Intelligent airport management system and method

Abstract

The application provides a smart airport management system and method, comprising a data acquisition module, a data processing module, an airport management module and a display module; the data acquisition module is used for acquiring initial data; the data processing module is used for processing the initial data to obtain basic data; based on a running target, the basic data is processed to obtain index data; the airport management module is used for managing the airport based on the basic data and the index data; the display module is used for extracting target data from the basic data to form a target scene; by using the above-mentioned airport management system and method, the operation and management pressure of the airport can be reduced, the work efficiency of the airport can be improved, and the economic benefit of the small airport can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of airport management, in particular to an intelligent airport management system and method. BACKGROUND

[0002] Small airports have less staff, high operation and maintenance pressure, and the phenomenon of operation and management integration and one post with multiple jobs is widespread. Human resources are tight, division of labor is unclear, and necessary technical support is lacking, resulting in low management efficiency and poor risk resistance of small airports. Taking small airports in the southwest region, especially highland airports, as an example, these airports have poor natural environment, poor office and living conditions, and extremely tight human resources. There are problems such as poor stability of personnel organization, unevenness, lack of strong technical support for airport management, low efficiency of operation and management, and poor reliability of facilities and equipment.

[0003] Therefore, some embodiments in the specification provide an intelligent airport management system and method to improve the efficiency of airport management and the reliability of facilities. SUMMARY

[0004] The purpose of the present application is to provide an intelligent airport management system, comprising a data acquisition module, a data processing module, an airport management module and a display module. The data acquisition module is used to acquire initial data. The data processing module is used to process the initial data to obtain basic data. Based on the operation target, the basic data is processed to obtain index data. The airport management module is used to manage the airport based on the basic data and the index data. The display module is used to extract target data from the basic data to form a target scene.

[0005] Further, the display module is also used to display the target data and the index data through the target scene. By operating the target scene, the target data and the index data are obtained, and the airport is managed.

[0006] Further, the airport management module is also used to, when the basic data does not meet the requirements of the index data, based on the basic data, the equipment of the target scene is investigated to determine the problem equipment. Based on the problem equipment, the corresponding actual equipment is repaired. Based on the repaired equipment, repair basic data and repair index data are obtained to determine the repair quality. Based on the repair quality, the operation and maintenance capability of the airport is evaluated. Based on the operation and maintenance capability, the operation and maintenance personnel of the airport are allocated.

[0007] Further, the operation target is energy saving; the airport management module is further used for inputting the basic data into an energy consumption prediction model, and outputting predicted energy consumption from the model; the predicted energy consumption is predicted future energy consumption of the airport; and based on the predicted energy consumption and the index data, the equipment in the target scene is managed.

[0008] Further, the energy consumption prediction model is obtained by training an initial energy consumption prediction model; a neural network model is assigned a weight to obtain the initial energy consumption prediction model; a training sample group is generated by a generation model; the training sample includes sample basic data and a label; the label is sample energy consumption corresponding to the sample basic data; the training sample group is input into the initial energy consumption prediction model, a target function is constructed based on the output of the model and the label; and parameters of the initial energy consumption prediction model are updated based on the target function to obtain a trained energy consumption prediction model.

[0009] The purpose of the present application is to provide a smart airport management method, including obtaining initial data, processing the initial data to obtain basic data; extracting target data from the basic data to form a target scene; based on an operation target, processing the basic data to obtain index data; and based on the basic data and the index data, managing the airport.

[0010] Further, the target data and the index data are displayed through the target scene; the target data and the index data are obtained by operating the target scene, and the airport is managed.

[0011] Further, the management of the airport includes: when the basic data does not meet the requirements of the index data, the equipment in the target scene is investigated based on the basic data to determine the problem equipment; the corresponding actual equipment is repaired based on the problem equipment; repair basic data and repair index data are obtained based on the repaired equipment to determine the repair quality; the operation and maintenance capability of the airport is evaluated based on the repair quality; and the operation and maintenance personnel of the airport are allocated based on the operation and maintenance capability.

[0012] Further, the operation target is energy saving; the basic data is input into an energy consumption prediction model, and predicted energy consumption is output from the model; the predicted energy consumption is predicted future energy consumption of the airport; and based on the predicted energy consumption and the index data, the equipment in the target scene is managed.

