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

Abstract

The invention belongs to the technical field of rail transit, and provides a heavy haul train group constraint driving control system and method oriented to transport capacity-transport volume dynamic matching, and the main scheme is as follows: obtaining road network structure information, train parameter information and train operation state information; on the basis of the obtained information, the property of the section where the train is located is judged in combination with a judgment index, and train key dynamic parameters of the high-traffic-volume section and the traffic-volume saturation section on the road network are updated on the basis of the judged property of the section where the train is located; based on the updated train key dynamic parameters of the high traffic volume section and the traffic volume saturation section on the road network; the method comprises the following steps of: judging whether trains in a high traffic volume section and a traffic volume saturation section need to spontaneously form a group or break away from the group or not and can spontaneously form the group or break away from the group or not through a constraint device, a train running state evaluator and a running mode discriminator; the time and control force for forming or separating the group are calculated by the optimization controller.

Description

Technical Field

[0001] The present invention relates to the technical field of rail transit, and particularly to a constraint-driven control system and method for a heavy-haul train group facing dynamic matching of transport capacity and traffic volume. Background Art

[0002] Macroeconomic situation, industrial structure adjustment, energy structure change, industrial and agricultural production cycles, etc. have led to significant fluctuations in railway freight volume. Dynamically matching transport capacity and traffic volume to optimize the allocation of transport capacity resources, improve transport efficiency, and promote green transport is the key way to solve this problem. At present, the existing research on the matching of transport capacity and traffic volume in rail transit mainly has the following characteristics: (1) In terms of research objects: Focus on passenger transport, with less attention paid to freight transport. Existing research generally focuses on short-term peak passenger flows such as subway commuting tides, OD distribution (Origin-Destination Distribution), transfer rules, spatio-temporal distribution differences of high-speed rail business or tourist passenger flows, cross-line demand, etc. There is less research on the problems of mismatch between transport capacity and traffic volume caused by the continuous concentrated transportation of heavy-haul bulk long-distance goods, short-term traffic volume fluctuations, local transport capacity bottlenecks, etc., and the corresponding solutions are extremely limited; (2) In terms of control means: Focus on top-level designs for transport capacity excavation such as dispatching optimization, and there are gaps in adaptive solutions for specific sections such as transport capacity bottlenecks. High-speed rail and subway mainly achieve the matching of transport capacity and traffic volume through dispatching optimization. The research mainly focuses on aspects such as train timetable arrangement, inter-train interval time control, train running speed optimization, and improvement of the distribution and acceptance capabilities of stations. For example, emergency dispatching such as subway flow restriction, adding trains or skipping stations, and dynamic pricing of high-speed rail, seasonal timetable adjustment, etc. At present, three-aspect and four-aspect automatic block lines of heavy-haul railways in China have adopted yellow-light departures. End assembly operation stations are prone to form transport capacity bottlenecks due to the limitations of throat areas and departure line departure capabilities. The transport capacity has been excavated to the limit at the transport organization level. Existing control means aiming at objectives such as operation diagram adjustment, dispatching optimization, and improvement of locomotive and vehicle turnover rate are essentially excavating the transport capacity limit. Obviously, they are not suitable for the evacuation control of specific sections where the transport capacity of heavy-haul railways has reached the limit or even transport capacity bottlenecks have occurred.

[0003] (3) In terms of the scope of attention: The capacity allocation mainly focuses on fixed facilities such as stations - lines - road networks, and there is less joint optimization of mobile resources and fixed facilities. Existing studies generally separate fixed facilities from mobile resources. The optimization of fixed facilities mainly focuses on macroscopic aspects such as station capacity, line design, and road network layout, aiming to optimize the allocation of road network capacity in terms of time scale and spatial level to improve the carrying capacity of the railway network; while the optimization of mobile resources focuses more on the trains themselves, such as shortening the tracking interval to improve line passing capacity, enhancing the flexibility of train formation through virtual formation, and optimizing the turnover plan of EMUs or locomotives to improve the utilization rate of mobile equipment. They usually assume that the demands of fixed facilities and mobile resources are stable, but in fact, the rail transit system faces factors such as dynamic passenger and freight flow fluctuations and transportation demand changes. Therefore, the isolated optimization of mobile resources and fixed facilities lacks flexibility, which is not conducive to improving the overall efficiency of the large railway system and will also significantly increase the complexity of the dispatching control system.

