A method and device for random interval optimization of regional port land based on simulation

Abstract

The present invention discloses a method and device for random interval optimization of regional port land based on simulation, comprising: firstly constructing an objective function including port operation income, ecological service value income and ecological environment protection cost expenditure, and considering multiple constraints such as land resource allocation, total water resources, solid waste discharge, pollutant discharge, forest and grass coverage rate and ecological environment purification capacity. The probability distribution function of key indicator factors is generated by Monte Carlo simulation technology, and the original random interval optimization model is equivalently converted into multiple linear sub-models for easy solution. The historical relevant parameters of the target port are combined for standardization processing to obtain continuous statistical data that conforms to the linear sub-model. Finally, these data are input into the linear sub-model to obtain the optimal configuration plan of the land area of ​​the port berth area under different forest and grass coverage rates, providing a scientific basis for port land planning.

Description

Technical Field

[0001] The invention relates to the field of environmental planning, and in particular to a method and a device for optimizing random intervals of regional port land based on simulation. Background Art

[0002] In the current port planning and management, the rational allocation of port land is of great significance to improving port operation efficiency and protecting the ecological environment.

[0003] Traditional methods often rely on empirical judgment or simple mathematical models, which makes it difficult to fully consider various uncertain factors and their impact on port land allocation.

[0004] Therefore, a more scientific and systematic approach is needed to optimize regional port land allocation, especially when considering multiple factors such as operating income, ecological service value, and environmental protection costs. Summary of the invention

[0005] The purpose of the present invention is to provide a method and device for optimizing random intervals of regional port land based on simulation.

[0006] In a first aspect, an embodiment of the present invention provides a method for optimizing random intervals of regional port land based on simulation, comprising:

[0007] Constructing a random interval optimization model for regional port land use, the random interval optimization model for regional port land use includes an objective function based on port terminal operation income, ecological service system service value income and ecological environmental protection cost expenditure, and constraints based on land resource allocation, total water resources, solid waste emission indicators, pollutant emission indicators, forest and grass coverage and ecological environmental purification capacity;

[0008] The probability distribution function is generated by Monte Carlo simulation of key indicator factors, and the random interval optimization model of the port land in the region is equivalently converted into multiple linear sub-models by combining interval parameters;

[0009] Acquire historical relevant parameters of the target port, and perform standardization on the historical relevant parameters to obtain continuous statistical data that conforms to the multiple linear sub-models;

[0010] The continuous statistical data are input into the multiple linear sub-models to obtain an optimization plan for the land area configuration of the port berth area under different forest and grass coverage conditions of the target port.

[0011] In a possible implementation, the objective function is: ;

[0012] Among them, i represents the port berth type; I is the total number of port berth types; j represents the ecological land type; J is the total number of ecological land types; k represents the type of characteristic pollutants; K is the total number of pollutant types; is the revenue per unit throughput of berth area i in period t; is the throughput per unit port area of ​​berth i in period t; is the amount of pollutant generated per unit throughput k of berth area i during period t; is the cost of treating pollutant k in period t; is the ecological service value per unit area of ​​ecological land j in period t; is the port land area of ​​berth i in period t; is the area of ​​ecological land in period t.

[0013] In a possible implementation, the land resource allocation constraint is: ;

[0014] in, is the total land area of ​​the region in period t.

[0015] In a possible implementation, the total water resource constraint is: in, is the water consumption per unit throughput of berth area of ​​port i in period t; is the sewage generation per unit throughput of berth area of ​​port i in period t; is the wastewater recycling rate in period t; is the net primary productivity of vegetation on ecological land j in period t; is the transpiration coefficient of the vegetation in ecological land j during period t; is the total amount of available water resources in period t.

[0016] In a possible implementation, the solid waste emission index constraint is: ;

[0017] in, is the amount of solid waste generated per unit throughput of berth area of ​​port i in period t; is the recycling rate of solid waste in the berth area of ​​port i during period t; is the environmental capacity of solid waste in period t.

[0018] In a possible implementation, the pollutant emission index constraint is:

[0019] ;

[0020] in, is the treatment rate of characteristic pollutant k in period t; is the emission index of characteristic pollutant k in period t.

[0021] In a possible implementation, the forest and grass coverage ratio constraint is: ;

[0022] in, is the forest and grass vegetation coverage ratio of the i-port berth area in period t; is the proportion of forest and grass vegetation coverage of ecological land j in period t; is the proportion of forest and grass vegetation coverage in the region during period t.

[0023] In a possible implementation, the ecological environment purification capacity constraint is: ;

[0024] in, is the unorganized emission of characteristic pollutants in berth area k of port i during period t; is the purification capacity of characteristic pollutant k in ecological land j during period t.

[0025] In one possible implementation, the technical constraints are: ; .

[0026] In a second aspect, an embodiment of the present invention provides a regional port land random interval optimization device based on simulation, comprising:

[0027] A construction module is used to construct a random interval optimization model for regional port land, wherein the random interval optimization model for regional port land includes an objective function based on port terminal operation income, ecological service system service value income and ecological environmental protection cost expenditure, and constraints based on land resource allocation, total water resources, solid waste emission indicators, pollutant emission indicators, forest and grass coverage and ecological environmental purification capacity; a probability distribution function is generated by Monte Carlo simulation of key indicator factors, and the random interval optimization model for regional port land is equivalently converted into multiple linear sub-models in combination with interval parameters;

[0028] The optimization module is used to obtain historical relevant parameters of the target port and standardize the historical relevant parameters to obtain continuous statistical data that conform to the multiple linear sub-models; the continuous statistical data are input into the multiple linear sub-models to obtain the optimization plan for the land area configuration of the port berth area under different forest and grass coverage conditions of the target port.

