Carbon neutralization partition simulation method, system and device based on smart city

Abstract

The invention provides a carbon neutralization zoning simulation method, system and device based on a smart city, and relates to the technical field of carbon neutralization simulation, and the method mainly comprises the steps: carrying out the planning zoning of a map of a tested city; calculating the carbon emission of each partition and carrying out visualization processing to obtain geographic space data; calculating the carbon sink amount of each partition; collecting output value basic data of each industry of the detected partition and marking the output value basic data in geographic space data; carbon emission basic data, carbon sink basic data and output value basic data which are regularly acquired by a smart city system are stored in a smart city database based on a time sequence; predicting the future carbon emission, carbon sink and output value of the measured subarea; constructing a time sequence dynamic optimization model, and solving an industrial area layout scheme through a parallelization genetic algorithm; and generating suggestion information. According to the scheme, a quantifiable and adjustable carbon management tool is provided for overall planning of urban territorial space, and the scheme is particularly suitable for low-carbon planning and treatment of high-density coastal cities.

Description

Technical Field

[0001] The present invention relates to the field of carbon neutrality simulation technology, and in particular to a carbon neutrality zoning simulation method, system and device based on smart cities. Background Art

[0002] Cities, as the primary hubs of human activity, contribute over 75% of global carbon emissions. Against this backdrop, countries are proposing carbon peak and carbon neutrality goals. As the core vehicle for achieving these goals, cities urgently need to build scientific and accurate carbon neutrality simulation systems to optimize spatial resource allocation and promote low-carbon transformation.

[0003] Since the 1990s, the academic community has been exploring urban carbon emissions accounting methods. Representative achievements include: Greenhouse Gas Protocol for Cities (GPC): This standard proposes a production- and consumption-based accounting framework that divides urban carbon emissions into six sectors: energy activities, industrial processes, transportation, construction, and waste disposal. However, it fails to fully incorporate spatial planning elements. Spatial accounting methods: These utilize geographic information such as nighttime light data and points of interest (POIs) to achieve a spatial representation of carbon emissions through a top-down allocation model. However, these methods rely on statistical data allocation and ignore differences in activity intensity at the plot level, resulting in insufficient accuracy. Land use correlation analysis: A "land use-carbon emission intensity" correlation framework was constructed to estimate carbon emissions during the planning period by classifying urban and rural land use. However, this method is entirely based on the unit area method of land use type and does not consider technological differences among industrial sub-sectors, resulting in high uncertainty. Innovation in accounting framework: A national land space carbon emissions model based on "spatial layout-land use type-sector division" was proposed, combining planning elements with accounting methods to address the difficulty of aligning accounting for central urban areas with international standards. However, the model still relies on static parameters and cannot reflect the impact of changes in energy structure on carbon emissions in real time. Therefore, it is urgent to build a "space-carbon emissions-carbon sink" coupling analysis tool suitable for high-density cities to provide quantifiable and controllable technical support for the city's carbon neutrality tasks. Summary of the Invention

[0004] The purpose of the present invention is to provide a carbon neutrality zoning simulation method, system and device based on smart cities to solve at least one of the above-mentioned technical problems existing in the prior art.

[0005] In a first aspect, to solve the above technical problems, the present invention provides a carbon neutrality zoning simulation method based on a smart city, comprising the following steps: Step 1: Plan and zone the map of the city under test; based on standards and literature, collect basic carbon emission data, calculate the carbon emissions of each zone and visualize it to obtain geospatial data; collect basic carbon sink data and mark it in the geospatial data, and calculate the carbon sink of each zone; calculate the net emissions of the city under test based on carbon emissions and carbon sinks; collect basic output value data of each industry in the tested zone and mark it in the geospatial data; this will obtain basic data in the initial state, so as to facilitate subsequent quantitative evaluation of the impact of the spatial structure and scale of each zone on carbon emissions, carbon sinks and output value.

[0006] In a feasible implementation manner, the planning zones include primary planning zones and secondary planning zones; The first-level planning zones include urban development zones, farmland protection zones, ecological control zones, ecological protection zones, and marine development zones; The secondary planning zones of the urban development zone include residential areas, commercial and business areas, comprehensive service areas, cultural and tourism areas, industrial development areas, scientific and technological innovation development areas, logistics and warehousing areas, strategic reserved areas, green space and leisure areas, transportation hub areas, and areas with concentrated public facilities; The secondary planning zones of the marine development zone include fishery sea areas, special sea areas, recreational sea areas, industrial and mining communication sea areas, marine reserved areas, and transportation sea areas; Among them, residential areas, commercial and business areas, comprehensive service areas, cultural and tourism areas, industrial development areas, scientific and technological development areas, logistics and warehousing areas, green space and leisure areas, transportation hub areas, public facilities concentrated areas, industrial and mining communication sea areas, recreational sea areas, transportation sea areas and farmland protection areas are carbon emission land; Ecological protection areas, ecological control areas and farmland protection areas are considered terrestrial carbon sinks; Fishery sea areas and special sea areas are marine carbon sink areas.

[0007] In a feasible implementation manner, the specific method for obtaining the geospatial data includes: Step a1: Based on the planned zoning, obtain spatial distribution data, land use scale data, etc. of each zoning; Step a2: constructing a land use type code and land use area database; Step a3: Based on the carbon emission standard documents, construct an industry emission factor database and a framework for associating greenhouse gas emission inventory accounting categories with land use types; and obtain the initial value of carbon emission intensity based on carbon emission literature; Step a4: Through spatial overlay analysis, match land use scale data and land use type codes with corresponding carbon emission intensities to calculate carbon emissions for each type of land use; Step a5: spatially aggregate the carbon emissions of all districts in the city to generate a heat map of total carbon emissions and spatial distribution, and obtain geospatial data; In this way, the carbon emissions of each urban district, the total carbon emissions and the spatial distribution heat map can be obtained more accurately for subsequent simulation analysis.

[0008] In a feasible implementation, in step a1, spatial distribution data, land use scale data, etc. of each partition can be obtained through the geographic information public service platform.

