A method and device for generating a reference scheme for green roof construction in a river basin city

Abstract

An embodiment of the present invention provides a method and device for generating a reference plan for the construction of green roofs in a river basin city. The method includes: collecting river basin rainwater pipe network data, river basin historical rainfall data, river basin building distribution data, and river basin resident survey data; constructing a resident intelligent agent green roof decision-making model and an urban pipe network hydraulic model based on the above data, and then constructing a resident green roof decision-making and urban waterlogging response model in the river basin; setting model parameters, and initializing resident intelligent agent attributes and simulation scenarios through the model, and simulating rainfall events under climate change; running the model, iterating and updating resident intelligent agent attributes and simulation scenarios according to a preset iteration time, and counting the changes in resident intelligent agent decisions, urban green roof area changes, and waterlogging situation changes in each simulation; and accurately generating a reference plan for the construction of green roofs in a river basin city according to the model simulation results output after the model operation is completed.

Description

Technical Field

[0001] The present invention relates to the technical field of urban ecological planning, and in particular to a method and device for generating a reference plan for green roof construction in a river basin city. Background Art

[0002] Due to rapid urbanization and climate change, urban flooding has become frequent in recent years. This has created numerous uncertainties and risks for urban drainage systems, posing significant challenges to the healthy circulation of urban hydrological systems. Green roofs, as a key component of sponge cities, play a crucial role in reducing surface runoff. However, their current application remains relatively limited.

[0003] Green rooftops currently hold great potential. As stakeholders in green roofs, understanding the decision-making rules governing them will facilitate their adoption. Existing research has analyzed the effects of varying green roof physical parameters on stormwater retention using the Storm Water Management Model (SWMM) and the impact of human decision-making on changes in green roof area using the Agent-Based Model (ABM). However, these studies focus on changes in green roof physical parameters and the theoretically hypothesized human decision-making process, respectively. They lack a discussion of the impact of human decision-making on changes in green roof area and the resulting changes in urban waterlogging risk. Consequently, they are unable to accurately generate a reference plan for green roof construction in watershed cities. Summary of the Invention

[0004] In a first aspect, an embodiment of the present invention provides a method for generating a reference plan for green roof construction in a river basin city, the method comprising:

[0005] Collect basin rainwater pipe network data, basin historical rainfall data, basin building distribution data, and basin resident survey data;

[0006] Based on the watershed building distribution data and watershed resident survey data, we construct resident agents, build resident agent decision rules, build resident agent social network relationships, and then build a resident agent green roof decision model;

[0007] Based on the basin rainwater pipe network data and the basin's historical rainfall data, a hydraulic model of the urban pipe network is constructed;

[0008] Based on the resident green roof decision-making model and the urban pipe network hydraulic model, a model of resident green roof decision-making and urban waterlogging response in the watershed was constructed.

[0009] Set the model parameters of the resident green roof decision-making and urban waterlogging response model in the watershed. Initialize the resident agent attributes and simulation scenarios based on the watershed building distribution data and watershed resident survey data through the model. Simulate rainfall events under climate change based on the watershed's historical rainfall data.

[0010] Run the model of residents' green roof decision-making and urban waterlogging response in the watershed, iterate and update the residents' agent attributes and simulation scenarios according to the preset iteration time, and count the changes in residents' agent decisions, urban green roof area, and waterlogging situation in each simulation;

[0011] Based on the changes in residents' intelligent agent decisions, urban green roof area and waterlogging conditions in the model simulation results output after the completion of the operation of the residents' green roof decision-making and urban waterlogging response model in the basin, a reference plan for the construction of urban green roofs in the basin is generated.

[0012] In some implementations of the first aspect, the watershed resident survey data includes: residents' socioeconomic background characteristics, household housing characteristics, waterlogging perception, green roof preferences, and willingness to pay for green roofs.

[0013] In some implementations of the first aspect, the resident agent decision rules include: recognition of green roof advantages, concern about green roof risks, and perception of waterlogging risks.

