A rail transit traction energy-saving method, system, electronic device and readable storage medium based on dynamic power flow calculation

Abstract

The present invention discloses a traction energy-saving method, system, electronic device and readable storage medium based on dynamic power flow calculation for rail transit. The traction energy-saving method dynamically analyzes multi-dimensional coupling conditions such as power supply, vehicles, signals and tracks through modules such as ATO single-vehicle energy saving + ATS operation diagram planning multi-vehicle collaborative energy saving module, dynamic diagram adjustment module according to passenger flow requirements and regenerative braking energy feedback module, and performs dynamic power flow calculation from the perspective of energy flow through the traction power supply system power flow dynamic calculation and analysis module, dynamically coordinates technical means such as ATO energy saving, ATS energy saving and regenerative braking energy recovery device energy saving, conducts unified management and collaborative linkage, performs energy consumption monitoring and statistical analysis for different traction energy-saving measures, and tracks and evaluates long-term energy-saving operation effects; and continuously optimizes energy-saving control strategies based on analysis results through a prediction model between line energy consumption and operation data variables, thereby achieving truly better energy-saving effects.

Description

Technical Field

[0001] The present invention relates to the field of traction energy saving in rail transit systems, and in particular to a traction energy saving method, system, electronic equipment and readable storage medium based on dynamic power flow calculation in rail transit. Background Art

[0002] After several years of rapid development, rail transit has gradually transitioned from a period of rapid construction to a phase focused on high-quality operations. During this period, the primary focus was on ensuring operational efficiency and safety, with little consideration given to energy-efficient operation.

[0003] According to statistics released by the China Urban Construction Association, urban rail power consumption in 2023 will total 24.977 billion kWh, with electricity consumption continuing to grow and representing significant growth. Traction power consumption accounts for 50-60%, while station electromechanical power consumption accounts for 30-35%. Traction and electromechanical power consumption together account for over 80% of total energy consumption. Research on energy conservation in rail transit systems is currently limited to small-scale, single-disciplinary pilot programs. These initiatives are isolated and lack a holistic energy-saving design based on the energy flow of the power system. In some cases, these energy-saving measures can even lead to increased energy consumption. Therefore, further research is needed.

[0004] It will be understood that the above statements merely provide background technology related to the present invention and do not necessarily constitute prior art. Summary of the Invention

[0005] Based on the above-mentioned technical problems, the purpose of the present invention is to provide a traction energy-saving method, system, electronic device and readable storage medium based on dynamic flow calculation for rail transit. The traction energy-saving method is based on dynamic analysis of multi-dimensional coupling conditions such as power supply, vehicles, signals, and tracks, and performs dynamic flow calculation from the perspective of energy flow. It dynamically coordinates technical means such as ATO energy saving, ATS energy saving, and regenerative braking energy recovery device energy saving, conducts unified management, and coordinated linkage. It monitors energy consumption and conducts statistical analysis on different traction energy-saving measures, tracks and evaluates long-term energy-saving operation effects, and continuously optimizes energy-saving control strategies based on analysis results, thereby achieving truly better energy-saving effects.

[0006] In order to achieve the above object, the present invention is implemented through the following technical solutions:

[0007] A rail transit traction energy-saving method based on dynamic power flow calculation, comprising:

[0008] S1. Collecting rail transit real-time operation data through a rail transit real-time data module, wherein the rail transit real-time operation data includes real-time passenger flow data;

[0009] S2. Based on the real-time operation data of rail transit, an ATO energy-saving curve is obtained through the ATO single-vehicle energy-saving + ATS operation diagram planning multi-vehicle collaborative energy-saving module. The passenger flow data of the day is predicted through the passenger flow time series prediction module. The dynamic diagram adjustment module according to passenger flow requirements dynamically adjusts the operation diagram of the day based on the real-time passenger flow data collected in S1 and the passenger flow data of the day predicted by the passenger flow time series prediction module to obtain an updated operation diagram. A regenerative braking energy recovery device activation mechanism based on the updated operation diagram is obtained through the regenerative braking energy feedback module. A first operation strategy is formed based on the ATO energy-saving curve, the updated operation diagram, the regenerative braking energy recovery device activation mechanism, and the predicted passenger flow data of the day.

[0010] S3. The traction power supply system power flow dynamic calculation and analysis module dynamically records and analyzes the distribution of trains corresponding to the first operation strategy in the power supply zone, the relationship between the traction braking moment and energy consumption of the train ATO energy-saving curve, and the total actual energy consumption data based on the first operation strategy;

[0011] S4. Optimizing the first operation strategy using a prediction model between line energy consumption and operation data variables to obtain a second operation strategy;

[0012] S5. The traction power supply system power flow dynamic calculation and analysis module dynamically records and analyzes the distribution of trains corresponding to the second operation strategy in the power supply partition, the relationship between the traction braking moment and energy consumption of the train ATO energy-saving curve, and the total actual energy consumption data based on the second operation strategy;

[0013] S6. Compare the total actual energy consumption data corresponding to the first operation strategy with the total actual energy consumption data corresponding to the second operation strategy. When the total actual energy consumption data corresponding to the second operation strategy is lower than the total actual energy consumption data corresponding to the first operation strategy, operate according to the second operation strategy.

[0014] Optionally, in S6, when the total actual energy consumption data corresponding to the second operating strategy is higher than the total actual energy consumption data corresponding to the first operating strategy, the first operating strategy is regenerated through step S2, and steps S3 to S6 are repeated until the total actual energy consumption data corresponding to the second operating strategy is lower than the total actual energy consumption data corresponding to the first operating strategy.

