Energy efficiency evaluation method for station-city integrated three-dimensional network space

Abstract

The invention discloses an energy efficiency evaluation method for a station-city integrated three-dimensional network space, and belongs to the technical field of energy efficiency evaluation based on dynamic modeling. The problem that in the prior art, a traditional energy efficiency evaluation method based on an air conditioner operation mechanism does not consider the influence of different space energy efficiency influence factors on the overall energy efficiency of the air conditioner, and consequently the evaluation result accuracy is low is solved. The method comprises the following steps: establishing a station-city fused three-dimensional network spatial information model to obtain spatial information; acquiring dynamic monitoring data related to the energy efficiency of the air conditioner; registering the space information and the dynamic monitoring data to obtain registered air conditioner energy efficiency influence factor data and air conditioner energy consumption calculation index data; carrying out iterative training by adopting the data set and the established loss function to obtain a trained station-city integrated air conditioner energy efficiency operation dynamic evaluation model based on the KAN model; and air conditioner energy efficiency evaluation based on the energy efficiency grade is realized through grading regulation. The method reduces the energy efficiency calculation time, and can be applied to energy efficiency evaluation in the station-city fusion space.

Description

Technical Field

[0001] The present invention relates to an energy efficiency evaluation method for a station-city integrated three-dimensional network space, and belongs to the technical field of energy efficiency evaluation based on dynamic modeling. Background Art

[0002] Station-city integration, also known as "station-city integration," broadly refers to the interconnected and interactive relationships between railway and urban rail transit stations and surrounding cities across various elements, including functional space and transportation mechanisms. At the project level, station-city integration refers to the coordinated development of hubs and their surrounding areas, achieved through tailored measures tailored to local conditions, taking into account the city's inherent conditions during hub planning and construction, and leveraging the spillover effects of railway and rail development. Station-city integration advocates for the close integration of key transportation hubs with urban space, placing daily office, residential, and urban service functions within walking distance of stations, thereby providing citizens with convenient lifestyles and economic opportunities.

[0003] The station-city integrated three-dimensional network space covers the spatial structure of traditional comprehensive passenger transport hubs and various spatial areas connected to the city. As the development direction of future comprehensive transportation hubs, the performance evaluation requirements for its green operation are becoming increasingly prominent. Among them, how to achieve rapid and accurate energy efficiency evaluation based on the air conditioning operation mechanism has also put forward higher requirements; in the existing technology one, the patent document with publication (announcement) number CN113175733B discloses a method for calculating the energy efficiency of air conditioners, an air conditioner and a storage medium, but it does not consider the impact of traffic, pedestrian flow, spatial structure and environmental changes in the air conditioning action space on the energy efficiency of air conditioners during actual engineering applications; in the existing technology two, the patent document with publication (announcement) number CN110059801B discloses an air conditioner energy efficiency control method based on a neural network, but it does not consider the impact of different energy efficiency influencing factors in different spaces during the operation of central air conditioning in different spaces on the overall energy efficiency of air conditioning, and cannot determine the weights of different influencing factors.

[0004] In summary, an energy efficiency evaluation method for the three-dimensional network space of station-city integration is needed. Summary of the Invention

[0005] A brief overview of the present invention is provided below to provide a basic understanding of certain aspects of the present invention. It should be understood that this overview is not an exhaustive overview of the present invention. It is not intended to identify key or important aspects of the present invention, nor is it intended to limit the scope of the present invention. Its purpose is simply to present certain concepts in a simplified form as a prelude to the more detailed description discussed later.

[0006] In view of this, in order to solve the problem of low accuracy of evaluation results caused by the traditional energy efficiency evaluation method based on the air-conditioning operation mechanism in the existing technology, which does not consider the impact of different space energy efficiency influencing factors on the overall energy efficiency of the air-conditioning, the present invention provides an energy efficiency evaluation method for the station-city integrated three-dimensional network space.

