Heavy-load train group constraint drive control system and method for dynamic matching of capacity and volume

Abstract

The present invention belongs to the field of rail transit technology, and proposes a heavy-load train group constraint drive control system and method for dynamic matching of capacity and volume. The main scheme is: obtaining network structure information, train parameter information and train operation status information; judging the nature of the section where the train is located based on the acquired information in combination with judgment indicators, and updating the key dynamic parameters of the train in the high-volume section and the volume-saturated section on the network based on the judged nature of the section where the train is located; based on the updated key dynamic parameters of the train in the high-volume section and the volume-saturated section on the network, judging whether the trains in the high-volume section and the volume-saturated section need to spontaneously form a group or leave the group and whether they can spontaneously form a group or leave the group through a constrainer, a train operation status evaluator and an operation mode discriminator; for those that meet the conditions for forming a group or leaving the group, the time and control force for forming a group or leaving the group are calculated through an optimization controller.

Description

Technical Field

[0001] The present invention relates to the field of rail transportation technology, and in particular to a heavy-load train group constraint drive control system and method for dynamic matching of transport capacity and transport volume. Background Art

[0002] The macroeconomic situation, industrial restructuring, energy structure changes, and industrial and agricultural production cycles have led to significant fluctuations in railway freight volume. Dynamic matching of capacity and volume to optimize resource allocation, improve transportation efficiency, and promote green transportation is a key approach to solving this problem. Currently, existing research on rail transit capacity and volume matching mainly presents the following characteristics:

[0003] (1) Research objects: Focus on passenger transport, followed by freight transport. Existing studies generally focus on short-term peak passenger flows such as subway commuter tides, OD distribution (origin-destination distribution), transfer patterns, differences in the temporal and spatial distribution of high-speed rail business or tourist passenger flows, cross-line demand, etc., while there is little research on the capacity-volume mismatch caused by the continuous concentrated transportation of heavy-loaded bulk medium and long-distance freight, short-term volume fluctuations, and localized capacity congestion points, and the corresponding solutions are also extremely limited;

[0004] (2) Control measures: The focus is on top-level design for capacity tapping, such as scheduling optimization, while there is a lack of adaptive solutions for specific sections such as capacity bottlenecks. High-speed rail and subways mainly achieve capacity-volume matching through scheduling optimization. Research focuses on train schedule arrangement, train interval time control, train speed optimization, station allocation and capacity improvement. For example, emergency scheduling such as subway flow control, additional trains or over-station operation, high-speed rail dynamic pricing, seasonal timetable adjustment, etc. At present, my country's heavy-duty railway three-indicator and four-indicator automatic block lines have adopted yellow light departure. The terminal assembly operation station is easily blocked by the bottleneck area and the departure line departure capacity, and the capacity has been tapped to the limit at the transportation organization level. The existing control measures aimed at adjusting the operation diagram, scheduling optimization, and improving the turnover rate of locomotives and vehicles are essentially tapping the capacity limit. Obviously, they are not suitable for the relief and control of specific sections where heavy-duty railways have reached their capacity limit or even have capacity bottlenecks.

[0005] (3) Scope of focus: Capacity configuration is mainly centered on fixed facilities such as stations, lines, and road networks, and there is less joint optimization of mobile resources and fixed facilities. Existing studies generally separate fixed facilities from mobile resources. Fixed facility optimization mainly focuses on macro-level aspects such as station capacity, line design, and road network layout, aiming to optimize the allocation of road network capacity from a temporal and spatial perspective to improve the carrying capacity of the railway network; while mobile resource optimization focuses more on the train itself, such as compressing tracking intervals to improve line capacity, improving train formation flexibility through virtual formation, and optimizing EMU or locomotive turnover plans to improve mobile equipment utilization. They usually assume that the demand for fixed facilities and mobile resources is stable, but in reality, rail transit systems face dynamic passenger and freight flow fluctuations, changes in transportation demand, and other factors. Therefore, the isolated optimization of mobile resources and fixed facilities lacks flexibility, which is not conducive to improving the overall efficiency of the railway system and will significantly increase the complexity of the dispatching control system.

[0006] In summary, there are problems in the dynamic matching of capacity and volume of existing heavy-haul railways, such as insufficient technical solutions, lack of adaptive solutions for capacity bottleneck sections, poor flexibility and high complexity of system architecture. Summary of the Invention

[0007] The purpose of the present invention is to provide a heavy-load train group constraint drive control system and method for dynamic matching of capacity and volume, aiming to solve the problems of insufficient dynamic matching scheme of capacity and volume of existing heavy-load railways, lack of adaptive solution for bottleneck sections of transportation capacity, poor flexibility and high complexity of system architecture, etc.

