Rail transit station energy consumption control method and device and electronic equipment

Abstract

The present application provides a kind of rail transit station energy consumption control method, device and electronic equipment, can determine the environmental information in rail transit station, operation information and at least one energy consumption control object;Respectively determine the current control strategy of each energy consumption control object under environmental information and operation information;Determine the station energy consumption control target of rail transit station under environmental information and operation information;According to the multi-objective control strategy optimization mode of pre-defined and station energy consumption control target, the current control strategy of each energy consumption control object is overall optimized, to obtain the multi-objective optimal control strategy of rail transit station;According to multi-objective optimal control strategy, each energy consumption control object is controlled.The present application can effectively improve the efficiency of rail transit station, enhance the reliability of rail transit station energy consumption control.

Description

Technical Field

[0001] This invention relates to the field of rail transit station control technology, and in particular to a method, device and electronic equipment for controlling energy consumption in rail transit stations. Background Technology

[0002] As the main artery of urban public transportation, rail transit plays an important role in national economic development.

[0003] With the expansion of the rail transit network and the continuous increase in operating mileage, energy consumption is also increasing. Therefore, it is imperative to strengthen station energy consumption management, focusing on intelligent energy consumption management and making breakthroughs in key technologies for energy conservation and carbon reduction.

[0004] However, the existing energy consumption control technologies for rail transit stations have low reliability. Summary of the Invention

[0005] This invention provides a method, device, and electronic equipment for controlling energy consumption in rail transit stations, which addresses the shortcomings of low reliability in existing rail transit station energy consumption control technologies and enhances the reliability of energy consumption control in rail transit stations.

[0006] This invention provides a method for controlling energy consumption in rail transit stations, comprising:

[0007] Identify environmental information, operational information, and at least one energy consumption control object within the rail transit station;

[0008] Determine the current control strategy for each of the energy consumption control objects under the environmental information and the operational information;

[0009] Determine the station energy consumption control target for the rail transit station under the environmental information and the operational information;

[0010] According to the predefined multi-objective control strategy optimization method and the station energy consumption control objective, the current control strategy of each energy consumption control object is optimized as a whole to obtain the multi-objective optimal control strategy of the rail transit station.

[0011] According to the multi-objective optimal control strategy, each of the energy consumption control objects is controlled.

[0012] Optionally, the step of optimizing the current control strategy of each energy consumption control object according to a predefined multi-objective control strategy optimization method and the station energy consumption control objective to obtain the multi-objective optimal control strategy of the rail transit station includes:

[0013] Based on the current control strategy of each energy consumption control object, a multi-objective control strategy sequence to be optimized is constructed;

[0014] Under the environmental information and the operational information, a single-objective optimal control strategy is determined for each of the energy consumption control objects;

[0015] Based on the single-objective optimal control strategies described above, a sequence of single-objective optimal control strategies is constructed.

[0016] Based on the predefined optimization processing method, the single-objective optimal control strategy sequence and the station energy consumption control objective, the multi-objective control strategy sequence is iteratively optimized to obtain the multi-objective optimal control strategy.

[0017] The predefined optimization processing methods include: grey comprehensive correlation analysis, passenger flow simulation and deduction, equipment prediction analysis and overall optimization method.

[0018] Optionally, the iterative optimization of the multi-objective control strategy sequence based on the predefined optimization processing method, the single-objective optimal control strategy sequence, and the station energy consumption control objective to obtain the multi-objective optimal control strategy includes:

[0019] Determine the grey comprehensive correlation degree sequence between the single-objective optimal control strategy sequence and the multi-objective control strategy sequence;

[0020] Obtain passenger flow simulation results and equipment prediction analysis results;

[0021] Based on the overall optimization method, the grey comprehensive correlation sequence, the passenger flow simulation results, and the equipment prediction analysis results, the current control strategies in the multi-objective control strategy sequence are iteratively optimized until the preset optimization requirements are met, so as to obtain the multi-objective optimal control strategy.

[0022] Optionally, the step of iteratively optimizing each of the current control strategies in the multi-objective control strategy sequence based on the overall optimization method, the grey comprehensive correlation degree sequence, the passenger flow simulation results, and the equipment prediction analysis results, until a preset optimization requirement is met, to obtain the multi-objective optimal control strategy, includes:

[0023] Determine the passenger flow distribution simulation weights and electromechanical equipment performance load weights corresponding to the passenger flow simulation results and the equipment prediction analysis results, respectively.

[0024] The multi-objective control strategy sequence is initialized to obtain an initialized sequence. The applicability of the initialized sequence is evaluated based on the grey comprehensive correlation degree sequence, the passenger flow distribution simulation and deduction weight, and the electromechanical equipment performance load weight. The fitness value of each element in the applicability of the initialized sequence is compared with the corresponding optimal value. Based on the comparison results, it is determined whether to continue iterative optimization until the preset optimization requirements are met, so as to obtain the multi-objective optimal control strategy.

[0025] Optionally, determining the grey comprehensive correlation degree sequence between the single-objective optimal control strategy sequence and the multi-objective control strategy sequence includes:

[0026] Determine the grey correlation degree, absolute correlation degree, and relative correlation degree between the single-objective optimal control strategy sequence and the multi-objective control strategy sequence;

[0027] Based on the gray correlation degree, the absolute correlation degree, and the relative correlation degree, the gray comprehensive correlation degree sequence is obtained.

[0028] The present invention also provides an energy consumption control device for rail transit stations, comprising: a first determining unit, a second determining unit, a third determining unit, a first optimizing unit, and a first control unit; wherein:

[0029] The first determining unit is used to determine environmental information, operational information, and at least one energy consumption control object within the rail transit station;

[0030] The second determining unit is used to determine the current control strategy of each of the energy consumption control objects under the environmental information and the operational information, respectively;

[0031] The third determining unit is used to determine the station energy consumption control target of the rail transit station under the environmental information and the operational information;

[0032] The first optimization unit is used to optimize the current control strategy of each energy consumption control object in an overall manner according to the predefined multi-objective control strategy optimization method and the station energy consumption control objective, so as to obtain the multi-objective optimal control strategy of the rail transit station.

[0033] The first control unit is used to control each of the energy consumption control objects according to the multi-objective optimal control strategy.

[0034] Optionally, the first optimization unit includes: a first construction unit, a fourth determination unit, a second construction unit, and a second optimization unit;

[0035] The first construction unit is used to construct a sequence of multi-objective control strategies to be optimized based on the current control strategies of each of the energy consumption control objects;

[0036] The fourth determining unit is used to determine the single-objective optimal control strategy for each of the energy consumption control objects under the environmental information and the operational information.

[0037] The second construction unit is used to construct a sequence of single-objective optimal control strategies based on each of the single-objective optimal control strategies.

