Railway passenger station group energy consumption scheduling method and system based on multi-target coupling

Abstract

The invention provides a railway passenger station group energy consumption scheduling method and system based on multi-target coupling. The method comprises the following steps: acquiring historical data of stations; extracting basic data corresponding to three preset targets from the historical data, and constructing input vectors corresponding to the three targets; respectively inputting the vectors into three preset calculation models corresponding to the three targets, wherein the three models respectively output equipment operation parameter sets corresponding to the three targets; calculating a satisfaction parameter set corresponding to the equipment operation parameter set based on the three equipment operation parameter sets, and calculating a single-target satisfaction value based on the satisfaction parameter set corresponding to each target; determining a tendency target of the current equipment operation parameter set based on the plurality of single-target satisfaction values, judging whether the tendency target is a tendency target of a set demand, and if the current tendency target is the tendency target of the set demand, applying the current equipment operation parameter set; and if not, recalculating the tendency target.

Description

Technical Field

[0001] The present invention relates to the technical field of railway passenger station management, and in particular to a railway passenger station group energy consumption scheduling method and system based on multi-objective coupling. Background Art

[0002] The management of internal facilities of railway passenger stations is a key factor in ensuring smooth, comfortable and safe travel for passengers, and its importance is becoming increasingly prominent in the modern railway transportation system. This strategy is not only related to the direct experience of passengers, but also has a profound impact on the overall operational efficiency and service quality of railway passenger stations.

[0003] First, by carefully analyzing passenger needs and behavior patterns, railway passenger stations can accurately configure various service facilities, such as ticket machines, self-service ticket machines and security equipment, to ensure that passengers can quickly find the services they need, reduce waiting and searching time, and improve travel efficiency. Secondly, railway passenger stations emphasize the modernization and intelligence of internal facilities. With the advancement of science and technology, new technologies are constantly being integrated into the internal facilities of railway passenger stations, such as intelligent navigation, face recognition and automatic ticket checking. The widespread application of these technologies and facilities not only improves the travel experience of passengers, but also enhances the management efficiency of passenger stations, prompting the railway department to keep up with the times, continuously optimize the configuration of facilities, and ensure that the internal facilities always maintain the leading level in the industry. In addition, as a large public place, the internal facilities of railway passenger stations need to withstand high-frequency use and testing. By formulating a scientific maintenance and maintenance plan to ensure that the facilities are in good condition, it can not only extend the service life of the facilities, but also reduce the failure rate and reduce the impact on passenger travel.

[0004] However, the existing technology often manually adjusts the operating parameters of equipment inside railway passenger stations, and manual adjustment is difficult to ensure the adaptability to multiple goals, such as safety, comfort and energy saving. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a railway passenger station group energy consumption scheduling method and system based on multi-objective coupling to eliminate or improve one or more defects existing in the prior art.

[0006] One aspect of the present invention provides a method for energy consumption scheduling of a railway passenger station group based on multi-objective coupling, the method comprising the following steps:

[0007] Acquire historical data of the station, wherein the historical data of the station includes historical weather data, station equipment data and historical passenger data;

[0008] Extracting basic data corresponding to three preset goals from the historical data, and constructing input vectors corresponding to the three goals, where the three preset goals are safety, comfort and energy saving;

[0009] The vectors corresponding to the three targets are respectively input into three preset calculation models corresponding to the three targets, and the three models respectively output sets of equipment operation parameters corresponding to the three targets;

[0010] Calculate the satisfaction parameter set corresponding to the equipment operation parameter set based on the three equipment operation parameter sets, and calculate the single target satisfaction value based on the satisfaction parameter set corresponding to each target;

[0011] Based on multiple single-objective satisfaction values, determine the tendency target of the current equipment operation parameter set, and determine whether the current tendency target is the tendency target of the set requirement. If the current tendency target is the tendency target of the set requirement, the current equipment operation parameter set is applied; if the current tendency target is not the tendency target of the set requirement, re-extract the basic data corresponding to the three preset targets from the historical data, recalculate the equipment operation parameter set and the tendency target of the current equipment operation parameter set, until the calculated tendency target is the tendency target of the set requirement.

[0012] Using the above scheme, three goals are pre-set during the calculation process of this scheme, namely safety, comfort and energy saving. The three goals are all related to the operation of the station. This scheme can output the optimal solution that meets the preset goals and can automatically output the equipment operation parameters. On the one hand, it can improve the adjustment speed of the equipment operation parameters and improve the processing efficiency; on the other hand, it can make the operation of the final equipment efficiently match the predetermined goals and ensure the compatibility with the predetermined goals.

[0013] In some embodiments of the present invention, in the step of inputting the vectors corresponding to the three targets into three preset calculation models corresponding to the three targets respectively, the three calculation models all adopt the NSGA-II algorithm with improved congestion.

[0014] In some embodiments of the present invention, in the step of calculating the satisfaction parameter set of the corresponding device operating parameter set based on three device operating parameter sets, each device operating parameter in the operating parameter set is calculated using a relatively large satisfaction function or a relatively small satisfaction function to obtain the satisfaction parameter corresponding to each device operating parameter, and then obtain the satisfaction parameter set of the corresponding operating parameter set.

[0015] In some embodiments of the present invention, in the step of calculating each device operating parameter in the operating parameter set using a large-scale satisfaction function or a small-scale satisfaction function, each device operating parameter in the operating parameter set is calculated with a preset reference value of the device operating parameter to determine whether to use a large-scale satisfaction function or a small-scale satisfaction function.

[0016] In some embodiments of the present invention, in the step of calculating each device operating parameter in the operating parameter set using a partial large satisfaction function or a partial small satisfaction function to obtain a satisfaction parameter corresponding to each device operating parameter, if a partial large satisfaction function is used, the calculation is based on the following formula:

[0017]

[0018] If a small satisfaction function is used, it is calculated based on the following formula:

[0019]

[0020] Wherein, μ represents the satisfaction parameter; g represents the equipment operation parameter corresponding to the satisfaction parameter μ; g max Indicates the maximum value of the device operating parameter in the operating parameter set where the device operating parameter g is located; g min Indicates the minimum value of the device operating parameter in the operating parameter set to which the device operating parameter g belongs.

[0021] In some embodiments of the present invention, in the step of calculating the single target satisfaction value based on the satisfaction parameter set corresponding to each target, the weighted average of the satisfaction parameters in the satisfaction parameter set for each target is calculated to obtain the single target satisfaction value corresponding to the target.