[0029] Compared with the prior art, the beneficial effects provided by the present invention include: the present invention discloses a simulation-based random interval optimization method and device for regional port land, including: firstly constructing an objective function including port operation income, ecological service value income and ecological environment protection cost expenditure, and considering multiple constraints such as land resource allocation, total water resources, solid waste emissions, pollutant emissions, forest and grass coverage and ecological environment purification capacity.

[0030] The probability distribution function of key indicator factors is generated through Monte Carlo simulation technology, and the original random interval optimization model is equivalently converted into multiple linear sub-models for easy solution.

[0031] Combined with the historical parameters of the target port, the standardized processing was carried out to obtain continuous statistical data that conformed to the linear sub-model. Finally, these data were input into the linear sub-model to obtain the optimal allocation plan for the land area of ​​the port berth area under different forest and grass coverage conditions, providing a scientific basis for port land planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below.

[0033] It should be understood that the following drawings depict only certain embodiments of the present invention and therefore should not be viewed as limiting the scope.

[0034] For ordinary technicians in this field, other related drawings can be obtained based on these drawings without any creative work.

[0035] Figure 1 A schematic flow chart of the steps of a method for random interval optimization of regional port land based on simulation provided by an embodiment of the present invention;

[0036] Figure 2 A schematic diagram of a random interval optimization framework for regional port land based on simulation provided by an embodiment of the present invention;

[0037] Figure 3 A schematic block diagram of the structure of a random interval optimization device for regional port land based on simulation provided by an embodiment of the present invention;

[0038] Figure 4 A schematic block diagram of the structure of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0039] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0040] Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0041] The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.

[0042] The specific implementation modes of the present invention are described in detail below in conjunction with the accompanying drawings.

[0043] In order to solve the technical problems in the aforementioned background technology, Figure 1 A flow chart of a simulation-based random interval optimization method for regional port land provided in an embodiment of the present disclosure is provided, and the simulation-based random interval optimization method for regional port land is introduced in detail below.

[0044] Step S201, constructing a random interval optimization model for regional port land, wherein the random interval optimization model for regional port land includes an objective function constructed based on port terminal operation income, ecological service system service value income and ecological environmental protection cost expenditure, and constraints constructed based on land resource allocation, total water resources, solid waste emission indicators, pollutant emission indicators, forest and grass coverage rate and ecological environmental purification capacity;

[0045] Step S202, generating a probability distribution function through Monte Carlo simulation of key indicator factors, and converting the regional port land random interval optimization model into multiple linear sub-models in combination with interval parameters;

[0046] Step S203, obtaining historical related parameters of the target port, and standardizing the historical related parameters to obtain continuous statistical data that conform to the multiple linear sub-models;

[0047] Step S204, inputting the continuous statistical data into the multiple linear sub-models to obtain an optimization plan for the land area configuration of the port berth area under different forest and grass coverage conditions of the target port.

[0048] In the embodiment of the present invention, illustratively, the server first constructs a random interval optimization model for regional port land.

[0049] This model includes an objective function based on the port terminal operation income, the ecological service system service value income and the ecological environmental protection cost expenditure.

[0050] At the same time, the model also contains a series of constraints based on land resource allocation, total water resources, solid waste emission indicators, pollutant emission indicators, forest and grass coverage and ecological environment purification capacity.

[0051] The server uses the Monte Carlo simulation method to simulate the probability distribution function of key indicator factors (such as port throughput, ecological service value, etc.).

[0052] These distribution functions reflect the uncertainty and variability of the indicator factors.

[0053] For example, the server may simulate the annual changes in port throughput and generate a probability distribution function that indicates the possible range of values ​​of throughput in different years and their probabilities.

[0054] The server uses the Monte Carlo simulation method to simulate the probability distribution function of key indicator factors (such as port throughput, ecological service value, etc.).

[0055] These distribution functions reflect the uncertainty and variability of the indicator factors.

[0056] For example, the server may simulate the annual changes in port throughput and generate a probability distribution function that indicates the possible range of values ​​of throughput in different years and their probabilities.

[0057] The server obtains historical relevant parameters of the target port, such as port throughput, ecological service value, and land use in the past ten years.

[0058] The server normalizes these historical data to ensure that they meet the input requirements of multiple linear sub-models and form continuous statistical data.

[0059] The server inputs the processed continuous statistical data into multiple linear sub-models and runs the model calculations.

[0060] The server outputs the optimal allocation plan for the land area of ​​the port berth area under different forest and grass coverage conditions. For example, in the low ecological and environmental protection cost scenario, the optimal solution may be to use 20% of the land for the port berth area; while in the high ecological and environmental protection cost scenario, the optimal solution may be to use 15% of the land for the port berth area.

[0061] Through these steps, the server can effectively handle the complexity and uncertainty in the energy system and provide multiple feasible optimization solutions for policy analysis and terminal decision-making.

[0062] In order to more clearly describe the solution provided by the embodiment of the present application, a more detailed description is given below.

[0063] In an embodiment of the present invention, Monte Carlo simulation is illustratively a random sampling technique used to estimate the quantification and probability distribution of uncertainty.

[0064] It obtains numerical results by repeated random sampling, thus simulating the behavior of the actual process or system.

