Basin city green roof construction reference scheme generation method and device

Abstract

The embodiment of the invention provides a drainage basin city green roof construction reference scheme generation method and device. The method comprises the steps that drainage basin rainwater pipe network data, drainage basin historical rainfall data, drainage basin building distribution data and drainage basin resident survey data are collected; building a resident agent green roof decision-making model and an urban pipe network hydraulic model based on the data, and further building a resident green roof decision-making and urban waterlogging response model in the drainage basin; setting model parameters, initializing resident agent attributes and simulation scenes through the model, and simulating rainfall event occurrence under climate change; the model is operated, iteration is carried out according to preset iteration time, resident agent attributes and simulation scenes are updated, and resident agent decision change, urban green roof area change and waterlogging condition change of each time of simulation are counted; and according to a model simulation result output after the operation of the model is finished, accurately generating a drainage basin city green roof construction reference scheme.

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 scheme for building green roofs in a river basin city. Background Art

[0002] Due to the rapid development of urbanization and the impact of climate change, urban waterlogging has occurred frequently in recent years, which has brought many uncertain risks to the urban drainage system and posed a huge challenge to the benign cycle of the urban hydrological cycle system. As one of the important components of sponge cities, green roofs play an important role in reducing surface runoff. However, the application of green roofs is still relatively low.

[0003] At present, there is great potential for greening building roofs. As one of the stakeholders of green roofs, residents’ understanding of their decision-making rules will help promote the application of green roofs. Existing studies use the Storm Water Management Model (SWMM) to analyze the effect of changes in physical parameters of different green roofs on rainwater retention, and use the Agent-Based Model (ABM) to analyze the impact of human decision-making on the change of green roof area. However, the above studies focus on the changes in physical parameters of green roofs and the human decision-making process based on theoretical assumptions, respectively. There is a lack of discussion on the changes in green roof area caused by human decision-making processes and the changes in urban waterlogging risks caused by them, and it is impossible to accurately generate a reference plan for the construction of green roofs in basin 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 building a green roof 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 basin building distribution data and basin resident survey data, construct resident intelligent agents, construct resident intelligent agent decision rules, construct resident intelligent agent social network relationships, and then construct a resident intelligent agent green roof decision model;

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

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

[0009] Set the model parameters of the green roof decision-making and urban waterlogging response model for residents in the basin, and initialize the resident agent attributes and simulation scenarios based on the basin building distribution data and basin resident survey data through the green roof decision-making and urban waterlogging response model for residents in the basin, and simulate the occurrence of rainfall events under climate change based on the basin's historical rainfall data;

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

[0011] According to the changes in residents' intelligent agent decisions, urban green roof area and 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, a reference plan for the construction of green roofs in the basin cities 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 implementable embodiments of the first aspect, the decision rules of the resident intelligent agent include: recognition of the advantages of green roofs, concern about the risks of green roofs, and perception of waterlogging risks.

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

[0015]

[0016] in, A. Recognize the advantages of green roofs; i,t A is the degree of awareness of the green roof advantage 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 by green roofs; t Indicates 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 of resident agent i in year t; i,t―1 is the risk concern level of green roof of resident agent i in year t-1; β 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 Indicates whether there are risks in 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 is the waterlogging risk perception level of resident agent i in year t; F t Indicates the severity of waterlogging; μ i is the sensitivity of resident agent i to waterlogging events; 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 residents are unwilling to pay extra for the green roof, and when the output is 1, it means that they are willing to pay extra.

[0024] In some possible implementations of the first aspect, a hydraulic model of an urban pipe network is constructed based on the basin rainwater pipe network data and the basin historical rainfall data, including:

[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 implementable methods of the first aspect, the green roof decision-making of residents in the watershed and the urban waterlogging response model couple the green roof decision-making model of the resident intelligent agent 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 green roof decision-making model of the resident intelligent agent, thereby affecting the green roof decision of the resident intelligent agent.

