Dynamic partition grouping method for local decentralized group train virtual reconnection

Abstract

The application discloses a dynamic partition grouping method for local decentralized group train virtual reconnection, and is based on a local decentralized structure. The method implements local optimization under the principle of maintaining centralized control of a train operation control system, partitions and groups a network, avoids large-scale train-ground large-capacity data interaction, and reduces train cross-zone switching scenes. The modularity of the train operation control network is used as an overall quality evaluation index to guide the partition decision of the train operation control network. The method supports automatic adjustment of the partition and grouping mode of the train according to real-time environmental changes, task requirements and system states during train operation. Moreover, the decision scheme of dynamic partition grouping also helps mobile equipment on the line to adaptively adjust the partition, thereby realizing overall load balancing of the train operation control network. In addition, the symmetry principle in group theory is introduced to identify and utilize the symmetric structure in the system in train scheduling, thereby simplifying and accelerating the optimization calculation.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of rail transit, and in particular to a dynamic partition grouping method for local decentralized group train virtual reconnection. BACKGROUND

[0002] Currently, the train operation control system based on train-ground communication is widely used in the rail transit industry in China. The system is characterized by train-ground high-capacity communication, and relies on the ground control center to send a movement authorization to the on-board device to ensure the safe and efficient operation of the train on the line. In the train operation control system of the ground center, the train needs to obtain the movement authorization of the ground control center to continue running forward. If the train cannot obtain the authorization or the timeliness of the authorization is insufficient, it will cause emergency braking. Therefore, it is very important to maintain stable and continuous communication resources for the train to ensure the safety of train operation and improve the operation efficiency.

[0003] The CTCS-3 level train control system of high-speed railway realizes continuous and bidirectional safe information transmission between the ground and the train through GSM-R technology. GSM-R (Global System for Mobile Communications-Railway) is a global mobile communication system specially designed for railway communication, which ensures high reliability and safety of communication when the train is running at high speed. The communication cells are distributed along the railway line to form a stable communication network structure, which can provide seamless communication coverage when the train passes through different cells. Each cell is equipped with a base station to support real-time position information update of the train, transmission of control signals, and exchange of instructions with the train control center. The handover process refers to the process that when the communication indicators of the communication base station cannot meet the communication needs of the train, the conditions for triggering the handover are met, the user disconnects the connection with the source base station, and a new connection is established with the target base station to realize the handover. The main purpose is to realize the uninterrupted communication of mobile equipment through channel switching to ensure the safe operation of the train. Generally, the reasons for triggering the handover include: the distance between the mobile equipment and the base station is far, the communication quality is poor, and the load in the partition is unbalanced.

[0004] However, when the train is running at high speed, the communication interruption or timeout problem in the handover process will have a certain impact on the safe operation of the train; for example, the communication interruption or timeout problem in the handover process will cause the train control system level to fail to switch in time, or the position information to fail to update in time. Such problems are mainly caused by abnormal radio cell reselection and position update of the train during handover, and occur frequently in the actual operation of high-speed railway. At present, there is no accurate positioning means, even if the CTCS-3 level train-ground communication is normal in the subsequent operation, such faults are still identified as radio faults.

[0005] The root cause of the above problems is that the centralized system structure itself is not suitable for implementing a large number of train-ground mass communication, and the network delay problem leads to system performance degradation. The existing solution is mainly to optimize the vehicle-mounted radio and the communication network, which cannot completely solve such problems. In addition to improving the reliability of the communication system, to improve the problem of train operation caused by the handover of the train control system, the load of the train operation control network can be balanced from the macroscopic perspective of the signal system as a whole, the resource allocation and scheduling can be optimized, the fault tolerance and recovery strategy can be designed, and the excessive dependence on train-ground communication can be reduced. However, there is no effective solution at present.

[0006] Therefore, the present application is proposed. SUMMARY

[0007] The purpose of the present application is to provide a dynamic partition grouping method for local decentralized group train virtual reconnection, which supports dynamic partition grouping, adaptive resource allocation, multi-level collaborative mechanism and autonomous operation control of trains on the line in the running environment, and guarantees the safety and efficiency of autonomous operation of virtual reconnection trains.

[0008] The purpose of the present application is achieved by the following technical solutions:

[0009] A dynamic partition grouping method for local decentralized group train virtual reconnection, comprising:

[0010] Based on the concept of local decentralization, a train operation control network is constructed by using trains and ground centers. The trains and ground centers serve as nodes in the train operation control network, referred to as train nodes and ground control nodes. The communication connection between the train nodes and the ground control nodes, as well as the communication connection between the trains, are all edges in the train operation control network.

[0011] The modularity of the train operation control network is calculated based on the communication connection between different nodes, whether different nodes belong to the same community, and the total number of communication connections of the train operation control network, and is used as an overall quality evaluation index to guide the partition decision of the train operation control network. The train nodes in each community form several virtual grouping train formations to complete the partition grouping of the operation control network.