[0038] The second optimization unit is used to iteratively optimize the multi-objective control strategy sequence based on a predefined optimization processing method, the single-objective optimal control strategy sequence, and the station energy consumption control objective, so as to obtain the multi-objective optimal control strategy.

[0039] The predefined optimization processing methods include: grey comprehensive correlation analysis, passenger flow simulation and deduction, equipment prediction analysis and overall optimization method.

[0040] Optionally, the second optimization unit includes: a fifth determining unit, a first obtaining unit, and a third optimization unit; wherein:

[0041] The fifth determining unit is used to determine the grey comprehensive correlation degree sequence between the single-objective optimal control strategy sequence and the multi-objective control strategy sequence;

[0042] The first obtaining unit is used to obtain passenger flow simulation results and equipment prediction analysis results;

[0043] The third optimization unit is used to iteratively optimize each of the current control strategies in the multi-objective control strategy sequence based on the overall optimization method, the gray comprehensive correlation degree sequence, the passenger flow simulation and deduction results, and the equipment prediction and analysis results, until the preset optimization requirements are met, so as to obtain the multi-objective optimal control strategy.

[0044] Optionally, the third optimization unit includes: a sixth determining unit, an initialization unit, an evaluation unit, a comparison unit, and a seventh determining unit;

[0045] The sixth determining unit is used to determine the passenger flow distribution simulation weight and the electromechanical equipment performance load weight corresponding to the passenger flow simulation results and the equipment prediction analysis results, respectively.

[0046] The initialization unit is used to initialize the multi-objective control strategy sequence to obtain the initialized sequence;

[0047] The evaluation unit is used to evaluate the applicability of the initialized sequence based on the grey comprehensive correlation degree sequence, the passenger flow distribution simulation and deduction weights, and the electromechanical equipment performance load weights.

[0048] The comparison unit is used to compare the fitness value of each element in the fitness of the initialized sequence with the corresponding optimal value.

[0049] The seventh determining unit is used to determine whether to continue iterative optimization based on the comparison results until the preset optimization requirements are met, so as to obtain the multi-objective optimal control strategy.

[0050] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the energy consumption control method for rail transit stations as described above.

[0051] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the energy consumption control method for rail transit stations as described above.

[0052] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the energy consumption control method for rail transit stations as described above.

[0053] The present invention provides a method, apparatus, and electronic equipment for energy consumption control in rail transit stations. These can determine environmental information, operational information, and at least one energy consumption control object within the rail transit station; determine the current control strategy for each energy consumption control object under the environmental and operational information; determine the station's energy consumption control objective under the environmental and operational information; optimize the current control strategy of each energy consumption control object as a whole according to a predefined multi-objective control strategy optimization method and the station's energy consumption control objective to obtain a multi-objective optimal control strategy for the rail transit station; and control each energy consumption control object according to the multi-objective optimal control strategy. This invention can simultaneously optimize the current control strategy of each energy consumption control object as a whole according to the multi-objective control strategy optimization method and the station's energy consumption control objective. That is, considering the coupling and correlation between the current control strategies of each energy consumption control object, and maximizing the overall efficiency of the rail transit station, it can simultaneously optimize the current control strategies of all energy consumption control objects to obtain a multi-objective optimal control strategy for the rail transit station. Furthermore, it can control each energy consumption control object according to the multi-objective optimal control strategy, effectively improving the efficiency of the rail transit station and enhancing the reliability of energy consumption control. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0055] Figure 1 This is one of the flowcharts illustrating the energy consumption control method for rail transit stations provided in this embodiment of the invention;

[0056] Figure 2 This is the second flowchart illustrating the energy consumption control method for rail transit stations provided in this embodiment of the invention.

[0057] Figure 3 This is the third flowchart illustrating the energy consumption control method for rail transit stations provided in this embodiment of the invention;

[0058] Figure 4 This is a schematic diagram of the data processing flow provided in an embodiment of the present invention;

[0059] Figure 5 This is a schematic diagram of the structure of the rail transit station energy consumption control device provided in an embodiment of the present invention;

[0060] Figure 6 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0062] The following is combined Figures 1-4 The present invention describes the energy consumption control method for rail transit stations.

[0063] like Figure 1 As shown, this invention proposes a first method for energy consumption control in rail transit stations, which may include the following steps:

[0064] S101. Determine the environmental information, operational information, and at least one energy consumption control object within the rail transit station;

[0065] Environmental information can include the values ​​of physical indicators related to the environment, such as temperature, humidity, oxygen content, air pollution index, and light intensity.

[0066] The operational information may include station passenger flow distribution, train arrival and departure information, station operating timetable information, station production operation information, and the health of electromechanical equipment.

[0067] Specifically, the present invention can obtain environmental and operational information within rail transit stations through a comprehensive monitoring platform for rail transit stations or related sensors.

[0068] Among them, the energy consumption control objects can be the actual needs of station energy consumption management and control, and the controllable physical indicators in the rail transit station can be summarized, specifically temperature, humidity, air oxygen content, light, energy consumption of electromechanical equipment, safety or comfort, etc.

[0069] Specifically, this invention can select one or more indicators from a large number of controllable physical indicators as control objects. Each selected indicator can be used as an energy consumption control object.

[0070] S102. Determine the current control strategy for each energy consumption control object under environmental and operational information;

[0071] The current control strategy can be the control method and control scheme used for the energy consumption control object to optimize the energy consumption of the energy consumption control object under the above environmental information and the above operational information. For example, the control method and control scheme used for the energy consumption control object to optimize temperature energy consumption.

[0072] Specifically, the present invention can determine the current control strategy for each energy consumption control object.

[0073] S103. Determine the energy consumption control targets for rail transit stations under environmental and operational information.

[0074] Among them, the station energy consumption control target can be the energy consumption control target and effect formulated and set according to the actual energy consumption control needs of the rail transit station, in order to improve the overall efficiency of the station.

[0075] Optionally, station energy consumption control targets may include multiple levels of control targets with different priorities. Specifically, station energy consumption control targets may include three levels of control targets: high priority, medium priority, and low priority.

[0076] High-priority control objectives may include a secure environmental state and secure system equipment management;

[0077] Among them, medium-priority control objectives may include a safe and comfortable environmental state and safe and efficient system equipment management;

[0078] Low-priority control objectives may include safe, comfortable, and adaptive environmental conditions, as well as safe, efficient, and precise use of system equipment.

[0079] S104. Based on the predefined multi-objective control strategy optimization method and the station energy consumption control objective, optimize the current control strategy of each energy consumption control object as a whole to obtain the multi-objective optimal control strategy of the rail transit station.

[0080] It should be noted that the current control strategy of each energy consumption control object can be regarded as a target control strategy to be optimized, and the current control strategies of multiple energy consumption control objects can be regarded as a multi-objective control strategy to be optimized.

[0081] Among them, the multi-objective control strategy optimization method can be an optimization method used to optimize the multi-objective control strategy as a whole.

