Roof productive transformation layout scheme generation and optimization method

Abstract

The invention discloses a roof productive transformation layout scheme generation and optimization method. The method comprises the following steps: step 1, establishing a block 3D model; 2, picking up a block geometric model and a selected transformation roof; 3, simulating a physical environment, and screening a suitable roof; 4, calculating the annual average yield per unit area of each suitable roof when different production strategies are applied; step 5, calculating cost benefit coefficients Selfk when different strategies are applied, step 6, inputting the cost benefit coefficients into a multi-objective optimization module according to a selection sequence to obtain an optimal configuration scheme of the roof reconstruction strategy, namely Fmax, Emax and Costmin in the optimal configuration scheme; and step 7, generating a resource index, an economic index and an environment index of each configuration scheme by the multi-dimensional evaluation calculation model to serve as decision support for optimizing the configuration scheme. According to the invention, comprehensive, rapid and accurate decision support can be provided for roof reconstruction.

Description

Technical Field

[0001] The present invention relates to the technical field of urban renewal, and in particular to a method for generating and optimizing a layout plan for productive rooftop renovation, which is suitable for planning and designing rooftop renovation in urban blocks, and can derive multiple sets of optimal planning methods based on basic site information and design objectives. Background Art

[0002] The urbanization process is developing rapidly. Currently, more than half of the world's population lives in cities, and it is estimated that by 2050, about 70% of the world's population will live in cities. The expansion of urban scale is bound to encroach on suburban land mainly used for agricultural production, which will bring huge pressure on food production and distribution; at the same time, the spatial distance between agricultural production and consumption links will be further expanded, resulting in an increase in agricultural mileage, bringing additional environmental pressure and economic costs. A large number of unused roof spaces in cities can be used as a direct carrier for urban agriculture and clean energy production. Therefore, designers need to reasonably evaluate and plan the physical and energy production of the target blocks in the planning stage to achieve a scientific and efficient productive transformation design plan.

[0003] Currently, there are few design methods for improving productivity in urban renewal, and most of them evaluate and plan for a single type of resource. In agriculture, the total output is generally estimated based on empirical values ​​and the area available for planting. In energy, the current design scheme is simulated and calculated by renewable energy output calculation software to obtain the overall simulated power output of the system, and the scheme is adjusted based on the calculation results and the design target. This design method relies too much on empirical values ​​and ignores the impact of complex urban forms on environmental factors that affect resource production, such as solar radiation, sunshine time, and temperature. Therefore, it cannot accurately evaluate the production potential of resources; when planning and designing, there is no rational planning of multiple resources, which is easy to cause overproduction of certain resources and underproduction of other resources, which not only causes waste of resources and production space, but also violates the original intention of localized resource production. In summary, conventional design methods are limited by the designer's design experience, conventional process thinking patterns, and technical tool limitations. They have the limitations of low accuracy in evaluating the potential of production capacity, weak optimization of schemes, and huge workload. Traditional design methods not only require designers to have certain urban agriculture and renewable energy design experience, but also consume a lot of time and energy on repeated result comparisons and program modifications, so they are not suitable for the work needs of grassroots planning and construction practitioners. At present, with the advancement of the concept of "urban dual repair" in urban renewal in my country, local design practitioners have an urgent need for simple and fast configuration methods and practical tools for productive transformation of urban block roofs. Summary of the invention

[0004] The purpose of the present invention is to provide a method for generating and optimizing a rooftop productive transformation layout plan in response to the technical defects existing in the prior art, so as to carry out urban agriculture and photovoltaic power generation transformation on rooftops in specific areas, thereby solving the technical problems of inaccurate potential assessment, discontinuous environmental simulation and potential calculation, insufficient space utilization caused by the inability to coordinate multiple resources, and inefficient result comparison and repeated modification.

[0005] The technical solution adopted to achieve the purpose of the present invention is:

[0006] A method for generating and optimizing a layout plan for a productive rooftop transformation comprises the following steps:

[0007] Step 1, establishing a 3D model of an urban block that needs to undergo productive rooftop transformation in a simulation module;

[0008] Step 2, picking up the block geometry model and the selected remodeled roof in the block 3D model obtained in step 1;

[0009] Step 3: extract the regional environmental information big data throughout the year, bring the information such as the solar trajectory path, radiation intensity information, and hourly sunshine time throughout the year in the region into the environmental simulation operator, perform physical environmental simulation on the selected renovated roof obtained in step 2, and select the roof that meets the physical environmental conditions as the suitable roof;

[0010] Step 4, establishing an agricultural potential calculation model and a photovoltaic potential calculation model, and calculating the annual average yield per unit area of ​​each suitable roof when applying different production strategies, wherein the production strategies include open-air planting, rooftop photovoltaics, rooftop greenhouses, and photovoltaic greenhouses;