[0008] The present invention solves the technical problem and adopts the following technical solution:

[0009] On the one hand, the present invention provides a heavy-load train group constraint drive control system for dynamic matching of transport capacity and transport volume, comprising:

[0010] The information acquisition module is used to obtain the road network structure information, train parameter information and train operation status information, and transmit it to the train dynamics module;

[0011] The train dynamics module is used to determine the nature of the train section based on the information transmitted by the information module and the judgment indicators, and update the key train dynamic parameters of the high-traffic section and the saturated section on the railway network based on the determined nature of the train section, and transmit them to the constraint-driven optimization control module;

[0012] The constraint-driven optimization control module is used to determine whether trains in high-volume sections and saturated sections on the updated road network need to spontaneously form or leave groups, and whether they can spontaneously form or leave groups, based on the key dynamic parameters of trains in high-volume sections and saturated sections on the road network through constraints, train operation status evaluators and operation mode discriminators. For those who meet the conditions for forming or leaving groups, the time and control force for forming or leaving groups are calculated through the optimization controller.

[0013] On the other hand, the present invention also provides a heavy-haul train group constraint drive control method for dynamic matching of transport capacity and transport volume, which is applied to the heavy-haul train group constraint drive control system for dynamic matching of transport capacity and transport volume, and includes the following steps:

[0014] Obtaining road network structure information, train parameter information and train operation status information;

[0015] Based on the acquired network structure information, train parameter information, and train operation status information, the train section nature is determined in combination with judgment indicators, and the key train dynamic parameters in the high-volume and saturated sections of the network are updated based on the determined train section nature;

[0016] Based on the key dynamic parameters of trains in high-volume sections and saturated sections on the updated road network, the constraints, train operation status evaluators and operation mode discriminators are used to determine whether trains in high-volume sections and saturated sections need to spontaneously form groups or leave groups, and whether they can spontaneously form groups or leave groups. For those that meet the conditions for forming or leaving groups, the time and control force for forming or leaving groups are calculated through the optimization controller.

[0017] As a further optimization, the road network structure information includes the horizontal and vertical sections of the line, station locations, section capacity and station capacity;

[0018] The train parameter information includes train type, structural dimensions, number of trains, load and traction and braking characteristics;

[0019] The train operation status information includes the current position of the train, the current operation mode of the train operation control device and the length of the block section at the current position.

[0020] As a further optimization, the obtained network structure information, train parameter information and train operation status information are combined with the judgment index to judge the nature of the section where the train is located, including:

[0021] The network structure information and train parameter information are used as public information resources to build a public information resource library. All trains on the network can access and read the public information resources in the public information resource library at any time and any location through the communication network.

[0022] Send the acquired running status information to all trains on the network through the communication network;

[0023] According to the current position information of the train, the number of trains in each line section of the road network is counted, the judgment index is determined, and the nature of the section where the train is located is judged according to the calculation formula of the nature of the section where the train is located.

[0024] As a further optimization, the judgment index is: the ratio of the difference between the section or station throughput capacity of the section where the train is located and the number of trains in the section or station on the network to the section or station throughput capacity of the section.

[0025] As a further optimization, the key dynamic parameters of trains in the high-volume sections and saturated-volume sections on the updated road network include slope additional resistance, curve additional resistance, tunnel additional resistance and basic operation resistance.

[0026] As a further optimization, constraint conditions are set in the constraint controller, and the constraint conditions include train dynamics constraint, train safety interval constraint, train travel time constraint and departure headway constraint.

[0027] As a further optimization, the train running state evaluator confirms the train running state according to an evaluation rule, and the train running state includes traction, braking, coasting and cruising.

[0028] As a further optimization, the operation mode discriminator determines whether the train is a train in a fixed block operation mode or a train in a group operation mode;

[0029] For trains in fixed block operation mode, the discrimination rules are used to determine whether the trains have the conditions to form a group spontaneously;

[0030] For trains in group operation mode, for any two adjacent trains in the group, when any of their spacing and headway exceeds the expected spacing or allowed headway of the group trains, it can be determined that they have the behavior of exiting the group.

