A method, system, terminal device and medium for urban agglomeration spatial resilience collaborative planning based on cooperative game

Abstract

The application discloses a kind of urban agglomeration space resilience collaborative planning method, system, terminal equipment and medium based on cooperative game, and it is related to the technical field of city planning.The method comprises the following steps: obtaining urban agglomeration basic data, dividing into uniformly assigned spatial unit set according to attribute;Build a multi-disaster scenario set and quantify the risk loss parameters of each unit;Define the candidate land use state and planning constraint condition set for each unit;Based on the candidate land use state of unit, build the comprehensive benefit function of the corresponding city;Build cooperative game with city as participant, maximize the urban agglomeration benefit as target, solve the optimal strategy in the planning configuration set that meets the constraint;Calculate the marginal contribution of each city to the urban agglomeration benefit under the optimal strategy, and distribute the cooperative benefit based on it.The present application provides an improved mechanism in terms of multi-disaster prevention and regional collaborative optimization, which helps to improve the resilience level of urban agglomeration space planning and ensure the fairness and stability of regional cooperation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban planning, and in particular to a city cluster spatial resilience collaborative planning method and system based on cooperative game, a terminal device and a medium. BACKGROUND

[0002] With spatial planning, development and safety are coordinated, and the focus is on promoting the construction of safe and reliable resilient cities. City clusters are facing complex risks such as cross-domain, multiple disasters, and chain impacts, and single-city optimization may lead to suboptimal group performance, so the resilience needs to be improved collaboratively. Among them, city resilience specifically refers to the comprehensive ability of the city to resist multiple risks and quickly recover.

[0003] In the prior art, most of the research focuses on a single city or a single disaster, and there is a lack of methods for unified assessment of the superimposed effects of multiple disasters and quantification of overall resilience at the city cluster scale. Most of the research is from the perspective of a single city or a single department, and there is no collaborative planning and decision-making framework that takes the overall benefits of city clusters as the goal and considers the interests of cities. The contribution of each city to the overall benefits has not been fully quantified, so there is a lack of benefit distribution methods linked to this contribution, making it difficult to achieve a balance of interests and stable cooperation among cities. This makes it difficult for existing technologies to coordinate multiple disaster risk prevention, spatial collaborative optimization, and cooperative benefit distribution at the city cluster scale, and to provide strong support for improving regional resilience.

[0004] Therefore, there is an urgent need for a city cluster collaborative planning method to address the shortcomings of existing technologies. SUMMARY

[0005] The technical problem to be solved by the present application is that existing urban planning has shortcomings in achieving comprehensive prevention of multiple disaster risks, cross-city collaborative planning, and fair distribution of cooperative benefits, resulting in insufficient regional resilience. To this end, the present application proposes a complete method system to address the above shortcomings.

[0006] To solve the above technical problems, the technical solutions adopted by the present application are as follows:

[0007] In a first aspect, the present application provides a city cluster spatial resilience collaborative planning method based on cooperative game, which comprises:

[0008] Obtain city cluster basic data and divide them into a set of uniformly assigned spatial units according to attributes;

[0009] Construct a set of multiple disaster scenarios, quantify the risk loss parameters of each unit, and identify high-risk areas;

[0010] Define a set of candidate land use states and planning constraint conditions for each spatial unit; wherein the set of planning constraint conditions includes safety resilience constraint conditions;

[0011] construct a city comprehensive benefit function of the corresponding city based on the candidate land use state of the spatial unit; wherein the city comprehensive benefit function comprises a risk dimension;

[0012] construct a cooperative game with cities as participants to maximize the comprehensive benefit of the urban agglomeration, and solve the optimal collaborative planning strategy in the planning configuration set that meets the planning constraint condition set;

[0013] Calculate the marginal contribution of each city to the urban agglomeration benefit under the optimal collaborative planning strategy, and distribute the cooperative benefit based on this.

[0014] In an implementation manner, the candidate land use state represents the comprehensive land property and function configuration of the spatial unit under the corresponding planning configuration;

[0015] The candidate land use state comprises a plurality of attribute parameters, and the attribute parameters comprise at least one of land area, land use type, volume rate, total planning population, total asset value, whether to set up a refuge facility, refuge facility level, refuge capacity, public service level index, protection engineering level, and ecological and management control attribute label.

[0016] In an implementation manner, the planning constraint condition set is used to limit the attribute parameters of the spatial unit.

[0017] The planning constraint condition set further comprises at least one of legal and regulatory constraints, social public interest and cultural protection constraints, and economic development constraints.

[0018] In an implementation manner, the city comprehensive benefit function further comprises at least one of an economic dimension, an ecological dimension, a social dimension, and a cost dimension.

[0019] In an implementation manner, based on the planning constraint condition set and the candidate land use state of the spatial unit, a cooperative game model is constructed with cities as game participants, and the spatial optimal collaborative planning strategy of the urban agglomeration is solved by maximizing the city comprehensive benefit function, comprising:

[0020] obtain a planning configuration set of all spatial units in the urban agglomeration under the constraint condition of the planning constraint condition set;

[0021] In the planning configuration set, based on the cooperative game mechanism, the planning configuration of all spatial units in the urban agglomeration is solved when the city comprehensive benefit function is maximized.

[0022] Integrate the planning configuration of all spatial units in the urban agglomeration to obtain the spatial optimal collaborative planning strategy of the urban agglomeration.

[0023] In an implementation manner, the cooperation benefit of each city is calculated based on the spatial optimal collaborative planning strategy of the urban agglomeration and the marginal contribution of each city to the comprehensive benefit function of the urban agglomeration, including:

[0024] The marginal contribution of each city to the comprehensive benefit function of the urban agglomeration under the spatial optimal collaborative planning strategy of the urban agglomeration is calculated.

[0025] For each city, the cooperation benefit is calculated based on the marginal contribution to the comprehensive benefit function of the urban agglomeration.

[0026] In an implementation manner, the method further includes:

[0027] For each spatial unit, all attribute parameters in the candidate land use state under the spatial optimal collaborative planning strategy of the urban agglomeration are extracted and translated into a set of planning indicators.

[0028] Based on a geographic information system, the set of planning indicators is mapped into a spatial layer to generate a land use planning map, a disaster risk prevention and control map, and a benefit distribution schematic diagram, thereby obtaining a set of visual planning layers.

[0029] Based on the set of visual planning layers, artificial judgment is made on implementation obstacles, and structural risks and benefit distribution fairness are evaluated, and the spatial optimal collaborative planning strategy of the urban agglomeration is optimized based on the judgment and evaluation results.

[0030] Based on the optimized spatial optimal collaborative planning strategy of the urban agglomeration, an urban agglomeration spatial planning suggestion scheme is generated.

[0031] In a second aspect, the embodiments of the present application also provide an urban agglomeration spatial resilience collaborative planning system based on cooperative game, which includes:

[0032] A data and division module is configured to obtain urban agglomeration basic data and divide the data into a set of uniformly valued spatial units according to attributes.