[0009] In a feasible implementation manner, in step a2, the land use type code includes a primary code and a secondary code; The first-level codes include: CZ for urban development zone; NT for farmland protection zone; STKZ for ecological control zone; STBH for ecological protection zone; HY for marine development zone; The secondary codes include: CZ-jz for residential area; CZ-ss for commercial and business area; CZ-zh for comprehensive service area; CZ-wh for cultural and tourism area; CZ-gy for industrial development area; CZ-zh for scientific and technological development area; CZ-wl for logistics and warehousing area; CZ-yl for strategic reserved area; CZ-ld for green space and leisure area; CZ-sn for transportation hub area; CZ-gg for public facilities concentration area; HY-yy for fishery sea area; HY-ts for special sea area; HY-yq for recreational sea area; HY-gk for industrial, mining and communication sea area; HY-yl for marine reserved area; HY-jt for transportation sea area.

[0010] In a feasible embodiment, the industry carbon emission factor database in step a3 includes a carbon emission factor matrix constructed by energy and corresponding carbon emission factors; the energy includes electricity, natural gas, heating and gasoline; the carbon emission factors are determined in accordance with the IPCC Guidelines for National Greenhouse Gas Inventories; For example, calculating the carbon emission factor for natural gas The specific formula can be: ; Among them, low calorific value: refer to the default value, which is 389.3 (unit: GJ / 10,000 ), representing the energy value of fuel per unit volume; Carbon content per unit calorific value: This can be based on the fuel element composition and is set to 0.0153 (unit: tC / GJ), reflecting the mass of carbon per unit energy; Carbon oxidation rate: can be taken as 0.99 (i.e. 99% of carbon is completely oxidized to carbon dioxide) based on the actual measured data of combustion efficiency; Conversion coefficient: can be calculated based on the molecular weight ratio ( / C=44 / 12), the value is 3.67.

[0011] In a feasible implementation, the association framework includes a mapping matrix between land use type, energy consumption and carbon emission intensity.

[0012] In a feasible implementation, the carbon emission calculation formula for each type of land use includes: Carbon emissions from residential areas (in tonnes of carbon dioxide equivalent): ; in, Indicates the natural gas consumption in GJ; Indicates the low calorific value of natural gas, in GJ / 10,000 standard cubic meters. ); Indicates the carbon content per unit calorific value, in tons of carbon per gigajoules (tC / GJ); represents the carbon oxidation rate; express Conversion factor; represents the number of personal motor vehicles; represents the average annual mileage; Indicates fuel consumption per 100 kilometers; Indicates the gasoline volume conversion coefficient; Indicates the total amount of electricity consumed in 10,000 kWh ( ); It represents the carbon emission factor of electricity, with the unit being tons of carbon dioxide equivalent per 10,000 kWh; Carbon emissions from industrial development zones : ; in, Indicates the heat supply; represents the thermal carbon emission factor; Indicates the total amount of gasoline used; represents the gasoline carbon emission factor; Carbon emissions from commercial business districts : ; Carbon emissions in comprehensive service areas : ; Carbon emissions in the Science and Technology Innovation Development Zone : ; Carbon emissions in areas with concentrated public facilities : ; Carbon emissions from sea areas used for industry, mining and communications : ; Carbon emissions in cultural tourism areas : ; in, Indicates the number of tourists; represents the per capita carbon footprint; Carbon emissions in transportation hub areas : ; in, Indicates passenger flow; Carbon emissions from logistics and warehousing areas : ; in, It represents the highway freight turnover, in ton-kilometers (t·km); Expresses road carbon intensity in kg CO2 equivalent / ton-km ( ); Indicates the waterway cargo turnover; represents the carbon intensity of waterways; Carbon emissions from green space and leisure areas : ; in, Indicates the Area of ​​plant-like carbon sequestration factor; It represents the carbon sequestration factor carbon sink ratio; Carbon emissions from sea areas used for transportation : ; in, Indicates throughput in tons; Indicates the unit cargo turnover intensity, in kg CO2 equivalent / ton-kilometer; Carbon emissions from recreational marine areas :

[0013] Carbon emissions from farmland protection areas : ; in, Indicates the total amount of fertilizer applied; represents the fertilizer carbon emission factor; Indicates the Area of ​​crop carbon sequestration factor.

[0014] In a feasible implementation, the spatial overlay analysis in step a4 includes associating the land use codes with the carbon emission factor matrix through the spatial join tool of the ArcGIS Pro system, thereby facilitating the calculation of carbon emissions for a single partition.

[0015] In a feasible implementation, the spatial aggregation refers to calculating the total carbon emissions in a step-by-step manner based on land use type coding.

[0016] In a feasible implementation, the specific method for generating the spatial distribution heat map includes: Step a51: Use the weighted kernel density estimation (WKDE) method to incorporate carbon emissions as a weight factor into the spatial density analysis. The specific formula includes: ; in, Indicates spatial location Carbon emission density value at Indicates the number of carbon emission points; Indicates the The weight factor of each carbon emission point, in tons of carbon dioxide equivalent per square kilometer; represents the kernel function; Indicates bandwidth, i.e. search radius, for example, set it to 500 meters; Step a52: Collect and pre-process carbon emission point data in a unified GeoJSON format; the carbon emission point data includes latitude and longitude and carbon emissions; Step a53: Generate grid density using the weighted kernel density estimation method for the carbon emission point data; Step a54: Based on the grid density, a geographic information tool is used to perform visual output to obtain geographic spatial data.

[0017] In a feasible implementation manner, the visual output includes: When the carbon emission density is less than 5 tons / km², cool colors are used; When the carbon emission density is greater than 20 tons / km², a red-black gradient color is used; Overlay contour lines; Mark the carbon emission intensity threshold warning line; This makes it easier to visually display the step-by-step distribution of carbon emission density.

[0018] In a feasible implementation, carbon sink land is manually labeled in geospatial data using geographic information tools: green fill indicates terrestrial carbon sink land; light blue fill indicates marine carbon sink land.

[0019] In a feasible implementation manner, the specific calculation method of the carbon sink includes land carbon sink calculation and ocean carbon sink calculation; Terrestrial carbon sink The specific calculation formulas include: ; in, Indicates the The land area of ​​carbon sink-like elements, in hectares (ha). Carbon sink elements mainly include mountains (forests), water (inland lakes, rivers), fields and grass. The land area of ​​each element can be determined based on planning zoning and satellite remote sensing observations; Indicates the The carbon sequestration rate of carbon sink-like elements, expressed in tons of carbon per hectare per year (tC / ha•yr), can be initially determined based on the "Guidelines for Accounting for Carbon Sinks in Terrestrial Ecosystems" or the "Table of Carbon Sequestration Rates in Agricultural Soils"; The coefficient representing the conversion of carbon dioxide to carbon; The ocean carbon sink specifically includes mangrove carbon sink, shellfish carbon sink and sediment carbon sink, etc.; The mangrove carbon sequestration The specific calculation formula includes: ; in, It represents biomass in tons; It represents the carbon content of biomass, with the unit of ton of carbon / ton (tC / t); Shellfish carbon sequestration The specific calculation formula includes: ; in, Indicates the stocking density, in units of individuals / hectare; Indicates the average shell weight of a single shellfish in grams per piece; represents the shell carbon content coefficient; The sediment carbon sink The specific calculation formula includes: ; in, Indicates the ocean area in square meters; Indicates the Organic carbon content of layer sediments, expressed as percentage; represents the coefficient of sediment coverage over ocean area.

