A hierarchical progressive marine ecological red line fine regulation method and system

Abstract

The application discloses a hierarchical progressive marine ecological red line fine management method and system, belongs to the technical field of intelligent marine ecological environment management, and comprises the following steps: acquiring multi-source monitoring data of a target sea area and a rule violation behavior deduction model; a virtual twin marine environment corresponding to the target sea area is constructed; a virtual risk event is injected into the virtual environment, an optimized dynamic plan is generated through game deduction of a management party response strategy and a rule violation party evasion strategy, a real risk monitoring signal is received and matched with the plan to obtain a target execution strategy, management resources are dispatched for disposal and disposal process data is collected, and the disposal data is fed back to the virtual environment and the deduction model to realize model calibration and plan updating. The application adopts virtual twinning and game deduction, combines multi-source data fusion and a dynamic feedback mechanism, can realize hierarchical progressive closed-loop management of marine ecological red line management, including pre-event simulation deduction, in-event accurate response and post-event continuous optimization, and improves the predictability of management.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring technology for marine ecological environment, and in particular to a hierarchical and progressive method and system for refined monitoring of marine ecological red lines. Background Technology

[0002] Marine ecological red lines are ecological functional zones, environmentally sensitive areas, and vulnerable areas designated by the state to maintain the health and safety of the marine ecosystem. Their implementation and supervision are core administrative tasks in marine resource and environmental management. This supervision work highly relies on the effective acquisition, comprehensive analysis, and rapid response decision-making of various dynamic information across vast sea areas. Therefore, data processing technology plays a crucial role, directly affecting the efficiency and effectiveness of the supervision work.

[0003] Among related technologies, Chinese invention patent CN119090154B discloses a method and system for early warning management of estuaries and bays based on digital twins, including: collecting multi-source data of estuaries and bays and integrating it into a unified digital twin system; using the collected multi-source data to construct a digital twin model of the estuary and bays, and analyzing the dynamic trends of the physical water quality environment, hydrological environment and ecological environment of the estuary and bays; and conducting real-time monitoring of the estuary and bay environment by deploying sensors and monitoring equipment.

[0004] However, the aforementioned existing technical solutions have the following technical shortcomings. Existing technologies focus on building digital twin models and conducting real-time monitoring and trend analysis, which is essentially a reactive, ex-post regulatory model. While it can identify and warn of problems, it cannot pre-generate optimized response plans by simulating confrontations between violators and regulators before risk events occur. Existing technologies provide dynamic environmental trend analysis, but when actual risks occur, formulating the optimal response strategy may still heavily rely on the personal experience and on-site judgment of on-site commanders. When facing multiple concurrent risks or complex scenarios, existing technologies may be insufficient in the global optimization and scheduling of regulatory resources, easily leading to resource allocation conflicts or low response efficiency. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a hierarchical and progressive method and system for refined supervision of marine ecological red lines. By employing virtual twins and game theory simulations, combined with multi-source data fusion and dynamic feedback mechanisms, it can achieve hierarchical and progressive closed-loop management of marine ecological red line supervision, including pre-event simulation and simulation, precise in-event response, and post-event continuous optimization, thereby enhancing the predictability of supervision.

[0006] The above objectives can be achieved through the following approach: A hierarchical and progressive method and system for refined supervision of marine ecological red lines includes: acquiring multi-source monitoring data and violation simulation models of the target sea area; constructing a virtual twin marine environment corresponding to the target sea area; injecting virtual risk events into the virtual environment and generating optimized dynamic contingency plans through game theory simulations of regulatory response strategies and violator avoidance strategies; receiving real-world risk monitoring signals and matching them with the contingency plans to obtain target execution strategies; scheduling regulatory resources for disposal and collecting disposal process data; and feeding the disposal data back to the virtual environment and simulation models to achieve model calibration and contingency plan updates.

[0007] Optionally, constructing a virtual twin marine environment corresponding to the target sea area based on the multi-source monitoring data includes: performing spatiotemporal alignment and fusion processing on the multi-source monitoring data to generate standardized environmental data; acquiring an environmental dynamic model to describe the laws of environmental evolution, extracting geospatial features, ecological environment features, and human activity features from the standardized environmental data, and associating them with the environmental dynamic model to generate a dynamic environmental baseline; and integrating the dynamic environmental baseline with a preset database containing static geographic information to form a virtual twin marine environment.

[0008] Optionally, the step of injecting virtual risk events into the virtual twin ocean environment based on the violation behavior simulation model, and performing game simulations of regulatory response strategies and violation avoidance strategies to generate optimized dynamic contingency plans includes: acquiring a risk event library, a response strategy library, and an avoidance strategy library containing multiple risk event templates; accessing the risk event library and setting virtual risk events in the virtual twin ocean environment; accessing the response strategy library and the avoidance strategy library, and generating multiple strategy pair combinations for the virtual risk events; simulating the evolution process of each strategy pair combination in parallel in the virtual twin ocean environment, and calculating the regulatory gain / loss ratio and violation gain / loss ratio corresponding to each strategy pair combination; and selecting regulatory response strategies that meet specific win rate conditions based on the regulatory gain / loss ratio and violation gain / loss ratio, and packaging them into optimized dynamic contingency plans.

[0009] Optionally, receiving the real-world risk monitoring signal and matching it with the optimized dynamic contingency plan to obtain the target execution strategy includes: parsing the real-world risk monitoring signal to extract real-world risk type features and real-world risk spatial features; matching the real-world risk type features and real-world risk spatial features with the triggering conditions of the optimized dynamic contingency plan to obtain an initial matching contingency plan; when the number of initial matching contingency plans is greater than one or there are multiple concurrent real-world risk monitoring signals, loading the real-world risk event corresponding to the initial matching contingency plan into the virtual twin ocean environment, and performing global scheduling simulation based on the real-time status of the current regulatory resources to generate a global optimized scheduling instruction; and determining the target execution strategy from the initial matching contingency plan according to the global optimized scheduling instruction.

[0010] Optionally, the step of scheduling regulatory resources for processing according to the target execution strategy includes: parsing the target execution strategy to obtain resource type requirements and action sequences; obtaining a regulatory resource status table containing the real-time status of various types of regulatory resources; locking the available regulatory resources in the regulatory resource status table according to the resource type requirements and the action sequences; generating a task instruction set; sending the task instruction set to the corresponding available regulatory resources; and receiving the execution status information fed back by the available regulatory resources.

[0011] Optionally, feeding back the handling process data to the virtual twin marine environment and the violation behavior simulation model for model calibration and contingency plan updates includes: extracting the actual behavioral characteristics of the violator, the actual environmental response characteristics, and the actual regulatory effectiveness characteristics from the handling process data; using the actual behavioral characteristics of the violator and the actual environmental response characteristics to correct the parameters in the violation behavior simulation model; using the actual regulatory effectiveness characteristics to evaluate the actual profit-loss ratio of the target execution strategy, and adjusting the specific win rate conditions used to screen the optimized dynamic contingency plan based on the evaluation results.

[0012] Optionally, after generating the optimized dynamic contingency plan, the method further includes: periodically injecting new virtual risk events randomly into the virtual twin ocean environment and repeatedly executing the game simulation to obtain the regulatory response success rate; identifying areas in the virtual twin ocean environment where the regulatory response success rate is lower than a preset specific threshold based on the regulatory response success rate, and marking them as defense weaknesses; and generating enhanced monitoring suggestions and supplementary contingency plans for the defense weaknesses.

[0013] Optionally, acquiring multi-source monitoring data of the target sea area includes: receiving real-time or near-real-time data streams; receiving patrol records and structured reporting information; and performing credibility weighting and conflict resolution on the real-time or near-real-time data streams, the patrol records, and the structured reporting information to form multi-source monitoring data.