[0022] In some embodiments of the present invention, the tendency target of the setting requirement is a single tendency target or multiple tendency targets, and the multiple tendency targets include single-target satisfaction thresholds corresponding to multiple targets, and in the step of determining the tendency target of the current set of equipment operating parameters based on multiple single-target satisfaction values, determining whether the current tendency target is the tendency target of the setting requirement;

[0023] If the tendency target of the set requirement is a single tendency target, compare the satisfaction values ​​of each single target, take the target corresponding to the largest single target satisfaction value as the tendency target of the current equipment operation parameter set, and determine whether the current tendency target is the tendency target of the set requirement;

[0024] If the tendency target of the set requirement is a multi-tendency target, each single-target satisfaction value is compared with the corresponding single-target satisfaction threshold to determine whether the current tendency target is the tendency target of the set requirement.

[0025] In some embodiments of the present invention, after the step of inputting the vectors corresponding to the three targets into three preset calculation models corresponding to the three targets respectively, and the three models respectively output the device operation parameter sets corresponding to the three targets, the step further includes:

[0026] The main target among the three targets is obtained, and the equipment operating parameter sets of targets other than the main target are used as constraint conditions for calculating the main target operating parameter set, and the equipment operating parameter set corresponding to the main target is recalculated.

[0027] In some embodiments of the present invention, in the step of acquiring a main target among the three targets, the main target is at least one of the three targets.

[0028] The second aspect of the present invention also provides a railway passenger station group energy consumption scheduling system based on multi-objective coupling, the system includes a computer device, the computer device includes a processor and a memory, the memory stores computer instructions, the processor is used to execute the computer instructions stored in the memory, when the computer instructions are executed by the processor, the system implements the steps implemented by the method described above.

[0029] The third aspect of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps implemented by the aforementioned railway passenger station group energy consumption scheduling method based on multi-objective coupling.

[0030] Additional advantages, purposes, and features of the present invention will be described in part in the following description, and will become apparent to those skilled in the art after studying the following, or may be learned from the practice of the present invention. The purposes and other advantages of the present invention can be specifically pointed out and obtained in the specification and the accompanying drawings.

[0031] Those skilled in the art will appreciate that the objectives and advantages that can be achieved with the present invention are not limited to the above specific description, and the above and other objectives that can be achieved by the present invention will be more clearly understood from the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings described herein are used to provide a further understanding of the present invention, constitute a part of this application, and do not constitute a limitation of the present invention.

[0033] Figure 1 It is a schematic diagram of the process of the present invention;

[0034] Figure 2 It is a general flow chart of the energy consumption scheduling method of railway passenger station group based on multi-objective coupling of the present invention;

[0035] Figure 3 This is a schematic diagram of the "passenger station group" of the present invention, the main index parameter gears and the operation mode division;

[0036] Figure 4 The objective function solution flow chart of the railway passenger station group energy consumption scheduling method based on multi-objective coupling of the present invention;

[0037] Figure 5 It is a multi-objective balanced optimization flow chart of the railway passenger station group energy consumption scheduling method and system based on multi-objective coupling of the present invention;

[0038] Figure 6 The flowchart of the NSGA-II algorithm for improved congestion of the present invention;

[0039] Figure 7 This is a flow chart for comparing the consistency between the inclination target and station requirements of the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0041] It should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, only structures and / or processing steps closely related to the solutions according to the present invention are shown in the accompanying drawings, while other details that are not closely related to the present invention are omitted.

[0042] In the specific implementation process, since railway passenger stations are dispersed in various places, no matter if the station wants to achieve balanced optimization of energy consumption under the goals of "safety", "comfort" or "energy saving", it is inevitable to consider the influence of region, season and climate. On the other hand, due to the huge daily passenger flow of the station, the massive passenger flow and rich variety of equipment and facilities also have a huge impact on the energy consumption of the station. Therefore, it is also extremely important to optimize the energy consumption of the station from the perspective of region, time and passenger flow.

[0043] like Figure 1 As shown, the present invention proposes a railway passenger station group energy consumption scheduling method based on multi-objective coupling, and the steps of the method include:

[0044] In the specific implementation process, this plan also includes adding preset conditions. First, the preset conditions are screened by region, and then the final comparison parameters are determined by solar term screening, and compared with the obtained parameters.

[0045] Step S100, obtaining historical data of the station, wherein the historical data of the station includes historical weather data, station equipment data and historical passenger data;

[0046] In some embodiments of the present invention, the historical data of the station can be obtained from the station management system, and the station historical data includes: station environmental comfort information (temperature, humidity, brightness, PM2.5 concentration, platform wind speed, etc.), station equipment operation data (equipment operation and maintenance records, equipment real-time operation status, equipment failure information, etc.), passenger flow data in different areas of the station (historical passenger flow, passenger flow density in each area of ​​the station during typical time periods, etc.), and station historical safety data (number of platform white line crossings, number of escalator passengers falling / escalator abnormal operation, etc.).

[0047] During the specific implementation process, energy consumption mainly refers to electricity consumption. The station's electricity consumption includes the comprehensive value of basic equipment power consumption and photovoltaic power generation, battery power supply, wind power generation and hydropower generation.

[0048] During the specific implementation process, the station's equipment includes basic equipment and facilities, travel service equipment and electromechanical equipment; the basic equipment and facilities include ticketing equipment, travel service equipment and electromechanical equipment. The ticketing equipment here includes ticket gates, automatic ticket vending machines, ticket vending and collection machines, etc.; travel service equipment includes broadcasting equipment, guidance equipment, inquiry equipment, monitoring equipment, security equipment, clock equipment, etc., and electromechanical equipment includes electric / escalator equipment, lighting equipment, air-conditioning equipment and fire-fighting equipment, etc.

[0049] Specifically, the power consumption of ticketing equipment is Q pw =Q zds +Q jp +Q sq +Q po , where Q zds , Q jp , Q sq , Q po They are the power consumption of automatic ticket vending machines, ticket gates, ticket vending and collection machines, and other ticketing equipment; the power consumption of travel service equipment is Q lf =Q broad +Q moni +Q guide +Q sense +Q check +Q clock , where Q broad , Q moni , Q guide , Q sense , Q check , Q clock The power consumption of broadcasting equipment, monitoring equipment, guidance equipment, inquiry equipment, security equipment and clock equipment is Q respectively; the power consumption of electromechanical equipment is Q jd =Q elev +Q lighting +Q cond +Q fire +Q other, where Q elev , Q lighting , Q cond , Q fire , Q other They are respectively the power consumption of electric / escalator equipment, lighting equipment, air-conditioning equipment, fire-fighting equipment and other electromechanical equipment.