[0082] Among them, the multi-objective optimal control strategy can be the current optimal control strategy for each energy consumption control object, taking into account the coupling and correlation between the current control strategies of each energy consumption control object, and maximizing the overall efficiency of the rail transit station.

[0083] Specifically, after determining the station energy consumption control target, the present invention can optimize the multi-objective control strategy composed of the current control strategies of all energy consumption control objects in an overall manner according to the multi-objective control strategy optimization method and the station energy consumption control target, so as to obtain the multi-objective optimal control strategy for rail transit stations.

[0084] Understandably, the optimal control strategy for an energy-consuming control object itself is often different from its corresponding optimal control strategy in a multi-objective optimal control strategy. This is because the optimal control strategy for an energy-consuming control object only considers the lowest energy consumption it can achieve, without taking into account its impact on the control strategies of other energy-consuming control objects or the overall efficiency of the rail transit station. Specifically, when only considering adjusting the control strategy of one energy-consuming control object to the optimal control strategy, the energy consumption value associated with that energy-consuming control object will decrease, but it may negatively impact the control strategies of other energy-consuming control objects, potentially leading to a decrease in the efficiency of the rail transit station.

[0085] It should be noted that the multi-objective control strategy optimization method adopted in this invention, as well as the station energy consumption control objectives and effects set, can simultaneously optimize the current control strategies of all energy consumption control objects while taking into account the coupling and correlation between the current control strategies of each energy consumption control object and maximizing the overall efficiency of the rail transit station, thereby obtaining the optimal multi-objective control strategy for the rail transit station.

[0086] S105. Control each energy consumption control object according to the multi-objective optimal control strategy.

[0087] Specifically, after obtaining the multi-objective optimal control strategy, the present invention can control the corresponding energy consumption control objects according to the control strategies of each energy consumption control object in the multi-objective optimal control strategy, so that each energy consumption control object can reach the control state corresponding to the control strategy, thereby improving the overall efficiency of rail transit stations.

[0088] It should be noted that in existing technologies, the overall environmental control and system equipment usage are not closely linked to the operational status of the station. Specifically, environmental control focuses solely on environmental control, while operation focuses solely on operation. This may lead to a lack of precise control over factors such as temperature, humidity, air quality, and lighting, resulting in energy waste, unused or overused system equipment. It is necessary to analyze the station's overall control needs and objectives based on environmental information, and to formulate more scientific and precise energy control strategies based on operational information.

[0089] Furthermore, the complex operating conditions and diverse specialties within rail transit stations make energy conservation challenging. For example, the station's ventilation and water systems involve chillers, refrigeration circulating pumps, cooling circulating pumps, cooling towers, and multiple fan units. Other examples include escalator and elevator systems, lighting systems, and large-screen display systems. The energy consumption relationships between these systems and their overall impact on station energy consumption are unclear. Conventional group control systems cannot provide the necessary decision support information in real time, and in actual operation, this information is not even roughly summarized.

[0090] Furthermore, the equipment operation and maintenance lacks a decision support system. The annual maintenance cost of the station's air conditioning system exceeds 10% of the annual energy consumption cost, and equipment maintenance directly affects the system's energy efficiency and stable operation. For equipment such as chiller units and modular air conditioning units, the investment is large and maintenance costs are high, yet corresponding equipment maintenance strategies have not been developed. Therefore, it is necessary to use performance monitoring and tracking technology to detect changes in equipment performance in real time, provide alerts for abnormal performance degradation, and offer targeted maintenance recommendations.

[0091] It should be noted that the present invention is achieved through... Figure 1Steps S101 to S105 can optimize the current control strategies of each energy consumption control object according to the multi-objective control strategy optimization method and the station energy consumption control objective. That is, considering the coupling and correlation between the current control strategies of each energy consumption control object and maximizing the overall efficiency of the rail transit station, the current control strategies of all energy consumption control objects can be optimized as a whole to obtain the multi-objective optimal control strategy of the rail transit station. Each energy consumption control object can be controlled according to the multi-objective optimal control strategy, which can effectively improve the efficiency of the rail transit station, enhance the reliability of the rail transit station energy consumption control, and effectively solve the problems existing in the prior art.

[0092] The energy consumption control method for rail transit stations proposed in this invention can determine environmental information, operational information, and at least one energy consumption control object within the rail transit station; determine the current control strategy of each energy consumption control object under the environmental and operational information; determine the station's energy consumption control objective under the environmental and operational information; optimize the current control strategy of each energy consumption control object as a whole according to a predefined multi-objective control strategy optimization method and the station's energy consumption control objective to obtain the multi-objective optimal control strategy for the rail transit station; and control each energy consumption control object according to the multi-objective optimal control strategy. This invention can simultaneously optimize the current control strategy of each energy consumption control object as a whole according to the multi-objective control strategy optimization method and the station's energy consumption control objective. That is, considering the coupling and correlation between the current control strategies of each energy consumption control object, and maximizing the overall efficiency of the rail transit station, it can simultaneously optimize the current control strategies of all energy consumption control objects to obtain the multi-objective optimal control strategy for the rail transit station, and control each energy consumption control object according to the multi-objective optimal control strategy, effectively improving the efficiency of the rail transit station and enhancing the reliability of energy consumption control.

[0093] based on Figure 1 This invention proposes a second method for energy consumption control in rail transit stations. In this method, step S104 may include steps S1041, S1042, S1043, and S1044, wherein:

[0094] S1041. Based on the current control strategy of each energy consumption control object, construct a sequence of multi-objective control strategies to be optimized;

[0095] Among them, the multi-objective control strategy sequence can be a multi-objective function optimization model sequence composed of the current control strategies of each energy consumption control object.

[0096] Specifically, after obtaining the current control strategy of each energy consumption control object, this invention can construct a multi-objective control strategy sequence based on the current control strategy of each energy consumption control object. For example, this invention can construct a multi-objective control strategy sequence based on the current control strategy of each energy consumption control object:

[0097] F1={f1(a,b),f2(a,b),f3(a,b),f4(a,b),f5(a,b),f6(a,b),f7(a,b),…,f N (a,b)};

[0098] Wherein, F1 is the multi-objective control strategy sequence; f1 is the current control strategy used for temperature to optimize temperature energy consumption; a represents environmental information, b represents operational information; f2 is the current control strategy used for humidity to optimize humidity energy consumption; f3 is the current control strategy used for air to optimize air energy consumption; f4 is the current control strategy used for lighting to optimize lighting energy consumption; f5 is the current control strategy used for electromechanical to optimize electromechanical energy consumption; f6 is the control strategy used for safety control to optimize safety control energy consumption; f7 is the control strategy used for comfort to optimize comfort energy consumption; f... N It is the control strategy used for the Nth energy consumption control object in order to optimize the energy consumption of the corresponding energy consumption control object.