[0033] A multi-disaster scenario set construction module is configured to construct a multi-disaster scenario set, quantify risk loss parameters of each unit, and identify high-risk areas.

[0034] A spatial unit attribute definition module is configured to define a set of candidate land use states and planning constraint conditions for each spatial unit; wherein the set of planning constraint conditions includes safety resilience constraint conditions.

[0035] A comprehensive benefit function construction module is configured to construct a city comprehensive benefit function of a corresponding city based on the candidate land use state of the spatial unit; wherein the city comprehensive benefit function includes a risk dimension.

[0036] a cooperative game module, configured to construct a cooperative game with cities as participants, to maximize the comprehensive income of the urban agglomeration, and to solve an optimal collaborative planning strategy in a planning configuration set satisfying the planning constraint condition set;

[0037] a benefit calculation module, configured to calculate the marginal contribution of each city to the income of the urban agglomeration under the optimal collaborative planning strategy, and to distribute the cooperative income based on the calculation.

[0038] In a third aspect, an embodiment of the present application further provides a terminal device, which comprises a memory, a processor, and a cooperative game-based urban agglomeration spatial resilience collaborative planning program stored in the memory and executable on the processor. When the processor executes the cooperative game-based urban agglomeration spatial resilience collaborative planning program, the steps of the cooperative game-based urban agglomeration spatial resilience collaborative planning method according to any one of the above solutions are implemented.

[0039] In a fourth aspect, an embodiment of the present application further provides a computer readable storage medium, which stores a cooperative game-based urban agglomeration spatial resilience collaborative planning program. When the processor executes the cooperative game-based urban agglomeration spatial resilience collaborative planning program, the steps of the cooperative game-based urban agglomeration spatial resilience collaborative planning method according to any one of the above solutions are implemented.

[0040] Beneficial effects: The cooperative game-based urban agglomeration spatial resilience collaborative planning method, system, terminal device, and computer readable storage medium provided by the present application introduce a unified spatial unit division and assignment method, consistently express the key attributes of different urban plots at the urban agglomeration scale, and to some extent, alleviate the fragmentation problem between multi-source planning data. The construction of multi-disaster scenarios and the quantification of unit risk loss, combined with the resilience constraints, identify and control high-risk areas at the planning stage, which is conducive to maintaining the spatial safety bottom line. The present application constructs a comprehensive income function including economic, risk, ecological, social, and cost dimensions, which is conducive to comparing different planning schemes under a multi-dimensional target system. Based on the cooperative game with cities as participants, the group optimal planning is solved, which helps to improve the overall income level and resilience of the region while taking into account the constraints of each city. In terms of income distribution, the present application distributes the cooperative income according to the marginal contribution of each city, which is conducive to enhancing the acceptability and stability of cross-city cooperation. In summary, the present application improves the resilience of urban agglomeration spatial planning to some extent and enhances the fairness and stability of regional cooperation. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 The flowchart of the specific implementation of the cooperative game-based urban agglomeration spatial resilience collaborative planning method provided by the embodiment of the present application is shown.

[0042] Figure 2A schematic diagram of core steps of a process of the urban agglomeration spatial resilience collaborative planning method based on cooperative game provided by the embodiment of the present application.

[0043] Figure 3 A schematic diagram of detailed steps of a process of the urban agglomeration spatial resilience collaborative planning method based on cooperative game provided by the embodiment of the present application.

[0044] Figure 4 A principle block diagram of the urban agglomeration spatial resilience collaborative planning device based on cooperative game provided by the embodiment of the present application.

[0045] Figure 5 A principle block diagram of the internal structure of the terminal device provided by the embodiment of the present application. DETAILED DESCRIPTION

[0046] In order to make the objectives, technical solutions and effects of the present application clearer and more explicit, the present application is further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0047] The flowchart shown in the drawings is only an example and does not necessarily include all contents and operations or steps, nor does it necessarily execute in the order described. For example, some operations or steps can be further divided, combined or partially merged, so the actual execution order may be changed according to the actual situation.

[0048] It should be understood that the terms used in the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the present application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0049] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present application, in the embodiments of the present application, the same items or similar items with basically the same functions and effects are distinguished by using "first", "second" and the like. For example, the first control information and the second control information are only used to distinguish different control information and do not limit the order.

[0050] Those skilled in the art can understand that the terms "first", "second" and the like do not limit the quantity and execution order, and the terms "first", "second" and the like do not necessarily mean different.

[0051] It should also be understood that the term "and / or" used in the present application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0052] Currently, urban agglomeration spatial planning faces common challenges in addressing multi-hazard risks and regional collaborative disaster prevention. First, assessments tend to focus on single cities or single hazard types, lacking a unified assessment and resilience quantification of the cumulative impacts of multiple hazards at the urban agglomeration scale. This makes it difficult to create a consistent risk map and core indicators, resulting in inconsistent decision-making basis. Second, planning decisions are still primarily based on single-city or single-department objectives, lacking a collaborative framework oriented towards the overall benefits of the urban agglomeration and taking into account inter-city externalities. This makes it difficult to coordinate and optimize key regional elements under the same goals and constraints. Third, cooperation mechanisms rarely quantify the contributions of each city to overall benefits, lack cost-sharing and benefit-distribution methods linked to contributions, and make it difficult to guarantee cooperation incentives and stability. These problems interact, making it difficult to form a closed loop from assessment to decision-making to implementation at the urban agglomeration scale. The ability to coordinate multi-hazard risk prevention, spatial collaborative optimization, and benefit balancing remains insufficient. To enhance regional resilience, a systematic improvement is needed around unified assessment standards, collaborative decision-making objectives, and contribution-oriented allocation rules. This will ensure that data, rules, and incentives work together within the same framework, enhancing executability and sustainability.

[0053] To address the challenges mentioned above, this solution provides a collaborative planning method for urban agglomeration spatial resilience based on cooperative game theory. For example... Figure 2 As shown, this scheme first constructs a mathematical model for urban resilience planning under multiple disaster scenarios. Then, based on the constructed mathematical model, a game theory framework is built to construct a payoff matrix, and the Shapley value method is used to solve for the optimal strategy set. Finally, the game theory solution is translated into a planning scheme, and optimized using professional knowledge to form the final planning scheme. Specifically, as... Figure 3As shown, this approach first completes data collection and modeling through data acquisition and spatial unit division, multi-hazard scenario construction, land use status modeling, and control baseline and constraint modeling. Subsequently, it completes the construction and solution of the game model through game theory framework construction, urban comprehensive benefit function construction, cooperative game solving, and benefit distribution. Finally, it generates the final planning scheme through result translation and planning scheme optimization. This scheme integrates multi-source data through unified spatial unit division to establish a consistent planning data foundation. Simultaneously, it constructs multi-hazard scenarios to assess the risks of each spatial unit under different hazard combinations. Based on this, urban agglomeration planning decisions are abstracted into candidate land use status combinations of each spatial unit, combined with control baseline constraints such as ecology and safety, to establish an urban benefit evaluation model covering multiple objectives including economy, risk, ecology, and society. Each urban management department, as a game participant, jointly optimizes the overall regional utility within the cooperative game framework, and the Shapley value method is used to fairly distribute cooperative gains according to urban contributions. Shapley values, a tool for fair allocation in cooperative game theory, are based on the idea that each participant's share is equal to the average of their marginal contributions to the combined payoff across all possible combinations. This addresses the problem of how to fairly distribute the total payoff generated by multiple participants cooperating. This leads to the establishment of a cross-city disaster prevention benefit coordination mechanism, promoting reasonable returns for each city through cooperation. Finally, through result translation and optimization of planning schemes, the final planning scheme is formed.