[0015] Optionally, the traction power supply system power flow dynamic calculation and analysis module performs analysis based on the traction power supply system power flow dynamic calculation, which specifically includes:

[0016] By real-time monitoring of traction power supply mode, traction power supply voltage, train dynamic position and weight, a dynamic circuit relationship between traction power supply and train is established;

[0017] The substation is equivalent to a power source, the overhead line is equivalent to a resistor, the train in traction is equivalent to a resistor, and the train in braking is equivalent to a power source. The entire section is divided into several sections. Under the condition of a dynamically moving train, the relationship between voltage, resistance, current, power and energy consumption in different sections is defined;

[0018] Real-time calculation of the traction power supply system's power flow by monitoring traction voltage and current, train power consumption and energy feed, and train dynamic distribution;

[0019] The total actual energy consumption data is collected through the power supply system and trains, and the distribution of trains in power supply areas and the relationship between the traction and braking moments of the train's ATO energy-saving curve and energy consumption are dynamically recorded and analyzed.

[0020] Optionally, in S2, the working method of the ATO single-vehicle energy saving + ATS operation diagram planning multi-vehicle collaborative energy saving module includes:

[0021] Based on the train's speed deviation, each ATO energy-saving curve is divided into multiple checkpoints, each of which records different ATO control parameters. When a train passes a checkpoint, ATO comprehensively considers the remaining time at the station and the upstream and downstream operating curves to calculate the appropriate ATO target speed.

[0022] Train coasting resistance is identified based on big data analysis, specifically for different trains with different loads at different kilometer marker positions. Coasting resistance is known in advance through data accumulation and cleaning.

[0023] Use convex optimization models and algorithms to perform offline planning of energy-saving speed curves;

[0024] By adjusting and utilizing surplus time, the train interval running time is adjusted, the utilization rate of train regenerative braking energy is improved, the energy-saving operation diagram is optimized, and the train is automatically adjusted;

[0025] Through ATO single-vehicle energy saving + ATS operation diagram planning multi-vehicle collaborative technology, the dynamic energy consumption of single vehicle and multiple vehicles can be optimized.

[0026] Optionally, in S2, obtaining a regenerative braking energy recovery device startup mechanism based on an operation diagram through a regenerative braking energy feedback module includes:

[0027] Based on the updated timetable, train interval running time, station stop time, and turnaround time are optimized. Simulated annealing intelligent algorithm technology is used to solve large-scale transportation optimization problems with multiple constraints in complex networks. This maximizes the overlap between the arrival and departure times of trains operating in the same and / or adjacent power supply zones, allowing outgoing trains to fully utilize the brake regeneration energy of incoming trains.

[0028] Leveraging the predictability of the signal system's operating parameters, a prediction algorithm is employed to coordinate with the regenerative braking energy recovery device. This notifies the regenerative braking energy recovery device when a running train is about to brake, maintaining the train's braking while immediately activating the regenerative braking energy recovery device to recover electrical energy.

[0029] Based on the above, a regenerative braking energy recovery device activation mechanism based on the updated operation diagram is obtained.

[0030] Optionally, in S4, based on the real-time rail transit operation data and the predicted passenger flow data of the day, the prediction model between the line energy consumption and the operation data variables is optimized in combination with the transportation plan;

[0031] The updated operation diagram and ATO energy-saving curve obtained in S2 are used as initial candidate solutions of the optimized prediction model between the line energy consumption and the operation data variables, and the updated operation diagram and ATO energy-saving curve are optimized to form a second operation strategy.

[0032] Optionally, the prediction model between the line energy consumption and the operation data variables can be expressed as:

[0033] E=f DNN (t_start i,j,k ,t_stop i,j,k ,t_hold i,j,k ,ato_curve i,j,k ,

[0034] weight k ,slope i,j ,p_flow i ,weather,E_brakingenergyi) 1＜i,j＜n,1＜k＜m

[0035] ato_curve i,j,k ∈Set_ato (1)

[0036] Among them, f DNN represents the deep neural network model function, E represents the line energy consumption, i and j represent the index of the i-th and j-th stations respectively, there are n stations in total, k represents the k-th train, there are m trains in total, t_start i,j,k t_stop i,j,k t_hold i,j,k 、ato_curve i,j,k They represent the departure time, arrival time, stop time at the jth station and the ATO energy-saving curve between stations of the kth train from the ith station to the jth station, Set_ato represents the ATO curve set, and weight krepresents the weight of the kth train, slope i,j represents the slope between the i-th station and the j-th station, p_flow i represents the passenger flow of the i-th station, weather represents whether it is rainy or snowy, E_brakingenergy i It represents the energy recovered by the linkage between the i-th station signal and the regenerative braking energy recovery device.

[0037] Optionally, based on the real-time rail transit operation data and the predicted passenger flow for the day, the prediction model between the optimized line energy consumption and the operation data variables is expressed as:

[0038]

[0039] in, is the optimized line energy consumption, t_start i,j,k ′、t_stop i,j,k ′、t_hold i,j,k ′ respectively represent the real-time departure time, arrival time, and stop time of the kth train from the ith station to the jth station, ato_curve i,j,k ′ is the inter-station ATO energy-saving curve obtained in S2.

[0040] Optionally, also include:

[0041] The health and abnormal energy consumption of train equipment are monitored by the abnormal energy consumption analysis module, and an alarm signal is issued when the abnormal energy consumption analysis module detects abnormal energy consumption.

[0042] Optionally, a rail transit traction energy-saving system based on dynamic power flow calculation is used to implement the aforementioned rail transit traction energy-saving method based on dynamic power flow calculation. The traction energy-saving system includes a rail transit real-time data module and a business knowledge module.

[0043] The rail transit real-time data module is used to collect rail transit real-time operation data, and the rail transit real-time operation data includes real-time passenger flow data;

[0044] The business knowledge module includes:

[0045] ATO single-vehicle energy saving + ATS operation diagram planning multi-vehicle collaborative energy saving module, which is used to obtain the preliminary ATO energy saving curve;

[0046] Passenger flow time series prediction module, which is used to predict the passenger flow data of the day;

[0047] Energy-saving module for dynamic diagram adjustment according to passenger flow requirements, which is used to obtain the operation diagram;

[0048] A regenerative braking energy feedback module, which is used to obtain a regenerative braking energy recovery device startup mechanism based on an operation diagram;

[0049] The traction power supply system power flow dynamic calculation and analysis module dynamically records and analyzes the distribution of corresponding trains in the power supply partition based on the operation strategy, the relationship between the traction braking moment and energy consumption of the train's ATO energy-saving curve, and obtains the total actual energy consumption data corresponding to the operation strategy;

[0050] A prediction model between line energy consumption and operational data variables is used to optimize operation strategies.

