Train operation control method and device and train

Abstract

The invention provides a train operation control method and device and a train, and can be applied to the technical field of train operation control. The method comprises the following steps: in response to a received current rail adhesion coefficient of a target section, based on a current rail adhesion grade corresponding to the rail adhesion coefficient and a historical rail adhesion grade, performing adjustment evaluation of a current train operation plan on a target line comprising the target section to obtain an evaluation result; under the condition that it is determined that the current train operation plan needs to be adjusted, the current train operation plan is adjusted based on the operation data and the adjustment strategies of the multiple trains operating on the target line, and candidate train operation plans are obtained; and verifying the candidate train operation plan based on the passenger flow data of each station of the target line route in the current time period and the respective braking distance constraint of the plurality of trains determined by the current track adhesion coefficient, so as to update the current train operation plan into the candidate train operation plan under the condition that the verification is passed.

Description

Technical Field

[0001] This disclosure relates to the field of train operation control technology, specifically to a train operation control method, equipment, and train. Background Technology

[0002] Rail adhesion refers to the ability of a train wheel to generate tangential force through friction between the contact surfaces of the train wheels and the rails. It directly affects the effective exertion of the train's traction and braking forces, and thus has a critical impact on train operation safety.

[0003] The research revealed that factors such as weather changes and track surface contamination can reduce track adhesion performance, leading to decreased train traction transmission efficiency and prolonged braking distance, which in turn affects train operation safety. Summary of the Invention

[0004] In view of the above problems, this disclosure provides a train operation control method, equipment and train.

[0005] According to a first aspect of this disclosure, a train operation control method is provided, comprising: in response to receiving the current track adhesion coefficient of a target section, performing an adjustment assessment of the current train operation plan for a target line including the target section based on the current track adhesion level corresponding to the track adhesion coefficient and the historical track adhesion level of the target section, and obtaining an assessment result; if the assessment result indicates that the current train operation plan needs to be adjusted, adjusting the current train operation plan based on the respective operating data of multiple trains operating on the target line and an adjustment strategy adapted to the current track adhesion level, and obtaining a candidate train operation plan; verifying the candidate train operation plan based on the passenger flow data of each station along the target line in the current time period and the braking distance constraints of multiple trains determined by the current track adhesion coefficient, so that if the verification passes, the current train operation plan is updated to the candidate train operation plan.

[0006] According to embodiments of this disclosure, a candidate train operation plan includes multiple stations where trains stop during their journey and the speeds of multiple trains traveling on multiple sections of the target line. The candidate train operation plan is validated based on passenger flow data at each station along the target line during the current time period and braking distance constraints for each train determined by the current track adhesion coefficient. This validation includes: determining the predicted passenger load factor for each train traveling on each section based on passenger flow data at each station along the target line during the current time period and the stations where trains stop during their journey; determining the predicted braking distance for each train traveling on each section using the predicted passenger load factor and the speed of each train traveling on each section; and obtaining the validation result of the candidate train operation plan through a first comparison result between each predicted passenger load factor and a preset passenger load threshold, and a second comparison result between each predicted braking distance and the corresponding braking distance constraint.

[0007] According to embodiments of this disclosure, based on passenger flow data of each station along the target route in the current time period and the stations where multiple trains stop during their journey, the predicted passenger load factor of multiple trains traveling in each section is determined, including: for each train, using historical passenger flow data, analyzing the passenger flow data of each station in the current time period to obtain the passenger flow data of each station at the time the train stops; and based on the passenger flow data of each station at the time the train stops and the carrying capacity of the train, determining the predicted passenger load factor of the train traveling in each section.

[0008] According to embodiments of this disclosure, the predicted braking distance of each train in each section is determined using the predicted passenger load factor and the speed of each train in each section. This includes: for each train, determining the initial braking distance of the train in each section based on the speed of the train in each section and the current track adhesion coefficient of each section; and correcting the initial braking distance of the train in each section based on a load correction factor corresponding to the predicted passenger load factor of the train in each section, a coasting correction factor corresponding to the current track adhesion level of each section, an environmental correction factor corresponding to the weather conditions, and a track state factor corresponding to the track type of each section, thereby obtaining the predicted braking distance of the train in each section.

[0009] According to an embodiment of this disclosure, the braking distance constraint is determined by: determining the safety protection coefficient of each section based on the current track adhesion level of each section; and using the safety protection coefficient of each section to compensate for the preset braking distance of each train when traveling in each section, thereby obtaining the braking distance constraint of each train when traveling in each section.

[0010] According to embodiments of this disclosure, a verification result for a candidate train operation plan is obtained through a first comparison result between each predicted passenger load factor and a preset passenger load threshold, and a second comparison result between each predicted braking distance and a corresponding braking distance constraint. This includes: if the first comparison result indicates that each predicted passenger load factor is less than or equal to the preset passenger load threshold, and the second comparison result indicates that each predicted braking distance is less than or equal to the corresponding braking distance constraint, then the verification result for the candidate train operation plan is determined to be successful; if the first comparison result indicates that there is a predicted passenger load factor greater than the preset passenger load threshold, or the second comparison result indicates that there is a predicted braking distance greater than the corresponding braking distance constraint, then the verification result for the candidate train operation plan is determined to be unsuccessful.

[0011] According to an embodiment of this disclosure, the method further includes: if the verification result is a verification failure, determining an adjustment range for the candidate train operation plan based on the reasons for failure determined by the first comparison result and the second comparison result; and adjusting the candidate train operation plan based on the adjustment range until the verification result is a verification success.

[0012] According to embodiments of this disclosure, the operational data includes: operational sections; based on the operational data of multiple trains operating on the target line and an adjustment strategy adapted to the current track adhesion level, the current train operation plan is adjusted to obtain a candidate train operation plan, including: determining the target train passing through the target section based on the operational sections of each train; updating the initial train control strategy of the target train in the current train operation plan when it travels in the target section based on the train control strategy corresponding to the current track adhesion level to obtain an intermediate train operation plan; and updating the intermediate train operation plan based on the adjustment strategy to obtain a candidate train operation plan.

[0013] According to embodiments of this disclosure, the database stores multiple initial adjustment strategies for initial train operation plans under preset track adhesion levels; the initial train operation plan is a train operation plan with track adhesion coefficients within the normal range; the adjustment intensity of the initial adjustment strategy on the initial train operation plan gradually increases as the preset track adhesion level decreases; the adjustment strategy is obtained by: determining an intermediate track adhesion level whose level ranking is between the historical track adhesion level and the current track adhesion level, and determining the level difference between the current track adhesion level and the historical track adhesion level; based on the initial adjustment strategies of the current track adhesion level, the historical track adhesion level, and the intermediate track adhesion level obtained from the database, determining the difference content of the initial adjustment strategy from the historical track adhesion level to the current track adhesion level; and obtaining the adjustment strategy based on the difference content and the level difference.

[0014] According to embodiments of this disclosure, an adjustment strategy is derived based on the difference content and the grade difference, including: when it is determined that the grade difference indicates that the current track adhesion level is lower than the historical track adhesion level, the adjustment strategy is to increase the adjustment intensity based on the difference content in the current train operation plan; when it is determined that the grade difference indicates that the current track adhesion level is higher than the historical track adhesion level, the adjustment strategy is to decrease the adjustment intensity based on the difference content in the current train operation plan.

[0015] According to embodiments of this disclosure, the differences include: screening out trains to be suspended based on preset vehicle parameters; the preset vehicle parameters include at least one of the following: carrying capacity, distance between the current location and the target section, track vacancy rate, passenger flow matching degree, risk level of the travel path, and train departure status; and enhancing the intensity of adjustments based on the current train operation plan according to the differences, including: weighted summing of the preset vehicle parameters of multiple trains included in the current train operation plan to obtain a suspension score for each train; determining the trains to be suspended from the multiple trains based on the suspension scores of each train; and marking the suspended trains as suspended in the current train operation plan.

[0016] According to embodiments of this disclosure, the train control strategy corresponding to the current track adhesion level includes: responding to a deviation between the actual speed of the target train traveling in the target section and the reference speed under the current track adhesion level, determining a corresponding preset control strategy based on the degree of deviation, wherein the correspondence between the degree of deviation and the preset control strategy is determined based on the probability of the target train slipping under multiple degrees of deviation.

[0017] According to embodiments of this disclosure, based on the current track adhesion level corresponding to the track adhesion coefficient and the historical track adhesion level of the target section, an adjustment assessment of the current train operation plan is performed on the target line including the target section to obtain an assessment result, including: comparing the current track adhesion level with the historical track adhesion level to obtain a comparison result; if the comparison result indicates that the current track adhesion level is inconsistent with the historical track adhesion level, it is determined that the assessment result indicates that the current train operation plan needs to be adjusted.