[0014] Optionally, matching the actual risk type characteristics and the actual risk space characteristics with the triggering conditions of the optimized dynamic contingency plan includes: establishing an index for the optimized dynamic contingency plan that includes a contingency plan identifier, risk type, risk space range, trigger signal combination, and historical scores of contingency plan effectiveness; querying the index according to the actual risk type characteristics and the actual risk space characteristics, and performing preliminary screening to obtain an initial matching contingency plan.

[0015] Based on the same inventive concept, this invention also provides a hierarchical and progressive refined monitoring system for marine ecological red lines. The system includes: a data acquisition and processing module for acquiring multi-source monitoring data of the target sea area and a violation behavior projection model describing the development patterns of violations; a virtual twin environment construction module for constructing a virtual twin marine environment corresponding to the target sea area based on the multi-source monitoring data; a risk projection and contingency plan generation module for injecting virtual risk events into the virtual twin marine environment based on the violation behavior projection model, and performing game theory simulations of the regulatory response strategy and the violator's avoidance strategy to generate an optimized dynamic contingency plan; a real-time monitoring and strategy matching module for receiving real-world risk monitoring signals and matching them with the optimized dynamic contingency plan to obtain a target execution strategy; a resource scheduling and disposal execution module for scheduling regulatory resources for disposal according to the target execution strategy and collecting disposal process data; and a data feedback and model optimization module for feeding back the disposal process data to the virtual twin marine environment and the violation behavior projection model for model calibration and contingency plan updates.

[0016] Compared with the prior art, the present invention has the following advantages: This invention generates a large number of targeted, optimized dynamic contingency plans in advance by constructing a virtual twin marine environment and conducting game-theoretic simulations. This method transforms the regulatory model from a reactive, post-event response to a proactive, pre-event planning approach. Regulatory decisions no longer rely solely on the on-site experience of personnel, but are based on simulation results driven by massive amounts of data. This improves the scientific rigor, foresight, and decision-making quality of regulatory work, fundamentally enhancing the ability to predict and deter potential violations.

[0017] This invention enables dynamic optimization and precise scheduling of regulatory resources. When a real risk occurs, the system can determine the optimal target execution strategy based on real-time risk characteristics and resource status through rapid matching and global optimization simulation. This avoids resource allocation errors and time delays caused by information lag or poor coordination in traditional models, ensuring that the most suitable regulatory forces can be deployed to the most critical positions in the shortest time, improving handling efficiency and saving administrative costs.

[0018] This invention establishes a complete closed-loop self-optimizing learning mechanism. By feeding data from the real-world handling process back to the virtual twin environment and simulation model, the system can continuously calibrate the model and update contingency plans. This self-iterative and evolutionary capability enables the regulatory system to continuously absorb real-world experience and dynamically adapt to changes in violation methods and environmental shifts, thereby ensuring the long-term effectiveness and advancement of regulatory strategies and capabilities.

[0019] This invention provides a technical means to proactively identify and strengthen weak links in a regulatory system. By conducting periodic random stress tests in a virtual environment, the system can systematically evaluate the defense capabilities of the entire regulatory network and automatically identify defense weaknesses with low response success rates. Based on this, the system can generate targeted suggestions for enhanced monitoring and supplementary contingency plans, thereby improving the robustness and resilience of the overall regulatory system.

[0020] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating a hierarchical and progressive method for refined supervision of marine ecological red lines, according to an embodiment of the present invention.

[0023] Figure 2 This is an embodiment of the present invention that constructs a virtual twin marine environment map.

[0024] Figure 3 This is a heatmap of the dynamic characteristics of the virtual twin environment according to an embodiment of the present invention.

[0025] Figure 4 This is a schematic diagram of a hierarchical and progressive marine ecological red line refined monitoring system according to an embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Reference Figure 1 One embodiment of the present invention proposes a hierarchical and progressive method for refined supervision of marine ecological red lines. By employing virtual twins and game theory simulation, combined with multi-source data fusion and dynamic feedback mechanisms, it can achieve hierarchical and progressive closed-loop management of marine ecological red line supervision, including pre-event simulation, in-event precise response, and post-event continuous optimization, thereby improving the predictability of supervision.

[0028] The method described in this embodiment specifically includes: S1. Acquire multi-source monitoring data of the target sea area and a deductive model for describing the development pattern of illegal activities; Specifically, the violation behavior inference model, used to describe the development patterns of violations, constructs a multi-agent evolutionary framework to achieve dynamic quantification and path prediction of the violator's behavior patterns. First, based on historical enforcement records and typical violation cases, the monitoring system decomposes the violator's action logic into discrete state nodes such as patrolling to find targets, committing violations, encountering regulatory evasion, and accelerating escape. By extracting the triggering features of these state transitions, a violation behavior inference model is constructed. The core of this model lies in simulating the violator's strategic tendencies under specific environmental pressures. At the operational level, the system treats the violator as an agent with autonomous decision-making capabilities, whose state is jointly influenced by its own attributes such as current position, speed, and load, as well as external environmental constraints such as water depth and visibility. The system uses state transition logic to dynamically simulate the violator's course selection and speed adjustment intentions at each moment, and introduces process disturbance terms to characterize the random fluctuations of the marine meteorological environment. To ensure the scientific nature of the decision-making logic, the model quantifies the violator's gains and risks through a utility evaluation mechanism, and ensures that the profit-driven motives and escape costs are compared on the same logical dimension by unifying the quantification standards of various parameters. Through multiple rounds of iterative simulation, the model can output the predicted flight path clusters of the violator over a future period and the probability distribution of their violating actions in different geographical locations.

[0029] Optionally, acquiring multi-source monitoring data of the target sea area includes: Receive real-time or near real-time data streams; Receive patrol records and structured reports; The real-time or near-real-time data stream, the patrol records, and the structured reporting information are weighted by credibility and conflict resolution to form multi-source monitoring data.

[0030] Specifically, parallel reception of heterogeneous data from multiple channels is implemented, and interfaces with various data sources are established to ensure comprehensive and timely capture of various information reflecting the state of the ocean. Three main reception channels are designed. Channel 1 receives real-time or near-real-time data streams. This channel continuously receives data from automated monitoring equipment such as satellite remote sensing ground stations, marine buoy networks, Automatic Identification System (AIS), and shore-based high-frequency radar through a publish-subscribe mechanism or API polling. Channel 2 receives patrol records. This channel periodically retrieves electronic patrol logs and on-site evidence photos submitted by law enforcement vessels, drones, and patrol personnel through an internal business system interface. These records are typically event-driven and discontinuous data. Channel 3 receives structured whistleblower information. This channel connects to public reporting platforms or specific information sharing networks to receive pre-formatted whistleblower leads, such as whistleblower forms containing time, location, event description, and preliminary evidence.

[0031] The system performs credibility weighting and conflict resolution, conducting quality assessments and preprocessing on the received raw data to resolve potential contradictions and inconsistencies between multi-source information, generating a unified and reliable multi-source monitoring data set. This step first assesses the credibility of each data source and assigns a credibility weight. For example, high-resolution commercial satellite imagery has a higher weight than prediction data based on empirical models, while verified official inspection records have a higher weight than unverified public reports. When different data sources provide different descriptions of the same physical quantity at the same spatiotemporal location—i.e., when data conflicts occur—the system activates the conflict resolution mechanism.

[0032] The final multi-source monitoring data set is formed by standardizing and packaging the pre-processed, high-quality data into a data product that can be directly accessed by downstream modules. The data, after reliability weighting and conflict resolution, is stored uniformly in a spatiotemporal database. All data is assigned standard timestamps, geographic coordinates, and unified physical units. This database constitutes the final multi-source monitoring data, a dynamically updated, comprehensive description of the target sea area's condition that integrates the advantages of multiple observation methods.

