Park dynamic carbon portrait generation method

Abstract

The invention discloses a park dynamic carbon portrait generation method and system, and the method comprises the steps: collecting building, equipment and traffic multi-source energy consumption data in real time through Internet of Things equipment, and calculating the carbon emission intensity in combination with a dynamic carbon emission factor library; constructing a three-dimensional park model, mapping the carbon emission data into a dynamic thermodynamic diagram layer according to a spatial hierarchical structure (park-building-floor-equipment), and synchronously generating a vehicle carbon emission intensity diagram; positioning a key emission source based on the energy flow-carbon flow mulberry-based graph, setting a grading early warning threshold value, and triggering a three-dimensional highlight alarm and carbon reduction strategy suggestion; an optimization algorithm is adopted to generate a multi-energy cooperative regulation and control instruction, and the instruction is pushed to the energy management system for execution; and closed-loop verification and iterative optimization are realized by monitoring emission reduction, economic benefits and carbon potential indexes. The space-time dynamic visualization of carbon emission, key source intelligent positioning and adaptive optimization closed loop are realized, the problems of lack of real-time performance and low traceability precision of park carbon management are solved, and the carbon emission intensity is reduced through actual measurement.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of low-carbon management, and particularly relates to a park dynamic carbon portrait generation method. BACKGROUND

[0002] Current energy saving and emission reduction is an important link in construction management, and all walks of life are actively taking measures to reduce energy consumption and emissions. As an important carrier of economic development, a park often contains a large number of enterprises, and for some manufacturing and production parks, energy management and low-carbon management become key links. At present, the low-carbon construction in the park is often monitored in real time by intelligent sensors, monitoring equipment and the like installed in each enterprise in the park, and data analysis and management are carried out through big data and artificial intelligence algorithms. However, in the above method, there are the following limitations: single visualization dimension: only static heat maps or basic statistical charts are supported, and there is a lack of spatial level association; weak traceability: unable to track the source of carbon emissions; lagging control response: dependent on manual analysis, difficult to timely warn and intervene. SUMMARY

[0003] The purpose of the present application is to provide a park dynamic carbon portrait generation method to solve the problems raised in the background art.

[0004] To achieve the above purpose, the present application provides the following technical scheme: a park dynamic carbon portrait generation method, comprising the following steps: 1) Real-time acquisition of multi-source energy consumption data in the park, calculation of carbon emission intensity through dynamic carbon emission factors, construction of a three-dimensional space model and mapping of carbon emission distribution according to the spatial level structure, and synchronous generation of vehicle carbon emission intensity map; 2) Construction of energy flow and carbon flow Sanjia diagram based on S1 data, and labeling of objects with high proportion and large emission reduction space in total carbon emission as key sources; 3) According to the labeled key sources, set a hierarchical warning threshold, when the real-time carbon emission exceeds the threshold, trigger the three-dimensional model highlighting, management terminal warning and automatically generate carbon reduction suggestions; 4) Quantify regional emission reduction potential based on carbon potential index, and generate optimization strategy through mixed integer programming algorithm; 5) Push the optimization strategy to the park energy management system for execution, monitor the implemented data and generate effect comparison report.

[0005] Preferably, the carbon emission intensity calculation in step 1) comprises: Building and equipment carbon emission: Wherein, E is the power consumption of buildings and equipment in the park, is the power carbon emission factor; Gas carbon emission: Wherein is the carbon emission factor of fuel gas, and 2.75 is the carbon emission factor of fuel gas; Through the driving mileage of the fuel automobile, the carbon emission data corresponding to the fuel consumption is estimated in combination with the vehicle model database; , wherein is a vehicle model factor, that is, the carbon emission weight per kilometer of the vehicle; and S is the driving mileage of the vehicle; Through the driving mileage of the electric automobile, the electricity consumption is estimated in combination with the vehicle model database (such as the electricity consumption per 100 kilometers), and the charging period is associated with the carbon emission factor of the power grid; , wherein S1 and S2 are the mileage corresponding to the proportion of the thermal power and the photovoltaic power generation in the driving mileage of the electric automobile, is the carbon emission factor of the photovoltaic charging.

[0006] Preferably, the specific steps of constructing the three-dimensional space model and mapping the carbon emission distribution according to the space hierarchy in the step 1) are as follows: the carbon emission data is mapped into a heat map layer according to the space hierarchy, the color scale intensity is positively correlated with the carbon emission amount, a vehicle carbon emission intensity map is designed, and a real-time updated three-dimensional carbon flow distribution map is generated.

[0007] Preferably, the space hierarchy division includes the park level, the building level, the floor level and the equipment level.

[0008] Preferably, the vehicle carbon emission intensity map is generated in the following manner: the red, yellow and green gradient color scales are adopted to represent the carbon emission intensity; the owner information, the vehicle information and the statistical period driving mileage are associated.

[0009] Preferably, the optimization strategy generated in the step 4) includes the micro-grid scheduling, the equipment load adjustment and the traffic control.