[0054] The above-mentioned solutions can, to some extent, enhance the region's resilience under multiple disaster scenarios, alleviate the lack of coordination in the distribution of benefits and the lack of coordination among cities, promote the optimal allocation of resources and the relatively reasonable sharing of responsibilities and interests among multiple cities, and at the same time promote the unified coordination of planning data and decision-making, forming a comprehensive solution that takes into account both safety and resilience and economic and social development.

[0055] This embodiment provides a collaborative planning method for urban agglomeration spatial resilience based on cooperative game theory, such as... Figure 1 As shown, the specific steps include the following:

[0056] Step S100: Obtain basic data of the urban agglomeration and divide it into a set of spatial units with uniformly assigned values ​​according to attributes.

[0057] In this embodiment, the urban agglomeration basic data is a multi-source information set supporting planning analysis. It refers to the basic spatial and statistical data related to spatial planning and disaster prevention within the urban agglomeration. This data collectively constitutes the information foundation for planning analysis and is used for subsequent risk assessment and benefit calculation. The spatial unit set divides the entire urban agglomeration into several independent analysis units. It refers to a set of regions with relatively homogeneous attributes that are easy to assign planning values ​​to. Through this division, macro-level planning problems at the urban agglomeration scale can be decomposed into micro-level decision-making problems at the unit level. Unified assignment is the operation of establishing a connection between urban basic data and spatial units. It refers to matching various types of basic data to corresponding spatial units according to preset rules and assigning attribute values.

[0058] Specifically, this implementation first acquires basic data on urban agglomerations, namely basic spatial and statistical data within the urban agglomeration, including but not limited to: land use, road network, public service facilities, important infrastructure, ecological space, cultural protection scope, historical records of various disasters (typhoons, floods, earthquakes, geological disasters, etc.), risk assessment and simulation results (such as water depth, seismic intensity, landslide susceptibility, etc.), population density, population structure, asset value, and other information of each spatial unit.

[0059] Subsequently, the urban agglomeration is divided. Specifically, the entire urban agglomeration region is divided into a finite number of spatial units (referred to as units) forming a set:

[0060]

[0061] in, Indicates the unit number, This indicates the total number of units.

[0062] The unit can be an administrative unit (street, township), a functional zone, or a regular grid, and there are no specific restrictions on its shape and size.

[0063] Next, the correspondence between cities and spatial units is established. Specifically, the city set is defined as follows:

[0064]

[0065] in, This represents the total number of cities.

[0066] Number the city The set of spatial units under jurisdiction is ,satisfy:

[0067]

[0068] in, This represents the empty set, meaning that all spatial units belong to one and only one city.

[0069] By unifying the spatial unit division, multi-source data such as planning, statistics, and disaster assessment are aligned to the same spatial resolution, providing a consistent data carrier for subsequent state modeling and game theory solutions.

[0070] Furthermore, spatial unit division can also adopt a multi-scale division strategy, using finer units for core or high-risk areas and coarser units for ordinary areas, in order to balance accuracy and efficiency.

[0071] Step S200: Construct a multi-hazard scenario set, quantify the risk loss parameters of each unit, and identify high-risk areas.

[0072] In this embodiment, a multi-hazard scenario set is used to describe scenarios of potential disaster combinations and intensities that urban agglomerations may face. Each scenario clearly defines key information such as disaster type, intensity, and impact range. Furthermore, each scenario can be assigned a weight or probability of occurrence, with the relevant probability values ​​determined by referring to historical data or model simulation results. Constructing multi-hazard scenarios helps to consider the cumulative effects of multiple disasters in the assessment, providing a reference for planning responses to various risk scenarios.

[0073] The disaster risk loss parameter is used to quantify the potential loss of spatial units under various disaster scenarios. This parameter helps reflect the risk differences among different units, thus providing a quantitative reference for subsequent construction of the payoff function and setting of constraints.

[0074] Specifically, in this embodiment, a set of multiple disaster scenarios is first constructed:

[0075]

[0076] Each scenario It represents a typical disaster or combination of disasters, such as a typhoon that occurs once every 50 years or a flood that occurs once every 100 years. This represents the total number of scenes.

[0077] For each scenario Assign weights or probability of occurrence ,satisfy:

[0078]

[0079] in, Describing a scenario Calculate relative importance or probability of occurrence in the planning process.

[0080] For each scenario and spatial units Given risk loss parameters For example, flood depth, inundation duration, peak ground acceleration, and landslide or debris flow susceptibility indices can serve as core parameters for quantifying unit risk, and are used for subsequent high-risk area identification and constraint setting. These parameters can be provided by physical models, statistical models, or empirical assessment methods, without limiting the specific model form.

[0081] based on High-risk areas can be identified using various formulaic methods. One method is the absolute threshold method, where the threshold is set as follows: The set of high-risk units is then defined as:

[0082]

[0083] That is, when the risk loss parameter of a certain unit is not lower than the statutory or agreed threshold, it is judged as high risk; applicable to situations where there are clear technical or regulatory thresholds.

[0084] The second method is the relative quantile method, which is suitable for situations where there is no clear absolute threshold or where it is necessary to identify the "top q% of risk hotspots". For cases where the actual proportion exceeds q due to parallel values, it can be conservatively retained or weighted by area or attribute. This invention does not limit the choice of method or parameter settings.

[0085] Step S300: Define a set of candidate land use status and planning constraints for each spatial unit; wherein, the set of planning constraints includes safety resilience constraints.

[0086] In this embodiment, the candidate land use status is a comprehensive land configuration form that a spatial unit can choose in the planning. It refers to the combination of land properties and functions of a spatial unit under a specific planning configuration. Each candidate status corresponds to a set of specific attribute parameters, such as land use type, plot ratio, refuge facility level, etc. By predefining the candidate status, complex planning decisions can be abstracted into a combination problem of unit selection status, which meets the needs of multi-scheme comparison in planning practice and provides clear strategy options for subsequent game analysis.

[0087] The set of planning constraints represents the minimum requirements for selecting spatial unit planning options. These constraints ensure that the planning scheme is compliant and meets safety and ecological objectives. Specifically, they can include legal and regulatory constraints, social public interest constraints, and economic development constraints. At least one constraint should be included: a safety resilience constraint. This constraint ensures the spatial unit's ability to cope with disasters and prevents unreasonable imbalances between safety and benefits in the planning scheme.

