Port planning and construction environment feasibility assessment method and system

Abstract

The invention relates to the technical field of port planning and construction, and particularly discloses a port planning and construction environment feasibility assessment method and system, and the method comprises the following steps: S1, obtaining planning parameters, real-time environment monitoring data, a historical ecological data set and social economic data of a target port; s2, constructing a dynamic coupling model of environmental bearing-ecological disturbance-pollution diffusion-social economic influence; and S3, based on the dynamic coupling model, adopting multi-scene space-time prediction simulation, and outputting an environmental feasibility comprehensive index, a key constraint factor and a social economic influence evaluation result. A dynamic coupling model of environment bearing, ecological disturbance, pollution diffusion and social economic influence is constructed, the four sub-models interact in real time through bidirectional data flow, full-dimension dynamic coupling evaluation of natural environment, ecological system, pollutant migration and social economic factors is achieved, and compared with traditional single-factor evaluation, the comprehensive evaluation accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of port planning and construction, and in particular to a method and system for assessing the environmental feasibility of port planning and construction. Background Art

[0002] In the field of port planning and construction, environmental feasibility assessment is a key link in ensuring the sustainable development of ports. Traditional port planning environmental assessment methods have many shortcomings: Limitations of multi-factor assessment: Most assessment models use a single factor or a small number of factors, failing to incorporate factors such as environmental carrying capacity, ecological disturbance, pollution diffusion, and socioeconomic impact into a unified dynamic coupling model. This makes it difficult to fully reflect the comprehensive impact of port planning on the multi-dimensional environment. Data collection and processing flaws: Data collection dimensions are limited, and there is a lack of an integrated air-space-ground real-time monitoring network. This makes it impossible to obtain multi-scale ecological data and real-time environmental parameters, resulting in insufficient timeliness and comprehensiveness of model input data. Insufficient model prediction accuracy: Traditional models often use static weights and fixed parameters, without incorporating intelligent algorithms such as deep learning and reinforcement learning. This makes it difficult to accurately simulate complex dynamic processes such as shoreline stress transmission and habitat response, and their ability to depict the interactive effects of multi-source pollutants is limited. Imbalance between socioeconomic and ecological protection: The evaluation system lacks a coupling mechanism between socioeconomic and ecological factors, and lacks quantum computing-based weight allocation and multi-objective optimization strategies, making it difficult to achieve coordinated optimization of environmental and socioeconomic benefits. Insufficient visualization in decision support: Due to the lack of digital twin technology support, it is impossible to dynamically overlay information such as environmental risks and ecological impacts with the port BIM model and GIS data, resulting in low visualization of assessment results and difficulty in assisting decision makers in scientific planning. Summary of the Invention

[0003] In view of the deficiencies in the prior art, the present invention provides a method and system for assessing the environmental feasibility of port planning and construction, which solves the problems in the above-mentioned background technology.

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for assessing the environmental feasibility of port planning and construction, comprising the following steps: S1. Obtain planning parameters, real-time environmental monitoring data, historical ecological datasets, and socioeconomic data for the target port; S2. Construct a dynamic coupling model of environmental carrying capacity, ecological disturbance, pollution diffusion, and socioeconomic impact. The dynamic coupling model integrates a natural environmental impact sub-model, an ecosystem response sub-model, a pollutant migration sub-model, and a socioeconomic impact sub-model. The four sub-models interact in real time through a bidirectional data stream. S3. Based on the dynamic coupling model, a multi-scenario spatiotemporal prediction simulation is used to output the comprehensive environmental feasibility index, key constraint factors, and socioeconomic impact assessment results; S4. When the comprehensive index is lower than the preset threshold, generate planning parameter optimization suggestions, ecological compensation strategies and socio-economic regulation plans.

[0005] Preferably, the natural environment impact sub-model introduces a shoreline stress wave conduction prediction algorithm based on deep learning, and its formula is: ; Among them, the weight coefficient Dynamically generated by trained deep convolutional neural networks to improve prediction accuracy; It stands for Natural Environment Impact Index, which is used to measure the impact of port planning on the natural environment; represents the rate of change of shoreline stress over time, is the initial shoreline stress; Laplace operator representing turbulence concentration; is the distance decay function, is the spatial distance, is the attenuation coefficient; is the dredging volume, is the sediment density, is the coastline area, is the sedimentation velocity.

[0006] Preferably, the ecosystem response sub-model includes a habitat connectivity correction term based on reinforcement learning, the formula of which is: ; Among them, the parameters Adaptively adjust historical data through reinforcement learning algorithms to optimize habitat protection; is the ecosystem response index; is the current value of biodiversity, is the initial value of biodiversity; is the change in habitat distance, is the initial habitat distance; Represents the degree of habitat fragmentation.

[0007] Preferably, the pollutant migration sub-model combines the real-time tidal dynamic field and the interaction effect of multi-source pollutants, and its formula is: ; in, Indicates multiple pollutants The interaction effect between is the mutual influence coefficient, is the impact factor; Indicates the rate of change of pollutant diffusion concentration over time; is the coefficient, is the divergence operator, For pollutant emission source strength, is the fluid velocity vector; is the pollutant attenuation coefficient; is a unit step function, is the rate of change of the ecosystem response index, Its gradient.

[0008] Preferably, the comprehensive environmental feasibility index adopts a weight distribution mechanism based on quantum computing, and its formula is: ; in: is the socioeconomic impact index; weight vector Solved by quantum algorithm, satisfying ; is a time decision function based on quantum state evolution, used to simulate the response state of the ecosystem at different time points; It is a comprehensive environmental feasibility index used to comprehensively evaluate the environmental feasibility of port planning.

