary and bay early warning management method based on digital twin according to claim 6 is characterized by: The calculation expression of the health risk assessment coefficient is: ； Among them, HC is the health risk assessment coefficient, MI is the meteorological interference index, SI is the hydrological and water quality assessment index, is the benchmark value of the hydrological and water quality assessment index. is the maximum value of the hydrological and water quality assessment index, PI is the pollution interference assessment index, and it should be noted that the value range of HC is between 0 and 1, where 0 means that the estuary and bay ecosystem is in an extremely high-risk state, and 1 means that the estuary and bay ecosystem is in the best health.

8. The estuary and bay early warning management method based on digital twin according to claim 7 is characterized by: In step 5, the process of determining the estuary and bay environmental warning level is as follows: Analyze the environmental standards and protection targets of the estuary and bay areas, and divide the warning levels into level 1, level 2, level 3 and level 4 based on the risk status assessment results and the severity of the risk level. The severity of the risk level increases gradually from level 1 to level 4. Combined with the health risk assessment coefficient, a corresponding risk assessment threshold is set for each warning level. When the risk assessment index exceeds the threshold of the corresponding level, the corresponding level of warning is triggered; Through environmental monitoring equipment and sensors, the environmental data of the estuaries and bays in the assessment area are collected in real time, the value of the health risk assessment coefficient is calculated and compared with the risk assessment threshold, and based on the comparison results, the warning level of the estuary and bay environment in the assessment area is determined; A plurality of the warning levels correspond to a plurality of the risk assessment thresholds, wherein the risk assessment thresholds include an upper threshold and a lower threshold; The multiple warning levels and the multiple risk assessment thresholds satisfy the following relationship: Level 1 warning ; Level 2 warning ; Level 3 warning ; Four warning levels ; Among them, HC is the health risk assessment coefficient, is the lower threshold corresponding to the first-level warning level and the upper threshold corresponding to the second-level warning level. is the lower threshold corresponding to the second-level warning level and the upper threshold corresponding to the third-level warning level. It is the lower threshold corresponding to the third warning level and the upper threshold corresponding to the fourth warning level.

9. The estuary and bay early warning management method based on digital twin according to claim 8 is characterized by: In step 6, the process of sending warning information and implementing intelligent warning management is as follows: According to the determined warning level, the warning system is automatically triggered to prepare to issue warning information, which includes the warning level, impact range, and recommended response measures; Send warning information through SMS, email, phone, and mobile applications; According to the warning level, initiate corresponding warning management measures, increase the frequency of environmental monitoring, limit pollutant emissions, and issue environmental health alerts. For level three and level four warning levels, immediately activate the emergency plan, which includes emergency response procedures, emergency resource allocation, personnel evacuation plans, and ecological restoration measures.

10. An estuary and bay early warning management system based on digital twins, used to implement the estuary and bay early warning management method based on digital twins as described in any one of claims 1 to 9, comprising an early warning management center, characterized in that: The early warning management center is communicatively connected with a data collection module, a digital twin model construction module, a data synchronization update module, a risk early warning module, an early warning level classification module and an intelligent early warning management module, wherein the modules are connected by electrical signals; The data collection module is used to collect and integrate data from different sources, including meteorological, hydrological, water quality and pollution data; The digital twin model construction module combines the hydrological model and the meteorological model to construct a digital twin model of the estuary and bay; The data synchronization update module is used to update the collected data to the digital twin model in real time to analyze the latest status of the estuary and bay environment; The risk warning module is used to evaluate the health status of the ecosystem and analyze the comprehensive risk status of the estuary and bay environment; The warning level classification module divides different warning levels according to risk status assessment results and environmental standards, and sets corresponding risk assessment thresholds; The intelligent early warning management module is used to automatically send early warning information to management personnel and initiate corresponding early warning management measures and emergency plans.

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