Deep learning method and system based on station-city integration space energy consumption model

Abstract

The application provides a kind of deep learning method and system based on station city fusion space energy consumption model, the method first adopts spatial region analysis method to analyze the core energy consumption system of core space region in station city fusion three-dimensional network space, and constructs the station city fusion three-dimensional network energy consumption model of core energy consumption system, then constructs carbon emission mechanism model through station city fusion three-dimensional network energy consumption model, then according to the deep learning neural network structure of initial input variable and output variable in carbon emission mechanism model, define the loss function of carbon emission mechanism model, then adopt iterative optimization function, the loss function is iteratively trained until the loss value is lower than the allowable value, and the deep learning neural network proxy model is output, finally, the green running performance of station city fusion three-dimensional network space is evaluated according to the specification requirements by using the deep learning neural network proxy model, and the evaluation result is obtained. Compared with the prior art, the method has higher evaluation efficiency and higher reliability.

Description

Technical Field

[0001] The present invention belongs to the technical field of station-city space energy consumption assessment, and specifically relates to a deep learning method and system based on a station-city integrated space energy consumption model. Background Art

[0002] Station-city integration, also known as "station-city integration," refers, at a macro level, to the interconnected and interactive relationships between railway and urban rail transit stations and surrounding cities across various elements, such as functional space and transportation mechanisms. At a project level, it involves integrating the city's specific conditions during hub planning and construction, leveraging the spillover effects of railway and rail development, and implementing tailored measures to achieve coordinated development between the hub and its surrounding areas. Station-city integration advocates for the close integration of key transportation stations with urban space, placing daily office, residential, and urban service functions within walking distance of stations, providing convenient living and economic conditions for citizens. The three-dimensional network space of station-city integration encompasses the spatial structure of traditional integrated passenger transport hubs and various spatial areas connecting to the city. As the future development direction of integrated transportation hubs, the performance evaluation requirements for green operations are becoming increasingly prominent. Current regulations, among other things, place stricter requirements on how to quickly and accurately assess carbon emissions using green evaluation indicators.

[0003] Existing technologies for carbon emission assessments of station-city integration primarily rely on data-driven deep learning network models. These models require manual preparation of large amounts of historical data for training, resulting in high training costs and a long training time. Furthermore, these data-driven deep learning network models often directly utilize foreign black-box models. When inputting data into the black-box models, no understanding of the data input and data relationships is performed. Consequently, the trained models are less reliable and fail to meet practical needs. Summary of the Invention

[0004] The present invention provides a deep learning method and system based on a station-city integrated spatial energy consumption model, which are used to solve the problems existing in the prior art.

[0005] In a first aspect, to achieve the above-mentioned objectives, the present invention provides a deep learning method based on a station-city integrated spatial energy consumption model, comprising:

[0006] S10: Use the spatial regional analysis method to analyze the core energy consumption system of the core spatial area within the station-city integrated three-dimensional network space, and construct a station-city integrated three-dimensional network energy consumption model corresponding to the core energy consumption system;

[0007] S20: Construct a carbon emission mechanism model based on the energy consumption types and characteristics of the station-city integrated three-dimensional network energy consumption model;

[0008] Among them, the core energy consumption systems include HVAC systems, lighting systems and elevator systems; energy consumption types include HVAC energy consumption, lighting energy consumption and elevator energy consumption; energy consumption characteristics include HVAC energy consumption characteristics, lighting energy consumption characteristics and elevator energy consumption characteristics;

[0009] S30: Based on the number of input variables, the matrix size of the input variables, and the number of output variables required in the carbon emission mechanism model, define the deep learning neural network structure of the input variables and output variables, and construct the loss function of the carbon emission mechanism model based on the deep learning neural network structure;

[0010] S40: using an iterative optimization function to iteratively train the loss function until a loss value obtained by the loss function is lower than a preset allowable value, and outputting a deep learning neural network agent model based on the carbon emission mechanism;

[0011] S50: Using a deep learning neural network agent model, the green operation performance of the station-city integrated three-dimensional network is evaluated in accordance with the requirements of the green building carbon emission specifications, and the evaluation results are obtained.

