Multi-energy flow simulation system based on energy-carbon interaction coupling mechanism

Abstract

The present invention provides a multi-energy flow simulation system based on the energy-carbon interaction coupling mechanism, which performs rasterization processing on the target area to obtain several target sub-areas included in the target area, determines the energy consumption correlation distribution map according to the personnel type data, building type data and power load type data in each target sub-area, performs image segmentation processing on the energy consumption correlation distribution map to obtain several sub-correlation distribution maps, inputs each sub-correlation distribution map into a multi-objective decision model to obtain the energy allocation strategy of the sub-correlation area corresponding to each sub-correlation distribution map output by the multi-objective decision model, and simulates the energy allocation strategy of each sub-correlation area and the energy consumption prediction data of the sub-correlation area to determine the carbon emissions corresponding to each sub-correlation area. By performing partition simulation on the data such as the power consumption type and personnel distribution of each target sub-area, the accuracy of the determined simulation results is improved.

Description

Background Art

[0002] The energy-carbon management system is an energy monitoring, allocation and carbon emission management system based on the Internet of Things architecture. It monitors and integrates various types of electricity consumption demands in the target area to allocate energy to the electrical equipment in the target area, so as to achieve the purpose of improving energy utilization efficiency and saving energy and reducing emissions. Before allocating energy to the electrical equipment in the target area, the energy-carbon management system needs to simulate the energy consumption data of the electrical equipment. However, when the existing simulation system is actually tested, the larger the geographical area corresponding to the simulation, the greater the differences in the types of electrical equipment and the size of personnel habits in the geographical area, which will lead to the low accuracy of the energy allocation plan corresponding to the simulated geographical area, which is not conducive to energy monitoring and carbon emission management in the geographical area. Summary of the invention

[0003] In view of the above technical problems, the technical solution adopted by the present invention is:

[0004] According to one aspect of the present application, a multi-energy flow simulation system based on energy-carbon interaction coupling mechanism is provided, which is used to perform the following steps:

[0005] Step S100, performing rasterization processing on the target area to obtain a plurality of target sub-areas included in the target area;

[0006] Step S200: determining an energy consumption correlation distribution map corresponding to the target area according to the personnel type data, building type data and power load type data in each target sub-area;

[0007] Step S300, performing image segmentation processing on the energy consumption correlation distribution map to obtain a plurality of sub-correlation distribution maps;

[0008] Step S400, inputting each sub-correlation distribution map into a preset multi-objective decision model to obtain an energy allocation strategy for a sub-correlation area corresponding to each sub-correlation distribution map output by the multi-objective decision model;

[0009] Step S500: Simulate the energy allocation strategy of each sub-associated area and the energy consumption forecast data of the sub-associated area to determine the carbon emissions corresponding to each sub-associated area.

[0010] In an exemplary embodiment of the present application, step S100 includes:

[0011] Step S110, obtaining a regional geographic map corresponding to the target area;

[0012] Step S120: rasterize the regional geographic map to obtain a plurality of sub-regional geographic maps included in the regional geographic map; each sub-regional geographic map corresponds to a target sub-region.

[0013] In an exemplary embodiment of the present application, step S200 includes:

[0014] Step S210: determining the first chromaticity component of the sub-region geographic map corresponding to the target sub-region according to the personnel type data in each target sub-region;

[0015] Step S220, determining the second chromaticity component of the sub-region geographic map corresponding to the target sub-region according to the building type data in each target sub-region;

[0016] Step S230, determining the color brightness component of the sub-region geographic map corresponding to the target sub-region according to the power load type data in each target sub-region;

[0017] Step S240: Determine an energy consumption correlation distribution map corresponding to the target area according to the first chromaticity component, the second chromaticity component and the color brightness component of each sub-area geographic map.