[0018] According to embodiments of this disclosure, the current track adhesion level is determined by: matching the track adhesion coefficient with multiple preset track adhesion coefficient ranges to determine the target track adhesion coefficient range to which the track adhesion coefficient belongs; and using the preset track adhesion level corresponding to the target track adhesion coefficient range as the current track adhesion level.

[0019] The second aspect of this disclosure provides a train operation control device, comprising: an evaluation module, configured to, in response to receiving the current track adhesion coefficient of a target section, evaluate the adjustment of the current train operation plan for a target line including the target section based on the current track adhesion level corresponding to the track adhesion coefficient and the historical track adhesion level of the target section, and obtain an evaluation result; an adjustment module, configured to, if the evaluation result indicates that the current train operation plan needs to be adjusted, adjust the current train operation plan based on the respective operating data of multiple trains operating on the target line and an adjustment strategy adapted to the current track adhesion level, and obtain a candidate train operation plan; and a verification module, configured to verify the candidate train operation plan based on the passenger flow data of each station along the target line in the current time period and the braking distance constraints of multiple trains determined by the current track adhesion coefficient, so that if the verification passes, the current train operation plan is updated to the candidate train operation plan.

[0020] A third aspect of this disclosure provides an electronic device comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors perform the train operation control method described above.

[0021] A fourth aspect of this disclosure provides a train, comprising: a sending module for sending the current track adhesion coefficient of a target section to the aforementioned electronic equipment; and an execution module for controlling the train operation according to the target train control strategy in response to receiving a target train control strategy sent by the aforementioned electronic equipment, the target train control strategy being determined by the electronic equipment from candidate train operation plans.

[0022] The fifth aspect of this disclosure also provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the train operation control method described above.

[0023] The sixth aspect of this disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described train operation control method.

[0024] According to the train operation control method disclosed herein, after receiving the track adhesion coefficient of the target section, the current track adhesion level is determined, and combined with historical track adhesion levels, an assessment is made as to whether the current train operation plan for the entire line needs to be adjusted. If adjustment is required, the current train operation plan is optimized in a targeted manner by combining the operating data of multiple trains and adjustment strategies adapted to the current track adhesion level, so that the adjustment action matches the track risk conditions and the actual operating status of the trains. After the adjustment is completed, the adjusted candidate train operation plan is verified from two aspects: passenger flow supply and demand balance and braking distance safety, thereby reducing station passenger congestion and backlog, as well as safety hazards such as train slippage, coasting, and rear-end collisions. Therefore, it at least partially solves the technical problem of safety hazards in train operation caused by a significant reduction in track adhesion performance. It achieves the technical effect of reducing the interference of track risk on train operation safety and improves the safety and reliability of train operation under low adhesion conditions. Attached Figure Description

[0025] The foregoing contents, as well as other objects, features, and advantages of this disclosure, will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0026] Figure 1 This diagram schematically illustrates an application scenario of the train operation control method according to an embodiment of the present disclosure.

[0027] Figure 2 A flowchart illustrating a train operation control method according to an embodiment of the present disclosure is shown schematically.

[0028] Figure 3 A data flow diagram illustrating the determination of the current track adhesion level according to an embodiment of the present disclosure is shown schematically;

[0029] Figure 4 A schematic diagram of the structure of a train according to an embodiment of the present disclosure is shown.

[0030] Figure 5 A flowchart illustrating a train operation control method according to another embodiment of the present disclosure is shown schematically;

[0031] Figure 6 A schematic diagram illustrating the structure of a train operation control device according to an embodiment of the present disclosure is shown; and

[0032] Figure 7 A block diagram schematically illustrates an electronic device suitable for implementing a train operation control method according to an embodiment of the present disclosure. Detailed Implementation

[0033] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0034] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0035] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0036] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0037] It should be noted that the train operation control method and equipment disclosed herein can be used in the field of train operation control technology, or in any field other than the field of train operation control technology, such as the field of computer technology. There is no limitation on the application field of the train operation control method and equipment disclosed herein.

[0038] Affected by factors such as weather changes and rail surface contamination, such as dampness, oil stains, fallen leaves, ice and snow, the track adhesion performance will decrease. This condition will have multiple negative impacts on the train's operational safety, efficiency and equipment life.

[0039] Therefore, embodiments of this disclosure provide a train operation control method, comprising: in response to receiving the current track adhesion coefficient of a target section, performing an adjustment evaluation of the current train operation plan for a target line including the target section based on the current track adhesion level corresponding to the track adhesion coefficient and the historical track adhesion level of the target section, and obtaining an evaluation result; if the evaluation result indicates that the current train operation plan needs to be adjusted, adjusting the current train operation plan based on the respective operation data of multiple trains operating on the target line and an adjustment strategy adapted to the current track adhesion level, and obtaining a candidate train operation plan; verifying the candidate train operation plan based on the passenger flow data of each station along the target line in the current time period and the braking distance constraints of multiple trains determined by the current track adhesion coefficient, so that if the verification passes, the current train operation plan is updated to the candidate train operation plan.

[0040] Figure 1 The diagram illustrates an application scenario of the train operation control method according to an embodiment of the present disclosure.

[0041] like Figure 1 As shown, application scenario 100 according to this embodiment may include a first train 101, a second train 102, a third train 103, a network 104, and an electronic device 105. Network 104 serves as a medium for providing communication links between the first train 101, the second train 102, the third train 103, and the electronic device 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0042] The first train 101, the second train 102, and the third train 103 interact with electronic devices 105 via network 104 to receive or send messages, etc. Various onboard systems are installed on the first train 101, the second train 102, and the third train 103, such as: Train Control and Management System (TCMS), Automatic Train Protection (ATP), and Automatic Train Operation (ATO).

[0043] The first train 101, the second train 102, and the third train 103 can provide track surface information, such as the current track adhesion coefficient of the target section, to the electronic equipment 105 through their respective onboard systems. They can also receive train control strategies or related control commands from the electronic equipment 105. The first train 101, the second train 102, and the third train 103 may be equipped with relevant computer equipment to ensure the operation of each onboard system.

[0044] Electronic device 105 can be a server providing various services, such as: performing grade matching on the current track adhesion coefficient of the target sections sent by the first train 101, the second train 102, and the third train 103, and evaluating the adjustment of the current train operation plan for the target line including the target sections to obtain evaluation results; and adjusting and verifying the current train operation plan when the evaluation results indicate that the current train operation plan needs to be adjusted.

[0045] It should be noted that the train operation control method provided in this embodiment can generally be executed by electronic device 105. The train operation control method provided in this embodiment can also be executed by electronic device or electronic device cluster that is different from electronic device 105 and is capable of communicating with the first train 101, the second train 102, and the third train 103 and / or electronic device 105.

[0046] It should be understood that Figure 1 The number of the first, second, and third trains, network devices, and electronic equipment shown is merely illustrative. Any number of these components can be used depending on implementation requirements.

[0047] The following will be based on Figure 1 The described scene, through Figures 2-4 The train operation control method of the disclosed embodiments will be described in detail.

[0048] Figure 2 A flowchart illustrating a train operation control method according to an embodiment of the present disclosure is shown schematically.

[0049] like Figure 2 As shown, the method includes operations S210 to S230.

[0050] In operation S210, in response to receiving the current track adhesion coefficient of the target section, an adjustment assessment of the current train operation plan is performed on the target line including the target section based on the current track adhesion level corresponding to the track adhesion coefficient and the historical track adhesion level of the target section, and an assessment result is obtained.

[0051] In operation S220, if the evaluation results indicate that the current train operation plan needs to be adjusted, the current train operation plan is adjusted based on the operation data of multiple trains running on the target line and the adjustment strategy adapted to the current track adhesion level, resulting in candidate train operation plans.

[0052] In operation S230, based on the passenger flow data of each station along the target route in the current time period and the braking distance constraints of multiple trains determined by the current track adhesion coefficient, the candidate train operation plan is verified, so that if the verification passes, the current train operation plan is updated to the candidate train operation plan.

[0053] The current track adhesion coefficient of the target section can be calculated and transmitted periodically by the trackside equipment of each target section, or it can be collected and transmitted by trains passing through the target section. For example, the train's TCMS determines and transmits the track adhesion coefficient of the target section to the electronic equipment used to implement the above-mentioned train operation control method.

[0054] Upon receiving the current track adhesion coefficient for the target section, the current track adhesion level can be determined based on the pre-stored mapping relationship between preset track adhesion coefficient ranges and preset track adhesion levels in the database. This current level is then compared and analyzed with historical track adhesion levels to determine whether adjustments to the current train operation plan for the target line are necessary.