[0004] To sum up, there are problems in the existing heavy-haul railway capacity - traffic volume dynamic matching, such as insufficient technical solutions, blank in the adaptive solutions for capacity bottleneck sections, poor flexibility of the system architecture, and high complexity. Summary of the Invention

[0005] The purpose of the present invention is to provide a constraint-driven control system and method for heavy-haul train groups for capacity - traffic volume dynamic matching, aiming to solve the problems of insufficient existing heavy-haul railway capacity - traffic volume dynamic matching solutions, blank in the adaptive solutions for capacity bottleneck sections, poor flexibility of the system architecture, and high complexity.

[0006] To solve its technical problems, the technical solution adopted by the present invention is: On the one hand, the present invention provides a constraint-driven control system for heavy-haul train groups for capacity - traffic volume dynamic matching, including: An information acquisition module, which is used to acquire road network structure information, train parameter information, and train operation status information, and transmit them to the train dynamics module; A train dynamics module, which is used to judge the nature of the section where the train is located based on the information transmitted by the information module and in combination with judgment indicators, and update the key train dynamics parameters in the high-traffic volume sections and traffic volume saturation sections on the road network based on the judged nature of the section where the train is located, and transmit them to the constraint-driven optimization control module; A constraint-driven optimization control module, which is used to judge whether the trains in the high-traffic volume sections and traffic volume saturation sections need to spontaneously form groups or leave groups and whether they can spontaneously form groups or leave groups based on the updated key train dynamics parameters in the high-traffic volume sections and traffic volume saturation sections on the road network through a constraint device, a train operation status evaluator, and an operation mode discriminator. For those that meet the conditions for forming groups or leaving groups, calculate the time and control force for forming groups or leaving groups through an optimization controller.

[0007] On the other hand, the present invention also provides a constraint-driven control method for heavy-haul train groups for dynamic matching of transport capacity and traffic volume, which is applied to the constraint-driven control system for heavy-haul train groups for dynamic matching of transport capacity and traffic volume, and includes the following steps: Obtain road network structure information, train parameter information, and train operation status information; Based on the obtained road network structure information, train parameter information, and train operation status information, combine judgment indicators to judge the nature of the section where the train is located, and update the key dynamic parameters of trains in high-traffic sections and traffic-saturated sections on the road network based on the judged nature of the section where the train is located; Based on the updated key dynamic parameters of trains in high-traffic sections and traffic-saturated sections on the road network, use a constraint device, a train operation status evaluator, and an operation mode discriminator to determine whether the trains in high-traffic sections and traffic-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, calculate the time and control force for forming groups or leaving groups through an optimization controller.

[0008] As a further optimization, the road network structure information includes line horizontal and vertical profiles, station locations, section passing capacities, and station passing capacities; The train parameter information includes train types, structural dimensions, formation numbers, load weights, and traction and braking characteristics; The train operation status information includes the current position of the train, the operation mode of the current train operation control equipment, and the block section length at the location.

[0009] As a further optimization, the step of combining judgment indicators to judge the nature of the section where the train is located based on the obtained road network structure information, train parameter information, and train operation status information includes: Take the road network structure information and train parameter information as public information resources, construct a public information resource library, and all trains on the road network can access and read the public information resources in the public information resource library at any time and any location through a communication network; Send the obtained operation status information to all trains on the road network through a communication network; According to the current position information of the train, count the number of trains in each line section on the road network, determine the judgment indicator, and judge the nature of the section where the train is located according to the calculation formula for the nature of the section where the train is located.

[0010] As a further optimization, the judgment indicator is: the ratio of the difference between the section or station passing capacity of the section where the train is located and the number of trains in the section or station on the road network to the section or station passing capacity of this section.

[0011] As a further optimization, the key train dynamic parameters in the updated road network for high - volume sections and volume - saturated sections include additional resistance on slopes, additional resistance on curves, additional resistance in tunnels, and basic running resistance.

[0012] As a further optimization, constraint conditions are set in the constraint device, and the constraint conditions include train dynamics constraints, safety intervals between trains constraints, train travel time constraints, and departure headway constraints.

[0013] As a further optimization, the train operation state evaluator confirms the train operation state according to the evaluation rules, and the train operation states are traction, braking, coasting, and cruising.