[0050] The photovoltaic array power generation is in, A is the total power generation of the station photovoltaic array in a certain period of time (kwh); light is the area of ​​the solar panel (m 2 );φ t is the total radiation energy of the station photovoltaic array (kwh / m 2 ), In the formula is the duration of illumination of the photovoltaic array during the statistical period (d), φ β is the total solar radiation on the tilted photovoltaic array surface, φ β =φ S ×[sin(α+β) / sinα]+D, φ in the formula S is the direct solar radiation on the horizontal plane, D is the scattered radiation, α is the solar altitude angle at noon, and β is the inclination angle of the photovoltaic array; η light is the conversion efficiency of the photovoltaic module; k is the correction factor, k = k 1 ×k 2 ×k 3 ×k 4 ×k 5 , where k 1 is the attenuation coefficient of the photovoltaic module in long-term operation, which is 0.8, k 2 k is the correction factor for the power reduction of photovoltaic modules due to the increase in temperature of the photovoltaic modules blocked by dust, which is taken as 0.82. 3 is the line correction coefficient, take 0.95, k 4 is the inverter power factor, take 0.85, k 5 is the correction coefficient for the orientation and tilt angle of the photovoltaic array, which is taken as 0.9;

[0051] The wind power generation is Q wind，t , In the formula, Q wind,t is the total power generation of the station wind power generator set in a certain period of time (kwh); η wind is the conversion efficiency of the wind turbine generator set, which is 0.3 to 0.5; η f is the efficiency of the generator, ranging from 0.9 to 0.95; is the average wind speed at the location of the wind power station (m / s); S wind is the rotor area (m 2 );

[0052] The hydroelectric power generation is Q water,t , Q water,t =E water ×H α ×η S ×10 4 , where Q water,t is the total power generation of the station's hydroelectric generator set during a certain period of time (kwh); E water is the rated capacity of the hydroelectric generator set (10,000 kW), H α is the number of hours of power generation (h), η S The power generation efficiency of the hydroelectric generator set;

[0053] The total storage capacity of the battery pack is Q xd,t , In the formula, Q xd,t is the total storage capacity of the station battery group in a certain period of time (kwh); The battery stores electricity for the period t-1. are the charging and discharging power of the battery, respectively, cd , η fd are the charging and discharging efficiency of the battery respectively.

[0054] Step S200, extracting basic data corresponding to three preset goals from the historical data, and constructing input vectors corresponding to the three goals, where the three preset goals are safety, comfort and energy saving;

[0055] Step S300, inputting the vectors corresponding to the three targets into three preset calculation models corresponding to the three targets respectively, and the three models respectively output the equipment operation parameter sets corresponding to the three targets;

[0056] In some embodiments of the present invention, the three calculation models all adopt the NSGA-II algorithm with improved congestion, and the objective functions of the three models are respectively the highest safety, the highest production and operation comfort, and the lowest power consumption.

[0057] The highest level of safety is determined by the number of abnormal behaviors occurring at the station, the passenger congestion, the passenger flow density within the station, and the percentage of abnormal events occurring within the station.

[0058] The highest production and operation comfort level is determined by temperature, humidity, brightness, CO2 concentration, PM2.5 concentration, noise, NOX concentration, harmful gas concentration, wind speed felt by staff near the platform at different train speeds, and reverberation time in the station.

[0059] In the specific implementation process, the binary tournament optimization process is introduced in the process of using the NSGA-II algorithm with improved congestion.

[0060] Step S400, calculating a satisfaction parameter set corresponding to the device operation parameter set based on the three device operation parameter sets, and calculating a single target satisfaction value based on the satisfaction parameter set corresponding to each target;

[0061] In the specific implementation process, if the three equipment operating parameter sets are G 1 =[g 11 ,g 12 ,...,g 15 ], G 2 =[g 21 ,g 22 ,...,g 29 ],G 3 =[g 31 ,g 32 ,g 33 ], then the satisfaction parameter sets corresponding to the equipment operation parameter sets are {μ 11 ,μ 12 ,μ 13 ,μ 14 ,μ 15},{μ 21 ,μ 22 ,...,μ 29},{μ 31 ,μ 32 ,μ 33}.

[0062] Step S500, based on multiple single-objective satisfaction values, determine the tendency target of the current equipment operation parameter set, and determine whether the current tendency target is the tendency target of the set requirement. If the current tendency target is the tendency target of the set requirement, the current equipment operation parameter set is applied; if the current tendency target is not the tendency target of the set requirement, the basic data corresponding to the three preset targets is re-extracted from the historical data, and the equipment operation parameter set and the tendency target of the current equipment operation parameter set are recalculated until the calculated tendency target is the tendency target of the set requirement.

[0063] In the specific implementation process, the optimal solution is obtained and the results between passenger stations in the passenger station group are matched:

[0064] After the operating parameters of the passenger station group are set according to three modes based on the optimal solution for the three goals of safety, comfort and energy saving, each station within the passenger station group will fine-tune the operating parameters based on its own operating process. If it is found that the adjusted operating parameters of one of the stations exceed the adjustable parameter range of the operating mode (for example, the parameters originally set according to the safety mode are adjusted to a large extent for a station in the group, and after adjustment, they match the comfort / energy-saving mode), the main indicator parameter range settings of the passenger station group will be readjusted, the needs of this station will be re-compared, and the matching will be recalculated.

[0065] Specifically include:

[0066] Energy consumption strategy formulation:

[0067] 1. Collaborative dispatch based on passenger flow: Adjust the operating status of energy-consuming equipment according to the real-time passenger flow of the station. During peak passenger flow periods, ensure that all necessary equipment operates at full capacity to meet the service needs of passengers; during low passenger flow periods, appropriately reduce the number of operating equipment or reduce the operating power (Note: passenger flow conditions can be predicted through passenger flow prediction models to estimate passenger flow conditions at each station in the group in advance). For example, dynamically adjust the operation of lighting, air conditioning and other equipment in the waiting hall based on train arrival and departure times and the predicted number of passengers entering and leaving the station. In case of large-scale delays, abnormal weather and other emergency situations, consider temporarily adding necessary equipment or opening security / ticket inspection channels appropriately while meeting the basic energy consumption requirements of the station. If the additional equipment exceeds the maximum energy consumption threshold of the station, virtual resources can also be considered, that is, all personnel and equipment resources of the entire passenger station group form a virtual resource library, which stores the resource configuration, use, and deployment of all passenger stations under the jurisdiction of the passenger station group. When an emergency occurs, each station can temporarily deploy corresponding equipment from the resource library. For example, if a station has a large-scale delay, passengers who take the train at the station cannot travel in time. At this time, the ticket inspection channel of the adjacent station on the same line can be opened, and passengers can take other trains at the adjacent station to complete their trip from the adjacent station after arriving at the adjacent station. This situation is applicable to stations on the same line forming a passenger station group or different passenger station groups.

[0068] 2. Application of time-of-use electricity price strategy: By utilizing the time-of-use electricity price policy, some high-energy-consuming equipment with flexible time adjustment (such as large cleaning equipment and maintenance work of some HVAC equipment) can be arranged to operate during the low electricity price period, thereby reducing electricity costs. At the same time, combined with energy storage technologies such as solar power supply, hydrogen power generation, and wind power generation, energy is stored when electricity prices are low and released during peak hours to further optimize energy consumption costs. The above situation applies to both single stations and passenger station groups. For passenger station groups, the electricity of each station in the group is stored uniformly. When the energy consumption allocation of a station is insufficient or energy is temporarily needed, the required electricity can be transferred from the storage system to support operations.