[0010] Preferably, the effect comparison report generated in the step 5) includes the carbon emission reduction amount, the energy saving economic benefit and the carbon potential index change curve.

[0011] The technical effects and advantages of the present application are as follows: 1. By collecting the energy consumption data of the buildings, the equipment and the traffic in the park in real time, and fusing the dynamic carbon emission factor library, the carbon emission intensity can be more accurately calculated; The three-dimensional park model is constructed, and the carbon emission data is mapped into a heat map layer according to the space hierarchy, which is helpful for intuitively displaying the spatial distribution of carbon emission and facilitating the managers to quickly identify the high carbon emission areas.

[0012] The energy flow Sankey diagram and the carbon flow Sankey diagram are constructed, which is helpful for analyzing the paths of energy flow and carbon emission, so as to locate the key carbon emission sources; The graded early warning threshold is set, when the real-time carbon emission amount exceeds the threshold, the early warning can be triggered in time, and the exceeding area is highlighted; The multi-energy collaborative optimization strategy is generated through the mixed integer programming algorithm, which is helpful for realizing the efficient utilization of energy and the reduction of carbon emission; The optimization strategy is pushed to the park energy management system and the intelligent building management and control platform for execution, so that the automatic implementation of the emission reduction measures is realized. BRIEF DESCRIPTION OF DRAWINGS

[0013] Figure 1 The flowchart of the present application. DETAILED DESCRIPTION

[0014] The specific embodiments of the present application will be further described below in conjunction with the accompanying drawings. It should be noted that the description of these embodiments is used to help understand the present application, but does not constitute a limitation on the present application. In addition, the technical features involved in the various embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0015] The present application provides a park dynamic carbon portrait generation method as shown in Figure 1 The park dynamic carbon portrait generation method comprises the following steps: Step S1: multi-source data fusion and three-dimensional carbon flow dynamic modeling Real-time collection of building, equipment energy consumption data and traffic energy consumption data in the park, calculation of carbon emission intensity through a dynamic carbon emission factor library; Real-time collection of building, equipment energy consumption data, monitoring of building power consumption and equipment power consumption through the deployment of smart meters, and collection of gas consumption through the installation of gas sensors; Building, equipment carbon emissions: Wherein, E is the power consumption of buildings and equipment in the park, is the power carbon emission factor; Gas carbon emissions: Wherein is the gas consumption, and 2.75 is the gas carbon emission factor; Through the driving mileage of the fuel car, combined with the vehicle type database (such as 100km fuel consumption), the carbon emission data corresponding to the fuel consumption is estimated; Wherein is the vehicle type factor, i.e. the carbon emission weight per kilometer of the vehicle; S is the driving mileage of the vehicle; Through the driving mileage of the electric car, combined with the vehicle type database (such as 100km power consumption), the power consumption is estimated, and the charging period is associated with the power grid carbon emission factor; Wherein S1 and S2 are the mileage corresponding to the proportion of thermal power and photovoltaic power generation in the driving mileage of the electric car, for example, the total mileage is 100km, and the thermal power accounts for 60% during the charging process of the electric car, then S1 is 60km, and S2 is 40km by analogy, is the photovoltaic charging carbon emission factor; A three-dimensional park model is constructed, carbon emission data is mapped to a heat map layer according to a spatial hierarchy (park-building-floor-equipment), and the color scale intensity is positively correlated with the carbon emission amount, and a vehicle carbon emission intensity map is designed; and a real-time updated three-dimensional carbon flow distribution map; Among them, the spatial hierarchy division includes: Park level: The overall park boundary, energy pipe network distribution and macro carbon emission heat trend are displayed.

[0016] Building level: It is refined to a single building, and the building function (production / office / storage) and carbon emission contribution ratio are labeled.

[0017] Floor level: Layered according to vertical height, showing energy consumption differences of different floors (such as high energy consumption floors of data centers marked red).

[0018] Equipment level: Positioning key equipment (such as boilers, air compressors), and associating real-time operation data (power, carbon emission intensity).

[0019] The data mapping rules include: adopting a red-yellow-green gradient color scale, red representing high carbon emission (such as >50 kgCO / m²·h), green representing low carbon (such as <10 kgCO / m²·h), and the color scale intensity is linearly positively correlated with the carbon emission amount; real-time energy consumption data (such as smart meters, gas sensors) are obtained through the Internet of Things interface, and the heat map layer is refreshed every 5 minutes to reflect the instantaneous emission changes; the driving mileage of fuel vehicles and electric vehicles is transmitted remotely by the vehicle at regular intervals to calculate the emission amount.

[0020] The vehicle carbon emission intensity map adopts picture color plus text display, also using a red-yellow-green gradient color scale to represent carbon emission intensity, and the picture contains vehicle owner information or vehicle information and the driving mileage of the vehicle within the statistical period.