[0055] For example, if it is determined that the current track adhesion level is inconsistent with the historical track adhesion level, the current train operation plan needs to be adjusted. Alternatively, if the difference between the current track adhesion level and the historical track adhesion level exceeds a preset threshold, the current train operation plan also needs to be adjusted. Specific adjustment and evaluation methods can be set according to requirements.

[0056] If the assessment results indicate that the current train operation plan needs to be adjusted, real-time operation data of each train running on the target line can be collected. The operation data may include at least one of the following: the current section or location of the train (obtained through a Communication-Based Train Control (CBTC) signaling system), actual operating speed, passenger load factor (obtained based on door weighing sensors), operating section (i.e., the section of the planned route), subsequent operating sections, track vacancy status, planned stops, and preset stop duration, etc.

[0057] At the same time, adjustment strategies adapted to the current track adhesion level can be determined. For example, if the current track adhesion level is low and the historical track adhesion level is higher than the current track adhesion level, the relevant trains can be suspended based on the current train operation plan.

[0058] The adjustment strategy may include at least one of the following: increasing or decreasing the number of trains running or scheduled to run on the target line, as well as rules related to increasing or decreasing trains, adjusting the train control strategy for each train, adjusting the running times of each train, adjusting trains of particular concern, increasing or decreasing backup security checkpoints, suspending operations on target sections, and activating backup lines. The train control strategy may include the train's speed on each section (which is the expected speed, and the specific speed can be controlled by the driver based on actual conditions), the baseline speed on each section (which is the speed baseline and generally cannot be exceeded), and preset control strategies corresponding to different degrees of deviation if the actual speed deviates from the baseline speed under different track adhesion levels.

[0059] By combining the operating data of multiple trains and the adjustment strategies adapted to the current track adhesion level, the current train operation plan can be adjusted to obtain a candidate train operation plan that matches the track risk conditions and the actual operating status of the trains.

[0060] Candidate train operation plans can also be verified to determine whether they meet operational requirements such as passenger flow supply and demand balance and braking distance safety. During the verification process, passenger flow data for the current time period can be collected at each station along the target line, including real-time inbound and outbound passenger flow at each station, as well as the overall line load factor. Simultaneously, braking distance constraints are determined by combining the current track adhesion coefficient with historical braking distances and safety protection coefficients.

[0061] Passenger flow suitability verification is performed on candidate train operation plans based on passenger flow data. For example, the current passenger flow at each station is compared with the stopping time and train frequency configuration of the corresponding trains in the candidate plans to determine the predicted passenger load factor of multiple trains traveling on each section. It is then verified whether this exceeds a preset passenger load threshold. If it does, the candidate train operation plan is deemed to have failed the passenger flow suitability verification.

[0062] Passenger flow data can be combined with station gate data and platform camera AI statistics to generate real-time station entry volume, overall line load factor, and other data, and peak / off-peak periods can be marked. There is no limit to the statistical interval of passenger flow data, for example, it can be updated every 5 minutes.

[0063] Furthermore, braking safety verification of candidate train operation plans can be performed based on the braking distance constraints of each train. For example, it can be determined whether each train exceeds the corresponding braking distance constraint during braking; if so, the candidate train operation plan is deemed to have failed the braking safety verification.

[0064] If a candidate train operation plan meets both the requirements of passenger flow adaptability and braking safety verification, the verification is deemed successful, and the current train operation plan is updated to the candidate train operation plan. If either dimension fails the verification, the verification is deemed unsuccessful. Based on the issues raised in the verification feedback, the corresponding parameters in the candidate train operation plan are retrospectively adjusted, such as extending the dwell time at target stations, increasing the number of trains, or reducing train speeds, and the verification is repeated until it passes.

[0065] In some embodiments, the above-mentioned verification results can be obtained by combining equipment risk verification with the above two verification methods. For example, attributes of each section can be collected by train or trackside equipment, including whether it contains turnouts or curves, and whether it is a transfer connection section. During verification, the status of turnouts and signals in the target section can be checked. If there is a risk of jamming during turnout switching, the equipment risk verification can be considered to have failed, that is, the final verification result is also a failure. When the above-mentioned equipment risk verification fails, the train can be instructed to pass through the turnout at a lower speed, for example, less than 15 km / h.

[0066] In some embodiments, a route topology map can be pre-stored, marking the connection relationship between the main line or backup line, the location of turnouts and speed limit requirements, the depot access point, etc., to ensure accurate scheduling route planning.

[0067] According to embodiments of this disclosure, after receiving the track adhesion coefficient of the target section, its current track adhesion level is determined, and combined with historical track adhesion levels, an assessment is made as to whether the current train operation plan for the entire line needs to be adjusted. If adjustment is required, the current train operation plan is specifically optimized by combining the operating data of multiple trains and adjustment strategies adapted to the current track adhesion level, so that the adjustment action matches the track risk conditions and the actual operating status of the trains. After the adjustment is completed, the adjusted candidate train operation plan is verified from two aspects: passenger flow supply and demand balance and braking distance safety, thereby reducing station passenger congestion and backlog, as well as safety hazards such as train slippage, coasting, and rear-end collisions. Therefore, it at least partially solves the technical problem that the significant reduction in track adhesion performance leads to safety hazards in train operation. It achieves the technical effect of reducing the interference of track risk on train operation safety, and improves the safety and reliability of train operation under low adhesion conditions.

[0068] According to embodiments of this disclosure, the current track adhesion level is determined in the following manner.

[0069] The track adhesion coefficient is matched with multiple preset track adhesion coefficient ranges to determine the target track adhesion coefficient range to which the track adhesion coefficient belongs; the preset track adhesion level corresponding to the target track adhesion coefficient range is taken as the current track adhesion level.

[0070] Figure 3 A data flow diagram illustrating the determination of the current track adhesion level according to an embodiment of the present disclosure is shown schematically.

[0071] like Figure 3 As shown, the database can pre-store the mapping relationship between preset track adhesion coefficient ranges and preset track adhesion levels. For example, using μ to represent the preset track adhesion coefficient, the preset track adhesion level is defined as follows: μ > 0.15 is normal; 0.08 < μ ≤ 0.15 is medium adhesion; 0.05 < μ ≤ 0.08 is low adhesion; 0.03 < μ ≤ 0.05 is relatively low adhesion; and μ ≤ 0.03 is extremely low adhesion. This mapping relationship is determined based on the influence of track adhesion coefficient on train traction and braking force, combined with a large amount of wheel-rail dynamics test data.

[0072] In practical applications, the current track adhesion coefficient of the target segment can be matched with a preset track adhesion coefficient range to determine the target track adhesion coefficient range to which the current track adhesion coefficient belongs. Furthermore, the preset track adhesion level corresponding to the target track adhesion coefficient range can be determined through the above mapping relationship and used as the current track adhesion level of the target segment.

[0073] According to embodiments of this disclosure, multiple preset track adhesion coefficient ranges are pre-defined, and a mapping relationship is established between each range and a corresponding preset track adhesion level. In practical applications, the real-time acquired track adhesion coefficients are quickly compared and matched with the preset ranges to rapidly determine the current track adhesion level, thereby shortening the response time for level identification and reducing level misjudgments.

[0074] According to embodiments of this disclosure, an adjustment assessment of the current train operation plan is performed on a target line including the target section based on the current track adhesion level corresponding to the track adhesion coefficient and the historical track adhesion level of the target section, and the assessment result may include the following operations.

[0075] The current track adhesion level is compared with the historical track adhesion level to obtain the comparison result. If the comparison result indicates that the current track adhesion level is inconsistent with the historical track adhesion level, the assessment result is determined that the current train operation plan needs to be adjusted.

[0076] When assessing whether the current train operation plan needs adjustment, this can be achieved by comparing the current track adhesion level with the historical track adhesion level. If they are consistent, the current train operation plan does not need adjustment. Conversely, if they are inconsistent, the current train operation plan needs adjustment.

[0077] According to embodiments of this disclosure, by using the comparison between the current track adhesion level and the historical track adhesion level as the standard for whether the current train operation plan needs to be adjusted, the evaluation results can be made more objective, providing a basis for subsequent adjustments and verifications.

[0078] According to embodiments of this disclosure, the operational data includes: the operating section; based on the operational data of multiple trains operating on the target line and an adjustment strategy adapted to the current track adhesion level, the current train operation plan is adjusted to obtain a candidate train operation plan, which may include the following operations.