[0051] Optionally, also include:

[0052] The big data module is used to store operation data.

[0053] Optionally, also include:

[0054] The equipment health and abnormal energy consumption analysis module is used to monitor the health and abnormal energy consumption of train equipment. When the abnormal energy consumption analysis module detects abnormal energy consumption, it can issue an alarm signal.

[0055] Optionally, the business knowledge module can establish the ATO single-vehicle energy saving + ATS operation diagram planning multi-vehicle collaborative energy saving module, passenger flow time series prediction module, dynamic diagram adjustment energy saving module according to passenger flow requirements, regenerative braking energy feedback module, traction power supply system flow dynamic calculation and analysis module and prediction model between line energy consumption and operation data variables through knowledge graph technology.

[0056] Optionally, an electronic device comprises: a memory and a processor, wherein a computer program is stored on the memory, and when the computer program is executed by the processor, the steps of the aforementioned rail transit traction energy-saving method based on dynamic power flow calculation are implemented.

[0057] Optionally, a readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the aforementioned rail transit traction energy-saving method based on dynamic power flow calculation are implemented.

[0058] Compared with the prior art, the present invention has the following advantages:

[0059] The present invention provides a traction energy-saving method, system, electronic device and readable storage medium for rail transit based on dynamic flow calculation. The traction energy-saving method is based on dynamic analysis of multi-dimensional coupling conditions such as power supply, vehicles, signals, and tracks, and performs dynamic flow calculation from the perspective of energy flow. It dynamically coordinates technical means such as ATO energy saving, ATS energy saving, and regenerative braking energy recovery device energy saving, conducts unified management, and coordinated linkage. It monitors energy consumption and conducts statistical analysis on different traction energy-saving measures, tracks and evaluates long-term energy-saving operation effects, and continuously optimizes energy-saving control strategies based on analysis results, thereby achieving truly better energy-saving effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for the description. Obviously, the drawings described below are one embodiment of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort:

[0061] Figure 1 This is a schematic diagram of a traction energy-saving method for rail transit based on dynamic power flow calculation according to the present invention;

[0062] Figure 2 An equivalent circuit diagram for traction power flow calculation according to the present invention;

[0063] Figure 3 A schematic diagram of the coordinated linkage between a regenerative braking energy recovery device and a signal system of the present invention;

[0064] Figure 4 This is a schematic diagram of a rail transit traction energy-saving system based on dynamic power flow calculation according to the present invention. DETAILED DESCRIPTION

[0065] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0066] It should be noted that, in this document, the terms "include," "comprise," "have," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements but also other elements not explicitly listed, or also includes elements inherent to such process, method, article, or terminal device. In the absence of further limitations, elements defined by the phrase "include..." or "comprising..." do not exclude the presence of additional elements in the process, method, article, or terminal device that includes the elements.

[0067] It should be noted that the drawings are all in very simplified form and use non-precise ratios, and are only used to conveniently and clearly assist in illustrating the embodiments of the present invention.

[0068] like Figure 1 FIG. 1 is a schematic diagram of a traction energy-saving method for rail transit based on dynamic power flow calculation according to the present invention, the method comprising:

[0069] S1. Collecting rail transit real-time operation data through a rail transit real-time data module, wherein the rail transit real-time operation data includes real-time passenger flow data;

[0070] S2. Based on the real-time operation data of rail transit, an ATO energy-saving curve is obtained through the ATO single-vehicle energy-saving + ATS operation diagram planning multi-vehicle collaborative energy-saving module. The passenger flow data of the day is predicted through the passenger flow time series prediction module. The dynamic diagram adjustment module according to passenger flow requirements dynamically adjusts the operation diagram of the day based on the real-time passenger flow data collected in S1 and the passenger flow data of the day predicted by the passenger flow time series prediction module to obtain an updated operation diagram. A regenerative braking energy recovery device activation mechanism based on the updated operation diagram is obtained through the regenerative braking energy feedback module. A first operation strategy is formed based on the ATO energy-saving curve, the updated operation diagram, the regenerative braking energy recovery device activation mechanism, and the predicted passenger flow data of the day.

[0071] S3. The traction power supply system power flow dynamic calculation and analysis module dynamically records and analyzes the distribution of trains corresponding to the first operation strategy in the power supply zone, the relationship between the traction braking moment and energy consumption of the train ATO energy-saving curve, and the total actual energy consumption data based on the first operation strategy;

[0072] S4. Optimizing the first operation strategy using a prediction model between line energy consumption and operation data variables to obtain a second operation strategy;

[0073] S5. The traction power supply system power flow dynamic calculation and analysis module dynamically records and analyzes the distribution of trains corresponding to the second operation strategy in the power supply partition, the relationship between the traction braking moment and energy consumption of the train ATO energy-saving curve, and the total actual energy consumption data based on the second operation strategy;

[0074] S6. Compare the total actual energy consumption data corresponding to the first operating strategy with the total actual energy consumption data corresponding to the second operating strategy. If the total actual energy consumption data corresponding to the second operating strategy is lower than the total actual energy consumption data corresponding to the first operating strategy, operate according to the second operating strategy. Specifically, determine whether the second operating strategy, which is optimized based on the prediction model between line energy consumption and operational data variables, is more optimized than the existing first operating strategy. If it is more optimized, i.e., more energy-efficient, operate according to the new operating strategy.

