A plan-driven radio block center virtual marshaling control method and system

Abstract

The application discloses a plan-driven radio block center virtual marshalling control method and system, and is applied to the technical field of data processing.The radio block center is taken as a core, a marshalling plan of a centralized dispatching system, train data, trackside and interlocking information are acquired first, cross-source mapping is established through associated line and station basic data, and initial legality checking is completed, and target associated plan data is generated.Then, structured control data is obtained through splitting processing, topological relationship generation and a priority caching mechanism, and targeted control commands are generated in combination with real-time states of trains and state machine logic.Through optimization of state machine migration conditions and weights, a dynamic optimization model is constructed, and finally, based on the model and train-ground and train-train communication states, virtual marshalling dynamic control results and abnormal protection instructions are output, and efficient and safe train virtual marshalling control is realized.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a plan-driven virtual grouping control method and system for radio block centers. Background Technology

[0002] In traditional manual train marshalling operations, both marshalling and demarcation must be carried out physically at specific stations. There are special requirements for train type, train length, and axle load. The marshalling and demarcation operations are all performed manually by operators, which poses safety risks. In addition, a large number of manual confirmation operations are required, which requires a high level of expertise from the operators and results in high labor costs.

[0003] While automated train formation reduces manual labor through various automated systems, it still cannot overcome the requirements of train type and specific station. Trains still need to be physically coupled, and both formation and decoupling operations can only be carried out at specific stations, making dynamic formation impossible. The availability of the system still has considerable room for improvement.

[0004] Current research on virtual train formation technology mainly focuses on vehicle-to-vehicle communication, model building, and the development of virtual train formation control systems. Research on virtual train formation control technology for existing CTCS train control systems and wireless block centers is limited. This hinders the practical application of virtual train formation technology in existing CTCS train control systems. Summary of the Invention

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] A plan-driven virtual train formation control method for a radio block center includes: the radio block center receiving and acquiring train formation plans, train parameters and real-time operation data, trackside equipment and interlocking route information from the centralized dispatching system; associating the train formation plans from the centralized dispatching system with the basic line and station data configured by the radio block center, establishing a cross-source association mapping between train formation plan information and line topology and station tracks, adding an initial plan validity verification tag to each associated data, and generating target associated plan data; decomposing the target associated plan data based on the structure of plan number-train information-station operation-verification result, introducing virtual train formation topology relationship generation logic to achieve unified features of multi-source plan data, and establishing a priority system for legally executable train formation plans. A caching mechanism is implemented to generate structured virtual train formation control data. For different stations' virtual train formation / deformation control requirements, based on the plan-train-station association mapping in the structured virtual train formation control data, and combined with the real-time train operation status and formation state machine logic, targeted virtual train formation control commands and state transition instructions are generated. The train formation execution status and train-to-ground communication feedback information are processed to optimize the state transition conditions and association judgment weights of the virtual train formation state machine, generating a dynamic formation control optimization model. Based on the dynamic formation control optimization model, the real-time train operation data and the formation plan information updated by the centralized dispatching system are processed, and combined with the train-to-ground communication and train-to-train communication status, dynamic control results and anomaly protection instructions for the virtual train formation are generated.

[0007] Another aspect of this application is a plan-driven radio block center virtual grouping control system, the system being configured to execute executable instructions to perform the plan-driven radio block center virtual grouping control method described above.

[0008] According to another aspect of this application, an electronic device includes: a first processor; and a memory for storing executable instructions of the first processor; wherein the first processor is configured to execute the above-described plan-driven radio block center virtual grouping control method by executing the executable instructions.

[0009] According to another aspect of this application, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a second processor, implements the above-described plan-driven radio block center virtual grouping control method.