[0018] In an exemplary embodiment of the present application, step S210 includes:

[0019] Step S211: Obtain the number of each type of target personnel in each target sub-area to obtain a list set of target personnel numbers A=(A 1 ,A 2 ,...,A m ,...,A n ), where m=1,2,...,n, and n is the number of target sub-regions; A m is the list of target personnel quantity corresponding to the mth target sub-area;

[0020] A m =(A m1 ,A m2 ,...,A mi ,...,A mj ), i=1,2,...,j, j is the number of personnel types of the target personnel, A mi is the number of target personnel corresponding to the i-th personnel type in the m-th target sub-area;

[0021] Step S212: Determine A according to the preset personnel quantity mapping table. mi The corresponding first chromaticity B mi The personnel quantity mapping table stores the association between the number of each type of target personnel and a number of first chromaticities;

[0022] Step S213: i=1 j B mi / j is determined as the first chromaticity component of the sub-region geographic map corresponding to the m-th target sub-region.

[0023] In an exemplary embodiment of the present application, step S220 includes:

[0024] Step S221: Obtain the use area of ​​each building in each target sub-area to obtain a list set C of the use areas of several buildings. 1 ,C 2 ,...,C m ,...,C n Among them, C m is the list set of building use areas corresponding to the mth target sub-area;

[0025] C m =(C m1 ,C m2 ,...,C me ,...,C mf ), e=1,2,...,f, f is the number of building types of several buildings, C me is a list of building use areas corresponding to the e-th building type in the m-th target sub-area;

[0026] C me =(C me1 ,C me2 ,...,C meg ,...,C meh(me) ), g = 1, 2, ..., h(me), h(me) is the number of buildings of the e-th building type in the m-th target sub-area, C meg is the usable area of ​​the g-th building of the e-th building type in the m-th target sub-area;

[0027] Step S222: According to C me , determine the average usable area D corresponding to the e-th building type in the m-th target sub-area me =∑ g=1 h(me) C meg / h(me);

[0028] Step S223: Determine D according to the preset building area mapping table. me The corresponding second chromaticity E me The building area mapping table stores the correlation between the average usable area corresponding to each building type and a plurality of second chromaticities;

[0029] Step S224: e=1 f E me / f is determined as the second chromaticity component of the sub-region geographic map corresponding to the m-th target sub-region.

[0030] In an exemplary embodiment of the present application, step S230 includes:

[0031] Step S231: Obtain the total power of each electrical load in each target sub-area to obtain a load power list set F=(F 1 ,F 2 ,...,F m ,...,F n ), where F m is the load power list corresponding to the mth target sub-area;

[0032] F m =(F m1 ,F m2 ,...,F ma ,...,F mb(m) ); a=1,2,...,b(m); b(m) is the number of power loads in the mth target sub-area; F ma is the total power of the ath electrical load in the mth target sub-area;

[0033] Step S232: Determine the average load power G corresponding to the mth target sub-area m =∑ a=1 b(m) F ma / b(m);

[0034] Step S233: Determine the G according to the preset load power mapping table. m The corresponding color brightness; the load power mapping table stores the association between each load power and a number of color brightness;

[0035] Step S234: G m The corresponding color brightness is determined as the color brightness component of the sub-region geographic map corresponding to the m-th target sub-region.

[0036] In an exemplary embodiment of the present application, step S240 includes:

[0037] Step S241, channel-superimposing the first chromaticity component, the second chromaticity component and the color brightness component of each sub-region geographical map to color-mark each sub-region geographical map;

[0038] Step S242: combining a plurality of color-marked sub-region geographical maps according to the position of the target sub-region corresponding to each sub-region geographical map in the target region, so as to obtain an energy consumption correlation distribution map corresponding to the target region.

[0039] In an exemplary embodiment of the present application, step S300 includes:

[0040] Step S310: perform superpixel segmentation processing on the energy consumption correlation distribution map to obtain a plurality of sub-correlation distribution maps; each sub-correlation distribution map corresponds to a sub-correlation area.

[0041] In an exemplary embodiment of the present application, after receiving any sub-association distribution graph, the multi-objective decision model performs the following steps:

[0042] Step S410, obtaining a plurality of historical correlation distribution graphs and an energy allocation strategy corresponding to each historical correlation distribution graph; each historical correlation distribution graph has a color mark;

[0043] Step S420, matching the color mark in the sub-association distribution map with the color mark in each historical association distribution map to obtain a number of corresponding color matching degrees;

[0044] Step S430: Determine the energy allocation strategy corresponding to the historical association distribution graph with the largest color matching degree as the energy allocation strategy for the sub-association area corresponding to the sub-association distribution graph.