[0014] As a further optimization, the operation mode discriminator determines whether the train is a train in the fixed - block operation mode or a train in the group operation mode; For a train in the fixed - block operation mode, it is determined whether the train meets the conditions for spontaneously forming a group through the discrimination rules; For a train in the group operation mode, for any two adjacent trains in the group, when either their spacing or headway exceeds the expected spacing or the permitted headway of the group trains, it can be determined that they have the behavior of exiting the group.

[0015] As a further optimization, the optimization controller performs optimization control on trains that meet the conditions for spontaneously forming a group and trains that are already in a group, and calculates the time for each train to optimally form or exit the group, as well as the corresponding train speed and control force, to guide the train to complete the process of spontaneously forming or exiting the group.

[0016] The beneficial effects of the present invention are as follows: making full use of the flexibility of the heavy - haul train group operation technology and its high compatibility with existing communication signal facilities and train operation control systems, a switching control scheme for dynamic matching of transport capacity - volume is designed with the road network line capacity as the threshold for the mobile train resource fluctuation scenario in the open railway environment. Through the joint optimization of mobile resources and fixed facilities, it promotes the spontaneous formation or exit of trains in the transport capacity bottleneck section, realizes the smooth switching between the group operation control and the existing automatic block control mode, and solves the technical problem that it is difficult for the existing scheme to achieve flexible matching of transport capacity - volume in the transport capacity bottleneck section. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the system composition structure of the heavy - haul train group constraint - driven control system for dynamic matching of transport capacity - volume in Embodiment 1 of the present invention; Figure 2 It is a flowchart of the heavy - haul train group constraint - driven control method for dynamic matching of transport capacity - volume in Embodiment 2 of the present invention; Figure 3Schematic diagram of the road network structure layout in Embodiment 3 of the present invention; Figure 4 Schematic diagram of the horizontal and vertical profiles of the line and the station positions in Embodiment 3 of the present invention; Figure 5 Schematic diagram of the additional resistance of the train on the ramp in Embodiment 3 of the present invention; Figure 6 Schematic diagram of the additional resistance of the train on the curve in Embodiment 3 of the present invention; Figure 7 Schematic diagram of the additional resistance of the train in the tunnel in Embodiment 3 of the present invention; Figure 8 Schematic diagram of the working process of the constraint-driven optimization control module in Embodiment 3 of the present invention; Figure 9 Schematic diagram of the simulation results of applying the patent method to the road network shown in the case in Embodiment 3 of the present invention; Figure 10 Speed curves of each train in Embodiment 3 of the present invention; Figure 11 Control force curves of each train in Embodiment 3 of the present invention; Figure 12 In Embodiment 3 of the present invention Figure 10 Train running basic resistance curve corresponding to the speed. Specific implementation manner

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and illustrated in the drawings here can be arranged and designed in various different configurations.

[0019] Embodiment 1

[0020] What this embodiment provides is a constraint-driven control system for a heavy-haul train group oriented to the dynamic matching of transport capacity and traffic volume. The schematic diagram of its system composition structure can be seen in Figure 1 Among them, this system includes: An information acquisition module, which is used to acquire road network structure information, train parameter information, and train operation state information, and transmit them to the train dynamics module; A train dynamics module, which is used to judge the nature of the section where the train is located based on the information transmitted by the information module in combination with judgment indicators, and update the key dynamic parameters of the trains in the high-traffic volume sections and traffic volume saturation sections on the road network based on the judged nature of the section where the train is located, and transmit them to the constraint-driven optimization control module; A constraint-driven optimization control module is used to determine whether trains in high-traffic sections and traffic-saturated sections of the road network need to spontaneously form groups or leave groups and whether they can spontaneously form groups or leave groups based on the updated key train dynamics parameters in the high-traffic sections and traffic-saturated sections of the road network. Through a constraint device, a train operation status evaluator, and an operation mode discriminator, for those meeting the conditions for forming or leaving groups, the optimization controller calculates the time and control force for forming or leaving groups.

