Dynamic power flow calculation-based traction energy saving method for rail transit, system, electronic device, and readable storage medium

Abstract

Disclosed in the present invention are a dynamic power flow calculation-based traction energy saving method for rail transit, a system, an electronic device, and a readable storage medium. According to the traction energy saving method, dynamic analysis is performed on multi-dimensional coupling conditions such as power supply, vehicles, signals, and tracks by means of modules such as an ATO single vehicle energy saving + ATS operation chart planning multi-vehicle coordinated energy saving module, a passenger flow requirement-based dynamic map adjustment module, and a regenerative braking energy feed module, dynamic power flow calculation is performed from the perspective of energy flow by means of a power flow dynamic calculation and analysis module of a traction power supply system, and techniques such as ATO energy saving, ATS energy saving, and energy saving using a regenerative braking energy recovery apparatus are dynamically coordinated, to perform unified management and coordinated linkage, so that energy consumption monitoring and statistical analysis are performed for different traction energy saving measures, to track and evaluate a long-term energy saving operation effect. By means of continuous adjustment and optimization of an energy saving control policy according to analysis results by a line energy consumption-operational data variable prediction model, an enhanced energy saving effect in real sense can be achieved.

Description

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

[0001] This invention relates to the field of traction energy saving in rail transit systems, and specifically to a traction energy saving method, system, electronic device, and readable storage medium based on dynamic power flow calculation for rail transit. Background Technology

[0002] After several years of rapid development, rail transit has gradually transitioned from a phase of rapid construction to one focused on high-quality operation. During the rapid construction phase, the main consideration was ensuring operational efficiency and safety, with little regard for energy-saving operation.

[0003] According to publicly released statistics from the China Urban Construction Association, the total electricity consumption of urban rail transit in 2023 was 24.977 billion kilowatt-hours, showing a continuous upward trend and enormous energy consumption. Traction power consumption accounted for 50-60%, and station electromechanical power consumption accounted for 30-35%, with traction and electromechanical power consumption exceeding 80% of the total energy consumption. In the field of energy conservation research for rail transit systems, energy-saving solutions from various disciplines are currently in the small-scale, single-discipline pilot stage. Energy-saving measures from different disciplines are isolated, and there is no overall energy-saving design from the perspective of power system energy flow. Sometimes, energy-saving measures actually increase energy consumption. Therefore, further research is needed.

[0004] It is understood that the above statements only provide background information related to the present invention and do not necessarily constitute prior art. Summary of the Invention

[0005] Based on the aforementioned technical problems, the purpose of this invention is to provide a traction energy-saving method, system, electronic equipment, and readable storage medium for rail transit based on dynamic power flow calculation. This traction energy-saving method is based on dynamic analysis of multi-dimensional coupling conditions such as power supply, vehicles, signals, and tracks. It performs dynamic power flow calculation from the perspective of energy flow, dynamically coordinates energy-saving technologies such as ATO, ATS, and regenerative braking energy recovery devices, and manages them in a unified and coordinated manner. It monitors and statistically analyzes energy consumption for different traction energy-saving measures, tracks and evaluates the long-term energy-saving operation effect, and continuously optimizes the energy-saving control strategy based on the analysis results, thereby achieving a truly superior energy-saving effect.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solution:

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

[0008] S1. Real-time operation data of rail transit is collected through the rail transit real-time data module, wherein the real-time operation data of rail transit includes real-time passenger flow data;

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

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

[0011] S4. Optimize the first operation strategy by 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 in the power supply zone, the relationship between the traction braking time of the train ATO energy-saving curve and energy consumption, 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 operating strategy with the total actual energy consumption data corresponding to the second operating strategy. When 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.

[0014] Optionally, 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 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, and specifically includes:

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

[0017] The substation is equivalent to a power source, the overhead contact 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 segments, and under the condition of dynamically moving trains, the relationship between voltage, resistance, current, power and energy consumption in different segments is defined.

[0018] The power flow of the traction power supply system is calculated in real time by monitoring traction voltage and current, train power consumption and energy feed, and the dynamic distribution of the train.

[0019] By collecting total actual energy consumption data from the power supply system and trains, the distribution of trains in the power supply zones and the relationship between the traction and braking times 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 speed deviation of the train operation, each ATO energy-saving curve is divided into multiple checkpoints, and each checkpoint records different ATO train control parameters. When the train passes a checkpoint, ATO takes into account the remaining time at the station and the upstream and downstream operation curves to calculate the appropriate ATO target speed.

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

[0023] An offline planning method for the energy-saving speed curve is adopted using a convex optimization model and algorithm.