[0075] From the above, it can be seen that in the rail transit traction energy-saving method based on dynamic power flow calculation of the present invention, through the ATO single-vehicle energy-saving + ATS operation diagram planning multi-vehicle collaborative energy-saving module, the dynamic diagram adjustment module according to passenger flow requirements and the regenerative braking energy feedback module and other modules, the power supply, vehicle, signal, track and other multi-dimensional coupling conditions are dynamically analyzed, and the traction power supply system power flow dynamic calculation and analysis module is used to perform dynamic power flow calculation from the perspective of energy flow, dynamically coordinate ATO energy saving, ATS energy saving, regenerative braking energy recovery device energy saving and other technical means, and carry out unified management and collaborative linkage, perform energy consumption monitoring and statistical analysis for different traction energy-saving measures, and track and evaluate long-term energy-saving operation effects; and continuously adjust the energy-saving control strategy according to the analysis results through the prediction model between line energy consumption and operation data variables, so as to achieve a better energy-saving effect in a truly sense.

[0076] Furthermore, in step S6, when the total actual energy consumption data corresponding to the second operating strategy is higher than the total actual energy consumption data corresponding to the first operating strategy, the first operating strategy is regenerated in step S2, and steps S3 to S6 are repeated until the total actual energy consumption data corresponding to the second operating strategy is lower than the total actual energy consumption data corresponding to the first operating strategy. Based on this continuous cycle, the energy-saving strategy can be continuously optimized to ensure energy-saving operation of the entire system.

[0077] On the other hand, the present invention conducts dynamic calculation and analysis of the traction power supply dynamic flow through the dynamic relationship between the traction power supply system and the train operation, and considers the energy-saving problem from the perspective of the flow, so as to effectively integrate various energy-saving measures and effectively plan the energy-saving measures as a whole.

[0078] The dynamic calculation and analysis module of the traction power supply system flow is analyzed based on the dynamic calculation of the traction power supply system flow, which specifically includes: establishing a dynamic circuit relationship between the traction power supply and the train through real-time monitoring of the traction power supply mode (bilateral, unilateral, large bilateral, etc.), traction power supply voltage, dynamic position and weighing of the train and other information; treating the substation as the power source, the contact network as the resistor, the train in the traction state as the resistor, and the train in the braking state as the power source, dividing the entire section into several sections (for example, one section every 100 meters, which can also be set to other parameters), and defining the correlation between the voltage, resistance, current, power and energy consumption of different sections under the condition of a dynamically moving train; calculating the flow of the traction power supply system in real time by real-time monitoring of the traction voltage and current, train power consumption and energy feedback, and dynamic distribution of the train; collecting total actual energy consumption data through the power supply system and the train, dynamically recording and analyzing the distribution of the train in the power supply partition, and the relationship between the traction braking moment and energy consumption of the train ATO energy-saving curve, to provide a theoretical basis and verification method for subsequent energy-saving strategies. Figure 2 As shown in the figure, it is an equivalent circuit diagram of traction power flow calculation of the present invention. In the figure, U represents power supply and R represents resistance. Based on this method, combined with real-time driving information, dynamic calculation and analysis of traction power supply system power flow is performed. Of course, the dynamic calculation and analysis module of traction power supply system power flow of the present invention is not limited to Figure 2 The traction power supply system power flow dynamic calculation is performed for analysis in the manner described above. In other embodiments, other methods may also be used, and the present invention does not limit this.

[0079] In the S2, the ATO single-vehicle energy saving + ATS operation diagram planning multi-vehicle collaborative energy saving module gives a preliminary ATO energy saving curve by combining the train operation interval with the line basic data simulation. Furthermore, it realizes multi-vehicle collaboration through the overall planning of the ATS operation diagram, achieving the energy saving effect of single-vehicle + multi-vehicle operation. Specifically, the working method of the ATO single-vehicle energy saving + ATS operation diagram planning multi-vehicle collaborative energy saving module includes: based on the speed deviation of the train operation, each ATO energy saving curve, that is, the operation curve or speed curve is divided by multiple checkpoints, that is, multiple segmentation checkpoints are set on each ATO energy saving curve, and each checkpoint records different ATO vehicle control parameters; when the train passes a checkpoint, the ATO comprehensively considers the remaining time of the station and the upstream and downstream operation curves, and dynamically calculates the appropriate ATO target speed to minimize the number of traction and braking times of the train; train idling resistance identification based on big data analysis Identification, specifically different trains with different loads at different kilometer mark positions; knowing the idling resistance in advance through data accumulation and cleaning; using the convex optimization model and algorithm to perform offline planning of the energy-saving speed curve; adjusting the train interval running time by adjusting the surplus time, improving the utilization rate of the train's regenerative braking energy, optimizing the energy-saving operation diagram, and automatically adjusting the train according to different situations, where the surplus time includes the time for stops and return operations; optimizing the dynamic energy consumption of single vehicles + multiple vehicles through ATO single vehicle energy saving + ATS operation diagram planning multi-vehicle collaborative technology. It can be understood that the ATO single vehicle energy saving + ATS operation diagram planning multi-vehicle collaborative energy saving module is not limited to realizing its corresponding functions in the above-mentioned manner, and it can also realize the corresponding functions in other ways.

[0080] Furthermore, in S2, the passenger flow data for the current day is predicted using the passenger flow time series prediction module. In practical applications, the passenger flow time series prediction module can be established using the ARIMA algorithm based on the historical passenger flow data in the big data module, and the passenger flow for the current day can be predicted based on the passenger flow obtained from the real-time data platform. Of course, the passenger flow time series prediction module can also be established using other methods, which are not limited by the present invention.

[0081] Furthermore, in S2, the dynamic timetable adjustment module according to passenger flow requirements is based on the real-time passenger flow data collected in S1 and the passenger flow data of the day predicted by the passenger flow time series prediction module. On the premise of meeting the operation indicators, it changes the existing fixed running mode according to the diagram, dynamically analyzes the passenger flow data in real time, and dynamically adjusts the running diagram of the day according to the passenger flow data to obtain an updated running diagram and realize running according to the passenger flow requirements.

[0082] Furthermore, in S2, the regenerative braking energy feedback module fully utilizes the real-time traction braking status information of the train controlled by the signal system, coordinates the operation of trains in the same power supply section, and at the same time, when the traction system voltage dV / dt begins to increase, starts the regenerative braking energy recovery device to recover energy, fully utilizes the regenerative braking energy, and avoids the regenerative braking energy being consumed by the train resistance.