[0033] For example, the monitoring system receives heterogeneous data from a certain red-line monitoring area in parallel through three main channels. Channel 1 connects to the marine buoy network and obtains an observed dissolved oxygen concentration of 6.5 mg / L at a certain location; Channel 2 retrieves electronic patrol logs submitted by patrol personnel and obtains an on-site observation value of 6.8 mg / L at the same location; Channel 3 connects to the public reporting platform and obtains a preliminary descriptive value of 5.5 mg / L for the location. The system initiates a conflict resolution mechanism, assesses the quality of each data source and assigns credibility weights, setting verified official patrol records to have the highest weight, followed by automated buoy monitoring data, and unverified public reports to have the lowest weight. A credibility-weighted algorithm is used to fuse the observation values ​​from different sources, resolving potential contradictions between multi-source information to obtain the final standardized environmental data value for the grid. The system then standardizes and encapsulates the conflict-resolved data, unifying physical quantity units and assigning standard timestamps and geographic coordinates, storing it in a spatiotemporal database to form a multi-source monitoring data set for subsequent virtual twin environment construction.

[0034] S2. Construct a virtual twin marine environment corresponding to the target sea area based on the multi-source monitoring data; Optionally, constructing a virtual twin marine environment corresponding to the target sea area based on the multi-source monitoring data includes: The multi-source monitoring data is spatiotemporally aligned and fused to generate standardized environmental data; An environmental dynamic model is obtained to describe the laws of environmental evolution. Geospatial features, ecological environment features, and human activity features are extracted from the standardized environmental data and associated with the environmental dynamic model to generate a dynamic environmental baseline. By integrating the dynamic environmental baseline with a preset database containing static geographic information, a virtual twin marine environment is formed.

[0035] Specifically, spatiotemporal alignment and data fusion are performed to process multi-source monitoring data with varying sources, formats, and spatiotemporal references into a unified, standardized environmental data set that can be directly used by the model. This step first establishes a unified spatiotemporal reference, for example, by setting a time resolution. For 1 hour, spatial grid resolution and All data are 50 meters in length and use the WGS-84 coordinate system. For the time dimension, all time-series data, such as buoy telemetry data and satellite transit data, are resampled to a unified time node using cubic spline interpolation or Kalman filtering algorithms. For the spatial dimension, geographic data from different sources, such as raster-format remote sensing imagery and vector-format Automatic Identification System (AIS) trajectory data, are uniformly mapped to a standardized geographic grid through projection transformation and spatial interpolation. After alignment, a confidence-weighted fusion algorithm is used to fuse data from different sources within the same grid to generate standardized environmental data. The fusion process formula is as follows: , in, This represents the standardized environmental data values ​​after fusion, such as the chlorophyll concentration of a specific grid. For from the Observations from one data source. For the first The credibility weight corresponding to each data source is dynamically evaluated based on the sensor's historical accuracy, the timeliness of the data, and its consistency with other data sources, and the value ranges from 0.1 to 1.0.

[0036] A dynamic environmental baseline is generated by using standardized environmental data to drive a pre-defined dynamic environmental model, thereby producing a dynamic environmental baseline that reflects and predicts the actual physical and ecological processes of the target sea area. This step first acquires one or more coupled dynamic environmental models, such as a regional ocean model system for simulating hydrodynamic processes and a nutrient-phytoplankton-zooplankton-detritus model for simulating ecosystem changes. From the standardized environmental data generated in the previous step, key geospatial features such as water depth gradients, ecological environmental features such as sea surface temperature and salinity distribution, and human activity features such as channel density and aquaculture area extent are automatically extracted. These features serve as boundary conditions and assimilation data, which are input into the dynamic environmental model. Data assimilation techniques such as four-dimensional variational assimilation or ensemble Kalman filtering are employed to continuously calibrate and fuse the model's predicted state with actual observational features, ensuring that the dynamic environmental baseline output by the model remains highly consistent with the actual ocean conditions.

[0037] A virtual twin marine environment is created by integrating a dynamically evolving environmental baseline in real time with high-precision static geographic information. This results in a complete, multi-dimensional virtual twin marine environment, serving as the digital foundation for subsequent game theory simulations and regulatory decisions. The dynamic environmental baseline generated in the previous step, which includes dynamic data layers that change over time, such as ocean current fields, temperature fields, and pollutant diffusion fields, is integrated with a pre-defined static geographic information database. This database contains fundamental information that does not change over time or has extremely long variation periods, such as centimeter-level precision seabed topography data, legal vector boundaries of marine ecological red lines and protected areas, locations of fixed sewage outlets, distribution of artificial reefs, and layout of submarine pipelines. The integration process is implemented through a geographic information system engine, overlaying the dynamic data layer as an active layer onto the static geographic base map to form a data-driven, multi-layered, coupled four-dimensional digital model—the virtual twin marine environment. The construction of this virtual twin marine environment is as follows: Figure 2 As shown.

[0038] For example, the monitoring system first standardizes the multi-source monitoring data of the target sea area and sets the time resolution. For 1 hour, spatial grid resolution and All measurements are 50 meters, and the WGS-84 coordinate system is used uniformly. The system acquires chlorophyll concentration observations from three different sources within a certain sampling grid, namely, those from high-resolution satellites. =5.2mg / Ocean buoys =5.5mg / And the predictions of empirical models =4.8mg / Based on the historical accuracy and timeliness of the sensors, the system assigns credibility weights to each data source and sets satellite weights. =0.9, buoy weight =1.0, Empirical Model Weights =0.3. The standardized environmental data value of this grid is calculated using a confidence-weighted fusion algorithm. After obtaining standardized data, the system extracts geospatial features such as water depth gradient, ecological environment features such as sea surface temperature, and human activity features such as channel density, and inputs these as boundary conditions into the regional ocean model system. The above features are then processed using ensemble Kalman filtering techniques. The system assimilates real-time observed features with model-predicted states to generate a dynamic environmental baseline that reflects real physical processes. Finally, the system overlays this environmental baseline, containing dynamic data layers such as ocean current fields and pollutant diffusion fields, onto a static geographic information database containing centimeter-level seabed topography and legal boundaries of ecological red lines. Through a geographic information system engine, a four-dimensional digital model of a virtual twin marine environment is formed. The dynamic thermal characteristics of this virtual twin environment are shown in figures such as... Figure 3 As shown.

[0039] S3. Based on the aforementioned violation behavior simulation model, inject virtual risk events into the virtual twin ocean environment, and conduct game simulation of the regulatory response strategy and the violation party's avoidance strategy to generate an optimized dynamic contingency plan. Optionally, the step of injecting virtual risk events into the virtual twin ocean environment based on the violation simulation model, and conducting game-theoretic analysis of the regulatory response strategy and the violation party's avoidance strategy to generate an optimized dynamic contingency plan includes: Obtain a risk event library, response strategy library, and avoidance strategy library containing various risk event templates; access the risk event library; and set virtual risk events in the virtual twin ocean environment. Access the response strategy library and avoidance strategy library, and generate multiple strategy pair combinations for the virtual risk event; The evolution of each strategy pair combination is simulated in parallel within the virtual twin ocean environment, and the regulatory profit / loss ratio and violation profit / loss ratio corresponding to each strategy pair combination are calculated. Based on the aforementioned regulatory profit-loss ratio and violation profit-loss ratio, regulatory response strategies that meet specific success rate conditions are selected and packaged into optimized dynamic contingency plans.