[0010] Its beneficial effects are as follows: This invention provides a plan-driven radio block center virtual train formation control method and system, and constructs a plan-driven train virtual train formation control scheme. By acquiring train formation plans, train parameters, real-time operating data, and trackside interlocking information from the Centralized Train Control (CTC) system, target-related plan data is generated through cross-source association mapping and legality verification. The verification process includes nine core rules, outputs specific error codes, and feeds back the plan status to the CTC. Then, through splitting, topology generation, and priority caching, structured control data is formed. Topology generation converts the train-level plan into a station-level topology model to adapt to the station-by-station control requirements of the RBC. Control commands are generated by combining the real-time train status with the formation state machine logic of eight states. The control commands are newly issued in the existing CTCS protocol and transmitted in 2-second cycles. The state machine parameters are optimized to build a dynamic optimization model. The model is weighted according to multiple dimensions and presets dynamic adaptation nodes to adapt to different operating conditions. Finally, dynamic control results and anomaly protection instructions are output. The protection instructions cover four core anomaly scenarios, clearly define anomaly identification and protection operations, and the vehicle-to-ground communication interruption protection threshold is 6 seconds. This achieves efficient virtual formation control under the CTCS train control system without reconstructing the existing system architecture.

[0011] This invention boasts strong compatibility, requiring no modification to the core functions of the existing CTCS-3 train control system. Upgrades are achieved through the addition of new message packets and control commands. The RBC-CTC interface adds a formation plan message packet (marked 0xFE), and the train-to-ground communication adds control commands in packet 44 of the "M24 General Message," reducing the difficulty and cost of modification. It overcomes physical coupling and specific station limitations, enabling dynamic formation and improving train scheduling flexibility and transportation efficiency. Multi-source data fusion and state machine optimization ensure control accuracy. The state machine contains eight states and clearly defined transition conditions, with weighted judgments based on dimensions such as plan matching degree and positioning accuracy. An anomaly protection mechanism covers scenarios such as communication interruptions and plan conflicts, ensuring operational safety. It adapts to the operational needs of different types of trains and stations, supporting the normal operation of non-virtual formation trains. For trains that do not support virtual formation, the RBC does not send new control commands, maintaining basic control functions. It has broad applicability and fills the gap in RBC virtual formation control technology within the CTCS system. Attached Figure Description

[0012] Figure 1 A flowchart illustrating a plan-driven virtual grouping control method for radio block centers, provided as an embodiment of the present invention;

[0013] Figure 2 This is a schematic diagram of a plan-driven virtual grouping control system for radio block centers, provided as an embodiment of the present invention. Detailed Implementation

[0014] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention. Figure 1 This application describes a plan-driven virtual grouping control method and system for radio block centers according to exemplary embodiments thereof.

[0015] In the embodiments of this application, a plan-driven radio block center virtual grouping control method and system are provided, such as Figure 1 As shown:

[0016] S101, the radio block center receives and acquires the train formation plan, train parameters and real-time operation data, trackside equipment and interlocking route information from the centralized dispatching system.

[0017] In one implementation, the structured formation plan issued by the CTC is received through the interface protocol between the Radio Block Center (RBC) and the CTC (with the addition of a formation plan message packet extension and message flag 0xFE). This plan includes the core identifier of the plan, the train list, and the operation instructions of each train at the stations it passes through, ensuring that the formation operation has a clear basis for execution. The train formation plan with plan number 0x00000001 was collected. This plan includes 3 trains (train numbers 96001, 96003, and 96005). It is clear that train 96001 needs to complete the formation on track IG at station A (within-group order 1), maintain the formation on track IIG at station B (within-group order 1), and finally disassemble on track IG at station C; train 96003 needs to complete the formation on track IIG at station A (within-group order 2), maintain the formation on track IIG at station B (within-group order 2), and finally disassemble on track IG at station C; train 96005 needs to be added to the formation on track IG at station B (within-group order 3) and disassembled on track IIG at station C. All train formation and disassembly operations, the tracks they pass through, and the order within the groups are clearly marked. The plan data is encapsulated according to standard message packet fields, including a 4-byte plan identification number, a 1-byte number of trains, and a 4-byte train number.