[0011] When the production strategy is open-air planting or rooftop greenhouse, the annual average yield is the annual average agricultural yield; when the production strategy is rooftop photovoltaic, the annual average yield is the annual average photovoltaic yield; when the production strategy is photovoltaic greenhouse, the annual average yield includes the annual average agricultural yield and the annual average photovoltaic yield. The agricultural potential calculation model is used to calculate the annual average agricultural yield per unit area, and the photovoltaic potential calculation model is used to calculate the annual average photovoltaic yield per unit area;

[0012] Step 5: Run an iterative calculation based on the annual average output and transformation cost obtained in step 4 to obtain the cost-benefit coefficient of each roof under different strategies. k , used to indicate the degree to which a roof is more suitable for a certain strategy, and is used as a screening basis for the optimization operator;

[0013] Step 6: Substitute the cost-benefit coefficients of open-air planting, rooftop photovoltaics, rooftop greenhouses, and photovoltaic greenhouse rooftop transformation obtained in step 5 into Self kInput the "cost-benefit coefficient of system one", "cost-benefit coefficient of system two" and "cost-benefit coefficient of system three" in the multi-objective optimization module in the order of selection. The remaining transformation strategies are automatically input into the multi-objective optimization module as "cost-benefit coefficient of system four". Input the suitable roofs obtained in step 3 into the multi-objective optimization module. The constraint condition is that the total number of roofs to be transformed must not exceed the total number of roofs in the block, but roofs that have not been updated are allowed. Then, the proportion of each production strategy is obtained through the genetic algorithm, so as to obtain the optimal configuration scheme of the roof transformation strategy. In the optimal configuration scheme, the total agricultural output is maximized Fmax, the total photovoltaic output is maximized Emax, and the total transformation cost is minimized Costmi n ;

[0014] Step 7. In the multidimensional evaluation module, a multidimensional evaluation calculation model for the roof renovation plan is established based on the economic benefits and environmental impacts of various roof renovation strategies. Based on the optimized configuration plan obtained in step 6, the multidimensional evaluation calculation model generates resource indicators, economic indicators, and environmental indicators for each configuration plan as decision support for the optimized configuration plan.

[0015] In the above technical solution, in step 1, after the basic geometric shape is established in the Rhino software, it is imported into the Grasshopper platform to establish a 3D model of the block.

[0016] In the above technical solution, in step 3, the operational meteorological data in EPW format is imported into the simulation module to extract the solar trajectory path, radiation intensity information and temperature information in the area.

[0017] In the above technical solution, in step 3, a parametric model is established under the Grasshopper platform, an environmental simulation calculation kernel is formed through the Ladybug program in the platform, and suitable roofs are screened based on plant growth conditions and photovoltaic potential thresholds.

[0018] In the above technical solution, in step 4, the average annual agricultural output per unit area is calculated by an agricultural potential calculation model, and the agricultural potential calculation model is:

[0019] Y pt =1.522×10 -5 ×(1-α)(1-β)(1-γ)×q×f t ;

[0020] f t =4.301×10 -2 t-5.771×10- 4 t 2 ;

[0021] Y ptis the agricultural output per square meter per year, f t is the temperature correction function, α is the reflectivity, α=0.83L i / L0, L0 is the maximum leaf area index; L i is the leaf area index in a certain period of time; β is the leakage rate; γ is the light saturation limit, that is, the proportion of light exceeding the light saturation point; q is the total solar radiation projected per unit area per unit time, and t is the annual average temperature.

[0022] In the above technical solution, in step 4, the average annual photovoltaic output per unit area is calculated by a photovoltaic output calculation model, and the photovoltaic output calculation model is:

[0023] E e =I G ×PV×K×(1-R d ) N-1

[0024] Where E e is the photovoltaic output per square meter per year, I G is the solar radiation accumulated on the roof surface each year, obtained by the physical environment simulation in step 3. PV is the photoelectric conversion efficiency of the photovoltaic module, and K is the energy efficiency ratio.

[0025] In the above technical solution, in step 5, the cost-benefit coefficient Self k The calculation method is:

[0026]

[0027] Yield k is the annual output per unit area using k-type production strategies for each roof; Population is the total population of the block; Per k is the per capita resource demand; Cost k is the transformation cost per unit area of ​​type k production strategy; x k The roof area converted for type k production strategy, where k is open-air cultivation, rooftop photovoltaics, rooftop greenhouse or photovoltaic greenhouse.

[0028] In the above technical solution, in step 6, the calculation formula for total agricultural output is:

[0029]

[0030] F is the total agricultural output; n is the number of types of rooftop agricultural production transformation strategies; a j is the agricultural output per unit area of ​​the j-type production strategy in rooftop agricultural production; x j It is the area that can be used for production by type j of agricultural production.