[0040] Specifically, virtual risk events are set up and strategy combinations are generated to construct a clear initial adversarial scenario for game theory simulation. First, the built-in risk event library, response strategy library, and avoidance strategy library are accessed. The risk event library stores structured risk templates, such as "illegal dumping at night in a specific protected area" and "using a cloned vessel for cross-border fishing." Each template defines the event type, the possible spatiotemporal range, and the attributes of the participating entities. The response strategy library contains available action combinations for regulators, such as "drone close-in reconnaissance - law enforcement vessel interception" and "satellite remote sensing initial screening - coast guard patrol aircraft verification." The avoidance strategy library stores possible countermeasures that violators may take, such as "using adverse sea conditions as cover" and "releasing jamming signals." A risk event template is selected, instantiated in a virtual twin ocean environment, and its specific coordinates, timestamp, and scale are set to constitute a virtual risk event.

[0041] The system uses a Cartesian product approach to pair M regulatory response strategies extracted from the response strategy library with N violation avoidance strategies extracted from the avoidance strategy library. This full combination approach ensures coverage of all possible adversarial scenarios, ultimately generating a total of M×N independent strategy pair combinations, each representing an independent scenario.

[0042] Parallelized game theory deduction and payoff calculations are performed to simulate the complete adversarial process for each strategy pair combination and quantify its final outcome. An independent simulation instance is launched for each strategy pair combination. In each instance, the regulatory agent and the violator agent make decisions and take actions in a virtual twin ocean environment based on the action sequences in their respective strategies. The virtual twin ocean environment, based on its built-in environmental dynamics model, provides real-time feedback on the impact of environmental changes on the actions of both parties, such as the impact of ocean currents on the oil spill diffusion rate or the impact of visibility on the effectiveness of drone reconnaissance. The simulation continues until preset termination conditions are met, such as the successful cessation of the violation, the successful escape of the violator, or the simulation duration reaching its limit, such as 48 hours. After the simulation ends, the regulatory payoff ratio and the violation payoff ratio for each strategy pair combination are automatically calculated. (Regulatory payoff ratio) Ratio of illegal gains and losses The calculation formula is as follows: , , in, This represents the cost-benefit ratio for regulators. It is the monetized value of the avoided ecological and environmental losses calculated by comparing it with a baseline simulation of "unregulated" conditions. It is the amount of fine that can be collected after successful enforcement. It is the total cost of all regulatory resources involved in the operation, such as ships, aircraft, and personnel. and It is a weighting coefficient used to adjust the importance of ecological benefits and economic penalties in the overall assessment, with a value ranging from 0.5 to 1.5. This represents the benefit-cost ratio of the party violating the rules. These are illegal gains resulting from violations. It is the cost of committing illegal acts. It refers to the value of the seized equipment or vessels. This is another weighting factor. These values ​​are all extracted from simulation logs and state snapshots of the virtual environment.

[0043] The process involves strategy screening and contingency plan packaging. Based on profit and loss calculations, the optimal regulatory response strategy is selected and formalized into a standard contingency plan. The core of this step is defining a specific win rate condition. For each regulatory response strategy, its average performance against all N violator evasion strategies is calculated. First, a "win" condition for a single simulation is defined, i.e., when the regulatory profit-loss ratio... Greater than the threshold And the profit-loss ratio of violations Less than the threshold If the regulator wins a particular confrontation, the regulator is considered victorious. Then, the win rate of the regulator's response strategy is calculated, which is the number of wins of that strategy in N confrontations divided by N. All strategies with a win rate higher than a preset win rate threshold are then selected. The selected efficient regulatory response strategies are packaged with their corresponding virtual risk event triggering conditions, expected profit / loss ratios, and success rates to form structured, optimized, and dynamic contingency plans, which are then stored in a contingency plan library for future use.

[0044] For example, the monitoring system first retrieves a template of illegal dumping at night within a specific protected area from the risk event database, and then sets up a virtual risk event in a virtual twin ocean environment, with its center coordinates at the core location of a red-line zone and the occurrence time at 2:00 AM. The system then accesses the response strategy database and the avoidance strategy database, selecting a regulatory response strategy. A drone conducts close-range reconnaissance in conjunction with a law enforcement vessel to intercept the violation, and an avoidance strategy for the violator is selected. This involves utilizing adverse sea conditions as cover and releasing jamming signals. The system simulates the evolution of this strategy combination in parallel within the virtual environment, assuming the simulation lasts for 48 hours before terminating. Data is extracted from the simulation logs, and the monetized value of the avoided ecological and environmental losses is determined. A fine of 1 million yuan can be levied upon successful enforcement. The total cost of regulatory resources is 200,000 yuan. The total cost, including fuel and labor for the enforcement vessel, was 150,000 yuan. Illegal and unauthorized proceeds were also included. The cost of violating the rules is 500,000 yuan. The value of the seized equipment was 50,000 yuan. The amount is 300,000 yuan, and the weighting coefficients for each item are as follows: All values ​​are taken as 1.0. The regulatory profit / loss ratio is calculated according to the formula. and the ratio of illegal gains and losses Set a threshold for determining the regulator's victory. It is 5.0 and The value is 1.0, due to the results of this simulation. >5.0 and If the score is less than 1.0, the regulator is considered the winner in this scenario. If the regulator's strategy wins 85 times out of a total of N=100 different avoidance strategies, then its win rate is calculated. =85 / 100=0.85. This win rate is higher than the preset win rate threshold. =0.80, the system packages the regulatory response strategy and its triggering conditions into the contingency plan library to form an optimized dynamic contingency plan.

[0045] Optionally, after generating the optimized dynamic plan, the method further includes: New virtual risk events are periodically and randomly injected into the virtual twin ocean environment, and the game simulation is repeated to obtain the regulatory response success rate. Based on the monitoring response success rate, identify areas in the virtual twin ocean environment where the monitoring response success rate is lower than a preset specific threshold, and mark them as defense weak points; Generate enhanced monitoring recommendations and supplementary contingency plans targeting the aforementioned defense weaknesses.

[0046] Specifically, periodic stress tests of the overall regulatory effectiveness are conducted. Through large-scale random simulations, the system systematically evaluates the overall defense capabilities of existing regulatory resources and contingency plans in responding to unknown and sudden risks, generating a distribution map of regulatory response success rates covering the entire target sea area. This step is triggered by a scheduled task, for example, weekly. The system uses the Monte Carlo simulation method to randomly inject a large number of new virtual risk events, such as 10,000 to 50,000, into a virtual twin ocean environment. The generation of these events is not completely random but based on a probability distribution model that integrates risk factors such as historical high-incidence areas of violations, ecologically sensitive red line areas, and areas with dense shipping traffic, making the event generation more spatially and typologically similar to real threats. For each injected virtual risk event, the entire response process, from "risk signal being monitored" to "matching and optimizing dynamic contingency plans" to "scheduling resources for handling," is fully simulated. The final result of each simulation is judged as "success" or "failure," typically based on whether the situation is successfully controlled within a preset time window, such as 4 hours.

[0047] Weaknesses in the defense are identified and quantitatively analyzed, transforming the massive discrete results from the previous simulation into an intuitive and quantifiable map of defensive vulnerabilities. First, the target sea area is divided into standardized geographic grids, for example, 1 square kilometer per grid. Then, the vulnerability distribution within each grid cell is statistically analyzed. Total number of virtual risk events within and the number of incidents that were successfully handled. ,in For the first The outcome of this incident is categorized as 1 for success and 0 for failure. Subsequently, the regulatory response success rate for each grid cell is calculated.

[0048] , Calculated Compare with a preset specific threshold. All meet the requirements. Grid cells with values ​​below a certain threshold were identified and marked as defensive vulnerabilities. Further cluster analysis of failure logs for these vulnerabilities was performed to determine the main reasons for the low success rate, such as excessive response distance, lack of specific contingency plans, or insufficient available resources.