[0024] By adjusting the train's running time between sections and improving the utilization rate of train regenerative braking energy, the energy-saving operation schedule is optimized and automatic train adjustments are made.

[0025] By combining ATO single-vehicle energy saving with ATS operation chart planning and multi-vehicle collaborative technology, the dynamic energy consumption of single and multiple vehicles can be optimized.

[0026] Optionally, in S2, the regenerative braking energy recovery device startup mechanism based on the operation diagram obtained through the regenerative braking energy feed module includes:

[0027] Based on the updated operation chart, the train section running time, station stop time and turnaround time are optimized. The simulated annealing intelligent algorithm technology is used 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 zones overlap to the greatest extent, so that the exiting trains can make full use of the braking regenerative energy of the entering trains.

[0028] Taking advantage of the predictability of the signal system's operating parameters, a predictive algorithm is used in conjunction with the regenerative braking energy recovery device. When the train is about to begin braking, the regenerative braking energy recovery device is notified in advance, and while maintaining the train's braking, the regenerative braking energy recovery device is immediately activated to recover electrical energy.

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

[0030] Optionally, in S4, the prediction model between the line's energy consumption and operational data variables is optimized based on real-time rail transit operation data and predicted daily passenger flow data, combined with the transportation plan.

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

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

[0033]

[0034] in, Let E represent the deep neural network model function, i and j represent the indices of the i-th and j-th stations respectively, there are n stations in total, k represents the k-th train, and there are m trains in total. Let represent the departure time, arrival time, dwell time at station j, and ATO energy-saving curve between stations for train k from station i to station j, respectively. Represents the set of ATO curves. This represents the weight of the k-th train. This represents the gradient between station i and station j. This represents the passenger flow at the i-th station. Indicates whether it is rainy or snowy weather. This represents the energy recovered by the signal and regenerative braking energy recovery device at station i.

[0035] Optionally, based on real-time rail transit operation data and predicted daily passenger flow, the optimized prediction model between line energy consumption and operational data variables is expressed as follows:

[0036]

[0037] in, For the optimized line energy consumption, Let represent the real-time departure time, arrival time, and dwell time at station j for the k-th train from station i to station j, respectively. The ATO energy-saving curve between stations is obtained from S2.

[0038] Optional, also includes:

[0039] The abnormal energy consumption analysis module monitors the health status and abnormal energy consumption of the train equipment. When the abnormal energy consumption analysis module detects abnormal energy consumption, it issues an alarm signal.

[0040] Optionally, a traction energy-saving system for rail transit based on dynamic power flow calculation is provided to implement the aforementioned traction energy-saving method for rail transit based on dynamic power flow calculation. The traction energy-saving system includes a real-time rail transit data module and a business knowledge module.

[0041] The rail transit real-time data module is used to collect real-time rail transit operation data, which includes real-time passenger flow data.

[0042] The business knowledge module includes:

[0043] The ATO single-vehicle energy saving + ATS operation diagram planning multi-vehicle collaborative energy saving module is used to obtain the initial ATO energy saving curve;

[0044] The passenger flow time series prediction module is used to predict the passenger flow data for the day.

[0045] The energy-saving module, which dynamically adjusts the operating schedule according to passenger flow requirements, is used to obtain the operating schedule.

[0046] A regenerative braking energy feed module is used to obtain the start-up mechanism of the regenerative braking energy recovery device based on the operation diagram;

[0047] The traction power supply system dynamic calculation and analysis module dynamically records and analyzes the distribution of trains in the power supply zone, the relationship between the traction braking time and energy consumption of the train ATO energy-saving curve, and obtains the total actual energy consumption data corresponding to the operation strategy based on the operation strategy.

[0048] A predictive model between line energy consumption and operational data variables is used to optimize operational strategies.

[0049] Optional, also includes:

[0050] The big data module is used to store runtime data.

[0051] Optional, also includes:

[0052] 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.

[0053] Optionally, the business knowledge module can use knowledge graph technology to establish the ATO single-vehicle energy saving + ATS operation diagram planning multi-vehicle collaborative energy saving module, passenger flow time series prediction module, dynamic adjustment of the time diagram according to passenger flow needs energy saving module, regenerative braking energy feedback module, traction power supply system power flow dynamic calculation and analysis module, and prediction model between line energy consumption and operation data variables.

[0054] Optionally, an electronic device is provided, comprising: a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, it implements the steps of the aforementioned traction energy-saving method for rail transit based on dynamic power flow calculation.

[0055] Optionally, a readable storage medium stores a computer program that, when executed by a processor, implements the steps of the aforementioned traction energy-saving method for rail transit based on dynamic power flow calculation.