[0045] In an exemplary embodiment of the present application, step S500 includes:

[0046] Step S510, obtaining historical energy consumption data of each sub-associated area in each historical period; the length of each historical period is the same;

[0047] Step S520, determining the average value of a plurality of historical energy consumption data of each sub-associated area as energy consumption prediction data of the sub-associated area;

[0048] Step 530: Input the energy allocation strategy of each sub-associated area and the energy consumption forecast data of the sub-associated area into a preset simulation model for simulation, so as to obtain the carbon emissions corresponding to each sub-associated area output by the simulation model.

[0049] The present invention has at least the following beneficial effects:

[0050] The multi-energy flow simulation system based on the energy-carbon interaction coupling mechanism of the present invention performs rasterization processing on the target area to obtain several target sub-areas included in the target area, determines the energy consumption correlation distribution map corresponding to the target area according to the personnel type data, building type data and power load type data in each target sub-area, performs image segmentation processing on the energy consumption correlation distribution map to obtain several sub-correlation distribution maps, inputs each sub-correlation distribution map into the multi-objective decision model to obtain the energy allocation strategy of the sub-correlation area corresponding to each sub-correlation distribution map output by the multi-objective decision model, and simulates the energy allocation strategy of each sub-correlation area and the energy consumption prediction data of the sub-correlation area to determine the carbon emission corresponding to each sub-correlation area. By performing partition simulation on the data such as carbon production of different energy allocation schemes under the same power demand for the power type and personnel distribution of each target sub-area, the accuracy of the simulation results of the optimization scheme that can both improve energy efficiency and significantly reduce carbon emissions is improved, thereby realizing more accurate energy allocation for each target sub-area, so as to provide assistance for the park or urban design of the target area. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 A flowchart of an execution method of a multi-energy flow simulation system based on an energy-carbon interaction coupling mechanism provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0054] The multi-energy flow simulation system based on the energy-carbon interaction coupling mechanism described in this application is as follows: Figure 1 As shown, it is used to perform the following steps:

[0055] Step S100, performing rasterization processing on the target area to obtain a plurality of target sub-areas included in the target area;

[0056] Further, step S100 includes steps S110 to S120:

[0057] Step S110, obtaining a regional geographic map corresponding to the target area;

[0058] The target area is a geographical area where energy distribution is required, and the regional geographical map may be a plane map corresponding to the target area.

[0059] Step S120: rasterize the regional geographic map to obtain a plurality of sub-regional geographic maps included in the regional geographic map; each sub-regional geographic map corresponds to a target sub-region.

[0060] The target area is divided into several target sub-areas through rasterization processing, so as to facilitate the subsequent partition simulation of each target sub-area.

[0061] Step S200: determining an energy consumption correlation distribution map corresponding to the target area according to the personnel type data, building type data and power load type data in each target sub-area;

[0062] Among them, the personnel type data can be the number of personnel included in each personnel type (which can be age group or occupation type, etc.) in the target sub-area, the building type data can be the use area of ​​the buildings included in each building type (which can be residential buildings, office buildings, green ground, etc.) in the target sub-area, and the power load type data can be the total power of each power-consuming equipment in the target sub-area. The energy consumption of the corresponding target sub-area is determined by the personnel type data, building type data and power load type data, and then the energy consumption correlation distribution map of the target area is determined by counting the energy consumption of each target sub-area. The energy consumption correlation distribution map is a correlation relationship map between energy consumption and electricity demand in the target area.

[0063] Further, step S200 includes steps S210 to S240:

[0064] Step S210, determining the first chromaticity component of the sub-region geographic map corresponding to the target sub-region according to the personnel type data in each target sub-region;

[0065] Wherein, step S210 includes steps S211 to S213:

[0066] Step S211: Obtain the number of target personnel of each type in each target sub-area to obtain a list set of target personnel numbers A=(A 1 ,A 2 ,...,A m ,...,A n ), where m=1,2,...,n, and n is the number of target sub-regions; A m is the list of target personnel quantity corresponding to the mth target sub-area;

[0067] A m =(A m1 ,A m2 ,...,A mi ,...,A mj ), i=1,2,...,j, j is the number of personnel types of the target personnel, A mi is the number of target personnel corresponding to the i-th personnel type in the m-th target sub-area;

[0068] Step S212: Determine A according to the preset personnel quantity mapping table. mi The corresponding first chromaticity B mi ;

[0069] The personnel quantity mapping table is set according to the personnel energy consumption. The personnel quantity mapping table stores the association between the number of each type of target personnel and several first chromaticities. For example, if the number of each type of target personnel belongs to a certain range, then the first chromaticity corresponding to this range has an association with the target personnel of that type.