[0079] Based on the operating sections of each train, the target trains that pass through the target sections are determined; based on the train control strategy corresponding to the current track adhesion level, the initial train control strategy of the target train in the current train operation plan when it travels in the target section is updated to obtain the intermediate train operation plan; based on the adjustment strategy, the intermediate train operation plan is updated to obtain the candidate train operation plan.

[0080] The train's operating section refers to the section that the train may pass through when traveling on the target line. Therefore, by identifying this operating section, the target train that will actually pass through the target section according to the current train operation plan can be determined. Based on the current track adhesion level, the corresponding train control strategy is retrieved from the database. The initial train control strategy for the target train in the target section is then modified to correspond to the current track adhesion level. Other sections outside the target section can retain the initial train control strategy.

[0081] The train control strategy corresponding to the current track adhesion level may include: the reference speed corresponding to the current track adhesion level, and the method for determining the preset control strategy when the actual speed deviates from the reference speed.

[0082] Based on the intermediate train operation plan, further optimization can be achieved by combining adjustment strategies, thereby obtaining candidate train operation plans.

[0083] For example, under the guidance of the adjustment strategy, some trains can be suspended, and the headway between the target train and adjacent trains can be adjusted by referring to the safe headway corresponding to the current track adhesion level and combining the speed and braking distance of each train in each section. Furthermore, the dwell time of the target train at each station within the target section can be dynamically adjusted based on the real-time passenger load factor, predicted passenger load factor, and preset passenger load thresholds in the adjustment strategy. If the passenger load factor is close to or reaches the carrying capacity threshold, the dwell time can be appropriately extended to ensure safe and orderly passenger boarding and alighting; if the passenger load is low, the dwell time can be shortened to avoid wasting transport capacity.

[0084] According to embodiments of this disclosure, by first screening target trains passing through target sections, and then adjusting the initial train control strategy of the target trains using a train control strategy adapted to the current track adhesion level of the target section, invalid adjustments to unrelated trains are reduced, and the control strategy of the target trains is matched with the real-time adhesion conditions, improving adjustment efficiency and accuracy. Then, based on the train control strategy adapted to the current track adhesion level, the intermediate train operation plan obtained after the above adjustments is further adjusted, enabling the overall train operation plan to meet the requirements of individual trains while also achieving coordinated adjustments between multiple trains. This reduces the adverse impact of track adhesion performance fluctuations on train operation safety and operational efficiency.

[0085] According to embodiments of this disclosure, the train control strategy corresponding to the current track adhesion level includes: responding to a deviation between the actual speed of the target train traveling in the target section and the reference speed under the current track adhesion level, determining a corresponding preset control strategy based on the degree of deviation, wherein the correspondence between the degree of deviation and the preset control strategy is determined based on the probability of the target train slipping under multiple degrees of deviation.

[0086] There are no restrictions on how the degree of deviation can be represented. For example, the speed deviation rate can be used. It can be calculated as follows: Speed ​​deviation rate = (Current speed - Reference speed) / Reference speed × 100%.

[0087] Different reference speeds can be set for different preset track adhesion levels to ensure train braking safety. For example, under normal or medium adhesion, because μ is relatively high, the wheel-rail adhesion is sufficient, so the reference speed can be the original speed limit (v0). Under low, very low, or extremely low adhesion, the reference speed can be kv0 (k is the adhesion coefficient correction factor, dynamically calculated).

[0088] In determining the correspondence between the degree of deviation and the preset control strategy, the correlation between the degree of deviation and the risk of wheel-rail slippage can be quantified. Specifically, for different preset track adhesion levels, the probability of slippage risk occurring for the target train under multiple degrees of deviation can be quantified, thereby determining the boundary deviation degree for slippage risk prevention and control. This quantification result can serve as the theoretical basis for subsequently establishing the correspondence between the degree of deviation and the preset control strategy. Slippage risk includes wheel spin and coasting.

[0089] Furthermore, there are no restrictions on the methods for quantifying the aforementioned correlation patterns; they can include historical data simulation, wheel-rail mechanics models, or actual vehicle testing.

[0090] For example, when the preset track adhesion level is medium adhesion, the above correlation law is determined through simulation using 100 sets of historical data as shown below. Using δ to represent the speed deviation rate, when δ≤10%, the risk of slippage is <0.5%; when δ>15%, the risk rises to 5%. Based on the midpoint of this level range of 0.115, combined with the above quantitative results, the following deviation ranges and preset control strategies can be defined: δ≤8%: No operation, background recording of μ and v (actual speed) fluctuation data. 8%<δ≤12%: A dynamic warning pops up on the train's Human-Machine Interface (HMI), for example: the prompt changes with the μ value, specifically: "μ=0.10, speed is close to 10% deviation, smooth driving is recommended"; δ>12%: a warning pop-up combined with voice prompts, while simultaneously calculating the maximum safe acceleration corresponding to the current μ, displaying "Recommended traction level ≤X%" on the HMI (X decreases as μ decreases).

[0091] For example, when the preset track adhesion level is low adhesion, the reference travel speed can be 0.8v0 due to the decrease in μ (through braking distance simulation, the braking safety margin meets the requirements at 0.8v0, so as to reduce the risk of extended braking distance under low adhesion conditions). The above correlation can be shown as follows. When μ=0.08 (the upper limit of low adhesion conditions), the speed deviation rate δ corresponding to the idling critical traction force is 20% (that is, when δ=20%, further increasing the traction force will trigger idling); when μ=0.05 (the lower limit of low adhesion conditions), the speed deviation rate δ corresponding to the idling critical traction force is 15% (the lower μ is, the weaker the wheel-rail adhesion, and the smaller the speed deviation rate corresponding to the idling critical). Therefore, based on the above correspondence between μ and the idling critical deviation rate, the blocking traction threshold δ is derived by linear correlation, which is the speed deviation rate threshold for triggering traction blocking intervention. Specifically, it can be shown in the following formula (1).

[0092] δ pull=20%-(0.08-μ) / 0.03×5%; (1)

[0093] Based on the above formula (1), it can be seen that when μ=0.065, δ_traction = 17.5%. This ensures that δ_traction decreases linearly with the decrease of μ, adapting to the idling control requirements under different μ values. On this basis, the speed deviation rate threshold δ_control that triggers braking intervention can also be set to δ_traction + 5%. The purpose is to prioritize intervention by blocking traction. When the speed deviation rate exceeds δ_traction and the deviation still does not converge after traction blocking intervention (i.e., δ > δ_control), braking intervention is then initiated to avoid operational fluctuations caused by premature braking intervention.

[0094] Therefore, the deviation range under low adhesion can be divided and preset control strategies can be set, such as: δ≤δtraction-3%: no operation, calculate "the current maximum allowable traction force" in real time and limit the driver's operation limit; δtraction-3%<δ≤δtraction: HMI pop-up combined with countdown (if there is no deceleration within 10s, traction will be automatically blocked); δtraction<δ≤δcontrol: traction will be automatically blocked and "recovery condition: δ≤δtraction-3%" will be displayed; δ>δcontrol: apply weak common braking until δ≤δtraction-3% is released.

[0095] For example: when the preset track adhesion level is low, the reference travel speed is 0.6 v0. Since the risk of wheel spin increases sharply as μ decreases, the above correlation law can be calculated by the wheel-rail mechanics model, where the speed deviation rate threshold can be as shown in the following formula (2), and the upper limit of the braking level B can be the same as the calculation formula of the above formula (2).

[0096] δ pull=15%-(0.05-μ) / 0.02×5%; (2)

[0097] Based on the above formula (2), it can be seen that when μ=0.05, δ_traction = 15%, and when μ=0.03, δ_traction = 10%. Based on the above correlation law, the deviation range and preset control strategy can be as follows: δ≤δ_traction-5%: limit the traction level to ≤30%, monitor the wheel speed difference in real time (if the preset threshold is exceeded, an early warning will be issued); δ_traction-5%<δ≤δ_traction: pop-up window combined with forced reduction of traction level; δ_traction<δ≤δ_traction+5%: automatically block traction and apply B-level braking simultaneously; δ>δ_traction+5%: increase the braking level to B+5% (but not exceeding 20%), and send a "need to extend interval" request.

[0098] In some embodiments, a correlation between actual speed and acceleration or deceleration can also be established. For example, when the preset track adhesion level is extremely low, the reference travel speed is 0.3v0. The maximum permissible acceleration a max As shown in the following formula (3), the traction force F ≤ μ × wheel weight can be ensured. Braking deceleration a brake As shown in the following formula (4), a certain safety margin can be reserved.

[0099] a max =0.1×μ×g; (3)

[0100] a brake =0.08×μ×g; (4)

[0101] Where g is the acceleration due to gravity.