[0065] In regional port land optimization, Monte Carlo simulation can be used to estimate the uncertainty of port throughput.

[0066] The server can randomly select a range of possible throughput values, based on historical data and possible future trends, and then calculate the probability distribution of these values.

[0067] Key indicator factors refer to variables or parameters that have a significant impact on the results of the optimization model.

[0068] In port land use optimization, these may include port throughput, ecological service value, land cost, etc.

[0069] For example, port throughput is a key indicator factor because it directly affects the port's operating revenue and the land area required.

[0070] If throughput increases, the port may need more land to expand berths and handling facilities.

[0071] A probability distribution function is a mathematical function that describes the probabilities of all possible values ​​that a random variable can take.

[0072] It provides the probability distribution of a random variable at different values.

[0073] For port throughput, the probability distribution function might indicate that there is a 20% probability of 1 million tons, a 30% probability of 1.5 million tons, and so on.

[0074] This helps understand the possible values ​​of throughput and their probabilities.

[0075] An interval parameter is a parameter with uncertainty, whose value range is within an interval rather than a definite value.

[0076] In optimization models, interval parameters are used to represent uncertainty.

[0077] The cost of ecological environmental protection may be an interval parameter because its specific value depends on a variety of uncertain factors (such as policy changes, market fluctuations, etc.).

[0078] The server may set the ecological environment protection cost to a range, such as between 1 million and 1.5 million yuan.

[0079] Equivalent transformation refers to converting a complex optimization model into one or more simpler and easier-to-solve models while maintaining the core features and solution objectives of the original model.

[0080] In port land optimization, the original random interval optimization model may be very complex and difficult to solve directly.

[0081] The server can transform it into multiple linear sub-models through equivalent transformation, each sub-model represents a specific scenario or condition (such as low, medium, and high ecological environmental protection costs).

[0082] In this way, the original complex model is transformed into a simpler and easier to solve linear sub-model.

[0083] A linear submodel is a simplified form of an optimization model where both the objective function and the constraints are linear.

[0084] The linear submodel is easier to solve because there are mature algorithms and tools for linear programming problems.

[0085] In port land optimization, a linear sub-model may represent the optimization problem under a low ecological environmental protection cost scenario.

[0086] The objective function and constraints of this model are linear, and the server can use a linear programming algorithm to solve the optimal land allocation plan.

[0087] In addition, the historical relevant parameters of the target port are the relevant data and parameters of the target port over a period of time in the past, which are crucial for constructing and optimizing the port land use model.

[0088] These data may include port throughput, land use, ecological service value, ecological environmental protection costs, etc.

[0089] The server may obtain port throughput data for the past ten years from the port management agency's database, historical data on ecological environmental protection costs from the environmental protection department, and records of land use from the land management department.

[0090] Standardization is the process of converting the collected historical data into a uniform format or scale so that they can be used in the model.

[0091] This often involves cleaning, transforming, and normalizing data to ensure accuracy and consistency.

[0092] The server may need to clean the collected throughput data to remove outliers or missing values.

[0093] It might then convert the throughput data into yearly or quarterly averages for use in the model.

[0094] For ecological environmental protection costs, the server may convert them into costs per hectare of land to be consistent with land use data.

[0095] Continuous statistics refer to data sets that have been standardized and arranged as time series or other continuous variables.

[0096] In the context of port land use optimization, these data may include port throughput, land use area, ecological environmental protection costs, etc., which are continuously changing and have been organized in a format suitable for model input.

[0097] It is assumed that the server has collected and standardized the throughput, land use area, and ecological environmental protection cost data of the target port over the past ten years.

[0098] These data now form a continuous time series dataset, with each time point having a corresponding throughput value, land use area value, and ecological environmental protection cost value.

[0099] In addition, multiple linear sub-models refer to a series of simpler linear models obtained by equivalent transformation of the original complex optimization model.

[0100] Each linear sub-model represents a specific scenario or condition, such as different ecological environmental protection costs or different policy constraints.

[0101] In the optimization of port land use, the server may construct three linear sub-models, representing three different ecological environmental protection cost scenarios: low, medium, and high.

[0102] Each model has the same objective function (such as maximizing the sum of port operation income and ecological service value, while minimizing the cost of ecological environmental protection), but the coefficients or constraints of ecological environmental protection cost are different. By solving multiple linear sub-models, the optimal land allocation scheme under different forest and grass coverage conditions is obtained.

[0103] Each plan will show how the land area of ​​the port berth area should be allocated to achieve the best result under a specific forest and grass coverage ratio.

[0104] After the server ran three linear sub-models, it obtained three different optimal land allocation plans.

[0105] The first option is to use 20% of the land for port berth areas under the low ecological environmental protection cost scenario, while maintaining a high forest and grass coverage rate; the second option is to use 15% of the land for port berth areas under the medium ecological environmental protection cost scenario; the third option is to use only 10% of the land for port berth areas under the high ecological environmental protection cost scenario to preserve more ecological land.

[0106] These plans provide policymakers and port managers with land allocation recommendations under different ecological and environmental protection requirements.

[0107] In this embodiment of the present invention, the objective function is: ; Among them, i represents the port berth type; I is the total number of port berth types; j represents the ecological land type; J is the total number of ecological land types; k represents the type of characteristic pollutants; K is the total number of pollutant types; is the revenue per unit throughput of berth area i in period t; is the throughput per unit port area of ​​berth i in period t; is the amount of pollutant generated per unit throughput k of berth area i during period t; is the cost of treating pollutant k in period t; is the ecological service value per unit area of ​​ecological land j in period t; is the port land area of ​​berth i in period t; is the area of ​​ecological land in period t.