[0083] Specifically, the regenerative braking energy recovery device activation mechanism based on the operation diagram obtained through the regenerative braking energy feedback module includes: optimizing the train interval operation time, station stop time and return time based on the updated operation diagram, and using simulated annealing intelligent algorithm technology to solve the large-scale transportation optimization problem with multiple constraints in complex networks, so that the entry and exit operation times of trains running in the same and / or adjacent power supply sections overlap to the greatest extent, so that the exiting trains can fully utilize the braking regeneration energy of the incoming trains, effectively reduce the traction energy consumption of the exiting trains, and achieve a better energy-saving solution for the entire line. Based on the above, the regenerative braking energy feedback module achieves the maximum balance between acceleration and braking by coordinating trains in the same power supply section, so that the energy generated by braking can be used by the accelerating train to the maximum extent.

[0084] Furthermore, the regenerative braking energy recovery module utilizes the predictability of the operating parameters of the signal system and adopts a prediction algorithm to coordinate with the regenerative braking energy recovery device. When the running train is about to start braking, it notifies the regenerative braking energy recovery device in advance, and immediately starts the regenerative braking energy recovery device to recover electric energy while maintaining the train braking, rather than starting it after the voltage reaches a certain level, thus avoiding a large amount of energy being absorbed by the braking resistor. Based on the above, a regenerative braking energy recovery device startup mechanism based on the updated operation diagram is obtained. Figure 3 FIG. 1 is a schematic diagram of the coordinated linkage between the regenerative braking energy recovery device and the signal system in one embodiment of the present invention. It is understood that the coordinated linkage between the regenerative braking energy recovery device and the signal system is not limited to Figure 3 In other embodiments, other methods may be used as long as the corresponding functions can be achieved, and the present invention does not limit this.

[0085] The prediction model between line energy consumption and operational data variables in S4 is based on historical operational data stored in the big data module, including line energy consumption, ATS operation diagrams, single-vehicle ATO operation curves, train weighing, line slope morphology, passenger flow, climate information, regenerative braking energy recovery activation mechanism, etc., and uses a deep neural network to establish a prediction model between line energy consumption and other operational data variables. The prediction model between line energy consumption and operational data variables can be expressed as:

[0086] E=f DNN (t_start i,j,k ,t_stopi,j,k ,t_hold i,j,k ,ato_curve i,j,k ,

[0087] weight k ,slope i,j ,p_flow i ,weather,E_brakingenergy i ) 1＜i,j＜n,1＜k＜m

[0088] ato_curve i,j,k ∈Set_ato (1)

[0089] Among them, f DNN represents the deep neural network model function, E represents the line energy consumption, i and j represent the index of the i-th and j-th stations respectively, there are n stations in total, k represents the k-th train, there are m trains in total, t_start i,j,k t_stop i,j,k t_hold i,j,k 、ato_curve i,j,k They represent the departure time, arrival time, stop time at the jth station and the ATO energy-saving curve between stations of the kth train from the ith station to the jth station, Set_ato represents the ATO curve set, and weight k represents the weight of the kth train, slope i,j represents the slope between the i-th station and the j-th station, p_flow i represents the passenger flow of the i-th station, weather represents whether it is rainy or snowy, E_brakingenergy i It represents the energy recovered by the linkage between the i-th station signal and the regenerative braking energy recovery device.

[0090] In practical applications, based on the acquired real-time rail transit operation data (such as weather data) and the predicted passenger flow data for the day, combined with constraints such as transportation plans, the prediction model between the line energy consumption and the operation data variables can be optimized, that is, the objective function can be optimized. The optimized prediction model between the line energy consumption and the operation data variables can be expressed as:

[0091]

[0092] in, is the optimized line energy consumption, t_start i,j,k ′、t_stop i,j,k ′、t_hold i,j,k′ respectively represent the real-time departure time, arrival time, and stop time of the kth train from the ith station to the jth station, ato_curve i,j,k ′ is the inter-station ATO energy-saving curve obtained in S2.

[0093] Furthermore, the updated operation diagram and ATO energy-saving curve obtained in S2 are used as the initial candidate solutions of the optimized prediction model between the energy consumption of the line and the operating data variables. The optimization problem is solved using heuristic optimization methods such as genetic algorithm or particle swarm algorithm to obtain the updated optimal operation diagram and optimal ATO energy-saving curve for the day to form a second operation strategy, thereby changing the existing fixed running mode according to the diagram into a dynamic adjustment operation mode required by real-time passenger flow during real-time operation.

[0094] Based on the above, the present invention utilizes deep neural networks to establish a predictive model linking line energy consumption and other operational data variables based on the energy-saving curves and energy consumption data recorded in the traction energy-saving system for each specific section of each day under different conditions (such as train weighing, track adhesion coefficient caused by climate change, etc.). This allows for the mining of more optimal energy-saving curves under the same conditions through the accumulation of historical data. In practical applications, the system records the planned energy-saving operation diagram for each day and, combined with daily passenger flow information, mines the optimal operation diagram required to transport the same passenger flow, while meeting various basic operational indicators. In real-time operation, the existing fixed-diagram running mode is changed to a dynamic operation mode adjusted according to the real-time passenger flow.

[0095] Furthermore, the rail transit traction energy-saving method based on dynamic power flow calculation also includes: monitoring the health and abnormal energy consumption of train equipment through an abnormal energy consumption analysis module, and issuing an alarm signal when the abnormal energy consumption analysis module detects abnormal energy consumption.

[0096] It is understandable that the various modules involved in the rail transit traction energy-saving method based on dynamic power flow calculation of the present invention are not limited to the above-mentioned implementation methods, and the corresponding functions can also be realized through other existing methods, and the present invention does not impose any restrictions on this.