[0102] Based on the above theory, preset control strategies can be established for different speed ranges, such as: speed < 0.3 km / h - 0-2 km / h: according to a maxAutomatic traction control; 0.3v0-2km / h≤speed≤0.3v0: Maintain current traction with real-time fine-tuning; Speed>0.3v0: Press a brake Automatically apply braking until the speed is ≤0.3v0-2km / h; record wheel speed difference, μ value, and control commands throughout the process, and generate an operation report for extremely low adhesion conditions.

[0103] According to embodiments of this disclosure, the train control strategy precisely correlates the current track adhesion level with the reference travel speed, and defines the correspondence between the degree of deviation and the preset control strategy based on the probability of slippage risk. This enables graded and more precise intervention for the speed deviation of the target train in the target section, reducing the probability of wheel-rail slippage risks such as wheel spin and coasting.

[0104] According to embodiments of this disclosure, the database stores multiple initial adjustment strategies for initial train operation plans under preset track adhesion levels; the initial train operation plan is a train operation plan with track adhesion coefficients within the normal range; the adjustment intensity of the initial adjustment strategy on the initial train operation plan gradually increases as the preset track adhesion level decreases; the adjustment strategy is obtained in the following manner.

[0105] Determine the intermediate orbit adhesion level that is ranked between the historical orbit adhesion level and the current orbit adhesion level, and determine the level difference between the current orbit adhesion level and the historical orbit adhesion level; based on the initial adjustment strategies of the current orbit adhesion level, the historical orbit adhesion level, and the intermediate orbit adhesion level obtained from the database, determine the differences in the initial adjustment strategies from the historical orbit adhesion level to the current orbit adhesion level; based on the differences and the level difference, obtain the adjustment strategy.

[0106] Initial adjustment strategies for the initial train operation plan under multiple preset track adhesion levels can be as follows: No adjustments are made when the preset track adhesion level is normal. When the preset track adhesion level is medium, trains passing through the target section are marked as key monitoring targets, and an alert is triggered when their braking distance exceeds a preset threshold. When the adhesion level is low, trains are selected for suspension according to corresponding preset vehicle parameters. When the adhesion level is relatively low, additional preset vehicle parameters are added to the low adhesion level to select trains for suspension, adjust train intervals and passenger flow capacity thresholds, and call in reserve trains for backup. When the adhesion level is extremely low, the suspension scope covers empty trains en route, station entrances at both ends of the target section are closed, and section flow control is implemented.

[0107] Since the aforementioned initial adjustment strategies are all based on the adjustment methods of the initial train operation plan when the preset track adhesion level is normal, determining the appropriate adjustment strategy requires identifying the differences and level differences between the historical and current track adhesion levels. This level difference will then determine the direction of the adjustment strategy, such as: increasing intervention relative to the initial train operation plan (i.e., continuing to apply the relevant initial adjustment strategies), or decreasing intervention relative to the initial train operation plan (i.e., reverting to previously applied relevant initial adjustment strategies).

[0108] According to embodiments of this disclosure, by quantifying the differences and grade gaps between historical and current track adhesion levels, the direction of adjustment strategies can be clearly defined, achieving dynamic adaptation to the current adhesion conditions. Through a control logic that enhances intervention when adhesion levels worsen and regresses intervention when they improve, the adjustment strategy always aligns with actual adhesion risk control needs.

[0109] According to embodiments of this disclosure, an adjustment strategy is derived based on differences in content and grade differences, which may include the following operations.

[0110] If the grade difference indicates that the current track adhesion level is lower than the historical track adhesion level, the adjustment strategy is to increase the adjustment intensity based on the current train operation plan according to the difference content; if the grade difference indicates that the current track adhesion level is higher than the historical track adhesion level, the adjustment strategy is to decrease the adjustment intensity based on the current train operation plan according to the difference content.

[0111] When the current track adhesion level is determined to be lower than the historical track adhesion level, it can be seen that the adhesion condition of the target section is deteriorating. Therefore, the initial adjustment strategy included in the difference content can be applied on the basis of the current train operation plan, that is, the adjustment intensity can be enhanced.

[0112] When the current track adhesion level is determined to be higher than the historical track adhesion level, it can be known that the adhesion condition of the target section is improving. Therefore, the initial adjustment strategy included in the difference content can be rolled back based on the current train operation plan, that is, the adjustment intensity can be reduced.

[0113] According to embodiments of this disclosure, by using the control logic of continuing and enhancing the initial adjustment strategy when the adhesion condition deteriorates and reverting the initial adjustment strategy when the condition improves, real-time adaptation of the adjustment intensity and the dynamic changes in the adhesion level is achieved, thereby improving the continuity and stability of the train operation plan and thus enhancing the operational reliability and condition adaptation accuracy of the train in scenarios with dynamic changes in adhesion conditions.

[0114] According to embodiments of this disclosure, the differences include: screening suspended trains based on preset vehicle parameters; the preset vehicle parameters include at least one of the following: carrying capacity, distance between the current location and the target section, track vacancy rate, passenger flow matching degree, risk level of the travel route, and train departure status; and strengthening the adjustment intensity based on the current train operation plan according to the differences, including:

[0115] The preset vehicle parameters of multiple trains included in the current train operation plan are weighted and summed to obtain the shutdown score of each train; based on the shutdown score of each train, the trains to be shut down are determined from the multiple trains; the trains to be shut down are marked as shut down in the current train operation plan.

[0116] When the difference includes filtering trains to be taken out of service according to preset vehicle parameters and the adjustment direction is to increase the adjustment intensity, the corresponding vehicle can be selected to be taken out of service based on the filtering strategy included in the difference.

[0117] For the differences determined by different historical track adhesion levels and the current track adhesion level, the preset vehicle parameters used in the screening strategy may be different, and the weights of each preset vehicle parameter may also be different.

[0118] For example, when changing from medium adhesion to low adhesion, the preset vehicle parameters can be the train's carrying capacity, the distance between the current position and the target section, the track vacancy rate, passenger flow matching degree, and the risk level of the travel path. The risk level of the travel path can be determined based on indicators such as the current track adhesion level of each section in the formal path and whether it is a turnout.

[0119] For example, when changing from low adhesion to lower adhesion, the preset vehicle parameters can be the train's carrying capacity, the distance between the current location and the target section, the availability of the track, passenger flow matching, the risk level of the travel path, the train's departure status, and whether the train is empty.

[0120] The preset vehicle parameters of multiple trains included in the current train operation plan can be weighted and summed to obtain the shutdown score for each train. The trains corresponding to the top N shutdown scores in descending order are then identified as the shutdown trains. N is a positive integer greater than 0, determined based on historical simulation results; N may differ for different track adhesion levels.

[0121] According to embodiments of this disclosure, by dynamically adapting preset vehicle parameters and weights for decommissioning screening to different track adhesion level change scenarios, and combining the decommissioning score obtained by weighted summation to screen decommissioning trains, the decommissioning decision is adapted to changes in adhesion conditions. During screening, priority is given to decommissioning trains with poor adaptability in indicators such as carrying capacity and passenger flow matching, or trains with high risk levels in their travel routes and close proximity to the target section, thereby specifically reducing safety hazards under low adhesion conditions. Furthermore, by setting differentiated parameters, the risk characteristics of different change scenarios are taken into account, improving the safety of train scheduling in dynamic adhesion condition change scenarios.

[0122] According to embodiments of this disclosure, the candidate train operation plan includes multiple stations where trains stop during their journey and the speeds of multiple trains traveling on multiple sections of the target line; wherein, the verification of the candidate train operation plan based on passenger flow data of each station along the target line in the current time period and braking distance constraints of each train determined by the current track adhesion coefficient may include the following operations.

[0123] Based on passenger flow data at each station along the target route during the current time period and the stations where multiple trains stop during their journey, the predicted passenger load factor for multiple trains traveling in each section is determined. Using the predicted passenger load factor and speed of each train traveling in each section, the predicted braking distance of each train traveling in each section is determined. By comparing the first comparison result of each predicted passenger load factor with a preset passenger load threshold and the second comparison result of each predicted braking distance with the corresponding braking distance constraint, the verification result of the candidate train operation plan is obtained.

[0124] The sections of the target route can be divided according to the adjacent stops along the target route.

[0125] Combining current passenger flow data for each station along the target route with multiple train stop sequences extracted from candidate train operation plans, the predicted passenger capacity for each train is calculated based on passenger flow distribution patterns at its stops. For example, inbound passenger flow is allocated according to train stop time, and transfer passenger flow is determined by line connectivity. This calculation then incorporates the train's carrying capacity, such as its rated passenger capacity, to determine the predicted passenger load factor for each train in the corresponding section.