[0012] Through the above technical solution, before constructing the deep learning neural network agent model, the deep learning method of the present invention first constructs a carbon emission mechanism model under the operation status of the station-city integrated three-dimensional network space, and defines the loss function and iteratively optimizes the carbon emission mechanism model to obtain the deep learning neural network agent model. Compared with the existing technology, the deep learning method of the present invention can independently complete the model training through real-time monitoring of physical data, and the evaluation efficiency is higher.

[0013] Preferably, step S10 includes:

[0014] S11: Determine the core spatial area of ​​the station-city integrated three-dimensional network;

[0015] The core space area includes at least one or more of the following: waiting room, platform, transfer space and concentrated commercial space;

[0016] S12: Use spatial area analysis method to analyze the core energy consumption system in the core spatial area;

[0017] Among them, the core energy consumption system includes at least one or more of the HVAC system, lighting system and elevator system;

[0018] S13: Constructing a corresponding energy consumption model based on the core energy consumption system, including:

[0019] Constructing an air conditioning energy consumption model for the HVAC system; and / or

[0020] Constructing a lighting energy consumption model corresponding to the lighting system; and / or

[0021] Construct an elevator energy consumption model corresponding to the elevator system;

[0022] Among them, the station-city integrated three-dimensional network energy consumption model includes at least one or more of the air conditioning energy consumption model, lighting energy consumption model and elevator energy consumption model.

[0023] Preferably, the air conditioning energy consumption model is specifically:

[0024] (1)

[0025] in, is the annual energy consumption of air conditioning, i.e. HVAC energy consumption; is the cooling / heating power of the air conditioner; The operating time of the air conditioner; The coefficient of performance of the air conditioner is the ratio of the cooling / heating effect of the air conditioner to the energy consumed; 、 and It belongs to the HVAC energy consumption characteristics.

[0026] Preferably, the lighting energy consumption model is specifically:

[0027] (2)

[0028] in, is the annual energy consumption of the lighting system, i.e., lighting energy consumption; For the day Lighting power density value of each room; For the Lighting area of ​​each room; For the j day i Lighting time of each room; The lighting power density of emergency lighting; Evaluate the building's footprint for the purpose; 、 、 、 and It belongs to the lighting energy consumption characteristics.

[0029] Preferably, the elevator energy consumption model is specifically as follows:

[0030] (3)

[0031] in, is the annual energy consumption of the elevator, i.e., the energy consumption of the elevator; for specific energy consumption; is the average annual operating hours of the elevator; is the elevator speed; is the rated load capacity of the elevator; Energy consumption when the elevator is in standby mode; The average annual standby hours of the elevator; 、 、 、 、 and This is the energy consumption characteristic of the elevator.

[0032] Preferably, the carbon emission mechanism model in step S20 is specifically:

[0033]

[0034] in, For target assessment buildings use r Carbon emissions from each refrigerant; For the HVAC system r Refrigerant charge amount; For the life of the HVAC system; For the r The global warming potential of the refrigerants; Evaluate the carbon emissions per unit floor area during the building operation phase for the target; Evaluate the building for the target Annual consumption of energy of this type; For the Carbon emission factors of energy sources; Evaluate the annual carbon reduction of green space carbon sequestration systems in buildings for the target; Evaluate the building's design life for the purpose; Evaluate the building's footprint for the purpose; The data type is parameter matrix; The matrix elements include 、 and In the Energy consumption data of the energy consumption type, i.e. 、 and , No. Types of energy include electric energy, gas and oil energy, and municipal thermal energy; For the The energy consumption system consumes the energy provided by the renewable energy system. Energy consumption of the category.

[0035] Preferably, step S30 includes:

[0036] S31: Initialize the number of input variables, the matrix size of the input variables, and the number of output variables in the carbon emission mechanism model, and define the number of input and output neurons, the number of neural network layers, and the number of neurons in each neural network layer of the deep learning neural network structure;

[0037] S32: After data preparation is completed, the loss function of the carbon emission mechanism model is constructed based on the network structure of the deep learning neural network.

[0038] Preferably, the loss function of the carbon emission mechanism model in step S32 is specifically:

[0039] (7)

[0040]

[0041] in, is the data domain weight, is the mechanism domain weight; is the loss function In the data domain-based sub-items; N is the number of iterations, The target evaluation building is obtained by random training of the deep learning model. Forecast of annual energy consumption, is the loss function of the carbon emission mechanism model.