[0018] After the train establishes a communication session with the RBC, the RBC receives static attribute data reported by the train in real time. This data is used to identify the train's identity, determine its train formation compatibility, and ensure that only trains meeting the requirements participate in virtual train formation. The RBC receives parameter data reported by train 96003, including its unique identifier ID (0x000000A3), train number 96003, train length 240 meters, train integrity status "normal" (no carriage separation), and train type "RBC-controllable train." These parameters confirm that the train meets the basic conditions for participating in virtual train formation. The initial train formation status is "unformed," with parameter values ​​of 0.

[0019] Through the train-to-ground wireless communication link, dynamic operating status data reported by the train is continuously received and reported every 2 seconds, allowing real-time monitoring of key information such as train position and speed, providing a basis for determining train formation timing and controlling safe distances. The system acquires the real-time location information of train 96003 (100 meters outside the A-station entry signal corresponding to the transponder and offset), its operating speed of 30 km / h, and its formation status feedback of "not in formation" (parameter value 0). As the train moves, data is continuously collected showing its position entering the A-station IIG track and its speed decreasing to 5 km / h (preparing for formation). After acquiring the aforementioned position, speed, and status information of 96003, the RBC, combined with the status of 96001 (96001 is located on the A-station IG track and is within the planned operation area), determines that 96003 meets the requirements for entering CT (train-to-train communication) status and immediately sends a "establish train-to-train communication" command to train 96003. After receiving the command, 96003 initiates the vehicle-to-vehicle communication establishment process with 96001. After the communication is successfully established, its group status is updated to "Establish vehicle-to-vehicle communication" (parameter value 1), and relevant dynamic data is continuously reported to the RBC at 2-second intervals.

[0020] The Trackless Braking System (RBC) communicates with the station interlocking system and trackside occupancy inspection equipment to collect information such as track occupancy status and route availability. This information supports the assessment of safety conditions for marshalling operations and avoids risks associated with marshalling operations due to abnormal trackside conditions. The RBC detected "idle" feedback from the trackside occupancy inspection equipment on track IIG at station A, indicating that the interlocking system had opened the route from the entrance signal to track IIG, and no other trains were occupying that track or adjacent protected sections. Simultaneously, the RBC detected that the corresponding marshalling / disassembly operation compliance indicator for that track was "marshalling permitted," ensuring that train 96003 could safely enter that track to perform marshalling operations.

[0021] All data acquisition follows the CTCS-3 train control system vehicle-to-ground communication protocol specification. The train formation plan is transmitted through a newly added dedicated message packet (message flag 0xFE), train parameters and real-time operation data are transmitted through a periodic reporting mechanism (period 2s), and trackside and interlocking information is synchronized through a real-time interactive link to ensure the timeliness and consistency of various data transmissions, providing reliable input for subsequent legality verification, topology generation and status control.

[0022] S102, perform correlation processing between the marshalling plan of the centralized dispatching system and the basic data of lines and stations configured by the radio block center, establish cross-source correlation mapping between marshalling plan information and line topology and station tracks, add an initial verification tag for the legality of the plan to each piece of correlation data, and generate target correlation plan data.

[0023] In one implementation, the multi-dimensional train formation plan data collected for train operation control is preprocessed to generate line and station characteristic data adapted to the virtual formation control of the Radio Block Center (RBC). This formation plan data includes a plan identification number, number of trains, train number, number of stations passed through, station number, formation sequence, track number, and formation / deformation plan instructions. Each field has a fixed byte length and a range of valid values; for example, 0x1A indicates formation and 0x8A indicates deformation. Valid track numbers range from 0x01 to 0xFE. The associated data consists of pre-configured line and station basic data from the RBC. First, the multi-dimensional train formation plan data collected during train operation control is cleaned and standardized to remove invalid and redundant data, ensuring the data format is compatible with the virtual formation control logic of the RBC, thus forming the line and station characteristic data.