[0021] Step S2: Energy flow-carbon flow traceability analysis and key source positioning Based on the output data of step S1, an energy flow Sankey diagram is constructed, classified according to energy types (electricity, gas) and terminal energy use activities (production, lighting, air conditioning); The energy flow data is converted into a carbon flow Sankey diagram, and the arrow width represents the carbon emission amount, and the device-level carbon emission details are supported by clicking the node; The objects with high proportion in the total carbon emission of the park and significant emission reduction space are marked as key sources; Step S3: Dynamic threshold early warning and responsibility partitioning According to the key sources positioned in step S2, set hierarchical warning thresholds (first level: industry benchmark value; second level: park target value; third level: legal limit); When the real-time carbon emission amount exceeds the threshold, the following linkage operations are triggered: Highlighting the over-standard area in the three-dimensional model; Push the early warning information to the management terminal and associate the traceability analysis result of step S2; Automatically generate preliminary carbon reduction suggestions (such as adjusting air conditioning temperature, switching to clean energy); Step S4: Multi-energy collaborative carbon potential optimization and strategy generation Based on the responsibility partitioning of step S3, define the carbon potential index and quantify the emission reduction potential of each area; Through the mixed integer programming algorithm, generate multi-energy collaborative optimization strategies, including: Microgrid scheduling (priority of photovoltaic / energy storage); Device load adjustment (turn off unnecessary high-energy-consuming devices); Traffic control (electric vehicles have priority); Step S5: Carbon reduction measure implementation and effect closed loop verification Push the optimization strategy of step S4 to the park energy management system and intelligent building management and control platform for execution; Real-time monitoring of carbon emission data after the implementation of measures, generating an effect comparison report, including: Carbon emission reduction (tCO / h); Energy saving economic benefits (yuan / day); Carbon potential index change curve; If the expected effect is not achieved, feedback to step S2 to reanalyze the key sources and iterate the optimization strategy.

[0022] Although the embodiments of the present application have been shown and described above, it can be understood that the above-mentioned embodiments are exemplary and cannot be understood as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above-mentioned embodiments within the scope of the present application.

Claims

1. A method for generating dynamic carbon profiles of a park, characterized in that, The method comprises the following steps: 1) Real-time acquisition of multi-source energy consumption data in the park, calculation of carbon emission intensity by dynamic carbon emission factor, construction of a three-dimensional space model and mapping of carbon emission distribution according to the spatial hierarchy structure, and synchronous generation of a vehicle carbon emission intensity map; 2) Based on the S1 data, construct energy flow and carbon flow Sanjia diagrams, and mark objects with high proportion and large emission reduction space in the total carbon emission amount as key sources; 3) According to the marked key sources, set a hierarchical warning threshold, when the real-time carbon emission exceeds the threshold, trigger the three-dimensional model highlighting, management terminal warning and automatically generate carbon reduction suggestions; 4) Quantify the regional emission reduction potential based on the carbon potential index, and generate an optimization strategy through a mixed integer programming algorithm; 5) Push the optimization strategy to the park energy management system for execution, monitor the post-implementation data and generate an effect comparison report.

2. The method of claim 1, wherein: The carbon emission intensity calculation in step 1) includes: Carbon emissions of buildings and equipment: wherein E is the electricity consumption of buildings and equipment within the park, is the carbon emission factor of electricity; Gas carbon emissions: wherein is the gas usage, 2.75 is the gas carbon emission factor; The carbon emission data corresponding to the fuel consumption is estimated by the driving mileage of the fuel automobile in combination with the vehicle model database; wherein is a vehicle model factor, i.e., the carbon emission weight per kilometer of the vehicle; and S is the driving mileage of the vehicle. The power consumption is estimated by the driving mileage of the electric vehicle in combination with a vehicle model database (such as the power consumption per 100 km), and the charging period is associated with the carbon emission factor of the power grid; wherein S1 and S2 are the mileage corresponding to the proportion of thermal power and photovoltaic power in the driving mileage of the electric vehicle, is the carbon emission factor of photovoltaic charging.

3. The method of claim 1, wherein: The specific steps of constructing a three-dimensional space model and mapping carbon emission distribution according to the spatial hierarchy structure in step 1) are: mapping carbon emission data into a heat map layer according to the spatial hierarchy, and the color scale intensity is positively correlated with the carbon emission amount, and a vehicle carbon emission intensity map is designed; and a real-time updated three-dimensional carbon flow distribution map.

4. The method of claim 3, wherein: The spatial hierarchy division includes park level, building level, floor level and equipment level.

5. The method of claim 1, wherein: The vehicle carbon emission intensity map generation method is: using red, yellow and green gradient color scales to represent carbon emission intensity; associating car owner information, vehicle information and statistical cycle mileage.

6. The method of claim 1, wherein: The optimization strategy generated in step 4) includes: micro-grid dispatching, device load adjustment and traffic control.

7. The method of claim 1, wherein: The effect comparison report generated in step 5) includes: carbon emission reduction amount, energy saving economic benefit and carbon potential index change curve.

Images