[0042] Preferably, the green operation performance of the station-city integrated three-dimensional network space is evaluated, and the evaluation results are as follows:

[0043] (9)

[0044] in, According to the U.S. Department of Energy standard, 15kg CO2 / m 2 ; is the value in formula (7) After iterative calculation, the target assessment is the carbon emissions per unit building area during the building operation phase; if , then the target assessment building is a green building. , then the target evaluation building is a non-green building. In the second aspect, to achieve the above purpose, the present invention provides a deep learning system based on the station-city integrated space energy consumption model. The system adopts the above deep learning method based on the station-city integrated space energy consumption model. The system includes:

[0045] The energy consumption model construction module is used to analyze the core energy consumption system of the core space area within the station-city integrated three-dimensional network space using the spatial regional analysis method, and to construct the station-city integrated three-dimensional network energy consumption model corresponding to the core energy consumption system;

[0046] The carbon emission mechanism model construction module is used to construct a carbon emission mechanism model based on the energy consumption type and energy consumption characteristics of the station-city integrated three-dimensional network energy consumption model;

[0047] A loss function definition module is used to define the deep learning neural network structure of input variables and output variables based on the number of input variables, the matrix size of the input variables, and the number of output variables in the carbon emission mechanism model, and to construct the loss function of the carbon emission mechanism model based on the deep learning neural network structure;

[0048] An iterative optimization module is used to iteratively train the loss function using an iterative optimization function until the loss value obtained by the loss function is lower than a preset allowable value, and output a deep learning neural network agent model based on the carbon emission mechanism;

[0049] The evaluation module is used to evaluate the green operation performance of the station-city integrated three-dimensional network by adopting a deep learning neural network agent model in accordance with the requirements of the green building carbon emission specifications and obtain the evaluation results.

[0050] The deep learning method and system based on the station-city integrated spatial energy consumption model of the present invention have at least the following advantages:

[0051] 1) Compared with the existing technology, the deep learning method of the present invention can independently complete the model training through real-time monitoring of physical data, and the evaluation efficiency is higher.

[0052] 2) Compared with existing technologies, the deep learning method of the present invention fully understands the data input and data relationships, and the trained model is more reliable.

[0053] 3) Compared with the existing technology, the deep learning method of the present invention imposes physical information constraints during the PINN training process, so it can learn a more generalizable model with fewer data samples. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 paying any creative work.

[0055] Figure 1 This is a flowchart of the deep learning method based on the station-city integrated spatial energy consumption model in Example 1 of the present invention.

[0056] Figure 2 This is a structural diagram of the deep learning system based on the station-city integrated spatial energy consumption model in Example 2 of the present invention.

[0057] Figure 3 This is a structural diagram of a computing device in Example 3 of the present invention. DETAILED DESCRIPTION

[0058] 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.

[0059] This embodiment discloses a deep learning method and system based on a station-city integrated spatial energy consumption model, which is used to solve the problems existing in the prior art.

[0060] Example 1

[0061] like Figure 1 As shown, this embodiment discloses a deep learning method based on a station-city integrated space energy consumption model, including:

[0062] S10: Use the spatial regional analysis method to analyze the core energy consumption system of the core spatial area within the station-city integrated three-dimensional network space, and construct a station-city integrated three-dimensional network energy consumption model corresponding to the core energy consumption system.

[0063] In this embodiment, the core spatial areas within the aforementioned station-city integrated three-dimensional network space include waiting rooms, platforms, transfer areas, and centralized commercial spaces. The core energy-consuming systems in each core spatial area are generally the HVAC system, lighting system, and elevator system. Spatial regional analysis is an existing technology in geographic information systems (GIS) that utilizes geospatial data to perform various analyses to reveal spatial relationships, patterns, and trends. The energy consumption model for the station-city integrated three-dimensional network includes energy consumption models for air conditioning, lighting, and elevators.

[0064] Specifically, the above step S10 includes:

[0065] S11: Determine the core spatial area of ​​the station-city integrated three-dimensional network;

[0066] S12: Use spatial area analysis method to analyze the core energy consumption system in the core spatial area;

[0067] S13: Constructing a corresponding energy consumption model based on the core energy consumption system, including:

[0068] Constructing an air conditioning energy consumption model for the HVAC system; and / or

[0069] Constructing a lighting energy consumption model corresponding to the lighting system; and / or

[0070] Construct an elevator energy consumption model corresponding to the elevator system.