[0108] In the embodiment of the present invention, for example, it is assumed that a port has two berth types: bulk berth (i=1) and container berth (i=2), and two ecological land types: woodland (j=1) and grassland (j=2).

[0109] At the same time, two pollutants are considered: sulfur dioxide (k=1) and nitrogen oxides (k=2).

[0110] The server first collected relevant data from the past year, including unit throughput revenue of each berth type, throughput per unit port area, pollutant generation per unit throughput, and the treatment cost of each pollutant.

[0111] At the same time, the server also obtains the ecological service value per unit area of ​​each ecological land, as well as the current area of ​​ports and ecological land.

[0112] Next, the server substitutes this data into the target function for calculation.

[0113] For example, for a bulk berth (i=1), the server calculates its total revenue per unit throughput, total throughput per unit port area, and total treatment costs due to pollutant generation in the past year.

[0114] The server then adds this result to the ecological service value of woodlands and grasslands to get the total value of the bulk berth over the past year.

[0115] The server performs the same calculation for container berths and adds the total values ​​of the two berth types to get the total operating value of the port over the past year.

[0116] Finally, the server will optimize the port land allocation based on this objective function, that is, determine how the area of ​​port and ecological land should be allocated in the coming year to maximize the total operating value of the port.

[0117] Through such calculation and optimization process, the server can provide port managers with a set of scientific land allocation plans, which not only takes into account the economic benefits of the port, but also takes into account the needs of ecological and environmental protection.

[0118] In the embodiment of the present invention, the land resource allocation constraints are: ;

[0119] in, is the total land area of ​​the region in period t.

[0120] In the embodiment of the present invention, for example, it is assumed that the total land area of ​​a port area in period t is 100 hectares.

[0121] The port has two berth types: bulk berth (i=1) and container berth (i=2), and two ecological land types: woodland (j=1) and grassland (j=2).

[0122] When optimizing the port land allocation, the server will first calculate the port land area required for bulk berths and container berths in period t, as well as the ecological land area required for forest land and grassland in period t.

[0123] The server then adds these areas together to ensure that they do not exceed the total area limit for the region's land.

[0124] Specifically, if the bulk berth requires 30 hectares, the container berth requires 20 hectares, the woodland requires 25 hectares, and the grassland requires 20 hectares in period t, then the sum of these areas is 95 hectares, and there is no regional total land area limit of more than 100 hectares.

[0125] However, if the server finds during the calculation process that according to the current land use allocation plan, the total area of ​​bulk berths, container berths, woodland and grassland will exceed 100 hectares, then it will adjust the land use allocation plan to ensure that the land resource allocation constraints are met.

[0126] Through such calculation and adjustment process, the server can ensure that the generated port land configuration plan is feasible and does not exceed the total area limit of the regional land.

[0127] This provides port managers with a scientific and practical land allocation plan, which helps to achieve the coordinated development of port operations and ecological environmental protection.

[0128] In the embodiment of the present invention, the total amount of water resources is constrained as follows: ;

[0129] in, is the water consumption per unit throughput of berth area of ​​port i in period t; is the sewage generation per unit throughput of berth area of ​​port i in period t; is the wastewater recycling rate in period t; is the net primary productivity of vegetation on ecological land j in period t; is the transpiration coefficient of the vegetation in ecological land j during period t; is the total amount of available water resources in period t.

[0130] In the embodiment of the present invention, for example, it is assumed that the total amount of available water resources of a port in period t is 5 million cubic meters.

[0131] The port has two berth types: bulk berth (i=1) and container berth (i=2), and two ecological land types: woodland (j=1) and grassland (j=2).

[0132] When optimizing the port land allocation, the server will first calculate the water consumption and sewage generation per unit throughput of bulk berths and container berths in period t.

[0133] It then takes into account the recycling rate of the wastewater and calculates how much wastewater is actually discharged into the environment.

[0134] At the same time, the server also calculates the net primary productivity and transpiration coefficient of forest land and grassland in period t to determine the amount of water they consume through transpiration.

[0135] Finally, the server adds up all these water amounts and compares them to the total amount of water available.

[0136] If the total amount of water exceeds the total amount of available water resources, the server will adjust the land use allocation plan or port operation strategy to ensure that the total amount of water resources constraints are met.

[0137] Specifically, if the total water consumption and sewage discharge (actual discharge after deducting the recycling rate) of bulk berths and container berths in period t is 4 million cubic meters, and the water consumed by transpiration from woodland and grassland is 800,000 cubic meters, then the total water volume is 4.8 million cubic meters, which does not exceed the total available water resource limit of 5 million cubic meters.

[0138] However, if the total water volume exceeds 5 million cubic meters, the server will adjust the land use configuration or operation strategy accordingly, such as reducing the throughput of the berth area, increasing the sewage recycling rate, increasing the area of ​​ecological land to increase the water consumption by transpiration, etc., to ensure that the total water resource constraints are met.

[0139] Through such calculation and adjustment process, the server can ensure that the generated port land allocation plan is feasible and does not exceed the total amount of water resources available in the region.

[0140] This provides port managers with a scientific and practical land allocation plan, which helps to achieve the coordinated development of port operations and water resource protection.

[0141] In the embodiment of the present invention, the solid waste discharge index constraint is: ;

[0142] in, is the amount of solid waste generated per unit throughput of berth area of ​​port i in period t; is the recycling rate of solid waste in the berth area of ​​port i during period t; is the environmental capacity of solid waste in period t.