[0021] Embodiment 2

[0022] Based on Embodiment 1, the present embodiment provides a constraint-driven control method for heavy-haul train groups for dynamic matching of transport capacity and traffic volume. The flowchart is shown in Figure 2 , where the method includes the following steps: S1. Obtain road network structure information, train parameter information, and train operation status information; S2. Based on the obtained road network structure information, train parameter information, and train operation status information, combine judgment indicators to judge the nature of the section where the train is located, and update the key train dynamics parameters in the high-traffic sections and traffic-saturated sections of the road network based on the judged nature of the section where the train is located; S3. Based on the updated key train dynamics parameters in the high-traffic sections and traffic-saturated sections of the road network, through a constraint device, a train operation status evaluator, and an operation mode discriminator, determine whether trains in the high-traffic sections and traffic-saturated sections need to spontaneously form groups or leave groups and whether they can spontaneously form groups or leave groups. For those meeting the conditions for forming or leaving groups, the optimization controller calculates the time and control force for forming or leaving groups.

[0023] In the actual application process, in this embodiment, the road network structure information mainly includes the horizontal and vertical sections of the line, the location of stations, the passing capacity of sections, and the passing capacity of stations, etc. The relevant information can be directly provided by the line design or operation department; The train parameter information mainly includes train type, structural dimensions, formation number, load, and traction and braking characteristics, etc. The formation number and load information are provided by the transport organization department, and other relevant information is directly obtained by looking up the design specifications according to the locomotive and vehicle models; The train operation status information mainly includes the current position of the train, the operation mode of the current 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.

[0024] It should be noted that the judgment of the nature of the section where the train is located based on the obtained road network structure information, train parameter information, and train operation status information in combination with judgment indicators may include: Taking the road network structure information and train parameter information as public information resources, a public information resource library is constructed, and all trains on the road network can access and read the public information resources in the public information resource library at any time and at any location through the communication network; Send the obtained operation status information to all trains on the road network through the communication network; According to the current position information of the train, count the number of trains in each line section on the road network, determine the judgment index, and judge the nature of the section where the train is located according to the calculation formula of the section nature where the train is located.

[0025] In this embodiment, the judgment index can be: the ratio of the difference between the passing capacity of the section or station where the train is located and the number of trains in the section or station on the road network to the passing capacity of the section or station.

[0026] The calculation formula for the nature of the section where the train is located is: , In the formula, the subscript represents the th line section or station, the preferred value of the line section length is 10 km, is the passing capacity of the section or station of this section, obtained from step S1, is the number of trains in the line section or station on the road network, is the nature of the section where the train is located, divided into three categories: low traffic volume, high traffic volume, and traffic volume saturation. When is less than 0.10, it is determined as a traffic volume saturation section; when it is greater than or equal to 0.25, it is determined as a low traffic volume section; and when it is between these two values, it is a high traffic volume section.

[0027] 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.

[0028] It should be pointed out that in this embodiment, the key train dynamic parameters in the high traffic volume sections and traffic volume saturation sections on the updated road network mainly include ramp additional resistance, curve additional resistance, tunnel additional resistance, and basic running resistance, etc. The above parameters are calculated according to the industry standard TB / T 1407.1-2018. In addition, these key parameters related to train dynamics are updated in real time dynamically with the change of train position during the whole process.

[0029] In this embodiment, the train information with updated key kinetic parameters is sent to the constraint-driven optimization control module. The constraint device, train operation state evaluator, and operation mode discriminator are used to determine whether the trains in high-capacity and saturated-capacity sections need to spontaneously form or break away from a group, and whether they can spontaneously form or break away from a group. For those meeting the conditions for forming or breaking away from a group, the optimization controller calculates the corresponding time and control force for forming or breaking away from the group.

[0030] It should be noted that constraint conditions are set in the constraint device, including train dynamics constraints, safety intervals between trains, train journey time constraints, and departure headway constraints. These constraint conditions are the basis for subsequent train operation state evaluation, operation mode discrimination, and optimization control.

[0031] Specifically, for the train dynamics constraints: the constraints on train speed, acceleration, and jerk are used to reflect the line conditions, locomotive traction and braking performance, and train handling stability, as shown in Equations (2)-(4).

[0032] , In the formula, the subscript represents the th vehicle in the th train, and

[0033] is a time variable. Equation (2) is the constraint on train speed , where the maximum speed takes the smaller value of the line speed limit and the maximum operating speed of the locomotive . is the minimum allowable speed that can pass at the current position, and if not otherwise specified, this value is taken as 0. Equation (3) is the constraint on acceleration and jerk , and are the minimum and maximum accelerations, where the maximum acceleration takes the maximum acceleration allowed by the line and the maximum acceleration that the locomotive can provide . 3 . Since the interaction between the vehicles in the train is considered, Equation (4) provides a constraint on the coupler force , being the maximum value of the coupler force.