[0056] Compared with the prior art, the present invention has the following advantages:

[0057] The present invention discloses a traction energy-saving method, system, electronic equipment, and readable storage medium for rail transit based on dynamic power flow calculation. This traction energy-saving method is based on dynamic analysis of multi-dimensional coupling conditions such as power supply, vehicles, signals, and tracks. It performs dynamic power flow calculation from the perspective of energy flow, dynamically coordinates energy-saving technologies such as ATO (Automatic Train Operation), ATS (Automatic Train System), and regenerative braking energy recovery devices, and implements unified management and coordinated operation. It monitors and statistically analyzes energy consumption for different traction energy-saving measures, tracks and evaluates long-term energy-saving operation effects, and continuously optimizes energy-saving control strategies based on the analysis results, thereby achieving truly superior energy-saving effects. Attached Figure Description

[0058] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the drawings in the following description are one embodiment of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort:

[0059] Figure 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;

[0060] Figure 2 is an equivalent circuit diagram for traction power flow calculation according to the present invention;

[0061] Figure 3 is a schematic diagram of the coordinated linkage between a regenerative braking energy recovery device and a signal system according to the present invention.

[0062] Figure 4 is a schematic diagram of a traction energy-saving system for rail transit based on dynamic power flow calculation according to the present invention. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] It should be noted that, in this document, the terms "comprising," "including," "having," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Unless otherwise specified, an element defined by the phrase "comprising..." or "including..." does not exclude the presence of additional elements in the process, method, article, or terminal device that includes said element.

[0065] It should be noted that the accompanying drawings are all in a very simplified form and use non-precise ratios, and are only used to facilitate and clearly illustrate the purpose of the embodiments of the present invention.

[0066] Figure 1 shows 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 includes:

[0067] S1. Real-time operation data of rail transit is collected through the rail transit real-time data module, wherein the real-time operation data of rail transit includes real-time passenger flow data;

[0068] S2. Based on real-time rail transit operation data, the ATO energy-saving curve is obtained through the ATO single-vehicle energy-saving + ATS operation plan planning multi-vehicle collaborative energy-saving module. The passenger flow time series prediction module predicts the passenger flow data for the day. The dynamic timetable adjustment module adjusts the operation plan for the day based on the real-time passenger flow data collected in S1 and the passenger flow data predicted by the passenger flow time series prediction module to obtain an updated operation plan. The regenerative braking energy recovery device activation mechanism is obtained based on the updated operation plan through the regenerative braking energy feedback module. The first operation strategy is composed of the ATO energy-saving curve, the updated operation plan, the regenerative braking energy recovery device activation mechanism, and the predicted passenger flow data for the day.

[0069] S3. The traction power supply system dynamic calculation and analysis module dynamically records and analyzes the distribution of trains in the power supply zone, the relationship between the traction braking time and energy consumption of the train ATO energy-saving curve, and the total actual energy consumption data based on the first operating strategy.

[0070] S4. Optimize the first operation strategy by using a prediction model between line energy consumption and operation data variables to obtain a second operation strategy;

[0071] S5. The traction power supply system power flow dynamic calculation and analysis module dynamically records and analyzes the distribution of trains in the power supply zone, the relationship between the traction braking time of the train ATO energy-saving curve and energy consumption, and the total actual energy consumption data based on the second operation strategy.

[0072] S6. Compare the total actual energy consumption data corresponding to the first operating strategy and 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, then operate according to the second operating strategy. That is, determine whether the second operating strategy, after optimization of 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, then operate according to the new operating strategy.

[0073] As described above, the traction energy-saving method for rail transit based on dynamic power flow calculation of the present invention uses modules such as the ATO single-vehicle energy-saving + ATS operation plan multi-vehicle collaborative energy-saving module, the dynamic timetable adjustment module according to passenger flow needs, and the regenerative braking energy feedback module to dynamically analyze the multi-dimensional coupling conditions of power supply, vehicles, signals, and tracks. Furthermore, the traction power supply system power flow dynamic calculation and analysis module performs dynamic power flow calculation from the perspective of energy flow, dynamically coordinating ATO energy saving, ATS energy saving, and regenerative braking energy recovery device energy saving technologies for unified management and coordinated operation. Energy consumption monitoring and statistical analysis are conducted for different traction energy-saving measures, and long-term energy-saving operation effects are tracked and evaluated. Finally, the energy-saving control strategy is continuously optimized based on the analysis results through a predictive model between line energy consumption and operational data variables, thereby achieving a truly superior energy-saving effect.

