Subway train energy-saving dispatching optimization method and system

Abstract

The invention provides a subway train energy-saving scheduling optimization method and system, and belongs to the technical field of energy-saving scheduling, and the method specifically comprises the steps: carrying out the recognition processing of an energy-saving optimization strategy of an energy-saving scheduling interval through an energy-saving scheduling recognition strategy, and obtaining a recognition processing result, determining an optimized control interval in the energy-saving dispatching interval according to the identification processing result and the coincident data of the trains in the energy-saving dispatching interval and other energy-saving dispatching intervals in different operation periods, and determining the energy-saving dispatching interval based on the data of the optimized control interval and the coincident data of the trains in the energy-saving dispatching interval and the optimized control interval. And the energy-saving scheduling optimization method of the energy-saving scheduling interval is determined, so that the recognition processing efficiency of the energy-saving strategy of the subway train is improved.

Description

Technical Field

[0001] This invention belongs to the field of energy-saving dispatching technology, and in particular relates to an energy-saving dispatching optimization method and system for subway trains. Background Technology

[0002] The current subway train scheduling and operation are based on fixed operating curves: fixed station running times and driving modes (such as maximum acceleration-cruising-coasting-maximum braking) are mostly used, and refined energy-saving speed adjustment is not carried out according to real-time load, track gradient and speed limit.

[0003] To address the aforementioned technical problems, the invention patent application CN201410211784.2, "A Method for Reducing Metro Traction Energy Consumption," establishes starting / braking energy models for a single metro train operating at a single station, traction energy models for a single metro train operating throughout the entire day and along the entire line, and traction energy models for all metro trains operating throughout the day and along the entire line. It adjusts train scheduling diagrams, increases energy offsetting, and appropriately reduces train speed limits to reduce metro traction energy consumption. However, the above technical solution has the following technical problems: When identifying and processing the optimal energy-saving strategy for subway trains, existing technical solutions have neglected the number of transfer passengers at different stations. Generally speaking, when the number of transfer passengers is large, the risk of subway train delays is higher. Therefore, how to determine the energy-saving strategy identification and processing method by combining the number of transfer passengers at different stations during the subway train's operation in the passenger capacity range, so as to improve the efficiency of energy-saving instruction identification and processing while minimizing the risk of delays, has become an urgent technical problem to be solved.

[0004] To address the aforementioned technical problems, this application provides a method and system for optimizing energy-saving dispatching of subway trains. Summary of the Invention

[0005] To achieve the objectives of this invention, the following technical solution is adopted: Specifically, this application provides a method for optimizing energy-saving scheduling of subway trains, which includes: S1 uses the subway train operation data to determine the running segment data in different passenger capacity ranges, and combines it with the transfer passenger data of different stations in the running segment to determine the energy-saving dispatching segment in the passenger capacity range. Using the energy-saving dispatching segment data, and combining it with the fully loaded stations in the running segment of the energy-saving dispatching segment, the energy-saving dispatching identification strategy of the energy-saving dispatching segment is determined. S2 uses the energy-saving scheduling identification strategy to identify the energy-saving optimization strategy of the energy-saving scheduling section and obtains the identification processing result. Based on the identification processing result and the overlap data of trains with other energy-saving scheduling sections in different operating periods, the optimal control section in the energy-saving scheduling section is determined. S3 determines the energy-saving scheduling optimization method for the energy-saving scheduling section based on the optimized control section data and the overlap data of trains in the energy-saving scheduling section and the optimized control section.

[0006] The beneficial effects of this invention are as follows: This application identifies energy-saving dispatching sections within passenger capacity ranges, enabling the intelligent identification of train passenger capacity ranges suitable for implementing energy-saving dispatching strategies (such as optimizing cruising speed) from massive operational data. The implementation of specific energy-saving strategies must not come at the expense of passenger service experience (especially transfer efficiency) and on-time reliability. By analyzing the stability of the operating segment and station load under a specific passenger capacity range, the application ultimately determines whether the range has the conditions for safe and efficient implementation of energy-saving dispatching, thereby achieving the identification of energy-saving dispatching sections.

[0007] In this application, an energy-saving scheduling optimization method for energy-saving scheduling sections is determined based on optimized control section data and train overlap data between energy-saving scheduling sections and optimized control sections. Since a batch of stable sections (optimized control sections) with good energy-saving effects have been identified in the system but are no longer subject to further optimization of energy-saving strategies, there are cases where the energy-saving effects of some stable sections may still have potential for improvement. To enable future re-optimization of these stable sections, the energy-saving scheduling identification strategy for energy-saving scheduling sections is determined by assessing the correlation with the operation of optimized control sections. This further improves the efficiency of identifying and processing the optimal energy-saving scheduling instructions for energy-saving scheduling sections, thereby reducing overall risk and laying a safe foundation for subsequent identification and optimization of stable sections.

[0008] Furthermore, the subway train's operation data is based on the passenger volume of the subway train at different times.

[0009] Furthermore, the operating time data within the passenger capacity range is determined based on the distribution data of the subway train's passenger capacity operating time within the passenger capacity range on different dates.

[0010] Furthermore, the passenger volume data of the station includes the passenger volume of the station.

[0011] Furthermore, the method for determining the energy-saving scheduling interval within the passenger capacity interval is as follows: Using the runtime data within the passenger capacity range, determine the matching runtime for the passenger capacity range; Based on the passenger volume data of different stations in the matching runtime, determine the stations in the matching runtime where the number of transfer passengers does not meet the requirements, and designate them as fully loaded stations. Based on the matching runtime segments of the passenger capacity range and the fully loaded stations in different matching runtime segments, it is determined whether the passenger capacity range is an energy-saving scheduling range.

[0012] Furthermore, the method for determining the energy-saving scheduling optimization method for the energy-saving scheduling interval is as follows: Based on the optimized control interval data, determine the number of optimized control intervals; Based on the overlap data of trains in the energy-saving dispatching section and the optimized control section, the overlap data of the matching operation process of the energy-saving dispatching section and the matching operation process of the optimized control section are determined. Based on the overlap data, the optimized control section that overlaps in the matching operation process of the energy-saving dispatching section is determined and used as the associated optimized section. The energy-saving scheduling optimization method for the energy-saving scheduling interval is determined based on the number of the optimized control intervals and the associated optimized intervals in the different matching operation processes of the energy-saving scheduling intervals.

[0013] Secondly, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described method for optimizing energy-saving scheduling of subway trains when running the computer program.

[0014] Other features and advantages will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0015] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0016] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0017] Figure 1 This is a flowchart of a subway train energy-saving scheduling optimization method; Figure 2 This is a flowchart illustrating the method for determining the energy-saving scheduling interval within the passenger capacity range; Figure 3 This is a flowchart illustrating the method for determining the energy-saving scheduling identification strategy within an energy-saving scheduling interval. Detailed Implementation

[0018] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that the invention will be thorough and complete, and the concept of the exemplary embodiments will be fully conveyed to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and therefore their detailed description will be omitted.

[0019] The terms “a,” “one,” “the,” and “the” are used to indicate the existence of one or more elements / components / etc.; the terms “including” and “having” are used to indicate an open-ended meaning of inclusion and that other elements / components / etc. may exist in addition to the listed elements / components / etc.