[0049] Targeted reinforcement recommendations and supplementary contingency plans are generated, transforming identified problems into specific, actionable improvement measures, thus forming a closed loop for defense system optimization. Based on the causal analysis in the previous step, two types of outputs are automatically generated. First, enhanced monitoring recommendations: if the failure is primarily due to detection and identification delays, recommendations include increasing the satellite revisit frequency in the area, increasing the frequency and density of UAV patrols, or adjusting the scanning strategy of shore-based radar. Second, supplementary contingency plans are generated: if the failure is due to existing plans being unable to effectively address specific risk types that are prevalent in the area, this scenario—"a specific type of virtual risk event occurring at a weak point in the defense"—is automatically treated as a new, high-priority issue. The game theory simulation process is restarted, generating one or more optimized supplementary contingency plans specifically for this purpose, and incorporating them into the main contingency plan library.

[0050] For example, after generating an optimized dynamic contingency plan, the monitoring system initiates periodic full-domain stress tests, randomly injecting a total of N=10,000 new virtual risk events into a virtual twin ocean environment using the Monte Carlo simulation method. The system divides the target sea area into multiple standard geographic grids of 1 square kilometer each, and targets a specific geographic grid unit within that grid. To count the total number of virtual risk events that randomly occur in this area. There are 100 events. The system simulates the handling process for each of these events and determines the number of events that are successfully handled. The number is 65. The regulatory response success rate of this grid cell is calculated using the formula. Given that the preset threshold is 0.70, due to the grid's... If the data falls below a threshold, the system marks it as a weak point in the defense. By clustering and analyzing the failure logs of this weak point, the system identifies the main cause of the failure as a response delay due to the area's distance from the nearest law enforcement vessel base. Based on this, the system automatically generates enhanced monitoring recommendations, suggesting increasing the frequency of drone patrols in the area to compensate for detection delays. Simultaneously, it initiates a game theory simulation process to generate specific supplementary contingency plans for the high-risk characteristics of this area and stores them in the contingency plan database, thereby achieving closed-loop reinforcement of the defense system.

[0051] S4. Receive the actual risk monitoring signal and match the actual risk monitoring signal with the optimized dynamic plan to obtain the target execution strategy; Optionally, receiving the actual risk monitoring signal and matching the actual risk monitoring signal with the optimized dynamic contingency plan to obtain the target execution strategy includes: Analyze the real-world risk monitoring signals to extract real-world risk type features and real-world risk spatial features; The actual risk type characteristics and actual risk space characteristics are matched with the triggering conditions of the optimized dynamic contingency plan to obtain an initial matching contingency plan; When the number of initial matching plans is greater than one or there are multiple concurrent real-world risk monitoring signals, the real-world risk events corresponding to the initial matching plans are loaded into the virtual twin ocean environment, and global scheduling simulation is performed in conjunction with the real-time status of the current regulatory resources to generate global optimized scheduling instructions. The target execution strategy is determined from the initial matching plan based on the global optimization scheduling instruction.

[0052] Specifically, the system analyzes and extracts features from real-world risk monitoring signals, transforming raw, heterogeneous alarm information into structured, standardized risk feature vectors that can be matched and extrapolated by the system. When a real-world risk monitoring signal is received, such as an AIS data packet showing an abnormal vessel trajectory or a frame of satellite synthetic aperture radar (SAR) imagery suggesting an oil spill area, the corresponding analysis module is activated. For AIS data, the analysis module extracts information such as vessel ID, time-series coordinates, speed, and heading, and compares it with the boundaries of prohibited and protected areas on the electronic nautical chart, generating a "violation of regulations" type of real-world risk feature and a real-world risk spatial feature containing the polygon coordinates of the violation area. For SAR imagery, the image recognition algorithm module performs target detection, identifies suspected oil spill areas, and outputs their confidence level, estimated area, and center geographic coordinates, forming a "suspected illegal discharge" type of risk feature.

[0053] Perform initial contingency plan matching based on the index. Utilizing the risk features extracted in the first step, quickly filter out all potentially relevant optimized dynamic contingency plans from the large contingency plan database. Maintain an inverted index for optimized dynamic contingency plans. This index uses the contingency plan identifier as the primary key and includes fields such as risk type, risk spatial range (usually defined as a geofence), trigger signal combination (e.g., the combination of "AIS signal disappearance" and "entry into sensitive sea area"), and historical scores of contingency plan effectiveness. Use the extracted real-world risk type features and real-world risk spatial features as query conditions to retrieve data from the index. For example, a "suspected illegal discharge" signal located in Protected Area A will match all entries in the contingency plan with the risk type "illegal discharge" and spatial range covering Protected Area A. The output of this step is a set of one or more initially matched contingency plans.

[0054] When complex decision-making conditions are triggered, a global scheduling simulation is executed to resolve ambiguities in contingency plan selection or resource conflicts between multiple tasks through high-fidelity simulation. This step is triggered when the number of initially matched contingency plans is greater than one, or when multiple concurrent risk monitoring signals from geographically similar locations or requiring the use of similar resources are received within a short period. The characteristics of these real-world risk events, including their type, location, and scale, are loaded into the virtual twin ocean environment as initial conditions. Simultaneously, the real-time status of current regulatory resources is monitored, including the position, speed, fuel reserves, and available UAV sorties of law enforcement vessels. Subsequently, a rapid and forward-looking scheduling simulation is performed for each initially matched contingency plan or combination of plans. The simulation process calculates the global scheduling utility function for each plan. value: , in, It is the first The overall utility score of each scheduling scheme. The prediction success rate of this scheme is determined by whether the violation is successfully contained in the simulation. It is the predicted time to complete the disposal task. It is the predicted total resource cost for implementing the plan. These are weighting coefficients that are dynamically adjusted based on current regulatory strategies to balance the relationship between success rate, response time, and cost. For example, in major ecological risk events, The weight will increase. These are all quantitative results obtained through simulations conducted in a virtual twin ocean environment at speeds tens of times faster than real time. The choice is to... The optimal solution is determined, and corresponding global optimization scheduling instructions are generated.

[0055] Based on the global optimization scheduling instructions, the final target execution strategy is determined, and the optimal scheduling scheme derived from the deduction is solidified into a unique and explicit action instruction. The global optimization scheduling instructions are analyzed; these instructions clarify which initial matching plan should be adopted, or, in a multi-task scenario, how to allocate specific regulatory resources and action priorities for each risk event. Accordingly, a unique plan is selected from the set of initial matching plans, or the plan is adaptively adjusted based on the instructions to form the final target execution strategy.

[0056] For example, the monitoring system receives real-world risk monitoring signals, and the analysis module extracts the risk type characteristics of illegal discharge and the spatial characteristics of specific protected areas. The system uses these characteristics to query the contingency plan index and selects two initial matching contingency plans whose matching degree both exceed a threshold. To address the ambiguity in contingency plan selection, the system loads the risk event into a virtual twin marine environment and incorporates the real-time status of current enforcement vessels and drones for global scheduling simulation. For the scheduling scheme generated by contingency plan one, its simulation prediction success rate is set. =0.9, the predicted time to complete the disposal task =1.2 hours, predict total resource cost =0.8 million yuan. Set weighting coefficients. =0.5, =0.3, =0.2. Calculate the overall utility score of this plan according to the formula. Regarding the scheduling scheme generated by Plan 2, its simulation prediction success rate is... =0.85, response time =1.5 hours, resource cost =0.3 million yuan. Calculate its overall utility score. The system determines Plan 1 by comparison. Greater than Plan 2 The system determines that Plan 1 is the solution that maximizes utility, generates corresponding global optimization scheduling instructions, and ultimately determines it as the target execution strategy.