[0009] Preferably, the planning parameter optimization suggestion is solved by a method combining a multi-objective evolutionary algorithm and deep reinforcement learning, and meets the following conditions: ; in, For ecological compensation costs, The socioeconomic impact indicator aims to optimize both environmental and socioeconomic benefits; It is the absolute value of the deviation between the actual change of the planning parameter and the target change.

[0010] Preferably, the environmental-ecological coupling impact value is calculated based on the natural environment impact index and the ecosystem response index, including: using the formula The environmental-ecological coupling impact value is calculated; among them, represents the environmental-ecological coupling impact value, is the weight coefficient, which is determined by the regression model trained with historical data. Represents the natural environment impact index of the target spatial unit, The ecosystem response index represents the target spatial unit, which is used to reflect the interaction between the natural environment and the ecosystem; Based on the pollutant diffusion concentration and environmental-ecological coupling impact value, the pollution-ecological comprehensive risk is assessed, including: using the formula ; Calculate the pollution-ecological comprehensive risk value; Among them, Represents the pollution-ecological comprehensive risk value, is the weight coefficient, determined by the hierarchical analysis method, Represents the pollutant migration concentration of the target space unit, Represents the environmental-ecological coupling impact value of the target spatial unit. This risk value is used to measure the comprehensive impact of pollutants on the ecological environment; The above method generates planning adjustment priorities based on the socio-economic impact index and pollution-ecological comprehensive risk value, including: using the formula ; Calculate the planning adjustment priority; Among them, Represents the planning adjustment priority. The larger the value, the higher the adjustment priority. is the weight coefficient, which is determined by expert scoring combined with entropy weight method. Represents the socioeconomic impact index of the target spatial unit, Represents the comprehensive pollution-ecological risk value of the target spatial unit and is used to guide the order of planning parameter optimization.

[0011] A port planning and construction environmental feasibility assessment system, comprising: An integrated intelligent monitoring network, consisting of drones, underwater robots, satellite remote sensing devices, and IoT sensor nodes, enables real-time collection and transmission of multi-dimensional data; Dynamically coupled computing engine, deploying a dynamic coupling model and integrating high-performance computing and artificial intelligence acceleration modules; The digital twin decision-making platform loads the port's BIM model and GIS data, overlaying multiple layers of information on environmental risks, ecological impacts, and socioeconomic impacts to dynamically visualize assessment results. The dynamic coupling computing engine has a built-in adaptive weight optimization network and adopts a federated learning strategy to share model parameters among multiple nodes. The formula is: ; Among them, the model parameters are updated collaboratively among the nodes to improve the generalization ability and stability of the model; Indicates the The weight in The results after the update, For the The weight of the update; is the learning rate, is a linear rectification function, The weight of the observed environmental feasibility comprehensive index gradient; is the regularization coefficient, is the difference norm between two adjacent weight updates.

[0012] Preferably, the port planning and construction environmental feasibility assessment system is connected to the ecological bank and carbon trading platform interface. When the budget is exceeded, an ecological compensation and carbon trading plan will be automatically generated. The formula is: ; in: Increase species richness in the restoration area; is the amount of carbon sequestration; Increase ecosystem service functions; is the current market price of ecological credit; Indicates the amount of ecological compensation and carbon trading.

[0013] Preferably, the real-time environmental dynamic coefficient of the port area is calculated based on the tidal hydrological data and meteorological data collected by the integrated intelligent monitoring network, including: using the formula ; Calculate the real-time dynamic coefficient of the port area environment; Among them, Represents the real-time environmental dynamic coefficient of the port area, which is the weight coefficient and is determined by principal component analysis. is the tidal hydrological influencing factor, is the meteorological impact factor, which is used to modify the calculation parameters of the dynamic coupling model; The digital twin decision-making platform generates a visual risk warning level based on the environmental feasibility comprehensive index and the real-time environmental dynamic coefficient, including: using the formula ; Calculate the visual risk warning level; Among them, Represents the visual risk warning level. The larger the value, the higher the warning level. It is a weight coefficient determined by grey correlation analysis and is used to visually display the environmental risk status of port planning and construction.

[0014] The present invention provides a method and system for assessing the feasibility of port planning and construction environment, which has the following beneficial effects: 1. Multi-dimensional dynamic assessment capability: A dynamic coupling model of environmental carrying capacity, ecological disturbance, pollution diffusion, and socioeconomic impact has been constructed. The four sub-models interact in real time through two-way data streams, enabling a full-dimensional dynamic coupling assessment of the natural environment, ecosystem, pollutant migration, and socioeconomic factors. Compared with traditional single-factor assessments, the accuracy of the comprehensive assessment is improved.

[0015] 2. Intelligent algorithms improve prediction accuracy: The natural environment impact sub-model introduces a deep learning shoreline stress wave conduction algorithm, dynamically generates weight coefficients through a deep convolutional neural network, and reduces shoreline stress prediction errors. The ecosystem response sub-model uses reinforcement learning to adaptively adjust parameters, improving the simulation accuracy of habitat connectivity and effectively optimizing habitat protection effects.

[0016] 3. Real-time fusion of multi-source data: The integrated air-space-ground intelligent monitoring network integrates satellite remote sensing, drones, underwater robots, and IoT sensor data to achieve real-time collection and transmission of multi-scale data such as biological migration during tidal cycles and water quality parameters. The data update frequency is increased from the traditional hourly level to the minute level, providing high-precision real-time data support for the model.

[0017] 4. Quantum computing and multi-objective optimization: Based on the weight allocation mechanism of quantum computing, the FEI index weight vector is solved through quantum algorithms, and the computational efficiency is improved compared with traditional optimization algorithms. Combining the planning parameter optimization of multi-objective evolutionary algorithms and deep reinforcement learning, under the constraint of meeting FEI ≥ 0.8, the ecological compensation cost can be reduced by 20%-30%, while optimizing socioeconomic impact indicators.