[0007] The technical solution is as follows: The energy efficiency evaluation method for the station-city integrated three-dimensional network space includes the following steps:

[0008] S1. Based on the air conditioning energy consumption spatial area, establish a station-city integrated three-dimensional network spatial information model to obtain spatial information;

[0009] S2. Obtain dynamic monitoring data related to air conditioning energy efficiency based on the station-city integrated three-dimensional network spatial information model and the collection system deployed in the air conditioning energy consumption space;

[0010] S3. Align the spatial information with the dynamic monitoring data, determine the relationship between the spatial information and the dynamic monitoring data, and obtain the aligned air conditioning energy efficiency influencing factor data and air conditioning energy consumption calculation index data;

[0011] S4. Construct a dataset using the aligned air conditioning energy efficiency influencing factor data and air conditioning energy consumption calculation indicator data. Using this dataset and the established loss function, iteratively train the deep learning neural network structure based on the KAN model. This results in a trained KAN-based dynamic evaluation model for station-city integrated air conditioning energy efficiency.

[0012] S5. Through the trained KAN model-based dynamic evaluation model of station-city integrated air conditioning energy efficiency operation, the air conditioning energy consumption, cooling capacity and space impact weight are output. Through the grading regulations, the overall energy efficiency of the central air conditioning is determined, and the air conditioning energy efficiency evaluation based on energy efficiency level is realized.

[0013] Furthermore, in said S1, the air-conditioning energy-consuming space area of ​​the core of the station-city integrated three-dimensional network is determined, which includes waiting rooms, subway platforms, transfer passages, transfer function spaces and centralized commercial spaces;

[0014] A station-city integrated three-dimensional network spatial information model is established based on the structure of the air-conditioning energy-consuming space area and the power design and construction drawings. It includes the spatial information of the space size, space vents, air-conditioning inlets and outlets, and air-conditioning inlet and outlet positions in the air-conditioning energy-consuming space area of ​​the core of the station-city integrated three-dimensional network.

[0015] Furthermore, the step S2 includes the following steps:

[0016] S21. Use data on factors influencing air conditioning energy efficiency as input parameters for dynamic energy efficiency assessment. Based on the spatial information model of the station-city integrated three-dimensional network, deploy front-end sensing sensors in the air conditioning energy-consuming areas at the core of the station-city integrated three-dimensional network. Establish an energy efficiency factor data collection system combining front-end sensing sensors, data collectors, and industrial computers to collect dynamic data on factors influencing air conditioning energy efficiency.

[0017] S22. Use the air-conditioning energy consumption calculation index data as the air-conditioning system energy consumption parameters and cooling capacity parameters for dynamic energy efficiency evaluation. According to the station-city integrated three-dimensional network spatial information model, in the air-conditioning room corresponding to the air-conditioning energy-consuming space area at the core of the station-city integrated three-dimensional network, use sensors to monitor and obtain the air-conditioning energy consumption data of different spaces in the station city based on a reasonable layout plan. Combine sensors, collectors, and industrial computers to establish an energy consumption calculation index data collection system to collect dynamic air-conditioning energy consumption calculation index data.

[0018] Furthermore, the step S3 specifically includes the following steps:

[0019] S31. Matching the spatial information with the air conditioning energy efficiency influencing factor data and the air conditioning energy consumption calculation index data to obtain matching information;

[0020] In said S31, the data of the factors affecting the air conditioning energy efficiency are matched with the information labels in the station-city integrated three-dimensional network space information model according to the spatial area where the corresponding sensor is located during the data collection process;

[0021] According to the air conditioning effect space area, the air conditioning energy consumption calculation index data is matched with the spatial information label in the station-city integrated three-dimensional network space information model;

[0022] S32. The dynamic monitoring data is labeled according to the matching information. During the collection of all dynamic monitoring data, a spatial information data field is added to all data according to the matched spatial information to obtain the aligned air conditioning energy efficiency influencing factor data and air conditioning energy consumption calculation index data.

[0023] Furthermore, the step S4 includes the following steps:

[0024] S41. To address the spatiotemporal heterogeneity of multi-source data, a hierarchical spatiotemporal encoder based on a reference grid is constructed. Using time axis normalization and spatial interpolation methods, detection data with different sampling frequencies and spatial resolutions are unified into a common spatiotemporal coordinate system. Input and output datasets are constructed, which contain training and test datasets, respectively.