[0020] Example 1 To solve the above problems, according to one aspect of the present invention, such as Figure 1 As shown, a method for optimizing energy-saving scheduling of subway trains is provided, specifically including: S1 uses the subway train operation data to determine the running segment data in different passenger capacity ranges, and combines it with the transfer passenger data of different stations in the running segment to determine the energy-saving dispatching segment in the passenger capacity range. Using the energy-saving dispatching segment data, and combining it with the fully loaded stations in the running segment of the energy-saving dispatching segment, the energy-saving dispatching identification strategy of the energy-saving dispatching segment is determined. S2 uses the energy-saving scheduling identification strategy to identify the energy-saving optimization strategy of the energy-saving scheduling section and obtains the identification processing result. Based on the identification processing result and the overlap data of trains with other energy-saving scheduling sections in different operating periods, the optimal control section in the energy-saving scheduling section is determined. S3 determines the energy-saving scheduling optimization method for the energy-saving scheduling section based on the optimized control section data and the overlap data of trains in the energy-saving scheduling section and the optimized control section.

[0021] Furthermore, the subway train's operation data is based on the passenger volume of the subway train at different times.

[0022] Furthermore, the operating time data within the passenger capacity range is determined based on the distribution data of the subway train's passenger capacity operating time within the passenger capacity range on different dates.

[0023] Furthermore, the passenger volume data of the station includes the passenger volume of the station.

[0024] Specifically, such as Figure 2As shown, the method for determining the energy-saving scheduling interval within the passenger capacity interval is as follows: The fundamental goal of this method is to intelligently identify train passenger capacity sections suitable for implementing energy-saving scheduling strategies (such as optimizing cruising speed and adjusting inter-station travel times) from massive amounts of operational data. Its core logic is that the implementation of energy-saving strategies should not come at the expense of passenger service experience (especially transfer efficiency) and on-time reliability. Therefore, the decision-making process is a data-driven, multi-layered filtering and risk assessment process. By analyzing the stability of operating segments and station load conditions under specific passenger capacity sections, it ultimately determines whether the section meets the conditions for safe and efficient implementation of energy-saving scheduling.

[0025] S11 uses the operating segment data within the passenger capacity range to determine the matching operating segment for the passenger capacity range; Determine the matching runtime for the passenger capacity range: Matched operating segments: These refer to all time segments during actual operation where the passenger load of a subway train falls within the specific passenger load range being analyzed (e.g., 60%-70% of the rated passenger load). These time segments serve as the basic data sample for analyzing the operational characteristics of that passenger load range.

[0026] Directly analyzing "passenger capacity ranges" is static and cannot guide operations. It must be mapped to real, dynamic operating times. By statistically analyzing historical data, we can identify which actual time periods (e.g., 10:00-11:30 AM on weekdays) trains frequently reach that passenger capacity level. This transforms the abstract "passenger flow level" into a concrete, analyzable "set of time periods." This is a crucial first step from macro-level indicators to micro-level time scenarios, laying the foundation for subsequent assessments of operational status within specific spatiotemporal contexts.

[0027] Let's assume we're analyzing a passenger volume range of 70%-80%. By scanning historical operational data from the past month, we find that this range primarily occurs between 9:30 AM and 11:00 AM and between 2:00 PM and 3:30 PM on weekdays. These two time periods are therefore identified as the "matching operating hours" for this passenger volume range. All subsequent analyses will be based on data occurring within these two time periods.

[0028] S12 determines the stations in the matching operation period where the number of transfer passengers does not meet the requirements based on the passenger volume data of different stations in the matching operation period, and treats them as fully loaded stations. Identify fully loaded sites in the matching runtime segment: Transfer passengers: This includes passengers who board and alight at a particular station. Special attention is paid to transfer behavior here because the flow of people at major transfer stations has the greatest impact on train stopping time and platform order, making them key nodes in the operational plan.

[0029] Fully loaded station: A station where the total number of passengers boarding and alighting (i.e., the number of transfer passengers) exceeds a preset threshold during a certain matching operation period. This threshold is usually set based on a combination of platform capacity, passageway capacity, and safety redundancy.

[0030] Energy-saving scheduling may involve adjusting train speeds or times between stations, which can affect train arrival intervals. At stations with extremely high passenger transfer demand (full-load stations), boarding and alighting times are long. In such cases, the probability of train delays or time fluctuations due to energy-saving scheduling is high, leading to safety hazards and a decline in service quality. Identifying "full-load stations" essentially involves identifying vulnerable and sensitive critical nodes in the operating network, and is a core step in assessing the risks of energy-saving scheduling.

[0031] During the matching operating hours of 70%-80% of passenger capacity, specifically from 9:30 AM to 11:00 AM on weekdays, passenger boarding and alighting data for each station was analyzed. It was found that People's Square Station (a large three-line transfer station) had an average total passenger volume of 5000 people / hour during this period, far exceeding the preset threshold of 3000 people / hour. Therefore, People's Square Station was marked as a "fully loaded station" during this time period. Other ordinary stations had passenger volumes below the threshold and were not marked.

[0032] S13 determines whether the passenger capacity range is an energy-saving scheduling range based on the matching running segments of the passenger capacity range and the fully loaded stations in different matching running segments.

[0033] It should be noted that the matching runtime segment is the runtime segment within the passenger capacity range.

[0034] Specifically, the transfer passengers include those boarding and those alighting.

[0035] It is understood that the "full-load station" refers to a station where the number of transfer passengers exceeds a preset threshold.

[0036] Specifically, determining whether the passenger capacity range is an energy-saving scheduling range includes: Case 1: If the average duration of the matching running segments of the passenger capacity range on different dates is less than the preset duration threshold, then the passenger capacity range is determined not to belong to the energy-saving scheduling range, that is, the passenger capacity range for which the optimal energy-saving strategy identification and processing is performed.

[0037] In the above scenario, the average duration of the matched running segments is too short. If the actual running segment corresponding to a passenger capacity range is very short (e.g., less than 15 minutes each time per day), it indicates that the train is in that passenger flow state for a relatively short period. Designing and implementing an energy-saving scheduling strategy specifically for this short and unstable state would likely have very limited energy-saving benefits, and frequent scheduling changes could introduce operational complexity and risks, resulting in a low return on investment. Therefore, this strategy is directly excluded, and the focus is placed on more continuous periods.

[0038] Case 2: If the average duration of the matched running segments of the passenger capacity range on different dates is not less than the preset duration threshold, and if there are no fully loaded stations in different matched running segments, then the passenger capacity range is determined to be an energy-saving scheduling range.

[0039] A long time period with no fully loaded stations represents the ideal energy-saving scheduling scenario. Sufficiently long daily matching runs (e.g., exceeding one hour) ensure ample time for energy-saving measures to accumulate substantial benefits. Simultaneously, the absence of fully loaded stations means low passenger flow pressure at key network nodes, minimizing the risk of delays caused by minor adjustments to implementation speed or timing. Implementing energy-saving strategies within this period maximizes energy savings with minimal impact on service quality, thus directly defining it as an "energy-saving scheduling period."

[0040] Case 3: If there are fully loaded stations in the matching runtime segments, obtain the percentage of matching runtime segments with fully loaded stations. If the percentage of matching runtime segments with fully loaded stations is greater than the preset full load percentage threshold, then it is determined that the passenger load range does not belong to the energy-saving scheduling range.