[0097] Based on the same inventive concept, the present invention also discloses a rail transit traction energy-saving system based on dynamic power flow calculation (see Figure 4), a system for implementing the aforementioned rail transit traction energy-saving method based on dynamic power flow calculation. Specifically, the traction energy-saving system includes a rail transit real-time data module and a business knowledge module. The rail transit real-time data module is used to collect real-time rail transit operation data, which includes real-time passenger flow data. The business knowledge module is the business knowledge middle platform, which includes ATO single-vehicle energy saving + ATS operation diagram planning multi-vehicle collaborative energy saving module, passenger flow time series prediction module, dynamic diagram adjustment energy saving module according to passenger flow requirements, regenerative braking energy feedback module, traction power supply system flow dynamic calculation and analysis module and line energy consumption and operation data variable prediction model, wherein the ATO single-vehicle energy saving + ATS operation diagram planning multi-vehicle collaborative energy saving module can be used to obtain a preliminary ATO energy saving curve, the passenger flow time series prediction module can predict the passenger flow data of the day, the dynamic diagram adjustment energy saving module according to passenger flow requirements can be used to obtain the operation diagram, the regenerative braking energy feedback module can be used to obtain the regenerative braking energy recovery device starting mechanism based on the operation diagram, the traction power supply system flow dynamic calculation and analysis module can dynamically record and analyze the distribution of corresponding trains in the power supply partition, the relationship between the traction braking moment and energy consumption of the train ATO energy saving curve based on the operation strategy, and obtain the total actual energy consumption data corresponding to the operation strategy, and the prediction model between the line energy consumption and operation data variables can be used to optimize the operation strategy.

[0098] From the above, it can be seen that in the rail transit traction energy-saving system based on dynamic power flow calculation of the present invention, through the rail transit real-time data module and business knowledge module, combined with the dynamic driving information of the signal system, track basic information, dynamic passenger flow information (train weighing), power supply system topology relationship and dynamic voltage and current, through dynamic power flow calculation and analysis, various traction energy-saving means such as train operation and regenerative braking energy recovery device are coordinated to ensure that various energy-saving measures work together to achieve the goal of efficient energy saving.

[0099] Furthermore, the traction energy-saving system also includes a big data module, or "big data center," which stores operational data. In practical applications, this module is established and then used by the business knowledge module to conduct data mining based on this data, identifying optimal ATO curves, energy-saving operation diagrams, and coordinated strategies for regenerative braking energy recovery devices from historical data.

[0100] Furthermore, the traction energy-saving system also includes an equipment health and abnormal energy consumption analysis module, which monitors the health and abnormal energy consumption of the train's key equipment. When the module detects abnormal energy consumption, it can issue an alarm signal. In actual application, the module constructs an energy consumption model of the train's key equipment and analyzes its health. When abnormal energy consumption is detected, it issues a timely alarm to avoid prolonged high-energy-consumption operation.

[0101] In actual application, the traction energy-saving system collects real-time operation data of rail transit by establishing a real-time data module for rail transit, namely the real-time data middle platform. The real-time operation data of rail transit includes real-time passenger flow data, real-time driving information, train weighing information, real-time traction power supply system voltage and current information, traction power supply system energy consumption information, single vehicle energy consumption information and single vehicle regeneration energy information, etc.

[0102] Furthermore, within the traction energy-saving system's architecture, the big data module resides at the data center layer. By establishing a big data center and using historical data to build a prediction module linking operational diagrams, operational information, and other data with energy consumption, an optimization algorithm is employed to determine the optimal ATO energy-saving curve and route operation diagram planning based on real-time data and passenger flow forecasts. Based on this, in practical applications, the system automatically matches the optimal energy-saving strategy by inputting transportation planning requirements and compares the energy-saving efficiency of previous strategies to determine which one is more energy-efficient.

[0103] Furthermore, the traction energy-saving system establishes a business knowledge module, namely a business knowledge middle platform. The business knowledge module can establish the ATO single-vehicle energy saving + ATS operation diagram planning multi-vehicle collaborative energy saving module, passenger flow time series prediction module, dynamic diagram adjustment energy saving module according to passenger flow requirements, regenerative braking energy feedback module, traction power supply system flow dynamic calculation and analysis module, line energy consumption and operation data variable prediction model and equipment health and abnormal energy consumption analysis module through knowledge graph technology. Of course, the business knowledge middle platform can not only be used to establish the above modules, it can also establish modules with other functional roles, and the present invention does not limit this.

[0104] like Figure 4Figure 2 shows the architecture of a traction energy-saving system according to the present invention. The system comprises a cloud platform layer, a data middle platform layer, an intelligent computing layer, and a human-computer interaction layer. The cloud platform layer provides basic computing resources and cloud management enhancements, including independent CPU core binding for enhanced virtual machines, exclusive memory allocation without over-allocation, and protection against preemption of enhanced virtual machine computing resources. The data middle platform layer comprises an industrial real-time middle platform and a big data middle platform. The industrial real-time middle platform is responsible for collecting and processing real-time data, while the big data middle platform performs data cleaning, mining, and analysis on real-time and historical data processed by the real-time middle platform to support business modeling in the business knowledge middle platform. The intelligent computing layer includes business knowledge modules, primarily based on knowledge graph technology, big data, and AI algorithm technologies, to implement the modeling of various business analysis modules. The main modules involved in the intelligent computing layer include: stop / section operation time optimization module, full-map surplus time dynamic planning module, ATS+ATO multi-vehicle coordination module, regenerative braking energy recovery module, traction power supply system power flow dynamic calculation module, data mining optimal energy-saving curve module, dynamic timetable adjustment module based on passenger flow requirements, and equipment health and abnormal energy consumption analysis module.

[0105] Based on the same inventive concept, the present invention also provides an electronic device, which includes: a memory and a processor, wherein a computer program is stored on the memory, and when the computer program is executed by the processor, the steps of the aforementioned rail transit traction energy-saving method based on dynamic power flow calculation are implemented.

[0106] Based on the same inventive concept, the present invention also provides a readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the aforementioned rail transit traction energy-saving method based on dynamic power flow calculation are implemented.