[0031] The total photovoltaic output is calculated as:

[0032]

[0033] E is the total photovoltaic output; m is the number of types of rooftop power production transformation strategies; b is the total photovoltaic output; i is the photovoltaic output per unit area of ​​type i production strategy in rooftop electricity production; y i It is the area where Class I electricity production can be used for production.

[0034] The total cost of the transformation is calculated by the total cost calculation model of the transformation, and the total cost calculation model of the transformation is:

[0035]

[0036] Cost is the total cost of transformation; n is the number of types of rooftop agricultural production transformation strategies; Cost j is the unit cost of type j production strategy in rooftop agricultural production; x j is the area that can be used for production in the j-type agricultural production situation; m is the number of types of rooftop power production transformation strategies; y i is the area where Class I electricity production can be used for production; Cost i is the unit cost of type i production strategy in rooftop photovoltaic production.

[0037] In the above technical scheme, in step 7, the production potential calculation kernel in the multidimensional evaluation model calculates resource indicators, and the resource indicators include agricultural self-sufficiency rate and energy self-sufficiency rate; the economic indicator calculation kernel in the multidimensional evaluation model calculates economic indicators, and the economic indicators include return on investment and investment payback period; the environmental impact calculation kernel in the multidimensional evaluation model calculates environmental indicators, and the environmental indicators include global warming potential, water consumption, electricity consumption, agricultural mileage carbon reduction potential, and renewable energy carbon reduction potential.

[0038] In the above technical solution, the calculation formula for agricultural self-sufficiency rate is:

[0039]

[0040] Vegetable self is the agricultural self-sufficiency rate; k is the average annual output per unit of type k production strategy; x k The roof area that is transformed for type k production strategy; Population is the total population of the block; Per k is the per capita resource demand;

[0041] The formula for calculating energy self-sufficiency rate is:

[0042]

[0043] Among them, Electric self is the energy self-sufficiency rate; l is the average annual output per unit of type l production strategy; x l The roof area converted for type l production strategy; Population is the total population of the block; Per l l is the per capita resource demand;

[0044] The formula for calculating the return on investment is:

[0045]

[0046] ROI is the return on investment; t Cost is the annual income. t is the total annual expenditure; Investment is the initial cost;

[0047]

[0048] Investment is the initial cost; n is the number of types of rooftop agricultural production transformation strategies; Cost j is the unit cost of type j production strategy in rooftop agricultural production; x j is the area that can be used for production in the j-type agricultural production situation; m is the number of types of rooftop power production transformation strategies; b i is the average annual output per unit of production strategy type i in rooftop agricultural production; y i is the area where Class I electricity production can be used for production; Cost i is the unit cost of type i production strategy in rooftop photovoltaic production.

[0049] The specific calculation method of the capital return period is as follows:

[0050]

[0051] Paybacktime is the investment return period; Revenue t Cost is the annual income. t is the total annual expenditure; Investment is the initial cost.

[0052] The calculation method for the carbon reduction potential of agricultural mileage is:

[0053] F c =P veg ×J×L

[0054] where F c Reduce carbon emissions from agricultural mileage;veg represents the total agricultural transport volume; J represents the average carbon dioxide emissions per ton of agricultural transport per kilometer; L represents the agricultural transport mileage.

[0055] The calculation method for the carbon reduction potential of renewable energy is:

[0056] E c =P ele ×J

[0057] where F c Reduce carbon emissions from agricultural mileage; veg represents the total agricultural transport volume; J represents the average carbon dioxide emissions per ton of agricultural transport per kilometer; L represents the agricultural transport mileage.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] 1. The present invention establishes a method for generating and optimizing the layout plan of the productive transformation of the roof, which can quickly generate a relatively optimal set of plans under multiple conflicting objectives, and provides comprehensive, fast and accurate decision support for the productive transformation plan of the roof, so that designers do not need to manually calculate, compare and modify the plan, thereby improving the design efficiency.

[0060] 2. The present invention utilizes a precise environmental simulation operator and a resource yield calculation formula based on the physical environment. Only basic environmental information needs to be input to accurately calculate the agricultural and photovoltaic production potential in a complex urban environment, which is convenient for designers who lack knowledge of urban agriculture and photovoltaics to use.

[0061] 3. The operation method of the present invention is simple and fast. It relies on the visualization of the spatial effect of the 3D modeling software Rhinoceros and Grasshopper platform commonly used in the construction planning industry. The result is more intuitive, and in-depth design can be directly carried out based on the optimization result. It has strong practicality and popularization, and can adapt to the practical application of the design stage of roof productive transformation schemes in different regions. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 Shown is a principle flow chart of a method for generating and optimizing a layout plan for productive roof renovation.