[0013] Further, the energy consumption prediction model is obtained by training an initial energy consumption prediction model; weights are assigned to the neural network model to obtain the initial energy consumption prediction model; a training sample set is generated by a generation model; the training sample includes sample basic data and labels; the labels are sample energy consumptions corresponding to the sample basic data; the training sample set is input into the initial energy consumption prediction model, a target function is constructed based on the output of the model and the labels; parameters of the initial energy consumption prediction model are updated based on the target function to obtain a trained energy consumption prediction model.

[0014] The technical scheme of the embodiment of the present application has at least the following advantages and beneficial effects:

[0015] Some embodiments provided in the specification can effectively help small airports reduce operation and management pressure, provide reliable technical support and decision-making wisdom, improve airport work efficiency, improve the economic benefits of small airports, and help the benign operation and sustainable development of airports, and further improve the scientific decision-making, emergency response and risk resistance of small airports.

[0016] The digital collaborative integrated operation concept can realize that data run multiple routes, effectively reduce personnel allocation, and the construction of a digital operation and management wisdom engine and platform can greatly improve the intelligent level of the airport, reduce the number of airport on-site operation and maintenance personnel, improve work efficiency, and reduce operation and maintenance pressure. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 An exemplary block diagram of a wisdom airport management system provided for some embodiments of the present application is provided;

[0018] Figure 2 An exemplary flowchart of a wisdom airport management system provided for some embodiments of the present application is provided;

[0019] Figure 3 An exemplary flowchart of managing an airport provided for some embodiments of the present application is provided;

[0020] Figure 4 An exemplary flowchart of implementing an energy-saving airport provided for some embodiments of the present application is provided. DETAILED DESCRIPTION

[0021] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme of the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all the embodiments. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0022] Figure 1 An exemplary block diagram of a smart airport management system according to some embodiments of the present application. As shown, the system 100 includes a data acquisition module 110, a data processing module 120, an airport management module 130, and a display module 140. Figure 1

[0023] The data acquisition module 110 is configured to acquire initial data.

[0024] The data acquisition module 110 is configured to acquire initial data.

[0025] The data processing module 120 is configured to process the initial data to obtain basic data, and process the basic data based on a running target to obtain index data. Figure 2

[0026] The data processing module 120 is configured to process the initial data to obtain basic data, and process the basic data based on a running target to obtain index data.

[0027] The data processing module 120 can be implemented by a smart engine COA. The smart engine COA is configured to format, analyze, manage, and process the data acquired from the data acquisition module into index data. Specifically, the data processing module can process the basic data by statistical analysis to obtain running key data, running upload data, and facility equipment running data for small airports. The processed data is classified and uploaded to each subsystem of the airport management module. For example, the data processing module can package the originally isolated and scattered data of the business and facility equipment subsystems into evaluation or assessment index data of the airport by using a development platform such as Storm, Spark, and Hive, and establish a decision-making strategy based on the index data and the running target. The running target includes production efficiency, energy saving based on flight operation, and the like.

[0028] In some embodiments, after processing the data, the data processing module can also store the data by using a platform tool such as Hadoop, Hbase, and Kafka. The smart engine COA can be a collection platform for data processing and analysis, decision-making rules, and algorithms for small airport running management. The COA can be developed by using java, spring, mybatis, and the like.

[0029] ​​The data acquisition module can process the data to provide data that meets the data requirements of the COA to the COA. The data requirements of the COA can include requirements related to data interface formats and data fields. For example, the format of the time can be year year year month month day day hour hour minute minute second second. A data dictionary can be formed according to the data requirements of the COA to facilitate subsequent data format conversion. The data acquisition module can aggregate the processed data to form a relationship association table, an interface list table, etc. In some embodiments, the COA can obtain data from the data acquisition module through platforms such as Flume, DataX, Sqoop, etc.