[0070] Step S213: i=1 j B mi / j is determined as the first chromaticity component of the sub-region geographic map corresponding to the m-th target sub-region.

[0071] Step S220, determining the second chromaticity component of the sub-region geographic map corresponding to the target sub-region according to the building type data in each target sub-region;

[0072] Wherein, step S220 includes steps S221 to S224:

[0073] Step S221: Obtain the use area of ​​each building in each target sub-area to obtain a list set C of the use areas of several buildings. 1 ,C 2 ,...,C m ,...,C n Among them, C m is the list set of building use areas corresponding to the mth target sub-area;

[0074] C m =(C m1 ,C m2 ,...,C me ,...,C mf ), e=1,2,...,f, f is the number of building types of several buildings, C me is a list of building use areas corresponding to the e-th building type in the m-th target sub-area;

[0075] C me =(C me1 ,C me2 ,...,C meg ,...,C meh(me) ), g = 1, 2, ..., h(me), h(me) is the number of buildings of the e-th building type in the m-th target sub-area, C meg is the usable area of ​​the g-th building of the e-th building type in the m-th target sub-area;

[0076] Step S222: According to C me , determine the average usable area D corresponding to the e-th building type in the m-th target sub-area me =∑ g=1 h(me) C meg / h(me);

[0077] Step S223: Determine D according to the preset building area mapping table. me The corresponding second chromaticity E me ;

[0078] The building area mapping table is set according to the correlation between the building area and the energy consumption of the building. The building area mapping table stores the correlation between the average usable area corresponding to each building type and a plurality of second chromaticities.

[0079] Step S224: e=1 f E me / f is determined as the second chromaticity component of the sub-region geographic map corresponding to the m-th target sub-region.

[0080] Step S230, determining the color brightness component of the sub-region geographic map corresponding to the target sub-region according to the power load type data in each target sub-region;

[0081] Wherein, step S230 includes steps S231 to S234:

[0082] Step S231: Obtain the total power of each electrical load in each target sub-area to obtain a load power list set F=(F 1 ,F 2 ,...,F m ,...,F n ), where F m is the load power list corresponding to the mth target sub-area;

[0083] F m =(F m1 ,F m2 ,...,Fma ,...,F mb(m) ); a=1,2,...,b(m); b(m) is the number of power loads in the mth target sub-area; F ma is the total power of the ath electrical load in the mth target sub-area;

[0084] Step S232: Determine the average load power G corresponding to the mth target sub-area m =∑ a=1 b(m) F ma / b(m);

[0085] Step S233: Determine the G according to the preset load power mapping table. m The corresponding color brightness;

[0086] The load power mapping table is set according to the power of the electrical load and the energy consumption of the electrical load. The load power mapping table stores the association between each load power and a plurality of color brightnesses.

[0087] Step S234: G m The corresponding color brightness is determined as the color brightness component of the sub-region geographic map corresponding to the m-th target sub-region.

[0088] Step S240, determining an energy consumption correlation distribution map corresponding to the target area according to the first chromaticity component, the second chromaticity component and the color brightness component of each sub-area geographical map;

[0089] The first chromaticity component, the second chromaticity component and the color brightness component can be respectively the three components of the LAB color space (i.e., red-green chromaticity, yellow-blue chromaticity and brightness) or the three components of the RGB color space (i.e., red chromaticity component, green chromaticity component and blue chromaticity component). By determining these three components, each sub-region geographic map can be color-marked.

[0090] Wherein, step S240 includes step S241-step S242:

[0091] Step S241, channel-superimposing the first chromaticity component, the second chromaticity component and the color brightness component of each sub-region geographical map to color-mark each sub-region geographical map;

[0092] By checking the color mark of the sub-region geographic map, the energy consumption correlation of the target sub-region corresponding to the sub-region geographic map can be known.