[0020] In a feasible implementation, the specific calculation method of the net emissions is: the carbon emissions of the statistical subject minus the carbon sinks of the statistical subject; the statistical subject can be the measured city or several subdistricts of the measured city, without limitation.

[0021] Step 2: Store the basic carbon emission data, carbon sink data, and output value data regularly collected by the smart city system in the smart city database based on time series; predict the future carbon emissions, carbon sinks, and output values ​​of the tested partition based on the trained LSTM (Long Short-Term Memory) model, thereby effectively capturing the long-term dependencies in the time series; construct a time series dynamic optimization model, based on the future carbon emissions, carbon sinks, and output values ​​of the tested partition, through a parallelized genetic algorithm, dynamically adjust the fitness evaluation function, and iteratively solve the optimal future industrial area layout plan for the tested partition.

[0022] In a feasible implementation, the smart city system includes a carbon emission sensing system, a carbon sink sensing system, and an output value sensing system; The carbon emission sensing system includes chimney sensors, monitoring cameras, energy consumption IoT network, etc. The chimney sensor is used to collect carbon emissions from the chimney; The monitoring camera is used to collect passenger flow; The energy consumption IoT network (including smart electricity meters, smart water meters, smart natural gas meters, smart heating stations, smart gas stations, etc.) is used to collect electricity consumption, gas consumption, heat consumption, and oil consumption; The carbon sink sensing system includes remote sensing satellites, soil sensors, smart buoys and ocean sonar, etc. The remote sensing satellite is used to collect data on the area of ​​terrestrial vegetation and its carbon dioxide absorption efficiency; The smart buoy is used to collect the carbon dioxide absorption efficiency of mangroves; The ocean sonar is used to collect carbon sequestration information from shellfish and seabed sediments; The output value perception system is connected to the enterprise ERP system, etc., and is used to collect enterprise output value data.

[0023] In a feasible implementation, the time series dynamic optimization model includes a sliding time window based on a planning period, dynamic variables, an objective function, and dynamic constraints. In a feasible implementation, the parallelized genetic algorithm includes: Step b1: Use the objective function of the time series dynamic optimization model as the fitness evaluation function; configure the DEAP (Distributed Evolutionary Algorithms in Python) toolbox to support parallel processing; Step b2: Use the scoop (Simple Concurrent Object-Oriented Programming) framework to perform multi-process distributed computing. Step b3: Based on the sliding time window, regularly obtain the latest data in the smart city database, update the fitness evaluation function, iteratively execute the genetic algorithm, and extract the optimal solution.

[0024] In a feasible implementation, the industrial area layout plan includes the land area, output value and net carbon emission value of each industry in the zone, so as to obtain the zone benefit with the highest output value and the lowest net carbon emission value in the shortest time.

[0025] Step 3: Based on the industrial area layout plan, generate recommended information through preset strategies and send it to the server of the competent department of the tested area through smart contracts to ensure data security and prevent tampering. Based on the smart city database, regularly compare the predicted data (carbon emissions, carbon sinks, and output value) with the actual data to update the parameters of the LSTM model. This allows the industrial area layout plan to be effectively implemented and the LSTM model to be calibrated through closed-loop feedback.

[0026] In a feasible implementation, the preset strategy includes a land use adjustment strategy: the industries in the partition are first sorted according to the net carbon emission value, and several high-carbon industries with the largest net carbon emission value are screened out to generate recommendation information for reducing the land area; among the remaining industries, they are sorted according to output value, and several high-yield industries with the largest output value are screened out to generate recommendation information for expanding the land area.

[0027] In a feasible implementation, the preset strategy further includes a spatial layout assessment strategy, an industrial structure assessment strategy, and a carbon sequestration capacity assessment strategy; The spatial layout assessment strategy includes generating a carbon emission density grid based on geospatial data bound to land use codes through spatial interpolation (IDW / Kriging) methods, visually displaying carbon emission intensity based on preset color classification thresholds, and mapping it to preset spatial layout optimization recommendation terms; The proposed terms for space layout optimization specifically include: Industrial land reorganization proposals: relocate high-carbon industries from ecologically sensitive areas to centralized industrial parks and provide supporting green electricity supply facilities; Suggestions for revitalizing existing land: Utilize urban villages, old residential areas, and idle and inefficiently used commercial and office spaces to develop large-scale rental housing; Recommendations for work-life balance planning: Reduce carbon emissions from commuting; The industry assessment strategy includes analyzing the impact of economic activities on resource consumption and pollution emissions based on the existing Environmental Input-Output Model (EIO), and mapping this to pre-set recommended terms for industrial structure upgrades; The proposed provisions for industrial structure upgrading specifically include: Green industry substitution suggestions: Promote hydrogen energy industrial parks to replace traditional petrochemical projects; Circular economy pilot projects suggest: building zero-carbon building clusters, mandating the use of recycled building materials, and promoting recycled buildings; The carbon sink capacity assessment strategy includes dividing carbon sink land into high, medium, and low carbon sink zones based on preset thresholds; quantifying the demand for carbon sink land based on the net carbon value of each zone and mapping it to preset carbon sink function enhancement recommendations; The proposed provisions for strengthening carbon sequestration functions specifically include: The Marine Blue Carbon Project recommends: expanding the scale of mangrove restoration; Suggestions for improving the quality of urban green spaces: Improve the carbon sequestration efficiency of green spaces through three-dimensional greening and replacement of drought-resistant plants; Recommendations for ecological network restoration: Connect fragmented carbon sink nodes through ecological corridors to improve carbon sink connectivity.