[0034] For the safety distance constraint between trains: The safety distance constraint aims to ensure the collision avoidance safety of trains and is calculated according to Equation (5). That is, within the entire time range from the start of braking until the train comes to a complete stop, the expected distance between two adjacent trains is always greater than their additional safety distance, and the safety distance between trains can be calculated from its expected distance and speed characteristics.

[0035] , In the formula, and are the start time of train braking and the train braking stop time respectively, and their subscripts and represent two adjacent trains and respectively. The th train is in front of the th train along the running direction; is the expected distance between two adjacent trains and . For the relative braking distance within the group, the fixed block is the corresponding block section length; is the additional safety distance between two adjacent trains and . For 5000-ton heavy-haul trains, it is recommended to take 200 meters; is the safety distance between trains; and are the speed and acceleration corresponding to the maximum braking force that the train can provide; is the corresponding speed when the train can provide the minimum braking force; is the speed difference between the freight car with the tail number in train and the locomotive numbered 0 in train at time is the spacing correction coefficient. When , otherwise .

[0036] For the train travel time constraint: The train travel time refers to the time required for the train to pass through fixed line intervals such as two adjacent stations. In the traditional fixed block mode, this time is fixed. However, considering the random fluctuations in the operation time of heavy-haul railway transportation organization under new modes such as group operation, it is therefore approximately simulated by a low-variance normal distribution, as shown in Equation (6).

[0037] , For the departure headway constraint: The headway refers to the time interval between the leading ends of the locomotives of two adjacent trains passing through the same line section among multiple trains running on the same track. The headways between the trains within the group follow the normal distribution shown in Equation (7), and the headways between adjacent groups follow the normal distribution shown in Equation (8). The headway between adjacent groups is defined as the time interval between the leading ends of the locomotives of the leading trains in the two adjacent front and rear groups passing through the same line section. The leading train is the first train within the group.

[0038] , wherein, and are respectively the mean and variance of the normal distribution of the headways between the trains within the group; and are respectively the mean and variance of the normal distribution of the headways between the trains in adjacent groups.

[0039] It should be noted that the train operation status evaluator confirms the train operation status according to the evaluation rules, and the train operation status includes traction, braking, coasting and cruising.

[0040] The train operation status evaluator evaluates the train operation status by whether the speed in Equation (2) and the acceleration in Equation (3) are within a certain threshold. The specific evaluation rules are as follows: , wherein, is the acceleration threshold for the traction or braking state of the train, and the value range is 0.05 - 0.2 m / s 2 , and the recommended value is 0.1 m / s 2 ; is the acceleration threshold for the cruising state, and the value range is 0.01 - 0.05 m / s 2 , and the recommended value is 0.03 m / s 2 . The evaluation of the train operation status is a prerequisite for determining whether adjacent trains in the road network can spontaneously form a group or withdraw from a group.

[0041] It should be noted that the operation mode discriminator determines whether the train is a train in the fixed block operation mode or a train in the group operation mode; For the train in the fixed block operation mode, it is determined whether the train meets the conditions for spontaneously forming a group through the discrimination rules; 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, they can be judged to have the behavior of exiting the group.

[0042] The operation mode mainly considers two types: existing fixed block 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 or group operation.

[0043] 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 tendency to gradually decrease. When any one of the above three conditions can be met, it can be determined that they have the conditions for spontaneously forming a group.

[0044] , In the formula, is the time variable; is the initial moment, recorded as 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 train ahead will be rear-ended after the time.

[0045] 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 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 leaving the group.