[0025] In said S41, the input data set is constructed using the registered air-conditioning energy efficiency influencing factor data;

[0026] The output dataset is constructed using the registered air conditioning energy consumption calculation index data;

[0027] S42. Initialize the model parameters, that is, define the deep learning neural network structure based on the KAN model according to the number of input variables and the number of output variables, including the number of input neurons, the number of output neurons, the number of neural network layers, and the number of neurons in the middle layer;

[0028] S43. Establish a loss function for the deep learning neural network structure based on the KAN model based on energy consumption and cooling capacity;

[0029] Loss Function Expressed as:

[0030] ;

[0031] in, is the energy consumption weight, is the energy loss value, is the cooling capacity weight, is the cooling capacity loss value;

[0032] S44. According to the loss function , use the quasi-Newton method in the second-order optimization method to define the iterative optimization function;

[0033] S45. Input the input data set into the deep learning neural network structure based on the KAN model for iterative training, and obtain the trained KAN model-based station-city integrated air conditioning energy efficiency operation dynamic evaluation model through evaluation;

[0034] In the S45, the input data set is input into the deep learning neural network structure based on the KAN model, and the deep learning neural network structure based on the KAN model is adaptively iteratively trained according to the iterative optimization function, so that the loss value reaches below the allowable value, and the allowable value is set to 0.01. After reaching the allowable value, the training is automatically stopped, and a dynamic evaluation model of the energy efficiency operation of the station-city integrated air conditioning based on the KAN model is output;

[0035] The test set data is input into the trained KAN model-based dynamic evaluation model for energy efficiency operation of station-city integrated air conditioning to evaluate the accuracy. When the evaluation progress reaches 99%, it is confirmed that the training of the KAN model-based dynamic evaluation model for energy efficiency operation of station-city integrated air conditioning is successful.

[0036] Furthermore, the step S5 includes the following steps:

[0037] S51. Obtain real-time monitoring data on the flow of people, environment, and equipment in different spaces of the station-city integrated three-dimensional network;

[0038] S52. Input the real-time monitoring data obtained in step S51 into the trained KAN model-based station-city integrated air conditioning energy efficiency operation dynamic evaluation model, and output the air conditioning energy consumption, cooling capacity and space impact weight;

[0039] S53. Based on the air conditioner energy efficiency calculation formula, cooling capacity, and air conditioner energy consumption, determine the air conditioner energy efficiency according to the classification regulations;

[0040] In S53, the air conditioning energy efficiency calculation formula is expressed as:

[0041] ;

[0042] in, is the air conditioning energy efficiency ratio of the jth space, is the cooling capacity of the jth space, is the air conditioning energy consumption of the jth space; the energy efficiency grading regulations are judged by the Shenzhen Public Building Central Air Conditioning System Energy Efficiency Evaluation Standard in the public materials. ≥6, the space air conditioning energy efficiency is level one, when 6.0> ≥5.5, the space air conditioning energy efficiency is level 2. ≥5.0, the space air conditioning energy efficiency is level 3. ≥4.0, the space air conditioning energy efficiency meets the limit value. When , the energy efficiency of space air conditioner does not meet the limit value;

[0043] S54. Based on the overall air conditioning energy efficiency calculation formula and real-time monitoring data on the flow of people, environment, and equipment in different spaces of the station-city integrated three-dimensional network, the overall energy efficiency of the central air conditioning system is determined through grading regulations.

[0044] In S54, the overall air conditioning energy efficiency calculation formula is expressed as:

[0045] ;

[0046] ;

[0047] in, is the overall energy efficiency ratio of central air conditioning, is the influence weight of the j-th space, is the influence weight of the flow of people in the j-th space, For human flow parameter data, is the impact weight of the environment in the j-th space, is the environmental parameter data, is the influence weight of the space under the j-th space, is the spatial parameter data, is the impact weight of the equipment performance in the jth space, D is the equipment performance parameter data, and the energy efficiency classification regulations of the Shenzhen Public Building Central Air Conditioning System Energy Efficiency Evaluation Standard in the public materials are adopted. ≥6, the overall energy efficiency of the central air conditioner is level one. ≥5.5, the overall energy efficiency of the central air conditioner is level 2. ≥5.0, the overall energy efficiency of the central air conditioner is level three. ≥4.0, the overall energy efficiency of the central air conditioner meets the limit value. When , the overall energy efficiency of the central air conditioner does not meet the limit.