[0018] 5. Digital twin visualization decision-making: The digital twin decision-making platform loads the port BIM model and GIS data, superimposes multiple layers of information such as environmental risks and ecological impacts, and displays the assessment results in a three-dimensional dynamic visualization, allowing decision makers to intuitively grasp the environmental impact of port planning and assist in formulating scientific optimization plans.

[0019] 6. Ecological value quantification and trading: The system connects the ecological bank and carbon trading platform interfaces. By quantifying ecological service functions such as biodiversity increment and carbon sequestration, it automatically generates ecological compensation and carbon trading plans, promoting the transformation of port planning from "ecological loss" to a virtuous cycle of "ecological compensation-economic development", and enhancing the sustainable development capacity of port construction.

[0020] 7. Federated Learning and Model Generalization: The computing engine has a built-in adaptive weight optimization network and adopts a federated learning strategy to achieve collaborative updating of multi-node model parameters. While protecting data privacy, it improves the model's generalization capabilities and is suitable for port planning and evaluation in different geographical environments and climatic conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a flow chart of a method for assessing the environmental feasibility of port planning and construction according to the present invention; Figure 2 This is a principle block diagram of a port planning and construction environmental feasibility assessment system according to the present invention. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 making creative efforts are within the scope of protection of the present invention.

[0023] like Figure 1 As shown, the present invention provides a technical solution: a method for assessing the environmental feasibility of port planning and construction, comprising the following steps: S1. Obtain planning parameters, real-time environmental monitoring data, historical ecological datasets, and socioeconomic data for the target port; S2. Construct a dynamic coupling model of environmental carrying capacity, ecological disturbance, pollution diffusion, and socioeconomic impact. The dynamic coupling model integrates a natural environmental impact sub-model, an ecosystem response sub-model, a pollutant migration sub-model, and a socioeconomic impact sub-model. The four sub-models interact in real time through a bidirectional data stream. S3. Based on the dynamic coupling model, a multi-scenario spatiotemporal prediction simulation is used to output the comprehensive environmental feasibility index, key constraint factors, and socioeconomic impact assessment results; S4. When the comprehensive index is lower than the preset threshold, generate planning parameter optimization suggestions, ecological compensation strategies and socio-economic regulation plans.

[0024] The historical ecological dataset includes multi-scale ecological data based on satellite remote sensing and drone monitoring, including biological migration trajectories and habitat distribution changes caused by tidal cycles; the real-time environmental monitoring data includes water quality parameters and suspended matter particle size distribution collected by multi-source sensors (such as underwater laser scatterometers and spectrometers).

[0025] More specifically, the natural environment impact sub-model introduces a shoreline stress wave transmission prediction algorithm based on deep learning, and its formula is: ; Among them, the weight coefficient Dynamically generated by trained deep convolutional neural networks to improve prediction accuracy; It stands for Natural Environment Impact Index, which is used to measure the impact of port planning on the natural environment; represents the rate of change of shoreline stress over time, is the initial shoreline stress; Laplace operator representing turbulence concentration; is the distance decay function, is the spatial distance, is the attenuation coefficient; is the dredging volume, is the sediment density, is the coastline area, is the sedimentation velocity.

[0026] Data input and preprocessing: Shoreline stress data: Real-time shoreline stress values ​​are collected through stress sensors deployed along the port coast. , calculate its time rate of change and the initial shoreline stress Perform normalization processing; Turbulence concentration data: Turbulence concentration is obtained using underwater spectrometers and laser scattering instruments , through the Laplace operator Calculate its spatial gradient changes to characterize the intensity of turbulence disturbance to the environment; Dredging project parameters: obtain dredging volume in port planning , sediment density , combined with the coastline area and sediment settling velocity , quantifying the environmental impacts of dredging activities.

[0027] Deep Convolutional Neural Network (DCNN) training: Model architecture: Using a multi-layer convolutional neural network (such as ResNet), the input layer receives historical shoreline stress data, turbulence concentration data, dredging engineering data, etc., and the output layer dynamically generates weight coefficients ; Training data: Based on satellite remote sensing images, historical monitoring data and port engineering cases, a labeled data set containing shoreline morphological changes and ecological response results is constructed; the network parameters are optimized through the back propagation algorithm to make the predicted Minimize the error with the actual environmental impact value.

[0028] Dynamic coupling calculation: Spatial distance attenuation calculation: through distance attenuation function Quantifying shoreline stress and turbulence effects over spatial distance The attenuation effect, where the attenuation coefficient Determined by the port's topographic and hydrological characteristics; Multi-factor integration: The product of shoreline stress change rate, turbulence gradient and distance attenuation is spatially accumulated through integral operation, and then the impact of dredging activities is superimposed to finally obtain the natural environment impact index. .

[0029] Implementation cases and application scenarios: In a container port expansion project, shoreline stress sensors collect data every 30 minutes, underwater sensors monitor turbulence concentration in real time, and dredging volume is obtained in combination with planning documents. , sediment density ; Use the port's shoreline erosion data and ecological monitoring data from the past five years to train DCNN and generate weight coefficients ; Calculated by the formula , indicating that the impact of dredging activities on nearshore ecology is at a medium risk level, and the dock layout needs to be further optimized in combination with the ecosystem response sub-model.

[0030] More specifically, the ecosystem response sub-model includes a habitat connectivity correction term based on reinforcement learning, which is formulated as follows: ; Among them, the parameters Adaptively adjust historical data through reinforcement learning algorithms to optimize habitat protection; is the ecosystem response index; is the current value of biodiversity, is the initial value of biodiversity; is the change in habitat distance, is the initial habitat distance; Represents the degree of habitat fragmentation.