[0088] In one implementation, the candidate land use status represents the comprehensive land properties and functional configuration of a spatial unit under the corresponding planning configuration;

[0089] The candidate land use status includes several attribute parameters, which include at least one of the following control attribute labels: land area, land use type, plot ratio, total planned population, total asset value, whether refuge facilities are set up, refuge facility level, refuge capacity, public service level index, protection engineering level, and ecology.

[0090] In this embodiment, the land use status is first defined. Specifically, for each spatial unit... The set of its optional land use states is defined as follows:

[0091]

[0092] in, Representing spatial units The The condition of the land Representation unit The number of candidate states.

[0093] In the planning scheme, each unit You need to choose one of the states. . It can include multiple states, reflecting different land use types, plot ratios, and functional combinations.

[0094] Define the planning decision variables as:

[0095]

[0096] City The strategy variables are:

[0097]

[0098] in, spatial unit Land status decision variables, For unit The set of candidate land states; For the city A collection of spatial units within the jurisdiction; Represents city The planning scheme refers to the combination of the states of the spatial units under its jurisdiction.

[0099] Each candidate state Representative Unit Land use and functional allocation under a certain planning scheme. To facilitate subsequent modeling and solution, each state... Assign the following properties, including but not limited to:

[0100] 1) Land area

[0101] Representing spatial units The area.

[0102] 2) Land use types

[0103] Representation unit In state Land use types, such as residential land, industrial land, public service facilities land, and ecological land.

[0104] 3) Floor area ratio (development intensity)

[0105] Representation unit In state The planned plot ratio or equivalent development intensity index.

[0106] 4) Total planned population

[0107] Representation unit In state The corresponding planned population size. This value can be estimated based on basic data such as land use type, population density, and unit area.

[0108] 5) Total asset value

[0109] Representation unit In state The comprehensive value of the buildings, structures and other tangible assets contained therein can be estimated based on data such as land use type and unit area.

[0110] 6) Whether refuge facilities are provided

[0111]

[0112] when When, it indicates a unit In state Emergency shelters have been set up, including dedicated shelter facilities or public open spaces that can be converted into shelters during a disaster; when When this condition is met, it indicates that no refuge areas are set up in this state.

[0113] 7) Refuge facility level

[0114] Representation unit In state The technical level of the refuge facilities provided can be, for example:

[0115]

[0116] Different levels correspond to different technical indicators such as design evacuation duration, effective evacuation area per capita, service radius, and functional configuration.

[0117] 8) Refuge capacity

[0118] Representation unit In state The total number of people that can be accommodated in the facility. This can be estimated based on data such as the level of the refuge facility and the area of ​​each unit.

[0119] 9) Public Service Level Index

[0120] Representation unit In state The following are comprehensive indicators of the configuration level of public service facilities such as medical care, education, and emergency services.

[0121] 10) Protection Engineering Level

[0122] Representation unit In state The comprehensive level of the protection projects, including flood control, moisture prevention, wind protection, and earthquake resistance, can be correlated with relevant engineering design standards.

[0123] 11) Ecological and other control attribute labels

[0124] Ecological and other regulatory attribute labels are a set of Boolean or categorical attributes used to indicate status. Whether to use the unit As ecological land, ecological buffer space, flood storage and detention space, and whether it is located within various control zones such as ecological protection red lines, permanent basic farmland, and cultural protection areas.

[0125] In one implementation, the set of planning constraints is used to restrict the attribute parameters of spatial units;

[0126] The set of planning constraints also includes at least one of the following: legal and regulatory constraints, social public interest and cultural protection constraints, and economic development constraints.

[0127] In this embodiment, constraints such as laws and regulations, security, public interest and cultural protection, and economic development are uniformly expressed as state selection. The constraints are shown below. Representative constraint forms can be selected as needed during actual implementation.

[0128] The specific legal and regulatory constraints include:

[0129] 1) Ecological protection red line

[0130] Let the set of spatial units within the ecological protection red line be . Then the following is required:

[0131]

[0132] in, It only includes land status that maintains or enhances ecological functions.

[0133] 2) Protection of permanent basic farmland

[0134] Let the set of spatial units where permanent basic farmland is located be . ,City The permanent basic farmland reserve is defined as:

[0135]

[0136] in, As an indicator function, when the state Make unit The value is 1 if the land continues to be used as permanent basic farmland, and 0 otherwise.

[0137] The minimum requirement for the protection of permanent basic farmland is:

[0138]

[0139] in, For cities as stipulated by higher-level planning or relevant regulations The minimum amount of permanent basic farmland to be maintained.

[0140] 3) Coastline protection

[0141] Let the set of spatial units within the prohibited development shoreline be . Then the following is required:

[0142]

[0143] Safety resilience constraints include:

[0144] 1) Population exposure control in high-risk areas

[0145] For each disaster scenario Select the corresponding high-risk spatial unit set based on risk loss parameters. Then the scenario The following are the population exposure control constraints for high-risk areas:

[0146]

[0147] in, For unit In the plan The total population below For the context The upper limit of permissible population exposure.

[0148] 2) Control of exposure to critical assets in high-risk areas

[0149] Let the set of spatial units where important assets are located be . Then the scenario The key asset exposure control constraints in high-risk areas are as follows:

[0150]

[0151] in, For unit In the plan The total value of assets under [the following] For the context The maximum allowable exposure value for important assets in high-risk areas.

[0152] 3) Protection engineering level constraints

[0153] Let the set of spatial units for which protective engineering needs to be set up or upgraded be . Then the following is required:

[0154]

[0155] in, Representation unit In state The following protection engineering level, This is the minimum protection level determined according to relevant standards.

[0156] The constraints of social public interest and cultural protection include:

[0157] 1) Constraints on emergency shelter capacity and coverage

[0158] City Total refuge capacity is defined as:

[0159]

[0160] in For unit In the selected state The available refuge capacity.

[0161] The minimum emergency evacuation capacity constraint is manifested as follows:

[0162]

[0163] in, For the city Minimum evacuation capacity requirements determined based on population size and evacuation needs in multi-hazard scenarios.

[0164] If further control over shelter service coverage is required, constraints or other restrictions may be added on the proportion of the population that can access shelters within the specified evacuation time.

[0165] 2) Control of development intensity within the scope of cultural protection

[0166] Let the set of spatial units within the cultural protection area be . Then the following is required:

[0167]

[0168] in, It does not include high-intensity development or uses that conflict with cultural heritage protection requirements, ensuring that the intensity of development and the type of function within the cultural protection area comply with relevant regulations and protection objectives.

[0169] Economic development constraints include:

[0170] 1) Basic requirements for the scale of industrial land use

[0171] Suppose an industrial land use status determination function. Defined as: when the state Unit When used as industrial land When the state Do not use unit When used as industrial land Based on this, the city The area of ​​industrial land is:

[0172]

[0173] The bottom-line constraint on industrial development space is expressed as:

[0174]

[0175] in, This refers to the minimum required area of ​​industrial land determined according to higher-level planning and development goals.

[0176] 2) Basic requirements for employment carrying capacity

[0177] State The corresponding employment density is It can be set according to land use type, then the city Total number of jobs:

[0178]

[0179] With total population This creates an employment rate constraint.