[0074] 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 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. Based on the above continuous cyclical process, the energy-saving strategy can be continuously optimized to ensure the energy-saving operation of the entire system.

[0075] On the other hand, this invention performs dynamic power flow calculation and analysis of traction power supply by analyzing the dynamic relationship between the traction power supply system and train operation, and considers energy saving from the perspective of power flow, thereby effectively integrating various energy-saving measures and effectively planning the overall energy-saving measures.

[0076] The traction power supply system dynamic calculation and analysis module analyzes the power flow based on the dynamic calculation of the traction power supply system. Specifically, it includes: establishing a dynamic circuit relationship between the traction power supply and the train by real-time monitoring of traction power supply modes (double-sided, single-sided, large double-sided, etc.), traction power supply voltage, train dynamic position, and weighing information; treating the substation as a power source and the contact network as a resistor, the train in traction mode as a resistor and the train in braking mode as a power source; dividing the entire section into several segments (e.g., one segment every 100 meters, or other parameters can be set); defining the correlation between voltage, resistance, current, power, and energy consumption in different segments under dynamically moving train conditions; calculating the power flow of the traction power supply system in real-time by monitoring traction voltage and current, train power consumption and energy feedback, and the dynamic distribution of the train; dynamically recording and analyzing the distribution of trains in the power supply zones, the relationship between the traction braking time and energy consumption of the train's ATO energy-saving curve, and providing a theoretical basis and verification method for subsequent energy-saving strategies. Figure 2 shows an equivalent circuit diagram for traction power flow calculation according to the present invention. In the figure, U represents the power supply and R represents the resistance. Based on this method, combined with real-time train travel information, dynamic calculation and analysis of the traction power supply system power flow are performed. Of course, the traction power supply system power flow dynamic calculation and analysis module of the present invention is not limited to performing dynamic calculation and analysis of the traction power supply system power flow in the manner shown in Figure 2. In other embodiments, other methods may be used, and the present invention does not limit these methods.

[0077] In S2, the ATO single-car energy saving + ATS operation diagram planning multi-car collaborative energy saving module provides a preliminary ATO energy saving curve through simulation of train operation intervals combined with basic line data. Furthermore, through overall ATS operation diagram planning, it achieves multi-car collaboration, realizing the combined energy saving effect of single-car and multi-car operation. Specifically, the working method of the ATO single-car energy saving + ATS operation diagram planning multi-car collaborative energy saving module includes: based on the speed deviation of train operation, each ATO energy saving curve (operation curve or speed curve) is divided by multiple checkpoints, i.e., multiple segmentation checkpoints are set on each ATO energy saving curve, and each checkpoint records different ATO train control parameters; when the train passes a checkpoint, ATO comprehensively considers the remaining time at the station and the upstream and downstream operation curves to dynamically calculate a suitable ATO target speed to minimize the number of traction and braking operations; based on big data analysis, train coasting resistance is identified, specifically for different trains with different loads at different kilometer marker positions; coasting resistance is known in advance through data accumulation and cleaning; convex optimization (Convex) is employed. The system employs an optimization model and algorithm to perform offline planning of energy-saving speed curves. It optimizes the energy-saving operation schedule by adjusting train running times within sections and improving the utilization rate of regenerative braking energy through surplus time. Automatic train adjustments are made based on various situations, with surplus time including station stops and turnaround operations. The system also optimizes the dynamic energy consumption of single and multiple trains through ATO single-car energy saving and ATS operation schedule planning multi-car collaborative technology. It is understood that the ATO single-car energy saving + ATS operation schedule planning multi-car collaborative energy saving module is not limited to achieving its functions through the above methods; it can also achieve its functions through other means.

[0078] Furthermore, in step S2, the passenger flow time series prediction module predicts the passenger flow data for the day. In practical applications, the passenger flow time series prediction module can be established based on historical passenger flow data in the big data module using the ARIMA algorithm, and the passenger flow for the day can be predicted based on the passenger flow data obtained from the real-time data platform. Of course, the passenger flow time series prediction module can also be established in other ways, and this invention does not limit it.

[0079] Furthermore, in S2, the dynamic scheduling module based on passenger flow needs, based on the real-time passenger flow data collected in S1 and the passenger flow data predicted by the passenger flow time series prediction module for the day, changes the existing fixed-run mode according to the schedule, and dynamically analyzes the passenger flow data in real time. Based on the passenger flow data, it dynamically adjusts the daily schedule to obtain an updated schedule, thereby realizing the scheduling based on passenger flow needs.