[0069] 3. For a single station, real-time energy consumption scheduling strategies can be implemented at the equipment level, including lighting systems, HVAC systems, and other equipment.

[0070] ① Intelligent lighting control strategy:

[0071] Intelligent lighting control: The illumination values ​​of different areas in the station are obtained through sensors, and intelligent control is performed in combination with intelligent control strategies. For example, during the day, natural light is fully utilized to automatically turn off or dim the lighting fixtures near the windows; at night, the lighting brightness is automatically adjusted according to the passenger flow, and the brightness is appropriately reduced when the passenger flow in the waiting hall is sparse.

[0072] Time-sharing scheduling: Combine the train schedule and passenger flow patterns to develop a time-sharing opening and closing plan for lighting equipment. For example, from 30 minutes before the train arrives to 15 minutes after the train leaves, ensure that lighting equipment in waiting halls, platforms and other areas is running at full power; when the interval between two trains is long and the passenger flow is small, turn off some circuit lighting equipment or appropriately reduce the platform lighting power.

[0073] Lamp upgrade: gradually replace traditional lamps with energy-saving LED lamps to improve the energy efficiency of the lighting system. LED lamps have the advantages of high luminous efficiency, long life and low energy consumption, which can significantly reduce lighting energy consumption.

[0074] ②HVAC system scheduling strategy:

[0075] Frequency conversion control: The HVAC system is equipped with frequency conversion equipment to adjust the cooling or heating power of the air conditioner in real time according to the indoor and outdoor temperature, humidity and passenger flow. For example, during periods of high passenger flow, the cooling or heating power of the air conditioner can be appropriately increased to ensure the comfort of passengers; during periods of low passenger flow, the power can be reduced to reduce energy consumption.

[0076] Utilization of natural ventilation: In transitional seasons (spring and autumn) and when the climate is suitable, make full use of natural ventilation. By rationally designing the ventilation system of the passenger station, such as setting up openable ventilation windows and ventilation shafts, when weather conditions permit, turn off the air conditioning system and use natural ventilation to ventilate the passenger station, thereby reducing air conditioning energy consumption.

[0077] System optimization and maintenance: Regularly optimize and maintain the HVAC system, including cleaning the air conditioning filter, checking the refrigerant pressure and content, calibrating the temperature sensor, etc., to ensure that the system is in an efficient operating state.

[0078] ③Other equipment scheduling strategies

[0079] Elevators and escalators: Install energy-saving operation modules for elevators and escalators. For example, escalators use induction devices, which automatically slow down or stop when no one is riding; elevators use intelligent scheduling algorithms to reasonably arrange the elevator's running path based on passenger flow and floor calls, reduce the number of empty trips, and reduce energy consumption.

[0080] Other electromechanical equipment: For electromechanical equipment such as ticket machines and security inspection equipment, set a scheduled sleep mode or low power consumption mode. For example, when a ticket machine has not been operated for a period of time, it will automatically enter sleep mode to reduce the standby energy consumption of the equipment.

[0081] Using the above scheme, three goals are pre-set during the calculation process of this scheme, namely safety, comfort and energy saving. The three goals are all related to the operation of the station. This scheme can output the optimal solution that meets the preset goals and can automatically output the equipment operation parameters. On the one hand, it can improve the adjustment speed of the equipment operation parameters and improve the processing efficiency; on the other hand, it can make the operation of the final equipment efficiently match the predetermined goals and ensure the compatibility with the predetermined goals.

[0082] like Figure 2 As shown, in some embodiments of the present invention, the steps of this scheme include:

[0083] Step S101, obtaining historical data of a station, wherein the historical data of the station includes historical weather data, station equipment data and historical passenger data;

[0084] Step S102, extracting basic data corresponding to three preset goals from the historical data, and constructing input vectors corresponding to the three goals, where the three preset goals are safety, comfort and energy saving;

[0085] Step S103, classifying the basic data corresponding to the preset target (including: power consumption of various types of equipment corresponding to different time periods, values ​​of various environmental comfort parameters in different time periods, and various environmental comfort parameters of stations in different regions);

[0086] Step S104, combining the climate conditions of different regions in my country obtained from the survey with the relevant specifications for the operation parameter settings of existing railway passenger stations, providing the power consumption level division and environmental comfort parameter setting range suitable for stations in different regions;

[0087] Step S105, determining whether the parameter setting range meets the operation requirements of the station, if so, selecting the set power consumption level and environmental parameter range as the station operation parameter setting standard, if not, adjusting the operation parameter division level and range until it meets the station operation requirements;

[0088] Step S106, inputting the input vectors corresponding to the safety and energy-saving targets into the preset calculation models (in this patent, different types of optimization solvers such as GPLEX and Gurobi) to obtain the equipment operation parameter sets corresponding to the safety and energy-saving targets;

[0089] In some embodiments of the present invention, the calculation model corresponding to the "safety" and "energy saving" goals adopts the NSGA algorithm with improved congestion.

[0090] In step S107, considering that the "comfort" target involves strong parameter coupling and complex trade-off relationships, a multi-objective particle swarm algorithm is used for calculation.

[0091] The objective functions of the three models are highest safety, most comfortable production operation and lowest power consumption.

[0092] The safety is determined by the number of abnormal behaviors in the station, the passenger congestion, the passenger flow density in the station and the percentage of abnormal events in the station.

[0093] The production and operation comfort is jointly determined by the staff's physical comfort, the platform's working environment comfort, and the information level of the station's basic equipment. Among them, the factors affecting the staff's physical comfort include but are not limited to temperature, humidity, brightness, CO2 concentration, PM2.5 concentration, noise, NOX concentration, harmful gas concentration, etc. In addition to the above factors, the platform's working environment comfort also includes the wind speed felt by the staff near the platform at different train speeds and the reverberation time in the station. The information level of the station's basic equipment includes but is not limited to the degree of perfection and intelligence of relevant passenger service facilities, the rationality and accuracy of passenger operation plans and train operation control plans, the stability and reliability of passenger operations according to the plan, and the quality of passenger operation training.

[0094] The electricity consumption is determined by the power consumption of basic equipment and facilities, the power generation of photovoltaic arrays, the power generation of wind power, the power generation of hydropower, and the power storage of batteries.

[0095] Step S108, select the NSGAII optimization algorithm with improved congestion to obtain the optimal solution of the operating parameters under the matching mode of the main goal and different sub-goals.