[0180]

[0181] in, For unit The land area; In the planning scheme Next unit population size For the city Total population; For the city The minimum employment rate requirement.

[0182] Step S400: Based on the candidate land use status of spatial units, construct the urban comprehensive benefit function for the corresponding city; wherein, the urban comprehensive benefit function includes a risk dimension.

[0183] In one implementation, the urban comprehensive benefit function further includes at least one of the following dimensions: economic, ecological, social, and cost dimensions.

[0184] In this embodiment, the comprehensive benefit function of a city represents a mathematical model of the comprehensive value of a single city under the planning scheme. It is used to characterize the impact of planning on the city from multiple dimensions, specifically covering economic, risk, ecological, social and cost dimensions, each of which is reflected by quantitative indicators.

[0185] Furthermore, in setting the dimensions of the urban comprehensive benefit function, the combination of dimensions can be adjusted according to regional needs. For example, low-carbon pilot city clusters can add a carbon emission dimension, while regions with a high degree of aging can strengthen the social dimension related to elderly care services. Regarding the determination of dimension weights, weights can be calculated using the analytic hierarchy process (AHP) or determined through negotiation among relevant stakeholders to more flexibly reflect planning preferences. In calculating disaster risk loss parameters, composite disaster scenarios or the impact of secondary disasters can be incorporated to further enhance the parameters' ability to characterize multi-hazard risks.

[0186] In this embodiment, firstly, for each city Construct a set of planning schemes The relevant evaluation functions are used to characterize the planning results from different dimensions, and based on this, to form the city's overall payoff function. Specifically, these include: economic dimensions, Used to depict the city in planning schemes Economic output capacity under the following circumstances; risk dimension. It is used to characterize the comprehensive disaster risk level under multiple disaster scenarios; ecological dimension, Used to depict the state of the urban ecological environment; social dimension: It is used to depict the overall situation of social equity and other aspects; in terms of cost, It is used to depict the overall implementation cost of adjusting the status quo to a planned scheme.

[0187] From an economic perspective, the economic function Used to depict cities In the planning scheme Below, the economic output capacity corresponding to land use layout and development intensity configuration. For each spatial unit and its status Economic parameters per unit area can be given based on land use type:

[0188]

[0189] Set unit The area is ,but:

[0190]

[0191] It should be noted that the above is only an example, and the specific form can be determined as needed in actual applications. This invention does not limit the specific expression form of the economic function.

[0192] Regarding the risk dimension, this invention addresses multiple disaster scenario sets. Depicting the city The overall disaster risk level. For each disaster scenario. The set of high-risk spatial units can be obtained through the risk loss function. In the overall planning scheme Below, spatial unit The state is denoted as , by state The planned population of the unit can be obtained. Asset value Equal planning state variables.

[0193] In specific implementation methods, disaster scenarios can be... Next unit The disaster risk loss is quantified as follows:

[0194]

[0195] in, For high-risk spatial cell indicator functions, when the cell Belongs to the scenario The value of the high-risk spatial unit set below is taken as Otherwise ; and For the context The weighting coefficients for population and assets are used to convert population loss and asset loss to a unified dimension. This invention does not limit the specific values ​​of these coefficients.

[0196] For cities In disaster scenarios Lower City The total loss function is then:

[0197]

[0198] Given scenario weights (or probabilities of occurrence) Under the condition that (satisfy) The risk function is:

[0199]

[0200] Used to characterize cities under a comprehensive multi-hazard scenario The expected disaster risk level. In practical applications, risk parameters can be constructed as a correlation between disaster hazard and vulnerability. The specific form can be determined according to the actual analysis needs, only requiring that it meets the general logic of risk analysis.

[0201] Regarding the ecological dimension, the ecological function Used to evaluate planning schemes Lower City The overall ecological environment status reflects the comprehensive level of ecological space scale, quality, and ecosystem service capacity. It focuses on the ecological condition corresponding to the current plan, and can be expressed as:

[0202]

[0203] in, For unit In state Ecological parameters per unit area under the given conditions are used to characterize the ecological quality and average level of ecosystem services corresponding to this state, such as comprehensive indicators of biodiversity, ecological connectivity, and carbon sequestration capacity. For unit The land area.

[0204] In practical applications, the ecological evaluation score can be determined by assigning values ​​or by combining simple functions based on factors such as land use type and development intensity. This invention does not limit the specific function form.

[0205] Regarding the social dimension, the social function Used to comprehensively evaluate the performance of planning schemes in terms of social equity and balanced public services. For each city Let its social evaluation function be . Indicators reflecting the degree of inequality in public services can be selected. The larger the value, the worse the social equity. This invention does not limit the specific form of the indicators.

[0206] In practical applications, the population defined above can be used. Public service level index Construct a simple social function example based on differences in service levels. For example, a city... Population-weighted average service level:

[0207]

[0208] in, ; For unit In state The public service level index, such as the overall level of services like healthcare, education, and emergency response; In the plan Next unit Population size; For the city The total population. Then define the social function as a weighted sum of the service level deviations of each unit, for example:

[0209]

[0210] Among them, weight This indicates that the weighting is based on population.

[0211] Regarding the cost dimension, the cost function Used to depict the transformation of a city from its current land use and infrastructure layout to its planning scheme. The total implementation cost required can cover various costs such as engineering investment, relocation compensation, land consolidation, and institutional coordination. (Unit) The current status is From the current situation Adjust to planning status At that time, the comprehensive cost per unit area was Then the city The cost function is:

[0212]

[0213] The cost may consist of several parts, such as land type change cost, development intensity adjustment cost, and population migration cost. This invention does not limit the specific construction method.

[0214] Regarding comprehensive benefits, based on the evaluation functions of the above five dimensions, the city... In the planning scheme The comprehensive return function is defined as follows:

[0215]

[0216] in, These are weighting parameters used to reflect the city's... Preferences across economic, risk, ecological, social, and cost dimensions. This refers to the benefit functions calculated above, considering the economic, risk, ecological, social, and cost dimensions. In practical implementation, the dimensions of the function values ​​for each dimension can be adjusted as needed to facilitate comparison and weighted combination of different dimensions and orders of magnitude. This invention does not limit the method of dimension processing.

[0217] Here, no presuppositions are made. As the solution changes, it will inevitably either improve or worsen monotonically. In practical applications, during the construction of the comprehensive benefit function, positive weights are used to amplify the dimensions expected to improve, while negative signs or smaller weights are used to suppress the dimensions expected to decrease.

[0218] Step S500: Construct a cooperative game with cities as participants, aiming to maximize the comprehensive benefits of the city cluster, and solve for the optimal collaborative planning strategy in the planning configuration set that satisfies the set of planning constraints.

[0219] In this embodiment, the game participants are the decision-making entities of cooperative games, referring to the urban management departments or their spatial planning authorities. Specifically, they can be the administrative entities of prefecture-level or county-level cities within the urban agglomeration. Relevant professional departments, such as housing and construction and emergency management departments, can participate indirectly by influencing the construction of candidate land use status and the weight of the payoff function, without acting as independent game players.