[0014] In some implementations of the first aspect, the green roof advantage recognition is represented by the following formula:

[0015]

[0016] in, Awareness of the advantages of green roofs; i,t A is the degree of awareness of the green roof advantages of resident agent i in year t; max =1, A min =0;θ i F is the sensitivity of resident agent i to the degree of waterlogging relief achieved by green roofs; t Characterizes the severity of waterlogging, calculated as F t = the longest duration of the overflow node in the subcatchment this year / (the longest duration of the overflow node in the subcatchment last year + the longest duration of the overflow node in the subcatchment this year).

[0017] In some implementations of the first aspect, the green roof risk concern is characterized by the following formula:

[0018]

[0019] Among them, C i,tC is the risk concern level of green roof for resident agent i in year t; i,t―1 is the risk concern level of green roof for resident agent i in year t-1; i is the learning rate of resident agent i; C j,t―1 is the green roof risk concern level of neighboring resident agent j in the social network of resident agent i in year t-1; J represents the total number of neighboring resident agents in the social network of resident agent i; GR risk Indicates whether there are risks associated with installing a green roof on a building.

[0020] In some implementations of the first aspect, the waterlogging risk perception is represented by the following formula:

[0021]

[0022] in, is the perception of waterlogging risk; P i,t F is the waterlogging risk perception level of resident agent i in year t; t Represents the severity of waterlogging; μ i is the sensitivity of resident agent i to waterlogging events; δ i is the forgetting rate of resident agent i about the flooding event.

[0023] In some implementations of the first aspect, the social network structure of the resident intelligent agent adopts a small-world network model; the green roof decision model of the resident intelligent agent adopts a random forest model, the output of which is a binary variable, that is, when the output is 0, it means that the resident is unwilling to pay extra for the green roof, and when the output is 1, it means that the resident is willing to pay extra.

[0024] In some implementations of the first aspect, constructing an urban pipe network hydraulic model based on basin rainwater pipe network data and basin historical rainfall data includes:

[0025] The basin rainwater pipe network data and the basin historical rainfall data are imported into the SWMM model to construct the urban pipe network hydraulic model.

[0026] In some possible implementations of the first aspect, the green roof decision-making model of residents in the watershed and the urban waterlogging response model couple the green roof decision-making model of the resident intelligent body and the hydraulic model of the urban pipe network, wherein the waterlogging situation simulated by the hydraulic model of the urban pipe network will affect the waterlogging risk perception of the resident intelligent body in the green roof decision-making model of the resident intelligent body, thereby affecting the green roof decision of the resident intelligent body.

[0027] In a second aspect, an embodiment of the present invention provides a device for generating a reference plan for green roof construction in a river basin city, the device comprising:

[0028] The collection module is used to collect basin rainwater pipe network data, basin historical rainfall data, basin building distribution data, and basin resident survey data;

[0029] A construction module is used to construct a resident agent, a decision-making rule for the resident agent, and a social network relationship between the resident agents based on the watershed building distribution data and the watershed resident survey data, and then construct a green roof decision-making model for the resident agent;

[0030] The construction module is also used to build a hydraulic model of the urban pipe network based on the basin rainwater pipe network data and the basin's historical rainfall data;

[0031] The construction module is also used to build a resident green roof decision-making and urban waterlogging response model in the basin based on the resident intelligent agent green roof decision-making model and the urban pipe network hydraulic model;

[0032] The setup module is used to set the model parameters of the resident green roof decision-making and urban waterlogging response model in the watershed. The model is used to initialize the resident agent attributes and simulation scenarios based on the watershed building distribution data and watershed resident survey data, and to simulate rainfall events under climate change based on the watershed's historical rainfall data.