[0071] Furthermore, the above air conditioning energy consumption model is specifically as follows:

[0072] (1)

[0073] in, is the annual energy consumption of air conditioning, i.e. HVAC energy consumption; is the cooling / heating power of the air conditioner; The operating time of the air conditioner; The coefficient of performance of the air conditioner is the ratio of the cooling / heating effect of the air conditioner to the energy consumed; 、 and This is a characteristic of HVAC energy consumption. The above air conditioning energy consumption model can calculate the total annual energy consumption of the air conditioning system, distribution system, air handling system and terminal air handling system within the HVAC system.

[0074] Furthermore, the above lighting energy consumption model is specifically as follows:

[0075] (2)

[0076] in, is the annual energy consumption of the lighting system, i.e. lighting energy consumption, in kWh / year; For the day Lighting power density value of each room, in watts per square meter; For the Lighting area of ​​each room, in square meters; For the j day i Lighting time of each room, in hours; is the emergency lighting power density, in watts per square meter; A The floor space of the building being assessed for the target, in square meters. 、 、 、 and It belongs to the lighting energy consumption characteristics.

[0077] Furthermore, the elevator energy consumption model is as follows:

[0078] (3)

[0079] in, is the annual energy consumption of the elevator, i.e., the energy consumption of the elevator, in kWh / year; is the specific energy consumption, in mWh / kgm; is the average annual operating hours of the elevator; is the elevator speed in meters per second; is the rated load capacity of the elevator, in kilograms; Energy consumption of the elevator in standby mode, in watts; The average annual standby hours of the elevator; 、 、 、 、 and This is the energy consumption characteristic of the elevator.

[0080] After step S10, the deep learning method of the present invention further includes:

[0081] S20: Construct a carbon emission mechanism model based on the energy consumption types and characteristics of the station-city integrated three-dimensional network energy consumption model.

[0082] Among them, the core energy consumption systems include HVAC systems, lighting systems and elevator systems; energy consumption types include HVAC energy consumption, lighting energy consumption and elevator energy consumption; energy consumption characteristics include HVAC energy consumption characteristics, lighting energy consumption characteristics and elevator energy consumption characteristics.

[0083] Specifically, the carbon emission mechanism model in step S20 is:

[0084]

[0085] It should be noted that due to the greenhouse gas emissions generated by the use of refrigerants in HVAC systems, formula (4) should be used separately for calculation. r Indicates the refrigerant type serial number. Different types of refrigerants correspond to unique serial numbers. The relevant information of the corresponding refrigerant can be extracted based on the refrigerant serial number.

[0086] in, For target assessment buildings use r Carbon emissions from each refrigerant, in tCO2 / year; For the HVAC system r Refrigerant charge amount, in kg / unit; is the service life of the HVAC system in years; For the r The global warming potential of the refrigerants; The target is to assess the carbon emissions per unit building area during the building operation phase, in kgCO2 / m2; Evaluate the building for the target Annual consumption of energy of the type, in units of corresponding energy standard units (electricity kW, gas and oil m 3, municipal heat KJ) / year; For the Carbon emission factors of energy sources; For the Type 1 energy consumption system Energy consumption of the type, in units of corresponding energy standard units (electricity kW, gas and oil m 3 , municipal heat KJ) / year; The data type is parameter matrix; The matrix elements include 、 and In the Energy consumption data of the energy consumption type, i.e. 、 and , No. Types of energy include electric energy, gas and oil energy, and municipal thermal energy; For the The energy consumption system consumes the energy provided by the renewable energy system. Energy consumption of the type, in units of corresponding energy standard units (electricity kW, gas and oil m 3 , municipal heat KJ) / year; The annual carbon reduction of the green space carbon sequestration system in the building is evaluated for the target, in kgCO2 / year; Evaluate the design life of the building for the purpose, in years; A The floor space of the building being assessed for the target, in square meters.

[0087] It should be noted that the above formula (1) represents the model of the HVAC system at the output end, and the corresponding approximate model of the energy supply end is:

[0088]

[0089] The total energy supply of the above three energy sources is equal to the calculation result in formula (1), 、 、 It is the preset energy supply coefficient. 、 、 As The elements of the parameter matrix are calculated using the multivariate linear regression method in combination with formula (1). If some of the energy is generated by photovoltaic or hydropower, it also belongs to elements.