[0028] In the second aspect, based on the same inventive concept, the present application also provides a carbon neutrality zoning simulation system based on a smart city, including a data receiving module, a data processing module and a result generation module; The data receiving module is used to receive a map of planned zones of the tested city, basic carbon emission data, basic carbon sink data, and basic output value data; The data processing module includes a geospatial data unit, a data prediction unit, a dynamic optimization unit, and a suggestion information unit; The geospatial data unit calculates the carbon emissions of each sub-district based on the map and the basic carbon emission data and performs visualization processing to obtain geospatial data; the basic carbon sink data is annotated in the geospatial data and the carbon sink of each sub-district is calculated; the net emissions of the measured city are calculated based on the carbon emissions and carbon sinks; the basic output value data of each industry is annotated in the geospatial data; The data prediction unit predicts the future carbon emissions, carbon sinks, and output value of the measured subarea based on a trained LSTM (Long Short-Term Memory) model; compares the predicted data with the actual data at regular intervals and updates the parameters of the LSTM model; The dynamic optimization unit constructs a time-series dynamic optimization model, dynamically adjusts the fitness evaluation function based on the future carbon emissions, carbon sinks and output value of the measured partition through a parallel genetic algorithm, and iteratively solves the optimal future industrial area layout plan for the measured partition; The suggestion information unit generates suggestion information based on the industrial area layout plan and a preset strategy; The result generation module is used to send out the industrial area layout plan and the suggestion information.

[0029] In a feasible implementation manner, the data receiving module is connected to the smart city system of the measured city for accessing the smart city database.

[0030] On the third aspect, based on the same inventive concept, the present application also provides a carbon-neutral zoning simulation device based on smart cities, including a processor, a memory and a bus, wherein the memory stores instructions and data that can be read by the processor, and the processor is used to call the instructions and data in the memory to execute the carbon-neutral zoning simulation method based on smart cities as described above, and the bus connects the functional components for transmitting information.

[0031] By adopting the above technical solution, the present invention has the following beneficial effects: The present invention provides a carbon neutrality zoning simulation method, system and device based on smart cities, which provide quantifiable and controllable carbon management tools for urban land space master planning, assist in achieving the goals of carbon peak and carbon neutrality, and are particularly suitable for low-carbon planning and governance in high-density coastal cities; through visualization, it facilitates intuitive decision-making assistance; takes into account the differences in activity intensity at the plot scale, and improves planning accuracy; facilitates the subdivision of industries and reduces the uncertainty of planning schemes; based on the time-series dynamic optimization model, it can reflect the impact of energy structure changes on carbon emissions in real time, and obtain the optimal industrial area layout plan for the future. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0033] Figure 1 A flow chart of a carbon neutrality zoning simulation method based on smart cities provided by an embodiment of the present invention; Figure 2 A flowchart of a specific method for acquiring geographic spatial data provided by an embodiment of the present invention; Figure 3 A flow chart of a parallel genetic algorithm provided by an embodiment of the present invention; Figure 4 A flowchart of a specific method for generating reduction suggestion information provided by an embodiment of the present invention; Figure 5 A flowchart of a specific method for generating amplification suggestion information provided by an embodiment of the present invention; Figure 6 A carbon neutrality zoning simulation system diagram based on a smart city is provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0035] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0036] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0037] The present invention will be further explained below with reference to specific embodiments.

[0038] It should also be noted that the following specific embodiments or specific implementations are a series of optimized settings listed in the present invention to further explain the specific content of the invention, and these settings can be combined or used in association with each other.

[0039] Example 1: like Figure 1 As shown, this embodiment provides a carbon neutrality zoning simulation method based on a smart city, including the following steps: Step 1: Plan and zone the map of the city under test; based on standards and literature, collect basic carbon emission data (such as greenhouse gas emissions, carbon emission intensity, etc.), calculate the carbon emissions of each zone and visualize it to obtain geospatial data (such as heat maps); collect basic carbon sink data and mark it in the geospatial data, and calculate the carbon sink of each zone; calculate the net emissions of the city under test based on carbon emissions and carbon sinks; collect basic output value data of each industry in the tested zone and mark it in the geospatial data; this can obtain basic data in the initial state, so as to facilitate subsequent quantitative evaluation of the impact of the spatial structure and scale of each zone on carbon emissions, carbon sinks and output value.

[0040] Furthermore, the planning zones include primary planning zones and secondary planning zones; The first-level planning zones include urban development zones, farmland protection zones, ecological control zones, ecological protection zones, and marine development zones; The secondary planning zones of the urban development zone include residential areas, commercial and business areas, comprehensive service areas, cultural and tourism areas, industrial development areas, scientific and technological innovation development areas, logistics and warehousing areas, strategic reserved areas, green space and leisure areas, transportation hub areas, and areas with concentrated public facilities; The secondary planning zones of the marine development zone include fishery sea areas, special sea areas, recreational sea areas, industrial and mining communication sea areas, marine reserved areas, and transportation sea areas; Among them, residential areas, commercial and business areas, comprehensive service areas, cultural and tourism areas, industrial development areas, scientific and technological development areas, logistics and warehousing areas, green space and leisure areas, transportation hub areas, public facilities concentrated areas, industrial and mining communication sea areas, recreational sea areas, transportation sea areas and farmland protection areas are carbon emission land; Ecological protection areas (such as forests and wetlands), ecological control areas (such as grasslands and ecological corridors), and farmland protection areas are considered terrestrial carbon sinks; Fishery sea areas and special sea areas (such as mangroves) are marine carbon sinks.

[0041] Further, if Figure 2 As shown, the specific method for obtaining the geographic spatial data includes: Step a1: Based on the planned zoning, obtain spatial distribution data, land use scale data, etc. of each zoning; Step a2: constructing a land use type code and land use area database; Step a3: Based on carbon emission standard documents, construct an industry emission factor database and a framework for associating greenhouse gas emission inventory accounting categories with land use types; obtain initial carbon emission intensity values ​​based on carbon emission literature (such as the IEA Global Energy Transition Report); Step a4: Through spatial overlay analysis, match land use scale data and land use type codes with corresponding carbon emission intensities to calculate carbon emissions for each type of land use; Step a5: spatially aggregate the carbon emissions of all districts in the city to generate a heat map of total carbon emissions and spatial distribution, and obtain geospatial data; In this way, the carbon emissions of each urban district, the total carbon emissions and the spatial distribution heat map can be obtained more accurately for subsequent simulation analysis.

[0042] Furthermore, in step a1, spatial distribution data, land use scale data, etc. of each partition can be obtained through the geographic information public service platform.