[0033] The operation module is used to run the resident green roof decision-making and urban waterlogging response model in the watershed, iterate and update the resident agent attributes and simulation scenarios according to the preset iteration time, and count the changes in resident agent decisions, urban green roof area and waterlogging situation in each simulation;

[0034] The generation module is used to generate a reference plan for the construction of green roofs in the basin cities based on the changes in residents' intelligent agent decisions, changes in urban green roof areas and changes in urban waterlogging conditions in the model simulation results output after the operation of the residents' green roof decision-making and urban waterlogging response model in the basin.

[0035] In a third aspect, an embodiment of the present invention provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described above.

[0036] In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions are used to enable a computer to execute the method described above.

[0037] In an embodiment of the present invention, actual data can be used to evaluate the response relationship between human green roof decisions and urban waterlogging risks from a quantitative analysis perspective, and then accurately generate a reference plan for green roof construction in river basin cities, which will help decision makers provide a basis for formulating more accurate green roof promotion strategies, and has important practical significance for the promotion and application of green roofs and the construction of sponge cities.

[0038] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The above and other features, advantages, and aspects of the embodiments of the present invention will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are provided for a better understanding of the present invention and do not constitute a limitation of the present invention. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0040] Figure 1 A flow chart of a method for generating a reference plan for green roof construction in a river basin city provided by an embodiment of the present invention;

[0041] Figure 2 A schematic diagram of a green roof space configuration for a scenario where no subsidy is provided at the end of a simulation provided by an embodiment of the present invention;

[0042] Figure 3 A schematic diagram of a green roof space configuration for a subsidy scenario provided at the end of a simulation according to an embodiment of the present invention;

[0043] Figure 4 A schematic diagram of changes in willingness to pay of resident agents during a simulation period provided by an embodiment of the present invention;

[0044] Figure 5 A schematic diagram of the change in green roof area during the simulation period provided by an embodiment of the present invention;

[0045] Figure 6 A schematic diagram of changes in flood relief during a simulation period provided by an embodiment of the present invention;

[0046] Figure 7 A structural diagram of a device for generating a reference plan for building green roofs in a river basin city provided by an embodiment of the present invention;

[0047] Figure 8 The figure is a structural diagram of an exemplary electronic device capable of implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0048] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions 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. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0049] In addition, the term "and / or" in this invention merely describes an association relationship between related objects, indicating that three possible relationships exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Furthermore, the character " / " in this invention generally indicates that the related objects are in an "or" relationship.

[0050] In order to solve the technical problems arising from the background technology, the embodiment of the present invention provides a method, device, equipment and storage medium for generating a reference plan for the construction of green roofs in river basin cities. Specifically, the method collects river basin rainwater pipe network data, river basin historical rainfall data, river basin building distribution data and river basin resident survey data; constructs a resident intelligent green roof decision-making model and an urban pipe network hydraulic model based on the above data, and then constructs a resident green roof decision-making and urban waterlogging response model in the river basin; sets model parameters, and initializes resident intelligent body attributes and simulation scenarios through the model, and simulates rainfall events under climate change; runs the model, iterates and updates resident intelligent body attributes and simulation scenarios according to the preset iteration time, and counts the changes in resident intelligent body decisions, urban green roof area and waterlogging situation in each simulation; generates a reference plan for the construction of green roofs in river basin cities based on the model simulation results output after the model runs.

[0051] In this way, actual data can be used to evaluate the response relationship between human green roof decisions and urban waterlogging risks from a quantitative analysis perspective, and then accurately generate a reference plan for green roof construction in river basin cities, which will help decision makers provide a basis for formulating more accurate green roof promotion strategies. It has important practical significance for the promotion and application of green roofs and the construction of sponge cities.

[0052] In conjunction with the accompanying drawings, a method, device, equipment and storage medium for generating a reference plan for green roof construction in a river basin city provided by the embodiments of the present invention will be described in detail through specific embodiments.