[0099] S1042. Under the conditions of environmental information and operational information, determine the single-objective optimal control strategy for each energy consumption control object;

[0100] Among them, the single-objective optimal control strategy can be the control strategy used for a certain energy consumption control object in order to make the energy consumption of that energy consumption control object reach the optimal value.

[0101] Specifically, the present invention can determine the single-objective optimal control strategy for each energy consumption control object under the condition of obtained environmental and operational information.

[0102] S1043. Based on the optimal control strategies for each single objective, construct a sequence of optimal control strategies for each single objective;

[0103] Specifically, after determining the single-objective optimal control strategy for each energy consumption control object, this invention can construct a sequence of single-objective optimal control strategies based on these strategies. For example, this invention can construct a sequence of single-objective optimal control strategies based on the single-objective optimal control strategies for each energy consumption control object:

[0104] F0={f'1(a,b),f'2(a,b),f'3(a,b),f'4(a,b),f'5(a,b),f'6(a,b),…,f'N (a,b)};

[0105] Wherein, F0 is the single-objective optimal control strategy sequence, f'1 is the single-objective optimal control strategy for temperature; f'2 is the single-objective optimal control strategy for humidity; f'3 is the single-objective optimal control strategy for air; f'4 is the single-objective optimal control strategy for illumination; f'5 is the single-objective optimal control strategy for electromechanical systems; f'6 is the single-objective optimal control strategy for safety control; f' N It is the single-objective optimal control strategy for the Nth energy consumption control object.

[0106] S1044. Based on the predefined optimization processing method, the single-objective optimal control strategy sequence and the station energy consumption control objective, the multi-objective control strategy sequence is iteratively optimized to obtain the multi-objective optimal control strategy.

[0107] The predefined optimization methods include: grey comprehensive correlation analysis, passenger flow simulation and deduction, equipment prediction analysis, and overall optimization.

[0108] It should be noted that the inventors of this invention discovered during the research process that the grey system theory proposes the concept of grey relational analysis of each subsystem. By using mathematical methods, it seeks the numerical relationship between each subsystem (or factor) in the system, providing a quantitative measure of the development and change trend of a system, which is very suitable for the multi-objective dynamic optimization analysis of station energy consumption.

[0109] Specifically, this invention can utilize grey comprehensive correlation to analyze the complex interrelationship between environmental information a and operational information b within a station, deriving the correlation and degree of correlation between environmental and operational information. Combined with station passenger flow distribution simulation and prediction of electromechanical equipment performance load, it controls energy consumption in systems such as air conditioning, lighting, fresh air, escalators, passenger information service, and platform screen doors. Based on different needs and station status, it sets three levels of targets: "high priority," "medium priority," and "low priority," thereby improving the overall efficiency of the station.

[0110] Optionally, step S1044 may include steps S201, S202, and S203, wherein:

[0111] S201. Determine the grey comprehensive correlation degree sequence of the single-objective optimal control strategy sequence and the multi-objective control strategy sequence;

[0112] S202. Obtain passenger flow simulation results and equipment prediction analysis results;

[0113] S203. Based on the overall optimization method, the grey comprehensive correlation sequence, the passenger flow simulation and deduction results and the equipment prediction and analysis results, the current control strategies in the multi-objective control strategy sequence are iteratively optimized until the preset optimization requirements are met, so as to obtain the optimal multi-objective control strategy.

[0114] Optionally, step S201 above may include:

[0115] Determine the grey relational degree, absolute relational degree, and relative relational degree between the single-objective optimal control strategy sequence and the multi-objective control strategy sequence;

[0116] Based on grey relational degree, absolute relational degree, and relative relational degree, a grey comprehensive relational degree sequence is obtained.

[0117] Optionally, step S203 may include steps S2031 and S2032, wherein:

[0118] S2031. Determine the passenger flow distribution simulation weights and electromechanical equipment performance load weights corresponding to the passenger flow simulation results and equipment prediction analysis results, respectively.

[0119] S2032. Initialize the multi-objective control strategy sequence to obtain the initialized sequence. Evaluate the applicability of the initialized sequence based on the grey comprehensive correlation degree sequence, passenger flow distribution simulation deduction weight, and electromechanical equipment performance load weight. Compare the fitness value of each element in the applicability of the initialized sequence with the corresponding optimal value. Determine whether to continue iterative optimization based on the comparison results until the preset optimization requirements are met, so as to obtain the multi-objective optimal control strategy.

[0120] It should be noted that the processing in step S2032 is the overall optimization process. Optionally, the overall optimization method can be a process using the particle swarm optimization algorithm. The aforementioned elements can be the particles in the optimization process using the particle swarm optimization algorithm.

[0121] Specifically, this invention can calculate the sequence grey relational degree, absolute relational degree, and relative relational degree of F0 and F1, and obtain the grey comprehensive relational degree P{} sequence of F0 and F1. Based on the P{} sequence and the fusion of passenger flow distribution simulation and analysis and the predictive analysis of electromechanical equipment performance and load, a multi-objective optimal control strategy is constructed based on the particle swarm optimization algorithm for air conditioning system, lighting system, fresh air system, escalator system, passenger information service system, platform screen door system, etc., to achieve the optimal control objectives and effects of F1 in low, medium, and high dimensions.

[0122] The present invention can perform the following steps in the particle swarm optimization algorithm:

[0123] Initialize F1, including random parameters a and b;

[0124] The applicability of F1 is evaluated based on the P{} sequence, passenger flow distribution simulation weights, and electromechanical equipment performance load weights.

[0125] For each particle f in F1, its fitness value is compared with its optimal value f'. If all the higher-weighted f'1-f'7 are good, it can be taken as the current best position f'; if only the higher-weighted f'1-f'7 are average, then the F1 optimization iteration continues and the individual optimal solution is continuously updated.

[0126] The global optimal solution is continuously updated, and the multi-objective optimal control strategy is obtained by iterating F1 until it approaches F0 or reaches the maximum number of iterations.

[0127] It should be noted that, based on the above steps, the present invention can effectively guarantee and realize the generation of multi-objective optimal control strategies, effectively reduce energy consumption in rail transit stations, improve the efficiency of rail transit stations, and further enhance the reliability of energy consumption control in rail transit stations.

[0128] The energy consumption control method for rail transit stations proposed in this invention can effectively guarantee and realize the generation of multi-objective optimal control strategies, effectively reduce energy consumption in rail transit stations, improve the efficiency of rail transit stations, and further enhance the reliability of energy consumption control in rail transit stations.

[0129] like Figure 2 As shown, in the energy consumption control process of the third rail transit station energy consumption control method proposed in this invention, the invention can obtain environmental information and operational information through environmental monitoring. The environmental information may include temperature a1, humidity a2, light intensity a3, air pollution index a4, and weather forecast information (outside the station) a5, etc.; the operational information may include station passenger flow distribution status b1, train arrival and departure information b2, station operating timetable information b3, electromechanical equipment health b4, and information station production operation information b5, etc.