[0041] In the above scenario, the frequency of fully loaded stations is excessive. If the proportion of matched running periods with fully loaded stations is very high (e.g., exceeding 80%), it indicates that full loading is the norm within that passenger volume range, rather than an isolated incident. This means that operating within this range, the system itself is nearing the passenger flow pressure boundary. Any scheduling adjustments aimed at energy conservation (even minor ones) could become the "last straw," systematically increasing the risk of delays. Lacking the basic safety redundancy for implementing energy-saving scheduling, such adjustments should be excluded.

[0042] Scenario 4: If the proportion of matching running segments with fully loaded stations is not greater than the preset full load proportion threshold, the full load impact period is determined by the number of fully loaded stations in different matching running segments. It is then determined whether there is a full load impact period on different dates. If so, energy-saving strategy identification processing is performed in the passenger capacity range. Since speed adjustment and the impact of station boarding time may lead to a higher risk of train delays, it is determined that the passenger capacity range does not belong to the energy-saving scheduling range. If not, the passenger capacity range is determined to belong to the energy-saving scheduling range.

[0043] In the above situation, fully loaded stations may occasionally occur, and it is necessary to further determine the period of impact. The period of impact refers to the matching operation segment in which the number of fully loaded stations exceeds a certain threshold within a single matching operation segment. This marks the "danger window" of high passenger flow.

[0044] This situation deals with an intermediate state—full load is not the norm, but it does occur. In this case, it's necessary to determine whether full load is an isolated, occasional event or a fixed pattern. The method is to analyze whether the "full load impact period" consistently occurs on different dates (even if the full load stations themselves may differ). If it consistently occurs, it indicates a fixed, predictable "high-risk period" within this passenger volume range. Implementing energy-saving strategies globally within this range will significantly increase the risk once this "high-risk period" begins, compounded by the effects of energy-saving scheduling. Therefore, out of caution, this entire range is excluded from the energy-saving scheduling range. Conversely, if it only occurs occasionally on a very few dates, the overall risk is controllable and can be classified as an "energy-saving scheduling range," but special handling can be applied to a few abnormal dates in actual scheduling.

[0045] Consistent examples: Continuing the analysis, the passenger volume range of 70%-80% was analyzed. The average duration of the matched running time on different dates exceeded one hour (excluding case 1). In the past 20 working days, there were 5 days with fully loaded stations (People's Square Station) during the matched time period, accounting for 25%. Due to the existence of fully loaded stations, the analysis proceeds to case 3 or 4. The preset threshold for the percentage of fully loaded stations is 30%; 25% < 30%, therefore case 3 is excluded, and case 4 is considered.

[0046] Analysis of these 5 days revealed that: on 4 of the days, the number of fully loaded stations (more than 2) occurred only within the short window of 9:50-10:05 (i.e., the "load-affected period"), and not on the same day; on the other day, there was no full load. Therefore, the "load-affected period" did not appear on all different dates. Conclusion: This passenger volume range falls within the energy-saving scheduling range. Operators can try applying energy-saving strategies within this range (70%-80% passenger volume).

[0047] This method embodiment demonstrates a data-driven, risk-prevention-based, and refined management decision-making process. Its value lies in: From "one-size-fits-all" to "refined": This approach changes the traditional extensive model of implementing energy-saving strategies either along the entire line or at all times, and instead achieves interval-based and time-based energy-saving scheduling based on precise passenger flow status.

[0048] Balancing "energy saving" and "service": By introducing concepts such as "full-loaded stations" and "impact periods", passenger transfer experience and operational reliability are quantified as core risk indicators to ensure that energy-saving measures do not introduce unacceptable risks at key passenger flow nodes and times.

[0049] Enhancing the scientific nature of decision-making: Through multi-layered filtering (duration filtering, frequency filtering, and pattern filtering), the system systematically identifies high-yield, low-risk energy-saving potential ranges, improving the scientific nature and reliability of operational strategy formulation. This helps the subway system achieve significant energy savings and reduced operating costs while ensuring service levels.

[0050] Specifically, such as Figure 3 As shown, the method for determining the energy-saving scheduling identification strategy for the energy-saving scheduling interval is as follows: This method, based on the already determined "energy-saving scheduling intervals," further introduces the key dimension of real-time operational health to serve the core objective: to formulate an energy-saving scheduling identification strategy that can both tap into energy-saving potential and absolutely guarantee on-time performance and system stability. Its core logic is that even if a certain interval historically meets the energy-saving scheduling conditions (moderate passenger flow, controllable risk of full load), if delays (node ​​delays) have occurred in a specific actual operation, continuing to implement energy-saving speed adjustments that could affect the timetable would be extremely risky. Therefore, the final energy-saving scheduling identification strategy is a product of combining historical risk assessment and real-time status monitoring, ensuring that energy-saving instructions are only implemented when all three conditions are met: favorable timing (passenger flow conditions), advantageous location (station load), and satisfactory personnel (on-time operation).

[0051] S21 uses the energy-saving scheduling interval data to determine the percentage of runtime of the energy-saving scheduling interval on different dates; Running time percentage: This refers to the percentage of total operating time that a train spends within the energy-saving dispatch zone on a single operating day. It measures the time weight of that zone in daily operations.

[0052] Average percentage of total runtime: This refers to the average percentage of daily runtime within a statistical period (e.g., 30 days). It reflects the long-term time percentage level of that period.

[0053] This step involves initial screening from a strategic time value perspective. If an energy-saving scheduling interval accounts for a very small percentage of long-term operation (e.g., an average percentage of total time less than 5%), it means that the marginal benefit of developing and deploying refined energy-saving strategies for it (requiring real-time assessment of node latency) is low, while the system complexity and scheduling costs are relatively fixed. Therefore, the percentage of time spent is a fundamental economic indicator for determining whether it is worthwhile to configure high-level energy-saving strategy management for it.

[0054] Specific examples: Let's continue using the energy-saving scheduling range of 70%-80% passenger capacity, which has already been determined, as an example. Analyzing the data from the past 30 days, we calculate the percentage of daily running time, and the 30-day average is 18%. Therefore, the average percentage of total running time is 18%, and this value will be used for subsequent decisions.

[0055] S22 determines the matching running segments with full-loaded stations and the number of full-loaded stations in different matching running segments based on the full-loaded stations in the running segments of the energy-saving scheduling interval. Matching runtime periods with fully loaded stations: In historical data, mark the specific dates and time periods when passenger volume falls within the energy-saving scheduling range and fully loaded stations occur simultaneously.

[0056] Number of fully loaded sites in different matching runtime periods: Count the number of fully loaded sites in each of the aforementioned time periods when fully loaded sites occurred.

[0057] This step aims to refine the analysis from the perspective of historical risk patterns. It quantifies the spatial (number of stations) and temporal (date of occurrence) distribution characteristics of passenger flow pressure within the energy-saving scheduling interval. This is a crucial link between judging the "static interval" and formulating the "dynamic strategy," providing data support for distinguishing between "normal risks" and "occasional risks," thereby determining the tolerance of the strategy.

[0058] Specific examples (continuous S21): For the 70%-80% range, we analyzed the matching runtime periods over the past 30 days. We found that there were 7 days with fully loaded sites (6 days with single-site full load and 1 day with both sites full load). Therefore, the "matching runtime periods with fully loaded sites" are the specific time periods of these 7 days, and we need to record the corresponding "number of fully loaded sites" (1 for 6 days and 2 for 1 day).