[0220] The cooperative game model of this invention is a mathematical framework for coordinating the interests of cities and pursuing the overall optimization of the region. When constructing it, the set of planning constraints is used as the boundary and the candidate land use status of spatial units is used as the strategy option. The maximum benefit of different strategies is quantified by defining the alliance characteristic function, that is, the highest comprehensive benefit that any city alliance can achieve by cooperating to select strategies.

[0221] Maximizing the overall benefits of a city cluster, i.e., the alliance benefits, is the optimization objective of the game theory model. The solution process requires searching for the spatial unit state combination that maximizes the overall benefits of the city cluster while satisfying all constraints, thus obtaining the spatially optimal collaborative planning strategy for the city cluster.

[0222] Furthermore, in terms of game structure, a comprehensive city-wide alliance model can be avoided, and instead, local alliances or phased cooperation models can be constructed. Regarding the scope of participants, regional coordinating bodies can be included as virtual participants, responsible for balancing conflicts of interest among cities. For the model solution algorithm, a decomposition and coordination algorithm can be used to break down the overall problem into city-level sub-problems for iterative solving, or metaheuristic algorithms such as genetic algorithms can be introduced for global search. In terms of strategy space definition, cities can be allowed to prioritize the state choices of their subordinate units, aligning it with actual planning and decision-making processes.

[0223] In one implementation, based on the set of planning constraints and the candidate land use states of the spatial units, a cooperative game model is constructed with cities as the game participants. The optimal spatial collaborative planning strategy for the urban agglomeration is obtained by solving the cooperative game model with the objective of maximizing the comprehensive benefit function of the urban agglomeration. This specifically includes the following steps:

[0224] Step S510: Using the set of planning constraints as the limiting conditions, obtain the planning configuration set of all spatial units in the urban agglomeration;

[0225] Step S520: In the set of planning configurations, based on the cooperative game mechanism, solve for the planning configuration of all spatial units in the urban agglomeration when maximizing the comprehensive benefit function of the urban agglomeration.

[0226] Step S530: Integrate the planning configuration of all spatial units in the urban agglomeration to obtain the optimal spatial collaborative planning strategy for the urban agglomeration.

[0227] In this embodiment, it is necessary to define a framework for cooperative game theory.

[0228] First, define the set of participants as the set of cities:

[0229]

[0230] Each participant This indicates that the corresponding urban management department (or its spatial planning authority) is the entity with decision-making power over the selection of spatial unit states within the city. Relevant professional departments (housing and construction, water resources, transportation, emergency management, power, etc.) influence the state set. The construction, state attributes, and weight parameters in the city utility function indirectly participate, but are not independent players.

[0231] Subsequently, the strategy space of cooperative game theory is modeled. For cities... The set of spatial units it governs is In this invention, the city One specific strategy can be understood as applying this strategy to every spatial unit under the jurisdiction of the city. From its set of optional land states Select a state All these state choices can be summarized as a vector:

[0232]

[0233] City The policy space is defined as all possible policies. The set, namely:

[0234]

[0235] Similarly, a global strategy configuration for the entire city cluster is denoted as:

[0236]

[0237] It can also be equivalently written as a state selection vector for all spatial units:

[0238]

[0239] The global policy space is defined as follows:

[0240]

[0241] Under the above definition, in the non-cooperative scenario, each city operates within its own strategic space. Internal independent selection strategy In the case of cooperation (alliance), the alliance Cities within the region can jointly select their set of strategies. Together, we optimize the alliance's objective function.

[0242] After constructing a cooperative game framework, the planning configuration of all spatial units in the urban agglomeration is obtained by solving the problem to maximize the comprehensive benefits of the urban agglomeration.

[0243] First, the baseline utility of each city is calculated. Under non-cooperative conditions, each city... Independently select strategies under its own constraints ,make Maximize. The corresponding baseline utility can be viewed as the city's... The benefits of independent planning are denoted as:

[0244]

[0245] in This represents the current (or non-cooperative) solution. This utility level serves as the minimum acceptable level for participation in cooperation (participation constraint).

[0246] Then, feature functions are constructed. For any alliance Cities within the alliance can coordinate their strategies. To maximize the overall utility of the alliance while satisfying all regulatory bottom-line constraints and other technical constraints:

[0247]

[0248] in Weight within the alliance (can be determined by population, GDP, etc.) For the alliance A set of planning schemes that meet all control bottom-line constraints and other technical constraints under the participation of participants. For the city In the planning scheme The overall benefits.

[0249] Specifically, the overall benefit of a city cluster, i.e., the alliance benefit, is expressed by the characteristic function of the entire alliance:

[0250]

[0251] Its solution The corresponding solution is the optimal collaborative planning scheme for the alliance under the constraints of the bottom line of control.

[0252] Step S600: Calculate the marginal contribution of each city to the benefits of the city cluster under the optimal collaborative planning strategy, and allocate the cooperative benefits accordingly.

[0253] In this embodiment, marginal contribution is the incremental value of a single city to the overall benefits of the city cluster. It refers to the additional increase in overall benefits brought by the city after joining the alliance. During the calculation, all alliance subsets containing the city are traversed, the incremental benefits of the city in different alliance combinations are quantified and weighted averaged. This indicator can accurately reflect the actual contribution of the city in collaborative planning and provide a reference for benefit distribution.

[0254] Cooperation benefits are the distribution of benefits obtained by cities after participating in collaborative planning. In the calculation, the total benefits corresponding to the optimal strategy of the city cluster are allocated to each city according to the marginal contribution ratio of each city, so as to ensure that the city with the greater contribution receives more benefits.

[0255] Furthermore, in calculating marginal contributions, weighting factors can be introduced, such as considering the city's administrative level or historical contributions to adjust the marginal contribution value, rather than simply based on the incremental revenue. Regarding the rules for distributing cooperative benefits, cooperative game solutions such as alliance core solutions and Nash negotiation solutions can be used for revenue distribution, without being limited to the Shapley value. In calculating independent planning benefits, independent benefits under different policy scenarios can be simulated, such as considering constraints from higher-level planning or market changes, to make the benefit comparison more realistic.

[0256] In one implementation, the calculation of the cooperative revenue for each city, based on the spatially optimal collaborative planning strategy of the urban agglomeration and the marginal contribution of each city to the comprehensive revenue function of the urban agglomeration, specifically includes the following steps:

[0257] Step S610: Calculate the marginal contribution of each city to the comprehensive benefit function of the urban agglomeration under the spatial optimal collaborative planning strategy of the urban agglomeration;

[0258] Step S620: For each city, calculate the cooperation revenue based on the marginal contribution to the comprehensive revenue function of the city cluster.

[0259] In this embodiment, after obtaining the characteristic function Subsequently, the Shapley value was adopted as the rule for distributing cooperative profits.

[0260] For the entire alliance ,City Shapley value ( )for:

[0261]

[0262] in Indicates any city not included The alliance Represents city The average marginal contribution to the benefits of the alliance, i.e., the city cluster, under all possible joining orders is the fair share of benefits that it should receive in the case of cooperation.