[0030] The data processing module provides functions including data governance, data analysis, data decision, data storage, and machine learning. The data processing module includes a data collection unit, a wisdom engine unit, a scenario application unit, and an access presentation unit. The data collection unit is used to obtain standardized data from the information integration system (information integration system, departure system, broadcast system, clock, etc.), security management system (baggage inspection, cargo inspection, fire alarm, monitoring, face recognition, etc.), operation and maintenance management system (building control, energy management, intelligent lighting, etc.) of the data acquisition unit. The wisdom engine unit is used to provide basic technical tools including Internet of Things connection technology, big data technology, artificial intelligence, BIM engine, GIS engine, etc. to realize the collection, storage, and secondary processing of system data, and provide data services for the scenario application unit. The scenario application unit is constructed based on different user usage scenario requirements (such as crew personnel, operation and maintenance personnel, equipment monitoring personnel, etc.). The basis of scenario application is the data or capability assembly processed by the wisdom engine. Through scenario application, similar scenarios such as intelligent energy saving, operation guarantee, and operation monitoring can be solved. The change from building a large and complete single system to scenario application provides fast and targeted support. The access presentation unit is used to provide PC, large screen, mobile access and presentation terminals to meet the requirements of centralized control of the monitoring center, operator operation, and mobile office. In addition, the data processing module provides friendly and easy-to-use interaction effects according to the characteristics of each type of presentation terminal. The access presentation layer can be realized through the display module. The data processing module can realize the functions of data asset management, task scheduling, data quality management, data security management, business index library construction, generation of statistical reports, and data visualization. For more information about the data processing module 120, see Figure 2 and related descriptions.

[0031] The airport management module 130 is used to manage the airport based on the basic data and the index data.

[0032] The airport management module can include a production operation subsystem, a security monitoring subsystem, an operation and maintenance control subsystem, and a green energy saving subsystem. The airport management module can classify and manage different subsystems, receive index data transmitted from the data processing module, and form scenes corresponding to the multiple subsystems based on the index data. For more information about the airport management module 130, see Figure 2 and related descriptions thereof.

[0033] The display module 140 is configured to extract target data from the basic data to form a target scene.

[0034] The display module can include a PC web, a monitoring large screen, and an APP mobile terminal. The display module can display data processed and screened by the COA. The display module can also obtain control instructions from a user, and perform operations such as intelligent airport facility and equipment operation control, intelligent positioning, state feedback, management operation report generation, evaluation index output, and the like according to the instructions.

[0035] In some embodiments, the display module can realize visualization through technologies such as GIS, BIM, and TANDEM. The display module can receive control instructions from a user, and transmit the control instructions to the airport management module to realize management of the airport. For more information about the display module 140, see Figure 2 and related descriptions thereof.

[0036] Figure 2 An exemplary flowchart of a smart airport management system according to some embodiments of the present application. In some embodiments, the flow 200 can be performed by the system 100. As shown in Figure 2 the flow 200 includes the following steps:

[0037] At step 210, initial data is obtained, and the initial data is processed to obtain basic data. In some embodiments, step 210 can be performed by the data acquisition module 110.

[0038] The initial data can refer to data obtained from a subsystem. For example, data obtained from multiple subsystems such as an information integration system, a security management system, and an operation and maintenance management system. The data acquisition module 110 can gather data from the subsystems through an interface. For example, the data acquisition module 110 can obtain one or more of flight information, weather data, and other airport data in the information integration system through an integration system interface. For another example, the data acquisition module 110 can obtain one or more of security data, passenger information, cargo information, and system security in the security management system through a security system interface. For yet another example, the data acquisition module 110 can obtain one or more of device information, energy information, fire data, and alarm information in the operation and maintenance management system through an operation and maintenance management system interface.

[0039] The basic data can refer to data meeting the data requirements of COA (Coordination Operation Analytic). In some embodiments, the acquisition module 110 can extract initial data in a data interface format and data field requirement through a platform such as Flume, DataX, Sqoop, etc., to obtain data in a format meeting the COA data requirements. In some embodiments, the data interface format and data field requirement can be obtained through a data dictionary or a data extraction rule, and the data dictionary and the data extraction rule contain a relationship association table and an interface list of data integration, etc. The interface list can include a data source, a data field, an interface form, a data collection frequency, etc.

[0040] Step 220, target data is extracted from the basic data to form a target scene.