[0059] S23 determines the energy-saving scheduling identification strategy for the energy-saving scheduling interval based on the percentage of runtime of different energy-saving scheduling intervals on different dates, the matching runtime segments with fully loaded stations in the energy-saving scheduling interval, and the number of fully loaded stations in different matching runtime segments.

[0060] This step is the convergence point of all decisions, and its greatest evolution is that all final strategies now include the precondition of "no matching runtime segment with node delay".

[0061] Node delay: refers to the actual arrival / departure time of a train relative to the scheduled timetable at the current or nearest station / section. It is a real-time or near real-time operational status indicator.

[0062] This is the last and most crucial line of defense against risk. Energy-saving speed adjustments (such as optimizing cruising speed) may slightly alter interval travel times. If trains are already delayed, any operations that could further impact travel time should be avoided to prevent delays from escalating or spreading. This constraint ensures that energy-saving scheduling absolutely conforms to the highest priority objective of restoring punctuality and operational stability. The strategy defines "when to allow energy-saving attempts," while this condition determines "whether it can actually be implemented at this moment."

[0063] It is understandable that the sum of the percentages of the running time of different energy-saving scheduling intervals on different dates is determined by the percentage of the running time of different energy-saving scheduling intervals on different dates, and this is used as the total time percentage. If the average of the total time percentages on different dates is less than a preset time percentage threshold, then the energy-saving scheduling identification strategy of the energy-saving scheduling interval is determined to be to perform energy-saving strategy identification processing in the matching running segments of the energy-saving scheduling interval where there is no node delay. That is, by adjusting the vehicle speed, the optimal cruising speed and braking time are identified on the basis of meeting the running time requirements, thereby improving the energy-saving effect.

[0064] In the above steps, if the average percentage of total running time is less than a preset threshold (e.g., <50%), and the sum of the percentages of running time for different energy-saving scheduling intervals on different dates is low, then even if the optimal energy-saving scheduling strategy is obtained in different energy-saving scheduling intervals, the impact on the train's energy-saving effect is not very high. Therefore, in order to obtain the optimal energy-saving scheduling strategy as quickly as possible, the same energy-saving scheduling identification strategy is adopted for all energy-saving scheduling intervals, but real-time conditions must be superimposed: energy-saving strategy identification is only activated when the train is running in that time period and no node delay occurs. This ensures that energy-saving benefits are obtained in fragmented time without causing any disruption.

[0065] For example: If the average total duration of all energy-saving dispatch intervals accounts for 40% (<50%), then the strategy is: when the train enters all energy-saving dispatch intervals and the system detects all matching running segments where "there is no node delay", energy-saving strategy identification and processing is performed.

[0066] Additionally, it's understandable that if the average percentage of total duration across different dates is less than a preset duration percentage threshold, the following situations also apply: Scenario 1: Based on the percentage of runtime of the energy-saving scheduling interval on different dates, if the average percentage of runtime of the energy-saving scheduling interval on different dates is greater than the preset value of the runtime percentage, then the energy-saving scheduling identification strategy of the energy-saving scheduling interval is determined to be to perform energy-saving strategy identification processing in the matching runtime segments of the energy-saving scheduling interval where there is no node delay. That is, by adjusting the vehicle speed, the optimal cruising speed and braking duration are identified on the basis of meeting the running time requirements, thereby improving the energy-saving effect.

[0067] In the above steps, the total time accounts for a large proportion, and its own daily average proportion is also large (greater than the preset value for time proportion, such as >20%): These time periods represent a rich source of energy savings. Their time proportion is high and stable, offering the greatest potential for energy-saving benefits. Therefore, historical strategies should encourage full coverage, but must also be adapted to real-time conditions. This reflects the principle of "actively utilizing high-value periods, but prioritizing safety."

[0068] For example: If the average total duration of the 70%-80% interval is 25%, and its own daily duration accounts for an average of 22% (>20%), then the strategy is to identify and process energy-saving strategies for all matching running segments where the train enters the interval and there is "no node delay".

[0069] Scenario 2: If the average percentage of running time in different dates of the energy-saving scheduling interval is not greater than the preset percentage of running time, based on the matching running segments with fully loaded stations in the energy-saving scheduling interval, if it is determined that there are not fully loaded stations in the matching running segments of the energy-saving scheduling interval on different dates, then the energy-saving scheduling identification strategy of the energy-saving scheduling interval is to perform energy-saving strategy identification processing in the matching running segments of the energy-saving scheduling interval where there is no node delay. That is, by adjusting the vehicle speed, the optimal cruising speed and braking time are identified on the basis of meeting the running time requirements, thereby improving the energy-saving effect.

[0070] The total duration is a moderate percentage, and the fully loaded sites do not occur daily. These intervals represent periods with favorable conditions. On one hand, the number of fully loaded stations is relatively small, reducing the probability of train delays during the identification of optimal energy-saving scheduling strategies. On the other hand, the demand for implementing energy-saving scheduling strategies with good energy-saving effects is also high. Therefore, the default strategy can be set to be implemented on all matching days. By adding real-time delay conditions, a dual guarantee mechanism of "historical data permission + real-time status confirmation" is formed, making the strategy both proactive and robust.

[0071] A consistent example: the total duration of the 70%-80% interval accounts for an average of 15% (>10% but ≤20%), and the fully loaded stations do not occur every day (7 out of 30 days). The strategy is to identify and process energy-saving strategies for all matching running segments where the train enters the interval and there is "no node delay".

[0072] Scenario 3: If the energy-saving scheduling interval has fully loaded stations in the matching runtime segments on different dates, obtain the number of fully loaded stations in the different matching runtime segments, and determine whether the average number of fully loaded stations in the different matching runtime segments is less than a preset threshold for the number of fully loaded stations. If so, the energy-saving scheduling identification strategy for the energy-saving scheduling interval is determined to be to perform energy-saving strategy identification processing in the matching runtime segments in the energy-saving scheduling interval where the number of fully loaded stations is less than the preset value for the number of fully loaded stations and there is no node delay. That is, by adjusting the vehicle speed, the optimal cruising speed and braking duration are identified to improve the energy-saving effect while meeting the running time requirements. If not, the energy-saving scheduling identification strategy for the energy-saving scheduling interval is determined to be to perform energy-saving strategy identification processing in the matching runtime segments in the energy-saving scheduling interval where there are no fully loaded stations and no node delay. That is, by adjusting the vehicle speed, the optimal cruising speed and braking duration are identified to improve the energy-saving effect while meeting the running time requirements.

[0073] The total duration is of moderate proportion, and fully loaded sites appear every day. Further breakdown is based on the number of fully loaded sites: The persistent historical passenger flow pressure in these areas is a key focus of risk management. At this point, it's necessary to further tighten historical access criteria based on the level of pressure (number of fully loaded stations), while still coupling real-time delay conditions. This enables "opportunity discovery within risk" even in the most complex scenarios.

[0074] Sub-case D1: Low average number of fully loaded sites (e.g., <2): Risk is concentrated in a very few sites, with a limited impact. The strategy allows for energy saving during periods of historically low risk (few fully loaded sites) and under real-time, zero-latency conditions. This is about securing benefits within the constraints of risk.