[0090] Similarly, the above formulas (2)-(3) also represent the model of the corresponding system at the output end level. The corresponding functional end model is similar to the above description, and the element calculation method is consistent with the above method, which will not be repeated here.

[0091] S30: Based on the number of input variables, the matrix size of the input variables, and the number of output variables required in the carbon emission mechanism model, define the deep learning neural network structure of the input variables and output variables, and construct the loss function of the carbon emission mechanism model based on the deep learning neural network structure.

[0092] Specifically, step S30 includes:

[0093] S31: Initialize the number of input variables, the matrix size of the input variables, and the number of output variables in the carbon emission mechanism model, and define the number of input and output neurons, the number of neural network layers, and the number of neurons in each neural network layer of the deep learning neural network structure;

[0094] S32: After data preparation is completed, the loss function of the carbon emission mechanism model is constructed based on the network structure of the deep learning neural network.

[0095] It should be explained that the above-mentioned data preparation refers to the routine processing of the original input data, including data processing, integration, transformation and normalization, to finally obtain the data set.

[0096] Furthermore, the loss function of the carbon emission mechanism model is specifically:

[0097] (7)

[0098]

[0099] in, is the data domain weight, is the mechanism domain weight; is the loss function In the data domain-based sub-items; N is the number of iterations, The target evaluation building is obtained by random training of the deep learning model. Forecast of annual energy consumption, is the loss function of the carbon emission mechanism model.

[0100] S40: Using an iterative optimization function, the loss function is iteratively trained until the loss value obtained by the loss function is lower than a preset allowable value, and a deep learning neural network agent model based on the carbon emission mechanism is output.

[0101] In this embodiment, the iterative optimization function adopts the quasi-Newton method in the second-order optimization method, and the allowable value is 0.01.

[0102] Specifically, the dataset a and dataset b prepared in step S32 are input into formula (7) and formula (8) respectively, and the quasi-Newton method is used to perform adaptive iterative training until the loss value is lower than the allowable value, and then the adaptive iterative training is stopped.

[0103] Preferably, after obtaining the deep learning neural network proxy model based on the carbon emission mechanism, the test set c is input into the deep learning neural network proxy model based on the carbon emission mechanism, and its model accuracy is evaluated. If the evaluation accuracy reaches 99%, it means that the model training is successful.

[0104] S50: Using a deep learning neural network agent model, the green operation performance of the station-city integrated three-dimensional network is evaluated in accordance with the requirements of the green building carbon emission specifications, and the evaluation results are obtained.

[0105] Among them, the above-mentioned evaluation of the green operation performance of the station-city integrated three-dimensional network space yielded the following specific evaluation results:

[0106] (9)

[0107] in, According to the U.S. Department of Energy standard, 15kg CO2 / m 2 , for After iterative calculation, the target assessment is the carbon emissions per unit building area during the building operation phase; if , then the target assessment building is a green building. , the target assessment building is a non-green building.

[0108] Through the above steps S10-S50, the deep learning method of the present invention constructs a carbon emission mechanism model under the operational state of the station-city integrated three-dimensional network space, achieving accurate energy consumption assessment under the operational performance of the station-city integrated three-dimensional network space and improving the efficiency of real-time assessment. At the same time, the deep learning method of the present invention specifically constructs a deep learning neural network agent model, which is not a black box model. In addition, the deep learning method of the present invention fully understands the data input and data relationships, and the trained model is more reliable. Physical information constraints are imposed during the PINN training process, so a more generalizable model can be learned with fewer data samples.

[0109] Example 2

[0110] like Figure 2 As shown, this embodiment discloses a deep learning system based on the station-city integrated space energy consumption model. The system adopts the deep learning method based on the station-city integrated space energy consumption model in Example 1. The system of the present invention includes:

[0111] The energy consumption model construction module is used to analyze the core energy consumption system of the core space area within the station-city integrated three-dimensional network space using the spatial regional analysis method, and to construct the station-city integrated three-dimensional network energy consumption model corresponding to the core energy consumption system;

[0112] The carbon emission mechanism model construction module is used to construct a carbon emission mechanism model based on the energy consumption type and energy consumption characteristics of the station-city integrated three-dimensional network energy consumption model;

[0113] A loss function definition module is used to define the deep learning neural network structure of input variables and output variables based on the number of input variables, the matrix size of the input variables, and the number of output variables in the carbon emission mechanism model, and to construct the loss function of the carbon emission mechanism model based on the deep learning neural network structure;

[0114] An iterative optimization module is used to iteratively train the loss function using an iterative optimization function until the loss value obtained by the loss function is lower than a preset allowable value, and output a deep learning neural network agent model based on the carbon emission mechanism;

[0115] The evaluation module is used to evaluate the green operation performance of the station-city integrated three-dimensional network by adopting a deep learning neural network agent model in accordance with the requirements of the green building carbon emission specifications and obtain the evaluation results.