[0063] Figure 2 It is the Pareto optimal solution set for the productive transformation of the roofs of the building in the case block obtained based on the multi-objective optimization algorithm in step 6.

[0064] Figure 3 This is a schematic diagram of the roof productive transformation configuration plan obtained in step 6. Roofs of different colors represent different transformation plans. DETAILED DESCRIPTION

[0065] The present invention is further described in detail below in conjunction with specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0066] Example 1

[0067] A method for generating and optimizing a layout plan for a productive rooftop transformation comprises the following steps:

[0068] Step 1: Select the urban blocks that need to undergo productive rooftop transformation and create a 3D model of the blocks:

[0069] Rhino software was used as a program visualization and 3D modeling tool to draw the geometric model of the target block.

[0070] Step 2, picking up the block geometry model and the selected remodeled roof in the block 3D model obtained in step 1;

[0071] Using Grasshopper as the parametric programming platform, pick up the geometric model drawn in Rhino software in step 1 and the selected renovated roof respectively.

[0072] Step 3: extract the regional environmental information big data throughout the year, bring the information such as the solar trajectory path, radiation intensity information, and hourly sunshine time throughout the year in the region into the environmental simulation operator, grab the selected renovated roof obtained in step 2, perform physical environment simulation on it, and select the roof that meets the physical environment conditions as the suitable roof;

[0073] Step 4, establishing an agricultural potential calculation model and a photovoltaic potential calculation model in Grasshopper, and calculating the annual average yield per unit area of ​​each suitable roof when applying different production strategies, wherein the production strategies include open-air planting, rooftop photovoltaics, rooftop greenhouses and / or photovoltaic greenhouses;

[0074] When the production strategy is open-air planting or rooftop greenhouse, the average annual yield per unit area is k is the annual agricultural output. When the production strategy is rooftop photovoltaics, the annual average yield per unit area is Yield k is the annual output of photovoltaic power generation. When the production strategy is photovoltaic greenhouse, the annual average output per unit area is Yield k Includes annual agricultural output and annual photovoltaic output;

[0075] The average annual agricultural output per unit area is calculated using the agricultural potential calculation model, which is:

[0076] Y pt =1.522×10 -5 ×(1-α)(1-β)(1-γ)×q×f t

[0077] f t =4.301×10 -2 t-5.771×10 -4 t 2

[0078] Among them, Y pt is the agricultural output per square meter per year, f t is the temperature correction function. α is the reflectivity, which can be written as a linear function with the growth of leaf area during the entire crop growth period: α = 0.83L i / L0, where L0 is the maximum leaf area index; L i is the leaf area index for a certain period of time; β is the leakage rate, and the amount of light leaking to the soil surface varies with different crop groups and different growth stages; γ is the light saturation limit, that is, the proportion of light exceeding the light saturation point; q is the total solar radiation projected per unit area per unit time (wh / m 2 ). t is the annual average temperature, in degrees Celsius (℃).

[0079] The average annual photovoltaic output per unit area is calculated using the photovoltaic output calculation model, which is:

[0080] E e =I G ×η PV ×K×(1-R d ) N-1

[0081] Where E e is the photovoltaic output per square meter per year. G is the annual accumulated solar radiation on the roof surface, obtained from the physical environment simulation in step 3. PV is the photoelectric conversion efficiency of the photovoltaic module, which is determined by the type of photovoltaic module. K is the comprehensive efficiency coefficient, also known as the energy efficiency ratio, which is used to indicate the conversion efficiency of the entire system from the input to the output. It reflects the system efficiency of the entire photovoltaic system after energy loss caused by a series of reasons such as inverters, temperature, shading, dust, circuit loss, etc.

[0082] Step 5: Based on the average annual output and transformation cost obtained in step 4, iteratively calculate the cost-benefit coefficient of each roof when applying different production strategies (open-air planting, rooftop photovoltaic, rooftop greenhouse, photovoltaic greenhouse) k , the formula is as follows:

[0083]

[0084] Among them, Self k Yield is the cost-benefit coefficient for self-sufficiency of each roof; kis the annual output per unit area using the k-type production strategy for each roof; Population is the total population of the block, and the data comes from the seventh census of China; Per k is the per capita resource demand; Cost k is the transformation cost per unit area of ​​type k production strategy, obtained through market data; x k The roof area transformed for type k production strategy, k is open-air planting, rooftop photovoltaic, rooftop greenhouse or photovoltaic greenhouse. The data comes from the "Tianjin Statistical Yearbook 2023".