[0075] Example: If 70%-80% of the intervals are fully loaded daily, but the average number of fully loaded stations over 30 days is 1.2 (<2), identifying energy-saving optimization strategies, i.e., adjusting train speed, carries a high risk of delays due to the added impact of fully loaded stations. The strategy would be: within the matched running segments corresponding to those with fewer than 2 fully loaded stations, when a train enters that interval and there is "no node delay," energy-saving strategy identification and processing would be performed.

[0076] Sub-case D2: The average number of fully loaded stations is high (e.g., ≥2), indicating widespread historical risks. Identifying energy-saving optimization strategies in this case, such as adjusting train speed, carries a high risk of delays due to the added impact of fully loaded stations. The strategy must be extremely conservative, only allowing implementation on "absolutely safe days" (if any) with absolutely no fully loaded stations in historical data, and under real-time, zero-delay conditions. This essentially places this interval in the "observation zone," with an extremely narrow window for energy-saving opportunities.

[0077] Example: If 70%-80% of the intervals are fully loaded every day, and the average number of "fully loaded stations" is 2.5 (≥2), then the strategy is: during the matching running segments with no fully loaded stations, when a train enters the interval and there is "no node delay", energy-saving strategy identification and processing is performed.

[0078] This method represents a leap from "static interval analysis" to "dynamic risk perception and scheduling," and its core value lies in: A two-tiered safety decision-making system of "historical assessment + real-time perception" has been constructed: by introducing the real-time state condition of "node delay", the energy-saving scheduling strategy has a dynamic self-suppression function, ensuring that energy-saving operations will never be implemented when the operation is already under pressure, fundamentally eliminating the risk of delays caused by energy saving.

[0079] This achieves precise, contextualized triggering of energy-saving strategies: the final strategy command is a function of historical data (date, time period, full load mode) and real-time status (node ​​delay). This refines the granularity of energy-saving control from "intervals" to "a specific delay-free instant within an interval," reducing energy-saving scheduling intervals with high full load risk and mitigating technical issues arising from delays caused by the implementation of energy-saving scheduling strategies. The level of intelligence and precision is significantly improved.

[0080] The operational priority sequence is clearly defined: this method clearly defines the ironclad rule that "on-time and stable operation" takes precedence over "energy efficiency optimization." All energy-saving benefits are based on the premise that the system already has sufficient time redundancy (no delay), thereby ensuring the core reliability and punctuality commitment of subway service, making the energy-saving dispatching system more acceptable to operation managers and passengers.

[0081] It should be noted that the identification and processing result is the energy saving rate under the current optimal energy saving control strategy, wherein the energy saving rate is determined based on the ratio of energy saved to energy consumption in the original energy saving scheduling interval.

[0082] Specifically, the method for determining the optimal control interval within the energy-saving scheduling interval is as follows: The fundamental goal of this approach is to identify, from among numerous implementable energy-saving intervals (energy-saving scheduling intervals), those intervals that already possess highly efficient energy-saving strategies (high energy-saving rates), but whose continued dynamic adjustments might negatively interfere with related intervals. Designating these intervals as "optimization control intervals" means the system will "lock" their current highly efficient energy-saving commands and will not initiate a new round of energy-saving strategy identification and adjustment. The core logic is that in complex and interconnected operational networks, not all "energy-saving scheduling intervals" need continuous optimization; sometimes, keeping certain "energy-saving scheduling intervals" stable is fundamental to ensuring the safe and orderly implementation of the optimal energy-saving scheduling commands across the entire "energy-saving scheduling interval."

[0083] S31 uses the identification processing results in the energy-saving scheduling interval to determine the energy-saving rate of the energy-saving scheduling interval; Determine the energy saving rate of the energy-saving dispatch interval: Energy saving rate: This refers to the ratio of energy savings achieved within a given interval under the current energy-saving dispatch identification strategy and the identified better energy-saving control strategy, to the original baseline energy consumption. It represents the level of energy saving effect already achieved within this interval.

[0084] This is the selection criterion. Only those intervals that have achieved significant energy-saving effects (energy saving rate not lower than the preset threshold) are worth "locking in". If the energy-saving effect of a certain interval is not good, then it does not belong to the category that needs to be stably "locked in" and may still need to be adjusted or optimized.

[0085] Specific examples: For the energy-saving scheduling interval (denoted as interval A) with passenger capacity of 70%-80%, the system evaluates the currently applied energy-saving strategy and finds that it can achieve an energy saving rate of 5% (higher than the preset 3% threshold). This indicates that interval A is already an "interval with good energy-saving effect" and has the initial conditions to become a stable foundation (optimized control interval).

[0086] S32 takes a single run of a train in the energy-saving dispatching section as a matching run, and determines other energy-saving dispatching sections that also belong to the matching run during the matching run based on the overlap data between the matching run and the matching run of other energy-saving dispatching sections, and takes them as associated dispatching sections. Identify associated scheduling intervals: Matching operation process: refers to a single, complete journey of a subway train (such as from the starting station to the terminal station). During this process, the passenger capacity of the train will change dynamically, passing through multiple different energy-saving dispatching sections.

[0087] Associated scheduling intervals: In the same matching operation process, other energy-saving scheduling intervals that exist simultaneously or are closely adjacent to the currently analyzed energy-saving scheduling interval (such as interval A) in time, and thus influence each other in terms of operation plan. For example, another energy-saving scheduling interval B that the train enters immediately after leaving interval A is coupled with the other interval in terms of operation time.

[0088] This represents a crucial leap from point-based to line-based analysis. The implementation of energy-saving strategies is not isolated: identifying energy-saving strategies in section A might lead to trains entering section B later, thus compressing the time window available for energy-saving adjustments in section B, and potentially forcing section B to abandon its energy-saving strategies to catch up with the timetable. Identifying "related scheduling sections" essentially involves depicting the current section's "neighbors" or "influence circles" on the operating timeline, laying the foundation for assessing the cascading effects of local decisions.

[0089] Specific examples (continuously in S31): Analyze the train operation process of a certain train in section A. It is found that the train successively passed through three energy-saving dispatch sections during its operation: first section B (60%-70% passenger capacity), then section A (70%-80% capacity), and finally section C (80%-90% capacity). Since they are in continuous operation of the same train, sections B and C are identified as "associated dispatch sections" of section A in this operation. It is necessary to statistically analyze all operations that include section A to find the complete set of associated sections.

[0090] S33 determines whether the energy-saving scheduling interval is an optimized control interval based on the energy-saving rate of the energy-saving scheduling interval and the matching operation process that overlaps with different associated scheduling intervals.

[0091] It is understood that if the energy saving rate of the energy-saving scheduling interval is less than the preset energy saving rate threshold, then the energy-saving scheduling interval is determined not to belong to the optimized control interval.

[0092] Specifically, for the energy-saving scheduling interval (denoted as interval A) with passenger capacity of 70%-80%, the system evaluates the currently applied energy-saving strategy and finds that it can achieve an energy saving rate of 5% (higher than the preset 3% threshold). This indicates that interval A is already an "interval with good energy-saving effect" and has the initial conditions to become a stable foundation (optimized control interval).