[0080] Furthermore, in S2, the regenerative braking energy feed module fully utilizes the real-time traction and braking status information of the train controlled by the signal system to coordinate the operation of trains in the same power supply zone. At the same time, when the traction system voltage dV / dt begins to rise, the regenerative braking energy recovery device is activated to recover energy, making full use of the regenerative braking energy and avoiding the regenerative braking energy being consumed by the train resistance.

[0081] Specifically, the regenerative braking energy recovery device activation mechanism based on the operating schedule, obtained through the regenerative braking energy feedback module, includes: optimizing train interval travel time, station stop time, and turnaround time based on the updated operating schedule; using simulated annealing intelligent algorithm technology to solve the large-scale transportation optimization problem with complex network and multiple constraints; maximizing the overlap of train arrival and departure times within the same and / or adjacent power supply zones; enabling departing trains to fully utilize the regenerative braking energy of arriving trains; effectively reducing the traction energy consumption of departing trains; and achieving a better energy-saving solution for the entire line. Based on the above, the regenerative braking energy feedback module, through coordination with trains in the same power supply zone, achieves maximum balance between acceleration and braking, maximizing the utilization of braking energy by the accelerating trains.

[0082] Furthermore, the regenerative braking energy recovery module utilizes the predictability of the signal system's operating parameters and employs a predictive algorithm in coordination with the regenerative braking energy recovery device. When the train is about to brake, the regenerative braking energy recovery device is notified in advance, and it is activated immediately while maintaining train braking to recover electrical energy, rather than activating it only after the voltage has increased to 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 activation mechanism based on the updated operating diagram is obtained. Figure 3 shows 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 the scheme shown in Figure 3. In other embodiments, other methods can be used, as long as the corresponding function can be achieved. The present invention does not impose any restrictions on this.

[0083] 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 gradient 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:

[0084]

[0085] in, Let E represent the deep neural network model function, i and j represent the indices of the i-th and j-th stations respectively, there are n stations in total, k represents the k-th train, and there are m trains in total. Let represent the departure time, arrival time, dwell time at station j, and ATO energy-saving curve between stations for train k from station i to station j, respectively. Represents the set of ATO curves. This represents the weight of the k-th train. This represents the gradient between station i and station j. This represents the passenger flow at the i-th station. Indicates whether it is rainy or snowy weather. This represents the energy recovered by the signal and regenerative braking energy recovery device at station i.

[0086] In practical applications, the prediction model between the line's energy consumption and operational data variables can be optimized based on real-time rail transit operation data (such as weather data) and predicted daily passenger flow data, combined with constraints such as transportation plans. This optimization objective function is then used. The optimized prediction model between the line's energy consumption and operational data variables can be expressed as:

[0087]

[0088] in, For the optimized line energy consumption, Let represent the real-time departure time, arrival time, and dwell time at station j for the k-th train from station i to station j, respectively. The ATO energy-saving curve between stations is obtained from S2.

[0089] Furthermore, the updated operation schedule and ATO energy-saving curve obtained in S2 are used as the initial candidate solutions for the prediction model between the optimized line energy consumption and operation data variables. Heuristic optimization methods such as genetic algorithm or particle swarm optimization are used to solve the optimization problem to obtain the updated optimal operation schedule and optimal ATO energy-saving curve for the day, so as to form a second operation strategy. This changes the existing fixed-run mode according to the schedule to a dynamic timetable adjustment mode according to the real-time passenger flow during real-time operation.

[0090] Based on the above, this invention utilizes the energy-saving curves and energy consumption data recorded daily for each specific section under different conditions (such as train weighing, track adhesion coefficient caused by climate change, etc.) in the traction energy-saving system. It employs a deep neural network to establish a predictive model between line energy consumption and other operational data variables, thereby accumulating historical data to discover better energy-saving curves under the same conditions. In practical applications, the system records the planned energy-saving operation schedule daily, combines it with daily passenger flow information, and, while meeting various basic operational indicators, discovers a better operation schedule required to transport the same passenger flow. Furthermore, in real-time operation, it transforms the existing fixed-schedule train operation mode into a dynamic schedule adjustment mode based on real-time passenger flow requirements.

[0091] Furthermore, the traction energy-saving method for rail transit based on dynamic power flow calculation also includes: monitoring the health status 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.

[0092] It is understood that the various modules involved in the traction energy-saving method for rail transit based on dynamic power flow calculation of the present invention are not limited to the above-mentioned methods, and can also achieve the corresponding functions through other existing methods. The present invention does not limit this.