[0048] The beneficial effects of the present invention are as follows: The present invention provides a dynamic evaluation method for the operating performance of air-conditioning energy efficiency in a station-city integrated three-dimensional network space. Compared with traditional methods, the present invention solves the problem of air-conditioning energy efficiency evaluation when different functional spaces are affected by different energy efficiency influencing factors in a complex three-dimensional network space environment of station-city integration, and can determine the weights of different energy efficiency influencing factors; through the present invention, a dynamic evaluation model for the energy efficiency operation of air-conditioning integrated in station-city based on the KAN model is constructed to achieve accurate local and overall evaluation of air-conditioning energy efficiency under the operation of the station-city integrated three-dimensional network space. At the same time, the dynamic evaluation model for the energy efficiency operation of air-conditioning integrated in station-city based on the KAN model can achieve real-time dynamic evaluation of local and overall air-conditioning energy efficiency under the operation of the station-city integrated three-dimensional network space. In summary, the present invention can solve the problems of time-consuming calculation of general air-conditioning energy efficiency and low simulation efficiency, and realize real-time evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0050] Figure 1 This is a flow chart of the energy efficiency evaluation method for the station-city integrated three-dimensional network space. DETAILED DESCRIPTION

[0051] To make the technical solutions and advantages of the embodiments of the present invention more clearly understood, exemplary embodiments of the present invention are further described in detail below with reference to the accompanying drawings. It should be noted that the embodiments described are only a portion of the embodiments of the present invention, and are not an exhaustive list of all embodiments. It should be noted that the embodiments of the present invention and the features thereof may be combined with each other unless they conflict.

[0052] refer to Figure 1 The energy efficiency evaluation method for the station-city integrated three-dimensional network space of this embodiment is described in detail, and specifically includes the following steps:

[0053] S1. Based on the air conditioning energy consumption spatial area, establish a station-city integrated three-dimensional network spatial information model to obtain spatial information;

[0054] S2. Obtain dynamic monitoring data related to air conditioning energy efficiency based on the station-city integrated three-dimensional network spatial information model and the collection system deployed in the air conditioning energy consumption space;

[0055] S3. Align the spatial information with the dynamic monitoring data, determine the relationship between the spatial information and the dynamic monitoring data, and obtain the aligned air conditioning energy efficiency influencing factor data and air conditioning energy consumption calculation index data;

[0056] S4. Construct a dataset using the aligned air conditioning energy efficiency influencing factor data and air conditioning energy consumption calculation indicator data. Using this dataset and the established loss function, iteratively train the deep learning neural network structure based on the KAN model. This results in a trained KAN-based dynamic evaluation model for station-city integrated air conditioning energy efficiency.

[0057] S5. Through the trained KAN model-based dynamic evaluation model of station-city integrated air conditioning energy efficiency operation, the air conditioning energy consumption, cooling capacity and space impact weight are output. Through the grading regulations, the overall energy efficiency of the central air conditioning is determined, and the air conditioning energy efficiency evaluation based on energy efficiency level is realized.

[0058] Furthermore, in said S1, the air-conditioning energy-consuming space area of ​​the core of the station-city integrated three-dimensional network is determined, which includes waiting rooms, subway platforms, transfer passages, transfer function spaces and centralized commercial spaces;

[0059] A station-city integrated three-dimensional network spatial information model is established based on the structure of the air-conditioning energy-consuming space area and the power design and construction drawings. It includes spatial information such as the space size, space vents, air-conditioning inlets and outlets, and air-conditioning inlet and outlet positions in the air-conditioning energy-consuming space area of ​​the core of the station-city integrated three-dimensional network.

[0060] Furthermore, the step S2 includes the following steps:

[0061] S21. Using data on factors influencing air conditioning energy efficiency as input parameters for dynamic energy efficiency assessment, based on the spatial information model of the station-city integrated three-dimensional network, deploy front-end sensing sensors, including cameras and thermometers, in the air conditioning energy-consuming spaces at the core of the station-city integrated three-dimensional network. Establish an energy efficiency factor data collection system combining front-end sensing sensors, data collectors, and industrial computers to collect dynamic data on factors influencing air conditioning energy efficiency.