[0024] The marshalling plan data includes the plan identification number (used to uniquely identify the marshalling task), the number of trains (clearly specifying the total number of trains participating in the marshalling, with a legal value of 1~8), the train number (distinguishing different participating trains, unique within the same plan), the number of stations passed through (the total number of stations each train needs to pass through, with a legal value of 1~8), the station number (the unique code of the stations passed through), the marshalling order (the order of the train within the group, with a legal value of 1~8), the track number (the track code for the train to perform marshalling and demarcation operations within the station), and the marshalling and demarcation plan instructions (clearly specifying whether to perform "marshalling" or "demarshalling" operations at the corresponding station). It is based on the pre-configured line basic data of the RBC (including the spatial arrangement order of each station within the line, the permitted train operation level of the line, etc.) and station basic data (including the list of legal tracks for each station, the compliance scope of marshalling and demarcation operations at each station, etc.).

[0025] For example, train formation plan data with plan identification number 0x00000001 is collected. This plan contains 3 trains with train numbers 96001, 96003, and 96005. Train 96001 passes through 3 stations: 0x00000011 (Station A), 0x00000021 (Station B), and 0x00000031 (Station C). At Station A, the formation sequence is 1, the track number is 0x01, and the formation / deformation plan instruction is "formation" (0x1A). During preprocessing, this data is uniformly converted to a binary format recognizable by RBC, and redundant fields with incorrect formats are removed. Simultaneously, it is correlated with pre-stored basic data such as the list of valid tracks at Station A (0x01 to 0x05) and the spatial order of stations (Station A → Station B → Station C) to generate adapted line and station feature data.

[0026] The adapted line and station feature data undergoes targeted analysis to generate a list of core calculation and control variables. These variables include line topology mapping variables for virtual train formation control, station track matching variables, numbering determination variables for plan legality verification, train information verification variables, station operation compliance judgment variables, and cross-source data association identifier variables. The preprocessed line and station feature data is then analyzed to extract variables that play a crucial role in virtual train formation control, forming a list of core calculation and control variables that provides clear operational objects for subsequent calculations and judgments.

[0027] Core variables include: line topology mapping variables for virtual train formation control (such as station spatial order mapping within the line, distance mapping between stations, etc., used to determine the spatial logic of train formation); station track matching variables (such as the set of legal track codes for stations, the adaptation relationship between tracks and train formation / deformation operations, etc., used to verify the compliance of track usage); numbering determination variables for plan legality verification (such as the legal value range of plan identification number 0x1~0xFFFFFFFE, duplicate number determination identifier, etc., used to verify the validity of plan numbering); train information verification variables (such as the unique identification rules for train numbering, the upper limit threshold for the number of trains, etc., used to verify the compliance of train information); station operation compliance judgment variables (such as the set of legal values ​​for train formation / deformation plan instructions, the valid range of station numbers, etc., used to verify the validity of station operations); and cross-source data association identification variables (such as the association matching identifier between plan data and line / station basic data, used to confirm the validity of data association).

[0028] For example, for the line station feature data of the above-mentioned plan 0x00000001, the core variables generated after parsing include: the line topology mapping variable is "Station A (0x00000011) → Station B (0x00000021) → Station C (0x00000031)"; the station track matching variable is "Legitimate tracks of Station A {0x01, 0x02, 0x03}"; the number judgment variable for plan legality verification is "Legitimate range of plan identification number 0x1~0xFFFFFFFE"; the train information verification variable is "Unique train number, maximum number of trains 8"; the station operation compliance judgment variable is "Legitimate value of compilation and decomposition instruction {0x1A, 0x8A}"; and the cross-source data association identifier variable is "Matching identifier between plan data and line basic data = 1 (match successful)".

[0029] The adapted line and station feature data, core calculation and control variable list, original train formation plan data, and RBC-related data are synchronously processed and precisely calculated. Multi-dimensional line and station data are matched in real time to quickly calculate the mapping and matching results between train formation plan information and line topology, station tracks, etc. Then, based on nine core plan legality initial verification rules, legality determination planning is carried out. Verification rules include the legality of plan number, train quantity, train number, station number, track number, and train formation / decoupling operations, requiring that the station order be consistent with the line spatial order and that the train number for the same plan not be repeated. The adapted line and station feature data, core calculation and control variable list, and the original train formation plan data and RBC-related data are synchronously processed, completing key processing in two steps:

[0030] By traversing each data item in the train formation plan (such as station number, track number, etc.), and matching it one by one with the corresponding legal range in the pre-configured basic data of the line and station in the RBC, the mapping matching result of each data item is quickly calculated (1 for a successful match, 0 for a failed match), ensuring the adaptability of the train formation plan information with the line topology and station tracks. For example, in plan 0x00000001, the track number 0x01 of train 96001 at station A is matched. The cross-source association mapping algorithm is called to query the list of legal tracks (0x01 to 0x05) pre-stored in the RBC at station A. Track number 0x01 is determined to be within the legal range, and the matching result is 1. Similarly, the train formation / deformation plan instruction 0x1A of train 96003 at station A is matched. The set of legal values ​​for the formation / deformation instruction {0x1A, 0x8A} is queried, and a successful match is determined, resulting in a result of 1.

[0031] Based on the mapping matching results, the validity is determined according to the rule of "full matching is legal". That is, the initial verification of the plan passes only when the mapping matching result of all data items in the train formation plan is 1; if any data item has a matching result of 0, the verification fails. The matching results of all data items of plan 0x00000001 are summarized. The matching result of all data items such as plan identification number, number of trains, train number, station number, track number, and train formation instructions is 1. According to the initial verification rule, the plan is initially deemed legal.

[0032] Subsequently, based on the data analysis results of the mapping and matching, initial verification labels for the plan's legality are determined. These labels are divided into legal labels and nine categories of illegal labels. Illegal labels correspond one-to-one with plan execution status codes, such as error number 0x81 or station track error 0x85. Finally, combining the basic control requirements of the virtual marshalling system of the radio block center, the target-related plan data is output, and the corresponding plan execution status code is fed back to the CTC. For this plan, the feedback is "not executed 0x01". Based on the above mapping and matching results and legality judgment conclusions, corresponding initial verification labels ("legal" or "illegal") are added to the marshalling plan. If the verification is invalid, the specific invalid data items must be marked (e.g., "station track error" or "marshalling instruction error"). Plan 0x00000001 is initially deemed valid, so a "valid" verification tag is added to it; if train 96005 in a certain plan has a track number of 0x00 at station B (which is outside the valid track range of 0x01~0xFE at station B), then the matching result is 0, and an "invalid - station track error (0x85)" tag is added.

[0033] Finally, based on the basic control requirements of virtual marshalling in the radio block center (such as the need to specify marshalling and dismounting stations and the requirement that the number of trains not exceed 8), the marshalling plan data with verification tags is finally integrated. Illegal plans are eliminated, legal plans are retained, and associated line and station basic data are supplemented to generate target associated plan data, providing a direct basis for the subsequent generation of virtual marshalling topology relationships. The legal tags, core variables, matching results, and associated line and station basic data of plan 0x00000001 are integrated to form target associated plan data, confirming that the plan is executable and containing key associated information such as "train 96001 marshals with 96003 on track IG at station A" and "all three trains are dismounted at station C".

[0034] S103, the target associated plan data is split based on the structure of plan number-train information-station operation-verification result, and the virtual grouping topology relationship generation logic is introduced to realize the unification of multi-source plan data features. At the same time, a priority execution caching mechanism is established for legal and executable grouping plans to generate structured virtual grouping control data.

[0035] In one implementation, combining the structured characteristics of the target-related planning data (such as a hierarchical plan-train-station data structure) with the control requirements of virtual train formation (such as performing train formation / disassembly operations at the station level and clarifying the train order within the group), the data splitting module and the topology generation engine collaboratively negotiate to determine the data splitting and feature unification strategy. Through multi-dimensional plan decomposition (decomposition dimensions include plan identifier, train attributes, station operations, and verification results) and topology relationship mapping (mapping logic is "train passes through stations → train formation / disassembly operations within stations → train order within the group"), the plan, which is based on trains, is converted into a topology model based on stations as basic units. This clarifies the data splitting dimensions and the virtual train formation topology construction standards (the standards require that the topology relationships reflect the station order, the train list within the station, the train formation / disassembly instructions, and the position within the group).