[0143] In the embodiment of the present invention, for example, it is assumed that the solid waste environmental capacity of a port in period t is 300 tons.

[0144] The port has two types of berths: bulk berth (i=1) and container berth (i=2).

[0145] When optimizing the port land allocation, the server will first calculate the amount of solid waste generated per unit throughput of bulk berths and container berths in period t.

[0146] It then takes into account the recycling rate of solid waste and calculates the amount of solid waste actually released into the environment.

[0147] Specifically, if the amount of solid waste generated per unit throughput of a bulk berth in period t is 10 tons and the recycling rate is 80%, then the amount of solid waste actually discharged into the environment is 2 tons (i.e., 10 tons multiplied by (1-80%)).

[0148] Similarly, if the solid waste generated per unit throughput of the container berth in period t is 8 tons and the recycling rate is 90%, then the actual amount of solid waste discharged into the environment is 0.8 tons.

[0149] Finally, the server will add up the amount of solid waste actually discharged by all berths and compare it with the environment's holding capacity.

[0150] If the total emissions exceed the environment's carrying capacity, the server will adjust the land use allocation plan or port operation strategy to ensure that the solid waste emission index constraints are met.

[0151] In this example, the total amount of solid waste actually discharged from the bulk berth and container berth was 2.8 tons, which did not exceed the environmental capacity limit of 300 tons.

[0152] Therefore, this land use configuration plan is feasible.

[0153] However, if the total emissions exceed 300 tons, the server will adjust the land use configuration or operation strategy accordingly, such as reducing the throughput of the berth area, increasing the recycling rate of solid waste, etc., to ensure that the solid waste emission index constraints are met.

[0154] Through such calculation and adjustment process, the server can ensure that the generated port land configuration plan is feasible and does not exceed the environment's solid waste holding capacity limit.

[0155] This provides port managers with a scientific and practical land allocation plan, which helps to achieve the coordinated development of port operations and environmental protection.

[0156] In the embodiment of the present invention, the pollutant emission index constraint is: ;

[0157] in, is the treatment rate of characteristic pollutant k in period t; is the emission index of characteristic pollutant k in period t.

[0158] In the embodiment of the present invention, for example, it is assumed that a port has clear emission indicators for two characteristic pollutants, sulfur dioxide (k=1) and nitrogen oxides (k=2), in period t, which are 100 tons and 80 tons respectively.

[0159] The port has two types of berths: bulk berth (i=1) and container berth (i=2).

[0160] When optimizing the port land allocation, the server will first calculate the sulfur dioxide and nitrogen oxide generation per unit throughput of bulk berths and container berths in period t.

[0161] It then takes into account the treatment rates of those pollutants and calculates how much is actually released into the environment.

[0162] Specifically, if the amount of sulfur dioxide generated per unit throughput of a bulk berth in period t is 20 tons and the treatment rate is 70%, then the actual amount of sulfur dioxide discharged into the environment is 6 tons (i.e. 20 tons multiplied by (1-70%)).

[0163] Similarly, if the amount of nitrogen oxides produced per unit of throughput at a container berth in period t is 15 tons and the treatment rate is 85%, then the actual amount of nitrogen oxides emitted into the environment is 2.25 tons.

[0164] Finally, the server will add up the actual sulfur dioxide and nitrogen oxide emissions of all berths and compare them with their respective emission indicators.

[0165] If the total emissions of any pollutant exceed its emission index, the server will adjust the land use allocation plan or port operation strategy to ensure that the pollutant emission index constraints are met.

[0166] In this example, the total amount of sulfur dioxide actually emitted by the bulk berth and container berth was 6 tons, which did not exceed the emission target of 100 tons; the total amount of nitrogen oxides actually emitted was 2.25 tons, which did not exceed the emission target of 80 tons.

[0167] Therefore, this land use configuration plan is feasible.

[0168] However, if the total emissions of any pollutant exceed its emission index, the server will adjust the land use configuration or operation strategy accordingly, such as reducing the throughput of the berth area, increasing the pollutant treatment rate, etc., to ensure that the pollutant emission index constraints are met.

[0169] Through such calculation and adjustment process, the server can ensure that the generated port land configuration plan is feasible and will not exceed the prescribed pollutant emission indicators.

[0170] This provides port managers with a scientific and practical land allocation plan, which helps to achieve the coordinated development of port operations and environmental protection.

[0171] In the embodiment of the present invention, the forest and grass coverage ratio constraint is: ;

[0172] in, is the forest and grass vegetation coverage ratio of the i-port berth area in period t; is the proportion of forest and grass vegetation coverage of ecological land j in period t; is the proportion of forest and grass vegetation coverage in the region during period t.

[0173] In the embodiment of the present invention, for example, it is assumed that the forest and grass vegetation coverage ratio required in the area where a port is located in period t is 30%. The port has two berth types: bulk berth (i=1) and container berth (i=2), and two ecological land types: woodland (j=1) and grassland (j=2).

[0174] When optimizing the port land allocation, the server calculates the forest and grass vegetation coverage ratio of bulk berths and container berth areas, as well as the forest and grass vegetation coverage ratio of woodlands and grasslands.

[0175] It then adds those proportions together to ensure they meet or exceed the 30 percent regional forest and grass vegetation cover requirement.