[0012] After the train operation control network completes the partition grouping, for the symmetric structure in each community, symmetric optimization scheduling is performed based on group theory. The symmetric structure refers to the symmetric characteristics of the train nodes in the community in the train operation mode.

[0013] As can be seen from the technical solution provided by the present invention, based on a locally decentralized structure, local optimization is implemented while maintaining the principle of centralized control of the train operation control system. The network is partitioned and grouped, avoiding large-scale, high-capacity data interaction between the train and the ground, and reducing train switching scenarios. The modularity of the train operation control network is used as an overall quality evaluation index to guide the partitioning decision of the train operation control network. This supports the automatic adjustment of the partitioning and grouping of trains during train operation based on real-time environmental changes, task requirements, and system status, in order to adapt to complex and ever-changing operating environments, better cope with emergencies, and optimize overall efficiency. Furthermore, the dynamic partitioning and grouping decision scheme also helps moving equipment on the line to adaptively adjust partitions, thereby achieving overall load balancing of the train operation control network. In addition, the symmetry principle in group theory is introduced to identify and utilize the symmetric structure within the system in train scheduling, thereby simplifying and accelerating optimization calculations. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 A flowchart illustrating a dynamic partitioning and grouping method for virtual reconnection of locally decentralized group trains provided in an embodiment of the present invention;

[0016] Figure 2 This is a comparative diagram of a centralized system and a partially decentralized system provided in an embodiment of the present invention;

[0017] Figure 3 This is a schematic diagram of the communication structure between the train and the control center in a conventional railway signal control system provided in an embodiment of the present invention;

[0018] Figure 4 This is a schematic diagram of the communication structure between the train and the control center after dynamic partitioning and grouping, as provided in an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0020] First, the following explanations are provided for the terms that may be used in this article:

[0021] The terms "comprising", "containing", "including", "having" or other similar semantic descriptions are to be interpreted as non-exclusive inclusion. For example, the inclusion of a technical feature element (such as raw materials, components, ingredients, carriers, dosage forms, materials, dimensions, parts, components, mechanisms, devices, steps, processes, methods, reaction conditions, processing conditions, parameters, algorithms, signals, data, products or articles, etc.) is to be interpreted as including not only the explicitly listed technical feature element, but also other technical feature elements not explicitly listed but known in the art.

[0022] The term "consisting of" means excluding any technical feature element not explicitly listed. If this term is used in a claim, the term will make the claim closed so that it contains no technical feature element other than those explicitly listed, except for impurities normally associated with the recited technical feature element. If the term is only present in a certain clause of the claim, it only limits the elements explicitly listed in that clause, and the elements recited in other clauses are not excluded from the overall claim.

[0023] The present application aims at the problem of communication interruption due to inter-zone switching in the existing rail transit train operation control system, and proposes a dynamic partition grouping method for local decentralized group train virtual reconnection, which helps the mobile equipment on the line to adaptively adjust the partition to avoid inter-zone switching problems, and further supports the train in the partition to adopt a flexible grouping mode to reduce train-ground communication, reduce tracking interval, and improve the overall transportation efficiency of multiple trains in the partition.

[0024] A dynamic partition grouping method for local decentralized group train virtual reconnection provided by the present application will be described in detail below. The contents not described in detail in the embodiments of the present application belong to the prior art known to those skilled in the art. If no specific conditions are specified in the embodiments of the present application, the conventional conditions or the conditions recommended by the manufacturer are used. If no manufacturer is specified for the instruments used in the embodiments of the present application, they are all conventional products that can be obtained by market purchase.

[0025] As shown in FIG. 1, a flowchart of a dynamic partition grouping method for local decentralized group train virtual reconnection provided by an embodiment of the present application mainly includes the following steps: Figure 1

[0026] Step 1: Based on the concept of local decentralization, a train operation control network is constructed by using trains and ground centers.

[0027] ​In the embodiment of the present application, based on the concept of local decentralization, a train operation control network is constructed by using trains and ground centers as nodes, called train nodes and ground control nodes, the communication connection between the train nodes and the ground control nodes, and the communication connection between the trains are all edges in the train operation control network.

[0028] Step 2, using complex network algorithm, a fast partition grouping evaluation strategy is provided for the trains running on the line.

[0029] In the embodiment of the present application, based on the complex network theory, the multi-train dynamic grouping partition decision is guided, specifically: the modularity of the train operation control network is calculated by combining the communication connection between different nodes, whether the different nodes belong to the same community, and the total number of communication connections of the train operation control network, and is used as an overall quality evaluation index to guide the partition decision of the train operation control network, and the train nodes in each community form several virtual marshalling train formations to complete the partition grouping of the operation control network.

[0030] Step 3, based on group theory symmetry optimization, an efficient optimal decision is realized.

[0031] In the embodiment of the present application, after the train operation control network completes the partition grouping, for the symmetric structure in each community, the symmetry optimization scheduling is performed based on the group theory; wherein the symmetric structure refers to the symmetric characteristics of the train nodes in the community in the train operation mode.