[0036] For the legitimate plan numbered 0x00000001 in the target associated plan data, the splitting dimension was negotiated and determined to be "plan number - train number / parameter - station number / track / compilation instruction - legitimate verification label". The topology construction standard is "constructed according to the spatial order of line stations (station A → station B → station C), with each station associated with the trains participating in the compilation and the order within the group".

[0037] A two-stage processing mechanism of splitting and unifying is adopted to process the target-related planning data. First, the target-related planning data is split and analyzed in all dimensions according to the structure of plan number-train information-station operation-verification result. Then, based on the virtual train formation topology generation logic, the split data is unified to achieve multi-source planning data characteristics. The core logic is to iterate through all the stations passed by all trains in the plan, integrate all trains at the same station and clarify the order within the group, and generate standardized virtual train formation basic data to meet the core requirements of accuracy and control efficiency. The "split-unify" two-stage processing mechanism standardizes the target-related planning data to ensure that the data is compatible with the virtual train formation control logic. The target-related planning data is split according to the fixed structure of "plan number-train information-station operation-verification result" to extract core information of each dimension, ensuring that the data granularity meets the requirements of subsequent topology generation. Disassembling plan 0x00000001 yields plan number "0x00000001"; train information includes 96001 (length 240 meters, integrity normal), 96003 (length 200 meters, integrity normal), and 96005 (length 180 meters, integrity normal); station operations include station A (96001-IG-grouping-sequence 1, 96003-IIG-grouping-sequence 2), station B (96001-IIG-grouping-sequence 1, 96003-IIG-grouping-sequence 2, 96005-IG-grouping-sequence 3), and station C (96001-IG-decoupling-sequence 1, 96003-IG-decoupling-sequence 2, 96005-IIG-decoupling-sequence 3); the verification result is "valid".

[0038] Based on the virtual train formation topology generation logic (the core logic being "traversing all train stations along the route, integrating all trains that are formed and disassembled at the same station according to the line station order, and clarifying the order within the group"), the fragmented data after splitting is unified in terms of features. This is used to link the core requirements of virtual train formation accuracy (ensuring train formation and disassembly operations match the track) and control efficiency (centralized management at the station level) to generate standardized virtual train formation basic data. After integrating the split data through the topology generation logic, the standardized basic data is clearly presented as "Station A (forming: 96001-sequence 1, 96003-sequence 2) → Station B (forming: 96001-sequence 1, 96003-sequence 2, 96005-sequence 3) → Station C (disforming: 96001-sequence 1, 96003-sequence 2, 96005-sequence 3)", while also including the core parameters and verification results of each train.

[0039] The standardized virtual train formation data undergoes validity screening. Data failing verification is removed, redundant data after feature unification is streamlined, and valid, executable formation plans are prioritized for caching. This generates a preprocessed virtual train formation data scheme that includes data removal methods, redundancy streamlining strategies, and a priority caching mechanism. The standardized virtual train formation data undergoes multi-dimensional screening and preprocessing to remove invalid data, streamline redundant information, and prioritize caching of valid plans to ensure data availability. For plan data with the verification tag "invalid" (e.g., a plan with "station track error"), removal is initiated directly to prevent invalid data from entering subsequent control processes. For example, if train 96005 in a plan has track number 0x00 at station B (out of the valid range 0x01~0xFE) and the verification tag is "invalid - station track error," the entire plan is removed.

[0040] After feature unification, duplicate or irrelevant information (such as identical parameters repeatedly reported by trains, and redundant fields unrelated to train formation and decomposition operations) is simplified, retaining only the data required for core control. The standardized data of Plan 0x00000001 is simplified, removing passenger number statistics fields reported by trains that are unrelated to train formation, and retaining only core information such as train number, length, completeness, station operations along the route, and order within the group.