[0176] Specifically, if the forest and grass vegetation coverage ratio of the bulk berth area is 10%, the forest and grass vegetation coverage ratio of the container berth area is 15%, the forest and grass vegetation coverage ratio of the forest land is 60%, and the forest and grass vegetation coverage ratio of the grassland is 40%, then the sum of these ratios is 125%, far exceeding the regional forest and grass vegetation coverage ratio requirement of 30%.

[0177] Through such calculation and adjustment process, the server can ensure that the generated port land allocation plan meets the forest and grass coverage constraints, which helps to maintain the ecological environment of the region.

[0178] In the embodiment of the present invention, the ecological environment purification capacity constraint is: ;

[0179] in, is the unorganized emission of characteristic pollutants in berth area k of port i during period t; is the purification capacity of characteristic pollutant k in ecological land j during period t.

[0180] In the embodiment of the present invention, for example, it is assumed that a port is mainly concerned with the emission of two characteristic pollutants, sulfur dioxide (k=1) and nitrogen oxides (k=2), during period t.

[0181] The port has two berth types: bulk berth (i=1) and container berth (i=2), and two ecological land types: woodland (j=1) and grassland (j=2).

[0182] When optimizing the port land allocation, the server will first calculate the unorganized emissions of various pollutants from bulk berths and container berths in period t.

[0183] It then calculates the purification capacity of forest land and grassland for each type of pollutant in period t.

[0184] Specifically, if the unorganized emissions of sulfur dioxide in the bulk berth area are 50 tons and the unorganized emissions of sulfur dioxide in the container berth area are 30 tons, the purification capacity of sulfur dioxide by the forest land is 100 tons and the purification capacity of sulfur dioxide by the grassland is 20 tons; at the same time, the unorganized emissions of nitrogen oxides in the bulk berth area are 40 tons and the unorganized emissions of nitrogen oxides in the container berth area are 25 tons, the purification capacity of nitrogen oxides by the forest land is 75 tons and the purification capacity of nitrogen oxides by the grassland is 35 tons.

[0185] Finally, the server will add up the unorganized emissions of various pollutants from all berths and compare them with the purification capacity of the ecological land.

[0186] In this example, the total sulfur dioxide emissions were 80 tons, which did not exceed the ecological land's purification capacity of 120 tons; the total nitrogen oxide emissions were 65 tons, which did not exceed the ecological land's purification capacity of 110 tons.

[0187] Therefore, this land use configuration plan is feasible.

[0188] In the embodiment of the present invention, the technical constraints are: ; .

[0189] In the embodiment of the present invention, illustratively, in period t, the port land area of ​​berth zone i must be greater than 0, and at the same time, the ecological land area of ​​j must also be greater than 0 in period t.

[0190] Step 1: Define the period and region, period t: Assume that period t represents the year 2030, which is a critical time point for urban planning.

[0191] i Berth area: refers to a specific area in a port city planned for berthing ships and loading and unloading cargo.

[0192] jEcological land: refers to specific areas reserved for ecological protection and greening in urban planning.

[0193] Step 2: Collect and analyze data The server collects the planning data of i berth area and j ecological land in 2030 (period t) from the urban planning database.

[0194] By analyzing these data, the server determines the planned land area of ​​berth area i and the planned area of ​​ecological land j.

[0195] Step 3: Apply technical constraints

[0196] The server checks whether the planned land area of ​​berth zone i is greater than 0.

[0197] For example, if the planned land area of ​​berth area i is 50 hectares, then this condition is met.

[0198] Next, the server checks whether the planned area of ​​ecological land j is also greater than 0.

[0199] For example, if the planned area of ​​ecological land j is 30 hectares, then this condition is also met.

[0200] Step 4: Verify the results

[0201] If both conditions are met, the server outputs the verification result as "compliant with technical constraints".

[0202] In this example, because the area of ​​berth area i is 50 hectares and the area of ​​ecological land j is 30 hectares, both greater than 0, the server will confirm that the plan meets the technical constraints.

[0203] Step 5: Application to urban planning

[0204] The server provides this validation result to urban planners to ensure that they take these technical constraints into account when conducting further planning and design.

[0205] If plans do not meet technical constraints, the server will provide feedback to help planners adjust their proposals to ensure that all areas meet the minimum area requirements.

[0206] In this way, the server plays a key role in the urban planning process, ensuring that the planning scheme not only meets the needs of port operations but also protects the ecological environment.

[0207] In order to more clearly describe the solution provided by the embodiment of the present application, please refer to Figure 2 , Figure 2 The regional port land random interval optimization framework diagram based on simulation provided by the embodiment of the present invention specifically includes the following steps:

[0208] Step 1: Construct a random interval optimization model for regional port land use;

[0209] An inter-regional optimization model for regional port land use is constructed to fully characterize the random uncertainty of forest and grass coverage and the interval uncertainty of the model system input parameters in the process of regional port land use planning.

[0210] The model sets the maximization of system benefits as the objective function, which includes the port terminal operation income, the ecological service system service value income, and the ecological environmental protection cost expenditure.

[0211] Set constraints such as land resource allocation, total water resources, solid waste emission indicators, pollutant emission indicators, forest and grass coverage rate, and ecological environment purification capacity.

[0212] Step 2: Generate a probability distribution function through Monte Carlo simulation of key indicator factors, and introduce interval parameters to fully characterize the uncertainty characteristics of the model system; convert the regional port land random interval optimization model based on Monte Carlo simulation into multiple linear sub-models;

[0213] Step 3: Obtain input data by referring to similar ports and historical literature databases, and apply mathematical statistical methods to process the raw data into continuous statistical data required to meet the linear sub-model;

[0214] Step 4: Use the regional port land optimization model to calculate the sub-models under different probability distribution conditions and interval parameter combinations, and obtain the port berth area and proportion and regional land allocation plan under different forest and grass coverage conditions for decision makers to refer to.