[0116] Example 3

[0117] like Figure 3 As shown, this embodiment discloses a computer device, which includes an internal memory, a processor, a non-volatile storage medium, and a computer program stored in the non-volatile storage medium and capable of running on the processor. When the processor executes the computer program, the information scheduling method based on the large language model in Example 2 is implemented.

[0118] Those skilled in the art will understand that all or part of the steps of implementing the above-mentioned embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk, etc. Various media that can store program codes.

[0119] Those skilled in the art can make various other corresponding changes and deformations based on the technical solutions and concepts described above, and all of these changes and deformations should fall within the scope of protection of the claims of the present invention.

Claims

1. A deep learning method based on the station-city integrated space energy consumption model is characterized by: include: S10: Analyze the core energy consumption system of the core space area in the station-city integrated three-dimensional network space using a spatial regional analysis method, and construct a station-city integrated three-dimensional network energy consumption model corresponding to the core energy consumption system; S20: Constructing a carbon emission mechanism model based on the energy consumption type and energy consumption characteristics of the station-city integrated three-dimensional network energy consumption model; The core energy consumption systems include HVAC systems, lighting systems, and elevator systems; the energy consumption types include HVAC energy consumption, lighting energy consumption, and elevator energy consumption; and the energy consumption characteristics include HVAC energy consumption characteristics, lighting energy consumption characteristics, and elevator energy consumption characteristics. S30: defining a deep learning neural network structure of the input variables and the output variables according to the number of input variables, the matrix size of the input variables, and the number of output variables in the carbon emission mechanism model, and constructing a loss function of the carbon emission mechanism model according to the deep learning neural network structure; S40: using an iterative optimization function to iteratively train the loss function until a loss value obtained by the loss function is lower than a preset allowable value, and outputting a deep learning neural network agent model based on a carbon emission mechanism; S50: Using the deep learning neural network agent model, the green operation performance of the station-city integrated three-dimensional network is evaluated in accordance with the green building carbon emission specification requirements to obtain an evaluation result.

2. The deep learning method based on the station-city integrated space energy consumption model according to claim 1 is characterized in that: The step S10 includes: S11: Determine the core spatial area of ​​the station-city integrated three-dimensional network; The core space area includes at least one or more of the waiting room, platform, transfer space and centralized commercial space; S12: Analyze the core energy consumption system of the core spatial area using a spatial area analysis method; S13: Constructing a corresponding energy consumption model according to the core energy consumption system, including: Constructing an air conditioning energy consumption model corresponding to the HVAC system; and / or Constructing a lighting energy consumption model corresponding to the lighting system; and / or Constructing an elevator energy consumption model corresponding to the elevator system; Among them, the station-city integrated three-dimensional network energy consumption model includes at least one or more of the air-conditioning energy consumption model, the lighting energy consumption model and the elevator energy consumption model.

3. The deep learning method based on the station-city integrated space energy consumption model according to claim 2 is characterized in that: The air conditioning energy consumption model is specifically as follows: （1） in, is the annual energy consumption of air conditioning, i.e. the HVAC energy consumption; is the cooling / heating power of the air conditioner; The operating time of the air conditioner; The coefficient of performance of the air conditioner is the ratio of the cooling / heating effect of the air conditioner to the energy consumed; 、 and This belongs to the HVAC energy consumption characteristics.

4. The deep learning method based on the station-city integrated space energy consumption model according to claim 2 is characterized in that: The lighting energy consumption model is specifically as follows: （2） in, is the annual energy consumption of the lighting system, i.e. the lighting energy consumption; For the day Lighting power density value of each room; For the Lighting area of ​​each room; For the j day i Lighting time of each room; The lighting power density of emergency lighting; Evaluate the building's footprint for the purpose; 、 、 、 and This belongs to the lighting energy consumption characteristics.