[0043] Furthermore, in step a2, the land use type code includes a primary code and a secondary code; The first-level codes include: CZ for urban development zone; NT for farmland protection zone; STKZ for ecological control zone; STBH for ecological protection zone; HY for marine development zone; The secondary codes include: CZ-jz for residential area; CZ-ss for commercial and business area; CZ-zh for comprehensive service area; CZ-wh for cultural and tourism area; CZ-gy for industrial development area; CZ-zh for scientific and technological development area; CZ-wl for logistics and warehousing area; CZ-yl for strategic reserved area; CZ-ld for green space and leisure area; CZ-sn for transportation hub area; CZ-gg for public facilities concentration area; HY-yy for fishery sea area; HY-ts for special sea area; HY-yq for recreational sea area; HY-gk for industrial, mining and communication sea area; HY-yl for marine reserved area; HY-jt for transportation sea area.

[0044] Furthermore, the industry carbon emission factor database in step a3 includes a carbon emission factor matrix constructed by energy and corresponding carbon emission factors; the energy includes electricity, natural gas, heating and gasoline; the carbon emission factors are determined in accordance with the IPCC Guidelines for National Greenhouse Gas Inventories; For example, calculating the carbon emission factor for natural gas The specific formula can be: ; Among them, low calorific value: refer to the default value, which is 389.3 (unit: GJ / 10,000 ), representing the energy value of fuel per unit volume; Carbon content per unit calorific value: This can be based on the fuel element composition and is set to 0.0153 (unit: tC / GJ), reflecting the mass of carbon per unit energy; Carbon oxidation rate: can be taken as 0.99 (i.e. 99% of carbon is completely oxidized to carbon dioxide) based on the actual measured data of combustion efficiency; Conversion coefficient: can be calculated based on the molecular weight ratio ( =44 / 12), the value is 3.67.

[0045] Furthermore, the association framework includes a mapping matrix between land use type, energy consumption and carbon emission intensity.

[0046] Furthermore, the calculation formulas for carbon emissions of various types of land use include: Carbon emissions from residential areas (in tonnes of carbon dioxide equivalent): ; in, Indicates the natural gas consumption in GJ; Indicates the low calorific value of natural gas, in GJ / 10,000 standard cubic meters. ); Indicates the carbon content per unit calorific value, in tons of carbon per gigajoules (tC / GJ); represents the carbon oxidation rate; express Conversion factor; represents the number of personal motor vehicles; represents the average annual mileage; Indicates fuel consumption per 100 kilometers; Indicates the gasoline volume conversion coefficient; Indicates the total amount of electricity consumed in 10,000 kWh ( ); It represents the carbon emission factor of electricity, with the unit being tons of carbon dioxide equivalent per 10,000 kWh; Carbon emissions from industrial development zones : ; in, Indicates the heat supply; represents the thermal carbon emission factor; Indicates the total amount of gasoline used; represents the gasoline carbon emission factor; Carbon emissions from commercial business districts : ; Carbon emissions in comprehensive service areas : ; Carbon emissions in the Science and Technology Innovation Development Zone : ; Carbon emissions in areas with concentrated public facilities : ; Carbon emissions from sea areas used for industry, mining and communications : ; Carbon emissions in cultural tourism areas : ; in, Indicates the number of tourists; represents the per capita carbon footprint; Carbon emissions in transportation hub areas : ; in, Indicates passenger flow; Carbon emissions from logistics and warehousing areas : ; in, It represents the highway freight turnover, in ton-kilometers (t·km); Expresses road carbon intensity in kg CO2 equivalent / ton-km ( ); Indicates the waterway cargo turnover; represents the carbon intensity of waterways; Carbon emissions from green space and leisure areas : ; in, Indicates the Area of ​​plant-like carbon sequestration factor; It represents the carbon sequestration factor carbon sink ratio; Carbon emissions from sea areas used for transportation : ; in, Indicates throughput in tons; Indicates the unit cargo turnover intensity, in kg CO2 equivalent / ton-kilometer; Carbon emissions from recreational marine areas :

[0047] Carbon emissions from farmland protection areas : ; in, Indicates the total amount of fertilizer applied; represents the fertilizer carbon emission factor; Indicates the Area of ​​crop carbon sequestration factor.

[0048] Furthermore, the spatial overlay analysis in step a4 includes associating the land use codes with the carbon emission factor matrix through the spatial join tool of the ArcGIS Pro system, thereby facilitating the calculation of carbon emissions for a single partition.

[0049] Furthermore, the spatial aggregation refers to calculating the total carbon emissions in a step-by-step manner according to the land use type coding.

[0050] Furthermore, the specific method for generating the spatial distribution heat map includes: Step a51: Use the weighted kernel density estimation (WKDE) method to incorporate carbon emissions as a weight factor into the spatial density analysis. The specific formula includes: ; in, Indicates spatial location Carbon emission density value at Indicates the number of carbon emission points; Indicates the The weight factor of each carbon emission point, in tons of carbon dioxide equivalent per square kilometer; represents the kernel function; Indicates bandwidth, i.e. search radius, for example, set it to 500 meters; Step a52: Collect and pre-process carbon emission point data in a unified GeoJSON format; the carbon emission point data includes latitude and longitude and carbon emissions; Step a53: Generate grid density using the weighted kernel density estimation method for the carbon emission point data; Step a54: Based on the grid density, a geographic information tool (such as PyQGIS) is used to perform visualization output to obtain geospatial data.

[0051] Furthermore, the visual output includes: When the carbon emission density is less than 5 tons / km², a cool color (such as blue) is used; When the carbon emission density is greater than 20 tons / km², a red-black gradient color is used; Overlay contour lines; Mark the carbon emission intensity threshold (e.g. 10 tons / km²) warning line; This makes it easier to visually display the step-by-step distribution of carbon emission density.

[0052] Furthermore, in the geospatial data, carbon sink land is manually labeled using geographic information tools (such as ArcGIS): green fill represents terrestrial carbon sink land; light blue fill represents marine carbon sink land.