[0053] Figure 1 A flow chart of a method for generating a reference plan for green roof construction in a river basin city provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, the method 100 for generating a reference plan for green roof construction in a watershed city may include the following steps:

[0054] S110, collects basin rainwater pipe network data, basin historical rainfall data, basin building distribution data and basin resident survey data.

[0055] Among these, basin stormwater network data, historical rainfall data, and building distribution data are all common data and are not detailed here. Basin resident survey data can include: socioeconomic characteristics, household residential characteristics, waterlogging perception, green roof preferences, and willingness to pay for green roofs. Furthermore, socioeconomic characteristics can include gender, age, education level, and annual household income; household residential characteristics can include homeownership, housing price, floor area, and property management fees; waterlogging perception can include the duration of waterlogging and the extent of waterlogging impact; green roof preferences can include awareness of green roofs, concerns about green roofs, environmental awareness, and community engagement; and willingness to pay for green roofs can include willingness to pay for green roofs.

[0056] It is worth noting that in order to improve data quality, the collected data needs to be preprocessed, which includes: removing outliers, processing missing values, data standardization, etc.

[0057] S120, based on the watershed building distribution data and the watershed resident survey data, constructs the resident intelligent agent, constructs the resident intelligent agent decision rules, constructs the resident intelligent agent social network relationship, and then constructs the resident intelligent agent green roof decision model.

[0058] In some embodiments, the resident agent decision rules may include: recognition of green roof advantages, concerns about green roof risks, and perception of waterlogging risks, as shown below:

[0059] The perceived benefits of green roofs can be characterized by the following formula:

[0060]

[0061] in, Awareness of the advantages of green roofs; i,t A is the degree of awareness of the green roof advantages of resident agent i in year t; max =1,A min =0;θ i F is the sensitivity of resident agent i to the degree of waterlogging relief achieved by green roofs; t Characterizes the severity of waterlogging, calculated as F t = the longest duration of overflow node in this year’s subcatchment / (the longest duration of overflow node in last year’s subcatchment + the longest duration of overflow node in this year’s subcatchment). i Obeying uniform distribution, θ i~U(0.1,0.7).

[0062] Green roof risk concerns are characterized by the following formula:

[0063]

[0064] Among them, C i,t C is the risk concern level of green roof for resident agent i in year t; i,t―1 is the risk concern level of green roof for resident agent i in year t-1; i is the learning rate of resident agent i; C j,t―1 is the green roof risk concern level of neighboring resident agent j in the social network of resident agent i in year t-1; J represents the total number of neighboring resident agents in the social network of resident agent i; GR risk Indicates whether there is risk in installing a green roof on a building. For example, β i Obeys normal distribution, β i ~N(0.5,0.1).

[0065] The waterlogging risk perception is represented by the following formula:

[0066]

[0067] in, is the perception of waterlogging risk; P i,t F is the waterlogging risk perception level of resident agent i in year t; t Represents the severity of waterlogging; μ i is the sensitivity of resident agent i to waterlogging events; δ i is the forgetfulness rate of resident agent i about the flooding event. For example, μ i Obey the normal distribution, μ i ~N(0.4,0.1),δ i Obeying uniform distribution, δ i ~U(0.1,0.4).

[0068] In some embodiments, the social network structure of the resident intelligent agent can adopt a small-world network model; the green roof decision-making model of the resident intelligent agent can adopt a random forest model. Here, the random forest model is constructed through the watershed resident survey data, and the watershed resident survey data is divided into a training set and a test set in a 4:1 manner. The model input is socioeconomic background characteristics, family housing characteristics, waterlogging perception, and green roof preference. The model output is the willingness to pay for the green roof, which is a binary variable, that is, when the output is 0, it means that the resident is unwilling to pay extra for the green roof, and when the output is 1, it means that the resident is willing to pay extra.

[0069] S130: Build a hydraulic model of the urban pipe network based on the basin rainwater pipe network data and the basin's historical rainfall data.

[0070] In some embodiments, the basin rainwater pipe network data and the basin historical rainfall data can be imported into the SWMM model to construct a hydraulic model of the urban pipe network.