[0031] Data input and preprocessing: Biodiversity data: Obtain habitat distribution changes through satellite remote sensing and drone monitoring to extract current biodiversity values and initial value , calculate the biodiversity conservation rate .

[0032] Habitat distance data: Using GIS spatial analysis technology, calculate the change in habitat distance before and after port planning and initial habitat distance , quantifying the spatial impacts of habitat fragmentation.

[0033] Fragmentation data: Calculate habitat fragmentation index using landscape ecology indicators (such as patch density and edge density) , reflecting the integrity of the ecosystem.

[0034] Reinforcement learning algorithm training: State space: defined as ,in is the natural environment impact index (from the natural environment impact sub-model); Action Space: Parameter Adjustment ,Optimize parameter combinations through exploration-exploitation strategy; Reward function: Designing rewards based on biodiversity conservation goals ,in is the weight coefficient, calibrated through historical ecological restoration cases; Algorithm iteration: Using the Deep Q-Network (DQN) or Proximal Policy Optimization (PPO) algorithm, iteratively update parameters based on historical data (such as ecological monitoring data before and after port construction) , to maximize the cumulative reward.

[0035] Dynamic coupling calculation: Connectivity correction calculation: The hyperbolic tangent function is used to characterize the nonlinear response of the natural environment to the ecosystem. When it is large, the correction term reduces the weight of biodiversity and highlights the effects of habitat distance and fragmentation; Multi-factor integration: linearly combine the biodiversity retention rate, habitat distance decay index and fragmentation correction term to obtain the ecosystem response index , used to assess the comprehensive degree of disturbance to the ecosystem caused by port planning.

[0036] Implementation case and application scenario: in a coastal port ecological restoration project: obtaining the biodiversity index of the project area , changes in habitat distance caused by land reclamation , initial habitat distance , fragmentation index The mangrove restoration data of the past 10 years in this area was used as the training set to optimize the parameters. , at this time the reward function value (maximization objective). This formula indicates that the ecosystem response is at medium risk, and habitat connectivity restoration measures (such as building ecological corridors) should be prioritized.

[0037] More specifically, the pollutant migration sub-model combines the real-time tidal dynamic field and the interaction effect of multi-source pollutants, and its formula is: ; in, Indicates multiple pollutants The interaction effect between is the mutual influence coefficient, is the impact factor; Indicates the rate of change of pollutant diffusion concentration over time; is the coefficient, is the divergence operator, For pollutant emission source strength, is the fluid velocity vector; is the pollutant attenuation coefficient; is a unit step function, is the rate of change of the ecosystem response index, Its gradient.

[0038] Real-time data collection and preprocessing: Tidal dynamic field data: Acoustic Doppler current profilers (ADCPs) and water level sensors deployed in port waters collect fluid velocity vectors in real time. and tidal cycle data with a resolution of 10 minutes / time, which are used to calculate the dynamic conditions of convection and diffusion of pollutants; Pollutant emission data: Obtain pollutant emission source intensity of each emission source through IoT sensors , including the concentration and emission rate of pollutants such as COD, heavy metals, and petroleum, and supports simultaneous monitoring of multiple pollutants; Ecosystem response data: Real-time acquisition of ecosystem response indices from the ecosystem response sub-model Rate of change and its spatial gradient , reflecting the feedback effect of ecological changes on pollutant migration; Model parameter calibration and dynamic update: Attenuation coefficient :Based on historical water quality monitoring data, the natural attenuation coefficients of different pollutants (such as organic matter and heavy metals) are obtained through exponential decay fitting, such as COD Take 0.05~0.2 / day for sodium and 0.01~0.03 / day for heavy metals.

[0039] Interaction coefficient : Through laboratory simulation and historical pollution event data, construct a multi-pollutant interaction matrix, such as the synergistic toxicity coefficient of petroleum and heavy metals , characterizing the joint pollution effect.

[0040] Impact Factor :Dynamically adjust according to pollutant type and ecological sensitivity. In sensitive areas such as mangroves, Automatically increase by 20%~30%, strengthening the impact of ecological feedback on pollutant migration.

[0041] Dynamic coupling calculation process: Convection-diffusion calculation: using the divergence operator Calculate the coupling effect between pollutant emission sources and tidal flow fields, and quantify the spatial diffusion trend of pollutants under the influence of water flow; Ecological feedback modulation: via the unit step function To judge the significance of ecosystem response, When (ecological deterioration), the ecological feedback term is activated, and the gradient Adjust the migration path of pollutants.

[0042] Multi-pollutant interaction calculation: through summation Quantify the synergistic or antagonistic effects of multiple pollutants, such as heavy metals binding to organic matter to reduce bioavailability. Take 0.8~0.9.

[0043] More specifically, the environmental feasibility comprehensive index adopts a weight distribution mechanism based on quantum computing, and its formula is: ; in: is the socioeconomic impact index; weight vector Solved by quantum algorithm, satisfying ; is a time decision function based on quantum state evolution, used to simulate the response state of the ecosystem at different time points; It is a comprehensive environmental feasibility index used to comprehensively evaluate the environmental feasibility of port planning.

[0044] FEI model core architecture and implementation process: Multidimensional index preprocessing: Natural environment impact index : Calculated through the natural environment impact sub-model, it reflects the degree of disturbance of port planning on natural elements such as coastal stress and turbulence. The value range is [0,1]. The larger the value, the more significant the impact.

[0045] Ecosystem Response Index : Output from the ecosystem response sub-model, quantifying changes in ecological factors such as biodiversity and habitat connectivity, with a value of [0,1]. The larger the value, the worse the ecosystem stability.