[0046] , wherein is the position difference between the freight car with the tail number in the train at time and the locomotive with the number recorded as 0 in the train; isthe expected spacing between two adjacent trains and ; is the train spacing corresponding to the headway and are the minimum and maximum values of the allowable headways within the group, and the recommended values are 90 seconds and 300 seconds respectively; are respectively the position and time when the th train exits the group; are respectively the position and speed of the th train; are respectively the travel time and the time when the th train joins the group; is

[0047] It should be noted that in this embodiment, the optimization controller performs optimization control on the trains that meet the conditions for spontaneously forming a group and the trains already in the group, calculates the time for each train to optimally form or exit the group and the corresponding train speed and control force, and guides the trains to complete the process of spontaneously forming or exiting the group. The specific control process of the optimization controller is as follows: First, determine the objective function: Since the operation of the group does not reduce the aerodynamic resistance between trains, the optimization focuses more on the optimality of the single - vehicle energy consumption during the entire transportation process. That is, the optimization problem is defined as: determining an optimal train operation speed to minimize the running resistance of the train passing through the specified line. To make the optimization problem as simple as possible to meet the requirements of real - time calculation and control, considering that the final control force is related to the train resistance and external disturbances, and the smaller the resistance, the lower the train energy consumption, all the parameters that have no direct relation with the train operation speed, such as the additional resistance on the slope and the additional resistance on the curve in step S2, can be eliminated. Then the objective of the final train control can be expressed as minimizing the resistance influence driven by multiple constraints. The objective function of the th train is wherein is the basic running resistance in step S2.

[0048] Secondly, update the constraint conditions: The objective function Only the numerical minimization objective is provided, and the specific physical meaning of the problem is given by the four types of constraint conditions in equations (2)-(8) of step S3. Therefore, after determining the objective function, it is necessary to update the matching constraint conditions again to form a complete constraint-driven optimal control problem.

[0049] Finally, solve the optimal control problem: This constraint-driven optimal control problem is solved based on the gradient flow theory to obtain the time for each train to optimally form or leave the group and the corresponding train speed and control force. It should be emphasized that the four types of constraint conditions given in step S3: train dynamics constraints, safety interval constraints between trains, train travel time constraints, and departure headway constraints are all hard constraints and cannot be violated during the solution of the optimization problem.

[0050] Embodiment 3 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: Step 1: Enable the information acquisition module to acquire road network structure, train parameters, and operation status information.

[0051] (1) Road network structure information: The layout of the road network structure is as Figure 3 shown, where the total length of the calculation section of the main line is 165 km, and the horizontal and vertical profiles of the line and the station locations are as Figure 2 shown; in the existing semi-automatic block mode, at most 17 trains can exist on this line under the most ideal conditions; (2) Train parameter information: 5000-ton and 10,000-ton trains on this road network are composed of 54 and 108 C80 freight cars respectively, and there are two types of locomotives, SS4B and HXN3. Other key parameters are shown in Table 1; Table 1 Train parameter information , (3) Operation status information: Assume that all trains are currently at the originating station, the current train operation control equipment is in the fixed block mode, and the length of the block sections of the entire road network is 1200 m - 1600 m; the different arrival time intervals of 5000-ton and 10,000-ton trains are 3 minutes and 5 minutes respectively.

[0052] Step 2: Transmit the acquired information to the train dynamics module to update the key train dynamics parameters.

[0053] (1) The above Figure 3 , Figure 4 , and Table 1 are the constructed common information resource library; (2)-(4) is an interrelated and dynamically changing process. At the initial stage, all trains are at the station, so the number of trains in each line section of the road network is recorded as 0. After the start of operation, the dynamic inflow or outflow of trains at each station will cause fluctuations in traffic volume. Figures 5 - 7 It gives the dynamic change process of the additional resistance of the ramp, the additional resistance of the curve, and the additional resistance of the tunnel when the train completely passes through this main line. Since the basic running resistance is related to the speed, it will be given together with the final optimization control results of steps 3 and 4.

[0054] Step 3: This step is the optimization control driven by the constraints of the heavy-haul train group for the dynamic matching of transport capacity and traffic volume at the road network level. Figure 8 It gives a schematic diagram of the working process of the constraint-driven optimization control module.

[0055] Figure 9 It is the result of the continuous dynamic regulation of the transport capacity and traffic volume of trains in this open road network. Figure 9 In it, each line represents the mileage-time curve of an actual train running on this line. The denser the lines, the more trains there are at that place. At this time, it is necessary to spontaneously form groups to promote the improvement of transport capacity; while in the sparse area of the lines, trains usually follow the existing fixed block operation mode. In order to better highlight the advantages of the method of this embodiment in the dynamic matching of transport capacity and traffic volume through quantitative analysis, the simulation results of this patent within 200 minutes are compared with the actual operation data under the existing fixed block mode. Figure 9 It shows that: 1) Within 200 minutes, the number of trains passing through this line under the method of this embodiment is 52. At time t = 9230 seconds, the maximum number of trains existing on the main line at the same time is 24; while under the existing fixed block mode, at most 17 trains can exist on this main line under the most ideal situation. This comparison result highlights the significant improvement of the method of this embodiment on the line capacity; 2) Trains can spontaneously complete the behavior of forming or exiting groups at any position without collision, and also allow the free entry of moving trains in an open environment. This result highlights the high efficiency of the method of this embodiment in the dynamic matching of transport capacity and traffic volume.