[0126] Subsequently, based on the train's mass and the passenger mass corresponding to the predicted passenger load rate for each section, the predicted total mass of the train when traveling on each section is calculated. Then, the preset speeds of each train in each section of the candidate train operation plan are retrieved, and the predicted braking distance of each train when traveling on each section is obtained.

[0127] Then, by combining the first comparison results of the predicted passenger load factor of each train in each section with the preset passenger load threshold and the second comparison results of the predicted braking distance of each train in each section with the corresponding braking distance constraint, the comparison results of each train and each section are obtained, that is, the verification results of the candidate train operation plan, which provides a basis for subsequent plan optimization.

[0128] According to embodiments of this disclosure, by associating passenger flow data with train stops, a quantitative assessment of the predicted passenger load factor for each train and each section is achieved. Then, by combining the passenger load factor with the section's travel speed, the predicted braking distance is dynamically calculated, identifying overload risks and braking safety risks in advance. This dual-check mechanism ensures that candidate train operation plans meet operational compliance and safety redundancy requirements. Furthermore, the refined calculation based on the section dimension has a smaller deviation compared to overall estimation, making the verification results more closely reflect actual train operation scenarios.

[0129] According to embodiments of this disclosure, determining the predicted passenger load factor of multiple trains traveling in each section based on passenger flow data of each station along the target route during the current time period and the stations where multiple trains stop during their journey may include the following operations.

[0130] For each train, passenger flow data from historical time periods is used to analyze the passenger flow data of each station in the current time period to obtain the passenger flow data of each station at the train's stop time. Based on the passenger flow data of each station at the train's stop time and the train's carrying capacity, the predicted passenger load factor when the train travels in each section is determined.

[0131] Historical passenger flow data for the same type of scenario as the current time period can be retrieved in advance for each station on the target route during historical periods, such as weekdays, weekends, morning peak hours, and evening peak hours. After filtering out extreme outliers, a historical passenger flow database can be established by station.

[0132] The current passenger flow data of each station is matched with the historical passenger flow database, and the passenger flow data of each station at the train stop time is determined based on the matching results and the historical passenger flow database.

[0133] Subsequently, based on the train's stop sequence, the predicted passenger capacity is calculated segment by segment starting from the originating station. During the calculation, the predicted passenger capacity for each segment can be divided by the train's rated passenger capacity to obtain the predicted passenger load factor when the train is traveling on the corresponding segment.

[0134] According to embodiments of this disclosure, by linking historical passenger flow data with current time-period passenger flow data, the actual passenger flow at each station during train stops can be more accurately determined, making passenger capacity calculations more consistent with actual train operation scenarios. Furthermore, based on station-by-station dynamic calculations at the segment level, the predicted passenger load factor can more realistically reflect the passenger load status of the train at different stages of its journey.

[0135] According to embodiments of this disclosure, determining the predicted braking distance of each train in each section by using the predicted passenger load factor and the speed of each train in each section may include the following operations.

[0136] For each train, the initial braking distance is determined based on the train's speed in each section and the current track adhesion coefficient of each section. The initial braking distance is then corrected based on the load correction factor corresponding to the predicted passenger load rate in each section, the coasting correction factor corresponding to the current track adhesion level in each section, the environmental correction factor corresponding to the weather conditions, and the track state factor corresponding to the track type in each section, thus obtaining the predicted braking distance of the train in each section.

[0137] For each section of each train, the initial braking distance of the train when traveling in that section can be determined based on the train's speed in that section and the current track adhesion coefficient of that section. It can be derived using Newton's laws of motion, as shown in the following formula (5).

[0138] (5)

[0139] Where v represents the speed of the train in this section, g represents the gravitational acceleration (taken as 9.8 m / s²), μ represents the current track adhesion coefficient, and φ represents the braking efficiency coefficient (taken as 0.85~0.95, determined by the performance of the train braking system, with a default value of 0.9).

[0140] For example, at low adhesion (μ=0.065), with a train speed of 60km / h (equivalent to 16.7m / s), the initial braking distance is... rice.

[0141] Since the initial braking distance does not take into account interference factors in actual operation, multi-factor correction can be used to make the calculation results more realistic. The weights and values ​​of the correction factors can be different under different preset adhesion levels. See Table 1 below for details.

[0142] Table 1

[0143]

[0144] Predicted braking distance The calculation method can be shown in the following formula (6).

[0145] (6)

[0146] For example: with low adhesion μ=0.065, train occupancy rate 70% (K2=1.1), rainy weather (K3=1.1), traveling on a straight section (K4=1.0), and coasting correction factor K1=1.2, then... rice.

[0147] According to embodiments of this disclosure, the initial braking distance is calculated based on the train's speed in each section and the current track adhesion coefficient. Then, it is corrected in multiple dimensions using load correction factors, coasting correction factors, environmental correction factors, and track state factors, which more comprehensively covers the variables affecting the braking distance and makes the predicted braking distance more consistent with actual working conditions.

[0148] According to embodiments of this disclosure, the braking distance constraint is determined in the following manner.

[0149] Based on the current track adhesion level of each section, the safety protection coefficient of each section is determined; using the safety protection coefficient of each section, the preset braking distance of each train when traveling in each section is compensated, and the braking distance constraint of each train when traveling in each section is obtained.

[0150] The database can pre-store preset braking distances for each train under different scenarios in different sections. These scenarios can include varying passenger load factors and speeds, or different weather conditions. The preset braking distances can be determined based on the actual braking distances of trains during their historical operation under different scenarios.

[0151] Therefore, the preset braking distance of the train in each section can be determined by the train's passenger load factor and speed during the calculation. Combined with the safety protection coefficient of each section, the braking distance constraint of the train when traveling in each section can be obtained.

[0152] The safety protection factor corresponding to each preset track adhesion level can be predetermined. Then, during actual calculations, this factor is matched with the current track adhesion level of each section to determine the safety protection factor for each section. The safety protection factors corresponding to each preset track adhesion level are shown in Table 2 below.

[0153] Table 2

[0154]

[0155] The braking distance constraints of the train when it is running in each section can be obtained based on the following formula (7).

[0156] (7)

[0157] in, Characterizing the braking distance constraint of this section, Characterizing the preset braking distance of this section, This characterizes the safety protection coefficient of this section.

[0158] In some embodiments, the braking distance constraint may differ in data acquisition frequency, factor weights, selected factors, and verification criteria under different preset track adhesion levels. For example, under medium adhesion, it is calculated every 2 minutes.

[0159] According to embodiments of this disclosure, a corresponding safety protection coefficient is determined based on the current track adhesion level of each section. This coefficient is used to compensate for a preset braking distance, resulting in a braking distance constraint suitable for each section. This ensures that the braking safety standard is better matched to the adhesion conditions of each section. Furthermore, sections with lower adhesion levels can obtain sufficient safety redundancy through a higher protection coefficient, reducing safety risks caused by slippage or insufficient braking.

[0160] According to embodiments of this disclosure, the verification result of the candidate train operation plan is obtained by comparing each predicted passenger load factor with a preset passenger load threshold and each predicted braking distance with a corresponding braking distance constraint. This can include the following operations.

[0161] If the first comparison result indicates that all predicted passenger load factors are less than or equal to the preset passenger load threshold, and the second comparison result indicates that all predicted braking distances are less than or equal to the corresponding braking distance constraints, the verification result of the candidate train operation plan is determined to be verified as passed; if the first comparison result indicates that there is a predicted passenger load factor greater than the preset passenger load threshold, or the second comparison result indicates that there is a predicted braking distance greater than the corresponding braking distance constraint, the verification result of the candidate train operation plan is determined to be verified as failed.

[0162] The system determines whether the train's predicted passenger load factor in the specified section is less than or equal to a preset passenger load threshold, and whether the train's predicted braking distance in the specified section is less than or equal to the braking distance constraint of the corresponding section. If all sections of a train simultaneously meet the above comparison conditions, i.e., multiple predicted passenger load factors do not exceed the preset threshold and multiple predicted braking distances do not exceed the corresponding constraints, then the train's verification result is determined to be passed.

[0163] If a train fails to meet any of the comparison conditions in any section, i.e., the predicted passenger load exceeds the preset threshold or the predicted braking distance exceeds the corresponding constraint, the verification result of the train is determined to be unsuccessful.

[0164] The verification results of multiple trains are summarized to form the verification results of the candidate train operation plan. Among the above verification results, trains that pass and fail are distinguished as well as the specific sections that do not meet the conditions.