[0096] The main goal and different sub-goals are specifically matched in the following manner: select "safety" as the main goal and "energy saving" or "comfort" as the auxiliary goal, and obtain the power consumption of different types of equipment in the station under the two conditions of "safety + comfort" and "safety + energy saving", the environmental comfort setting, and the corresponding satisfaction function set; select "energy saving" as the main goal and "safety" or "comfort" as the auxiliary goal, and select "comfort" as the main goal and "safety" and "energy saving" as the auxiliary goals, and obtain various power consumption and operating parameter settings and satisfaction function sets in turn;

[0097] In the specific implementation process, the binary tournament optimization process is introduced in the process of using the NSGAII optimization algorithm with improved congestion, and the congestion method is optimized by calculating the "concentration" of the objective function.

[0098] Step S109, obtaining corresponding satisfaction function values ​​based on the proportion of indicators under multiple target combinations, and calculating single target satisfaction values ​​based on the satisfaction parameter set corresponding to each target;

[0099] Step S110, determining the tendency target of the corresponding operating parameter set in combination with the calculated satisfaction function value, and determining the type of the satisfaction function to determine whether it is a high tendency type or a low tendency type;

[0100] Step S111, determining whether the tendency target is consistent with the station operation demand;

[0101] Step S112, if they are consistent, the station operation parameters corresponding to the multi-objective optimal solution are set;

[0102] If they are inconsistent, the parameters of the improved multi-objective optimization algorithm in S108 are continuously adjusted until the two are consistent.

[0103] In some embodiments of the present invention, in the step of inputting the vectors corresponding to the three targets into three preset calculation models corresponding to the three targets respectively, the three calculation models all adopt the NSGA-II algorithm with improved congestion.

[0104] In the specific implementation process, the NSGA-II algorithm with improved congestion adopts the basic NSGA algorithm or NSGA-II algorithm. The full name of the NSGA algorithm is Non-dominated Sorting Genetic Algorithms, which is a genetic algorithm based on the Pareto optimal concept. The NSGA-II algorithm further improves the performance and efficiency of the algorithm by introducing improvement measures such as fast non-dominated sorting method, congestion and congestion comparison operator, and elite strategy.

[0105] In some embodiments of the present invention, in the step of calculating the satisfaction parameter set of the corresponding device operating parameter set based on three device operating parameter sets, each device operating parameter in the operating parameter set is calculated using a relatively large satisfaction function or a relatively small satisfaction function to obtain the satisfaction parameter corresponding to each device operating parameter, and then obtain the satisfaction parameter set of the corresponding operating parameter set.

[0106] In some embodiments of the present invention, in the step of calculating each device operating parameter in the operating parameter set using a large-scale satisfaction function or a small-scale satisfaction function, each device operating parameter in the operating parameter set is calculated with a preset reference value of the device operating parameter to determine whether to use a large-scale satisfaction function or a small-scale satisfaction function.

[0107] In some embodiments of the present invention, in the step of calculating each device operating parameter in the operating parameter set with a preset reference value of the device operating parameter to determine whether to use a large satisfaction function or a small satisfaction function,

[0108] The parameter ratio is calculated according to the following formula:

[0109] (device operating parameter - preset reference value of the device operating parameter) / preset reference threshold range of the device operating parameter;

[0110] Method 2: The mean algorithm is used to calculate the satisfaction with each equipment operating parameter, that is: equipment operating parameter setting satisfaction = (valid sample mean - 1) / 9 * 100. The valid sample adopts a 10-level scale, and the equipment parameter setting satisfaction is scored from 0 to 10 (questionnaire survey, 20 people are selected on-site for scoring). The satisfaction is expressed by calculating the average value of the valid samples and then converting it into a percentage.

[0111] Based on the satisfaction values ​​obtained above, the weighted average of the satisfaction parameters in the satisfaction parameter set of each target is directly calculated to support subsequent calculation and analysis.

[0112] Method 3: Calculate satisfaction according to the set of operating parameters under different goals. It can also be calculated based on the ASCI (American Customer Satisfaction Index) model, improve the original model, and increase the digitalization level of the station. The model contains 7 factors: digitalization level of the station, quality expectations, quality perception, perceived value, passenger satisfaction, passenger complaints, and passenger loyalty. Among them, the first four elements are premise variables, and the last three factors are result variables. The premise variables comprehensively determine and affect the result variables.

[0113] By combing the measurable variables of the seven factors, each variable was measured using the Likert 10-level scale. The scoring results of each measurable indicator of the seven factors were obtained through questionnaire surveys. After processing missing values, data confidence tests, and validity tests, the structural equations between the factors were obtained. The CR statistical test of the model was carried out using the parameter estimation method to evaluate the statistical significance of the parameters. Afterwards, the model was fitted and estimated under different fitting indexes by setting the chi-square value (degrees of freedom), and finally the optimal model parameters and the estimated values ​​of each path coefficient were obtained.

[0114] If the parameter proportion is greater than the preset percentage threshold, a relatively large satisfaction function is used; if the parameter proportion is not greater than the preset percentage threshold, a relatively small satisfaction function is used.

[0115] In some embodiments of the present invention, in the step of calculating each device operating parameter in the operating parameter set using a partial large satisfaction function or a partial small satisfaction function to obtain a satisfaction parameter corresponding to each device operating parameter, if a partial large satisfaction function is used, the calculation is based on the following formula:

[0116]

[0117] If a small satisfaction function is used, it is calculated based on the following formula:

[0118]

[0119] Wherein, μ represents the satisfaction parameter; g represents the equipment operation parameter corresponding to the satisfaction parameter μ; g max Indicates the maximum value of the device operating parameter in the operating parameter set where the device operating parameter g is located; g min Indicates the minimum value of the device operating parameter in the operating parameter set to which the device operating parameter g belongs.

[0120] Adopting the above scheme, this scheme is set with a relatively large satisfaction function or a relatively small satisfaction function, which can determine the applied function based on the difference between the parameters and the standard parameters, and can adapt to a more appropriate calculation scheme to obtain better satisfaction parameters to indicate the excellence of the current parameters.

[0121] In some embodiments of the present invention, in the step of calculating the single target satisfaction value based on the satisfaction parameter set corresponding to each target, the weighted average of the satisfaction parameters in the satisfaction parameter set for each target is calculated to obtain the single target satisfaction value corresponding to the target.

[0122] In the specific implementation process, in the step of calculating the single-objective satisfaction value based on the satisfaction parameter set corresponding to each objective, the satisfaction parameter in the satisfaction parameter set of each objective is updated based on the following formula;

[0123]

[0124] Among them, μ new represents the updated satisfaction parameter, μ old represents the satisfaction parameter before updating, μ 和 The sum of the satisfaction parameters in the satisfaction parameter set to which the satisfaction parameter belongs;

[0125] In the specific implementation process, after updating the satisfaction parameters in the satisfaction parameter set of each target, the weighted average of the satisfaction parameters in the satisfaction parameter set of each target is calculated to obtain the single target satisfaction value corresponding to the target.