[0031] As a further optimization, the optimization controller optimizes the control of trains that have the conditions to spontaneously form a group and are already in the group, and calculates the optimal time for each train to form a group or exit the group and the corresponding train speed and control force, guiding the train to complete the process of spontaneously forming or exiting the group.

[0032] The beneficial effects of the present invention are: making full use of the flexibility of heavy-load train group operation technology and its high compatibility with existing communication signal facilities and train operation control systems, and designing a switching control scheme for dynamic matching of capacity and volume in an open railway environment under the scenario of mobile train resource fluctuations based on the network line capacity. Through the joint optimization of mobile resources and fixed facilities, the trains in the capacity bottleneck section are promoted to spontaneously form or exit the group, and the smooth switching between group operation control and the existing automatic block control mode is realized, which solves the technical problem that the existing scheme is difficult to achieve flexible matching of capacity and volume in the capacity bottleneck section. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a schematic diagram of the system composition structure of a heavy-load train group constraint drive control system for dynamic matching of transport capacity and transport volume in Example 1 of the present invention;

[0034] Figure 2 This is a flow chart of a heavy-load train group constraint drive control method for dynamic matching of transport capacity and transport volume in Example 2 of the present invention;

[0035] Figure 3 This is a schematic diagram of the road network structure layout in Example 3 of the present invention;

[0036] Figure 4 Schematic diagram of the horizontal and vertical sections of the line and the locations of stations in Example 3 of the present invention;

[0037] Figure 5 Schematic diagram of additional resistance on a slope for a train in Example 3 of the present invention;

[0038] Figure 6 This is a schematic diagram of additional resistance to curves for a train in Example 3 of the present invention;

[0039] Figure 7 Schematic diagram of additional tunnel resistance of a train in Example 3 of the present invention;

[0040] Figure 8 Schematic diagram of the workflow of the constraint-driven optimization control module in Example 3 of the present invention;

[0041] Figure 9 Schematic diagram of simulation results of applying the patented method to the road network shown in the case in Example 3 of the present invention;

[0042] Figure 10 is the speed curve of each train in Example 3 of the present invention;

[0043] Figure 11 is the control force curve of each train in Example 3 of the present invention;

[0044] Figure 12 In Example 3 of the present invention, Figure 10Basic resistance curve of train operation corresponding to speed. DETAILED DESCRIPTION

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0046] Example 1

[0047] This embodiment provides a heavy-load train group constraint drive control system for dynamic matching of transport capacity and transport volume. Figure 1 , wherein the system comprises:

[0048] The information acquisition module is used to obtain the road network structure information, train parameter information and train operation status information, and transmit it to the train dynamics module;

[0049] The train dynamics module is used to determine the nature of the train section based on the information transmitted by the information module and the judgment indicators, and update the key train dynamic parameters of the high-traffic section and the saturated section on the railway network based on the determined nature of the train section, and transmit them to the constraint-driven optimization control module;

[0050] The constraint-driven optimization control module is used to determine whether trains in high-volume sections and saturated sections on the updated road network need to spontaneously form or leave groups, and whether they can spontaneously form or leave groups, based on the key dynamic parameters of trains in high-volume sections and saturated sections on the road network through constraints, train operation status evaluators and operation mode discriminators. For those who meet the conditions for forming or leaving groups, the time and control force for forming or leaving groups are calculated through the optimization controller.

[0051] Example 2

[0052] Based on Example 1, this embodiment provides a heavy-load train group constraint drive control method for dynamic matching of capacity and volume. Figure 2 , wherein the method comprises the following steps:

[0053] S1. Obtaining network structure information, train parameter information and train operation status information;

[0054] S2. Based on the acquired network structure information, train parameter information, and train operation status information, the nature of the train section is determined in combination with the judgment index, and key train dynamic parameters for high-volume sections and saturated sections of the network are updated based on the determined nature of the train section.

[0055] S3. Based on the key dynamic parameters of trains in high-volume sections and saturated sections on the updated road network, the constraints, train operation status evaluators and operation mode discriminators are used to determine whether trains in high-volume sections and saturated sections need to spontaneously form or leave groups, and whether they can spontaneously form or leave groups. For those that meet the conditions for forming or leaving groups, the time and control force for forming or leaving groups are calculated through the optimization controller.