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

[0028] The collection module is used to collect the 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 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;

[0030] The construction module is also 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;

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

[0032] The setting module is used to set the model parameters of the green roof decision-making and urban waterlogging response model of residents in the basin, and initialize the resident agent attributes and simulation scenarios according to the basin building distribution data and basin resident survey data through the green roof decision-making and urban waterlogging response model of residents in the basin, and simulate the occurrence of rainfall events under climate change according to the historical rainfall data of the basin;

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

[0034] The generation module is used to generate a reference plan for the construction of green roofs in the basin cities according to the changes in the decisions of residents' intelligent agents, the changes in the area of ​​urban green roofs and the changes in 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, thereby accurately generating a reference plan for green roof construction in river basin cities, which helps to provide a basis for decision makers to formulate 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 easily 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 used to better understand the present solution 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, wherein:

[0040] Figure 1 A flow chart of a method for generating a reference plan for building green roofs 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 providing a subsidy scenario at the end of a simulation provided by an embodiment of the present invention;

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

[0045] Figure 6 A schematic diagram of changes in waterlogging relief conditions 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 scheme 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] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 creative work are within the scope of protection of the present invention.

[0049] In addition, the term "and / or" in the present invention is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects before and after are in an "or" relationship.

[0050] In order to solve the technical problems arising from the background technology, the embodiments of the present invention provide a method, device, equipment and storage medium for generating a reference plan for the construction of green roofs in river basin cities. Specifically, the river basin rainwater pipe network data, the river basin historical rainfall data, the river basin building distribution data and the river basin resident survey data are collected; based on the above data, a resident intelligent green roof decision model and an urban pipe network hydraulic model are constructed, and then a resident green roof decision and urban waterlogging response model in the river basin is constructed; the model parameters are set, and the resident intelligent body attributes and simulation scenarios are initialized through the model, and rainfall events under climate change are simulated; the model is run, and the resident intelligent body attributes and simulation scenarios are iterated and updated according to the preset iteration time, and the changes in the resident intelligent body decision, the urban green roof area and the waterlogging situation are counted for each simulation; according to the model simulation results output after the model operation is completed, a reference plan for the construction of green roofs in river basin cities is generated.

[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 to formulate more accurate green roof promotion strategies and provide a basis for them. This 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 building green roofs in a river basin city provided by an embodiment 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 building green roofs 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 building a green roof 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 them, the basin rainwater pipe network data, basin historical rainfall data, and basin building distribution data are all common data and will not be elaborated here. The basin resident survey data can include: residents' socioeconomic background characteristics, family housing characteristics, waterlogging perception, green roof preference, and green roof willingness to pay. Further, socioeconomic background characteristics can include: gender, age, education level, annual family income, etc.; family housing characteristics can include: housing ownership, house price, house area, property fees, etc.; waterlogging perception can include: waterlogging time, waterlogging impact, etc.; green roof preference can include: cognition of green roofs, concerns about green roofs, environmental awareness, community participation, etc.; green roof willingness to pay 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, construct the resident intelligent agent, construct the resident intelligent agent decision rules, construct the resident intelligent agent social network relationship, and then construct the resident intelligent agent green roof decision model.

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

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

[0060]

[0061] in, A. Recognize the advantages of green roofs; i,t A is the degree of awareness of the green roof advantage 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 by green roofs; t Indicates the severity of waterlogging, calculated as F t = the longest duration of overflow node in this subcatchment this year / (the longest duration of overflow node in last year’s subcatchment + the longest duration of overflow node in this subcatchment this year). i Follows 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 of resident agent i in year t; i,t―1 is the risk concern level of green roof of resident agent i in year t-1; β 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 Indicates whether there is a risk in installing a green roof on a building. For example, β i Follows 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 is the waterlogging risk perception level of resident agent i in year t; F t Indicates the severity of waterlogging; μ i is the sensitivity of resident agent i to waterlogging events; i is the forgetfulness rate of resident agent i about the flooding event. For example, μ i Obeys normal distribution, μ i ~N(0.4,0.1),δ i Follows 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, household residential characteristics, waterlogging perception, and green roof preference. The model output is willingness to pay for green roofs, which is a binary variable, that is, when the output is 0, it means that the residents are unwilling to pay extra for the green roof, and when the output is 1, it means that they are willing to pay extra.