[0032] Based on the above scheme, a multi-level coordination mechanism of the train operation control system is also introduced, in the train operation control network, the control systems at each level make decisions independently, and maintain consistency through the coordination mechanism, realizing local autonomy and global coordination.

[0033] In order to more clearly show the technical solutions provided by the present application and the technical effects produced, the method provided by the embodiment of the present application is described in detail below with specific embodiments.

[0034] I. Local decentralization principle.

[0035] As Figure 2The left part is a centralized system, in which all control, decision and resource allocation are centralized in one or a few central nodes, such as the RBC (Radio Block Center) of the train operation control system. This central node usually undertakes the main functions of the system, such as management, coordination, data processing and decision-making, and all other nodes rely on this central node for operation. The advantage is that the decision and operation are unified, reducing conflicts and inconsistencies, and the management of data and resources is relatively simple and easy to control. The disadvantage is that the scalability of the centralized system is limited, and as the size of the system increases, the load of the central node increases, and once the central node fails, it may cause the entire system to collapse. The structure of the centralized system limits its ability to undertake large-scale and fine-grained management tasks, because once the demand increases or becomes more complex, it will inevitably be trapped in the choice between "sacrificing efficiency to ensure safety" and "improving efficiency to reduce safety margin".

[0036] Although the advanced train operation control system balances the load and improves the efficiency by adopting a distributed structure for the control center, mainly referring to the division of multiple partitions, each of which is managed by an RBC, and the use of multiple RBCs to handle all line resources in parallel. By this way, the tasks are distributed to multiple nodes, which can handle multiple tasks at the same time, thereby improving the overall efficiency of the system. Each node can independently execute part of the task, and these tasks can be performed simultaneously, reducing the processing time of the task and maintaining the concentration of control. But the complexity of the system will increase, because the system involves multiple nodes and their interaction, the state of each node, task allocation, communication protocol, etc. all need fine coordination and monitoring, and the complexity of managing and maintaining the distributed system increases significantly. And as the size of the system expands, it becomes more difficult to keep all nodes synchronized and coordinated. Network delays, node failures and data consistency problems can cause system performance to decline, and differences between nodes can cause inconsistent updates or performance bottlenecks, even facing huge security risks. In the face of more fine-grained train control requirements such as virtual reconnection, the delay problem and the complex life cycle management problem of the existing train control system will make it difficult to implement or limit its performance.

[0037] Early Internet architecture and traditional database systems are centralized system architecture, with the change of demand, the requirement of system scalability and the emergence of new technology, gradually evolved into distributed architecture, and then further to the decentralization technology. Drawing on the experience of the development of other modern application fields, combined with the new requirements of rail transit train operation control system, the present invention proposes to take the local decentralized system structure as the basis, such as Figure 2The right part is shown. In this structure, different parts of the network show a certain degree of decentralization, and there is a certain autonomy and independence between each part, but the central node or central cluster is not completely removed. It can be seen that this structure still has partial centralization, but the mutual connection and distribution between each sub-cluster show the characteristics of decentralization. This locally decentralized structure not only retains a certain central control, but also allows other parts of the system to have the ability to operate and make decisions independently, which is very suitable for implementing small-scale capacity enhancement technologies such as group operation control on existing train operation control systems.

[0038] II. Comparison between traditional communication scheme and communication scheme of the present application.

[0039] The present application aims at the problem of communication interruption or delay affecting train safety due to inter-zone switching in the existing rail transit train operation control system, and proposes a system structure based on local decentralization. By using complex network algorithm, a fast partition grouping strategy is provided for trains running on the line. Further based on the concept of group theory, the symmetry existing in the system is used to make optimal decisions, and finally the dynamic partition grouping of multiple trains is realized, so as to support the dynamic partition grouping, adaptive resource allocation, multi-level collaborative mechanism and autonomous operation control of trains in the running environment on the line.

[0040] 1. Existing communication scheme.

[0041] As shown in Figure 3 , it is a schematic diagram of train and control center communication structure of traditional railway signal control system. In the traditional train operation control system, such as CBTC system of urban rail transit or CTCS-3 system of high-speed railway, all running trains communicate with the same ground center, report the running speed and position of the train to it, and request the movement authorization of the train. This structure makes the train operation rely on the large-capacity communication channel between the train and the ground. When the train runs, it moves between the cells, and inter-zone switching must occur, which causes a certain degree of communication interruption and causes the ATP emergency braking of the train signal system, affects the comfort of the train, reduces the efficiency of the train operation, and increases the energy consumption of the train operation.

[0042] 2. Communication scheme of the present application.