[0071] S140, based on the resident intelligent green roof decision-making model and the urban pipe network hydraulic model, constructs the resident green roof decision-making and urban waterlogging response model in the basin.

[0072] In some embodiments, the resident agent green roof decision-making model can be coupled with the urban pipe network hydraulic model to construct a model for resident green roof decision-making and urban waterlogging response within a watershed. The waterlogging conditions simulated by the urban pipe network hydraulic model will influence the resident agent's waterlogging risk perception in the resident agent green roof decision-making model, thereby influencing the resident agent's green roof decision-making.

[0073] In short, urban flooding caused by rainfall will affect residents' perception of urban flooding risk, thereby affecting their decision-making on green roofs. The waterlogging risk perception formula is used to quantitatively characterize the impact of urban flooding severity on residents' perception of urban flooding risk, thereby establishing a connection between urban flooding and green roof decision-making.

[0074] S150, setting model parameters of the green roof decision-making and urban waterlogging response model for residents in the watershed, and initializing the resident intelligent body attributes and simulation scenarios based on the watershed building distribution data and watershed resident survey data through the green roof decision-making and urban waterlogging response model for residents in the watershed, and simulating the occurrence of rainfall events under climate change based on the historical rainfall data of the watershed.

[0075] As an example, the model parameters for the baseline scenario include a small-world network with 10 neighbors, a reconnection probability of 0.01, a community decision threshold of 0.7, a property fee increase of 0.025, a subsidy of 180, a green roof cost of 300, and a minimum green roof construction percentage of 0. Simulation scenarios include no subsidy (zero) and subsidies of varying amounts. This article discusses only the zero and 180 subsidy scenarios.

[0076] S160 runs the green roof decision-making and urban waterlogging response model for residents in the basin, iterates and updates the resident agent attributes and simulation scenarios according to the preset iteration time, and counts the changes in resident agent decisions, urban green roof area changes, and waterlogging situation changes in each simulation.

[0077] As an example, the iteration time here is set to 2000-2020. The willingness to pay of the resident agent is predicted once every simulation year. When the proportion of residents in the same building willing to pay for a green roof exceeds the community decision threshold, the roof of the building is greened. Once the roof is greened, residents in the building no longer make green roof decisions.

[0078] S170, generating a reference plan for the construction of urban green roofs in the watershed based on the changes in the decisions of residents' intelligent agents, the changes in the area of ​​urban green roofs, and the changes in the watershed waterlogging situation in the model simulation results output after the completion of the operation of the residents' green roof decision-making and urban waterlogging response model in the watershed.

[0079] The above steps are supplemented with some drawings below, as shown below:

[0080] Figure 2 、 Figure 3 Schematic diagrams of green roof space configurations for scenarios where no subsidy is provided and where subsidy is provided at the end of simulations provided by embodiments of the present invention, where calculations are performed using sub-catchment areas as units.

[0081] Figure 4 This is a schematic diagram of changes in the willingness to pay of resident agents during the simulation period provided by the embodiment of the present invention, such as Figure 4 As shown in the figure, during the 20-year simulation period, the number of agents willing to pay for green roofs increases with time, and the number of agents in the subsidy scenario is greater than that in the no-subsidy scenario.

[0082] Figure 5 This is a schematic diagram of the change in green roof area during the simulation period provided by the embodiment of the present invention, such as Figure 5 As shown in the figure, during the 20-year simulation period, the area of ​​green roofs in the study area continued to increase with time, and under the incentive of subsidies, the area of ​​green roofs installed was much larger than that in the scenario without subsidies.

[0083] Figure 6 This is a schematic diagram of changes in flood relief during the simulation period provided by an embodiment of the present invention, such as Figure 6 As shown in the figure, during the 20-year simulation period, the waterlogging mitigation effect showed an increasing trend over time, and because the green roof area in the subsidy scenario was larger than that in the non-subsidy scenario, the mitigation effect in the subsidy scenario was also much greater than that in the non-subsidy scenario.