[0056] In actual application, in this embodiment, the road network structure information mainly includes the horizontal and vertical sections of the line, station locations, section capacity, and station capacity, etc. The relevant information can be directly provided by the line design or operation department;

[0057] The train parameter information mainly includes train type, structural dimensions, number of trains, load, and traction braking characteristics. The number of trains and load information are provided by the transportation organization department, and other relevant information is directly obtained by looking up the design specifications based on the locomotive and vehicle models.

[0058] The train operation status information mainly includes the current position of the train, the current operation mode of the train operation control equipment and the length of the block section at the location, etc. The key information is read from the locomotive LKJ, that is, the train operation monitoring device.

[0059] It should be noted that the determination of the nature of the train section based on the acquired network structure information, train parameter information, and train operation status information in combination with the determination index may include:

[0060] The network structure information and train parameter information are used as public information resources to build a public information resource library. All trains on the network can access and read the public information resources in the public information resource library at any time and any location through the communication network.

[0061] Send the acquired running status information to all trains on the network through the communication network;

[0062] According to the current position information of the train, the number of trains in each line section of the road network is counted, the judgment index is determined, and the nature of the section where the train is located is judged according to the calculation formula of the nature of the section where the train is located.

[0063] In this embodiment, the judgment index can be: the ratio of the difference between the section or station throughput capacity of the section where the train is located and the number of trains in the section or station on the network to the section or station throughput capacity of the section.

[0064] The formula for calculating the nature of the section where the train is located is:

[0065] ,

[0066] In the formula, the subscript Indicates the Line sections or stations, the preferred length of a line section is 10 km, is the section or station throughput capacity of the section, obtained in step S1, For the line section or station on the railway network The number of trains in The nature of the section where the train is located is divided into three categories: low traffic volume, high traffic volume, and traffic volume saturation. When it is lower than 0.10, it is determined to be a saturated traffic section; when it is greater than or equal to 0.25, it is determined to be a low traffic section; and if it is between these two values, it is a high traffic section.

[0067] It should be noted that the coefficients 0.25 and 0.10 given in formula (1) are recommended values ​​and can be replaced by any other reasonable values.

[0068] It should be pointed out that, in this embodiment, the key dynamic parameters of trains in the high-volume sections and saturated-volume sections of the updated road network mainly include slope additional resistance, curve additional resistance, tunnel additional resistance and basic running resistance, etc. The above parameters are calculated in accordance with the industry standard TB / T 1407.1-2018. In addition, these key parameters related to train dynamics are dynamically updated in real time as the train position changes throughout the entire process.

[0069] In this embodiment, the train information after the key dynamic parameters are updated is sent to the constraint-driven optimization control module. The constraint, train operation status evaluator, and operation mode discriminator are used to determine whether the trains in the high-volume and traffic-saturated sections need to spontaneously form or leave the group, and whether they can spontaneously form or leave the group. For those that meet the conditions for forming or leaving the group, the optimization controller calculates the corresponding time and control force for forming or leaving the group.

[0070] It should be pointed out that the constraint controller is provided with constraint conditions, which include train dynamics constraint, inter-train safety interval constraint, train travel time constraint and departure headway constraint. These constraint conditions are the basis for subsequent train operation status assessment, operation mode identification and optimization control.

[0071] Specifically, for train dynamic constraints: the line conditions, locomotive traction and braking performance, and train handling stability are reflected by constraining the train speed, acceleration, and jerk, as shown in Equations (2)-(4).

[0072] ,

[0073] In the formula, the subscript Indicates the The first in the train vehicles, is the time variable.

[0074] Formula (2) is the train speed Constraints, maximum speed Take line speed limit and the maximum operating speed of the locomotive The smaller of is the minimum speed allowed to pass at the current position. If there is no special explanation, the value is 0. Formula (3) is the acceleration and jerk Constraints, and is the minimum and maximum acceleration, where the maximum acceleration Take the maximum acceleration allowed by the line and the maximum acceleration the locomotive can provide , The maximum jerk is 1.25 m / s. 3 Considering the train Vehicles Therefore, Equation (4) provides the relationship between the coupler force and the Constraints, is the maximum value of the coupler force.

[0075] Regarding the inter-train safety interval constraint: The safety interval constraint aims to ensure the collision avoidance safety of trains and is calculated according to Equation (5). That is, during the entire time range from the start of braking to the complete stop of the train, the expected distance between two adjacent trains is always greater than their additional safety distance, and the inter-train safety interval can be calculated based on their expected distance and speed characteristics.