[0057] Optionally, matching the actual risk type characteristics and the actual risk spatial characteristics with the triggering conditions of the optimized dynamic contingency plan includes: An index is established for the optimized dynamic contingency plan, including contingency plan identifier, risk type, risk spatial range, trigger signal combination, and historical score of contingency plan effectiveness; Based on the characteristics of the actual risk type and the characteristics of the actual risk space, the index is queried to perform preliminary screening and obtain an initial matching plan.

[0058] Specifically, the system establishes and dynamically maintains a contingency plan index, transforming unstructured contingency plan text into a structured data index that supports rapid retrieval, thus avoiding inefficient full-text scanning of the entire contingency plan database during matching. The system runs an index building service in the background. Whenever a new optimized dynamic contingency plan is generated or updated, this service automatically parses the plan content, extracts key metadata, and builds index entries. The index entry includes: a contingency plan identifier, a globally unique ID; a risk type, a standardized classification label, such as "illegal dumping - solid waste" or "cross-border fishing - trawling"; a risk spatial range, stored in a geographic information system-processable vector polygon data format, such as GeoJSON, and indexed by spatial indexes such as R-trees or quadtrees to accelerate spatial queries; a trigger signal combination, a set of keywords or codes describing typical monitoring signal characteristics that can activate this contingency plan, such as the combination of "AIS signal interruption" and "abnormal high chlorophyll concentration"; and a contingency plan effectiveness history score, a floating-point number between 0 and 1, which is dynamically updated based on the success rate and cost-effectiveness of the contingency plan in past actual operations through the aforementioned feedback calibration mechanism.

[0059] The system performs a multi-dimensional feature-based index query and preliminary screening. Utilizing the parsed real-world risk features, a multi-condition joint query is executed in the contingency plan index to quickly locate all potentially applicable contingency plans. When the system receives a real-world risk monitoring signal and extracts the real-world risk type and spatial features, it transforms these features into a structured query request. This request contains at least two core query clauses: an exact or fuzzy match query for the risk type, and a spatial relationship query for the risk spatial features, typically an "intersection" or "containment" query. This composite query request is submitted to the contingency plan index engine. The engine first uses the spatial index to quickly filter out all contingency plans whose risk spatial range overlaps with the location of the real-world risk. Then, within this subset, it uses an inverted index to filter out contingency plans whose risk types match. The query results are sorted in descending order based on the historical utility score of the contingency plan to ensure that historically better-performing contingency plans are given priority. The system can set a minimum matching score threshold; for example, only contingency plans with a comprehensive matching score higher than 0.6 will be selected. The matching score of this query process... It can be represented as: , in It is the final overall matching score. This is the type matching score. A perfect match is 1, and a partial match is a value between 0 and 1 based on semantic similarity. It is the spatial matching score, calculated based on the overlap between the risk point and the scope of the contingency plan, with a value range of 0 to 1. The historical score of the effectiveness of the contingency plan itself has also been normalized. , , These are their respective weighting coefficients, which sum to 1 and can be dynamically adjusted according to regulatory strategies, such as in core protected areas. The weight will be increased. All Plans whose values ​​exceed a preset threshold are selected to form the initial set of matching plans.

[0060] For example, the monitoring system builds an inverted index in the background to optimize dynamic contingency plans. This index includes plan identifiers, risk types, risk spatial ranges, trigger signal combinations, and historical scores of plan effectiveness. When the system receives a real-time risk monitoring signal and extracts the risk type characteristics of illegal discharge and the spatial characteristics of a core location within a red-line zone, it converts these characteristics into a structured query request and executes it in the index engine. For plan one in the index, its risk type is a perfect match, and a type matching score is set. =1.0, the risk points are located within the scope of its contingency plan and have a high degree of overlap, so a spatial matching score is set. =0.9, the historical effectiveness score of this contingency plan in past responses. =0.85. The system sets the weighting coefficient according to the regulatory strategy. =0.4, =0.4, =0.2. The comprehensive matching score for Plan One is calculated according to the formula. Regarding Plan Two, its risk types partially match, and the following settings are made: =0.7, spatial matching score =0.8, historical rating =0.75. Calculate the comprehensive matching score for Plan Two. The system sets the minimum matching threshold to 0.60. Due to the combined matching score of Plan 1 and Plan 2... If both exceed the threshold, the system will perform a preliminary screening, selecting both as the initial matching plan, and then submitting them to the subsequent global scheduling and simulation stage after sorting them in descending order of score.

[0061] S5. Dispatch and monitor resources according to the target execution strategy to handle the situation, and collect data on the handling process; Optionally, the step of scheduling regulatory resources for processing according to the target execution strategy includes: The target execution strategy is analyzed to obtain resource type requirements and action sequences; Obtain a regulatory resource status table containing the real-time status of various regulatory resources; based on the resource type requirements and the action sequence, identify the available regulatory resources in the regulatory resource status table and generate a task instruction set; The task instruction set is sent to the corresponding available monitoring resources, and the execution status information fed back by the available monitoring resources is received.

[0062] Specifically, the system performs in-depth analysis of the target execution strategy, breaking down high-level handling plans into a series of standardized, machine-understandable, and executable atomic task units. The received target execution strategy is a structured data object, such as a JSON or XML file. The parsing module first reads the strategy's metadata and then parses its core content, namely the action sequence, one by one. Each action sequence node defines a phased task, from which the system extracts key resource type requirements and action parameters. Resource type requirements specify the resource capabilities needed to execute the task, such as "requiring a drone with an endurance of at least 4 hours and equipped with an infrared thermal imaging gimbal" or "requiring a law enforcement vessel with a maximum speed exceeding 25 knots and the ability to seize assets." The action sequence specifies the temporal and spatial logic of the task, such as "Phase 1, 0 to 30 minutes, the drone flies to the coordinates and conducts hovering reconnaissance; Phase 2, 30 to 90 minutes, the law enforcement vessel conducts encirclement and interception along the planned route."

[0063] The system performs optimal matching-based resource locking and task instruction generation. From all available monitored resources, it matches and assigns the most suitable execution unit to each atomic task unit and generates specific execution instructions. The system first accesses a real-time monitored resource status table, a dynamic database that records the unique identifier, type, current location, status, remaining fuel or power, and sensor operating status of each monitored resource, such as law enforcement vessels, drones, and shore-based radar stations. Based on the parsed resource type requirements, the system employs a multi-objective optimization algorithm to filter available monitored resources. The algorithm aims to minimize a comprehensive cost function to select the optimal resource. This function can be expressed as: , in, Candidate resources The overall score is as follows: the lower the score, the better. and These are preset, dimensionless weighting coefficients, representing the degree of importance attached to response time and task endurance, respectively, and their sum is 1. It is a resource The estimated response time from the current location to the mission area is calculated using the maximum speed and distance. It is a baseline response time, such as 30 minutes, used to normalize the time term. This is the required battery life for this mission. It is a resource Current available battery life. Select. The lowest-level resource is selected, and its status is updated from "standby" to "assigned" in the resource status table to lock the resource. After locking, a formatted task instruction set is generated for the resource based on the specific parameters in the action sequence.

[0064] The system continuously tracks and issues task commands and their execution status, reliably transmitting generated commands to the front-end execution units and establishing an uninterrupted data feedback link for real-time monitoring of the process. Task command sets are distributed to the control terminals or autonomous control systems of selected available monitored resources via dedicated encrypted communication links, such as satellite communication or 4G / 5G private networks. Upon successful command issuance, the monitored resource begins executing the task. Simultaneously, the resource transmits execution status information, including its real-time location, speed, heading, equipment status, and sensor data, back to the monitoring center at a preset frequency, e.g., every 10 to 30 seconds. Upon receiving this information, the monitoring center updates the monitored resource status table in real time and dynamically plots the resource's movement trajectory and task progress on the situation display interface.