[0043] As shown in Figure 4As shown, the train and control center communication structure diagram after dynamic partition grouping provided by the application. The train operation control network provided by the application regards the trains running on the line and the ground center as multiple nodes in a complex network, and regards the communication connection between the train-ground and train-train as the edge of the complex network. Based on the concept of "local decentralization", the quality function (overall quality evaluation index) is optimized on the possible division of the network, the characteristic matrix vector of the network is formed, a method for partition detection and evaluation is formed through the vector expression, and a high-quality detection result is obtained. Further, according to the detection result of the complex network, the dynamic grouping partition decision of multiple trains is guided (such as Figure 4 As shown), and the scene of inter-zone switching is reduced. The trains in the partition can maintain "complete train-ground communication connection" (such as Figure 4 As shown in partition 1 in the middle), each train communicates with the ground center, and can also maintain "chain connection" (such as Figure 4 As shown in partition n), the lead train is autonomously decided through the election negotiation strategy, the lead train implements train-ground communication connection, and the other trains maintain virtual coupling small formation with the lead train through train-train communication, and autonomously negotiate the line resources in a small range.

[0044] Through the above comparison, it can be seen that the application aims to solve the problem that the train brake is caused by the communication interruption caused by the inter-zone switching of the existing train operation control system, and the train safety is affected by the data transmission safety. The characteristic matrix vector after optimizing the quality function of the train operation control network is divided, a partition detection and evaluation scheme is formed, multiple trains are dynamically grouped and partitioned in the running process, and the inter-zone switching scene of the trains is avoided.

[0045] In summary, the application aims to solve the problem that the train brake is caused by the communication interruption caused by the inter-zone switching of the existing train operation control system, and the train safety is affected by the data transmission safety. A dynamic partition grouping method of locally decentralized group train virtual reconnection is proposed. The characteristic matrix vector after optimizing the quality function of the complex network is divided, a partition detection and evaluation method is formed, multiple trains are dynamically grouped and partitioned in the running process, and the problem is abstracted based on the concept of group theory. According to the symmetry of the system, an efficient optimal decision is realized. Through this method, the train group actively optimizes the partition grouping in the running process, avoids or reduces the inter-zone switching scene of the trains as much as possible, and further supports the small formation of the trains in the partition to reduce the tracking interval and improve the overall transportation efficiency of the multiple trains in the partition.

[0046] Third, the application scheme is introduced.

[0047] 1. Introduction of train operation control network

[0048] In the embodiment of the present application, the train operation control network is composed of train nodes, ground control nodes and communication connections between the nodes, wherein the nodes represent the entity control devices in the real train control system, and the edges represent the communication connections between the entity control devices. The number of edges contained in the shortest path between two nodes represents the network distance (non-track distance) between the two train nodes. The average value of the network distance between any two nodes in the train operation control network is the average path length of the network, which represents the separation degree between the nodes in the whole train control network and reflects the global characteristics of the train control network. The total number of edges of a train node with other nodes is the degree of the train node. The degrees of the train nodes in the network are sorted from small to large, and the proportion P(k) of the nodes with degree k in the whole network obtained by statistics is the degree distribution of the network. From the probability point of view, P(k) is the probability that the degree of a randomly selected node in the network is k.

[0049] 2. Partitioned group decision scheme.

[0050] In order to realize the overall load balancing of the train operation control network, it is necessary to divide it into multiple communities, not only to have almost no edges between the communities, but also to ensure that the trains in the partition have specific performance as much as possible, so as to further realize the virtual coupling of the trains. To achieve this effect, simply calculating the edges is not a good method to intuitively quantify the community structure, because the lack of edges between communities is mostly because the number of edges between groups is less than expected. Since the real community structure in the train control network corresponds to the statistical edge arrangement, the modularity can be used to quantify and evaluate and guide the final decision. The value of modularity can be positive or negative, and the positive value indicates that there may be a reasonable partition structure. The specific scheme is as follows:

[0051] A ij represents the communication connection between node i and node j, A ij = 0 indicates that there is no communication connection between node i and node j, A ij ≠ 0 indicates that there is a communication connection between node i and node j; in the one-dimensional network scale, if there is a communication connection between node i and node j, A ij = 1, and in the case of multi-dimensional network superposition, A ij may also have a larger value; actually, A ij is the element of the adjacency matrix of the train operation control network represents the total number of communication connections of the train operation control network; C i , C j correspond to the communities of node i and node j, respectively, and if node i and node j belong to the same community, δ(C i , C j ) = 1, otherwise δ(C i , Cj ) = 0.

[0052] During the operation, each node randomly communicates (including vehicle-vehicle communication and vehicle-ground communication), and the expected number of connections between node i and node j can be represented as , where k i and k j are the degrees of the nodes (i.e., the nodes have several point-to-point direct communication with several nodes in the region), and k i is an example: A il is the communication connection between node i and node l.

[0053] Finally, the calculation method of the modularity Q of the train operation control network is represented as:

[0054]

[0055] The modularity Q of the train control network as a new attribute after the definition of the network of the train operation control system can evaluate the overall quality after the division of the train operation control network into multiple small communities, and can quantitatively evaluate whether the division is reasonable. From the above calculation method, it can be concluded that the physical meaning of the modularity function of the train control network is the proportion of the edges in the community minus the expected value of the edges of the randomly connected nodes under the same community structure. Therefore, the larger the Q value is, the more obvious the community structure of the network is. In the network with better modularity, there are many edges in the community, and only a few edges between the communities, which makes the trains in the community not delayed or interrupted due to cross-community communication, and also represents that the trains in the community have a certain degree of performance to implement virtual coupling.