[0093] Based on the same inventive concept, this invention also discloses a traction energy-saving system for rail transit based on dynamic power flow calculation (see Figure 4). This system is used to implement the aforementioned traction energy-saving method for rail transit based on dynamic power flow calculation. Specifically, the traction energy-saving system includes a real-time rail transit data module and a business knowledge module. The real-time rail transit data module is used to collect real-time rail transit operation data, which includes real-time passenger flow data. The business knowledge module, also known as the business knowledge platform, includes a multi-vehicle collaborative energy-saving module for ATO single-vehicle energy saving and ATS time-series planning, a passenger flow time-series prediction module, a dynamic time-series adjustment module based on passenger flow demand for energy saving, a regenerative braking energy feedback module, a traction power supply system power flow dynamic calculation and analysis module, and a prediction model between line energy consumption and operational data variables. Specifically, the ATO single-vehicle energy saving and ATS time-series planning 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 for the day; the dynamic time-series adjustment module based on passenger flow demand can be used to obtain the time-series plan; the regenerative braking energy feedback module can be used to obtain the activation mechanism of the regenerative braking energy recovery device based on the time-series plan; the traction power supply system power flow dynamic calculation and analysis module can dynamically record and analyze the distribution of trains in the power supply zone, the relationship between the traction braking time and energy consumption of the train ATO energy-saving curve, and obtain the total actual energy consumption data corresponding to the operation strategy; and the prediction model between line energy consumption and operational data variables can be used to optimize the operation strategy.

[0094] As can be seen from the above, in the traction energy-saving system of the present invention based on dynamic power flow calculation for rail transit, the real-time data module and business knowledge module of rail transit, combined with the dynamic train operation information of the signal system, track foundation information, dynamic passenger flow information (train weighing), power supply system topology relationship and dynamic voltage and current, through dynamic power flow calculation and analysis, coordinate and adjust various traction energy-saving means such as train operation and regenerative braking energy recovery device, so as to ensure that various energy-saving measures work together to achieve the goal of high efficiency and energy saving.

[0095] Furthermore, the traction energy-saving system also includes a big data module, namely a big data platform, which is used to store operational data. In practical applications, by establishing the big data module and performing data mining based on it through the business knowledge module, better ATO curves, better energy-saving operation diagrams, and better collaborative strategies for regenerative braking energy recovery devices can be found from historical data.

[0096] Furthermore, the traction energy-saving system also includes an equipment health and abnormal energy consumption analysis module. This module monitors the health and abnormal energy consumption of the train's main equipment. When the abnormal energy consumption analysis module detects abnormal energy consumption, it can issue an alarm signal. In practical applications, the equipment health and abnormal energy consumption analysis module constructs an energy consumption model of the train's main equipment and analyzes its health. When abnormal energy consumption is detected, it issues an alarm in a timely manner to avoid prolonged high-energy-consumption operation.

[0097] In practical applications, the traction energy-saving system collects real-time operation data of rail transit by establishing a real-time data module, i.e., a real-time data platform. The real-time operation data of rail transit includes real-time passenger flow data, real-time train operation information, train weighing information, real-time voltage and current information of the traction power supply system, energy consumption information of the traction power supply system, energy consumption information of a single vehicle, and regenerative energy information of a single vehicle.

[0098] Furthermore, in the system architecture of the traction energy-saving system, the big data module is located in the data platform layer. By establishing a big data platform and using historical data to create a prediction module between data such as operation charts and operational information and energy consumption, and based on real-time data and passenger flow forecasts, the system uses optimization algorithms to solve for the optimal ATO energy-saving curve and route operation plan under the current operational needs. Based on the above, in practical applications, by inputting transportation planning requirements, the system can automatically match a better energy-saving strategy and compare it with the energy-saving efficiency of previous strategies to determine whether it is more energy-efficient.

[0099] Furthermore, the traction energy-saving system establishes a business knowledge module, namely a business knowledge platform. This business knowledge module can utilize knowledge graph technology to establish modules such as the ATO single-vehicle energy-saving + ATS operation plan multi-vehicle collaborative energy-saving module, the passenger flow time series prediction module, the dynamic timetable adjustment energy-saving module based on passenger flow needs, the regenerative braking energy feedback module, the traction power supply system power flow dynamic calculation and analysis module, the prediction model between line energy consumption and operational data variables, and the equipment health and abnormal energy consumption analysis module. Of course, the business knowledge platform can be used not only to establish the above modules but also to establish modules with other functionalities; this invention does not limit this.