[0046] Pollutant diffusion concentration : Obtained through the pollutant migration sub-model and standardized to the interval [0,1]. The larger the value, the higher the pollution risk, so (1-Pdiff) is used to convert it into an environmental positive benefit indicator.

[0047] Socioeconomic Impact Index (SEI): Integrates indicators such as the economic benefits and employment impact of port planning, and takes a value between [0,1] after normalization. The larger the value, the higher the positive socioeconomic benefit.

[0048] Quantum algorithm weight solution process: Quantum model construction: Use quantum annealing algorithms (such as D-Wave quantum processors) or quantum gate models (such as IBM quantum computers) to build weight-optimized quantum circuits. The input is the multi-dimensional index of historical evaluation cases and the feasibility results of actual environments, and the output is to meet The weight vector .

[0049] Quantum state initialization: Encode weight parameters as quantum bits (qubits), prepare superposition states through operations such as Hadamard gates, and use quantum parallelism to simultaneously evaluate multiple weight combinations.

[0050] Energy Function Design: Defining the Objective Function , through the quantum tunneling effect, it quickly searches for the global optimal solution, and improves the solution efficiency by more than 50% compared with the traditional gradient descent algorithm.

[0051] Dynamic coupling calculation process: Tensor operations are implemented by (tensor product) and (tensor sum) operation, which couples the dimensional indicators and weights in multiple dimensions. For example, Represents the spatial mapping of positive benefits of the natural environment and corresponding weights, The operation realizes the parallel aggregation of multi-dimensional indicators.

[0052] Time decision function : It is constructed based on the quantum state evolution equation (such as the Schrödinger equation), with the input being the historical data of ecosystem succession (such as the mangrove growth cycle and the coral reef recovery rate), and the output being the quantum state probability amplitude that changes with time t, which is used to modulate the FEI weight distribution at different time points.

[0053] Implementation cases and application scenarios in a free trade zone port planning project: Natural Environment Impact Index =0.45 (reclamation projects cause moderate disturbance of shoreline stress); Ecosystem Response Index =0.32 (20% loss of mangrove habitat); pollutant diffusion concentration Pdiff=0.28 (emissions meet standards after treatment); socioeconomic impact index SEI=0.75 (estimated to create 12,000 jobs).

[0054] Quantum weight solution: Using D-Wave quantum annealer, input 10 years of historical assessment data of the region and optimize the weight vector ,satisfy .

[0055] FEI dynamic calculation: The time decision function Q(t) takes the quantum state parameter Q(5) = 0.85 in the fifth year of planning (simulating the improvement of ecological benefits in the mid-term of mangrove restoration); tensor operation results: (Q(t) is not considered); Final FEI= (below the threshold of 0.8), triggering planning adjustment suggestions: increase the area of ​​artificial mangrove planting, reduce Ec to 0.25, and recalculate FEI= , still needs further optimization.

[0056] According to the FEI iterative optimization planning scheme, the FEI was eventually increased to 0.82 by adding ecological corridors and deep pollutant treatment facilities, meeting the feasibility requirements.

[0057] More specifically, the planning parameter optimization suggestion is solved by combining a multi-objective evolutionary algorithm and deep reinforcement learning, and meets the following conditions: ; in, For ecological compensation costs, The socioeconomic impact indicator aims to optimize both environmental and socioeconomic benefits; It is the absolute value of the deviation between the actual change of the planning parameter and the target change.

[0058] Optimize the core architecture and implementation process of the model: Multi-objective problem definition and parameter encoding: Optimization goal: Minimize the following four indicators simultaneously - planning parameter deviation , pollutant diffusion concentration , ecological compensation costs , socioeconomic impact indicators (Note: The smaller the value, the lower the negative impact on the social economy); Parameter encoding: Encode port planning parameters (such as terminal location, dredging depth, breakwater height, etc.) into real vectors, such as the terminal front elevation Encoded as Continuous values ​​within the interval to support mutation and crossover operations of evolutionary algorithms.

[0059] Multi-objective evolutionary algorithm (MOEA) implementation process: Algorithm selection: Use the non-dominated sorting genetic algorithm (NSGA-II) or the multi-objective particle swarm optimization algorithm (MOPSO) to capture the trade-off relationship under multi-objective conflicts and form a Pareto optimal solution set; Population initialization: Randomly generate 100 to 200 initial solutions based on the feasible domain of the planning parameters. Each solution corresponds to a particle (in PSO) or individual (in GA). Fitness function: Combine the objective function with the constraints to design a fitness evaluation mechanism. or The constraint scheme imposes a penalty term.

[0060] Deep Reinforcement Learning (DRL) Co-Optimization Process: State space: defined as , including the environmental-economic indicators and parameter status of the current plan; Action space: a set of parameter adjustments , continuous action values ​​are output through the neural network (e.g. dock position adjustment ±50m).

[0061] Reward function: designed as ,in is the weight coefficient, which is used to guide the algorithm to prioritize the constraints; Training method: Use the Proximal Policy Optimization (PPO) or Deep Q-Network (DQN) algorithm, and use the Pareto solution set generated by the evolutionary algorithm as the initial experience pool to accelerate the convergence process.

[0062] Collaborative optimization iterative mechanism: MOEA generates candidate solutions: It generates a new generation of candidate solutions through operations such as selection, crossover, and mutation to expand the diversity of the solution space; DRL strategy optimization: fine-tune the strategy of the candidate solutions generated by MOEA, such as learning “when Optimization strategies such as "prioritizing dredging depth when dredging"; Solution set fusion: The Pareto solution set generated by MOEA is merged with the solution optimized by DRL, and the optimal solution is retained through methods such as congestion distance sorting to form the final optimization solution set.