[0056] During the train operation, various operation data (such as speed, position, acceleration, etc.) of each train node are taken as data blocks, and the data blocks are organized to form a data block sequence; a Merkle tree is constructed for the data block sequence to obtain a root node hash value; if any data block is changed, the entire Merkle tree structure will be changed, and the root node hash value will also be changed. Each node receives the train operation data of other train nodes, and verifies whether the data is tampered or damaged by comparing the root node hash value. This Merkle tree structure can quickly and accurately verify the integrity and correctness of the data, and ensure the safety and reliability of the train operation control network. On this basis, the entire train operation control network can be divided into communities in the framework of a tree structure (i.e., the aforementioned Merkle tree structure), and the leaves of the tree structure are each ground control node, and the internal connection of the tree corresponds to the communication connection between the ground control nodes.

[0057] Based on the above manner, the tree structure formed by the ground control nodes in the train operation control network and the communication connection among the ground control nodes can be stored as an integer array, the community is dynamically divided, and the pair of rows and columns is repeatedly merged when the corresponding community is merged, the calculation manner of the modularity Q of the train operation control network is converted into a matrix form, and is represented as:

[0058]

[0059] Wherein, S represents an n*R matrix, an element of an i-th row and an r-th column of the matrix is denoted as S ir , S ir =1 indicates that the i-th node belongs to the r-th community, n is the number of nodes, R is the number of communities, Tr() is a trace of a matrix, and T is a matrix transpose symbol; the modularity matrix B reflects the deviation of the actual connection in the network from the expected value of the connection, and is used to judge whether the community structure in the network is significant or not, and B ij is an element of an i-th row and a j-th column of the matrix B.

[0060] The modularity matrix B of the train operation control network is a matrix for coding the network connection structure, and can effectively describe the network structure and guide the train partition grouping decision, thereby guaranteeing the overall load balance of the network.

[0061] Each community contains multiple train nodes, the train nodes in the community form a plurality of virtual marshalling train formations through grouping, a lead car is selected through election and negotiation of the train nodes, and the other train nodes are used as follower cars, the follower cars maintain virtual coupling small marshalling through train-to-train communication with the lead car, and grouping is completed; the part of the flow can refer to the conventional technology, and the present application will not be repeated.

[0062] 3. Symmetry optimization based on group theory.

[0063] Local decentralized systems often exhibit certain symmetries, such as repeating paths, similar scheduling rules or patterns in train operation. Group theory can help identify these symmetries and translate them into mathematical group structures. These symmetries can be used to reduce redundant calculations, lower the complexity of calculations, and form standardized decision-making patterns in the system. For example, if certain train operation patterns have the same symmetrical characteristics, they can be grouped into the same class, and based on these relationships, effective grouping and path planning can be constructed to simplify the design of scheduling and control strategies. If the journey of some trains exhibits periodicity or symmetry within a certain time period, these characteristics can be used to optimize scheduling and resource allocation, making the overall operation of the system more stable and efficient. Through the representation theory of groups, complex optimization problems can be transformed into simpler linear representations, thereby accelerating calculations. For train control systems that require real-time scheduling and control, this optimization can significantly improve the reaction speed and decision-making efficiency of the system. In addition, through group theory, the system can identify invariants and symmetric operations, which are particularly important in dealing with unexpected situations. For example, in certain emergency situations, group theory can help identify elements that can remain invariant in the system, thereby providing a theoretical basis for emergency handling of the system and improving the robustness of the system.

[0064] Suppose a local decentralized train network consists of multiple regions, each with multiple train nodes, and these nodes are arranged in a certain symmetrical structure. Specifically, this symmetrical structure refers to the fact that train nodes in a community exhibit symmetrical characteristics in terms of train operation patterns, such as symmetrical regularity in the paths or scheduling schemes of trains in certain regions.

[0065] In the embodiments of the present invention, a region mainly refers to a physical region in train operation, and the aforementioned community is generally used to describe the topological structure of the train network, i.e., the group identified through the connection and interaction between nodes. In some cases, the two may overlap, for example, train nodes within a certain physical region can form a community in the topological sense.

[0066] Let G be a group acting on a set of train nodes X. The orbit Orb G (x) of a train node x∈X under the action of G represents all the positions that the train node x can reach:

[0067] Orb G (x)={g·x∣g∈G}

[0068] where g is an element of the group G, representing an operation or transformation.