[0093] Additionally, it should be noted that if the energy-saving rate of the energy-saving scheduling interval is not less than a preset energy-saving rate threshold, the following content is also included: S331 uses the associated scheduling interval data of the energy-saving scheduling interval in different matching operation processes to determine whether the average number of associated scheduling intervals in different matching operation processes is greater than the preset interval number threshold. If so, it is determined that the energy-saving scheduling interval belongs to the optimization control interval, so that energy-saving identification processing is not performed in the energy-saving scheduling interval, avoiding the technical problem of excessive delay risk caused by energy-saving strategy identification in too many associated scheduling intervals. If not, proceed to step S332. Assess the complexity of the associations – whether the impact is too broad: If a highly efficient energy-saving interval (such as interval A) is associated with a large number of other energy-saving intervals (the average number exceeds a threshold), then it is a critical hub in the network. Any small adjustment to this hub will amplify its fluctuations and propagate them to numerous associated intervals, forcing them to frequently recalculate their strategies, greatly increasing the overall risk and complexity of the system. The safest approach is to ensure that the operating state of this "hub" is highly stable and predictable. Therefore, it should be marked as an "optimal control interval," and its current excellent energy-saving strategy should be locked and not changed.

[0094] Example: Statistics show that each run of interval A affects an average of 4 downstream energy-saving intervals (exceeding the threshold of 3). The system determines that its impact is too broad. Conclusion: Interval A is designated as the "optimal control interval," its current optimal energy-saving command is locked, and dynamic energy-saving strategy re-identification is no longer performed on it.

[0095] S332 determines the proportion of the matching operation process that overlaps with the matching operation process of different associated scheduling intervals in the matching operation process of the associated scheduling interval, and uses it as the influence proportion. It then determines whether there is an associated scheduling interval whose influence proportion is greater than a preset influence proportion threshold. If so, proceed to step S333; otherwise, determine that the energy-saving scheduling interval does not belong to the optimization control interval.

[0096] S333 takes the associated scheduling interval with an impact ratio greater than the preset impact ratio threshold as the impact scheduling interval, and determines whether there is an impact scheduling interval with an energy saving rate less than the preset energy saving rate. If so, it is determined that the energy saving scheduling interval belongs to the optimization control interval, so that energy saving identification processing is not performed in the energy saving scheduling interval, avoiding the technical problem of excessive delay risk caused by energy saving strategy identification in too many associated scheduling intervals. If not, proceed to step S334. Assess the binding strength with vulnerable associated regions—whether a stable environment needs to be provided for "neighbors": If interval A and a specific associated interval (such as interval B) almost always operate continuously (with an influence ratio > 85%), then the coordination requirement between the two is extremely high. In this case, the key to decision-making lies in the energy-saving capability of interval B. If the energy-saving rate of interval B itself is very low (e.g., less than 2%), it indicates that it is significantly affected by interval A, and that the current energy-saving strategy's identification scheme is unable to obtain effective energy-saving instructions. In this situation, to reduce the impact on interval B, a reliable input condition is provided for interval B. Therefore, the interval A, which has already performed well, is "locked" as the optimal control interval.

[0097] A consistent example: When interval A exceeds 90%, the subsequent interval is always B. Upon investigation, the energy saving rate of interval B is only 1% (below the 2% threshold), making it a vulnerable interval in terms of adjustment capability. Conclusion: To provide a stable operating premise for the vulnerable interval B, interval A is designated as the "optimized control interval," and its current energy-saving command is locked.

[0098] S334 determines the optimization demand coefficient of the energy-saving scheduling interval based on the number of the affected scheduling intervals and the energy-saving rate of different affected scheduling intervals, and determines whether the energy-saving scheduling interval belongs to the optimization control interval based on the optimization demand coefficient.

[0099] It should be noted that the optimization demand coefficient is related to the number of the affected scheduling intervals and the energy saving rate of different affected scheduling intervals. The more affected scheduling intervals there are and the lower the energy saving rate of different affected scheduling intervals, the higher the optimization demand coefficient will be. Its value ranges from 0 to 1.

[0100] It is understood that when the optimized demand coefficient is greater than the preset demand coefficient threshold, the energy-saving scheduling interval is determined to belong to the optimized control interval.

[0101] Comprehensive quantitative assessment – ​​optimizing demand coefficients: Optimization Demand Coefficient: The higher this coefficient (closer to 1), the more urgent the need for the interval itself to be "locked in to stabilize the overall system" is, rather than the need for the interval itself to be optimized. Its calculation logic is: the more related intervals (influenced scheduling intervals) affected by it, and the lower the energy-saving capacity (energy-saving rate) of these related intervals themselves, the higher the "optimization demand coefficient" of the current interval (interval A).

[0102] When interval A is downstream of a group of intervals with poor energy-saving performance and weak adjustment capabilities, this association forms an "inefficient and sensitive" group. Within this group, any policy fluctuation in interval A could lead to the failure of the entire group's energy-saving attempt. In this situation, the most efficient system decision is to ensure that the high-performing interval A "maintains its exemplary state," using its own stability to create a reliable "testing ground" environment for the downstream inefficient and sensitive intervals. The high coefficient precisely illustrates the necessity of this "stability-driven overall improvement."

[0103] A continuation example: Downstream of interval A are two strongly correlated scheduling intervals (B and D), both with low energy-saving rates (2.5% and 3%). Therefore, the optimization demand coefficient for interval A is ((1-0.025)+(1-0.003))=1.972. If the preset threshold is 1.95, since 1.972>1.95, it indicates high value as a "stabilizing cornerstone." Conclusion: Determining interval A as the "optimized control interval" and locking its current energy-saving command provides a stable foundation for the energy-saving operation of intervals B and D.

[0104] The essence of this method embodiment lies in demonstrating the systems engineering principle that "superior stability is sometimes more valuable than extreme optimization": The role of "strategy anchor point" is defined: the "optimization control interval" is defined as the interval in the system where energy-saving strategies are mature, the state is stable, and it can serve as a reliable benchmark. Locking these intervals is to reduce the overall complexity and uncertainty of the system.

[0105] A synergistic efficiency guarantee mechanism has been established: by identifying correlations and vulnerabilities, the system can proactively set some sections that have achieved high energy-saving benefits to a stable state, thereby creating a predictable and low-interference implementation environment for other sections that are still being optimized or are relatively vulnerable, thus improving the success rate and security of energy-saving strategies in related sections.

[0106] This achieves a rebalancing of system reliability and energy efficiency: in the pursuit of higher energy efficiency, it intelligently identifies points requiring "strategy convergence," avoiding the oscillation risks caused by continuous, uncoordinated dynamic optimization. This ensures that the entire energy-saving dispatch system can still operate robustly and reliably under high energy efficiency conditions.

[0107] Specifically, the method for determining the energy-saving scheduling optimization method for the energy-saving scheduling interval is as follows: The core objective of this approach is to create conditions for further optimization of stable intervals by rapidly optimizing unstable intervals. Its innovative logic lies in the fact that a batch of stable intervals (optimized control intervals) with good energy-saving effects but whose strategies are "locked" have already been identified in the system. However, the energy-saving effects of these stable intervals may still have room for improvement. In order to re-optimize these stable intervals in the future, it is necessary to first bring the other "unstable" intervals (energy-saving scheduling intervals) associated with them to an optimized state as soon as possible, thereby reducing global risk and laying a safe foundation for further identification and optimization of stable intervals.

[0108] S41 determines the number of optimized control intervals based on the optimized control interval data; The number of optimized control intervals refers to the total number of stable intervals in the system that have been "locked" by the strategy and are no longer subject to dynamic energy-saving identification. They represent the current stability base of the system and the potential pool for optimization.