[0126] like Figure 3 As shown, the present invention sets three objective functions of "safety", "comfort" and "energy saving" and their respective constraints and mathematical models. In the process of solving the optimal solution of a single objective, the complex relationship and continuity of the constraints are taken into consideration, so different algorithms are used to obtain the optimal solution of a single objective. Through different objective combinations, the corresponding operating parameter configuration is obtained, and the appropriate satisfaction function is selected with the help of fuzzy evaluation. Combined with the single objective weight ratio, the final parameter settings of each indicator are derived.

[0127] Step S201, setting three objective functions, constraints and mathematical models of "safety", "comfort" and "energy saving";

[0128] Step S202, using an optimization solver to optimize the power consumption of the station power consumption basic data under the two goals of "safety" and "energy saving";

[0129] Step S203, using a multi-objective particle swarm algorithm to optimize parameters of the station environment comfort basic data under the "comfort" target;

[0130] Step S204, obtain the set of station operation parameter solutions under the three goals of "safety", "comfort" and "energy saving", which are Gk = {G1k, G2k, G3k)

[0131] Step S205, using the "orthodontic equilibrium optimization" algorithm (improved crowding NSGAII algorithm) to perform multi-objective equilibrium optimization;

[0132] Step S206, taking "safety" (G1k) as the goal, "energy saving" and "comfort" as constraints, substitute the set of operating parameter solutions under the single goal, and obtain the optimal solution sets QA1 and QA2 for power consumption of different types of equipment in the station under the two cases of "safety + energy saving" and "safety + comfort", and the optimal solution sets SA1 and SA2 for environmental comfort parameters

[0133] Step S207, calculate the satisfaction function set UA1={uA1-1,uA1-2,...uA1-5}, UA2={uA1-1,uA1-2,...uA1-5} corresponding to each power consumption index in the power consumption optimal solution set QA1 and QA2; calculate the satisfaction function set SA1={sA1-1,...sA1-3}, SA2={sA2-1,...sA2-3} corresponding to each comfort index in the environmental comfort optimal solution set SA1 and SA2.

[0134] Step S208: standardize the satisfaction sets UA1, UA2, SA1, and SA2 in turn to obtain a comprehensive evaluation matrix and a membership matrix

[0135] Step S209, taking "comfort" (G2k) as the goal, "safety" and "energy saving" as constraints, respectively, substitute the operating parameter solution set under the single goal, and obtain the optimal solution sets QS1 and QS2 of power consumption of different types of equipment in the station under the two cases of "comfort + energy saving" and "comfort + safety", and the optimal solution sets FS1 and FS2 of safe operation parameters

[0136] Step S210, calculate the satisfaction function set US1 = {uS1-1, uS1-2, ... uS1-5}, US2 = {uS1-1, uS1-2, ... uS1-5} corresponding to each power consumption index in the optimal solution set QS1 and QS2 of power consumption; the satisfaction function set FS1 = {fS1-1, ... fS1-3}, FS2 = {fS1-1, ... fS1-3} corresponding to each power consumption index in the satisfaction function set corresponding to each safety index in the optimal solution set FS1 and FS2 of safety operation parameters.

[0137] Step S211, standardize the satisfaction sets US1, US2, FS1, and FS2 in turn to obtain a comprehensive evaluation matrix and a membership matrix

[0138] Step S212, taking "energy saving" (G3k) as the goal, "safety" and "comfort" as the constraints, substitute the operating parameter solution set under the single goal, and obtain the optimal solution sets FJ1 and FJ2 of station safety operation parameters and the optimal solution sets SJ1 and SJ2 of environmental comfort parameters under the two cases of "energy saving + comfort" and "energy saving + safety".

[0139] Step S213, calculate the optimal solution set FJ1 of the safe operation parameters and the satisfaction function set of each specific indicator in FJ2: FJ1 = {fJ1-1, ... fJ1-3}, FJ2 = {fJ1-1, ... fJ1-3}; the optimal solution set SJ1 of the environmental comfort parameters and the satisfaction function set SJ1 = {sJ1-1, ... SJ1-3}, SJ2 = {sJ1-1, ... sJ1-3} corresponding to each safety indicator in SJ2

[0140] Step S214, standardizing satisfaction sets FJ1, FJ2, SJ1, SJ2 in sequence to obtain a comprehensive evaluation matrix and a membership matrix;

[0141] Step S215, use the fuzzy evaluation method to determine the type of satisfaction function (fuzzy set is large or small). According to the principle of membership, multiple rounds of comparison (such as: judging whether the energy saving effect is better under safety conditions, or the energy consumption is lower under comfortable conditions) are performed to select the satisfaction function set with the largest membership (such as UA1);

[0142] Step S216, using expert evaluation method to set the proportion of different indicators under each goal, such as: UA1 = W1 × uA1-1 + W2 × uA1-2 + ... + W5 × uA1-5};

[0143] Step S217, calculating the final satisfaction function value according to the weight of each indicator;

[0144] Step S218, combining the single objective weights to obtain the final setting results of each indicator parameter.

[0145] like Figure 4 As shown, the present invention adopts the "orthodontic equilibrium optimization" algorithm (improved crowding meter NSGAII algorithm) to perform multi-objective equilibrium optimization. By combing all solution individuals and performing non-dominated sorting between individuals, polling and screening the top ranked individuals, the preliminary screening of individuals is completed. Through crowding calculation, the crowding of the objective function is compared, and the objective function and individual set with relatively small crowding distance and high concentration are selected, and the optimal solution is obtained by judging the occupancy of the remaining solution space.

[0146] like Figure 6 As shown, step S301, initialize the population, set the number of objective functions W, population size N, number of iterations M, mutation probability mp, and crossover probability cp;

[0147] Step S302, through selection, crossover, and mutation, the population is iterated for M rounds, and the population is continuously updated to obtain the optimal solution sets of safety parameters and power consumption under a single objective, namely G = {G1i, G2j, G3k}, G1i = {G11, G12, .., G15}, G2j = {G21, G22, G23}, G3k = {G31.G32, G33};

[0148] Step S303, sort out the non-dominated order of all solution individual sets G1i, G2j, G3k, poll and compare and select the individuals with the highest order;

[0149] Step S304, determine whether the number of individuals of the remaining G1i, G2j, G3k is 0;

[0150] Step S305, forming a new solution individual set S1{G1i}, S2{G2j}, S3{G3k};

[0151] Step S306, for different objectives, after each iteration, the solution individual sets S1_1{G1i}~S1_M{G1i}, S2_1{G2j}~S2_M{G2j}, S3_1{G1i}~S3_M{G1i} and the objective functions C1_1~C1_M, C2_1~C2_M, C3_1~C3_M corresponding to each set are obtained;

[0152] Step S307, sorting the objective function values ​​from small to large;