[0084] In summary, the embodiment of the present invention uses the ABM and SWMM coupled model to generate reference plans for green roof construction in watershed cities. This model can simulate the changes in residents' green roof decisions under the context of urban waterlogging. By leveraging real-world watershed resident survey data, the simulation results can be closer to reality. Furthermore, the embodiment of the present invention can simulate the impact of different strategic scenarios on residents' green roof decisions. This helps decision makers assess the changes in green roof installation under different scenarios and their impact on urban waterlogging, providing decision support for the development of targeted green roof promotion strategies, making them more scientific and effective.

[0085] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should be aware that the present invention is not limited by the order of the actions described, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0086] The above is an introduction to a method embodiment. The following further illustrates the solution of the present invention through an apparatus embodiment.

[0087] Figure 7 The structural diagram of a device for generating a reference scheme for building green roofs in a river basin city provided by an embodiment of the present invention is as follows: Figure 7 As shown, the apparatus 700 for generating a reference plan for green roof construction in a watershed city may include:

[0088] The collection module 710 is used to collect the basin rainwater pipe network data, basin historical rainfall data, basin building distribution data and basin resident survey data.

[0089] The construction module 720 is used to construct a resident intelligent agent, a resident intelligent agent decision rule, a resident intelligent agent social network relationship, and then a resident intelligent agent green roof decision model based on the watershed building distribution data and the watershed resident survey data.

[0090] The construction module 720 is further used to construct a hydraulic model of the urban pipe network based on the basin rainwater pipe network data and the basin historical rainfall data.

[0091] The construction module 720 is also used to construct a resident green roof decision-making and urban waterlogging response model in the basin based on the resident intelligent agent green roof decision-making model and the urban pipe network hydraulic model.

[0092] The setting module 730 is used to set the model parameters of the green roof decision-making and urban waterlogging response model for residents in the watershed, and initialize the resident intelligent body attributes and simulation scenarios based on the watershed building distribution data and watershed resident survey data through the green roof decision-making and urban waterlogging response model for residents in the watershed, and simulate the occurrence of rainfall events under climate change based on the historical rainfall data of the watershed.

[0093] Operation module 740 is used to run the resident green roof decision-making and urban waterlogging response model in the basin, iterate and update the resident intelligent agent attributes and simulation scenarios according to the preset iteration time, and count the changes in resident intelligent agent decisions, urban green roof area changes and waterlogging situation changes in each simulation.

[0094] The generation module 750 is used to generate a reference plan for the construction of green roofs in the basin cities based on the changes in the decisions of the residents' intelligent agents, the changes in the area of ​​urban green roofs, and the changes in the watershed situation in the model simulation results output after the operation of the residents' green roof decision-making and urban waterlogging response model in the basin is completed.

[0095] It is understandable that Figure 7 Each module / unit in the apparatus 700 for generating a reference plan for building a green roof in a watershed city has the following features: Figure 1 The functions of the various steps in the method 100 for generating a reference plan for building green roofs in a watershed city are shown, and their corresponding technical effects can be achieved. For the sake of brevity, they are not described in detail here.

[0096] Figure 8 8 is a block diagram of an exemplary electronic device capable of implementing embodiments of the present invention. Electronic device 800 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 800 may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the invention described and / or claimed herein.

[0097] like Figure 8As shown, the electronic device 800 may include a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the electronic device 800 may also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0098] Multiple components in the electronic device 800 are connected to the I / O interface 805, including an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a magnetic disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the electronic device 800 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0099] The computing unit 801 can be a variety of general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as method 100. For example, in some embodiments, method 100 can be implemented as a computer program product, including a computer program tangibly embodied in a computer-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the method 100 described above can be performed. Alternatively, in other embodiments, the computing unit 801 can be configured to perform the method 100 in any other appropriate manner (e.g., by means of firmware).