[0063] More specifically, according to the natural environment impact index and the ecosystem response index, the environmental-ecological coupling impact value is calculated, including: using the formula The environmental-ecological coupling impact value is calculated; among them, represents the environmental-ecological coupling impact value, is the weight coefficient, which is determined by the regression model trained with historical data. Represents the natural environment impact index of the target spatial unit, The ecosystem response index represents the target spatial unit, which is used to reflect the interaction between the natural environment and the ecosystem; Based on the pollutant diffusion concentration and environmental-ecological coupling impact value, the pollution-ecological comprehensive risk is assessed, including: using the formula ; Calculate the pollution-ecological comprehensive risk value; Among them, Represents the pollution-ecological comprehensive risk value, is the weight coefficient, determined by the hierarchical analysis method, Represents the pollutant migration concentration of the target space unit, Represents the environmental-ecological coupling impact value of the target spatial unit. This risk value is used to measure the comprehensive impact of pollutants on the ecological environment; The above method generates planning adjustment priorities based on the socio-economic impact index and pollution-ecological comprehensive risk value, including: using the formula ; Calculate the planning adjustment priority; Among them, Represents the planning adjustment priority. The larger the value, the higher the adjustment priority. is the weight coefficient, which is determined by expert scoring combined with entropy weight method. Represents the socioeconomic impact index of the target spatial unit, Represents the comprehensive pollution-ecological risk value of the target spatial unit and is used to guide the order of planning parameter optimization.

[0064] Environmental-ecological coupling impact value ( ) Calculation: Integrated Natural Environment Impact Index Ecosystem Response Index , reflecting the interaction between the two through linear weighting; Data input: (degree of disturbance of the natural environment, [0,1]), (ecosystem stability, [0,1]); Weight determination: Based on historical data through regression model (such as Lasso regression) training, for example: a certain estuary port 、 , indicating that environmental impacts dominate; Calculation formula: ; Application example: When 、 hour, (Medium risk).

[0065] Pollution-ecological comprehensive risk value ( ) Assessment: Fusion pollutant diffusion concentration Environmental-ecological coupling value , quantifying the compound ecological impacts of pollution.

[0066] Data input: (pollution risk, [0,1]), (environmental-ecological interactions); Weight determination: Using the analytic hierarchy process (AHP), a judgment matrix is ​​constructed through expert scoring. For example: 、 (Pollution diffusion has a higher weight); Calculation formula: ; Application example: When 、 hour, (Medium risk).

[0067] Planning adjustment priority generation: combining socioeconomic impact index and pollution-ecological risks , determine the planning optimization order.

[0068] Data input: (positive economic benefit, [0,1]), (ecological risks); Weight determination: expert scoring combined with entropy weight method correction, for example: initial 、 , after entropy weight correction 、 (Ecological risk weight is higher); Calculation formula: ; Application example: When 、 hour, (Planning needs to be adjusted first).

[0069] like Figure 2 As shown, a port planning and construction environmental feasibility assessment system includes: An integrated intelligent monitoring network, consisting of drones, underwater robots, satellite remote sensing devices, and IoT sensor nodes, enables real-time collection and transmission of multi-dimensional data; Dynamically coupled computing engine, deploying a dynamic coupling model and integrating high-performance computing and artificial intelligence acceleration modules; The digital twin decision-making platform loads the port's BIM model and GIS data, overlaying multiple layers of information on environmental risks, ecological impacts, and socioeconomic impacts to dynamically visualize assessment results. The dynamic coupling computing engine has a built-in adaptive weight optimization network and adopts a federated learning strategy to share model parameters among multiple nodes. The formula is: ; Among them, the model parameters are updated collaboratively among the nodes to improve the generalization ability and stability of the model; Indicates the The weight in The results after the update, For the The weight of the update; is the learning rate, is a linear rectification function, The weight of the observed environmental feasibility comprehensive index gradient; is the regularization coefficient, is the difference norm between two adjacent weight updates.

[0070] More specifically, the port planning and construction environmental feasibility assessment system connects the ecological bank and carbon trading platform interfaces. When the budget is exceeded, an ecological compensation and carbon trading plan will be automatically generated. The formula is: ; in: Increase species richness in the restoration area; is the amount of carbon sequestered by mangroves, seagrass beds, etc.; Increase ecosystem services, including water purification and climate regulation; is the current market price of ecological credit; Indicates the amount of ecological compensation and carbon trading.

[0071] Eco-bank connects with carbon trading platform: the system Realize data exchange with ecological banks (such as a provincial ecological product trading center in China) and carbon trading platforms (such as the national carbon emission trading market) to obtain real-time ecological credit prices and trading rules.

[0072] Data synchronization: The following data will be synchronized daily: Species monitoring data in the restoration area : Obtain species number and type changes through drone aerial photography and ground sensors; Carbon sequestration data :Inverting the biomass and carbon storage changes of mangroves and seagrass beds based on satellite remote sensing; Ecological service function data : Integrate data from water quality monitoring stations and meteorological stations to quantify water purification and climate regulation effects, etc.

[0073] Budget trigger mechanism: When the ecological compensation cost When the preset budget threshold is exceeded (such as 15% of the total project investment), the system automatically activates the carbon trading module and calculates the tradable ecological credits (Trade_credits) through a formula to offset the cost overruns with ecological compensation income.

[0074] More specifically, the real-time environmental dynamic coefficient of the port area is calculated based on the tidal hydrological data and meteorological data collected by the integrated intelligent monitoring network, including: using the formula ; Calculate the real-time dynamic coefficient of the port area environment; Among them, Represents the real-time environmental dynamic coefficient of the port area, which is the weight coefficient and is determined by principal component analysis. is the tidal hydrological influencing factor, is the meteorological impact factor, which is used to correct the calculation parameters of the dynamic coupling model.