[0053] Furthermore, the specific calculation method of the carbon sink includes the calculation of land carbon sink and the calculation of ocean carbon sink; Terrestrial carbon sink The specific calculation formulas include: ; in, Indicates the The land area of ​​carbon sink-like elements, in hectares (ha). Carbon sink elements mainly include mountains (forests), water (inland lakes, rivers), fields and grass. The land area of ​​each element can be determined based on planning zoning and satellite remote sensing observations; Indicates the The carbon sequestration rate of carbon sink-like elements, expressed in tons of carbon per hectare per year (tC / ha•yr), can be initially determined based on the "Guidelines for Accounting for Carbon Sinks in Terrestrial Ecosystems" or the "Table of Carbon Sequestration Rates in Agricultural Soils"; The coefficient representing the conversion of carbon dioxide to carbon; The ocean carbon sink specifically includes mangrove carbon sink, shellfish carbon sink and sediment carbon sink, etc.; The mangrove carbon sequestration The specific calculation formula includes: ; in, It represents biomass in tons; It represents the carbon content of biomass, with the unit of ton of carbon / ton (tC / t); Shellfish carbon sequestration The specific calculation formula includes: ; in, Indicates the stocking density, in units of individuals / hectare; Indicates the average shell weight of a single shellfish in grams per piece; represents the shell carbon content coefficient; The sediment carbon sink The specific calculation formula includes: ; in, Indicates the ocean area in square meters; Indicates the Organic carbon content of layer sediments, expressed as percentage; represents the coefficient of sediment coverage over ocean area.

[0054] Furthermore, the specific calculation method of the net emissions is: the carbon emissions of the statistical subject minus the carbon sinks of the statistical subject; the statistical subject can be the measured city or several sub-districts of the measured city, without limitation.

[0055] Step 2: Store the basic carbon emission data, carbon sink data and output value data regularly collected by the smart city system in the smart city database based on time series; predict the future carbon emissions, carbon sinks and output value of the tested partition based on the trained conventional LSTM (Long Short-Term Memory) model, so as to effectively capture the long-term dependencies in the time series; construct a time series dynamic optimization model, based on the future carbon emissions, carbon sinks and output value of the tested partition, through a parallel conventional genetic algorithm, dynamically adjust the fitness evaluation function, and iteratively solve the optimal future industrial area layout plan for the tested partition.

[0056] Furthermore, the smart city system includes a carbon emission sensing system, a carbon sink sensing system and an output value sensing system; The carbon emission sensing system includes chimney sensors, monitoring cameras, energy consumption IoT network, etc. The chimney sensor is used to collect carbon emissions from the chimney; The monitoring camera is used to collect passenger flow; The energy consumption IoT network (including smart electricity meters, smart water meters, smart natural gas meters, smart heating stations, smart gas stations, etc.) is used to collect electricity consumption, gas consumption, heat consumption, and oil consumption; The carbon sink sensing system includes remote sensing satellites, soil sensors, smart buoys and ocean sonar, etc. The remote sensing satellite is used to collect data on the area of ​​terrestrial vegetation and its carbon dioxide absorption efficiency; The smart buoy is used to collect the carbon dioxide absorption efficiency of mangroves; The ocean sonar is used to collect carbon sequestration information from shellfish and seabed sediments; The output value perception system is connected to the enterprise ERP system, etc., and is used to collect enterprise output value data.

[0057] Furthermore, the time series dynamic optimization model includes a sliding time window based on the planning period; for example, the model is optimized once every quarter, with each planning (stage) covering the next three years; Also includes dynamic variables, including: No. Period Partition No. Land area of ​​each industry ; No. Period Partition No. The area of ​​carbon sink elements ; It also includes the objective function, including: ; in, Indicates the total planning time, which needs to be minimized in order to achieve the goal of emission reduction and efficiency improvement as quickly as possible; Indicate the duration of each planning stage; Indicates the planning stage; represents the discount rate, which is set according to the fiscal policy of the city under test; and It represents a dynamic weight that can be adjusted according to the policy priorities of the city being measured; Represents the cumulative output value. The specific formula is: ; in, Indicates the Partition No. The industry in The output value per unit area in a certain period of time may increase over time due to factors such as technological progress; represents the net emission penalty item in the planning stage, and the specific formula is: ; in, Indicates the Partition No. The carbon emission intensity of an industry can decrease over time due to factors such as technological progress; Indicates the Partition No. The carbon sequestration capacity of a carbon sink element can increase over time due to factors such as plant growth. The specific formula is: ; in, Represents the carbon sink maturity rate, which is used to simulate the growth of vegetation carbon sequestration capacity from planting to forestation; represents a natural constant; It also includes dynamic constraints, including: The total land constraint is expressed as follows: ; in, Indicates the The total area of ​​each subdistrict; Industrial continuity constraint, the specific expression is: ; in, Indicates the land area change threshold, which is used to limit the sudden decrease of industrial area; Carbon neutrality path constraint, the specific expression is: ; in, Indicates the first Net emissions at a point in time; Indicates the slope of the linearly decreasing target line.

[0058] Further, if Figure 3 As shown, the parallel genetic algorithm includes: Step b1: Use the objective function of the time series dynamic optimization model as the fitness evaluation function; configure the DEAP (Distributed Evolutionary Algorithms in Python) toolbox to support parallel processing; Step b2: Perform multi-process (e.g., 4-process) distributed computing using the conventional scoop (Simple Concurrent Object-Oriented Programming) framework. Step b3: Based on the sliding time window, regularly obtain the latest data in the smart city database, update the fitness evaluation function, iteratively execute the genetic algorithm, and extract the optimal solution.

[0059] Furthermore, the industrial area layout plan includes the land area, output value and net carbon emission value of each industry in the zone, so as to obtain the zone benefit with the highest output value and the lowest net carbon emission value in the shortest time.

[0060] Step 3: Based on the industrial area layout plan, generate recommended information through preset strategies and send it to the server of the competent department of the measured area through smart contracts (such as blockchain) to ensure data security and prevent tampering; based on the smart city database, regularly compare the predicted data (carbon emissions, carbon sinks and output value) with the actual data to update the parameters of the LSTM model; this can ensure the effective implementation of the industrial area layout plan and calibrate the LSTM model through closed-loop feedback.

[0061] Furthermore, the preset strategy includes a land use adjustment strategy: the industries in the zone are first sorted according to the net carbon emission value, and several high-carbon industries with the largest net carbon emission value are screened out to generate recommendation information for reducing the land area; among the remaining industries, they are sorted according to the output value, and several high-yield industries with the largest output value are screened out to generate recommendation information for expanding the land area.

[0062] Further, if Figure 4 As shown, the specific method for generating the reduction suggestion information includes: Step c1: Calculate the total reduced area , the specific formula is: ; in, Indicates the Current land area used by high-carbon industries; Indicates the preset total reduction ratio; Step c2: sort the high-carbon industries in descending order of carbon emission intensity per unit area; Step c3: Starting from the highest ranked high-carbon industry, reduce its area as needed until the , the specific formulas include: ; in, Indicates the Reduce the area of ​​high-carbon industries; express The remaining area is: .