[0069] S130, constructing a hydraulic model of the urban pipe network based on the basin rainwater pipe network data and the basin 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 model can be coupled with the urban pipe network hydraulic model to construct a resident green roof decision and urban waterlogging response model in the watershed. The waterlogging situation simulated by the urban pipe network hydraulic model will affect the waterlogging risk perception of the resident agent in the resident agent green roof decision model, thereby affecting the green roof decision of the resident agent.

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

[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 according to the watershed building distribution data and the 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 in the baseline scenario include the number of neighbors in the small-world network model is 10, the reconnection probability is 0.01, the community decision threshold is 0.7, the property fee increase ratio is 0.025, the subsidy is 180, the green roof cost is 300, and the minimum percentage of green roof construction is 0. The simulation scenarios include no subsidy, which is 0, and subsidies of different amounts. Here, only the two scenarios of subsidies of 0 and 180 are discussed.

[0076] S160, runs the green roof decision-making and urban waterlogging response model of 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 decision-making, 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, and the willingness to pay of the resident agent is predicted once every simulation year. When the proportion of residents in the same building who are willing to pay for a green roof exceeds the community decision threshold, the roof of the building will be greened. Once the roof is greened, the residents in the building will no longer make green roof decisions.

[0078] S170, generating a reference plan for the construction of urban green roofs in the basin according to the changes in the decisions of residents' intelligent agents, the changes in the area of ​​urban green roofs and the changes in waterlogging conditions in the model simulation results output after the operation of the green roof decision-making and urban waterlogging response model of residents in the basin is completed.

[0079] The above steps are supplemented with some drawings as follows:

[0080] Figure 2 , Figure 3 Schematic diagrams of green roof space configuration for a scenario where no subsidy is provided and a scenario where subsidy is provided at the end of a simulation provided by an embodiment of the present invention, wherein calculation is performed specifically with sub-catchment areas as units.

[0081] Figure 4 A schematic diagram of changes in the willingness to pay of residents’ intelligent agents during the simulation period provided by an embodiment of the present invention is shown in FIG. 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 non-subsidy scenario.

[0082] Figure 5 The schematic diagram of the change of green roof area during the simulation period provided by the embodiment of the present invention is as follows: 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 the scenario without subsidies.

[0083] Figure 6 Schematic diagram of changes in flood relief during the simulation period provided by the embodiment of the present invention, such as Figure 6 As shown in the figure, within the simulation period of 20 years, the waterlogging mitigation effect increases with time, and because the green roof area in the subsidy scenario is larger than that in the non-subsidy scenario, the mitigation effect in the subsidy scenario is also much greater than that in the non-subsidy scenario.

[0084] In summary, the embodiment of the present invention uses the ABM and SWMM coupling model to generate a reference plan for the construction of green roofs in watershed cities, which can simulate the changes in residents' green roof decisions under the background of urban waterlogging. Using real watershed resident survey data, the simulation results can be close to the actual situation. In addition, the embodiment of the present invention can also simulate the impact of different strategic scenarios on residents' green roof decisions, which helps relevant decision makers evaluate the changes in green roof installation under different scenarios and their impact on urban waterlogging, and provide decision support for the formulation of targeted green roof promotion strategies, making them more scientific and effective.

[0085] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described order of actions, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the 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 is a further explanation of the solution of the present invention through an apparatus embodiment.

[0087] Figure 7 A 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, such as Figure 7 As shown, the reference scheme generating device 700 for building green roofs in a river basin 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 also 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 of residents in the basin, and initialize the resident intelligent body attributes and simulation scenarios according to the basin building distribution data and the basin resident survey data through the green roof decision-making and urban waterlogging response model of residents in the basin, and simulate the occurrence of rainfall events under climate change based on the historical rainfall data of the basin.