[0076] ,

[0077] Where, and The train braking start time and train braking stop time are respectively and Represents two adjacent trains and , along the running direction The train is in the front of the train; For two adjacent trains and The desired spacing between them, for the relative braking distance within the group, the fixed block is the corresponding block section length; For two adjacent trains and For 5,000-ton heavy-load trains, the recommended additional safety distance is 200 meters. To provide safe spacing between trains; and are the speed and acceleration corresponding to the maximum braking force that the train can provide; The corresponding speed when the train can provide minimum braking force; for Time Train The tail number is Freight cars and trains The speed difference of the locomotive numbered 0; is the spacing correction coefficient, when hour ,otherwise .

[0078] Regarding the train travel time constraint: Train travel time refers to the time required for a train to pass through a fixed line interval such as two adjacent stations. In the traditional fixed block mode, this time is customized. However, considering the random fluctuations in the operation time of heavy-haul railway transportation organization under new modes such as group operation, it is approximated by a low-variance normal distribution, as shown in Equation (6).

[0079] ,

[0080] For departure headway constraints: the headway refers to the time interval between the locomotive heads of two adjacent trains passing through the same track section among multiple trains running on the same track. Obeying the normal distribution shown in formula (7), the headway between adjacent groups is It obeys the normal distribution shown in formula (8). The headway between adjacent groups is defined as the time interval between the locomotive heads of the respective lead trains in the two adjacent groups passing through the same track section. The lead train is the first train in the group.

[0081] ,

[0082] Where, and are the mean and variance of the normal distribution of the headway of trains within the group; and are the mean and variance of the normal distribution of the headway time between adjacent groups.

[0083] It should be noted that the train running state evaluator determines the train running state according to the evaluation rules, and the train running state includes traction, braking, coasting and cruising.

[0084] The train running state evaluator uses the speed in formula (2) and the acceleration in formula (3) Whether the train is within a certain threshold is used to evaluate the train operation status. The specific evaluation rules are as follows:

[0085] ,

[0086] Where, The acceleration threshold for the train traction or braking state, ranging from 0.05 to 0.2 m / s 2 , the recommended value is 0.1 m / s 2 ; The acceleration threshold for cruising state, ranging from 0.01 to 0.05 m / s 2 , the recommended value is 0.03m / s 2 The evaluation of train operation status is a prerequisite for determining whether adjacent trains in a network can spontaneously form or exit a group.

[0087] It should be noted that the operation mode discriminator determines whether the train is a train in a fixed block operation mode or a train in a group operation mode;

[0088] For trains in fixed block operation mode, the discrimination rules are used to determine whether the trains have the conditions to form a group spontaneously;

[0089] For trains in group operation mode, for any two adjacent trains in the group, when any of their spacing and headway exceeds the expected spacing or allowed headway of the group trains, it can be determined that they have the behavior of exiting the group.

[0090] The operation mode mainly considers two types: existing fixed block operation and emerging group operation. The current train operation control device operation mode information obtained in step S1 is read through the communication network to determine whether the current operation mode of each train is fixed block operation or group operation.

[0091] For trains in fixed block operation mode, the train operation status information in formula (9) is used to further determine whether the conditions for spontaneous group formation are met. The specific judgment rule is: define the initial distance between adjacent trains , relative speed , relative acceleration , rear-end collision time As shown in equations (10)-(13), if and only if any one of the conditions in equations (14)-(16) is satisfied, it can be determined that the conditions for spontaneous group formation are met. That is, ① Although the acceleration of the rear train is less than that of the front train, there is a non-negative time , causing the two trains to rear-end each other; or ② the acceleration of the rear train is equal to the acceleration of the front train, and the speed of the rear train is greater than the speed of the front train; or ③ the acceleration of the rear train is greater than the acceleration of the front train, and the distance between the two trains has a trend of gradually decreasing. When any one of the above three conditions can be met, it can be determined that the conditions for spontaneous group formation are met.

[0092] ,

[0093] Where, is the time variable; is the initial moment, which is the moment when the operation mode discriminator is enabled; Respectively The position, speed, and acceleration of each train; Respectively The position, speed, and acceleration of each train; along the running direction, the The train in In front of a train; is the rear-end collision time, i.e. the rear train After a certain time, the train ahead will be rear-ended.

[0094] For trains in group operation mode, equations (17) and (18) are used to further determine in real time whether the train has the behavior of leaving the group. That is, for any two adjacent trains in the group, when either their spacing or headway exceeds the expected spacing or allowed headway of the group trains, it can be determined that they have the behavior of leaving the group.