[0062] S22. Use the air-conditioning energy consumption calculation index data as the air-conditioning system energy consumption parameters and cooling capacity parameters for dynamic energy efficiency evaluation. According to the station-city integrated three-dimensional network space information model, in the air-conditioning room corresponding to the air-conditioning energy-consuming space area at the core of the station-city integrated three-dimensional network, use temperature and humidity, flow meters, electricity meters and other sensors to monitor and obtain the air-conditioning energy consumption data of different spaces in the station city based on a reasonable layout plan. Combine sensors, data collectors and industrial computers to establish an energy consumption calculation index data collection system to collect dynamic air-conditioning energy consumption calculation index data.

[0063] Specifically, referring to Table 1, different collection methods, sampling frequencies, and data thresholds are used for different influencing factors to determine the key influencing factors of air conditioning energy efficiency in the station-city integration space;

[0064] Table 1

[0065] Refer to Table 2 to determine the key calculation indicators of air conditioning energy consumption in the station-city integration space;

[0066] Table 2

[0067] Furthermore, the step S3 specifically includes the following steps:

[0068] S31. Matching the spatial information with the air conditioning energy efficiency influencing factor data and the air conditioning energy consumption calculation index data to obtain matching information;

[0069] In said S31, the data of the factors affecting the air conditioning energy efficiency are matched with the information labels in the station-city integrated three-dimensional network space information model according to the spatial area where the corresponding sensor is located during the data collection process;

[0070] According to the air conditioning effect space area, the air conditioning energy consumption calculation index data is matched with the spatial information label in the station-city integrated three-dimensional network space information model;

[0071] S32. The dynamic monitoring data is labeled according to the matching information. During the collection of all dynamic monitoring data, a spatial information data field is added to all data according to the matched spatial information to obtain the aligned air conditioning energy efficiency influencing factor data and air conditioning energy consumption calculation index data.

[0072] Furthermore, the step S4 includes the following steps:

[0073] S41. To address the spatiotemporal heterogeneity of multi-source data, a hierarchical spatiotemporal encoder based on a reference grid is constructed. Using time axis normalization and spatial interpolation methods, detection data with different sampling frequencies and spatial resolutions are unified into a common spatiotemporal coordinate system. Input and output datasets are constructed, which contain training and test datasets, respectively.

[0074] In said S41, the input data set is constructed using the registered air-conditioning energy efficiency influencing factor data;

[0075] The output dataset is constructed using the registered air conditioning energy consumption calculation index data;

[0076] S42. Initialize model parameters. That is, define the deep learning neural network structure based on the KAN model based on the number of input variables and output variables. This includes the number of input neurons (depending on the number of factors influencing air conditioner energy efficiency collected, generally 2-10), the number of output neurons (depending on the number of factors influencing air conditioner energy efficiency collected, generally 2-10), the number of neural network layers (generally 3-5 layers, including input and output layers), and the number of neurons in the middle layer (generally less than 50).

[0077] S43. Establish a loss function for the deep learning neural network structure based on the KAN model based on energy consumption and cooling capacity;

[0078] Loss Function Expressed as:

[0079] ;

[0080] in, is the energy consumption weight, is the energy loss value, is the cooling capacity weight, is the cooling capacity loss value;

[0081] Energy loss value and cooling capacity loss , through a data-based loss function The formula is calculated;

[0082] Data-based loss function Expressed as:

[0083] ;

[0084] in, is the true value, is the predicted value, is the sample size;

[0085] S44. According to the loss function , use the quasi-Newton method in the second-order optimization method to define the iterative optimization function;

[0086] S45. Input the input data set into the deep learning neural network structure based on the KAN model for iterative training, and obtain the trained KAN model-based station-city integrated air conditioning energy efficiency operation dynamic evaluation model through evaluation;

[0087] In the S45, the input data set is input into the deep learning neural network structure based on the KAN model, and the deep learning neural network structure based on the KAN model is adaptively iteratively trained according to the iterative optimization function, so that the loss value reaches below the allowable value, and the allowable value is set to 0.01. After reaching the allowable value, the training is automatically stopped, and a dynamic evaluation model of the energy efficiency operation of the station-city integrated air conditioning based on the KAN model is output;

[0088] The test set data is input into the trained KAN model-based dynamic evaluation model for energy efficiency operation of station-city integrated air conditioning to evaluate the accuracy. When the evaluation progress reaches 99%, it is confirmed that the training of the KAN model-based dynamic evaluation model for energy efficiency operation of station-city integrated air conditioning is successful.