[0093] Step S242: combining a plurality of color-marked sub-region geographical maps according to the position of the target sub-region corresponding to each sub-region geographical map in the target region, so as to obtain an energy consumption correlation distribution map corresponding to the target region.

[0094] The energy consumption correlation distribution map is a color-coded regional geographic map.

[0095] Step S300, performing image segmentation processing on the energy consumption correlation distribution map to obtain a plurality of sub-correlation distribution maps;

[0096] Further, as a feasible embodiment, step S300 includes step S310:

[0097] Step S310: perform superpixel segmentation processing on the energy consumption correlation distribution map to obtain a plurality of sub-correlation distribution maps; each sub-correlation distribution map corresponds to a sub-correlation area.

[0098] The method for performing image segmentation processing on the energy consumption correlation distribution map can adopt the superpixel segmentation of the existing technology, or can adopt image processing methods such as semantic segmentation or instance segmentation, so that the color markings in each determined sub-correlation distribution map are the same or similar, to indicate that the energy consumption correlation relationship in each sub-correlation distribution map is the same, which is convenient for subsequent data simulation.

[0099] Step S400, inputting each sub-correlation distribution map into a preset multi-objective decision model to obtain an energy allocation strategy for a sub-correlation area corresponding to each sub-correlation distribution map output by the multi-objective decision model;

[0100] The multi-objective decision-making model can adopt the existing mathematical model.

[0101] After receiving any sub-correlation distribution graph, the multi-objective decision model executes steps S410 to S430:

[0102] Step S410, obtaining a plurality of historical correlation distribution graphs and an energy allocation strategy corresponding to each historical correlation distribution graph; each historical correlation distribution graph has a color mark;

[0103] Step S420, matching the color mark in the sub-association distribution map with the color mark in each historical association distribution map to obtain a number of corresponding color matching degrees;

[0104] Step S430: Determine the energy allocation strategy corresponding to the historical association distribution graph with the largest color matching degree as the energy allocation strategy for the sub-association area corresponding to the sub-association distribution graph.

[0105] Energy allocation strategy is an energy allocation plan, which is set according to energy usage.

[0106] Step S500: Simulate the energy allocation strategy of each sub-associated area and the energy consumption forecast data of the sub-associated area to determine the carbon emissions corresponding to each sub-associated area;

[0107] The data simulation method uses the existing simulation model to simulate the energy consumption forecast data and energy allocation strategy, and the carbon emissions are also the values ​​set in the preset simulation model.

[0108] Further, step S500 includes steps S510 to S530:

[0109] Step S510, obtaining historical energy consumption data of each sub-associated area in each historical period; the length of each historical period is the same;

[0110] Step S520, determining the average value of a plurality of historical energy consumption data of each sub-associated area as energy consumption prediction data of the sub-associated area;

[0111] Energy consumption forecast data is determined based on historical energy consumption data.

[0112] Step 530: Input the energy allocation strategy of each sub-associated area and the energy consumption forecast data of the sub-associated area into a preset simulation model for simulation, so as to obtain the carbon emissions corresponding to each sub-associated area output by the simulation model.

[0113] The multi-energy flow simulation system based on the energy-carbon interaction coupling mechanism of the present invention performs rasterization processing on the target area to obtain several target sub-areas included in the target area, determines the energy consumption correlation distribution map corresponding to the target area according to the personnel type data, building type data and power load type data in each target sub-area, performs image segmentation processing on the energy consumption correlation distribution map to obtain several sub-correlation distribution maps, inputs each sub-correlation distribution map into the multi-objective decision model to obtain the energy allocation strategy of the sub-correlation area corresponding to each sub-correlation distribution map output by the multi-objective decision model, and simulates the energy allocation strategy of each sub-correlation area and the energy consumption prediction data of the sub-correlation area to determine the carbon emission corresponding to each sub-correlation area. By performing partition simulation on the data such as carbon production of different energy allocation schemes under the same power demand for the power type and personnel distribution of each target sub-area, the accuracy of the simulation results of the optimization scheme that can both improve energy efficiency and significantly reduce carbon emissions is improved, thereby realizing more accurate energy allocation for each target sub-area, so as to provide assistance for the park or urban design of the target area.