[0085] Step 6. Input the "cost-benefit coefficient of system 1", "cost-benefit coefficient of system 2" and "cost-benefit coefficient of system 3" of each strategy obtained in step 4 into the input end of the multi-objective optimization module as the basis for determining the purpose of the roof. The input order represents the priority of the roof renovation type selected by the user. After entering the first three cost-benefit coefficients, the system defaults to the "cost-benefit coefficient of system 4" for the strategy that has not been entered. The suitable roof obtained in step 3 is input into the multi-objective optimization module.

[0086] "System 1 cost-benefit coefficient", "System 2 cost-benefit coefficient", "System 3 cost-benefit coefficient", and "System 4 cost-benefit coefficient" are connected to the variable end of Octopus in the Grasshopper platform, and the "total agricultural output", "total photovoltaic output", and "total transformation cost" at the output end of the "multi-objective optimization module" are connected to the target end of Octopus.

[0087] The constraint is that the total number of roofs to be renovated must not exceed the total number of roofs in the block, but some roofs that have not been renovated are allowed. The specific expression is as follows: Among them, NumberRoof is the total number of roofs in the target block, s is the s-type production strategy, and Roof s is the number of roofs occupied by the s-type strategy.

[0088] The total agricultural output is calculated as:

[0089]

[0090] F is the total agricultural output, in kg; n is the number of types of rooftop agricultural production transformation strategies; a j is the agricultural output per unit area of ​​the j-type production strategy in rooftop agricultural production, in kg / m 2 ;x j is the area available for production of agricultural production type j, in m 2 .

[0091] The total photovoltaic output is calculated as:

[0092]

[0093] E is the total photovoltaic output, in kWh; m is the number of types of rooftop power production transformation strategies; b is the total photovoltaic output, in kWh; i is the photovoltaic output per unit area of ​​type i production strategy in rooftop electricity production, in kWh / m 2 ;y i is the area that can be used for production of type i electricity, in m 2 .

[0094] The total cost of the transformation is calculated by the total cost calculation model of the transformation, and the total cost calculation model of the transformation is:

[0095]

[0096] Cost is the total cost of transformation, in RMB; n is the number of types of rooftop agricultural production transformation strategies; Cost j is the unit cost of type j production strategy in rooftop agricultural production, in yuan / m 2 ;x j is the area that can be used for production in the j-type agricultural production situation, in m 2 ; m the number of types of rooftop power production transformation strategies; b i is the average annual output per unit of production strategy type i in rooftop agricultural production, in kwh / m 2 ;y i is the area that can be used for production of type i electricity, in m 2 Cost i is the unit cost of type i production strategy in rooftop photovoltaic production, in yuan / Kw / h.

[0097] The mathematical expression of Octopus multi-objective optimization is: max 、E max Cost min , which respectively represent the maximization of total agricultural output, the maximization of total photovoltaic output, and the minimization of total transformation cost.

[0098] The Pareto optimal solution set for the productive transformation of the roof of the case block building obtained based on the multi-objective optimization algorithm in step 6 is as follows: Figure 2 As shown in the figure, the schematic diagram of the configuration scheme for the productive transformation of the roof is as follows Figure 3 shown.

[0099] Step 7. In the multidimensional evaluation module, a multidimensional evaluation calculation model for the roof renovation plan is established based on the economic benefits and environmental impacts of various roof renovation strategies. Based on the optimized configuration plan obtained in step 6, the multidimensional evaluation calculation model generates resource indicators, economic indicators, and environmental indicators for each configuration plan as decision support for the optimized configuration plan.

[0100] The production potential calculation core in the multidimensional evaluation model calculates resource indicators, which include agricultural self-sufficiency rate and energy self-sufficiency rate; the economic indicator calculation core in the multidimensional evaluation model calculates economic indicators, which include return on investment and investment payback period; the environmental impact calculation core in the multidimensional evaluation model calculates environmental indicators, which include global warming potential, water consumption, electricity consumption, agricultural mileage carbon reduction potential, and renewable energy carbon reduction potential.

[0101] The formula for calculating agricultural self-sufficiency rate is:

[0102]

[0103] Vegetable self is the agricultural self-sufficiency rate, a k is the average annual output per unit of type k production strategy; x k The roof area that is transformed for type k production strategy; Population is the total population of the block, and the data comes from the seventh census of China; Per k is the per capita resource demand, and the data comes from the "Tianjin Statistical Yearbook 2023".

[0104] The specific method for energy self-sufficiency rate is as follows:

[0105]

[0106] Among them, Electric self is the energy self-sufficiency rate; l is the average annual output per unit of type l production strategy; x l The roof area that is transformed for type l production strategy; Population is the total population of the block, and the data comes from the seventh census of China; Per l l is the per capita resource demand, and the data comes from the "Tianjin Statistical Yearbook 2021".