[0075] Real-time environmental dynamic coefficient ( )calculate: Tidal hydrological data: Using ADCP (Acoustic Doppler Current Profiler) and water level sensors in the integrated intelligent monitoring network, key data such as tidal velocity, flow direction, and tide level are collected in real time. These data are filtered and de-noised to generate tidal hydrological impact factors. For example, parameters such as tidal kinetic energy and tidal shear force are standardized to the [0,1] interval to facilitate subsequent calculations.

[0076] Meteorological data: Obtain meteorological information such as wind speed, wind direction, rainfall, and temperature through weather stations and satellite remote sensing technology. Use principal component analysis to extract dominant meteorological factors (such as typhoon frequency and strong wind days) from these data to form meteorological influencing factors. , and also normalized to the interval [0,1].

[0077] Principal component analysis (PCA) to determine weight coefficients , : Data dimensionality reduction: Principal component analysis (PCA) is performed on historical tidal hydrological and meteorological data to calculate eigenvalues ​​and contribution rates, and extract principal components with cumulative contribution rates exceeding 85%. For example, after PCA analysis of a port, it was determined that tidal hydrological factors accounted for 60% of the principal component weight, and meteorological factors accounted for 40%, that is, =0.6, =0.4.

[0078] Dynamic update: To adapt to seasonal changes, weights are recalculated every quarter based on the latest data. For example, during the typhoon season, F weather The weight of may be automatically increased to 0.5 to reflect the greater impact of meteorological conditions on the dynamics of the port environment.

[0079] Core formula: ; This formula is used to calculate the real-time environmental dynamic coefficient, which comprehensively reflects the environmental dynamics of the port area.

[0080] Correction effect: It can be used to dynamically adjust the parameters of the dynamic coupling model. For example, when When it is greater than 0.7, the diffusion coefficient of the pollutant migration sub-model will automatically increase by 15% to improve the model's adaptability to extreme hydrological and meteorological conditions.

[0081] The digital twin decision-making platform generates a visual risk warning level based on the environmental feasibility comprehensive index and the real-time environmental dynamic coefficient, including: using the formula ; Calculate the visual risk warning level; Among them, Represents the visual risk warning level. The larger the value, the higher the warning level. It is a weight coefficient determined by grey correlation analysis and is used to visually display the environmental risk status of port planning and construction.

[0082] Environmental feasibility comprehensive index This index, derived from a comprehensive assessment of a dynamic coupling model, reflects the environmental feasibility of a planning scheme. Its value range is [0, 1], with lower values ​​indicating higher risk.

[0083] Real-time environmental dynamic coefficient : The above calculation result is used to reflect the intensity of dynamic changes in the current environment.

[0084] Determining weight coefficients using grey relational analysis , :Build 、 The grey correlation matrix of historical environmental risk events is used to calculate the correlation between each indicator and the risk level. For example, the historical data of a port shows that The correlation with risk events is 0.65. is 0.72. Therefore, =0.4, =0.6 to highlight the impact of the dynamic environment on risk.

[0085] Adaptive adjustment: When When it is greater than 0.5, It will automatically increase to 0.7 to strengthen the risk amplification effect of the dynamic environment.

[0086] Warning level generation and visualization: Core formula: ; This formula is used to calculate the warning level and comprehensively reflect the environmental risks of the port area.

[0087] Level Mapping: The numerical values ​​are mapped to color warning levels to visually display the risk distribution. The specific mapping rules are as follows: <0.3: Green (low risk); 0.3≤ <0.6: yellow (medium risk); ≥0.6: Red (high risk).

[0088] 3D Visualization: Based on the port BIM model, the digital twin platform uses visual effects such as color gradation and halo to intuitively display risk distribution. For example, red highlighted areas can indicate high-risk areas for reclamation projects under the influence of typhoons.

[0089] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for assessing the environmental feasibility of port planning and construction, characterized in that: The following steps are involved: S1. Obtain planning parameters, real-time environmental monitoring data, historical ecological datasets, and socioeconomic data for the target port; S2. Construct a dynamic coupling model of environmental carrying capacity, ecological disturbance, pollution diffusion, and socioeconomic impact. The dynamic coupling model integrates a natural environmental impact sub-model, an ecosystem response sub-model, a pollutant migration sub-model, and a socioeconomic impact sub-model. The four sub-models interact in real time through a bidirectional data stream. S3. Based on the dynamic coupling model, a multi-scenario spatiotemporal prediction simulation is used to output the comprehensive environmental feasibility index, key constraint factors, and socioeconomic impact assessment results; S4. When the comprehensive index is lower than the preset threshold, generate planning parameter optimization suggestions, ecological compensation strategies and socio-economic regulation plans.

2. A method for assessing the feasibility of port planning and construction environment according to claim 1, characterized in that: The natural environment impact sub-model introduces a shoreline stress wave transmission prediction algorithm based on deep learning, and its formula is: ； Among them, the weight coefficient Dynamically generated by trained deep convolutional neural networks to improve prediction accuracy; It stands for Natural Environment Impact Index, which is used to measure the impact of port planning on the natural environment; represents the rate of change of shoreline stress over time, is the initial shoreline stress; Laplace operator representing turbulence concentration; is the distance decay function, is the spatial distance, is the attenuation coefficient; is the dredging volume, is the sediment density, is the coastline area, is the sedimentation velocity.

3. A method for assessing the feasibility of port planning and construction environment according to claim 2, characterized in that: The ecosystem response sub-model includes a habitat connectivity correction term based on reinforcement learning, which is formulated as follows: ； Among them, the parameters Adaptively adjust historical data through reinforcement learning algorithms to optimize habitat protection; is the ecosystem response index; is the current value of biodiversity, is the initial value of biodiversity; is the change in habitat distance, is the initial habitat distance; Represents the degree of habitat fragmentation.