[0069] As understood by those skilled in the art, a group is a structured set in mathematics, which usually needs to satisfy the properties of closure, associativity, identity element, and inverse element; in the train control network described in the embodiments of the present application, the group G represents a set of operations or transformations on the running modes of the train nodes, for example: different selections or symmetric transformations of the train paths; scheduling or switching of the train positions; certain structured arrangement or grouping among the train nodes. These operations satisfy the algebraic properties of the group and represent the symmetry or structured transformation in the train scheduling. By using the characteristics of the group structure, the train paths can be more effectively managed, resources can be more effectively allocated, the scheduling can be simplified, and the consistency and stability of the system can be maintained.

[0070] In the train scheduling, if different train nodes need to pass through the same track section under the action of the group, they can be considered as symmetric and form a group, and then the scheduling strategy can be the same.

[0071] In the group action, the stabilizer Stab G (x) is a set of group elements that keep the node x unchanged:

[0072] Stab G (x) = {g ∈ G | g · x = x}

[0073] The stabilizer group represents which operations will not change the system state under symmetry. By using this characteristic, the invariant paths can be identified in the path planning, thereby simplifying the scheduling.

[0074] For example, it can be assumed that the paths of the train running have a symmetric structure. For example, it is assumed that the train runs from the path p1 to the path p2, and the symmetry of the path is controlled by the group G. In the path selection, the symmetry of the path can be analyzed by the group action, and the optimal path can be selected.

[0075] Based on this, when two train running modes (for example, the paths p1 and p2) have the symmetric characteristic under the action of the group G, the optimal solution of one of the train running modes is calculated, the optimal solution is subjected to the symmetry operation to obtain the optimal solution of the other train running mode, which is represented as:

[0076] p2 = g · p1

[0077] where p1 is the optimal solution of one of the train running modes (for example, the path p1), p2 is the optimal solution of the other train running mode (for example, the path p2), and g ∈ G. The optimal solution refers to the best scheme for achieving the set optimal target under the given constraint condition. For example, the optimal target can be the optimization target of meeting the time optimization, resource optimization, load balance optimization, safety optimization, symmetry optimization, etc.

[0078] Let the set of train operation modes be P, and the set P is divided into different equivalence classes (e.g., same track) by the action of the group G, denoted as: P / G={Orb G (p)∣p∈P};where P / G represents the result of the set P after being divided by the group G, p is a single train operation mode in the set P, Orb G (p) is all the positions that the train operation mode p can reach, i.e., Orb G (p)={g·p∣g∈G};Although the train operation mode p here is different from the train node x described above, both types of independent variables can be processed under the framework of group action, which conforms to the application logic of group theory and meets the requirements of the train control system at different levels.

[0079] In this way, only one scheduling calculation needs to be performed for each equivalence class, greatly reducing the amount of calculation. The final scheduling scheme can generate the scheduling plan of all train operation modes through the symmetry operation.

[0080] The optimal objective of the scheduling scheme is to minimize the objective function f(p), and the optimal solution of the symmetry optimization scheduling based on group theory is:

[0081] min p∈P f(p)=min p∈P / G f(p)。

[0082] In the embodiments of the present application, the optimal objective can be each type of optimization objective provided above, and the objective function corresponding to each type of optimization objective can be implemented according to conventional techniques, which will not be described herein.

[0083] It can be seen that optimization only needs to be performed in the equivalence class representative path under the action of the group, greatly reducing the calculation complexity.

[0084] Four, implement a multi-level coordination mechanism of the train operation control system.

[0085] Based on the above scheme of the embodiment of the present application, in the local decentralized structure, the train control systems of different levels, including the regional control center, the trackside control unit, the on-board autonomous control device, etc., cooperate through the above-mentioned partitioning and grouping method to realize efficient and stable operation of the overall system; the ground equipment involved in the train control systems of different levels serves as a ground control node, and the on-board autonomous control device on the train serves as a train node; that is, the above-mentioned regional control center, trackside control unit, etc. can be regarded as a ground control node in a broad sense, responsible for different levels of ground control tasks, and the on-board autonomous control device is a train node, responsible for autonomous operation and microscopic control of the train. Under this mechanism, the control units at all levels form a synergistic effect through communication and data sharing to achieve the goal of global optimization. For train group operation control, the regional control center can issue regional-level macro instructions to the on-board system, and the on-board system can make micro adjustments at the train level according to local conditions. The control systems at all levels make decisions independently under certain rules and maintain consistency through the synergy mechanism. This synergy mechanism not only retains the flexibility of local autonomy but also ensures global coordination. In fact, for a completely autonomous train system, unmanned driving is the ultimate goal, and all control and decision-making in the system are automatically completed by the system.

[0086] The above scheme provided by the embodiment of the present application mainly obtains the following beneficial effects:

[0087] 1. Optimizing the system structure helps the train to avoid communication interruption and delay problems in zone switching.

[0088] The existing train operation control system is based on a centralized structure, and the zones are divided according to physical distance. In the zone switching scenario, the train will have a communication interruption problem. The method described in the present application proposes a local decentralized system structure, which implements local optimization while maintaining the principle of centralized control of the train operation control system. Through partitioning and grouping, the train in the partition autonomously selects "complete train-ground communication connection" or "chain connection". In this way, large-scale train-ground data interaction is avoided, and the train zone switching scenario is reduced.