[0130] Specifically, the present invention can input the obtained environmental and operational information into the station energy management platform, which then uses evaluation methods such as temperature assessment, humidity assessment, light assessment, air quality assessment, and electromechanical equipment performance and energy consumption assessment to process the environmental and operational information accordingly, thereby obtaining the current control strategies for the relevant energy consumption control objects, such as temperature control strategies, humidity control strategies, light control strategies, air quality control strategies, and electromechanical equipment performance control strategies.

[0131] Specifically, this invention can determine control objectives and effects based on the controlled object and actual energy consumption requirements. The controlled object may include air conditioning systems, lighting systems, fresh air systems, escalator systems, passenger information service systems, and platform screen door systems, etc.; the control objectives and effects may include three levels of control objectives: high priority, medium priority, and low priority. High-priority control objectives may include a safe environmental state and safe system equipment management; medium-priority control objectives may include a safe and comfortable environmental state and safe and efficient system equipment management; and low-priority control objectives may include a safe and comfortable environmental state and safe and efficient system equipment management.

[0132] Subsequently, the present invention can process the current control strategy of the relevant energy consumption control objects according to the above-mentioned predefined optimization processing method and station energy consumption control objectives, and generate a multi-objective optimization control strategy based on grey comprehensive correlation analysis and simulation deduction.

[0133] Subsequently, the present invention can control each system in the control object according to a multi-objective optimization control strategy, so as to control the relevant energy consumption control object, maximize the reduction of energy consumption of rail transit stations, and realize the efficiency improvement of rail transit stations.

[0134] It should be noted that this invention can realize an intelligent energy consumption management system. This intelligent energy consumption management system, with comprehensive monitoring as its core, utilizes highly visualized images to intuitively and accurately assess and manage the electricity and water consumption of various systems; it establishes a statistical database of station energy consumption operation management information, providing various information services and conducting data analysis; it establishes trend analysis contingency plans to enrich and expand the localized information database; simultaneously, it can utilize digital intelligent monitoring technology to perform 24 / 7 information capture, analysis, and processing, improving incident handling efficiency and achieving a high degree of equipment and information sharing and intelligent decision-making.

[0135] Specifically, this invention utilizes an energy detection module to monitor the power consumption of station elevator systems, lighting, environmental control equipment (fans, chillers), platform screen door systems, and fire-fighting equipment; a water consumption detection module monitors the station's main water supply pipeline, restroom water supply pipeline, and cooling tower water supply pipeline; and a ventilation-water linkage module can acquire the temperature of air ducts and public areas, intelligently control the switching of fans and chillers, and use an energy consumption management platform to statistically analyze and monitor the station's power and water consumption. Based on the above information, this invention can make intelligent decisions, forming a demonstrative and scalable station energy consumption control system in the field of rail transit technology.

[0136] Specifically, this invention can achieve holographic perception of environmental and operational information within a station, obtaining not only environmental information such as temperature, humidity, light intensity, and air pollution index, but also operational information such as station passenger flow distribution, train arrival and departure information, station operating timetable information, station production operation information, and the health of electromechanical equipment. This invention can prioritize and weight environmental and operational information and input it into the station energy management platform's expert database to formulate precise energy consumption and system management strategies based on business, time, and region.

[0137] Specifically, this invention can analyze the overall control needs and objectives of a station based on station environmental information, and formulate more scientific and precise energy control strategies based on operational information. It can also provide a more precise and comfortable environment by dividing the station into grid-like areas based on passenger flow distribution; by using train arrival and departure information and station operating timetables, it can ensure high performance of equipment in advance during train arrivals and departures; and by monitoring and simulating the vibration, noise, temperature, pressure, and sound of electromechanical equipment, as well as assessing the health status of the equipment, it can predict equipment performance evolution to prevent overload, abnormal energy consumption, and other issues.

[0138] Platform screen doors, escalators, air conditioners, and other electromechanical equipment experience varying degrees of performance degradation after a period of operation, leading to an abnormal increase in energy consumption. Conventional automation systems cannot detect these changes. This invention employs nonlinear support vector machine technology to identify the performance models of the equipment online, track performance changes of key equipment in real time, provide maintenance suggestions for equipment with significantly degraded performance, and rationally arrange the work schedules of each device based on equipment performance differences and system load requirements to achieve optimal overall system operating efficiency.

[0139] It should also be noted that the present invention can set up station energy consumption control strategies based on passenger service experience and equipment performance warnings.

[0140] Specifically, this invention can increase the density of air quality monitoring during periods of high passenger flow and utilize ventilation equipment in conjunction to ensure passengers have a good experience and good health.

[0141] Specifically, the present invention can be centrally generated by the control platform based on real-time information such as passenger distribution and density, and each system can then link together to execute strategies such as the intensity, color scheme, and spectrum of lighting light.

[0142] The energy consumption control method for rail transit stations proposed in this invention can effectively enhance the reliability of energy consumption control in rail transit stations.

[0143] like Figure 3 As shown, the fourth method for controlling energy consumption in rail transit stations proposed in this invention may include the following steps:

[0144] S301. Obtain multi-dimensional environmental information (i.e., the aforementioned environmental information) and multi-dimensional operational information (i.e., the aforementioned operational information);

[0145] S302, Construct an environmental state sample sequence;

[0146] S303, Constructing operational information sample sequences;

[0147] S304. Construct a reference sample sequence for the environmental control effect;

[0148] S305. Construct a reference sample sequence for operational control effectiveness;

[0149] S306. Calculate the correlation coefficients between environmental status samples, operational information samples, environmental control effects, and operational control effects.

[0150] S307. Calculate and rank the correlation between environmental status samples, operational information samples, environmental control effects, and operational control effects.

[0151] S308, integrating passenger flow distribution simulation and analysis with electromechanical equipment performance and load prediction analysis;

[0152] S309. Construct a multi-objective control strategy, namely a multi-objective optimal control strategy, for systems such as air conditioning, lighting, fresh air, elevators and escalators, passenger information service, and platform screen doors.

[0153] S310. Achieve control objectives and effects according to a multi-objective control strategy;

[0154] In the process of executing the above steps S302, S303, S304, S305 and S306, the present invention can be carried out according to the control objectives and effects respectively.

[0155] The energy consumption control method for rail transit stations proposed in this invention can effectively enhance the reliability of energy consumption control in rail transit stations.

[0156] In the fifth energy consumption control method for rail transit stations proposed in this invention, an improved genetic algorithm is employed to consider the operational planning of all equipment, including air conditioning, lighting, fresh air systems, escalator and elevator systems, passenger information service systems, and platform screen door systems, thereby increasing the scope of optimization and the overall system energy efficiency. Furthermore, this invention can also perform trend analysis on real-time station energy consumption data based on cluster analysis methods, providing a theoretical basis for the later operation of network-level stations, rationally selecting station energy control processes, and reducing energy consumption while ensuring safe station operation and comfort. Figure 4 The data processing flow shown in the invention may further include the following data processing flow:

[0157] S401. Obtain the required information such as the number of train departures, station maintenance structure, and passenger flow from the network-level shared data, and identify the time points of stations with the same energy consumption characteristics.