[0215] In summary, the embodiments of the present invention firstly introduce random and interval methods into land resource planning and management to characterize system complexity and uncertainty; secondly, introduce ecosystem services into land resource utilization planning, which provides decision makers with a more comprehensive decision-making solution compared with traditional research that focuses on the analysis of a single demand.

[0216] Therefore, the present invention has developed a simulation-based random interval optimization method for regional port land use, which can not only fully characterize the multiple uncertainties existing in regional land resource planning and management, balance system benefits and multiple default risks, but also effectively jointly manage the ecological environment related to land resources to achieve efficient utilization of resources and environment.

[0217] Please refer to Figure 3 , Figure 3 A regional port land random interval optimization device 110 based on simulation provided by an embodiment of the present invention includes:

[0218] Construction module 1101 is used to construct a random interval optimization model for regional port land, wherein the random interval optimization model for regional port land includes an objective function constructed based on port terminal operation income, ecological service system service value income and ecological environment protection cost expenditure, and constraint conditions constructed based on land resource allocation, total water resources, solid waste emission indicators, pollutant emission indicators, forest and grass coverage rate and ecological environment purification capacity; a probability distribution function is generated by Monte Carlo simulation of key indicator factors, and the random interval optimization model for regional port land is equivalently converted into multiple linear sub-models in combination with interval parameters;

[0219] The optimization module 1102 is used to obtain historical relevant parameters of the target port and standardize the historical relevant parameters to obtain continuous statistical data that conform to the multiple linear sub-models; the continuous statistical data are input into the multiple linear sub-models to obtain the optimization plan for the land area configuration of the port berth area under different forest and grass coverage conditions of the target port.

[0220] It should be noted that the implementation principle of the aforementioned simulation-based regional port land random interval optimization device 110 can refer to the implementation principle of the aforementioned simulation-based regional port land random interval optimization method, which will not be repeated here.

[0221] It should be understood that the division of the various modules of the above device is only a division of logical functions, and in actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated.

[0222] And these modules can all be implemented in the form of software calling through processing elements; or all be implemented in the form of hardware; or some modules can be implemented in the form of software calling through processing elements, and some modules can be implemented in the form of hardware.

[0223] For example, the simulation-based random interval optimization device 110 for regional port land can be a separately established processing element, or it can be integrated in one of the chips of the above-mentioned device. In addition, it can also be stored in the memory of the above-mentioned device in the form of program code, and called and executed by one of the processing elements of the above-mentioned device. The functions of the above-mentioned simulation-based random interval optimization device 110 for regional port land.

[0224] The implementation of other modules is similar.

[0225] In addition, all or part of these modules can be integrated together or implemented independently.

[0226] The processing element described herein may be an integrated circuit having signal processing capabilities.

[0227] In the implementation process, each step of the above method or each module above can be completed by an integrated logic circuit of hardware in a processor element or an instruction in software form.

[0228] For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more application specific integrated circuits (ASICs), or, one or more microprocessors (digital signal processors, DSPs), or, one or more field programmable gate arrays (FPGAs), etc.

[0229] For another example, when a certain module above is implemented in the form of a processing element scheduling a program code, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processors that can call program codes.

[0230] For another example, these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0231] An embodiment of the present invention provides a computer device 100, which includes a processor and a non-volatile memory storing computer instructions. When the computer instructions are executed by the processor, the computer device 100 executes the aforementioned regional port land random interval optimization device 110 based on simulation.

[0232] like Figure 4 As shown, Figure 4 The computer device 100 provided in the embodiment of the present invention is a structural block diagram. The computer device 100 comprises a simulation-based regional port land random interval optimization device 110, a memory 111, a processor 112 and a communication unit 113.

[0233] To achieve data transmission or interaction, the memory 111 , the processor 112 , and the communication unit 113 are electrically connected to each other directly or indirectly.

[0234] For example, these components may be electrically connected to each other via one or more communication buses or signal lines.

[0235] The simulation-based regional port land random interval optimization device 110 includes at least one software function module that can be stored in the memory 111 in the form of software or firmware or fixed in the operating system (OS) of the computer device 100.

[0236] The processor 112 is used to execute the simulation-based regional port land random interval optimization device 110 stored in the memory 111, such as the software function modules and computer programs included in the simulation-based regional port land random interval optimization device 110.

[0237] An embodiment of the present invention provides a readable storage medium, which includes a computer program. When the computer program is running, it controls the computer device where the readable storage medium is located to execute the aforementioned simulation-based random interval optimization method for regional port land.

[0238] The foregoing description, for purposes of illustration, has been presented with reference to specific embodiments.

[0239] However, the illustrative discussions above are not intended to be exhaustive or to limit the disclosure to the precise forms disclosed.

[0240] Numerous modifications and variations are possible in light of the above teaching.

[0241] The embodiments were chosen and described in order to best explain the principles of the disclosure and its practical application, to thereby enable others skilled in the art to best utilize the disclosure and utilize various embodiments with various modifications as are suited to the particular use contemplated.