[0114] An embodiment of the present invention further provides a computer program product, which includes program code. When the program product is run on an electronic device, the program code is used to enable the electronic device to execute the steps of the method according to various exemplary embodiments of the present invention described above in this specification.

[0115] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0116] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by combining software with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0117] In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

[0118] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as a system, method or program product. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as a "circuit", "module" or "system".

[0119] The electronic device according to this embodiment of the present invention is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0120] The electronic device is presented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: the at least one processor mentioned above, the at least one storage device mentioned above, and a bus connecting different system components (including storage devices and processors).

[0121] The storage stores program codes, which can be executed by the processor, so that the processor executes the steps according to various exemplary embodiments of the present invention described in the above “Exemplary Method” section of this specification.

[0122] The memory may include readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read only memory (ROM).

[0123] The storage may also include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0124] The bus may represent one or more of several types of bus structures including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.

[0125] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface. Furthermore, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter.

[0126] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present invention can also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present specification.

[0127] The program product may be any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0128] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0129] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0130] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0131] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0132] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0133] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A multi-energy flow simulation system based on the energy-carbon interaction coupling mechanism, characterized in that: Used to perform the following steps: Step S100, performing rasterization processing on the target area to obtain a plurality of target sub-areas included in the target area; Step S200: determining an energy consumption correlation distribution map corresponding to the target area according to the personnel type data, building type data and power load type data in each target sub-area; Step S300, performing image segmentation processing on the energy consumption correlation distribution map to obtain a plurality of sub-correlation distribution maps; Step S400, inputting each of the sub-correlation distribution graphs into a preset multi-objective decision model to obtain an energy allocation strategy for a sub-correlation area corresponding to each of the sub-correlation distribution graphs output by the multi-objective decision model; Step S500: Simulate the energy allocation strategy of each sub-associated area and the energy consumption forecast data of the sub-associated area to determine the carbon emissions corresponding to each sub-associated area; Wherein, the step S200 includes steps S210 to S240: Step S210: determining the first chromaticity component of the sub-region geographic map corresponding to the target sub-region according to the personnel type data in each target sub-region; Step S220: determining the second chromaticity component of the sub-region geographic map corresponding to the target sub-region according to the building type data in each target sub-region; Step S230, determining the color brightness component of the sub-region geographic map corresponding to the target sub-region according to the power load type data in each target sub-region; Step S240, determining an energy consumption correlation distribution map corresponding to the target area according to the first chromaticity component, the second chromaticity component and the color brightness component of each sub-area geographical map; Wherein, the step S210 includes steps S211 to S213: Step S211: Obtain the number of each type of target personnel in each target sub-area to obtain a list set of target personnel numbers A=(A1, A2, ..., A m ,...,A n ), wherein m=1,2,...,n, and n is the number of the target sub-regions; A m A list of the number of target personnel corresponding to the mth target sub-area; A m =(A m1 ,A m2 ,...,A mi ,...,A mj ); i=1,2,...,j; j is the number of personnel types of the target personnel; A mi is the number of target personnel corresponding to the i-th personnel type in the m-th target sub-area; Step S212: Determine A according to the preset personnel quantity mapping table. mi The corresponding first chromaticity B mi The personnel quantity mapping table stores the association between the number of each type of target personnel and a number of first chromaticities; Step S213: i=1 j B mi / j is determined as the first chromaticity component of the sub-region geographic map corresponding to the mth target sub-region.

2. The multi-energy flow simulation system based on the energy-carbon interaction coupling mechanism according to claim 1 is characterized in that: The step S100 includes: Step S110, obtaining a regional geographic map corresponding to the target area; Step S120: rasterize the regional geographic map to obtain a plurality of sub-regional geographic maps included in the regional geographic map; each of the sub-regional geographic maps corresponds to a target sub-region.