[0158] S402. Perform preliminary processing on the averaged daily energy consumption data of the stations to complete the station classification;

[0159] S403. By reviewing the energy consumption data of various stations throughout the year and classifying the distribution of station energy consumption data, we can identify the different types of stations.

[0160] S404. Based on factors such as geographical location, station type and passenger flow, analyze the measured data of various types of stations and their energy consumption, and calculate the monthly energy consumption forecast data for different station types.

[0161] S405 Finally, based on the measured data, we can obtain the monthly energy consumption changes for the coming year, and thus understand the annual trend.

[0162] The energy consumption control method for rail transit stations proposed in this invention can perform trend analysis on real-time station energy consumption data based on cluster analysis, providing a theoretical basis for the later operation of network-level stations, and rationally selecting station energy consumption control processes to reduce energy consumption while ensuring safe operation and comfort of the station.

[0163] Based on the aforementioned energy consumption control methods for rail transit stations, this invention proposes a sixth energy consumption control method for rail transit stations. In this sixth method, the invention proposes an adaptive variable load optimization control mode for the entire station ventilation and air conditioning system, based on a safety-oriented multi-disciplinary collaborative approach to energy consumption.

[0164] Specifically, this invention addresses the operational characteristics of rail transit ventilation and air conditioning systems and the features of environmental and equipment monitoring systems. It introduces an intelligent optimization control device for the entire "air-water" system of air conditioning, employing artificial intelligence and machine learning algorithms to evolve programmable logic controller (PLC) technology into computer controller technology. This analyzes numerous control parameters to find the optimal control strategy, solving the problem of inefficient control in the environmental and equipment monitoring system and reducing energy consumption. Simultaneously, through dedicated interfaces and protocols for the environmental and equipment monitoring system, it seamlessly integrates and coordinates control with the system, achieving adaptive variable load optimization control for the entire station ventilation and air conditioning system.

[0165] Among them, the ventilation and water linkage module can be connected to the rail transit station integrated monitoring platform through the interface with the integrated monitoring system, and the communication protocol is MODBUS TCP. It reads basic equipment information, operating status information, fault information, switch control information, and environmental parameter information from the rail transit station integrated monitoring platform.

[0166] This invention enables equipment status monitoring. Specifically, through the human-machine interface of the integrated monitoring system, it dynamically displays the operating, stopping, and fault status of each exhaust fan, as well as the operating time, energy consumption, fault information, and various temperature parameters involved in system control and regulation. Furthermore, this invention provides a process flow monitoring interface for the controlled area, allowing for a direct view of the equipment's operating status and environmental parameter information from each sensor measurement point via a flowchart interface.

[0167] The present invention may include fault alarm function and recording function;

[0168] The present invention can have data and event recording functions; specifically, the present invention can record the operating power and operating time of each wind turbine and generate data reports.

[0169] In this invention, the interface with the environmental and equipment monitoring system allows the acquisition of operational feedback signals, fire signals, and forced revoke-of-authority signals from the integrated monitoring system. It should be noted that the wind and water linkage control system can communicate with the environmental and equipment monitoring system using TCP / IP as the transport layer protocol and supporting standard protocols such as BACnet, OPC, MODBUS, Restful, and oBIX at the application layer. The environmental and equipment monitoring system can provide the integrated monitoring system with signals for energy-saving system operation feedback, energy-saving system restoration, and energy-saving system switching through the original communication module. Specifically, when a fire signal is issued, the environmental and equipment monitoring system automatically switches to fire mode control. Similarly, in the energy-saving system, the environmental and equipment monitoring system also needs to provide a fire signal to the energy-saving control module. When the energy-saving control module receives the fire signal, it automatically relinquishes control authority, allowing the environmental control system to revert to the operating mode of the environmental and equipment monitoring system.

[0170] In its interface with the frequency converter, the ventilation and water linkage control system can communicate with the frequency converter via RS485 serial communication. The application layer supports standard protocols such as BACnet, OPC, MODBUS, RESTful, and oBIX. The interface allows for flexible switching of control permissions between the system and the environmental and equipment monitoring system. When a fire signal is issued, the environmental and equipment monitoring system automatically switches to fire mode control, and the ventilation and water linkage control system automatically relinquishes control, allowing the environmental control system to revert to the operating mode of the environmental and equipment monitoring system.

[0171] It should be noted that this invention aims to intelligently empower urban rail transit stations to save energy and reduce carbon emissions. It proposes an intelligent energy consumption management system architecture and functions, utilizing IoT technology to achieve real-time data collection and monitoring of various energy sources such as wind, water, electricity, heat, and gas. By analyzing energy consumption patterns and trends, it formulates reasonable and scientific energy-saving strategies. Based on big data analysis, it comprehensively monitors the energy consumption of urban rail transit station equipment, enhancing energy quality and safety monitoring and improving energy efficiency. Simultaneously, it introduces a wind-water linkage system for real-time monitoring of ventilation and water systems, integrating the station's air conditioning water system and other major air conditioning systems onto a single platform to achieve equipment operation data collection and remote linkage. Furthermore, this invention can automatically adjust according to equipment load changes, achieving automated equipment control and improving operational efficiency. Additionally, based on similar daily energy consumption analyses of stations, this invention can provide a reference for the later operation of stations across the entire network.

[0172] The energy consumption control method for rail transit stations proposed in this invention introduces a ventilation and water linkage system to monitor the ventilation system and water system in real time. It integrates the station's air conditioning water system, air conditioning system and other systems and technologies into a single platform to realize the collection and remote linkage of equipment operation data.

[0173] The energy consumption control device for rail transit stations provided by the present invention is described below. The energy consumption control device for rail transit stations described below can be referred to in correspondence with the energy consumption control method for rail transit stations described above.

[0174] like Figure 5 As shown, the present invention proposes an energy consumption control device for rail transit stations, which may include: a first determining unit 501, a second determining unit 502, a third determining unit 503, a first optimizing unit 504, and a first control unit 505; wherein:

[0175] The first determining unit 501 is used to determine environmental information, operational information and at least one energy consumption control object within the rail transit station.

[0176] The second determining unit 502 is used to determine the current control strategy of each energy consumption control object under environmental information and operational information;

[0177] The third determining unit 503 is used to determine the station energy consumption control target of the rail transit station under environmental information and operational information.

[0178] The first optimization unit 504 is used to optimize the current control strategy of each energy consumption control object in an overall manner according to the predefined multi-objective control strategy optimization method and the station energy consumption control objective, so as to obtain the multi-objective optimal control strategy of the rail transit station.