[0107] The specific method of initial cost is as follows:

[0108]

[0109] Investment is the initial cost, in yuan; n is the number of types of rooftop agricultural production transformation strategies; Costj is the unit cost of type j production strategy in rooftop agricultural production, in yuan / m 2 ;x j is the area that can be used for production in the j-type agricultural production situation, in m 2 ; m the number of types of rooftop power production transformation strategies; b i is the average annual output per unit of production strategy type i in rooftop agricultural production, in kwh / m 2 ;y i is the area that can be used for production of type i electricity, in m 2 Cost i is the unit cost of type i production strategy in rooftop photovoltaic production, in yuan / Kw / h.

[0110] The specific calculation method of return on investment (ROI) is as follows:

[0111]

[0112] ROI is the return on investment; t Cost is the annual income. t is the total annual expenditure; Investment is the initial cost.

[0113] The specific calculation method of investment payback period is as follows:

[0114]

[0115] Paybacktime is the investment return period; Revenue t Cost is the annual income. t is the total annual expenditure;

[0116] Investment is the initial cost.

[0117] The calculation method for the carbon reduction potential of agricultural mileage is:

[0118] F c =P veg ×J×L

[0119] where F c is the carbon reduction of agricultural mileage, in P veg represents the total agricultural transport volume, in tons (t), which is equivalent to agricultural production in this study; J represents the average carbon dioxide emissions per ton of agricultural transport per kilometer, in L represents agricultural transport mileage, in kilometers (km).

[0120] The carbon reduction potential of renewable energy is calculated using the following formula:

[0121] E c =P ele ×J

[0122] Where E c is the carbon reduction of photovoltaic power generation, in P ele represents the total photovoltaic power generation, in kilowatt-hours; J represents the amount of carbon dioxide emissions reduced per kilowatt-hour of average photovoltaic power generation.

[0123] In addition to the benefits to the environment, the environmental sustainability index also considers the environmental costs of the productive renewal strategy of the roof throughout its life cycle, and comprehensively evaluates the negative impact on the environment during the construction, operation, production and product distribution of the roof system. Due to the different units of various materials, this invention will use the global warming potential (GWP) to uniformly convert the expenditures of each part. The specific data refers to relevant national standards and past literature in China and is calculated according to the specific roof strategy.

[0124] The structure of the roof system includes (i) installation of the structure; (ii) maintenance of the system, including the maintenance and replacement of equipment within the system; and (iii) disposal of components after their lifespan. The environmental impacts considered in the operation part are (i) water; (ii) energy; and (iii) fertilizers, pesticides, and seeds.

[0125] Table 1 Economic and environmental parameters of roof systems

[0126]

[0127] Example 2

[0128] This embodiment takes the rooftop productive transformation design project of Xuefu Street, Nankai District, Tianjin as the specific analysis background. The known conditions in the planning stage are: the street includes 9 residential communities and two high-rise office buildings. According to the survey data, the permanent population is 32,706. The building types include low-rise buildings, multi-story buildings, medium-rise buildings and high-rise buildings. The building density is large and the urban form is relatively complex. According to the previous survey, the CAD drawings and 3D point cloud models of the area have been obtained.

[0129] By using Rhino software, Grasshopper platform, TT Toolbox program, Octopus program and the roof productive transformation layout plan generation and optimization method of the present invention, a variety of roof productive renewal design plans and calculation evaluation indicators are quickly generated in the selected block.

[0130] In this embodiment, the deterministic parameters include the square plane and number of the roofs to be renovated, the type of photovoltaic components, the efficiency of photovoltaic modules, the total population of the block, the per capita vegetable consumption, the per capita electricity consumption, and the EPW format climate data file of Tianjin. Using the renovation configuration method of the present invention, a set of roof productive renovation design schemes that meet multiple predetermined goals and their resource, economic and environmental evaluations can be obtained, such as Figure 2 Finally, 57 solutions were obtained as Pareto optimal solutions, as shown in Table 2.

[0131] Table 2 Optimization results list

[0132]

[0133]

[0134]

[0135] The process method of the present invention can solve the lack of design methods for productive transformation of rooftop space in urban blocks and the technical difficulties of cumbersome processes and repetitive work in existing design processes; it can save a lot of time and cost and improve calculation accuracy, and simulate the physical environment of urban blocks based on meteorological data, and use multi-objective optimization methods to achieve the established design goals; the use of this tool does not require designers to master the knowledge of urban agriculture and photovoltaic power generation, only the basic information of the site and the design goals need to be input to obtain multiple sets of optimal solutions, and the economic and environmental indicators of each plan can be automatically calculated to provide support for the decision-making of the design plan; the final result is presented to the user in a visual form, the user and the tool are highly interactive, and the practicability and promotion are strong.