[0095] ,

[0096] Where, for Time Train The tail number is Freight cars and trains The position difference of the locomotive numbered 0; For two adjacent trains and The desired spacing between Time distance to the vehicle head The corresponding train spacing; and The minimum and maximum headway times allowed within a group are recommended to be 90 seconds and 300 seconds respectively; Respectively The location and time of each train exiting the group; Respectively The position and speed of each train; Respectively The travel time of each train and the time to join the group; is the collection of trains that form a group.

[0097] It should be noted that in this embodiment, the optimization controller optimizes the control of trains that meet the conditions for spontaneous group formation and are already in a group, calculates the optimal time for each train to form a group or exit a group, and the corresponding train speed and control force, and guides the trains to complete the process of spontaneous group formation or exit. The specific control process of the optimization controller is as follows:

[0098] First, determine the objective function: Since group operation cannot reduce the aerodynamic resistance between trains, the optimization focuses more on the optimal energy consumption of a single vehicle during the entire transportation process, that is, the optimization problem is defined as: determine an optimal train running speed so that the train's running resistance through the specified line is minimized. In order to make the optimization problem as simple as possible to meet the needs of real-time computational control, considering that the final control force is related to the train resistance and external interference, the smaller the resistance, the lower the train energy consumption. Therefore, all parameters that are not directly related to the train running speed, such as the additional resistance of the slope and the additional resistance of the curve in step S2, can be eliminated. Then the final train control goal can be expressed as minimizing the resistance effect driven by multiple constraints. The objective function of a train Expressed as:

[0099] ,

[0100] Where, This is the basic running resistance in step S2.

[0101] Next, update the constraints: the objective function Only a numerical minimization objective is provided, while the specific physical meaning of the problem is given by the four types of constraints in Equations (2)-(8) in step S3. Therefore, after determining the objective function, the matching constraints need to be updated again to form a complete constraint-driven optimization control problem.

[0102] Finally, the optimization control problem is solved: This constraint-driven optimization control problem is solved based on gradient flow theory to determine the optimal time for each train to form or exit a group, as well as the corresponding train speed and control force. It is important to emphasize that the four types of constraints given in step S3: train dynamics constraints, inter-train safety spacing constraints, train travel time constraints, and departure headway constraints are all hard constraints and cannot be violated during the optimization problem solution process.

[0103] Example 3

[0104] Taking the actual operation scenario of the Baotou-Shenmu Railway in China as an example, the detailed implementation process of this technical solution is given:

[0105] Step 1: Enable the information acquisition module to obtain the network structure, train parameters, and operation status information.

[0106] (1) Road network structure information: Road network structure layout such as Figure 3 As shown, the main line calculation section is 165 km long, and the horizontal and vertical sections of the line and the station locations are shown in Figure 2 As shown in the figure, under the existing semi-automatic block mode, the line can have 17 trains running at the same time under the most ideal conditions;

[0107] (2) Train parameter information: The 5,000-ton and 10,000-ton trains on this network are composed of 54 and 108 C80 freight cars, respectively, and the locomotives are SS4B and HXN3. Other key parameters are shown in Table 1.

[0108] Table 1 Train parameter information

[0109] ,

[0110] (3) Operation status information: Assume that all trains are currently at the departure station, the current train operation control equipment is in fixed block mode, and the block section length of the entire network is 1200 meters to 1600 meters; the time intervals between the simultaneous arrival of 5,000-ton and 10,000-ton trains are 3 minutes and 5 minutes respectively.

[0111] Step 2: Transmit the acquired information to the train dynamics module to update the key dynamic parameters of the train.

[0112] (1) The above Figure 3 、 Figure 4 ,Table 1 is the constructed public information resource library;

[0113] (2)-(4) are an interrelated dynamic process. In the initial stage, all trains are at stations, so the number of trains in each line section of the network is recorded as 0. After the start of operation, the dynamic influx or outflow of trains at each station will cause fluctuations in traffic volume. Figure 5-Figure 7The dynamic evolution of the additional resistance on slopes, curves, and tunnels is presented as the train passes through the trunk line. Because the basic operating resistance is speed-dependent, it is presented along with the final optimized control results from steps 3 and 4.

[0114] Step 3: This step is the constraint-driven optimization control of heavy-load train groups for network-level capacity-volume dynamic matching. Figure 8 A schematic diagram of the workflow of the constraint-driven optimization control module is given.