[0041] The target data can refer to data used to form a target scene. The target scene can refer to a scene of an airport displayed to different personnel. The target scene can include one or more of a scene for managing flight information, a scene for security management, and a scene for operating and maintaining an airport, etc. In some embodiments, the display module 140 can filter target data from the basic data based on the requirements of different scenes to construct a target scene and display it. For example, the display module 140 can extract data in multiple dimensions such as equipment information, energy information, fire data, and alarm information to form a scene for operating and maintaining an airport. In some embodiments, the display module 140 can identify the position of a logged-in user and construct a target scene based on the position.

[0042] Step 230, the basic data is processed based on a running target to obtain index data.

[0043] The running target can refer to a running target of an airport. The running target can include one or more of a safe airport, a green airport, a smart airport, and a humanistic airport. In some embodiments, the running target can be set according to actual needs.

[0044] The index data can refer to standard data for indicating the operation of an airport. In some embodiments, different index data can be used for different running targets. For example, the index data for a green airport can be related to the energy consumption and resource consumption of the airport. For example, the index data for a safe airport can be related to the equipment operation and flight safety of the airport. In some embodiments, the basic data can include airport data in multiple time periods, the routine data of the airport can be determined based on the basic data, the operation of the airport can be analyzed based on the basic data, the place that needs to be optimized in the routine data can be determined based on the running target, and the routine data can be optimized by changing the operation to complete the running target of the airport.

[0045] In some embodiments, the index data can be obtained by processing the basic data through a data processing module. The data processing module can be a wisdom engine COA. The data processed and analyzed by the COA can effectively evaluate the airport evaluation system of the national industry supervisory department; can be based on the actual operation of small airports to create intelligent operation and control logic and create benefits for the airport; under the support of effective data input, the wisdom engine can efficiently and safely output index data in the operation and management of small airports, and based on the visualization platform selected by the airport, display data, and a series of intelligent airport facility and equipment operation and control, fault positioning, state feedback, and management operation report generation under the authorization and permission of evaluation index output.

[0046] The wisdom engine COA includes a running index data processing unit, a running upload data processing unit, and a facility and equipment running output processing unit. The data of originally isolated and scattered business and facility and equipment subsystems are packaged into effective index data for evaluating or examining the airport, and decision-making strategies and rules such as production efficiency and energy saving based on flight operation are established according to the index data.

[0047] In step 240, the airport is managed based on the basic data and the index data.

[0048] In some embodiments, the airport management module can improve the basic data based on the index data, and adjust the subsystems of the airport through the improved basic data to complete the management of the airport. The subsystems of the airport include a production operation subsystem, a safety monitoring subsystem, an operation and maintenance control subsystem, and a green energy saving subsystem.

[0049] For more information about managing the airport, see Figure 3 and related descriptions.

[0050] In step 250, the target data and the index data are displayed through a target scene.

[0051] In some embodiments, the target scene can be modeled based on the basic data, and the target data can be linked with the index data and the model. The user can obtain the target data and the index data related to the model by clicking the model and the like. In some embodiments, the visualization display of the scene and the data can be realized through GIS, BIM, and TANDEM and the like. The airport can be managed through an easy-to-interact way.

[0052] In some embodiments, the target scene can be displayed through a PC web terminal, a monitoring large screen, and / or a mobile terminal APP and the like.

[0053] In step 260, the target data and the index data are obtained by operating the target scene, and the airport is managed.

[0054] In some embodiments, the user can issue instructions under the target scene to adjust the operation process of the related device. To achieve the management of the airport.

[0055] Figure 3 An exemplary flowchart for managing the airport is provided for some embodiments of the present application. In some embodiments, the flow 300 can be executed by the airport management module 130. As shown, the flow 300 includes the following steps: Figure 3

[0056] Step 310, when the basic data does not meet the requirements of the index data, the device in the target scene is investigated based on the basic data to determine the problem device.

[0057] For example, when the power consumption of the airport is higher than the index requirement, the basic data related to the power supply can be compared with the corresponding index data to determine the abnormal power consumption device.

[0058] Step 320, based on the problem device, the corresponding actual device is repaired.

[0059] The target scene can simulate the airport to form a twin airport of the actual airport. Through the problem device determined in the target scene, the corresponding actual device can be found.

[0060] Step 330, based on the repaired device, the repair basic data and repair index data are obtained to determine the repair quality.