[0263] If factors such as city size and status need to be considered, a weighted Shapley value can be used, and no restrictions are imposed here.

[0264] The allocation result must satisfy:

[0265]

[0266] That is, city Average marginal contribution of urban agglomeration under all possible access orders It must be greater than its average return. This means that the utility each city gains after cooperation is no less than its baseline utility without cooperation, thus ensuring that cooperation is rationally individualized. If directly using the Shapley value cannot meet this condition, it can be corrected by adjusting the characteristic function parameters or by using weighted Shapley values, kernel solutions, etc., and this invention does not limit this to any particular method.

[0267] In one implementation, the urban agglomeration spatial resilience collaborative planning method based on cooperative game theory further includes the following steps:

[0268] Step S710: For each spatial unit, extract all attribute parameters of the candidate land use status under the optimal collaborative planning strategy of the urban agglomeration and translate them into a set of planning indicators.

[0269] Step S720: Based on the geographic information system, map the planning indicator set into spatial layers to generate a land use planning map, a disaster risk prevention and control map, and a benefit distribution diagram, thus obtaining a set of visualized planning layers;

[0270] Step S730: Based on the set of visualized planning layers, manually identify implementation obstacles, assess structural risks and the fairness of benefit distribution, and optimize the optimal collaborative planning strategy for urban agglomeration space based on the judgment and assessment results.

[0271] Step S740: Based on the optimized urban agglomeration spatial optimal collaborative planning strategy, generate a spatial planning proposal for the urban agglomeration.

[0272] First, the proposed solutions output by the model can be interpreted and evaluated. Specifically, a feasibility study of the spatial pattern should be conducted to analyze whether there are micro-level implementation obstacles to the collaborative optimization spatial solutions output by the model. For example, does the suggested relocation area involve major infrastructure or stable communities, and are its social costs underestimated? Systemic structural risks should be assessed to determine whether the solution introduces new systemic risks due to over-optimization. For example, whether the emergency access network lacks redundancy, and whether over-reliance on a single node could lead to paralysis during a disaster. The fairness of benefit distribution should be examined, analyzing benefit distribution schemes based on Shapley values. It is crucial to identify cities that contribute to overall regional security, such as providing ecological buffer space, but whose own economic benefits are actually harmed after cooperation, and to assess their potential impact on the stability of the cooperative alliance.

[0273] Subsequently, the generated planning strategies were revised and optimized. Specifically, redundancy design was introduced, and backup lines were planned for critical lifeline projects based on the backbone network suggested by the model, thereby improving system reliability. Multifunctional spaces that can be used for both peacetime and disaster relief were promoted, serving citizens' daily lives in peacetime and quickly converted into refuge areas or temporary flood storage areas during disasters. A multi-dimensional value compensation system was improved, systematically incorporating the value of ecological products, contributions to safety and security, and other implicit benefits that are difficult to monetize into the compensation scope, laying the foundation for the subsequent development of diversified compensation policies.

[0274] Based on the optimized plan, it can be further specified into planning guidelines and action recommendations for cities to implement collaboratively. For example, the following recommendations could be formulated.

[0275] First, the plan generates optimization suggestions for urban agglomeration spatial use control and risk zoning, proposing specific recommendations for adjusting or adding disaster risk prevention and control zoning in spatial planning. Extremely high-risk areas and key ecological buffer zones identified by the model are included in the key control scope, and the development intensity of high-risk areas is reasonably controlled to promote safe and intensive urban development. Second, the plan proposes an infrastructure resilience planning scheme for the urban agglomeration, identifying key plots requiring new construction or upgraded protection levels; it also proposes an emergency shelter planning scheme, specifically formulating tiered emergency shelter layouts and construction standards, and proposing plans for newly added or expanded shelters and evacuation routes. Third, based on the optimized planning scheme, the plan proposes a compensation mechanism, which may include horizontal fiscal compensation, with the compensation amount calculated based on the marginal contribution quantified by the Shapley value; development rights transfer and trading, allowing cities undertaking more risk buffer functions to transfer a portion of their construction land quotas to cities with more urgent development needs, under the control of the regional total construction land quota; and ecological compensation and green rewards, establishing an urban agglomeration ecological compensation fund to provide continuous financial rewards to cities that protect and restore ecological spaces with important disaster prevention and mitigation functions. Fourth, at the policy and organizational level, it is recommended to formulate guiding documents for regional collaborative planning and interest coordination, clarifying the principles and procedures for coordination.

[0276] Through the above methods, the analytical framework and results provided by this approach can offer decision support for specific spatial planning and regional governance practices.

[0277] In summary, compared with existing urban agglomeration spatial planning and multi-hazard prevention technologies, this invention has key technological innovations in the following aspects: First, it constructs a unified set of spatial units, integrating heterogeneous data from planning, statistics, and disaster assessment at the same scale, reducing biases caused by scale inconsistencies, providing a stable data carrier for subsequent state modeling and collaborative computing, and improving the coordination and accuracy of cross-regional analysis. Second, it establishes multi-hazard scenario combinations, simulating the impact of multiple hazards and considering superposition effects at the spatial unit level, ensuring that the scheme covers major hazard types simultaneously during evaluation, reducing the risk of omissions from a single hazard perspective, and enhancing the adaptability of planning under different extreme events. Third, it abstracts land use decisions into the selection of candidate land states for spatial units, pre-setting discrete states of land use type, development intensity, and functional combination, and selecting the best state during planning, simplifying the processing of continuous variables, facilitating rapid model solution and multi-scheme comparison. Fourth, by introducing cooperative game theory, it models multi-city collaborative planning as a game problem, maximizing the comprehensive benefits of the urban agglomeration to avoid the overall regional risk escalation caused by individual city planning, and enhancing the overall regional resilience. Fifth, adopting the Shapley allocation rule based on marginal contribution quantifies the benefits and compensation of cooperation, enabling cities that assume more responsibility for ecological buffering or risk avoidance to receive corresponding returns, alleviating the decline in participation motivation caused by uneven benefits, and promoting long-term stable collaboration.

[0278] like Figure 4 As shown in the figure, this embodiment of the invention provides a collaborative planning system for urban agglomeration spatial resilience based on cooperative game theory. The system includes: a data and partitioning module 10, a multi-hazard scenario set construction module 20, a spatial unit attribute definition module 30, a comprehensive benefit function construction module 40, a cooperative game theory module 50, and a benefit calculation module 60.

[0279] Specifically, the data and partitioning module 10 is used to acquire basic data of the urban agglomeration and divide it into a set of spatial units with uniformly assigned values ​​according to attributes; the multi-hazard scenario set construction module 20 is used to construct a multi-hazard scenario set, quantify the risk loss parameters of each unit, and identify high-risk areas; the spatial unit attribute definition module 30 is used to define a set of candidate land use states and planning constraints for each spatial unit; wherein, the set of planning constraints includes safety resilience constraints; the comprehensive benefit function construction module 40 is used to construct a comprehensive benefit function for the corresponding city based on the candidate land use states of the spatial unit; wherein, the comprehensive benefit function includes a risk dimension; the cooperative game module 50 is used to construct a cooperative game with cities as participants, aiming to maximize the comprehensive benefit of the urban agglomeration, and solve for the optimal collaborative planning strategy in the set of planning configurations that satisfy the set of planning constraints; the benefit calculation module 60 is used to calculate the marginal contribution of each city to the benefit of the urban agglomeration under the optimal collaborative planning strategy, and allocate cooperative benefits accordingly.