[0109] The number of stable intervals is a macro-level indicator for judging the urgency of collaborative optimization. If the number of stable intervals is small, it means that the system as a whole is still in the initial optimization stage, and proceeding step by step is sufficient. However, if the number of stable intervals is large (exceeding the threshold), it means that there is a large-scale potential cluster in the system that needs in-depth optimization. In order to "activate" this potential cluster, the optimization process of its surrounding environment must be accelerated first. Therefore, this number is the "master switch" that determines whether to start the global accelerated optimization mode.

[0110] Specific examples: A subway line has 10 energy-saving dispatching sections. Analysis revealed that 5 of these sections were identified as "optimized control sections" (stable sections) due to high correlation risk, with an average energy saving rate of 6%. These 5 sections represent high-potential optimization targets, but their strategies are locked and cannot be directly improved. The number of optimized control sections = 5 (assuming a preset threshold of 3).

[0111] S42 determines the overlap data between the matching operation process of the energy-saving dispatching section and the matching operation process of the optimized control section based on the overlap data of the trains in the energy-saving dispatching section and the optimized control section, and determines the optimized control section that overlaps in the matching operation process of the energy-saving dispatching section based on the overlap data, and uses it as the associated optimized section. Identify the relationship between energy-saving dispatch intervals and stable intervals: Associated Optimization Interval: For a given energy-saving scheduling interval X being evaluated (which is an unlocked, dynamically optimizable interval), the optimal control interval (stable interval) that occurs simultaneously with it during a single train run. It identifies in which operating scenarios interval X is directly associated with the stable interval to be optimized.

[0112] This is crucial for finding optimization breakthroughs and defining optimization priorities. If interval X frequently appears "tied" with multiple stable intervals during runtime, then rapidly optimizing interval X has dual strategic value: 1) it directly improves the efficiency of interval X itself; 2) more importantly, by optimizing interval X, the operating input environment of those stable intervals to be optimized can be significantly improved, reducing the overall risk of their operating chain, thereby clearing obstacles for "unlocking" and optimizing these stable intervals in the future. Identifying "associative optimization intervals" is to find those optimizable intervals that have a key impact on the optimization process of the stable interval cluster.

[0113] Specific examples (continuous S41): We focus on an optimizable energy-saving scheduling interval Y. Analyzing its entire operation, we find that in 80% of the journeys, interval Y travels sequentially with two optimized control intervals: a stable interval S1 and a stable interval S2. Therefore, S1 and S2 are the "correlated optimized intervals" of interval Y. This means that the progress of optimizing interval Y directly affects the stability of the operational chains containing S1 and S2.

[0114] S43 determines the energy-saving scheduling optimization method for the energy-saving scheduling interval based on the number of the optimized control intervals and the associated optimized intervals in the different matching operation processes of the energy-saving scheduling intervals.

[0115] Furthermore, if the number of optimized control intervals is greater than the preset control interval number threshold, then the energy-saving scheduling optimization method for all energy-saving scheduling intervals is to perform energy-saving strategy identification processing in the matching running segment where there is no node delay in the energy-saving scheduling interval. That is, by adjusting the vehicle speed, the optimal cruising speed and braking duration are identified on the basis of meeting the running time requirements, thereby improving the energy-saving effect.

[0116] This step is a future-oriented accelerated decision-making process. Based on the size of the stable interval (S41) and its specific correlation (S42), it determines whether a more aggressive and rapid optimization identification strategy should be adopted for the current energy-saving scheduling interval. Its purpose is not simply to obtain immediate energy-saving benefits, but to bring the optimizable interval to the "optimization complete" state as quickly as possible, thereby creating safe system conditions for subsequent "unlocking-re-optimization" of the stable interval cluster.

[0117] Global acceleration condition: Number of stable intervals > preset threshold (e.g., >3). When a considerable number of stable intervals (optimization potential pool) exist in the system, it indicates a strong need for future optimization. To create conditions for future in-depth optimization, an "acceleration mode" must now be activated for all still optimizable intervals (energy-saving scheduling intervals). That is, as long as there is no real-time delay, energy-saving identification is allowed, prompting them to find the optimal strategy as quickly as possible and reach a stable state. This is laying the "infrastructure" for future system-level optimization.

[0118] Furthermore, if the number of optimized control intervals is greater than a preset control interval number threshold, the following content is also included: S431 determines whether there is an associated optimization interval in the matching operation process of the energy-saving scheduling interval based on the associated optimization interval in different matching operation processes of the energy-saving scheduling interval. If yes, proceed to step S432. If no, determine that the energy-saving scheduling optimization method of the energy-saving scheduling interval is to perform energy-saving strategy identification processing according to the original energy-saving scheduling identification strategy. Determining whether to assume responsibility for "paving the way": In global acceleration mode, examine the specific interval Y. If interval Y is not associated with any stable interval in any run, it means that its optimization process is unrelated to the "unlocking" of the stable interval cluster. For such "isolated" intervals, there is no strategic task of paving the way for the future, so optimization can proceed at the original pace without acceleration.

[0119] Specific implementation: If interval Y never overlaps with any optimized control interval in the same operation process, its optimization method remains unchanged, and its original, possibly limited, energy-saving scheduling identification strategy is executed step by step.

[0120] S432 obtains the proportion of the matching operation process with associated optimization interval in the matching operation process of the energy-saving scheduling interval, and determines whether the proportion of the matching operation process with associated optimization interval in the matching operation process of the energy-saving scheduling interval is greater than the preset matching process proportion threshold. If so, it is determined that the energy-saving scheduling optimization method of the energy-saving scheduling interval is to perform energy-saving strategy identification processing in the matching operation segment without node delay in the energy-saving scheduling interval, that is, by adjusting the vehicle speed, on the basis of meeting the running time requirements, the optimal cruising speed and braking time are identified to improve the energy-saving effect. If not, proceed to step S433. Determine and optimize the correlation strength of the control interval: If interval Y is correlated with the stable interval, the strength of this correlation needs to be evaluated. If the vast majority (percentage > threshold, e.g., 70%) of interval Y's operation is closely tied to the stable interval, then it is a key precursor node for stable interval cluster optimization. It must undergo the most aggressive acceleration optimization to reach its optimal state as quickly as possible, thus providing a safe environment for subsequent stable interval optimization as early as possible.

[0121] A consistent example: 80% of the matching runs of interval Y are associated with stable intervals S1 and S2 (>70%). This indicates a high degree of correlation between interval Y and S1 and S2. Therefore, the optimization method for interval Y is immediately upgraded to the most aggressive mode: in all its matching runs, as long as there is no node delay, energy-saving strategy identification is performed. The goal is to enable the optimization of interval Y to quickly identify the optimal energy-saving control command.

[0122] S433 determines the energy-saving scheduling optimization method for the energy-saving scheduling interval based on the average number of associated optimization intervals in different matching operation processes of the energy-saving scheduling interval.