[0063] Further, if Figure 5 As shown, the specific method for generating the expansion suggestion information includes: Step d1: Reduce the total area of ​​high-carbon industries , as the total area of ​​expansion of high-yield industries; Step d2: Arrange the high-yield industries in descending order of output value per unit area; Step d3: Starting from the highest ranked high-yield industry, allocate , until exhausted, the specific formula includes: ; in, Indicates the the expansion of high-yield industries; express The remaining area is: .

[0064] In this way, under the premise that the zoned land area remains unchanged, more accurate land area adjustment recommendation information can be obtained through greedy reduction and greedy expansion, which is conducive to the low-carbon and high-growth urban development goals.

[0065] Example 2: like Figure 6 As shown, this embodiment provides a carbon neutrality zoning simulation system based on smart cities, including a data receiving module, a data processing module and a result generating module; The data receiving module is used to receive a map of planned zones of the tested city, basic carbon emission data, basic carbon sink data, and basic output value data; The data processing module includes a geospatial data unit, a data prediction unit, a dynamic optimization unit, and a suggestion information unit; The geospatial data unit calculates the carbon emissions of each sub-district based on the map and the basic carbon emission data and performs visualization processing to obtain geospatial data; the basic carbon sink data is annotated in the geospatial data and the carbon sink of each sub-district is calculated; the net emissions of the measured city are calculated based on the carbon emissions and carbon sinks; the basic output value data of each industry is annotated in the geospatial data; The data prediction unit predicts the future carbon emissions, carbon sinks, and output value of the measured subarea based on a trained LSTM (Long Short-Term Memory) model; compares the predicted data with the actual data at regular intervals and updates the parameters of the LSTM model; The dynamic optimization unit constructs a time-series dynamic optimization model, dynamically adjusts the fitness evaluation function based on the future carbon emissions, carbon sinks and output value of the measured partition through a parallel genetic algorithm, and iteratively solves the optimal future industrial area layout plan for the measured partition; The suggestion information unit generates suggestion information based on the industrial area layout plan and a preset strategy; The result generation module is used to send out the industrial area layout plan and the suggestion information.

[0066] Furthermore, the data receiving module is connected to the smart city system of the measured city for accessing the smart city database.

[0067] Example 3: This embodiment provides a carbon-neutral zoning simulation device based on a smart city, including a processor, a memory and a bus. The memory stores instructions and data that can be read by the processor. The processor is used to call the instructions and data in the memory to execute the carbon-neutral zoning simulation method based on a smart city as described above. The bus connects the functional components for transmitting information.

[0068] In another embodiment, this solution can be implemented as an integrated device that can include corresponding modules for performing each or several steps in each of the above embodiments. The modules can be one or more hardware modules specifically configured to perform the corresponding steps, or implemented by a processor configured to perform the corresponding steps, or stored in a computer-readable medium for implementation by the processor, or implemented by some combination thereof.

[0069] The processor performs the various methods and processes described above. For example, the method implementation in this solution can be implemented as a software program, which is tangibly contained in a machine-readable medium, such as a memory. In some embodiments, part or all of the software program can be loaded and / or installed via a memory and / or a communication interface. When the software program is loaded into the memory and executed by the processor, one or more steps in the method described above can be performed. Alternatively, in other embodiments, the processor can be configured to perform one of the above methods by any other appropriate means (e.g., by means of firmware).

[0070] The device can be implemented using a bus architecture. The bus architecture can include any number of interconnecting buses and bridges, depending on the specific application and overall design constraints of the hardware. The bus connects various circuits including one or more processors, memories, and / or hardware modules. The bus can also connect various other circuits such as peripherals, voltage regulators, power management circuits, external antennas, etc.

[0071] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Component (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A carbon neutrality zoning simulation method based on smart cities, characterized in that: include: Step 1: Plan and partition the map of the city to be tested; Based on standards and literature, basic carbon emission data is collected, carbon emissions of each sub-district are calculated and visualized to obtain geospatial data; basic carbon sink data is collected and annotated in the geospatial data, and carbon sinks of each sub-district are calculated; based on carbon emissions and carbon sinks, net emissions of the measured cities are calculated; basic output value data of each industry in the measured sub-district is collected and annotated in the geospatial data; Step 2: The basic carbon emission data, carbon sink data, and output value data collected regularly by the smart city system are stored in the smart city database based on a time series. Based on the trained LSTM model, the future carbon emissions, carbon sinks, and output values ​​of the tested partition are predicted. A time series dynamic optimization model is constructed. Based on the future carbon emissions, carbon sinks, and output values ​​of the tested partition, a parallel genetic algorithm is used to dynamically adjust the fitness evaluation function and iteratively solve the optimal future industrial area layout plan for the tested partition. Step 3: Based on the industrial area layout plan, generate recommendation information through preset strategies and send it to the server of the competent department of the tested area through smart contracts; based on the smart city database, compare the predicted data with the actual data at regular intervals and update the parameters of the LSTM model.

2. The method according to claim 1, characterized in that The planning zones include primary planning zones and secondary planning zones; The first-level planning zones include urban development zones, farmland protection zones, ecological control zones, ecological protection zones, and marine development zones; The secondary planning zones of the urban development zone include residential areas, commercial and business areas, comprehensive service areas, cultural and tourism areas, industrial development areas, scientific and technological development areas, logistics and warehousing areas, strategic reserved areas, green space and leisure areas, transportation hub areas, and areas with concentrated public facilities; The secondary planning zones of the marine development zone include fishery sea areas, special sea areas, recreational sea areas, industrial, mining and communication sea areas, marine reserved areas and transportation sea areas.

3. The method according to claim 1, characterized in that The specific method for obtaining the geospatial data includes: Step a1: Based on the planned zoning, obtain the spatial distribution data and land use scale data of each zoning; Step a2: constructing a land use type code and land use area database; Step a3: Based on the carbon emission standard documents, construct an industry emission factor database and a framework for associating greenhouse gas emission inventory accounting categories with land use types; and obtain the initial value of carbon emission intensity based on carbon emission literature; Step a4: Through spatial overlay analysis, match land use scale data and land use type codes with corresponding carbon emission intensities to calculate carbon emissions for each type of land use; Step a5: spatially aggregate the carbon emissions of all districts in the city to generate a heat map of total carbon emissions and spatial distribution, and obtain geospatial data.