[0061] The repair basic data can be the basic data obtained after repairing the device. The repair index data can be the index data obtained based on the processing after repairing the device. For example, the index data can be obtained by extracting the features of the historical basic data of the airport, and the historical basic data extracted by the repair index data includes the repair basic data. The airport management module 130 can obtain the repair basic data and the repair index data by obtaining the basic data and the index data.

[0062] In some embodiments, the airport management module 130 can input the repair basic data, the repair index data, the basic data and the index data into a quality evaluation model, and the model outputs the repair quality.

[0063] Step 340, based on the repair quality, the operation and maintenance capability of the airport is evaluated.

[0064] In some embodiments, the speed of repair and the quality of repair can be determined based on the repair quality, and the operation and maintenance capability of the airport can be determined based on the speed of repair and the quality of repair.

[0065] In some embodiments, the capability of the operation and maintenance personnel can also be determined, and the operation and maintenance capability of the airport can be determined based on the capability of the operation and maintenance personnel. ​

[0066] Step 350, based on the operation and maintenance capability, the airport operation and maintenance personnel are allocated.

[0067] In some embodiments, the airport operation and maintenance personnel can be allocated based on the equipment they are good at repairing.

[0068] Figure 4 An exemplary flowchart for implementing an energy-saving airport is provided for some embodiments of the present application. In some embodiments, flow 400 can be executed by airport management module 130. As shown, flow 400 includes the following steps: Figure 4

[0069] Step 410, weights are assigned to the neural network model to obtain an initial energy consumption prediction model.

[0070] In some embodiments, the weights of the neural network model can be assigned according to experience. For example, the weights of the two layers of the neural network are initialized.

[0071] Step 420, a training sample set is generated by the generation model; the training sample includes sample basic data and labels; the labels are sample energy consumptions corresponding to the sample basic data.

[0072] In some embodiments, random noise can be determined based on the basic data. For example, a distribution function is determined according to the distribution of the basic data, and the generation method of the random noise is determined based on the distribution function. The initial generation model generates a first training sample set based on the random noise, and the first training sample set includes first training samples and first labels. The first training samples can include the basic data generated by the initial generation model. The first labels can be the energy consumption generated by the initial generation model. A second training sample set is obtained from the basic data, and the second training sample set includes second training samples and second labels; the second training samples can include historical basic data. The second labels can be the energy consumption corresponding to the historical basic data.

[0073] The first training sample set and the second training sample set are input into the initial discriminant model, and the model judges the truth and falsehood of the first training sample set and the second training sample set.

[0074] The parameters of the initial discriminant model and the initial generation model are updated based on the discriminant result. The parameters of the initial discriminant model and the initial generation model can be updated in various ways of updating model parameters.

[0075] When the initial discriminant model judges the first training sample set as true, the initial generation model trained is used as the generation model. The training sample is generated by the generation model.

[0076] Step 430, the training sample set is input into the initial energy consumption prediction model, and a target function is constructed based on the output of the model and the labels.

[0077] ​There are two propagation modes in the process of model training. Forward propagation and back propagation. In the process of forward propagation of the model, the output of the hidden layer and the output layer can be calculated, and then the weights of the hidden layer and the output layer are updated through back propagation to update the model.

[0078] In some embodiments, the objective function can be constructed according to the difference between the output of the initial energy consumption prediction model and the label.

[0079] Step 440, updating the parameters of the initial energy consumption prediction model based on the objective function to obtain a trained energy consumption prediction model

[0080] When the objective function reaches the condition, it is determined that the model training is completed, and the initial energy consumption prediction model is taken as the trained energy consumption prediction model. The objective function reaching the condition can include the objective function converging or the number of iterations reaching a threshold.

[0081] In some embodiments, different prediction models can be designed for different running targets to adjust the running mode of the airport based on the prediction results to achieve the corresponding running target.