[0136] The above is only a preferred embodiment of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for generating and optimizing a rooftop productive transformation layout plan, characterized in that: The following steps are involved: Step 1, establishing a 3D model of an urban block that needs to undergo productive rooftop transformation in the simulation module; Step 2, picking up the block geometry model and the selected remodeled roof in the block 3D model obtained in step 1; Step 3: extract the regional environmental information big data throughout the year, bring the information such as the solar trajectory path, radiation intensity information, and hourly sunshine time throughout the year in the region into the environmental simulation operator, perform physical environmental simulation on the selected renovated roof obtained in step 2, and select the roof that meets the physical environmental conditions as the suitable roof; Step 4, establishing an agricultural potential calculation model and a photovoltaic potential calculation model, and calculating the annual average yield per unit area of ​​each suitable roof when applying different production strategies, wherein the production strategies include open-air planting, rooftop photovoltaics, rooftop greenhouses, and photovoltaic greenhouses; When the production strategy is open-air planting or rooftop greenhouse, the annual average yield is the annual average agricultural yield; when the production strategy is rooftop photovoltaic, the annual average yield is the annual average photovoltaic yield; when the production strategy is photovoltaic greenhouse, the annual average yield includes the annual average agricultural yield and the annual average photovoltaic yield. The agricultural potential calculation model is used to calculate the annual average agricultural yield per unit area, and the photovoltaic potential calculation model is used to calculate the annual average photovoltaic yield per unit area; Step 5: Run an iterative calculation based on the annual average output and transformation cost obtained in step 4 to obtain the cost-benefit coefficient of each roof under different strategies. k , used to indicate the degree to which a roof is more suitable for a certain strategy, and is used as a screening basis for the optimization operator; Step 6: Substitute the cost-benefit coefficients of open-air planting, rooftop photovoltaics, rooftop greenhouses, and photovoltaic greenhouse rooftop transformation obtained in step 5 into Self k Input the "cost-benefit coefficient of system 1", "cost-benefit coefficient of system 2" and "cost-benefit coefficient of system 3" in the multi-objective optimization module in the order of selection. The remaining transformation strategies are automatically input into the multi-objective optimization module as "cost-benefit coefficient of system 4". Input the suitable roofs obtained in step 3 into the multi-objective optimization module. The constraint condition is that the total number of roofs to be transformed must not exceed the total number of roofs in the block, but it is allowed that there are roofs that have not been updated. Then, the proportion of each production strategy is obtained through the genetic algorithm, so as to obtain the optimal configuration scheme of the roof transformation strategy. In the optimal configuration scheme, the total agricultural output is maximized F max , the total photovoltaic output is maximized E max , Minimize the total cost of transformation min ; Step 7. In the multidimensional evaluation module, a multidimensional evaluation calculation model for the roof renovation plan is established based on the economic benefits and environmental impacts of various roof renovation strategies. Based on the optimized configuration plan obtained in step 6, the multidimensional evaluation calculation model generates resource indicators, economic indicators, and environmental indicators for each configuration plan as decision support for the optimized configuration plan.

2. The method for generating and optimizing a rooftop productive transformation layout plan according to claim 1, characterized in that: In step 1, the basic geometric shape is created in Rhino software and then imported into Grasshopper platform to create a 3D model of the block.

3. The method for generating and optimizing a rooftop productive transformation layout plan according to claim 1, characterized in that: In step 3, operational meteorological data in EPW format is imported into the simulation module to extract the solar trajectory path, radiation intensity information and temperature information in the area.

4. The method for generating and optimizing a rooftop productive transformation layout plan according to claim 1, characterized in that: In step 3, a parameterized model is established on the Grasshopper platform, an environmental simulation calculation kernel is formed through the Ladybug program in the platform, and suitable roofs are screened based on plant growth conditions and photovoltaic potential thresholds.

5. The method for generating and optimizing a rooftop productive transformation layout plan according to claim 1, characterized in that: In step 4, the average annual agricultural output per unit area is calculated by an agricultural potential calculation model, and the agricultural potential calculation model is: Y pt =1.522×10 -5 ×(1-a)(1-b)(1-c)×q×f t ; f t =4.301×10 -2 t-5.771×10 -4 t 2 ; Y pt is the agricultural output per square meter per year, f t is the temperature correction function, α is the reflectivity, α=0.83L i / L0, L0 is the maximum leaf area index; L i is the leaf area index in a certain period of time; β is the leakage rate; γ is the light saturation limit, that is, the proportion of light exceeding the light saturation point; q is the total solar radiation projected per unit area per unit time, and t is the annual average temperature.