[0115] Figure 9 This is the result of the continuous dynamic regulation of train capacity and volume in the open network. Figure 9 In the figure, each line represents the actual mileage-time curve of a train on that line. The denser the lines, the more trains there are, and in this case, spontaneous grouping is necessary to increase capacity. In areas with sparse lines, trains typically continue to operate using the existing fixed block mode. To better highlight the advantages of this embodiment's method in dynamically matching capacity and volume through quantitative analysis, the simulation results of this patent over a period of 200 minutes were compared with actual operating data under the existing fixed block mode. Figure 9 The results show that: 1) Within 200 minutes, 52 trains passed through the line using the method in this embodiment. At time t = 9230 seconds, the maximum number of trains simultaneously on the mainline was 24; under the existing fixed block mode, the mainline can ideally have 17 trains simultaneously. This comparison highlights the significant improvement in line capacity achieved by the method in this embodiment. 2) Trains can spontaneously form or exit groups at any location without collision, and mobile trains can also freely join in an open environment. This result highlights the efficiency of the method in this embodiment in dynamically matching capacity and volume.

[0116] Figure 10 、 Figure 11 : is the train speed and corresponding control force curve simulated in this embodiment. Figure 10 The speed curve clearly gives the actual speed characteristics of all trains in this line section. The results show that: 1) at stations where trains merge into or depart from, all trains maintain a relatively high speed, which reflects the essential attribute of dynamic train deployment in a dynamic network environment; 2) at line lengths of 80 to 100 km, the speed trajectory curve is extremely dense and the fluctuations are more dramatic. The dense speed curve is because this section accommodates a large number of trains at the same time, which is a typical high-volume section; the dramatic fluctuations are because trains in high-volume sections need to spontaneously form or exit groups to achieve volume-to-volume matching, and the speed fluctuations are mainly to complete this process. Figure 11 The control and Figure 10The speed curve is completely corresponding, which accurately reflects the speed that each train in the network needs to achieve. Figure 10 The control force required for the speed curve shown is shown.

[0117] Figure 12 For Figure 10 The basic resistance curve of train operation corresponding to the speed. This set of curves describes the variation characteristics of the basic resistance of all trains on the line. The basic resistance is the main component of the objective function in formula (19). Figure 12 The fundamental reason for the fluctuation of basic running resistance along the line mileage is the constant change in speed.

[0118] In summary, this embodiment enables dynamic switching of group control modes. Based on real-time thresholds of network line capacity, it can trigger a switch to group operation mode during periods of multiple train confluence or during capacity bottlenecks. By jointly optimizing mobile resources (trains) and fixed infrastructure (signaling systems and track resources), it achieves adaptive switching between group control mode and the existing automatic block control mode. The core of this mechanism is to dynamically adjust the composition and disassembly of train groups through real-time capacity assessment, ensuring a dynamic match between capacity and volume.

[0119] This embodiment also incorporates a constraint-driven global optimization control framework, integrating train dynamics constraints (such as acceleration and deceleration performance) and collision avoidance safety requirements (minimum safe distance) into the optimization model. This creates a collaborative optimization framework that prioritizes safety while balancing energy consumption and efficiency. Through a priori train status assessments (such as speed and position predictions) and dynamic decision-making mechanisms (spontaneous grouping and withdrawal), global optimal control is achieved in an open railway environment, rather than traditional suboptimal solutions.

[0120] Furthermore, this embodiment utilizes a multi-scale integrated architecture encompassing train-station-line-network-diagram. By integrating three key functional modules: information acquisition (real-time train-ground data), train dynamics modeling (single-vehicle / group dynamics), and constraint-driven optimal control (network-level planning and single-vehicle control), this system achieves full-chain optimization, from single-train speed trajectories to network-level operational diagrams. This architecture transcends the limitations of traditional hierarchical control, enabling simultaneous handling of both macro-network scheduling and micro-level single-vehicle control, improving resource utilization and system flexibility.