[0065] For example, the monitoring system first performs a deep analysis of the target execution strategy, extracting the resource type requirement as needing an enforcement vessel with a maximum speed exceeding 25 knots and the capability to seize assets, and specifying the action sequence as Phase One: arriving at the target sea area along the planned route within 30 minutes. The system accesses the real-time monitoring resource status table, identifies two candidate enforcement vessels, and calculates their comprehensive scores. For the candidate resources... The estimated response time for it to reach the mission area from its current location. The current available battery life is 20 minutes. The timeframe is 10 hours. This applies to candidate resources. Its estimated response time The current available battery life is 15 minutes. Set the baseline response time to 5 hours. The required battery life for this mission is 30 minutes. For 4 hours, weighting coefficient It is 0.6. The value is 0.4. Candidate resources are calculated according to the formula. rating Calculate candidate resources rating The system selects the resource with the lower score. Lock the resource and update its status from standby to assigned. The system then allocates resources according to the action sequence. A task instruction set containing coordinates and route is generated and sent to the ship's control terminal via a dedicated encrypted communication link. The ship's real-time position and speed information is received every 30 seconds, enabling continuous tracking of the handling process.

[0066] S6. Feedback the data of the handling process to the virtual twin marine environment and the illegal behavior simulation model for model calibration and contingency plan update.

[0067] Optionally, feeding back the data from the handling process to the virtual twin marine environment and the violation simulation model for model calibration and contingency plan updates includes: Extract the actual behavioral characteristics of the violating party, the actual environmental response characteristics, and the actual effectiveness characteristics of the regulatory party from the data of the disposal process; The parameters in the violation behavior inference model are corrected by using the actual behavioral characteristics of the violating party and the actual response characteristics of the environment. By utilizing the actual effectiveness characteristics of the regulatory body, the actual profit-loss ratio of the target execution strategy is evaluated, and the specific win rate conditions used to screen the optimized dynamic plan are adjusted based on the evaluation results.

[0068] Specifically, this involves in-depth extraction of multi-dimensional characteristics of the handling process, transforming unstructured on-site handling records into quantitative indicators that can be used for model correction and strategy evaluation. From the collected handling process data, including the navigation logs of law enforcement vessels, video streams transmitted from drones, on-site reports from law enforcement personnel, and final case closure documents, three core features are extracted using multimodal data analysis technology. First, the actual behavioral characteristics of the violating party: by performing kinematic analysis on the actual trajectory of the violating vessel, parameters such as its average escape acceleration, maximum turning angular velocity, and evasion tactics employed after being detected are extracted. Second, the actual environmental response characteristics: by comparing environmental parameters obtained from satellites or on-site sensors before and after the handling, such as the actual diffusion area and drift velocity of pollutants in an oil spill, with simulation results under the same conditions in a virtual twin ocean environment, the model prediction bias is calculated. Third, the actual effectiveness characteristics of the regulatory body: accurately calculating the actual response time, fuel consumption, and personnel man-hours of each regulatory resource in this handling operation, as well as the final enforcement results, such as the amount of fines and the value of seized items.

[0069] The model parameters are corrected based on real-world feedback. The actual features extracted in the previous step are used to calibrate the internal parameters of the virtual simulation model, narrowing the gap between the virtual and physical worlds. The actual behavioral characteristics of the violator are used as input to correct the violation behavior simulation model. This model is typically built based on multi-agent systems or reinforcement learning, and includes parameters describing the violator's decision-making logic, such as the "risk aversion coefficient" or the "probability distribution of strategy selection after detection." Online learning algorithms, such as Bayesian inference or gradient descent, are used to compare the observed behavioral characteristics with the model's predicted behavior, and the error is backpropagated to fine-tune these internal parameters. Simultaneously, the model prediction bias calculated from the actual environmental response characteristics is fed back to the environmental dynamics model of the virtual twin marine environment. Data assimilation techniques are used to adjust its physical parameters, such as the bottom friction coefficient in the ocean current model or the turbulent diffusion coefficient in the pollutant diffusion model, thereby improving the accuracy of the environmental evolution simulation.

[0070] The system re-evaluates the effectiveness of strategies and dynamically adjusts the selection criteria for contingency plans. Based on actual enforcement results, it objectively evaluates the true effectiveness of implemented strategies and optimizes the generation criteria for future contingency plans accordingly. The system utilizes extracted characteristics of the regulator's actual effectiveness to recalculate the actual profit-loss ratio of the current target implementation strategy, denoted as... Then, this Compared to the expected profit and loss ratio derived from game theory during the plan generation stage. By comparing the results, an effectiveness bias is obtained. : , Maintain a [something] about The moving average is calculated. When this moving average is positive, it indicates that the system simulation is too optimistic, and the threshold for selecting contingency plans needs to be raised. Conversely, if it is negative, it indicates that the simulation is too conservative. Based on this, a specific win rate condition, i.e., a win rate threshold, is established for selecting and optimizing dynamic contingency plans. Adaptive adjustments are made. The adjustment logic can be expressed as: , in, This is the updated win rate threshold. This is the current threshold. It is a gain coefficient that controls the adjustment rate, and its value is usually between 0.05 and 0.2. As a normalized error term, it guides the direction and magnitude of adjustments. This adjustment ensures that the strategies in the contingency plan library always reflect the effectiveness expectations closest to actual combat.

[0071] For example, after completing a response to illegal discharge, the monitoring system extracts data on the response process from the navigation logs of the enforcement vessel and on-site sensors. The system first analyzes the actual trajectory of the offending vessel, extracting behavioral characteristics such as its average escape acceleration after being detected. It then compares the actual observed and simulated values ​​of the oil spill area to extract actual environmental response characteristics, thereby revising the violation behavior model and the environmental dynamics model. Subsequently, the system calculates the actual response time, resource consumption, and enforcement benefits of the operation, recalculating the actual cost-benefit ratio of the strategy employed. The value is 7.2. The expected profit / loss ratio, derived from game theory during the plan generation phase, is known. The value is 8.0. The performance deviation is calculated using the formula. Since the performance deviation is positive, it indicates that the original system's projections were overly optimistic, and the screening threshold for contingency plans needs to be raised. The system obtains the current win rate threshold. The gain coefficient is set to 0.80 to control the adjustment rate. The value is 0.1. The specific win rate condition used for screening and optimizing dynamic plans is adjusted according to the formula. Updated win rate threshold The value was increased to 0.81. Through this adaptive adjustment mechanism, it is ensured that the subsequently generated optimized dynamic plans can more realistically reflect the expected actual combat effectiveness.

[0072] Based on the same inventive concept, such as Figure 4 As shown, the present invention also provides a hierarchical and progressive marine ecological red line refined monitoring system, the system comprising: The data acquisition and processing module is used to acquire multi-source monitoring data of the target sea area and a violation inference model to describe the development pattern of violations. A virtual twin environment construction module is used to construct a virtual twin marine environment corresponding to the target sea area based on the multi-source monitoring data; The risk simulation and contingency plan generation module is used to inject virtual risk events into the virtual twin ocean environment based on the violation simulation model, and to conduct game simulation of the regulatory response strategy and the violation avoidance strategy to generate an optimized dynamic contingency plan. The real-time monitoring and strategy matching module is used to receive real-time risk monitoring signals and match the real-time risk monitoring signals with the optimized dynamic plan to obtain the target execution strategy; The resource scheduling and disposal execution module is used to schedule monitored resources for disposal according to the target execution strategy and collect disposal process data; The data feedback and model optimization module is used to feed back the data of the handling process to the virtual twin marine environment and the illegal behavior simulation model for model calibration and plan update.