6. The method for generating and optimizing a rooftop productive transformation layout plan according to claim 1, characterized in that: In step 4, the average annual photovoltaic output per unit area is calculated using a photovoltaic output calculation model, which is: E e =I G × PV ×K×(1-R d ) N-1 Where E e is the photovoltaic output per square meter per year, I G is the annual accumulated solar radiation on the roof surface, obtained from the physical environment simulation in step 3. PV is the photoelectric conversion efficiency of the photovoltaic module, and K is the energy efficiency ratio.

7. The method for generating and optimizing a rooftop productive transformation layout plan according to claim 1, characterized in that: In step 5, the cost-benefit coefficient Self k The calculation method is: Yield k is the annual output per unit area using k-type production strategies for each roof; Population is the total population of the block; Per k is the per capita resource demand; Cost k is the transformation cost per unit area of ​​type k production strategy; x k The roof area converted for type k production strategy, where k is open-air cultivation, rooftop photovoltaics, rooftop greenhouse or photovoltaic greenhouse.

8. The method for generating and optimizing a rooftop productive transformation layout plan according to claim 1, characterized in that: In step 6, the calculation formula for total agricultural output is: F is the total agricultural output; n is the number of types of rooftop agricultural production transformation strategies; a j is the agricultural output per unit area of ​​the j-type production strategy in rooftop agricultural production; x j It is the area that can be used for production by type j of agricultural production. The total photovoltaic output is calculated as: E is the total photovoltaic output; m is the number of types of rooftop power production transformation strategies; b is the total photovoltaic output; i is the photovoltaic output per unit area of ​​type i production strategy in rooftop electricity production; y i It is the area where Class I electricity production can be used for production. The total cost of the transformation is calculated by the total cost calculation model of the transformation, and the total cost calculation model of the transformation is: Cost is the total cost of transformation; n is the number of types of rooftop agricultural production transformation strategies; Cost j is the unit cost of type j production strategy in rooftop agricultural production; x j is the area that can be used for production in the j-type agricultural production situation; m is the number of types of rooftop power production transformation strategies; y i is the area where Class I electricity production can be used for production; Cost i is the unit cost of type i production strategy in rooftop photovoltaic production.

9. The method for generating and optimizing a rooftop productive transformation layout plan according to claim 1, characterized in that: In step 7, the production potential calculation core in the multidimensional evaluation model calculates resource indicators, and the resource indicators include agricultural self-sufficiency rate and energy self-sufficiency rate; the economic indicator calculation core in the multidimensional evaluation model calculates economic indicators, and the economic indicators include return on investment and investment payback period; the environmental impact calculation core in the multidimensional evaluation model calculates environmental indicators, and the environmental indicators include global warming potential, water consumption, electricity consumption, agricultural mileage carbon reduction potential, and renewable energy carbon reduction potential.

10. The method for generating and optimizing a rooftop productive transformation layout plan according to claim 9, characterized in that: The formula for calculating agricultural self-sufficiency rate is: Vegetable self is the agricultural self-sufficiency rate; k is the average annual output per unit of type k production strategy; x k The roof area that is transformed for type k production strategy; Population is the total population of the block; Per k is the per capita resource demand; The formula for calculating energy self-sufficiency rate is: Among them, Electric self is the energy self-sufficiency rate; l is the average annual output per unit of type l production strategy; x l The roof area converted for type l production strategy; Population is the total population of the block; Per l l is the per capita resource demand; The formula for calculating the return on investment is: ROI is the return on investment; t Cost is the annual income. t is the total annual expenditure; Investment is the initial cost; Investment is the initial cost; n is the number of types of rooftop agricultural production transformation strategies; Cost j is the unit cost of type j production strategy in rooftop agricultural production; x j is the area that can be used for production in the j-type agricultural production situation; m is the number of types of rooftop power production transformation strategies; b i is the average annual output per unit of production strategy type i in rooftop agricultural production; y i is the area where Class I electricity production can be used for production; Cost i is the unit cost of type i production strategy in rooftop photovoltaic production. The specific calculation method of the capital return period is as follows: Paybacktime is the investment return period; Revenue t Cost is the annual income. t is the total annual expenditure; Investment is the initial cost. The calculation method for the carbon reduction potential of agricultural mileage is: F c =P veg ×J×L where F c Reduce carbon emissions from agricultural mileage; veg represents the total agricultural transport volume; J represents the average carbon dioxide emissions per ton of agricultural transport per kilometer; L represents the agricultural transport mileage. The calculation method for the carbon reduction potential of renewable energy is: AND c =P ele ×J where F c Reduce carbon emissions from agricultural mileage; veg represents the total agricultural transport volume; J represents the average carbon dioxide emissions per ton of agricultural transport per kilometer; L represents the agricultural transport mileage.

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