4. The method according to claim 3, characterized in that In step a2, the land use type code includes a primary code and a secondary code; The first-level codes include: CZ for urban development zone; NT for farmland protection zone; STKZ for ecological control zone; STBH for ecological protection zone; HY for marine development zone; The secondary codes include: CZ-jz for residential area; CZ-ss for commercial and business area; CZ-zh for comprehensive service area; CZ-wh for cultural and tourism area; CZ-gy for industrial development area; CZ-zh for scientific and technological development area; CZ-wl for logistics and warehousing area; CZ-yl for strategic reserved area; CZ-ld for green space and leisure area; CZ-sn for transportation hub area; CZ-gg for public facilities concentration area; HY-yy for fishery sea area; HY-ts for special sea area; HY-yq for recreational sea area; HY-gk for industrial, mining and communication sea area; HY-yl for marine reserved area; HY-jt for transportation sea area.

5. The method according to claim 4, characterized in that The calculation formulas for carbon emissions of various types of land use include: Carbon emissions from residential areas : ； in, Indicates natural gas consumption; Indicates the low calorific value of natural gas; Indicates the carbon content per unit calorific value; represents the carbon oxidation rate; express Conversion factor; represents the number of personal motor vehicles; represents the average annual mileage; Indicates fuel consumption per 100 kilometers; Indicates the gasoline volume conversion coefficient; Indicates the total amount of electricity used; represents the carbon emission factor of electricity; Carbon emissions from industrial development zones : ； in, Indicates the heat supply; represents the thermal carbon emission factor; Indicates the total amount of gasoline used; represents the gasoline carbon emission factor; Carbon emissions from commercial business districts : ； Carbon emissions in comprehensive service areas : ； Carbon emissions in the Science and Technology Innovation Development Zone : ； Carbon emissions in areas with concentrated public facilities : ； Carbon emissions from sea areas used for industry, mining and communications : ； Carbon emissions in cultural tourism areas : ； in, Indicates the number of tourists; represents the per capita carbon footprint; Carbon emissions in transportation hub areas : ； in, Indicates passenger flow; Carbon emissions from logistics and warehousing areas : ； in, Indicates highway freight turnover; represents the carbon intensity of roads; Indicates the waterway cargo turnover; represents the carbon intensity of waterways; Carbon emissions from green space and leisure areas : ； in, Indicates the Area of ​​plant-like carbon sequestration factor; It represents the carbon sequestration factor carbon sink ratio; Carbon emissions from sea areas used for transportation : ； in, represents throughput; Indicates the unit cargo turnover intensity; Carbon emissions from recreational marine areas : Carbon emissions from farmland protection areas : ； in, Indicates the total amount of fertilizer applied; represents the fertilizer carbon emission factor; Indicates the Area of ​​crop carbon sequestration factor.

6. The method according to claim 3, characterized in that The specific method for generating the spatial distribution heat map includes: Step a51: Use the weighted kernel density estimation method to incorporate carbon emissions as a weight factor into the spatial density analysis. The specific formula includes: ； in, Indicates spatial location Carbon emission density value at Indicates the number of carbon emission points; Indicates the Weighting factor for each carbon emission point; represents the kernel function; Indicates bandwidth; Step a52: Collect and pre-process carbon emission point data in a unified GeoJSON format; the carbon emission point data includes latitude and longitude and carbon emissions; Step a53: Generate grid density using the weighted kernel density estimation method for the carbon emission point data; Step a54: Based on the grid density, a geographic information tool is used to perform visual output to obtain geographic spatial data.

7. The method according to claim 6, characterized in that The visual output includes: When the carbon emission density is less than 5 tons / km², cool colors are used; When the carbon emission density is greater than 20 tons / km², a red-black gradient color is used; Overlay contour lines; Mark the carbon emission intensity threshold warning line.

8. The method according to claim 6, characterized in that The specific calculation method of the carbon sink includes land carbon sink calculation and ocean carbon sink calculation; Terrestrial carbon sink The specific calculation formulas include: ； in, Indicates the The area of ​​land used as carbon sinks, including mountains, water, fields, and grass; Indicates the Carbon sequestration rate of carbon sink-like elements; The coefficient representing the conversion of carbon dioxide to carbon; The ocean carbon sink specifically includes mangrove carbon sink, shellfish carbon sink and sediment carbon sink; The mangrove carbon sequestration The specific calculation formula includes: ； in, represents biomass; represents the carbon content of biomass; Shellfish carbon sequestration The specific calculation formula includes: ； in, Indicates stocking density; Indicates the average shell weight of a single shellfish; represents the shell carbon content coefficient; The sediment carbon sink The specific calculation formula includes: ； in, Indicates the ocean area; Indicates the Organic carbon content of layer sediments; It represents the coefficient of sediment coverage over ocean area.

9. A carbon neutrality zoning simulation system based on smart cities, characterized by: It includes a data receiving module, a data processing module and a result generating module; The data receiving module is used to receive a map of planned zones of the tested city, basic carbon emission data, basic carbon sink data, and basic output value data; The data processing module includes a geospatial data unit, a data prediction unit, a dynamic optimization unit, and a suggestion information unit; The geospatial data unit calculates the carbon emissions of each zone based on the map and the basic carbon emissions data and performs visualization processing to obtain geospatial data; Mark the basic carbon sink data in the geospatial data and calculate the carbon sink amount of each sub-region; calculate the net emissions of the measured cities based on carbon emissions and carbon sinks; mark the basic output value data of each industry in the geospatial data; The data prediction unit predicts the future carbon emissions, carbon sinks and output value of the measured partition based on the trained LSTM model; compares the predicted data with the actual data at regular intervals and updates the parameters of the LSTM model; The dynamic optimization unit constructs a time-series dynamic optimization model, dynamically adjusts the fitness evaluation function based on the future carbon emissions, carbon sinks and output value of the measured partition through a parallel genetic algorithm, and iteratively solves the optimal future industrial area layout plan for the measured partition; The suggestion information unit generates suggestion information based on the industrial area layout plan and a preset strategy; The result generation module is used to send out the industrial area layout plan and the suggestion information.

10. A carbon neutral zoning simulation device based on smart city, characterized in that: It includes a processor, a memory and a bus, the memory stores instructions and data read by the processor, the processor is used to call the instructions and data in the memory to execute the method according to any one of claims 1 to 8, and the bus connects the functional components to transmit information.

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