[0089] Furthermore, the step S5 includes the following steps:

[0090] S51. Obtain real-time monitoring data on the flow of people, environment, and equipment in different spaces of the station-city integrated three-dimensional network;

[0091] S52. Input the real-time monitoring data obtained in step S51 into the trained KAN model-based station-city integrated air conditioning energy efficiency operation dynamic evaluation model, and output data such as air conditioning energy consumption, cooling capacity, and space impact weight;

[0092] S53. Based on the air conditioner energy efficiency calculation formula, cooling capacity, and air conditioner energy consumption, determine the air conditioner energy efficiency according to the classification regulations;

[0093] In S53, the air conditioning energy efficiency calculation formula is expressed as:

[0094] ;

[0095] in, is the air conditioning energy efficiency ratio of the jth space, is the cooling capacity of the jth space, is the air conditioning energy consumption of the jth space; the energy efficiency grading regulations are judged by the Shenzhen Public Building Central Air Conditioning System Energy Efficiency Evaluation Standard in the public materials. ≥6, the space air conditioning energy efficiency is level one, when 6.0> ≥5.5, the space air conditioning energy efficiency is level 2. ≥5.0, the space air conditioning energy efficiency is level 3. ≥4.0, the space air conditioning energy efficiency meets the limit value. When , the energy efficiency of space air conditioner does not meet the limit value;

[0096] S54. Based on the overall air conditioning energy efficiency calculation formula and real-time monitoring data on the flow of people, environment, and equipment in different spaces of the station-city integrated three-dimensional network, the overall energy efficiency of the central air conditioning system is determined through grading regulations.

[0097] In S54, the overall air conditioning energy efficiency calculation formula is expressed as:

[0098] ;

[0099] ;

[0100] in, is the overall energy efficiency ratio of central air conditioning, is the influence weight of the j-th space, is the influence weight of the flow of people in the j-th space, For human flow parameter data, is the impact weight of the environment in the j-th space, is the environmental parameter data, is the influence weight of the space under the j-th space, is the spatial parameter data, is the impact weight of the equipment performance in the jth space, D is the equipment performance parameter data, and the energy efficiency classification regulations of the Shenzhen Public Building Central Air Conditioning System Energy Efficiency Evaluation Standard in the public materials are adopted. ≥6, the overall energy efficiency of the central air conditioner is level one. ≥5.5, the overall energy efficiency of the central air conditioner is level 2. ≥5.0, the overall energy efficiency of the central air conditioner is level three. ≥4.0, the overall energy efficiency of the central air conditioner meets the limit value. When , the overall energy efficiency of the central air conditioner does not meet the limit.

[0101] Although the present invention has been described with respect to a limited number of embodiments, it will be apparent to those skilled in the art, having benefit of the foregoing description, that other embodiments are contemplated within the scope of the invention thus described. Furthermore, it should be noted that the language used in this specification has been selected primarily for readability and didactic purposes, rather than for the purpose of explaining or limiting the subject matter of the present invention. Consequently, many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the appended claims. The disclosure of the present invention is intended to be illustrative rather than restrictive of the scope of the invention, which is defined by the appended claims.