[0165] According to embodiments of this disclosure, passenger load factor verification ensures that train operations in each section do not exceed passenger load thresholds, improving capacity matching and passenger safety. Braking distance verification ensures that braking performance in each section meets corresponding constraints, reducing safety risks caused by insufficient braking. Furthermore, clear pass / fail determination results allow for rapid identification of overloaded or over-braking trains and sections, providing direction for optimizing candidate plans and improving the safety and feasibility of train operation plans.

[0166] According to embodiments of this disclosure, the train operation control method may further include the following operations.

[0167] If the verification result is that the verification fails, the adjustment range for the candidate train operation plan is determined based on the reasons for failure determined by the first comparison result and the second comparison result; the candidate train operation plan is adjusted based on the adjustment range until the verification result is that the verification passes.

[0168] When the verification result is "failed," the first comparison result and the second comparison result can be analyzed to determine the reason for failure. For example, if the predicted passenger load of train A in section A is greater than the preset passenger load threshold, the reason for failure can be determined to be overloading. If the predicted braking distance of train B in section B is greater than the corresponding braking distance constraint, the reason for failure can be determined to be the existence of braking risk.

[0169] The reasons for failure can be used to determine the scope of adjustments to the candidate train operation plan. For example, for the overload problem, the adjustment scope includes increasing the number of carriages of the corresponding train A, optimizing the departure time of train A to avoid peak passenger flow, increasing the number of vehicles on the same route as train A, and implementing station passenger flow control, etc.

[0170] By defining the adjustment range as described above, the corresponding target adjustment strategy can be determined, and then the candidate train operation plan can be adjusted. After the adjustment, the plan can be verified until it passes the verification.

[0171] If multiple adjustments still fail to pass, an "emergency plan" will be triggered, and manual intervention will be used for decision-making.

[0172] According to embodiments of this disclosure, the adjustment scope is defined based on the reasons for failure, reducing resource waste and efficiency loss caused by indiscriminate modifications, and the adjustment focuses on the problem section and related trains, thus solving the problem more accurately.

[0173] According to embodiments of this disclosure, an intelligent scheduling algorithm is activated based on multi-dimensional data such as the overall line operation status, real-time passenger flow, and train location. During scheduling, trains located at the terminal station and whose subsequent operating sections are significantly affected by low adhesion are prioritized and sent a service withdrawal instruction. For example, on a certain subway line, if the system detects low adhesion in an intermediate section, trains waiting to depart from the terminal station and scheduled to pass through that low-adhesion section will be temporarily suspended and withdrawn from service. By reducing the number of trains in operation, the line's operating density is lowered, mitigating congestion problems that may be caused by decreased train operating efficiency due to low adhesion, avoiding safety risks caused by frequent acceleration, deceleration, or braking of trains, and improving the stability of the overall operational order.

[0174] In some embodiments, to adapt to the limited wheel-rail adhesion under low adhesion conditions, the candidate train operation plan may further include: the signaling system of each train can automatically optimize the traction and braking control of the train. Regarding traction, when facing even lower adhesion conditions, the original traction level can be downgraded, reducing the output power of the traction motor. For example, the original maximum traction level of 10 can be reduced to level 6-7 to reduce the traction force during train start-up and acceleration, preventing wheel slippage due to excessive traction force. Similarly, in the braking stage, when facing even lower adhesion conditions, the braking level can also be reduced by adjusting the brake cylinder pressure to decrease the braking force between the brake pads and the wheels, preventing wheel lock-up and slippage. Simultaneously, the signaling system can dynamically fine-tune the traction and braking levels based on real-time changes in train speed, acceleration, and adhesion coefficient, achieving precise matching of traction, braking force, and track adhesion, effectively improving train slippage and slippage conditions, and ensuring train operation stability.

[0175] Figure 4 A schematic block diagram of a train according to an embodiment of the present disclosure is shown.

[0176] like Figure 4 As shown, the train includes a sending module 410 and an execution module 420.

[0177] The transmitting module 410 is used to transmit the current track adhesion coefficient of the target section to the electronic device.

[0178] The execution module 420 is used to control the train operation according to the target train control strategy sent by the electronic equipment in response to receiving the target train control strategy, which is determined by the electronic equipment from the candidate train operation plan.

[0179] After calculating the current track adhesion coefficient of the target section, the train can send it to the electronic equipment. Upon receiving the track adhesion coefficient, the electronic equipment can then execute the aforementioned train operation control method. For example, upon successful verification, the current train operation plan is updated to a candidate train operation plan, and the target train control strategies for each train included in the candidate train operation plan are sent to the corresponding train.

[0180] Figure 5 A flowchart illustrating a train operation control method according to another embodiment of the present disclosure is shown schematically.

[0181] like Figure 5 As shown, the method includes operations S501 to S504.

[0182] In operation S501, determine and send the track adhesion coefficient of the target segment.

[0183] The transmitting module may include a train control and management system. In implementation, the train control and management system can determine the track adhesion coefficient of the target section and transmit it via a communication network.

[0184] In operation S502, determine whether the target train control strategy has been received. If received, execute operation S503. If not received, execute operation S505.

[0185] In operation S503, the initial train control strategy is updated to the target train control strategy.

[0186] The execution module may include various train automatic protection systems, automatic train operation systems, etc., which can control train operation according to the target train control strategy.

[0187] When operating S504, the train is running according to the initial train control strategy.

[0188] Based on the above-described train operation control method, this disclosure also provides a train operation control device. The following will be combined with... Figure 6 The device is described in detail.

[0189] Figure 6 A schematic block diagram of a train operation control device according to an embodiment of the present disclosure is shown.

[0190] like Figure 6 As shown, the train operation control device 600 in this embodiment includes an evaluation module 610, an adjustment module 620, and a verification module 630.

[0191] The evaluation module 610 is used to respond to receiving the current track adhesion coefficient of the target section, and to evaluate the adjustment of the current train operation plan of the target line including the target section based on the current track adhesion level corresponding to the track adhesion coefficient and the historical track adhesion level of the target section, and to obtain the evaluation result. In one embodiment, the evaluation module 610 can be used to perform the operation S210 described above, which will not be repeated here.

[0192] The adjustment module 620 is used to adjust the current train operation plan based on the operation data of multiple trains running on the target line and the adjustment strategy adapted to the current track adhesion level when the evaluation result indicates that the current train operation plan needs to be adjusted, so as to obtain a candidate train operation plan. In one embodiment, the adjustment module 620 can be used to perform the operation S220 described above, which will not be repeated here.

[0193] The verification module 630 is used to verify the candidate train operation plan based on the passenger flow data of each station along the target route in the current time period and the braking distance constraints of multiple trains determined by the current track adhesion coefficient. If the verification passes, the current train operation plan is updated to the candidate train operation plan. In one embodiment, the verification module 630 can be used to perform the operation S230 described above, which will not be repeated here.

[0194] According to embodiments of this disclosure, any plurality of modules among the evaluation module 610, adjustment module 620, and verification module 630 may be combined into one module, or any one of these modules may be split into multiple modules. Alternatively, at least a portion of the functionality of one or more of these modules may be combined with at least a portion of the functionality of other modules and implemented in one module. According to embodiments of this disclosure, at least one of the evaluation module 610, adjustment module 620, and verification module 630 may be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three implementation methods. Alternatively, at least one of the evaluation module 610, adjustment module 620, and verification module 630 may be at least partially implemented as a computer program module, which, when run, can perform corresponding functions.

[0195] Figure 7 A block diagram schematically illustrates an electronic device suitable for implementing a train operation control method according to an embodiment of the present disclosure.

[0196] like Figure 7 As shown, an electronic device 700 according to an embodiment of the present disclosure includes a processor 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage portion 708 into a random access memory (RAM) 703. The processor 701 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 701 may also include onboard memory for caching purposes. The processor 701 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0197] RAM 703 stores various programs and data required for the operation of electronic device 700. Processor 701, ROM 702, and RAM 703 are interconnected via bus 704. Processor 701 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 702 and / or RAM 703. It should be noted that the programs may also be stored in one or more memories other than ROM 702 and RAM 703. Processor 701 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in said one or more memories.

[0198] According to embodiments of this disclosure, the electronic device 700 may further include an input / output (I / O) interface 705, which is also connected to a bus 704. The electronic device 700 may also include one or more of the following components connected to the I / O interface 705: an input section 706 including a keyboard, mouse, etc.; an output section 707 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN card, modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as needed. A removable medium 711, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 710 as needed so that computer programs read from it can be installed into the storage section 708 as needed.

[0199] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.

[0200] According to embodiments of this disclosure, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this disclosure, the computer-readable storage medium may include ROM 702 and / or RAM 703 and / or one or more memories other than ROM 702 and RAM 703 described above.