[0073] It should be noted that the electrical connections between the various units described above do not necessarily represent direct or indirect connections. Any indirect connection method can be applied to the embodiments of the present invention as long as it achieves the purpose of the present invention. The above descriptions are merely exemplary embodiments of the present invention and should not be construed as limiting the scope of the present invention.

[0074] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A tiered and progressive method for refined supervision of marine ecological red lines, characterized in that, The method includes: Acquire multi-source monitoring data of the target sea area and a deductive model for describing the development pattern of illegal activities; A virtual twin marine environment corresponding to the target sea area is constructed based on the multi-source monitoring data; Based on the aforementioned violation simulation model, virtual risk events are injected into the virtual twin ocean environment, and a game simulation of the regulatory response strategy and the violation avoidance strategy is conducted to generate an optimized dynamic contingency plan. Receive real-time risk monitoring signals and match the real-time risk monitoring signals with the optimized dynamic contingency plan to obtain the target execution strategy; According to the target execution strategy, regulatory resources are scheduled for disposal, and data on the disposal process is collected; The data from the handling process is fed back to the virtual twin marine environment and the violation simulation model for model calibration and contingency plan updates.

2. The method for refined supervision of marine ecological red lines in a hierarchical and progressive manner according to claim 1, characterized in that, The construction of a virtual twin marine environment corresponding to the target sea area based on the multi-source monitoring data includes: The multi-source monitoring data is spatiotemporally aligned and fused to generate standardized environmental data; An environmental dynamic model is obtained to describe the laws of environmental evolution. Geospatial features, ecological environment features, and human activity features are extracted from the standardized environmental data and associated with the environmental dynamic model to generate a dynamic environmental baseline. By integrating the dynamic environmental baseline with a preset database containing static geographic information, a virtual twin marine environment is formed.

3. The method for refined supervision of marine ecological red lines in a hierarchical and progressive manner according to claim 1, characterized in that, The process of injecting virtual risk events into the virtual twin ocean environment based on the aforementioned violation simulation model, and conducting game-theoretic analysis of the regulatory response strategy and the violation party's avoidance strategy to generate an optimized dynamic contingency plan includes: Obtain a risk event library, response strategy library, and avoidance strategy library containing various risk event templates; access the risk event library; and set virtual risk events in the virtual twin ocean environment. Access the response strategy library and avoidance strategy library, and generate multiple strategy pair combinations for the virtual risk event; The evolution of each strategy pair combination is simulated in parallel within the virtual twin ocean environment, and the regulatory profit / loss ratio and violation profit / loss ratio corresponding to each strategy pair combination are calculated. Based on the aforementioned regulatory profit-loss ratio and violation profit-loss ratio, regulatory response strategies that meet specific success rate conditions are selected and packaged into optimized dynamic contingency plans.

4. The hierarchical and progressive refined supervision method for marine ecological red lines according to claim 1, characterized in that, The step of receiving real-time risk monitoring signals and matching the real-time risk monitoring signals with the optimized dynamic contingency plan to obtain the target execution strategy includes: Analyze the real-world risk monitoring signals to extract real-world risk type features and real-world risk spatial features; The actual risk type characteristics and actual risk space characteristics are matched with the triggering conditions of the optimized dynamic contingency plan to obtain an initial matching contingency plan; When the number of initial matching plans is greater than one or there are multiple concurrent real-world risk monitoring signals, the real-world risk events corresponding to the initial matching plans are loaded into the virtual twin ocean environment, and global scheduling simulation is performed in conjunction with the real-time status of the current regulatory resources to generate global optimized scheduling instructions. The target execution strategy is determined from the initial matching plan based on the global optimization scheduling instruction.

5. The method for refined supervision of marine ecological red lines in a hierarchical and progressive manner according to claim 1, characterized in that, The step of scheduling and processing regulatory resources according to the target execution strategy includes: The target execution strategy is analyzed to obtain resource type requirements and action sequences; Obtain a regulatory resource status table containing the real-time status of various regulatory resources; based on the resource type requirements and the action sequence, identify the available regulatory resources in the regulatory resource status table and generate a task instruction set; The task instruction set is sent to the corresponding available monitoring resources, and the execution status information fed back by the available monitoring resources is received.

6. The method for refined supervision of marine ecological red lines in a hierarchical and progressive manner according to claim 3, characterized in that, The step of feeding back the data from the handling process to the virtual twin marine environment and the violation simulation model for model calibration and contingency plan updates includes: Extract the actual behavioral characteristics of the violating party, the actual environmental response characteristics, and the actual effectiveness characteristics of the regulatory party from the data of the disposal process; The parameters in the violation behavior inference model are corrected by using the actual behavioral characteristics of the violating party and the actual response characteristics of the environment. By utilizing the actual effectiveness characteristics of the regulatory body, the actual profit-loss ratio of the target execution strategy is evaluated, and the specific win rate conditions used to screen the optimized dynamic plan are adjusted based on the evaluation results.

7. The hierarchical and progressive refined supervision method for marine ecological red lines according to claim 1, characterized in that, The process of generating the optimized dynamic plan also includes: New virtual risk events are periodically and randomly injected into the virtual twin ocean environment, and the game simulation is repeated to obtain the regulatory response success rate. Based on the monitoring response success rate, identify areas in the virtual twin ocean environment where the monitoring response success rate is lower than a preset specific threshold, and mark them as defense weak points; Generate enhanced monitoring recommendations and supplementary contingency plans targeting the aforementioned defense weaknesses.

8. The method for refined supervision of marine ecological red lines in a hierarchical and progressive manner according to claim 1, characterized in that, The acquisition of multi-source monitoring data for the target sea area includes: Receive real-time or near real-time data streams; Receive patrol records and structured reports; The real-time or near-real-time data stream, the patrol records, and the structured reporting information are weighted by credibility and conflict resolution to form multi-source monitoring data.

9. A graded and progressive method for refined supervision of marine ecological red lines according to claim 4, characterized in that, The step of matching the actual risk type characteristics and the actual risk spatial characteristics with the triggering conditions of the optimized dynamic contingency plan includes: An index is established for the optimized dynamic contingency plan, including contingency plan identifier, risk type, risk spatial range, trigger signal combination, and historical score of contingency plan effectiveness; Based on the characteristics of the actual risk type and the characteristics of the actual risk space, the index is queried to perform preliminary screening and obtain an initial matching plan.

10. A hierarchical and progressive refined monitoring system for marine ecological red lines, applied to the hierarchical and progressive refined monitoring method for marine ecological red lines as described in any one of claims 1-9, characterized in that, The system includes: The data acquisition and processing module is used to acquire multi-source monitoring data of the target sea area and a violation inference model to describe the development pattern of violations. A virtual twin environment construction module is used to construct a virtual twin marine environment corresponding to the target sea area based on the multi-source monitoring data; The risk simulation and contingency plan generation module is used to inject virtual risk events into the virtual twin ocean environment based on the violation simulation model, and to conduct game simulation of the regulatory response strategy and the violation avoidance strategy to generate an optimized dynamic contingency plan. The real-time monitoring and strategy matching module is used to receive real-time risk monitoring signals and match the real-time risk monitoring signals with the optimized dynamic plan to obtain the target execution strategy; The resource scheduling and disposal execution module is used to schedule monitored resources for disposal according to the target execution strategy and collect disposal process data; The data feedback and model optimization module is used to feed back the data of the handling process to the virtual twin marine environment and the illegal behavior simulation model for model calibration and plan update.

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