[0056] Figure 10 、 Figure 11 It is the train speed and the corresponding control force curve simulated in this embodiment. Figure 10The speed curve clearly presents the speed characteristics of all trains actually operating on this line section. The results show that: 1) At stations where trains merge in or depart, each train maintains a relatively high speed, which reflects the essential attribute of dynamic train dispatching in a dynamic road network environment; 2) At the 80 - 100 km line mileage, the speed trajectory curves are extremely dense and fluctuate more violently. The density of the speed curves is because a relatively large number of trains are accommodated in this section, which belongs to a typical high - traffic volume section; the violent fluctuation is because trains in the high - traffic volume section need to spontaneously form or withdraw from groups to achieve volume - volume matching, and the speed fluctuations are mainly to complete this process. Figure 11 The control force of Figure 10 is exactly corresponding to the speed curve, which accurately reflects the control force that each train in the road network needs to provide to reach Figure 10 the speed shown in the speed curve.

[0057] Figure 12 To be corresponding to Figure 10 the basic train operation resistance curves corresponding to the speed. This set of curves describes the variation characteristics of the basic operation resistance of all trains on this line. The basic operation resistance is the main component of the objective function in Equation (19), Figure 12 and the fundamental reason for the fluctuation of the basic operation resistance along the line mileage in

[0058] In summary, this embodiment can have a group control mode that can be dynamically switched, and can trigger a switch to the group operation mode during the multi - train merging period or in the capacity bottleneck section based on the real - time threshold of the road network line capacity. Through the joint optimization of mobile resources (trains) and fixed facilities (signal systems, track resources), it realizes the adaptive switch between the group control mode and the existing automatic block control mode. The core of this mechanism lies in dynamically adjusting the composition and disassembly of train groups through real - time capacity assessment to ensure the dynamic matching of transport capacity and traffic volume.

[0059] Meanwhile, this embodiment can also have a global optimization control framework for constraints - driven, integrating train dynamics constraints (such as acceleration and deceleration performance), collision avoidance safety requirements (minimum safety distance), etc. into the optimization model, and constructing a collaborative optimization framework with safety as the core, taking into account energy consumption and efficiency. Through priori train operation state assessment (such as speed and position prediction) and dynamic decision - making mechanisms (spontaneous grouping / withdrawing from groups), it ensures the achievement of global optimal control in an open railway environment, rather than the traditional sub - optimal solution.

[0060] In addition, this embodiment adopts a multi-scale integrated architecture of "vehicle-station-line-network-diagram". Through the highly integrated three major functional modules of information acquisition (real-time vehicle-ground data), train dynamics modeling (single vehicle / group dynamics), and constraint-driven optimal control (network-level planning and single vehicle-level control), the full-chain optimization from the speed trajectory of a single train to the network-level operation diagram is realized. This architecture breaks through the limitations of traditional hierarchical control, can handle both macroscopic network scheduling and microscopic single vehicle control simultaneously, and improves resource utilization and system flexibility.

[0061] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A constraint-driven control system for heavy-haul train groups oriented to dynamic matching of transport capacity and traffic volume, characterized in that It includes: An information acquisition module, which is used to acquire road network structure information, train parameter information, and train operation status information, and transmit them to the train dynamics module; A train dynamics module, which is used to judge the nature of the section where the train is located based on the information transmitted by the information module, in combination with judgment indicators, and update the key train dynamics parameters in the high-traffic sections and traffic-saturated sections of the road network based on the judged nature of the section where the train is located, and transmit them to the constraint-driven optimization control module; A constraint-driven optimization control module, which is used to judge whether the trains in the high-traffic sections and traffic-saturated sections of the road network need to spontaneously form groups or leave groups and whether they can spontaneously form groups or leave groups based on the updated key train dynamics parameters in the high-traffic sections and traffic-saturated sections of the road network through a constraint device, a train operation status evaluator, and an operation mode discriminator. For those that meet the conditions for forming groups or leaving groups, calculate the time and control force for forming groups or leaving groups through an optimization controller.