[0153] Step S308, setting the crowding distance (crowding distance = the sum of the individual distances closest to the targets C1, C2, and C3) of the minimum value of the objective function (boundary individual) to infinity, and calculating the crowding degree of each remaining individual;

[0154] Step S309, selecting the objective function C and the corresponding individual set S with a relatively small crowding distance (the longest distance) and a high concentration;

[0155] Step S313, whether both exceed the threshold;

[0156] Step S314, determine whether all solution spaces have been calculated (the number of remaining uncalculated solutions is 0);

[0157] Step S315, forming the optimal target values ​​and optimal solutions for "safety", "comfort" and "energy saving" respectively;

[0158] Step S316, calculate the "concentration" of a certain objective function: θi is the influence of any objective on the variance of the total sample (the set of all optimal solutions); ARi is the "absorption" of a single set of eigenvectors (the optimal eigenvector corresponding to any objective), which indicates the explanatory power of the eigenvector on the total variance of the feature sample; Wij is the weight of any objective in the jth eigenvector, σ(Ej2) is the variance of the jth eigenvector, and the concentration is between 0 and 1;

[0159] Step S317, after integrating the concentration and congestion of the objective function, compare them with the set threshold value in a round-robin manner;

[0160] Step S318, determining whether both exceed the threshold;

[0161] Step S319, eliminating inappropriate solution data to avoid invalid iteration times;

[0162] Figure 5 For the tendency target comparison flow chart, such as Figure 5 and 7 As shown, step S401, historical data and real-time operation data of the station are obtained, and the station data includes environmental comfort information (temperature, humidity, brightness, PM2.5 concentration, platform wind speed, etc.), equipment operation data (equipment operation and maintenance records, equipment real-time operation data, equipment failure information, etc.), passenger flow data in different areas (historical passenger flow, passenger flow density in typical time periods in each area, etc.), safety data (number of platform white line crossings, number of escalator passenger falls, number of escalator abnormal operation, etc.);

[0163] Step S402, extracting basic data corresponding to three preset goals from the station historical data and real-time operation data, and constructing input vectors corresponding to the three goals, where the three preset goals are safety, comfort, and energy saving;

[0164] Step S403, inputting the input vectors corresponding to the three targets into preset calculation models corresponding to the three targets respectively, and outputting the device operation parameter sets corresponding to the three targets respectively;

[0165] Step S404, obtaining satisfaction function values ​​under different objective combinations and satisfaction function values ​​of a single objective based on an improved multi-objective equilibrium algorithm;

[0166] Step S405, comparing two satisfaction function values ​​(e.g., comparing the satisfaction function value UA1 obtained for “energy saving” under the “safety + comfort” condition with the satisfaction function value UJN obtained for the single goal of “energy saving”);

[0167] Step S406: Conduct actual measurements based on the operating parameters corresponding to different satisfaction functions, and use the weighted average of multiple rounds of measured data as a "reference value", and identify the satisfaction function (which may be high or low) that is more in line with the subjective needs of the staff and has a smaller difference from the reference value as a "consistent" function;

[0168] Step S407, applying the optimal solution corresponding to the "consistent type" function as the operating parameter for setting.

[0169] like Figure 2 As shown, in some embodiments of the present invention, the tendency target of the setting requirement is a single tendency target or multiple tendency targets, and the multiple tendency targets include single-target satisfaction thresholds corresponding to multiple targets, and in the step of determining the tendency target of the current equipment operation parameter set based on multiple single-target satisfaction values, determining whether the current tendency target is the tendency target of the setting requirement;

[0170] If the tendency target of the set requirement is a single tendency target, compare the satisfaction values ​​of each single target, take the target corresponding to the largest single target satisfaction value as the tendency target of the current equipment operation parameter set, and determine whether the current tendency target is the tendency target of the set requirement;

[0171] In the specific implementation process, if the tendency target of the setting requirement is a single tendency target, in the step of determining whether the current tendency target is the tendency target of the setting requirement, if the tendency target of the setting requirement is safe, then determine whether the current tendency target is safe.

[0172] If the tendency target of the set requirement is a multi-tendency target, each single-target satisfaction value is compared with the corresponding single-target satisfaction threshold to determine whether the current tendency target is the tendency target of the set requirement.

[0173] In the specific implementation process, if the tendency target of the set requirement is a multi-tendency target, then each single target satisfaction value is compared with the corresponding single target satisfaction threshold to determine whether the current tendency target is the tendency target of the set requirement. If each single target satisfaction value is greater than the corresponding single target satisfaction threshold, then the current tendency target is determined to be the tendency target of the set requirement; if any one of the single target satisfaction values ​​is not greater than the corresponding single target satisfaction threshold, then the current tendency target is determined not to be the tendency target of the set requirement.

[0174] The above scheme is adopted. This scheme provides a single-tendency target or a multi-tendency target method. In the specific implementation process, the staff can set a single target to be met in some scenarios. If it is determined that safety in time period A is the most important, a single-tendency target method can be adopted. The staff can also set multiple targets in some scenarios, that is, multiple targets all need to meet certain standards, so that work parameters that are more suitable for specific scenarios can be obtained.

[0175] If the current trend target is the trend target of the setting requirement, the current equipment operation parameter set will be applied; if the current trend target is not the trend target of the setting requirement, the basic data corresponding to the three preset targets will be re-extracted from the historical data, and the equipment operation parameter set and the tendency target of the current equipment operation parameter set will be recalculated until the calculated tendency target is the tendency target of the setting requirement.

[0176] In a specific implementation process, in the process of applying the current device operation parameter set, the device operation parameters of each device in the device operation parameter set are applied to the specific device.

[0177] In some embodiments of the present invention, after the step of inputting the vectors corresponding to the three targets into three preset calculation models corresponding to the three targets respectively, and the three models respectively output the device operation parameter sets corresponding to the three targets, the step further includes:

[0178] The main target among the three targets is obtained, and the equipment operating parameter sets of targets other than the main target are used as constraint conditions for calculating the main target operating parameter set, and the equipment operating parameter set corresponding to the main target is recalculated.

[0179] In some embodiments of the present invention, in the step of acquiring a main target among the three targets, the main target is at least one of the three targets.

[0180] In the specific implementation process, in the step of obtaining the main target among the three targets, taking the equipment operation parameter set of the targets other than the main target as the constraint condition for calculating the main target operation parameter set, and recalculating the equipment operation parameter set corresponding to the main target, the main target can be safety, comfort or energy saving, or a combination of safety, comfort and, specifically, safety + comfort, safety + energy saving or safety + comfort + energy saving;

[0181] When safety is set as the main goal, the set of equipment operating parameters for comfort and energy saving is used as the constraint conditions of the calculation model corresponding to safety in the calculation process; when comfort is set as the main goal, the set of equipment operating parameters for safety and energy saving is used as the constraint conditions of the calculation model corresponding to safety in the calculation process; when energy saving is set as the main goal, the set of equipment operating parameters for safety and comfort is used as the constraint conditions of the calculation model corresponding to safety in the calculation process; when safety + comfort is set as the main goal, the set of equipment operating parameters for energy saving is used as the constraint conditions of the calculation model corresponding to safety + comfort in the calculation process.