[0089] 2. Dynamic partitioning and grouping increase system flexibility.

[0090] The above scheme of the present application supports automatic adjustment of the partitioning and grouping of the train during operation according to real-time environmental changes, task requirements, and system status. In traditional systems, the partitioning and grouping of the train are usually pre-set, but in a complex and variable operating environment, a static partitioning and grouping method cannot cope with sudden situations or optimize overall efficiency. The technology proposed in the present application forms a partition detection and evaluation method by dividing the characteristic matrix vector of the complex network optimization quality function, which guides the dynamic grouping and partitioning decision-making of multiple trains during operation and can improve the above-mentioned problems.

[0091] 3. Realize the whole load balance of train operation control network.

[0092] The cause of triggering train unscheduled braking is sometimes the problem of network load imbalance, that is, multiple trains are congested in a certain area at a certain time, causing communication delay and triggering train emergency braking. From the perspective of signal control system, the application proposes a decision method for dynamic partitioning and grouping of trains in the running process, helping the mobile equipment on the line to adaptively adjust the partition, thereby realizing the whole load balance of train operation control network.

[0093] 4. Realize the optimization scheduling decision of train group.

[0094] The method of the application can realize the functions of dynamic demand adjustment and resource allocation. Here, the resources mainly refer to the occupation of track sections, traction power supply, communication bandwidth and other basic elements required in the train running process. In the traditional system, resource allocation is often fixed, and it is difficult to flexibly respond to sudden changes. However, the partitioning and grouping technology proposed by the application introduces the division and cooperation of different communities and nodes, as well as the collaborative mechanism (each level of control unit coordinates through communication and data sharing), which can realize the adaptive resource allocation function, allowing the system to make a quick response in real-time conditions to ensure the efficient use of resources. In a large train operation control network, the symmetry principle in group theory is used to identify and utilize the symmetric structure in the system in train scheduling, thereby simplifying and accelerating the optimization calculation. For example, the transportation demand and track section usage of some trains are equivalent in symmetry (such as having similar routes and timetable plans), so these equivalent problems can be combined into one problem for solution, that is, considered as a group of super-long trains. This way greatly reduces redundant calculation, thereby improving the overall calculation efficiency and effectively shortening the scheduling decision time, which shows a significant advantage in real-time systems.

[0095] 5. Realize the multi-level coordination mechanism of train operation control system.

[0096] The method of the application can realize a multi-level coordination mechanism. This collaborative mechanism not only retains the flexibility of local autonomy, but also ensures global coordination, improves the collaborative effect of the system and supports hierarchical management and maintenance. Through the cooperation of different levels, the multi-level coordination mechanism realizes a stronger collaborative effect. For example, when the boundary of different regions is transferred, the control systems of different regions can realize smooth train transfer through the coordination mechanism, avoiding scheduling conflicts when the train enters a new region. At the same time, the system can realize the cross-regional scheduling of resources through the coordination mechanism, such as temporarily adjusting the train flow between regions to balance the load. In addition, the multi-level coordination mechanism makes the management and maintenance of the system more modular and hierarchical. Different control levels can be updated, maintained and managed independently without directly affecting other levels.

[0097] 6. Application of data analysis technology to train control system.

[0098] The network modularity calculation and group theory symmetry optimization method provided by the application further applies data analysis technology to the train control system, helps the digital transformation of the railway communication signal system, supports the implementation and application of the digital twin technology of the train control system, and redefines the train control system from the perspective of networking, digitization and intelligentization.

[0099] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiments can be implemented by software, or can be implemented by means of software plus necessary general hardware platforms. Based on such understanding, the technical solutions of the above embodiments can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a U disk, a mobile hard disk, etc.), and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments of the application.

[0100] The above description is only a preferred embodiment of the application, but the protection scope of the application is not limited thereto, and any changes or replacements within the technical scope disclosed by the application can be easily thought of by those skilled in the art, which should be covered within the protection scope of the application. Therefore, the protection scope of the application should be subject to the protection scope of the claims.

Claims

1. A dynamic partition grouping method for local decentralized group train virtual reconsist, characterized in that, The application comprises: Based on the concept of local decentralization, a train operation control network is constructed by using trains and ground centers as nodes, called train nodes and ground control nodes, the communication connection between the train nodes and the ground control nodes, and the communication connection between the trains as edges in the train operation control network; The modularity of the train operation control network is calculated by combining the communication connection between different nodes, whether different nodes belong to the same community, and the total number of communication connections of the train operation control network, which is used as an overall quality evaluation index to guide the partition decision of the train operation control network, and the train nodes in each community form several virtual marshalling train formations to complete the partition grouping of the operation control network. After the train operation control network completes the partition grouping, the symmetric optimization scheduling is performed on the symmetric structure in each community based on group theory, wherein the symmetric structure refers to the symmetric characteristics of the train nodes in the community in the train operation mode.