[0100] Figure 4 shows a schematic diagram of the architecture of a traction energy-saving system according to the present invention. This traction energy-saving system includes a cloud platform layer, a data middleware layer, an intelligent computing layer, and a human-computer interaction layer. The cloud platform layer provides basic computing resources and cloud management enhancement functions, specifically including functions such as independent CPU core binding for enhanced virtual machines, exclusive memory usage without over-allocation, and ensuring that the computing resources of enhanced virtual machines are not preempted. The data middleware layer includes an industrial real-time middleware and a big data middleware. The industrial real-time middleware is responsible for the collection and processing of real-time data, while the big data middleware performs data cleaning, mining, and analysis on the real-time data processed by the real-time middleware and historical data to support business modeling of the business knowledge middleware. The intelligent computing layer includes a business knowledge module, which is mainly based on knowledge graph technology, big data, and AI algorithm technology to realize the modeling of various business analysis modules. The main modules involved in the intelligent computing layer include: station / section running 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 map adjustment module according to passenger flow requirements, and equipment health and abnormal energy consumption analysis module.

[0101] Based on the same inventive concept, the present invention also provides an electronic device, the electronic device comprising: a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, it implements the steps of the aforementioned traction energy-saving method for rail transit based on dynamic power flow calculation.

[0102] Based on the same inventive concept, the present invention also provides a readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the aforementioned traction energy-saving method for rail transit based on dynamic power flow calculation.

[0103] In summary, the present invention discloses a traction energy-saving method, system, electronic equipment, and readable storage medium for rail transit based on dynamic power flow calculation. This traction energy-saving method utilizes modules such as an ATO single-vehicle energy-saving + ATS operation plan multi-vehicle collaborative energy-saving module, a dynamic timetable adjustment module based on passenger flow requirements, and a regenerative braking energy recovery module to dynamically analyze the multi-dimensional coupling conditions of power supply, vehicles, signals, and tracks. Furthermore, it employs a traction power supply system power flow dynamic calculation and analysis module to perform dynamic power flow calculations from an energy flow perspective, dynamically coordinating ATO energy saving, ATS energy saving, and regenerative braking energy recovery device energy saving technologies for unified management and coordinated operation. It also monitors and statistically analyzes energy consumption for different traction energy-saving measures, tracking and evaluating the long-term energy-saving operation effects. Finally, it continuously optimizes energy-saving control strategies based on the analysis results using a predictive model between line energy consumption and operational data variables, thereby achieving truly superior energy-saving results.

[0104] Although the present invention has been described in detail through the preferred embodiments above, it should be understood that the above description should not be considered as a limitation of the present invention. Various modifications and substitutions to the present invention will be apparent to those skilled in the art after reading the above description. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. A rail transit traction energy-saving method based on dynamic power flow calculation, characterized in that, Include: S1. Real-time operation data of rail transit is collected through the rail transit real-time data module, wherein the real-time operation data of rail transit includes real-time passenger flow data; S2. Based on real-time rail transit operation data, the ATO energy-saving curve is obtained through the ATO single-vehicle energy-saving + ATS operation plan planning multi-vehicle collaborative energy-saving module. The passenger flow time series prediction module predicts the passenger flow data for the day. The dynamic timetable adjustment module adjusts the operation plan for the day based on the real-time passenger flow data collected in S1 and the passenger flow data predicted by the passenger flow time series prediction module to obtain an updated operation plan. The regenerative braking energy recovery device activation mechanism is obtained based on the updated operation plan through the regenerative braking energy feedback module. The first operation strategy is composed of the ATO energy-saving curve, the updated operation plan, the regenerative braking energy recovery device activation mechanism, and the predicted passenger flow data for the day. S3. The traction power supply system dynamic calculation and analysis module dynamically records and analyzes the distribution of trains in the power supply zone, the relationship between the traction braking time and energy consumption of the train ATO energy-saving curve, and the total actual energy consumption data based on the first operating strategy. S4. Optimize the first operation strategy by 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 in the power supply zone, the relationship between the traction braking time of the train ATO energy-saving curve and energy consumption, 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 operating strategy with the total actual energy consumption data corresponding to the second operating strategy. When 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.

2. The traction energy-saving method for rail transit based on dynamic power flow calculation as described in claim 1, characterized in that, 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 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, and specifically includes: By real-time monitoring of traction power supply mode, traction power supply voltage, train dynamic position and weighing, a dynamic circuit relationship between traction power supply and train is established. The substation is equivalent to a power source, the overhead contact 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 segments, and under the condition of dynamically moving trains, the relationship between voltage, resistance, current, power and energy consumption in different segments is defined. The power flow of the traction power supply system is calculated in real time by monitoring traction voltage and current, train power consumption and energy feed, and the dynamic distribution of the train. By collecting total actual energy consumption data from the power supply system and trains, the distribution of trains in the power supply zones and the relationship between the traction and braking times of the train's ATO energy-saving curve and energy consumption are dynamically recorded and analyzed.