3. The multi-energy flow simulation system based on the energy-carbon interaction coupling mechanism according to claim 1 is characterized in that: The step S220 includes: Step S221: Obtain the use area of ​​each building in each target sub-area to obtain a plurality of building use area list sets C1, C2, ..., C m ,...,C n Among them, C m is a list set of building use areas corresponding to the mth target sub-area; C m =(C m1 ,C m2 ,...,C me ,...,C mf ); e=1,2,...,f; f is the number of building types of the buildings; C me A list of building use areas corresponding to the e-th building type in the m-th target sub-area; C me =(C me1 ,C me2 ,...,C meg ,...,C meh(me) ), g = 1, 2, ..., h(me), h(me) is the number of buildings of the e-th building type in the m-th target sub-area, C meg is the usable area of ​​the g-th building of the e-th building type in the m-th target sub-area; Step S222: According to C me , determine the average usable area D corresponding to the e-th building type in the m-th target sub-area me =∑ g=1 h(me) C meg / h(me); Step S223: Determine D according to the preset building area mapping table. me The corresponding second chromaticity E me The building area mapping table stores the correlation between the average usable area corresponding to each building type and a plurality of second chromaticities; Step S224: e=1 f E me / f is determined as the second chromaticity component of the sub-region geographic map corresponding to the mth target sub-region.

4. The multi-energy flow simulation system based on the energy-carbon interaction coupling mechanism according to claim 3 is characterized in that: The step S230 includes: Step S231: Obtain the total power of each electrical load in each target sub-area to obtain a load power list set F=(F1, F2, ..., F m ,...,F n ), where F m is a load power list corresponding to the mth target sub-area; F m =(F m1 ,F m2 ,...,F ma ,...,F mb(m) ); a=1,2,...,b(m); b(m) is the number of power loads in the mth target sub-area; F ma is the total power of the ath electrical load in the mth target sub-area; Step S232: Determine the average load power G corresponding to the mth target sub-area. m =∑ a=1 b(m) F ma / b(m); Step S233: Determine the G according to the preset load power mapping table. m The load power mapping table stores the association between each load power and a number of color brightnesses; Step S234: G m The corresponding color brightness is determined as the color brightness component of the sub-region geographic map corresponding to the m-th target sub-region.

5. The multi-energy flow simulation system based on the energy-carbon interaction coupling mechanism according to claim 4 is characterized in that: The step S240 includes: Step S241, channel-superimposing the first chromaticity component, the second chromaticity component and the color brightness component of each of the sub-regional geographical maps, so as to color-mark each of the sub-regional geographical maps; Step S242: combining a plurality of color-marked sub-region geographical maps according to the position of the target sub-region corresponding to each sub-region geographical map in the target region, so as to obtain an energy consumption correlation distribution map corresponding to the target region.

6. The multi-energy flow simulation system based on the energy-carbon interaction coupling mechanism according to claim 5 is characterized in that: The step S300 includes: Step S310: perform superpixel segmentation processing on the energy consumption correlation distribution map to obtain a plurality of sub-correlation distribution maps; each of the sub-correlation distribution maps corresponds to a sub-correlation area.

7. The multi-energy flow simulation system based on the energy-carbon interaction coupling mechanism according to claim 6 is characterized in that: After receiving any of the sub-association distribution graphs, the multi-objective decision model performs the following steps: Step S410, obtaining a plurality of historical correlation distribution graphs and an energy allocation strategy corresponding to each of the historical correlation distribution graphs; each of the historical correlation distribution graphs has a color mark; Step S420, matching the color mark in the sub-association distribution map with the color mark in each of the historical association distribution maps to obtain a number of corresponding color matching degrees; Step S430: Determine the energy allocation strategy corresponding to the historical association distribution diagram with the largest color matching degree as the energy allocation strategy for the sub-association area corresponding to the sub-association distribution diagram.

8. The multi-energy flow simulation system based on the energy-carbon interaction coupling mechanism according to claim 7 is characterized in that: The step S500 includes: Step S510, obtaining the historical energy consumption data of each sub-associated area in each historical period; the length of each historical period is the same; Step S520, determining an average value of a plurality of the historical energy consumption data of each of the sub-associated areas as energy consumption prediction data of the sub-associated area; Step 530: Input the energy allocation strategy of each of the sub-associated areas and the energy consumption forecast data of the sub-associated areas into a preset simulation model for simulation, so as to obtain the carbon emissions corresponding to each of the sub-associated areas output by the simulation model.

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