[0123] Furthermore, when the average number of associated optimization intervals in different matching operation processes of the energy-saving scheduling interval is greater than the preset threshold for the number of associated optimization intervals, the energy-saving scheduling optimization method of the energy-saving scheduling interval is determined to be to perform energy-saving strategy identification processing in the matching operation segments of the energy-saving scheduling interval where there is no node delay. That is, by adjusting the vehicle speed, the optimal cruising speed and braking duration are identified on the basis of meeting the requirements of the running time, so as to improve the energy-saving effect. Otherwise, proceed to the next step. Fine-grained acceleration based on correlation density: If the correlation strength between interval Y and the stable intervals is at a moderate level, then the correlation density (the average number of stable intervals correlated each time) needs to be examined further. If the average number of correlations is high (> the threshold), it indicates that the optimization of interval Y can benefit multiple stable intervals simultaneously, and its "paving the way" value is still very high, making acceleration worthwhile. Otherwise, a more refined decision is needed: if the original strategy is already very aggressive, maintain it; if the original strategy is conservative, appropriately relax the restrictions and implement "mild acceleration," promoting the optimization process while controlling risks, accumulating stability bit by bit for the future.

[0124] A coherent example: Only 50% of the operation of interval Y is related to S1 and S2 (<70%), leading to S433. Calculations show that in these related processes, the average number of stable intervals associated is 1.5 (assuming a threshold of 2), which does not meet the target. Next, the original "energy-saving scheduling identification strategy" of interval Y is examined. Assume its original strategy is "identification only during periods with no fully loaded stations and no delay." Since the original strategy is not the most aggressive "unconditional identification," the system calibrates its optimization method to an accelerated intermediate strategy: "In matching operation periods where the number of fully loaded stations is less than a preset value (e.g., relaxed from 0 to allow 1) and there is no node delay, energy-saving strategy identification is performed." Although this is not at full speed, it significantly accelerates the speed of optimization convergence, gradually laying a better foundation for the future optimization of the associated stable intervals S1 and S2.

[0125] If the energy-saving scheduling identification strategy of the energy-saving scheduling interval is to perform energy-saving strategy identification processing in all matching running segments of the energy-saving scheduling interval where there is no node delay, that is, to identify the optimal cruising speed and braking duration by adjusting the vehicle speed while meeting the running time requirements, thereby improving the energy-saving effect, then the original energy-saving scheduling identification strategy will still be used for identification processing. In other cases, the energy-saving scheduling optimization method of the energy-saving scheduling interval is determined to be to perform energy-saving strategy identification processing in all matching running segments of the energy-saving scheduling interval where the number of fully loaded stations is less than the preset value of the number of fully loaded stations and there is no node delay, that is, to identify the optimal cruising speed and braking duration by adjusting the vehicle speed while meeting the running time requirements, thereby improving the energy-saving effect.

[0126] This method embodiment reflects the dynamic system optimization concept of "focusing on the present while planning for the future," and its core value lies in: A collaborative link between "current optimization" and "future optimization" has been established: the current strategy adjustments for the optimizable range are explicitly linked to the future goal of in-depth optimization of the cluster in the stable range. This ensures that every current strategy decision serves a longer-term system energy efficiency improvement plan.

[0127] A "strategic priority" optimization mechanism was defined: by analyzing the relationships, it is possible to identify those intervals that have a key leverage effect on the overall future optimization process, and prioritize their resource allocation (computing resources, strategy aggression), which greatly improves the efficiency of the overall optimization process.

[0128] This achieves a "self-driven" and "virtuous cycle" in the system optimization process: the system is not content with its current static optimum, but actively creates conditions to propel itself from a state of "stability in some intervals" to one of "stability and better performance in more intervals." By accelerating the optimization of unstable intervals first to reduce global risk, it paves the way for subsequent optimization tasks in more challenging (stable intervals), forming a continuously self-improving and self-upgrading intelligent closed loop. This marks the evolution of the energy-saving scheduling system from a "passive response" stage to a more advanced stage of "proactive planning."

[0129] Example 2 Secondly, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described method for optimizing energy-saving scheduling of subway trains when running the computer program.

[0130] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0131] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0132] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A method for optimizing energy-saving dispatching of subway trains, characterized in that, Specifically, it includes: Using the operation data of subway trains, the operation segment data in different passenger capacity ranges is determined. Combined with the transfer passenger data of different stations in the operation segment, the energy-saving dispatching segment in the passenger capacity range is determined. Using the data of the energy-saving dispatching segment, combined with the fully loaded stations in the operation segment of the energy-saving dispatching segment, the energy-saving dispatching identification strategy of the energy-saving dispatching segment is determined. The energy-saving scheduling identification strategy is used to identify the energy-saving optimization strategy of the energy-saving scheduling section to obtain the identification processing result. Based on the identification processing result and the overlap data of trains with other energy-saving scheduling sections in different operating periods, the optimization control section in the energy-saving scheduling section is determined. Based on the optimized control interval data and the overlap data of trains in the energy-saving dispatch interval and the optimized control interval, the energy-saving dispatch optimization method for the energy-saving dispatch interval is determined.

2. The subway train energy-saving scheduling optimization method as described in claim 1, characterized in that, The subway train's operation data is based on the passenger volume of the subway train at different times.

3. The subway train energy-saving scheduling optimization method as described in claim 1, characterized in that, The operating time data within the passenger capacity range is determined based on the distribution data of the subway train's passenger capacity operating time within the passenger capacity range on different dates.

4. The subway train energy-saving scheduling optimization method as described in claim 1, characterized in that, The passenger volume data for the station includes the number of passengers at the station.

5. The subway train energy-saving scheduling optimization method as described in claim 1, characterized in that, The method for determining the energy-saving scheduling interval within the passenger capacity range is as follows: Using the runtime data within the passenger capacity range, determine the matching runtime for the passenger capacity range; Based on the passenger volume data of different stations in the matching runtime, determine the stations in the matching runtime where the number of transfer passengers does not meet the requirements, and designate them as fully loaded stations. Based on the matching runtime segments of the passenger capacity range and the fully loaded stations in different matching runtime segments, it is determined whether the passenger capacity range is an energy-saving scheduling range.

6. The subway train energy-saving scheduling optimization method as described in claim 5, characterized in that, The matching runtime segment is the runtime segment within the passenger capacity range.

7. The subway train energy-saving scheduling optimization method as described in claim 5, characterized in that, The term "full-load station" refers to a station where the number of transfer passengers exceeds a preset threshold.

8. The subway train energy-saving scheduling optimization method as described in claim 1, characterized in that, The method for determining the energy-saving scheduling optimization method for the energy-saving scheduling interval is as follows: Based on the optimized control interval data, determine the number of optimized control intervals; Based on the overlap data of trains in the energy-saving dispatching section and the optimized control section, the overlap data of the matching operation process of the energy-saving dispatching section and the matching operation process of the optimized control section are determined. Based on the overlap data, the optimized control section that overlaps in the matching operation process of the energy-saving dispatching section is determined and used as the associated optimized section. The energy-saving scheduling optimization method for the energy-saving scheduling interval is determined based on the number of the optimized control intervals and the associated optimized intervals in the different matching operation processes of the energy-saving scheduling intervals.

9. The subway train energy-saving scheduling optimization method as described in claim 8, characterized in that, If the number of optimized control intervals is greater than the preset control interval number threshold, then the energy-saving scheduling optimization method for all energy-saving scheduling intervals is to perform energy-saving strategy identification processing in the matching runtime segment where there is no node delay in the energy-saving scheduling interval.

10. A computer system, comprising: A memory and processor connected by communication, and a computer program stored in the memory and capable of running on the processor, characterized in that, when the processor runs the computer program, it executes a subway train energy-saving scheduling optimization method according to any one of claims 1-9.

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