[0201] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to cause the computer system to implement the methods provided in the embodiments of this disclosure.

[0202] When the computer program is executed by the processor 701, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0203] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 709, and / or installed from a removable medium 711. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0204] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 709, and / or installed from the removable medium 711. When the computer program is executed by the processor 701, it performs the functions defined in the system of this disclosure embodiment. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0205] According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can execute entirely on a user's computing device, partially on a user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0206] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0207] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0208] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. The scope of this disclosure is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. A train operation control method, characterized in that, include: In response to receiving the current track adhesion coefficient of the target section, an adjustment assessment of the current train operation plan is performed on the target line including the target section based on the current track adhesion level corresponding to the track adhesion coefficient and the historical track adhesion level of the target section, and an assessment result is obtained; If the evaluation results indicate that the current train operation plan needs to be adjusted, the current train operation plan is adjusted based on the operation data of multiple trains operating on the target line and the adjustment strategy adapted to the current track adhesion level to obtain a candidate train operation plan. Based on the passenger flow data of each station along the target route in the current time period and the braking distance constraints of each of the multiple trains determined by the current track adhesion coefficient, the candidate train operation plan is verified, so that if the verification passes, the current train operation plan is updated to the candidate train operation plan.

2. The method according to claim 1, characterized in that, The candidate train operation plan includes multiple stations where the train will stop during its journey and multiple speeds of the train when it travels on multiple sections of the target line; The verification of the candidate train operation plan based on passenger flow data of each station along the target route in the current time period and braking distance constraints of multiple trains determined by the current track adhesion coefficient includes: Based on the passenger flow data of each station along the target route in the current time period and the stations where the trains stop during their journey, the predicted passenger load factor of the trains when traveling in each section is determined. The predicted braking distance of each train when it is traveling in each section is determined by using the predicted passenger load factor and the speed of each train when it is traveling in each section. The verification result of the candidate train operation plan is obtained by comparing the first comparison result of each predicted passenger load factor with the preset passenger load threshold and the second comparison result of each predicted braking distance with the corresponding braking distance constraint.

3. The method according to claim 2, characterized in that, The method of determining the predicted passenger load factor of multiple trains traveling in each section based on passenger flow data of each station along the target route in the current time period and the stations stopped by the trains during their journey includes: For each train, passenger flow data from historical time periods is used to analyze the passenger flow data of each station in the current time period to obtain the passenger flow data of each station at the time the train stops. Based on passenger flow data at each station at the time the train stops and the carrying capacity of the train, the predicted passenger load factor of the train when traveling in each section is determined.

4. The method according to claim 2, characterized in that, The step of determining the predicted braking distance of each train in each section by using the predicted passenger load factor and the speed of each train in each section includes: For each of the trains, the initial braking distance of the train when traveling in each of the sections is determined based on the speed of the train traveling in each of the sections and the current track adhesion coefficient of each of the sections; Based on the load correction factor corresponding to the predicted passenger load rate of the train when traveling in each section, the coasting correction factor corresponding to the current track adhesion level of each section, the environmental correction factor corresponding to the weather conditions, and the track state factor corresponding to the track type of each section, the initial braking distance of the train when traveling in each section is corrected to obtain the predicted braking distance of the train when traveling in each section.

5. The method according to claim 1 or 4, characterized in that, The braking distance constraint is determined in the following way: Based on the current track adhesion level of each of the aforementioned sections, the safety protection factor of each of the aforementioned sections is determined; By utilizing the safety protection coefficient of each section, the preset braking distance of each train when traveling in each section is compensated, thereby obtaining the braking distance constraint of each train when traveling in each section.

6. The method according to claim 2, characterized in that, The verification result of the candidate train operation plan is obtained by comparing the first comparison result of each predicted passenger load factor with the preset passenger load threshold and the second comparison result of each predicted braking distance with the corresponding braking distance constraint, including: If the first comparison result indicates that each of the predicted passenger load factors is less than or equal to the preset passenger load threshold, and the second comparison result indicates that each of the predicted braking distances is less than or equal to the corresponding braking distance constraint, then the verification result of the candidate train operation plan is determined to be verified as passed. If the first comparison result indicates that the predicted passenger load factor is greater than the preset passenger load threshold, or if the second comparison result indicates that the predicted braking distance is greater than the corresponding braking distance constraint, the verification result of the candidate train operation plan is determined to be that the verification failed.

7. The method according to claim 6, characterized in that, The method further includes: If the verification result is that the verification fails, the adjustment range for the candidate train operation plan is determined based on the reasons for failure determined by the first comparison result and the second comparison result. The candidate train operation plan is adjusted based on the adjustment range until the verification result is a successful verification.

8. The method according to claim 1, characterized in that, The operational data includes: the operating section; the adjustment of the current train operation plan based on the operational data of multiple trains operating on the target line and an adjustment strategy adapted to the current track adhesion level to obtain candidate train operation plans, including: Based on the operating sections of each of the trains, the target trains that pass through the target sections are determined; Based on the train control strategy corresponding to the current track adhesion level, the initial train control strategy of the target train in the current train operation plan when it travels in the target section is updated to obtain an intermediate train operation plan; Based on the adjustment strategy, the intermediate train operation plan is updated to obtain candidate train operation plans.

9. The method according to claim 1 or 8, characterized in that, The database stores initial adjustment strategies for initial train operation plans under multiple preset track adhesion levels; the initial train operation plan is a train operation plan with a track adhesion coefficient within the normal range; the adjustment intensity of the initial adjustment strategy on the initial train operation plan gradually increases as the preset track adhesion level decreases; the adjustment strategy is obtained through the following method: Determine the intermediate track adhesion level that is ranked between the historical track adhesion level and the current track adhesion level, and determine the level difference between the current track adhesion level and the historical track adhesion level; Based on the initial adjustment strategies of the current track adhesion level, the historical track adhesion level, and the intermediate track adhesion level obtained from the database, the differences in the initial adjustment strategies from the historical track adhesion level to the current track adhesion level are determined. The adjustment strategy is derived based on the differences in content and the grade difference.

10. The method according to claim 9, characterized in that, The adjustment strategy derived based on the differences in content and the grade difference includes: If the grade difference indicates that the current track adhesion level is lower than the historical track adhesion level, the adjustment strategy is to increase the adjustment intensity based on the difference in the current train operation plan. If the grade difference indicates that the current track adhesion level is higher than the historical track adhesion level, the adjustment strategy is to reduce the adjustment intensity based on the difference in the current train operation plan.

11. The method according to claim 10, characterized in that, The differences include: filtering suspended trains according to preset vehicle parameters; the preset vehicle parameters include at least one of the following: carrying capacity, distance between the current location and the target section, track availability, passenger flow matching degree, risk level of the travel route, and train departure status; the enhancement of the adjustment intensity based on the current train operation plan according to the differences includes: The preset vehicle parameters of the multiple trains included in the current train operation plan are weighted and summed to obtain the shutdown score of each train. Based on the decommissioning scores of each of the trains, trains that are decommissioned are identified from among the multiple trains. The suspended train is marked as suspended in the current train operation plan.

12. The method according to claim 8, characterized in that, The train control strategy corresponding to the current track adhesion level includes: In response to a deviation between the actual speed of the target train traveling in the target section and the reference speed under the current track adhesion level, a corresponding preset control strategy is determined based on the degree of deviation. The correspondence between the degree of deviation and the preset control strategy is determined based on the probability of the target train slipping under multiple degrees of deviation.

13. The method according to claim 1, characterized in that, The method involves adjusting and evaluating the current train operation plan of the target line, including the target section, based on the current track adhesion level corresponding to the track adhesion coefficient and the historical track adhesion level of the target section, to obtain the evaluation results, including: The current track adhesion level is compared with the historical track adhesion level to obtain the comparison result; If the comparison results indicate that the current track adhesion level is inconsistent with the historical track adhesion level, the assessment result is determined to be that the current train operation plan needs to be adjusted.

14. The method according to claim 1, characterized in that, The current track adhesion level is determined in the following way: The track adhesion coefficient is matched with multiple preset track adhesion coefficient ranges to determine the target track adhesion coefficient range to which the track adhesion coefficient belongs; The preset track adhesion level corresponding to the target track adhesion coefficient range is taken as the current track adhesion level.

15. An electronic device comprising: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors perform the method according to any one of claims 1 to 14.

16. A train, characterized in that, include: A transmitting module is used to transmit the current track adhesion coefficient of the target segment to the electronic device as described in claim 15; An execution module is configured to, in response to receiving a target train control strategy sent by the electronic device, control the train operation according to the target train control strategy, wherein the target train control strategy is determined by the electronic device from the candidate train operation plan.

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