4. A method for assessing the feasibility of port planning and construction environment according to claim 3, characterized in that: The pollutant migration sub-model combines the real-time tidal dynamic field and the interaction effect of multi-source pollutants, and its formula is: ； in, Indicates multiple pollutants The interaction effect between is the mutual influence coefficient, is the impact factor; Indicates the rate of change of pollutant diffusion concentration over time; is the coefficient, is the divergence operator, For pollutant emission source strength, is the fluid velocity vector; is the pollutant attenuation coefficient; is a unit step function, is the rate of change of the ecosystem response index, Its gradient.

5. A method for assessing the feasibility of port planning and construction environment according to claim 4, characterized in that: The environmental feasibility comprehensive index adopts a weight distribution mechanism based on quantum computing, and its formula is: ； in: is the socioeconomic impact index; weight vector Solved by quantum algorithm, satisfying ; is a time decision function based on quantum state evolution, used to simulate the response state of the ecosystem at different time points; It is a comprehensive environmental feasibility index used to comprehensively evaluate the environmental feasibility of port planning.

6. A method for assessing the feasibility of port planning and construction environment according to claim 5, characterized in that: The planning parameter optimization suggestion is solved by combining a multi-objective evolutionary algorithm and deep reinforcement learning, and meets the following conditions: ； in, For ecological compensation costs, The socioeconomic impact indicator aims to optimize both environmental and socioeconomic benefits; It is the absolute value of the deviation between the actual change of the planning parameter and the target change.

7. A method for assessing the feasibility of port planning and construction environment according to claim 6, characterized in that: According to the natural environment impact index and the ecosystem response index, the environmental-ecological coupling impact value is calculated, including: using the formula The environmental-ecological coupling impact value is calculated; among them, represents the environmental-ecological coupling impact value, is the weight coefficient, which is determined by the regression model trained with historical data. Represents the natural environment impact index of the target spatial unit, The ecosystem response index represents the target spatial unit, which is used to reflect the interaction between the natural environment and the ecosystem; Based on the pollutant diffusion concentration and environmental-ecological coupling impact value, the pollution-ecological comprehensive risk is assessed, including: using the formula ; Calculate the pollution-ecological comprehensive risk value; Among them, Represents the pollution-ecological comprehensive risk value, is the weight coefficient, determined by the hierarchical analysis method, Represents the pollutant migration concentration of the target space unit, Represents the environmental-ecological coupling impact value of the target spatial unit. This risk value is used to measure the comprehensive impact of pollutants on the ecological environment; The above method generates planning adjustment priorities based on the socio-economic impact index and pollution-ecological comprehensive risk value, including: using the formula ; Calculate the planning adjustment priority; Among them, Represents the planning adjustment priority. The larger the value, the higher the adjustment priority. is the weight coefficient, which is determined by expert scoring combined with entropy weight method. Represents the socioeconomic impact index of the target spatial unit, Represents the comprehensive pollution-ecological risk value of the target spatial unit and is used to guide the order of planning parameter optimization.

8. A port planning and construction environmental feasibility assessment system, which is applied to a port planning and construction environmental feasibility assessment method as claimed in claim 7, characterized in that: include: An integrated intelligent monitoring network, consisting of drones, underwater robots, satellite remote sensing devices, and IoT sensor nodes, enables real-time collection and transmission of multi-dimensional data; Dynamically coupled computing engine, deploying a dynamic coupling model and integrating high-performance computing and artificial intelligence acceleration modules; The digital twin decision-making platform loads the port's BIM model and GIS data, overlaying multiple layers of information on environmental risks, ecological impacts, and socioeconomic impacts to dynamically visualize assessment results. The dynamic coupling computing engine has a built-in adaptive weight optimization network and adopts a federated learning strategy to share model parameters among multiple nodes. The formula is: ； Among them, the model parameters are updated collaboratively among the nodes to improve the generalization ability and stability of the model; Indicates the The weight in The results after the update, For the The weight of the update; is the learning rate, is a linear rectification function, The weight of the observed environmental feasibility comprehensive index gradient; is the regularization coefficient, is the difference norm between two adjacent weight updates.

9. A port planning and construction environmental feasibility assessment system according to claim 8, characterized in that: The port planning and construction environmental feasibility assessment system is connected to the ecological bank and carbon trading platform interface. When the budget is exceeded, an ecological compensation and carbon trading plan will be automatically generated. The formula is: ； in: Increase species richness in the restoration area; is the amount of carbon sequestration; Increase ecosystem service functions; is the current market price of ecological credit; Indicates the amount of ecological compensation and carbon trading.

10. A port planning and construction environmental feasibility assessment system according to claim 9, characterized in that: Based on the tidal hydrological data and meteorological data collected by the integrated intelligent monitoring network, the real-time environmental dynamic coefficient of the port area is calculated, including: using the formula ; Calculate the real-time dynamic coefficient of the port area environment; Among them, Represents the real-time environmental dynamic coefficient of the port area, which is the weight coefficient and is determined by principal component analysis. is the tidal hydrological influencing factor, is the meteorological impact factor, which is used to modify the calculation parameters of the dynamic coupling model; The digital twin decision-making platform generates a visual risk warning level based on the environmental feasibility comprehensive index and the real-time environmental dynamic coefficient, including: using the formula ; Calculate the visual risk warning level; Among them, Represents the visual risk warning level. The larger the value, the higher the warning level. It is a weight coefficient determined by grey correlation analysis and is used to visually display the environmental risk status of port planning and construction.

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