2. The constraint-driven control method for a heavy-haul train group oriented to the dynamic matching of transport capacity and traffic volume is applied to the constraint-driven control system for a heavy-haul train group oriented to the dynamic matching of transport capacity and traffic volume according to claim 1, and is characterized in that It includes the following steps: Acquire road network structure information, train parameter information, and train operation status information; Based on the acquired road network structure information, train parameter information, and train operation status information, judge the nature of the section where the train is located in combination with judgment indicators, and update the key train dynamics parameters in the high-traffic sections and traffic-saturated sections of the road network based on the judged nature of the section where the train is located; Based on the updated key train dynamics parameters in the high-traffic sections and traffic-saturated sections of the road network, judge whether the trains in the high-traffic sections and traffic-saturated sections need to spontaneously form groups or leave groups and whether they can spontaneously form groups or leave groups through a constraint device, a train operation status evaluator, and an operation mode discriminator. For those that meet the conditions for forming groups or leaving groups, calculate the time and control force for forming groups or leaving groups through an optimization controller.

3. The constrained driving control method for a heavy-haul train group oriented to dynamic matching of transport capacity and traffic volume according to claim 2, characterized in that, The road network structure information includes line horizontal and vertical profiles, station locations, section passing capacities, and station passing capacities; The train parameter information includes train type, structural dimensions, formation number, load, and traction and braking characteristics; The train operation status information includes the current position of the train, the operation mode of the current train operation control equipment, and the block section length at the location.

4. The constraint-driven control method for heavy-haul train groups oriented to dynamic matching of transport capacity and traffic volume according to claim 2, characterized in that The 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: Taking the road network structure information and train parameter information as public information resources, constructing a public information resource library, and all trains on the road network can access and read the public information resources in the public information resource library at any time and any location through a communication network; Sending the acquired operation status information to all trains on the road network through a communication network; According to the current position information of the train, counting the number of trains in each line section of the road network, determining the judgment indicator, and judging the nature of the section where the train is located according to the calculation formula for the nature of the section where the train is located.

5. The constrained drive control method for heavy-haul train groups oriented to dynamic matching of transport capacity and volume according to claim 4, characterized in that The judgment indicator is: the ratio of the difference between the section or station passing capacity of the section where the train is located and the number of trains in the section or station on the road network to the section or station passing capacity of that section.

6. The constrained driving control method for heavy-haul train groups oriented to dynamic matching of transport capacity and traffic volume according to claim 2, wherein The key dynamic parameters of trains in the high-traffic sections and traffic-saturated sections of the updated road network include additional resistance on slopes, additional resistance on curves, additional resistance in tunnels, and basic running resistance.

7. The constraint-driven control method for heavy-haul train groups oriented to dynamic matching of transport capacity and traffic volume according to claim 2, characterized in that, Constraints are set in the constraint device, and the constraints include train dynamics constraints, safety interval constraints between trains, train journey time constraints, and departure headway constraints.

8. The constrained driving control method for heavy-haul train groups oriented to dynamic matching of transport capacity and traffic volume according to claim 2, wherein, The train operation status evaluator confirms the train operation status according to the evaluation rules, and the train operation status includes traction, braking, coasting, and cruising.

9. The constrained drive control method for a heavy-haul train group oriented to dynamic matching of transport capacity and traffic volume according to claim 2, characterized in 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; For trains in the fixed block operation mode, it is determined whether the train meets the conditions for spontaneously forming a group through the discrimination rules; For trains in the group operation mode, for any two adjacent trains in the group, when either their spacing or headway exceeds the expected spacing or the permitted headway of the group trains, it can be determined that they have the behavior of exiting the group.

10. The constraint-driven control method for heavy-haul train groups oriented to dynamic matching of transport capacity and traffic volume according to claim 2, wherein The optimization controller performs optimization control on trains that meet the conditions for spontaneously forming a group and trains that are already in a group, and calculates the time for each train to optimally form or exit the group, as well as the corresponding train speed and control force, to guide the train to complete the process of spontaneously forming or exiting the group.

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