[0121] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A heavy-load train group constraint drive control system for dynamic matching of transport capacity and transport volume, characterized by: include: The information acquisition module is used to obtain the road network structure information, train parameter information and train operation status information, and transmit it to the train dynamics module; The train dynamics module is used to determine the nature of the train's section based on the information transmitted by the information module and the judgment index, and update the key train dynamic parameters of the high-volume section and the saturated section on the network based on the determined nature of the train's section, and transmit them to the constraint-driven optimization control module. The judgment index is: the ratio of the difference between the section or station throughput capacity of the train section and the number of trains in the section or station on the network to the section or station throughput capacity of the section; The constraint-driven optimization control module is used to determine whether trains in high-volume sections and saturated sections on the updated road network need to spontaneously form or leave groups and whether they can spontaneously form or leave groups through constraints, train operation status evaluators and operation mode discriminators based on the key dynamic parameters of trains in high-volume sections and saturated sections on the road network. For those that meet the conditions for forming or leaving groups, the time and control force for forming or leaving groups are calculated through the optimization controller. The updated key dynamic parameters of trains in high-volume sections and saturated sections on the road network include slope additional resistance, curve additional resistance, tunnel additional resistance and basic operation resistance.

2. A heavy-load train group constraint drive control method for dynamic matching of transport capacity and transport volume, applied to the heavy-load train group constraint drive control system for dynamic matching of transport capacity and transport volume according to claim 1, characterized in that: The steps include: Obtaining road network structure information, train parameter information and train operation status information; Based on the acquired network structure information, train parameter information, and train operation status information, the nature of the section in which the train is located is determined in combination with a judgment index, and based on the determined nature of the section in which the train is located, key train dynamic parameters in high-volume sections and saturated sections of the network are updated. The judgment index is: the ratio of the difference between the section or station throughput capacity of the section in which the train is located and the number of trains in the section or station on the network to the section or station throughput capacity of the section; Based on the updated key dynamic parameters of trains in high-volume sections and saturated sections on the road network, the constraints, train operation status evaluators and operation mode discriminators are used to determine whether trains in high-volume sections and saturated sections need to spontaneously form groups or leave groups, and whether they can spontaneously form groups or leave groups. For those that meet the conditions for forming groups or leaving groups, the time and control force for forming groups or leaving groups are calculated through the optimization controller. The updated key dynamic parameters of trains in high-volume sections and saturated sections on the road network include slope additional resistance, curve additional resistance, tunnel additional resistance and basic operation resistance.

3. The heavy-load train group constraint driving control method for dynamic matching of transport capacity and transport volume according to claim 2 is characterized in that: The road network structure information includes the horizontal and vertical sections of the line, station locations, section capacity and station capacity; The train parameter information includes train type, structural dimensions, number of trains, load and traction and braking characteristics; The train operation status information includes the current position of the train, the current operation mode of the train operation control device and the length of the block section at the current position.

4. The heavy-load train group constraint drive control method for dynamic matching of transport capacity and transport volume according to claim 2 is characterized in that: The method of judging the nature of the section where the train is located based on the acquired road network structure information, train parameter information, and train operation status information in combination with judgment indicators includes: The network structure information and train parameter information are used as public information resources to build a public information resource library. All trains on the network can access and read the public information resources in the public information resource library at any time and any location through the communication network. Send the acquired running status information to all trains on the network through the communication network; According to the current position information of the train, the number of trains in each line section of the road network is counted, the judgment index is determined, and the nature of the section where the train is located is judged according to the calculation formula of the nature of the section where the train is located.

5. The heavy-load train group constraint driving control method for dynamic matching of transport capacity and transport volume according to claim 2 is characterized in that: The constraint device is provided with constraint conditions, which include train dynamics constraint, inter-train safety interval constraint, train travel time constraint and departure headway constraint.

6. The heavy-load train group constraint driving control method for dynamic matching of transport capacity and transport volume according to claim 2 is characterized in that: The train running state evaluator determines the train running state according to an evaluation rule, and the train running state includes traction, braking, coasting and cruising.

7. The heavy-load train group constraint driving control method for dynamic matching of transport capacity and transport volume according to claim 2 is characterized in that: The operation mode discriminator determines whether the train is in a fixed block operation mode or a group operation mode; For trains in fixed block operation mode, the discrimination rules are used to determine whether the trains have the conditions to form a group spontaneously; For trains in group operation mode, for any two adjacent trains in the group, when any of their spacing and headway exceeds the expected spacing or allowed headway of the group trains, it can be determined that they have the behavior of exiting the group.

8. The heavy-load train group constraint driving control method for dynamic matching of transport capacity and transport volume according to claim 2 is characterized in that: The optimization controller optimizes the control of trains that have the conditions to form a group spontaneously and are already in the group, and calculates the optimal time for each train to form a group or exit the group and the corresponding train speed and control force, guiding the trains to complete the process of spontaneously forming or exiting the group.

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