Claims

1. A simulation-based random interval optimization method for regional port land, characterized in that: include: Constructing a random interval optimization model for regional port land use, the random interval optimization model for regional port land use includes an objective function based on port terminal operation income, ecological service system service value income and ecological environmental protection cost expenditure, and constraints based on land resource allocation, total water resources, solid waste emission indicators, pollutant emission indicators, forest and grass coverage and ecological environmental purification capacity; The probability distribution function is generated by Monte Carlo simulation of key indicator factors, and the random interval optimization model of the port land in the region is equivalently converted into multiple linear sub-models by combining interval parameters; Acquire historical relevant parameters of the target port, and perform standardization on the historical relevant parameters to obtain continuous statistical data that conforms to the multiple linear sub-models; Inputting the continuous statistical data into the multiple linear sub-models to obtain an optimization plan for the land area configuration of the port berth area under different forest and grass coverage conditions of the target port; The objective function is: Among them, i represents the port berth type; I is the total number of port berth types; j represents the ecological land type; J is the total number of ecological land types; k represents the type of characteristic pollutants; K is the total number of pollutant types; PA it is the revenue per unit throughput of berth area i in period t; is the throughput per unit port area of ​​berth i in period t; is the amount of pollutant generated per unit throughput k of berth area i in period t; PM kt is the cost of treating pollutant k in period t; is the ecological service value per unit area of ​​ecological land j in period t; is the port land area of ​​berth i in period t; is the area of ​​ecological land in period j in t; The land resource allocation constraints are: Among them, TS t is the total land area of ​​the region during period t; The total water resources constraint is: Among them, CW it is the water consumption per unit throughput of berth area of ​​port i in period t; RW is the sewage generation per unit throughput of berth area of ​​port i in period t; t is the wastewater recycling rate in period t; is the net primary productivity of vegetation on ecological land j in period t; TW is the transpiration coefficient of the vegetation in ecological land j at period t; t is the total amount of available water resources in period t; The solid waste emission index constraints are: Among them, SW it is the solid waste generation per unit throughput of berth area of ​​port i in period t; RU it is the recycling rate of solid waste in the berth area of ​​port i during period t; TP t is the environmental capacity of solid waste in period t; The pollutant emission index constraints are: Among them, RT kt is the treatment rate of characteristic pollutant k in period t; SE kt is the emission index of characteristic pollutant k in period t; The forest and grass coverage ratio constraint is: in, is the forest and grass vegetation coverage ratio of the i-port berth area in period t; is the forest and grass vegetation coverage ratio of ecological land j in period t; TF t is the proportion of forest and grass vegetation coverage in the region during period t; The ecological environment purification capacity constraints are: Among them, FE ikt is the unorganized emission of characteristic pollutants in berth area k of port i during period t; is the purification capacity of characteristic pollutant k of ecological land j in period t; The technical constraints are:

2. A simulation-based random interval optimization device for regional port land, characterized in that: include: A construction module is used to construct a random interval optimization model for regional port land, wherein the random interval optimization model for regional port land includes an objective function based on port terminal operation income, ecological service system service value income and ecological environmental protection cost expenditure, and constraints based on land resource allocation, total water resources, solid waste emission indicators, pollutant emission indicators, forest and grass coverage and ecological environmental purification capacity; a probability distribution function is generated by Monte Carlo simulation of key indicator factors, and the random interval optimization model for regional port land is equivalently converted into multiple linear sub-models in combination with interval parameters; An optimization module is used to obtain historical relevant parameters of the target port, and standardize the historical relevant parameters to obtain continuous statistical data that conforms to the multiple linear sub-models; input the continuous statistical data into the multiple linear sub-models to obtain an optimization plan for the land area configuration of the port berth area under different forest and grass coverage conditions of the target port; The objective function is: Among them, i represents the port berth type; I is the total number of port berth types; j represents the ecological land type; J is the total number of ecological land types; k represents the type of characteristic pollutants; K is the total number of pollutant types; PA it is the revenue per unit throughput of berth area i in period t; is the throughput per unit port area of ​​berth i in period t; is the amount of pollutant generated per unit throughput k of berth area i in period t; PM kt is the cost of treating pollutant k in period t; is the ecological service value per unit area of ​​ecological land j in period t; is the port land area of ​​berth i in period t; is the area of ​​ecological land in period j in t; The land resource allocation constraints are: Among them, TS t is the total land area of ​​the region during period t; The total water resources constraint is: Among them, CW it is the water consumption per unit throughput of berth area of ​​port i in period t; RW is the sewage generation per unit throughput of berth area of ​​port i in period t; t is the wastewater recycling rate in period t; is the net primary productivity of vegetation on ecological land j in period t; TW is the transpiration coefficient of the vegetation in ecological land j at period t; t is the total amount of available water resources in period t; The solid waste emission index constraints are: Among them, SW it is the solid waste generation per unit throughput of berth area of ​​port i in period t; RU it is the recycling rate of solid waste in the berth area of ​​port i during period t; TP t is the environmental capacity of solid waste in period t; The pollutant emission index constraints are: Among them, RT kt is the treatment rate of characteristic pollutant k in period t; SE kt is the emission index of characteristic pollutant k in period t; The forest and grass coverage ratio constraint is: in, is the forest and grass vegetation coverage ratio of the i-port berth area in period t; is the forest and grass vegetation coverage ratio of ecological land j in period t; TF t is the proportion of forest and grass vegetation coverage in the region during period t; The ecological environment purification capacity constraints are: Among them, FE ikt is the unorganized emission of characteristic pollutants in berth area k of port i during period t; is the purification capacity of characteristic pollutant k of ecological land j in period t; The technical constraints are:

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