2. The dynamic zoned grouping method of partial decentralized group train virtual re-connection according to claim 1, wherein, The calculation of the modularity of the train operation control network comprises: A ij represents the communication connection between node i and node j, A ij is 0, which means that there is no communication connection between node i and node j, A ij is not 0, which means that there is a communication connection between node i and node j; using represents the total number of communication connections of the train operation control network; using C i , C j corresponds to the community where node i and node j are located, if node i and node j belong to the same community, then δ(C i , C j ) = 1, otherwise δ(C i , C j ) = 0; The calculation method of the modularity Q of the train operation control network is: where k i , k j corresponds to the degree of the node i, j.

3. The dynamic zoned grouping method of partial decentralized group train virtual reconnection according to claim 2, characterized in that, During the train operation, various operation data of the train nodes are taken as data blocks, and the data blocks are organized to form a data block sequence; the Merkle tree is constructed for the data block sequence to obtain a hash value of a root node; Whenever the train operation data of the train nodes are received, the hash value of the root node is compared to verify whether the data is tampered or damaged.

4. The dynamic zoned grouping method of partially decentralized group train virtual re-connection according to claim 3, wherein, The entire train operation control network is divided into communities in the framework of the Merkle tree structure, the leaves of the tree structure are the ground control nodes, and the internal connections of the tree correspond to the communication connections between the ground control nodes.

5. The dynamic zoned grouping method of partial decentralized group train virtual re-connection according to claim 4, wherein, The adjacency matrix of the Merkle tree structure is stored as an integer array, and when the corresponding communities are merged, the rows and columns are repeatedly merged in pairs, and the calculation method of the modularity Q of the train operation control network is converted into a matrix form, which is represented as: where S represents an n * R matrix, the element in the i-th row and r-th column of which is denoted as S ir , S ir = 1 indicates that the i-th node belongs to the r-th community, n is the number of nodes, R is the number of communities, Tr() is the trace of a matrix, and T is the matrix transpose symbol; the modularity matrix B reflects the deviation of the actual connection from the expected value of connection and is used to determine whether the community structure in the network is significant, B ij is the element in the i-th row and j-th column of the matrix B.

6. The dynamic partition grouping method of the local decentralized group train virtual reconnection according to any one of claims 1-5, characterized in that, After the train operation control network is partitioned, the train nodes and the ground control nodes in the community maintain complete train-ground communication connection, and each train node communicates with the ground control node; or the trains in the community maintain chain connection, and the lead vehicle is autonomously decided by the election negotiation strategy, the lead vehicle communicates with the ground control node, and the other train nodes maintain virtual coupling small marshalling through train-train communication, and autonomously negotiate the line resources.

7. The dynamic zoned grouping method of partial decentralized group train virtual re-connection of claim 1, wherein, The symmetric optimization scheduling based on the group theory for the symmetric structure in each community comprises: Let G be a group acting on the set of train nodes, when two train operation modes have symmetric characteristics under the action of the group G, the optimal solution of one of the train operation modes is calculated, and the optimal solution is subjected to symmetric operation to obtain the optimal solution of the other train operation mode.

8. The dynamic zoned grouping method of partially decentralized group train virtual re-connection according to claim 7, wherein, The symmetry operation on the optimal solution obtains another optimal solution of train operation mode, and is expressed as: p2=g·p1 Wherein, p1 is an optimal solution of one train operation mode, p2 is an optimal solution of another train operation mode, g is an element in the group G, and the optimal solution refers to the best scheme for achieving the set optimal target under the given constraint condition.

9. The dynamic partition grouping method of partial decentralized group train virtual reconnection according to claim 7 or 8, characterized in that, Further comprising: Let P be a set of train operation modes, and P / G be the equivalence classes of P divided by the group G, denoted as: P / G = {Orb G (p) | p e P}; where P / G denotes the result of partitioning the set P by the group G, p is a single train operation mode in the set P, Orb G (p) is the set of all locations that the train operation mode p can reach, Orb G (p) = {g p | g G}, g is an element in the group G, representing an operation or transformation; A scheduling calculation is performed for each equivalence class, and a final scheduling scheme generates a scheduling plan of all train operation modes through symmetry operation, and the optimal target of the scheduling scheme is to minimize the objective function f(p), so that the scheduling is optimized based on symmetry of group theory, and the optimal solution is expressed as: min p∈P f(p) = min p∈P / G f(p).

10. The dynamic zoned grouping method of partial decentralized group train virtual re-connection of claim 1, wherein, Further comprising: A multi-level coordination mechanism of the train operation control system is introduced, in the train operation control network, control systems at different levels independently make decisions, and consistency is maintained through the coordination mechanism, so that local autonomy and global coordination are realized; wherein ground equipment involved in different levels of train control system is used as a ground control node, and on-board autonomous control equipment involved in the train is used as a train node.

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