[0093] The operation module 740 is used to run the green roof decision-making and urban waterlogging response model of residents 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 the resident intelligent agent decision, the urban green roof area and the waterlogging situation 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 according to 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] Understandably, Figure 7 Each module / unit in the watershed city green roof construction reference scheme generating device 700 has the following functions: 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 will not be repeated here.

[0096] Figure 8 800 is a block diagram of an exemplary electronic device capable of implementing an embodiment 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 processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown in the present invention, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required in the present invention.

[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 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 through a computer network such as the Internet and / or various telecommunication networks.

[0099] The computing unit 801 may be a variety of general and / or special 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 running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above, such as method 100. For example, in some embodiments, the method 100 may be implemented as a computer program product, including a computer program, which is tangibly contained in a computer-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 800 via ROM 802 and / or a communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of the method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 801 may 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), load 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, which may be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general programmable processor, which may 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. These program codes 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 the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0102] In the context of the present invention, a computer-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a computer-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, 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 effect achieved by executing the method in an embodiment of the present invention. For the sake of concise description, 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 processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and the present invention is not limited here.

[0106] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for generating a reference scheme 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, construct a resident intelligent agent, construct a resident intelligent agent decision rule, construct a resident intelligent agent social network relationship, and then construct a resident intelligent agent green roof decision model; Based on the rainwater pipe network data of the watershed and the historical rainfall data of the watershed, a hydraulic model of the urban pipe network is constructed; Based on the resident intelligent green roof decision model and the urban pipe network hydraulic model, a resident green roof decision model and urban waterlogging response model in the watershed is constructed; Setting model parameters of the green roof decision-making and urban waterlogging response model for residents in the watershed, and initializing the resident agent attributes and simulation scenarios according to the watershed building distribution data and the 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 according to the historical rainfall data of the watershed; 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 decision-making, urban green roof area changes, and waterlogging changes in each simulation; According to the changes in residents' intelligent agent decisions, urban green roof area and 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, a reference plan for the construction of urban green roofs in the basin is generated.

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, waterlogging perception, green roof preferences and willingness to pay for green roofs.

3. The method according to claim 1, characterized in that The decision-making rules of the resident intelligent agent include: recognition of green roof advantages, concern about green roof risks, and perception of waterlogging risks.

4. The method according to claim 3, characterized in that The perceived benefits of green roofs are characterized by the following formula: in, A. Recognize the advantages of green roofs; i,t A is the degree of awareness of the green roof advantage 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 by green roofs; t Indicates 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).

5. The method according to claim 3, characterized in that: 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 of resident agent i in year t; i,t―1 is the risk concern level of green roof of resident agent i in year t-1; β 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 Indicates whether there are risks in installing a green roof on a building.

6. The method according to claim 3, characterized in that The waterlogging risk perception is represented by the following formula: in, is the perception of waterlogging risk; P i,t is the waterlogging risk perception level of resident agent i in year t; F t Indicates the severity of waterlogging; μ i is the sensitivity of resident agent i to waterlogging events; i is the forgetting rate of resident agent i about the flooding event.

7. The method according to claim 1, characterized in that 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.

8. The method according to claim 1, characterized in that The step of constructing a city 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.

9. The method according to claim 1, characterized in that: 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.

10. A device for generating a reference plan for building green roofs in a river basin city, characterized in that: The device comprises: The collection module is used to collect the 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 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; 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 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; A setting module is used to set the model parameters of the green roof decision-making and urban waterlogging response model of residents in the watershed, and initialize the resident agent attributes and simulation scenarios according to the watershed building distribution data and the watershed resident survey data through the green roof decision-making and urban waterlogging response model of residents in the watershed, and simulate the occurrence of rainfall events under climate change according to the historical rainfall data of the watershed; An operation module is used to operate the green roof decision-making and urban waterlogging response model of residents in the watershed, iterate and update the resident agent attributes and simulation scenarios according to a preset iteration time, and count the changes in the resident agent decision, the urban green roof area and the waterlogging situation in each simulation; A generation module is used to generate a reference plan for the construction of green roofs in the basin cities according to 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.

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