[0280] Based on the above embodiments, the present invention also provides a terminal device, the principle block diagram of which can be as follows: Figure 5 As shown, the terminal device includes a processor, memory, network interface, display screen, and temperature sensor connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a collaborative planning method for urban agglomeration spatial resilience based on cooperative game theory. The display screen can be an LCD screen or an e-ink screen. The temperature sensor is pre-installed inside the terminal device to detect the operating temperature of the internal components.

[0281] Those skilled in the art will understand that Figure 5The schematic diagram shown is only a partial structural diagram related to the present invention and does not constitute a limitation on the terminal device to which the present invention is applied. The specific terminal device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0282] In one embodiment, a terminal device is provided, including a memory and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by one or more processors, the one or more programs including instructions for performing operations as described in the embodiments of the methods above.

[0283] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0284] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0285] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A collaborative planning method for urban agglomeration spatial resilience based on cooperative game theory, characterized in that, include: Acquire basic data on urban agglomerations and divide them into sets of spatially assigned uniformly valued units based on their attributes; Construct a multi-hazard scenario set, quantify the risk loss parameters of each unit, and identify high-risk areas; Define a set of candidate land use states and planning constraints for each spatial unit; wherein, the set of planning constraints includes safety resilience constraints; Based on the candidate land use status of spatial units, a comprehensive urban revenue function for the corresponding city is constructed; wherein, the comprehensive urban revenue function includes a risk dimension. A cooperative game is constructed with cities as participants, aiming to maximize the comprehensive benefits of the city cluster. The optimal collaborative planning strategy is solved in the set of planning configurations that satisfy the set of planning constraints. Calculate the marginal contribution of each city to the benefits of the city cluster under the optimal collaborative planning strategy, and allocate the cooperative benefits accordingly; For each spatial unit, all attribute parameters of candidate land use status under the optimal collaborative planning strategy of the urban agglomeration are extracted and translated into a set of planning indicators. Based on the geographic information system, the set of planning indicators is mapped to spatial layers to generate land use planning maps, disaster risk prevention and control maps and benefit distribution diagrams, resulting in a set of visualized planning layers; Based on the set of visual planning layers, implementation obstacles are manually identified, structural risks and the fairness of benefit distribution are assessed, and the optimal collaborative planning strategy for urban agglomeration is optimized based on the identification and assessment results. Based on the optimized urban agglomeration spatial optimal collaborative planning strategy, a spatial planning proposal for the urban agglomeration is generated. Based on the set of planning constraints and the candidate land use states of the spatial units, a cooperative game model is constructed with cities as the game participants. The goal is to maximize the comprehensive benefit function of the urban agglomeration. Solving the cooperative game model yields the spatially optimal collaborative planning strategy for the urban agglomeration, including: Using the aforementioned set of planning constraints as limitations, obtain the planning configuration set for all spatial units within the urban agglomeration; In the aforementioned planning configuration set, based on the cooperative game mechanism, the planning configuration of all spatial units in the urban agglomeration is obtained when the comprehensive benefit function of the urban agglomeration is maximized. By integrating the planning and configuration of all spatial units in the urban agglomeration, the optimal spatial collaborative planning strategy for the urban agglomeration is obtained. The spatially optimal collaborative planning strategy based on the urban agglomeration, and the marginal contribution of each city to the comprehensive benefit function of the urban agglomeration, calculates the cooperative benefit of each city, including: Calculate the marginal contribution of each city to the comprehensive benefit function of the urban agglomeration under the spatially optimal collaborative planning strategy of the urban agglomeration; For each city, the cooperation revenue is calculated based on its marginal contribution to the comprehensive revenue function of the city cluster.

2. The urban agglomeration spatial resilience collaborative planning method based on cooperative game theory as described in claim 1, characterized in that, The candidate land use status represents the comprehensive land nature and functional configuration of the spatial unit under the corresponding planning configuration; The candidate land use status includes several attribute parameters, including at least one of the following: land area, land use type, plot ratio, total planned population, total asset value, whether refuge facilities are set up, refuge facility level, refuge capacity, public service level index, protection engineering level, and ecological management attribute label.

3. The urban agglomeration spatial resilience collaborative planning method based on cooperative game theory according to claim 2, characterized in that, The set of planning constraints is used to limit the attribute parameters of spatial units; The set of planning constraints also includes at least one of the following: legal and regulatory constraints, social public interest and cultural protection constraints, and economic development constraints.

4. The urban agglomeration spatial resilience collaborative planning method based on cooperative game theory according to claim 1, characterized in that, The urban comprehensive benefit function also includes at least one of the following dimensions: economic, ecological, social, and cost dimensions.

5. A collaborative planning system for urban agglomeration spatial resilience based on cooperative game theory, characterized in that, The system, used to implement the collaborative planning method for urban agglomeration spatial resilience based on cooperative game theory as described in any one of claims 1-4, comprises: The data and partitioning module is used to acquire basic data of urban agglomerations and divide them into a set of spatially assigned units based on attributes. The multi-hazard scenario set construction module is used to construct multi-hazard scenario sets, quantify the risk loss parameters of each unit, and identify high-risk areas; The spatial unit attribute definition module is used to define a set of candidate land use states and planning constraints for each spatial unit; wherein, the set of planning constraints includes safety resilience constraints. The comprehensive benefit function construction module is used to construct the comprehensive urban benefit function of the corresponding city based on the candidate land use status of spatial units; wherein, the comprehensive urban benefit function includes a risk dimension; The cooperative game module is used to construct a cooperative game with cities as participants, aiming to maximize the comprehensive benefits of the city cluster, and to solve the optimal cooperative planning strategy in the set of planning configurations that satisfy the set of planning constraints. The revenue calculation module is used to calculate the marginal contribution of each city to the revenue of the city cluster under the optimal collaborative planning strategy, and allocate the cooperative revenue accordingly.

6. A terminal device, characterized in that, The terminal device includes a memory, a processor, and a cooperative game-based urban agglomeration spatial resilience collaborative planning program stored in the memory and executable on the processor. When the processor executes the cooperative game-based urban agglomeration spatial resilience collaborative planning program, it implements the steps of the cooperative game-based urban agglomeration spatial resilience collaborative planning method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a collaborative planning program for urban agglomeration spatial resilience based on cooperative game theory. When the collaborative planning program for urban agglomeration spatial resilience based on cooperative game theory is executed by a processor, it implements the steps of the collaborative planning method for urban agglomeration spatial resilience based on cooperative game theory as described in any one of claims 1-4.

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