5. The deep learning method based on the station-city integrated space energy consumption model according to claim 2 is characterized in that: The elevator energy consumption model is specifically as follows: （3） in, is the annual energy consumption of the elevator, i.e., the energy consumption of the elevator; for specific energy consumption; is the average annual operating hours of the elevator; is the elevator speed; is the rated load capacity of the elevator; Energy consumption when the elevator is in standby mode; The average annual standby hours of the elevator; 、 、 、 、 and This belongs to the energy consumption characteristics of the elevator.

6. The deep learning method based on the station-city integrated space energy consumption model according to claim 2 is characterized in that: The carbon emission mechanism model in step S20 is specifically: in, For target assessment buildings use r Carbon emissions from each refrigerant; The HVAC system r Refrigerant charge amount; The service life of the HVAC system; For the r The global warming potential of the refrigerants; Evaluate the carbon emissions per unit floor area during the building operation phase for the target; Evaluate the building for the target Annual consumption of energy of this type; For the Carbon emission factors of energy sources; Evaluate the annual carbon reduction of green space carbon sequestration systems in buildings for the target; Evaluate the building's design life for the purpose; Evaluate the building's footprint for the purpose; The data type is parameter matrix; k include a、l and e , a For air conditioning, l For lighting, e For the elevator, The matrix elements include 、 and In the Energy consumption data in the energy consumption category, i.e. 、 and , is the annual energy consumption of air conditioning, i.e. HVAC energy consumption; is the annual energy consumption of the lighting system, i.e., lighting energy consumption; is the annual energy consumption of the elevator, that is, the energy consumption of the elevator, Types of energy include electric energy, gas and oil energy, and municipal thermal energy; For the The energy consumption system consumes the energy provided by the renewable energy system. Energy consumption of the category.

7. The deep learning method based on the station-city integrated space energy consumption model according to claim 6 is characterized in that: The step S30 includes: S31: Initialize the number of input variables, the matrix size of the input variables, and the number of output variables in the carbon emission mechanism model, and define the number of input and output neurons, the number of neural network layers, and the number of neurons in each neural network layer of the deep learning neural network structure; S32: After the data preparation is completed, a loss function of the carbon emission mechanism model is constructed according to the network structure of the deep learning neural network.

8. The deep learning method based on the station-city integrated space energy consumption model according to claim 7 is characterized in that: The loss function of the carbon emission mechanism model in step S32 is specifically: （7） in, is the data domain weight, is the mechanism domain weight; is the loss function In the data domain-based sub-items; N is the number of iterations, The target evaluation building is obtained by random training of deep learning model. Forecast of annual energy consumption, is the loss function of the carbon emission mechanism model.

9. The deep learning method based on the station-city integrated space energy consumption model according to claim 1 is characterized in that: The green operation performance of the station-city integrated three-dimensional network is evaluated, and the evaluation results are as follows: （9） in, is the benchmark parameter, Evaluate the carbon emissions per unit floor area during the building operation phase for the target; for After iterative calculation, the target assessment is the carbon emissions per unit building area during the building operation phase; like , then the target assessment building is a green building; like , then the target assessment building is a non-green building.

10. A deep learning system based on a station-city integrated space energy consumption model adopts a deep learning method based on a station-city integrated space energy consumption model according to any one of claims 1 to 9, characterized in that: The deep learning system includes: An energy consumption model construction module is used to analyze the core energy consumption system of the core spatial area within the station-city integrated three-dimensional network space using a spatial regional analysis method, and to construct a station-city integrated three-dimensional network energy consumption model corresponding to the core energy consumption system; A carbon emission mechanism model construction module is used to construct a carbon emission mechanism model based on the energy consumption type and energy consumption characteristics of the station-city integrated three-dimensional network energy consumption model; A loss function definition module, configured to define a deep learning neural network structure of the input variables and the output variables according to the number of input variables, the matrix size of the input variables, and the number of output variables required in the carbon emission mechanism model, and to construct a loss function of the carbon emission mechanism model according to the deep learning neural network structure; an iterative optimization module, configured to iteratively train the loss function using an iterative optimization function until a loss value obtained by the loss function is lower than a preset allowable value, and output a deep learning neural network proxy model based on a carbon emission mechanism; An evaluation module is used to use the deep learning neural network agent model to evaluate the green operation performance of the station-city integrated three-dimensional network in accordance with the green building carbon emission specifications and obtain an evaluation result.

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