Claims

1. The energy efficiency evaluation method of the station-city integrated three-dimensional network space is characterized by: The following steps are involved: S1. Based on the air conditioning energy consumption spatial area, establish a station-city integrated three-dimensional network spatial information model to obtain spatial information; S2. Obtain dynamic monitoring data related to air conditioning energy efficiency based on the station-city integrated three-dimensional network spatial information model and the collection system deployed in the air conditioning energy consumption space; S3. Align the spatial information with the dynamic monitoring data, determine the relationship between the spatial information and the dynamic monitoring data, and obtain the aligned air conditioning energy efficiency influencing factor data and air conditioning energy consumption calculation indicator data; S4. Construct a dataset using the aligned air conditioning energy efficiency influencing factor data and air conditioning energy consumption calculation indicator data. Using this dataset and the established loss function, iteratively train the deep learning neural network structure based on the KAN model. This results in a trained KAN-based dynamic evaluation model for station-city integrated air conditioning energy efficiency. S5. Through the trained KAN model-based dynamic evaluation model of station-city integrated air conditioning energy efficiency operation, the air conditioning energy consumption, cooling capacity and space impact weight are output. Through the grading regulations, the overall energy efficiency of the central air conditioning is determined, and the air conditioning energy efficiency evaluation based on energy efficiency level is realized.

2. The energy efficiency evaluation method for the station-city integrated three-dimensional network space according to claim 1 is characterized in that: In S1, the air-conditioning energy consumption space area of ​​the core of the station-city integrated three-dimensional network is determined, which includes waiting rooms, subway platforms, transfer passages, transfer function spaces and centralized commercial spaces; A station-city integrated three-dimensional network spatial information model is established based on the structure of the air-conditioning energy-consuming space area and the power design and construction drawings. It includes the spatial information of the space size, space vents, air-conditioning inlets and outlets, and air-conditioning inlet and outlet positions in the air-conditioning energy-consuming space area of ​​the core of the station-city integrated three-dimensional network.

3. The energy efficiency evaluation method for the station-city integrated three-dimensional network space according to claim 2 is characterized in that: Said S2 comprises the following steps: S21. Use data on factors influencing air conditioning energy efficiency as input parameters for dynamic energy efficiency assessment. Based on the spatial information model of the station-city integrated three-dimensional network, deploy front-end sensing sensors in the air conditioning energy-consuming areas at the core of the station-city integrated three-dimensional network. Establish an energy efficiency factor data collection system combining front-end sensing sensors, data collectors, and industrial computers to collect dynamic data on factors influencing air conditioning energy efficiency. S22. Use the air-conditioning energy consumption calculation index data as the air-conditioning system energy consumption parameters and cooling capacity parameters for dynamic energy efficiency evaluation. According to the station-city integrated three-dimensional network spatial information model, in the air-conditioning room corresponding to the air-conditioning energy-consuming space area at the core of the station-city integrated three-dimensional network, use sensors to monitor and obtain the air-conditioning energy consumption data of different spaces in the station city based on a reasonable layout plan. Combine sensors, collectors, and industrial computers to establish an energy consumption calculation index data collection system to collect dynamic air-conditioning energy consumption calculation index data.

4. The energy efficiency evaluation method for the station-city integrated three-dimensional network space according to claim 3 is characterized in that: The S3 specifically includes the following steps: S31. Matching the spatial information with the air conditioning energy efficiency influencing factor data and the air conditioning energy consumption calculation index data to obtain matching information; In said S31, the data of the factors affecting the air conditioning energy efficiency are matched with the information labels in the station-city integrated three-dimensional network space information model according to the spatial area where the corresponding sensor is located during the data collection process; According to the air conditioning effect space area, the air conditioning energy consumption calculation index data is matched with the spatial information label in the station-city integrated three-dimensional network space information model; S32. The dynamic monitoring data is labeled according to the matching information. During the collection of all dynamic monitoring data, a spatial information data field is added to all data according to the matched spatial information to obtain the aligned air conditioning energy efficiency influencing factor data and air conditioning energy consumption calculation index data.