[0107] In summary, in a rail transit traction energy-saving method, system, electronic device and readable storage medium based on dynamic power flow calculation of the present invention, the traction energy-saving method dynamically analyzes the multi-dimensional coupling conditions such as power supply, vehicle, signal, and track through modules such as ATO single-vehicle energy saving + ATS operation diagram planning multi-vehicle collaborative energy-saving module, dynamic diagram adjustment module according to passenger flow requirements, and regenerative braking energy feedback module, and performs dynamic power flow calculation from the perspective of energy flow through the traction power supply system power flow dynamic calculation and analysis module, dynamically coordinates ATO energy saving, ATS energy saving, regenerative braking energy recovery device energy saving and other technical means, conducts unified management and collaborative linkage, performs energy consumption monitoring and statistical analysis for different traction energy-saving measures, and tracks and evaluates long-term energy-saving operation effects; and continuously optimizes the energy-saving control strategy according to the analysis results through the prediction model between line energy consumption and operation data variables, thereby achieving a truly better energy-saving effect.

[0108] Although the present invention has been described in detail through the above preferred embodiments, it should be understood that the above description is not intended to limit the present invention. After reading the above description, various modifications and substitutions of the present invention will become apparent to those skilled in the art. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. A traction energy-saving method for rail transit based on dynamic power flow calculation, characterized in that: Include: S1. Collecting rail transit real-time operation data through a rail transit real-time data module, wherein the rail transit real-time operation data includes real-time passenger flow data; S2. Based on the real-time operation data of rail transit, an ATO energy-saving curve is obtained through the ATO single-vehicle energy-saving + ATS operation diagram planning multi-vehicle collaborative energy-saving module. The passenger flow data of the day is predicted through the passenger flow time series prediction module. The dynamic diagram adjustment module according to passenger flow requirements dynamically adjusts the operation diagram of the day based on the real-time passenger flow data collected in S1 and the passenger flow data of the day predicted by the passenger flow time series prediction module to obtain an updated operation diagram. A regenerative braking energy recovery device activation mechanism based on the updated operation diagram is obtained through the regenerative braking energy feedback module. A first operation strategy is formed based on the ATO energy-saving curve, the updated operation diagram, the regenerative braking energy recovery device activation mechanism, and the predicted passenger flow data of the day. S3. The traction power supply system power flow dynamic calculation and analysis module dynamically records and analyzes the distribution of trains corresponding to the first operation strategy in the power supply zone, the relationship between the traction braking moment and energy consumption of the train ATO energy-saving curve, and the total actual energy consumption data based on the first operation strategy; S4. Optimizing the first operation strategy using a prediction model between line energy consumption and operation data variables to obtain a second operation strategy; S5. The traction power supply system power flow dynamic calculation and analysis module dynamically records and analyzes the distribution of trains corresponding to the second operation strategy in the power supply partition, the relationship between the traction braking moment and energy consumption of the train ATO energy-saving curve, and the total actual energy consumption data based on the second operation strategy; S6. Compare the total actual energy consumption data corresponding to the first operation strategy with the total actual energy consumption data corresponding to the second operation strategy. When the total actual energy consumption data corresponding to the second operation strategy is lower than the total actual energy consumption data corresponding to the first operation strategy, operate according to the second operation strategy.

2. The rail transit traction energy-saving method based on dynamic power flow calculation according to claim 1, characterized in that: In S6, when the total actual energy consumption data corresponding to the second operating strategy is higher than the total actual energy consumption data corresponding to the first operating strategy, the first operating strategy is regenerated through step S2, and steps S3 to S6 are repeated until the total actual energy consumption data corresponding to the second operating strategy is lower than the total actual energy consumption data corresponding to the first operating strategy.

3. The rail transit traction energy-saving method based on dynamic power flow calculation according to claim 1, characterized in that: The traction power supply system power flow dynamic calculation and analysis module performs analysis based on the traction power supply system power flow dynamic calculation, which specifically includes: By real-time monitoring of traction power supply mode, traction power supply voltage, train dynamic position and weight, a dynamic circuit relationship between traction power supply and train is established; The substation is equivalent to a power source, the overhead line is equivalent to a resistor, the train in traction is equivalent to a resistor, and the train in braking is equivalent to a power source. The entire section is divided into several sections. Under the condition of a dynamically moving train, the relationship between voltage, resistance, current, power and energy consumption in different sections is defined; Real-time calculation of the traction power supply system's power flow by monitoring traction voltage and current, train power consumption and energy feed, and train dynamic distribution; The total actual energy consumption data is collected through the power supply system and trains, and the distribution of trains in power supply areas and the relationship between the traction and braking moments of the train's ATO energy-saving curve and energy consumption are dynamically recorded and analyzed.

4. The rail transit traction energy-saving method based on dynamic power flow calculation according to claim 1, characterized in that: In S2, the working method of the ATO single-vehicle energy saving + ATS operation diagram planning multi-vehicle collaborative energy saving module includes: Based on the train's speed deviation, each ATO energy-saving curve is divided into multiple checkpoints, each of which records different ATO control parameters. When a train passes a checkpoint, ATO comprehensively considers the remaining time at the station and the upstream and downstream operating curves to calculate the appropriate ATO target speed. Train coasting resistance is identified based on big data analysis, specifically for different trains with different loads at different kilometer marker positions. Coasting resistance is known in advance through data accumulation and cleaning. Use convex optimization models and algorithms to perform offline planning of energy-saving speed curves; By adjusting and utilizing surplus time, the train interval running time is adjusted, the utilization rate of train regenerative braking energy is improved, the energy-saving operation diagram is optimized, and the train is automatically adjusted; Through ATO single-vehicle energy saving + ATS operation diagram planning multi-vehicle collaborative technology, the dynamic energy consumption of single vehicle and multiple vehicles can be optimized.