4. The traction energy-saving method for rail transit based on dynamic power flow calculation as described in 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 speed deviation of the train operation, each ATO energy-saving curve is divided into multiple checkpoints, and each checkpoint records different ATO train control parameters. When the train passes a checkpoint, ATO takes into account the remaining time at the station and the upstream and downstream operation curves to calculate the appropriate ATO target speed. Based on big data analysis, the coasting resistance of trains is identified, specifically for different trains with different loads at different kilometer marker positions; the coasting resistance is known in advance through data accumulation and cleaning. An offline planning method for the energy-saving speed curve is adopted using a convex optimization model and algorithm. By adjusting the train's running time between sections and improving the utilization rate of train regenerative braking energy, the energy-saving operation schedule is optimized and automatic train adjustments are made. By combining ATO single-vehicle energy saving with ATS operation chart planning and multi-vehicle collaborative technology, the dynamic energy consumption of single and multiple vehicles can be optimized.

5. The traction energy-saving method for rail transit based on dynamic power flow calculation as described in claim 1, characterized in that, In step S2, the regenerative braking energy recovery device startup mechanism based on the operation diagram obtained through the regenerative braking energy feed module includes: Based on the updated operation chart, the train section running time, station stop time and turnaround time are optimized. The simulated annealing intelligent algorithm technology is used 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 zones overlap to the greatest extent, so that the exiting trains can make full use of the braking regenerative energy of the entering trains. Taking advantage of the predictability of the signal system's operating parameters, a predictive algorithm is used in conjunction with the regenerative braking energy recovery device. When the train is about to begin braking, the regenerative braking energy recovery device is notified in advance, and while maintaining the train's braking, the regenerative braking energy recovery device is immediately activated to recover electrical energy. Based on the above, the startup mechanism of the regenerative braking energy recovery device based on the updated operation diagram is obtained.

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

7. The traction energy-saving method for rail transit based on dynamic power flow calculation as described in claim 6, characterized in that, The prediction model between the line energy consumption and operational data variables can be expressed as follows: in, Let E represent the deep neural network model function, i and j represent the indices of the i-th and j-th stations respectively, there are n stations in total, k represents the k-th train, and there are m trains in total. Let represent the departure time, arrival time, dwell time at station j, and ATO energy-saving curve between stations for train k from station i to station j, respectively. Represents the set of ATO curves. This represents the weight of the k-th train. This represents the gradient between station i and station j. This represents the passenger flow at the i-th station. Indicates whether it is rainy or snowy weather. This represents the energy recovered by the signal and regenerative braking energy recovery device at station i.

8. The traction energy-saving method for rail transit based on dynamic power flow calculation as described in claim 6, characterized in that, Based on real-time rail transit operation data and predicted daily passenger flow, the optimized prediction model between line energy consumption and operational data variables is expressed as follows: For the optimized line energy consumption, Let represent the real-time departure time, arrival time, and dwell time at station j for the k-th train from station i to station j, respectively. The ATO energy-saving curve between stations is obtained from S2.

9. The traction energy-saving method for rail transit based on dynamic power flow calculation as described in claim 1, characterized in that, Also includes: The abnormal energy consumption analysis module monitors the health status and abnormal energy consumption of the train equipment. When the abnormal energy consumption analysis module detects abnormal energy consumption, it issues an alarm signal.

10. A traction energy-saving system for rail transit based on dynamic power flow calculation, used to implement the traction energy-saving method for rail transit based on dynamic power flow calculation as described in any one of claims 1 to 9, characterized in that, The traction energy-saving system includes a real-time rail transit 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 includes: The ATO single-vehicle energy saving + ATS operation diagram planning multi-vehicle collaborative energy saving module is used to obtain the initial ATO energy saving curve; The passenger flow time series prediction module is used to predict the passenger flow data for the day. The energy-saving module, which dynamically adjusts the operating schedule according to passenger flow requirements, is used to obtain the operating schedule. A regenerative braking energy feed module is used to obtain the start-up mechanism of the regenerative braking energy recovery device based on the operation diagram; The traction power supply system dynamic calculation and analysis module dynamically records and analyzes the distribution of trains in the power supply zone, the relationship between the traction braking time and energy consumption of the train ATO energy-saving curve, and obtains the total actual energy consumption data corresponding to the operation strategy based on the operation strategy. A predictive model between line energy consumption and operational data variables is used to optimize operational strategies.

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

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

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

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

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