[0100] The various embodiments described above in the present invention can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0101] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. Such program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0102] In the context of the present invention, computer-readable media can be tangible media that can contain or store a program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable media can be a computer-readable signal medium or a computer-readable storage medium. Computer-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. More specific examples of computer-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0103] It should be noted that the present invention also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute method 100 and achieve the corresponding technical effects achieved by executing the method in an embodiment of the present invention. For the sake of brevity, they will not be repeated here.

[0104] In addition, the present invention also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the method 100 is implemented.

[0105] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in the present invention can be achieved. The present invention is not limited here.

[0106] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for generating a reference plan for green roof construction in a river basin city, characterized in that: The method comprises: Collect basin rainwater pipe network data, basin historical rainfall data, basin building distribution data, and basin resident survey data; Based on the watershed building distribution data and the watershed resident survey data, constructing a resident agent, constructing a resident agent decision rule, constructing a resident agent social network relationship, and then constructing a resident agent green roof decision model; Constructing a hydraulic model of the urban pipe network based on the rainwater pipe network data of the watershed and the historical rainfall data of the watershed; Based on the resident agent green roof decision model and the urban pipe network hydraulic model, a resident green roof decision-making and urban waterlogging response model in the watershed is constructed; Setting model parameters of the resident green roof decision-making and urban waterlogging response model in the watershed, initializing resident agent attributes and simulation scenarios based on the watershed building distribution data and the watershed resident survey data using the resident green roof decision-making and urban waterlogging response model in the watershed, and simulating rainfall events under climate change based on the watershed historical rainfall data; Run the resident green roof decision-making and urban waterlogging response model in the watershed, iterate and update the resident agent attributes and simulation scenarios according to the preset iteration time, and count the changes in resident agent decisions, urban green roof area changes, and waterlogging changes in each simulation; Generate a reference plan for urban green roof construction in the watershed based on the changes in resident agent decisions, urban green roof area, and waterlogging conditions in the model simulation results output after the completion of the operation of the resident green roof decision-making and urban waterlogging response model in the watershed; The decision-making rules of the resident agent include: recognition of green roof advantages, concern about green roof risks, and perception of waterlogging risks; The perceived benefits of green roofs are characterized by the following formula: in, Awareness of the advantages of green roofs; i,t A is the degree of awareness of the green roof advantages of resident agent i in year t; max =1,A min =0;θ i F is the sensitivity of resident agent i to the degree of waterlogging relief achieved by green roofs; t Characterizes the severity of waterlogging, calculated as F t = the longest duration of the overflow node in this year's subcatchment / (the longest duration of the overflow node in last year's subcatchment + the longest duration of the overflow node in this year's subcatchment); The green roof risk concerns are characterized by the following formula: Among them, C i,t C is the risk concern level of green roof for resident agent i in year t; i,t-1 is the risk concern level of green roof for resident agent i in year t-1; i is the learning rate of resident agent i; C j,t-1 is the green roof risk concern level of other neighboring resident agents j in the social network of resident agent i in year t-1; J represents the total number of other neighboring resident agents in the social network of resident agent i; GR risk Indicate whether there are risks associated with installing a green roof on a building; The waterlogging risk perception is represented by the following formula: in, is the perception of waterlogging risk; P i,t F is the waterlogging risk perception level of resident agent i in year t; t Represents the severity of waterlogging; μ i is the sensitivity of resident agent i to waterlogging events; δ i is the forgetting rate of resident agent i about the flooding event.

2. The method according to claim 1, characterized in that The survey data of the watershed residents include: residents' socioeconomic background characteristics, household housing characteristics, perception of waterlogging, green roof preferences and willingness to pay for green roofs.

3. The method according to claim 1, characterized in that The social network relationship of the resident intelligent agent adopts a small-world network model; the green roof decision model of the resident intelligent agent adopts a random forest model, the output of which is a binary variable, that is, when the output is 0, it means that the resident is unwilling to pay extra for the green roof, and when the output is 1, it means that the resident is willing to pay extra.