5. The rail transit traction energy-saving method based on dynamic power flow calculation according to claim 1, characterized in that: In S2, the regenerative braking energy recovery device startup mechanism based on the operation diagram is obtained through the regenerative braking energy feedback module, including: Based on the updated timetable, train interval running time, station stop time, and turnaround time are optimized. Simulated annealing intelligent algorithm technology is used to solve large-scale transportation optimization problems with multiple constraints in complex networks. This maximizes the overlap between the arrival and departure times of trains operating in the same and / or adjacent power supply zones, allowing outgoing trains to fully utilize the brake regeneration energy of incoming trains. Leveraging the predictability of the signal system's operating parameters, a prediction algorithm is employed to coordinate with the regenerative braking energy recovery device. This notifies the regenerative braking energy recovery device when a running train is about to brake, maintaining the train's braking while immediately activating the regenerative braking energy recovery device to recover electrical energy. Based on the above, a regenerative braking energy recovery device activation mechanism based on the updated operation diagram is obtained.

6. The rail transit traction energy-saving method based on dynamic power flow calculation according to claim 1, characterized in that: In S4, based on the real-time rail transit operation data and the predicted passenger flow data of the day, the prediction model between the line energy consumption and the operation data variables is optimized in combination with the transportation plan; The updated operation diagram and ATO energy-saving curve obtained in S2 are used as initial candidate solutions of the optimized prediction model between the line energy consumption and the operation data variables, and the updated operation diagram and ATO energy-saving curve are optimized to form a second operation strategy.

7. The rail transit traction energy-saving method based on dynamic power flow calculation according to claim 6, characterized in that: The prediction model between the line energy consumption and the operation data variables can be expressed as: E=f DNN (t_start i,j,k ,t_stop i,j,k ,t_hold i,j,k ,ato_curve i,j,k ,weight k ,slope i,j ,p_flow i ,weather,E_brakingenergy i ) 1＜i,j＜n,1＜k＜m ato_curve i,j,k ∈Set_ato (1) Among them, f DNN represents the deep neural network model function, E represents the line energy consumption, i and j represent the index of the i-th and j-th stations respectively, there are n stations in total, k represents the k-th train, there are m trains in total, t_start i,j,k t_stop i,j,k t_hold i,j,k 、ato_curve i,j,k They represent the departure time, arrival time, stop time at the jth station and the ATO energy-saving curve between stations of the kth train from the ith station to the jth station, Set_ato represents the ATO curve set, and weight k represents the weight of the kth train, slope i,j represents the slope between the i-th station and the j-th station, p_flow i represents the passenger flow of the i-th station, weather represents whether it is rainy or snowy, E_brakingenergy i It represents the energy recovered by the linkage between the i-th station signal and the regenerative braking energy recovery device.

8. The rail transit traction energy-saving method based on dynamic power flow calculation according to claim 6, characterized in that: Based on the real-time rail transit operation data and the predicted passenger flow of the day, the prediction model between the optimized line energy consumption and the operation data variables is expressed as follows: in, is the optimized line energy consumption, t_start i,h,k ′、t_stop i,j,k ′、t_hold i,j,k ′ respectively represent the real-time departure time, arrival time, and stop time of the kth train from the ith station to the jth station, ato_curve i,j,k ′ is the inter-station ATO energy-saving curve obtained in S2.

9. The rail transit traction energy-saving method based on dynamic power flow calculation according to claim 1, characterized in that: Also includes: The health and abnormal energy consumption of train equipment are monitored by the abnormal energy consumption analysis module, and an alarm signal is issued when the abnormal energy consumption analysis module detects abnormal energy consumption.

10. A rail transit traction energy-saving system based on dynamic power flow calculation, which is used to implement the rail transit traction energy-saving method based on dynamic power flow calculation according to any one of claims 1 to 9, characterized in that: The traction energy-saving system includes a rail transit real-time data module and a business knowledge module. The rail transit real-time data module is used to collect rail transit real-time operation data, and the rail transit real-time operation data includes real-time passenger flow data; The business knowledge module includes: ATO single-vehicle energy saving + ATS operation diagram planning multi-vehicle collaborative energy saving module, which is used to obtain the preliminary ATO energy saving curve; Passenger flow time series prediction module, which is used to predict the passenger flow data of the day; Energy-saving module for dynamic diagram adjustment according to passenger flow requirements, which is used to obtain the operation diagram; A regenerative braking energy feedback module, which is used to obtain a regenerative braking energy recovery device startup mechanism based on an operation diagram; The traction power supply system power flow dynamic calculation and analysis module dynamically records and analyzes the distribution of corresponding trains in the power supply partition based on the operation strategy, the relationship between the traction braking moment and energy consumption of the train's ATO energy-saving curve, and obtains the total actual energy consumption data corresponding to the operation strategy; A prediction model between line energy consumption and operational data variables is used to optimize operation strategies.

11. The rail transit traction energy-saving system based on dynamic power flow calculation according to claim 10, characterized in that: Also includes: The big data module is used to store operation data.

12. The rail transit traction energy-saving system based on dynamic power flow calculation according to claim 10, characterized in that: Also includes: The equipment health and abnormal energy consumption analysis module is used to monitor the health and abnormal energy consumption of train equipment. When the abnormal energy consumption analysis module detects abnormal energy consumption, it can issue an alarm signal.

13. The rail transit traction energy-saving system based on dynamic power flow calculation according to claim 10, characterized in that: The business knowledge module can establish the ATO single-vehicle energy saving + ATS operation diagram planning multi-vehicle collaborative energy saving module, passenger flow time series prediction module, dynamic diagram adjustment energy saving module according to passenger flow requirements, regenerative braking energy feedback module, traction power supply system flow dynamic calculation and analysis module and prediction model between line energy consumption and operation data variables through knowledge graph technology.

14. An electronic device, characterized in that: The electronic device includes: a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the steps of the rail transit traction energy-saving method based on dynamic power flow calculation as described in any one of claims 1 to 9 are implemented.

15. A readable storage medium, characterized in that The readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the rail transit traction energy-saving method based on dynamic power flow calculation are implemented as described in any one of claims 1 to 9.

Images