[0179] The first control unit 505 is used to control each energy consumption control object according to a multi-objective optimal control strategy.

[0180] It should be noted that the specific processing procedures and their technical effects of the first determining unit 501, the second determining unit 502, the third determining unit 503, the first optimizing unit 504, and the first control unit 505 can be referred to the present invention regarding... Figure 1 Steps S101, S102, S103, S104, and S105 will not be described in detail.

[0181] Optionally, the first optimization unit 504 includes: a first construction unit, a fourth determination unit, a second construction unit, and a second optimization unit;

[0182] The first building unit is used to construct a sequence of multi-objective control strategies to be optimized based on the current control strategies of each energy consumption control object;

[0183] The fourth determining unit is used to determine the single-objective optimal control strategy for each energy consumption control object under environmental and operational information.

[0184] The second building unit is used to construct a sequence of single-objective optimal control strategies based on each single-objective optimal control strategy.

[0185] The second optimization unit is used to iteratively optimize the multi-objective control strategy sequence based on a predefined optimization processing method, a single-objective optimal control strategy sequence and a station energy consumption control objective, so as to obtain a multi-objective optimal control strategy.

[0186] The predefined optimization methods include: grey comprehensive correlation analysis, passenger flow simulation and deduction, equipment prediction analysis, and overall optimization.

[0187] Optionally, the second optimization unit includes: a fifth determining unit, a first obtaining unit, and a third optimization unit; wherein:

[0188] The fifth determining unit is used to determine the grey comprehensive correlation degree sequence of the single-objective optimal control strategy sequence and the multi-objective control strategy sequence;

[0189] The first acquisition unit is used to obtain passenger flow simulation results and equipment prediction analysis results;

[0190] The third optimization unit is used to iteratively optimize each current control strategy in the multi-objective control strategy sequence based on the overall optimization method, the grey comprehensive correlation degree sequence, the passenger flow simulation and deduction results, and the equipment prediction and analysis results, until the preset optimization requirements are met, so as to obtain the optimal multi-objective control strategy.

[0191] Optionally, the third optimization unit includes: a sixth determination unit, an initialization unit, an evaluation unit, a comparison unit, and a seventh determination unit;

[0192] The sixth determining unit is used to determine the passenger flow distribution simulation weights and electromechanical equipment performance load weights corresponding to the passenger flow simulation results and equipment prediction analysis results, respectively.

[0193] An initialization unit is used to initialize the multi-objective control strategy sequence to obtain the initialized sequence.

[0194] The evaluation unit is used to evaluate the applicability of the initialized sequence based on the grey comprehensive correlation degree sequence, passenger flow distribution simulation and deduction weights, and electromechanical equipment performance load weights.

[0195] The comparison unit is used to compare the fitness value of each element in the fitness of the initialized sequence with the corresponding optimal value.

[0196] The seventh determining unit is used to determine whether to continue iterative optimization based on the comparison results until the preset optimization requirements are met, so as to obtain the multi-objective optimal control strategy.

[0197] Optionally, the fifth determining unit includes: an eighth determining unit and a second obtaining unit;

[0198] The eighth determining unit is used to determine the grey correlation degree, absolute correlation degree, and relative correlation degree between the single-objective optimal control strategy sequence and the multi-objective control strategy sequence;

[0199] The second acquisition unit is used to obtain the gray comprehensive correlation sequence based on gray correlation degree, absolute correlation degree and relative correlation degree.

[0200] The energy consumption control device for rail transit stations proposed in this invention can determine environmental information, operational information, and at least one energy consumption control object within the rail transit station; determine the current control strategy for each energy consumption control object under the environmental and operational information; determine the station's energy consumption control objective under the environmental and operational information; optimize the current control strategy of each energy consumption control object as a whole according to a predefined multi-objective control strategy optimization method and the station's energy consumption control objective to obtain the optimal multi-objective control strategy for the rail transit station; and control each energy consumption control object according to the optimal multi-objective control strategy. This invention can simultaneously optimize the current control strategy of each energy consumption control object as a whole according to the multi-objective control strategy optimization method and the station's energy consumption control objective. That is, considering the coupling and correlation between the current control strategies of each energy consumption control object, and maximizing the overall efficiency of the rail transit station, it can simultaneously optimize the current control strategies of all energy consumption control objects to obtain the optimal multi-objective control strategy for the rail transit station, and control each energy consumption control object according to the optimal multi-objective control strategy, effectively improving the efficiency of the rail transit station and enhancing the reliability of energy consumption control.

[0201] Figure 6 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 6 As shown, the electronic device may include: a processor 610, a communication interface 620, a memory 630, and a communication bus 640, wherein the processor 610, the communication interface 620, and the memory 630 communicate with each other through the communication bus 640. The processor 610 can call logical instructions in the memory 630 to execute a rail transit station energy consumption control method, which includes:

[0202] Identify environmental information, operational information, and at least one energy consumption control object within the rail transit station;

[0203] Determine the current control strategy for each energy consumption control object under environmental and operational information;

[0204] Determine the energy consumption control targets for rail transit stations based on environmental and operational information;

[0205] Based on the predefined multi-objective control strategy optimization method and the station energy consumption control objective, the current control strategy of each energy consumption control object is optimized as a whole to obtain the multi-objective optimal control strategy of the rail transit station.

[0206] The energy consumption control objects are controlled according to the multi-objective optimal control strategy.

[0207] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0208] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the rail transit station energy consumption control method provided by the above methods, the method including:

[0209] Identify environmental information, operational information, and at least one energy consumption control object within the rail transit station;

[0210] Determine the current control strategy for each energy consumption control object under environmental and operational information;

[0211] Determine the energy consumption control targets for rail transit stations based on environmental and operational information;

[0212] Based on the predefined multi-objective control strategy optimization method and the station energy consumption control objective, the current control strategy of each energy consumption control object is optimized as a whole to obtain the multi-objective optimal control strategy of the rail transit station.

[0213] The energy consumption control objects are controlled according to the multi-objective optimal control strategy.

[0214] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the energy consumption control method for rail transit stations provided by the methods described above, the method comprising:

[0215] Identify environmental information, operational information, and at least one energy consumption control object within the rail transit station;

[0216] Determine the current control strategy for each energy consumption control object under environmental and operational information;

[0217] Determine the energy consumption control targets for rail transit stations based on environmental and operational information;

[0218] Based on the predefined multi-objective control strategy optimization method and the station energy consumption control objective, the current control strategy of each energy consumption control object is optimized as a whole to obtain the multi-objective optimal control strategy of the rail transit station.

[0219] The energy consumption control objects are controlled according to the multi-objective optimal control strategy.