4. The method according to claim 1, wherein The constructing of the urban pipe network hydraulic model based on the basin rainwater pipe network data and the basin historical rainfall data includes: The rainwater pipe network data of the watershed and the historical rainfall data of the watershed are imported into the SWMM model to construct a hydraulic model of the urban pipe network.

5. The method according to claim 1, wherein The resident green roof decision-making and urban waterlogging response model in the watershed couples the resident intelligent agent green roof decision-making model and the urban pipe network hydraulic model, wherein the waterlogging situation simulated by the urban pipe network hydraulic model will affect the waterlogging risk perception of the resident intelligent agent in the resident intelligent agent green roof decision-making model, thereby affecting the green roof decision of the resident intelligent agent.

6. A device for generating a reference plan for green roof construction in a river basin city, characterized in that: The device comprises: The collection module is used to collect basin rainwater pipe network data, basin historical rainfall data, basin building distribution data, and basin resident survey data; A construction module is used to construct a resident agent, a resident agent decision rule, a resident agent social network relationship, and then a resident agent green roof decision model based on the watershed building distribution data and the watershed resident survey data; The construction module is further used to construct a hydraulic model of the urban pipe network based on the rainwater pipe network data of the watershed and the historical rainfall data of the watershed; The construction module is further used to construct a resident green roof decision-making and urban waterlogging response model in the basin based on the resident intelligent agent green roof decision-making model and the urban pipe network hydraulic model; a setting module for setting model parameters of a resident green roof decision-making and urban waterlogging response model within the watershed, initializing resident agent attributes and simulation scenarios based on the watershed building distribution data and the watershed resident survey data through the resident green roof decision-making and urban waterlogging response model within the watershed, and simulating rainfall events under climate change based on the watershed historical rainfall data; An operation module is used to run the resident green roof decision-making and urban waterlogging response model in the watershed, iterate and update the resident agent attributes and simulation scenarios according to a preset iteration time, and count the changes in resident agent decisions, urban green roof area changes, and waterlogging changes in each simulation; A generation module is used to generate a reference plan for the construction of urban green roofs in the watershed based on the changes in the decisions of the residents' intelligent agents, the changes in the area of ​​urban green roofs, and the changes in the watershed situation in the model simulation results output after the completion of the operation of the residents' green roof decision-making and urban waterlogging response model in the watershed; The decision-making rules of the resident agent include: recognition of green roof advantages, concern about green roof risks, and perception of waterlogging risks; The perceived benefits of green roofs are characterized by the following formula: in, Awareness of the advantages of green roofs; i,t A is the degree of awareness of the green roof advantages of resident agent i in year t; max =1,A min =0;θ i F is the sensitivity of resident agent i to the degree of waterlogging relief achieved by green roofs; t Characterizes the severity of waterlogging, calculated as F t = the longest duration of the overflow node in this year's subcatchment / (the longest duration of the overflow node in last year's subcatchment + the longest duration of the overflow node in this year's subcatchment); The green roof risk concerns are characterized by the following formula: Among them, C i,t C is the risk concern level of green roof for resident agent i in year t; i,t-1 is the risk concern level of green roof for resident agent i in year t-1; i is the learning rate of resident agent i; C j,t-1 is the green roof risk concern level of other neighboring resident agents j in the social network of resident agent i in year t-1; J represents the total number of other neighboring resident agents in the social network of resident agent i; GR risk Indicate whether there are risks associated with installing a green roof on a building; The waterlogging risk perception is represented by the following formula: in, is the perception of waterlogging risk; P i,t F is the waterlogging risk perception level of resident agent i in year t; t Represents the severity of waterlogging; μ i is the sensitivity of resident agent i to waterlogging events; δ i is the forgetting rate of resident agent i about the flooding event.

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