[0182] By adopting the above scheme, this scheme can integrate the most needed goals into the calculation during the process of calculating the device operation parameter set, so that the calculated operation parameter set can give priority to meeting the main goal, so as to improve the calculation efficiency of subsequent cyclic calculation processing and ensure the efficient satisfaction of the main goal.

[0183] An embodiment of the present invention also provides a railway passenger station group energy consumption scheduling system based on multi-objective coupling, the system includes a computer device, the computer device includes a processor and a memory, the memory stores computer instructions, the processor is used to execute the computer instructions stored in the memory, when the computer instructions are executed by the processor, the system implements the steps implemented by the method described above.

[0184] The embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps implemented by the aforementioned railway passenger station group energy consumption scheduling method based on multi-objective coupling are implemented. The computer-readable storage medium can be a tangible storage medium, such as a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a floppy disk, a hard disk, a removable storage disk, a CD-ROM, or any other form of storage medium known in the technical field.

[0185] It should be understood by those skilled in the art that the exemplary components, systems and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software or a combination of the two. Whether it is performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present invention are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier.

[0186] It should be clear that the present invention is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present invention.

[0187] In the present invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with or replace features of other embodiments.

[0188] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the embodiments of the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A railway passenger station group energy consumption scheduling method based on multi-objective coupling, characterized in that: The steps of the method include: Acquire historical data of the station, wherein the historical data of the station includes historical weather data, station equipment data and historical passenger data; Extracting basic data corresponding to three preset goals from the historical data, and constructing input vectors corresponding to the three goals, where the three preset goals are safety, comfort and energy saving; The vectors corresponding to the three targets are respectively input into three preset calculation models corresponding to the three targets, and the three models respectively output sets of equipment operation parameters corresponding to the three targets; Calculate the satisfaction parameter set corresponding to the equipment operation parameter set based on the three equipment operation parameter sets, and calculate the single target satisfaction value based on the satisfaction parameter set corresponding to each target; Based on multiple single-objective satisfaction values, determine the tendency target of the current equipment operation parameter set, and determine whether the current tendency target is the tendency target of the set requirement. If the current tendency target is the tendency target of the set requirement, the current equipment operation parameter set is applied; if the current tendency target is not the tendency target of the set requirement, re-extract the basic data corresponding to the three preset targets from the historical data, recalculate the equipment operation parameter set and the tendency target of the current equipment operation parameter set, until the calculated tendency target is the tendency target of the set requirement.

2. The energy consumption scheduling method for railway passenger station groups based on multi-objective coupling according to claim 1 is characterized in that: In the step of inputting the vectors corresponding to the three targets into the three preset calculation models corresponding to the three targets respectively, the three calculation models all adopt the NSGA-II algorithm with improved congestion.

3. The energy consumption scheduling method for railway passenger station groups based on multi-objective coupling according to claim 1 is characterized in that: In the step of calculating the satisfaction parameter set of the corresponding device operating parameter set based on the three device operating parameter sets, each device operating parameter in the operating parameter set is calculated using a relatively large satisfaction function or a relatively small satisfaction function to obtain the satisfaction parameter corresponding to each device operating parameter, and then obtain the satisfaction parameter set of the corresponding operating parameter set.

4. The energy consumption scheduling method for railway passenger station groups based on multi-objective coupling according to claim 3 is characterized in that: In the step of calculating each device operating parameter in the operating parameter set using a large-scale satisfaction function or a small-scale satisfaction function, each device operating parameter in the operating parameter set is calculated with a preset reference value of the device operating parameter to determine whether to use a large-scale satisfaction function or a small-scale satisfaction function.

5. The method for energy consumption scheduling of railway passenger stations based on multi-objective coupling according to claim 4 is characterized in that: In the step of calculating each device operating parameter in the operating parameter set using a partial large satisfaction function or a partial small satisfaction function to obtain the satisfaction parameter corresponding to each device operating parameter, if a partial large satisfaction function is used, the calculation is based on the following formula: If a small satisfaction function is used, it is calculated based on the following formula: Wherein, μ represents the satisfaction parameter; g represents the equipment operation parameter corresponding to the satisfaction parameter μ; g max Indicates the maximum value of the device operating parameter in the operating parameter set where the device operating parameter g is located; g min Indicates the minimum value of the device operating parameter in the operating parameter set to which the device operating parameter g belongs.

6. The energy consumption scheduling method for railway passenger station groups based on multi-objective coupling according to claim 1 is characterized in that: In the step of calculating the single-objective satisfaction value based on the satisfaction parameter set corresponding to each objective, the weighted average of the satisfaction parameters in the satisfaction parameter set of each objective is calculated to obtain the single-objective satisfaction value corresponding to the objective.

7. The method for energy consumption scheduling of a railway passenger station group based on multi-objective coupling according to any one of claims 1 to 6, characterized in that: The tendency target of the setting requirement is a single tendency target or multiple tendency targets, wherein the multiple tendency targets include single-target satisfaction thresholds corresponding to multiple targets, and in the step of determining the tendency target of the current equipment operation parameter set based on the multiple single-target satisfaction values, determining whether the current tendency target is the tendency target of the setting requirement; If the tendency target of the set requirement is a single tendency target, compare the satisfaction values ​​of each single target, take the target corresponding to the largest single target satisfaction value as the tendency target of the current equipment operation parameter set, and determine whether the current tendency target is the tendency target of the set requirement; If the tendency target of the set requirement is a multi-tendency target, each single-target satisfaction value is compared with the corresponding single-target satisfaction threshold to determine whether the current tendency target is the tendency target of the set requirement.

8. The energy consumption scheduling method for railway passenger station groups based on multi-objective coupling according to claim 1 is characterized in that: After the step of inputting the vectors corresponding to the three targets into three preset calculation models corresponding to the three targets respectively, and the three models respectively output the device operation parameter sets corresponding to the three targets, the method further includes the following steps: The main target among the three targets is obtained, and the equipment operating parameter sets of targets other than the main target are used as constraint conditions for calculating the main target operating parameter set, and the equipment operating parameter set corresponding to the main target is recalculated.

9. The railway passenger station group energy consumption scheduling method based on multi-objective coupling according to claim 8 is characterized in that: In the step of acquiring a main target among the three targets, the main target is at least one of the three targets.

10. A railway passenger station group energy consumption dispatching system based on multi-objective coupling, characterized in that: The system includes a computer device, which includes a processor and a memory. The memory stores computer instructions. The processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the system implements the steps implemented by the method as described in any one of claims 1 to 9.

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