[0220] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0221] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0222] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for controlling energy consumption in rail transit stations, characterized in that, include: Identify environmental information, operational information, and at least one energy consumption control object within the rail transit station; Determine the current control strategy for each of the energy consumption control objects under the environmental information and the operational information; Determine the station energy consumption control target for the rail transit station under the environmental information and the operational information; According to the predefined multi-objective control strategy optimization method and the station energy consumption control objective, the current control strategy of each energy consumption control object is optimized as a whole to obtain the multi-objective optimal control strategy of the rail transit station. According to the multi-objective optimal control strategy, each of the energy consumption control objects is controlled; The process of optimizing the current control strategy of each energy consumption control object according to a predefined multi-objective control strategy optimization method and the station energy consumption control objective to obtain the multi-objective optimal control strategy for the rail transit station includes: Based on the current control strategy of each energy consumption control object, a multi-objective control strategy sequence to be optimized is constructed; Under the environmental information and the operational information, a single-objective optimal control strategy is determined for each of the energy consumption control objects; Based on the single-objective optimal control strategies described above, a sequence of single-objective optimal control strategies is constructed. Based on the predefined optimization processing method, the single-objective optimal control strategy sequence and the station energy consumption control objective, the multi-objective control strategy sequence is iteratively optimized to obtain the multi-objective optimal control strategy. The predefined optimization processing methods include: grey comprehensive correlation analysis, passenger flow simulation and deduction, equipment prediction analysis and overall optimization method; The step of iteratively optimizing the multi-objective control strategy sequence based on a predefined optimization processing method, the single-objective optimal control strategy sequence, and the station energy consumption control objective to obtain the multi-objective optimal control strategy includes: Determine the grey comprehensive correlation degree sequence between the single-objective optimal control strategy sequence and the multi-objective control strategy sequence; Obtain passenger flow simulation results and equipment prediction analysis results; Based on the overall optimization method, the grey comprehensive correlation degree sequence, the passenger flow simulation and deduction results and the equipment prediction and analysis results, the current control strategies in the multi-objective control strategy sequence are iteratively optimized until the preset optimization requirements are met, so as to obtain the multi-objective optimal control strategy. The step of iteratively optimizing each current control strategy in the multi-objective control strategy sequence based on the overall optimization method, the grey comprehensive correlation sequence, the passenger flow simulation results, and the equipment prediction analysis results until a preset optimization requirement is met, in order to obtain the optimal multi-objective control strategy, includes: Determine the passenger flow distribution simulation weights and electromechanical equipment performance load weights corresponding to the passenger flow simulation results and the equipment prediction analysis results, respectively. The multi-objective control strategy sequence is initialized to obtain an initialized sequence. The applicability of the initialized sequence is evaluated based on the grey comprehensive correlation degree sequence, the passenger flow distribution simulation and deduction weight, and the electromechanical equipment performance load weight. The fitness value of each element in the applicability of the initialized sequence is compared with the corresponding optimal value. Based on the comparison results, it is determined whether to continue iterative optimization until the preset optimization requirements are met, so as to obtain the multi-objective optimal control strategy. The preset optimization requirement is that the iterated multi-objective control strategy sequence approximates the single-objective optimal control strategy sequence or reaches the maximum number of iterations.

2. The energy consumption control method for rail transit stations according to claim 1, characterized in that, The determination of the grey comprehensive correlation degree sequence between the single-objective optimal control strategy sequence and the multi-objective control strategy sequence includes: Determine the grey correlation degree, absolute correlation degree, and relative correlation degree between the single-objective optimal control strategy sequence and the multi-objective control strategy sequence; Based on the gray correlation degree, the absolute correlation degree, and the relative correlation degree, the gray comprehensive correlation degree sequence is obtained.

3. An energy consumption control device for rail transit stations, characterized in that, include: The system comprises a first determining unit, a second determining unit, a third determining unit, a first optimizing unit, and a first controlling unit; wherein: The first determining unit is used to determine environmental information, operational information, and at least one energy consumption control object within the rail transit station; The second determining unit is used to determine the current control strategy of each of the energy consumption control objects under the environmental information and the operational information, respectively; The third determining unit is used to determine the station energy consumption control target of the rail transit station under the environmental information and the operational information; The first optimization unit is used to optimize the current control strategy of each energy consumption control object in an overall manner according to the predefined multi-objective control strategy optimization method and the station energy consumption control objective, so as to obtain the multi-objective optimal control strategy of the rail transit station. The first control unit is used to control each of the energy consumption control objects according to the multi-objective optimal control strategy; The first optimization unit includes: a first construction unit, a fourth determination unit, a second construction unit, and a second optimization unit; The first construction unit is used to construct a sequence of multi-objective control strategies to be optimized based on the current control strategies of each of the energy consumption control objects; The fourth determining unit is used to determine the single-objective optimal control strategy for each of the energy consumption control objects under the environmental information and the operational information. The second construction unit is used to construct a sequence of single-objective optimal control strategies based on each of the single-objective optimal control strategies. The second optimization unit is used to iteratively optimize the multi-objective control strategy sequence based on a predefined optimization processing method, the single-objective optimal control strategy sequence, and the station energy consumption control objective, so as to obtain the multi-objective optimal control strategy. The predefined optimization processing methods include: grey comprehensive correlation analysis, passenger flow simulation and deduction, equipment prediction analysis and overall optimization method; The second optimization unit includes: a fifth determining unit, a first obtaining unit, and a third optimization unit; wherein: The fifth determining unit is used to determine the grey comprehensive correlation degree sequence between the single-objective optimal control strategy sequence and the multi-objective control strategy sequence; The first obtaining unit is used to obtain passenger flow simulation results and equipment prediction analysis results; The third optimization unit is used to iteratively optimize each of the current control strategies in the multi-objective control strategy sequence based on the overall optimization method, the grey comprehensive correlation degree sequence, the passenger flow simulation and deduction results and the equipment prediction and analysis results, until the preset optimization requirements are met, so as to obtain the multi-objective optimal control strategy. The third optimization unit includes: a sixth determination unit, an initialization unit, an evaluation unit, a comparison unit, and a seventh determination unit; The sixth determining unit is used to determine the passenger flow distribution simulation weight and the electromechanical equipment performance load weight corresponding to the passenger flow simulation results and the equipment prediction analysis results, respectively. The initialization unit is used to initialize the multi-objective control strategy sequence to obtain the initialized sequence; The evaluation unit is used to evaluate the applicability of the initialized sequence based on the grey comprehensive correlation degree sequence, the passenger flow distribution simulation and deduction weights, and the electromechanical equipment performance load weights. The comparison unit is used to compare the fitness value of each element in the fitness of the initialized sequence with the corresponding optimal value. The seventh determining unit is used to determine whether to continue iterative optimization based on the comparison results until the preset optimization requirements are met, so as to obtain the multi-objective optimal control strategy. The preset optimization requirement is that the iterated multi-objective control strategy sequence approximates the single-objective optimal control strategy sequence or reaches the maximum number of iterations.

4. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the energy consumption control method for rail transit stations as described in any one of claims 1 to 2.

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