[0082] The above is only the preferred embodiment of the present application and is not used to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A smart airport management system, characterized in that, It includes a data acquisition module, a data processing module, an airport management module, and a display module; The data acquisition module is used to acquire initial data; The data processing module is used to process the initial data to obtain basic data; Based on the operational objectives, the basic data is processed to obtain indicator data; The airport management module is used to manage the airport based on the basic data and the indicator data; The display module is used to extract target data from the basic data to form a target scene; The operational objective is energy conservation; the airport management module is also used for, The basic data is input into the energy consumption prediction model, and the model outputs the predicted energy consumption; the predicted energy consumption is the predicted future energy consumption of the airport. Based on the predicted energy consumption and the indicator data, the devices in the target scenario are managed; The energy consumption prediction model is obtained by training an initial energy consumption prediction model; Weights are assigned to the neural network model to obtain the initial energy consumption prediction model; A training sample set is generated by a generative model; the training sample set includes basic sample data and labels; the labels are the sample energy consumption corresponding to the basic sample data, the distribution function is determined according to the distribution of the basic data, and the generation method of random noise is determined based on the distribution function; The initial generative model generates a first training sample set based on random noise. The first training sample set includes a first training sample and a first label. The first training sample includes the basic data generated by the initial generation model; the first label is the energy consumption generated by the initial generation model. Obtain a second training sample group from the basic data. The second training sample group includes the second training sample and the second label. The second training sample includes historical baseline data; the second label is the energy consumption corresponding to the historical baseline data. The first training sample group and the second training sample group are input into the initial discrimination model, and the model judges whether the first training sample group and the second training sample group are true or false. The parameters of the initial discrimination model and the initial generation model are updated based on the discrimination results; When the initial discrimination model judges the first training sample group as true, the trained initial generative model is used as the generative model; training samples are generated through the generative model. The training sample set is input into the initial energy consumption prediction model, and an objective function is constructed based on the model's output and the labels. The parameters of the initial energy consumption prediction model are updated based on the objective function to obtain the trained energy consumption prediction model.

2. The intelligent airport management system according to claim 1, characterized in that, The display module is also used to display the target data and the indicator data through the target scenario; to obtain the target data and the indicator data by operating the target scenario, and to manage the airport.

3. The intelligent airport management system according to claim 1, characterized in that, The airport management module is also used for, When the basic data does not meet the requirements of the indicator data, the equipment in the target scenario is investigated based on the basic data to identify the problematic equipment; Based on the problematic equipment, the corresponding actual equipment was repaired. Based on the repaired equipment, obtain basic repair data and repair indicator data to determine the repair quality; Based on the aforementioned maintenance quality, assess the airport's operational capabilities; Based on the aforementioned operational capabilities, airport operational personnel are assigned.

4. A smart airport management method for implementing the smart airport management system as described in any one of claims 1-3, characterized in that, include, Obtain initial data, and process the initial data to obtain basic data; Target data is extracted from the basic data to form the target scene; Based on the operational objectives, the basic data is processed to obtain indicator data; Airports are managed based on the aforementioned basic data and indicator data.

5. The intelligent airport management method according to claim 4, characterized in that, It also includes, The target data and the indicator data are displayed in the manner described in the target scenario; By operating the target scenario, the target data and the indicator data are obtained, and the airport is managed.

6. The intelligent airport management method according to claim 4, characterized in that, The management of the airport includes; When the basic data does not meet the requirements of the indicator data, the equipment in the target scenario is investigated based on the basic data to identify the problematic equipment; Based on the problematic equipment, the corresponding actual equipment was repaired. Based on the repaired equipment, obtain basic repair data and repair indicator data to determine the repair quality; Based on the aforementioned maintenance quality, assess the airport's operational capabilities; Based on the aforementioned operational capabilities, airport operational personnel are assigned.

7. The intelligent airport management method according to claim 4, characterized in that, The operational objective is energy conservation; The basic data is input into the energy consumption prediction model, and the model outputs the predicted energy consumption; the predicted energy consumption is the predicted future energy consumption of the airport. Based on the predicted energy consumption and the indicator data, the devices in the target scenario are managed.

8. The intelligent airport management method according to claim 7, characterized in that, The energy consumption prediction model is obtained by training an initial energy consumption prediction model; Weights are assigned to the neural network model to obtain the initial energy consumption prediction model; A training sample set is generated by a generative model; the training sample set includes basic sample data and labels; the labels are the sample energy consumption corresponding to the basic sample data. The training sample set is input into the initial energy consumption prediction model, and an objective function is constructed based on the model's output and the labels. The parameters of the initial energy consumption prediction model are updated based on the objective function to obtain the trained energy consumption prediction model.

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