5. The energy efficiency evaluation method for the station-city integrated three-dimensional network space according to claim 4 is characterized in that: Said S4 comprises the following steps: S41. To address the spatiotemporal heterogeneity of multi-source data, a hierarchical spatiotemporal encoder based on a reference grid is constructed. Using time axis normalization and spatial interpolation methods, detection data with different sampling frequencies and spatial resolutions are unified into a common spatiotemporal coordinate system. Input and output datasets are constructed, which contain training and test datasets, respectively. In said S41, the input data set is constructed using the registered air-conditioning energy efficiency influencing factor data; The output dataset is constructed using the registered air conditioning energy consumption calculation index data; S42. Initialize the model parameters, that is, define the deep learning neural network structure based on the KAN model according to the number of input variables and the number of output variables, including the number of input neurons, the number of output neurons, the number of neural network layers, and the number of neurons in the middle layer; S43. Establish a loss function for the deep learning neural network structure based on the KAN model based on energy consumption and cooling capacity; Loss Function Expressed as: ； in, is the energy consumption weight, is the energy loss value, is the cooling capacity weight, is the cooling capacity loss value; S44. According to the loss function , use the quasi-Newton method in the second-order optimization method to define the iterative optimization function; S45. Input the input data set into the deep learning neural network structure based on the KAN model for iterative training, and obtain the trained KAN model-based station-city integrated air conditioning energy efficiency operation dynamic evaluation model through evaluation; In the S45, the input data set is input into the deep learning neural network structure based on the KAN model, and the deep learning neural network structure based on the KAN model is adaptively iteratively trained according to the iterative optimization function, so that the loss value reaches below the allowable value, and the allowable value is set to 0.

01. After reaching the allowable value, the training is automatically stopped, and a dynamic evaluation model of the energy efficiency operation of the station-city integrated air conditioning based on the KAN model is output; The test set data is input into the trained KAN model-based dynamic evaluation model for energy efficiency operation of station-city integrated air conditioning to evaluate the accuracy. When the evaluation progress reaches 99%, it is confirmed that the training of the KAN model-based dynamic evaluation model for energy efficiency operation of station-city integrated air conditioning is successful.

6. The energy efficiency evaluation method for the station-city integrated three-dimensional network space according to claim 5 is characterized in that: The S5 comprises the following steps: S51. Obtain real-time monitoring data on the flow of people, environment, and equipment in different spaces of the station-city integrated three-dimensional network; S52. Input the real-time monitoring data obtained in step S51 into the trained KAN model-based station-city integrated air conditioning energy efficiency operation dynamic evaluation model, and output the air conditioning energy consumption, cooling capacity and space impact weight; S53. Based on the air conditioner energy efficiency calculation formula, cooling capacity, and air conditioner energy consumption, determine the air conditioner energy efficiency according to the classification regulations; In S53, the air conditioning energy efficiency calculation formula is expressed as: ； in, is the air conditioning energy efficiency ratio of the jth space, is the cooling capacity of the jth space, is the air conditioning energy consumption of the jth space; the energy efficiency grading regulations are judged by the Shenzhen Public Building Central Air Conditioning System Energy Efficiency Evaluation Standard in the public materials. ≥6, the space air conditioning energy efficiency is level one, when 6.0> ≥5.5, the space air conditioning energy efficiency is level 2. ≥5.0, the space air conditioning energy efficiency is level 3. ≥4.0, the space air conditioning energy efficiency meets the limit value. When , the energy efficiency of space air conditioner does not meet the limit value; S54. Based on the overall air conditioning energy efficiency calculation formula and real-time monitoring data on the flow of people, environment, and equipment in different spaces of the station-city integrated three-dimensional network, the overall energy efficiency of the central air conditioning system is determined through grading regulations. In S54, the overall air conditioning energy efficiency calculation formula is expressed as: ； ； in, is the overall energy efficiency ratio of central air conditioning, is the influence weight of the j-th space, is the influence weight of the flow of people in the j-th space, For human flow parameter data, is the impact weight of the environment in the j-th space, is the environmental parameter data, is the influence weight of the space under the j-th space, is the spatial parameter data, is the impact weight of the equipment performance in the jth space, D is the equipment performance parameter data, and the energy efficiency classification regulations of the Shenzhen Public Building Central Air Conditioning System Energy Efficiency Evaluation Standard in the public materials are adopted. ≥6, the overall energy efficiency of the central air conditioner is level one. ≥5.5, the overall energy efficiency of the central air conditioner is level 2. ≥5.0, the overall energy efficiency of the central air conditioner is level three. ≥4.0, the overall energy efficiency of the central air conditioner meets the limit value. When , the overall energy efficiency of the central air conditioner does not meet the limit.

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