[0041] For valid executable plans that pass verification, a priority execution caching mechanism is established to store them in a high-speed cache area to ensure response efficiency during subsequent calls. The preprocessed data of plan number 0x00000001 is stored in the priority cache area and marked with "high" priority. Subsequent RBC calls to this plan can directly read from the cache without repeated processing.

[0042] By integrating the above filtering, simplification, and caching operations, a preprocessed virtual grouping data scheme is formed, which includes data removal methods (removal by verification tag), redundancy simplification strategies (retaining core control fields), and priority caching mechanisms (high-speed storage of legitimate plans).

[0043] The pre-processed virtual train formation data scheme is integrated and optimized. Fine-tuning is performed by incorporating feedback information from line topology, station tracks, train operation, and formation / deformation operations. Simultaneously, a data correction feedback system is constructed based on the configuration data from the Radio Block Center (RBC). This involves dynamically improving topological relationships, supplementing train-station association information, and optimizing the data structure by combining the association weights between planned data and virtual train formation control. This generates structured virtual train formation control data adapted to the RBC's virtual train formation control, with the final data structure at the "station-train-core control parameter" level. Core control parameters include train length, integrity, track adaptability, and operation time windows. The pre-processed virtual train formation data scheme undergoes final integration and detailed optimization, with a data correction system constructed based on multi-source feedback information to ensure the data structure adapts to the virtual train formation control requirements of the RBC. Detailed adjustments are made to the data based on line topology (e.g., signal distribution between stations), station tracks (e.g., track capacity limitations), train operation (e.g., train speed limits), and formation / deformation operation feedback information (e.g., historical formation / deformation success rates). For example, based on the capacity limit of the IG track on Station A (maximum adaptable length 250 meters), the train formation order description of 96001 was slightly adjusted, and the "track capacity adaptability" note was added; based on the historical train formation feedback of the IIG track on Station B ("suitable for train formations under 200 meters"), the rationality of the train formation operation of 96003 (200 meters) and 96005 (180 meters) was confirmed.

[0044] Based on the pre-configured line and station data of RBC, a dynamic correction mechanism is established to continuously optimize data accuracy by supplementing train and station correlation information (such as the train and track compatibility relationship and the time window for station coding and disassembly operations). For example, the compatibility indicator "fully compatible" is added for track IG at station A and track 96001, and the compatibility indicator "fully compatible" is added for track IIG at station B and track 96003 / 96005. The time window for coding and disassembly operations at each station is also added (station A: 08:00-08:30, station B: 09:00-09:30, station C: 10:00-10:30).

[0045] By combining the correlation weights between planned data and virtual train formation control (such as the highest weight for train formation instructions and the second highest weight for order within the group), the data structure is optimized to present a hierarchical structure of "station-train-core control parameters," which facilitates rapid call and parsing by the RBC. The optimized structured control data for Project 0x00000001 is as follows: Station A (compilation / decompilation command: marshalling; train list: 96001 (sequence 1, length 240 meters, integrity normal, track IG), 96003 (sequence 2, length 200 meters, integrity normal, track IIG)); Station B (compilation / decompilation command: marshalling; train list: 96001 (sequence 1), 96003 (sequence 2), 96005 (sequence 3, length 180 meters, integrity normal, track IG)); Station C (compilation / decompilation command: decompilation; train list: 96001 (sequence 1), 96003 (sequence 2), 96005 (sequence 3, track IIG)), ultimately generating structured virtual marshalling control data adapted to RBC virtual marshalling control.

[0046] S104, based on the plan-train-station association mapping in the structured virtual train formation / deformation control data, and combined with the real-time operation status of the train and the formation state machine logic, generates targeted virtual train formation control commands and state transition instructions to meet the virtual train formation / deformation control requirements of different stations.

[0047] In one implementation, all stations within the Radio Block Center (RBC) control area are categorized based on their marshalling and demarcation operation types (primarily divided into "marshalling operations" and "demarshalling operations"). The core control requirements for each station are clarified, and the list of trains participating in the marshalling and demarcation operations at that station and the basic operation information are associated to generate the corresponding control requirement type and associated train operation information for each station. The requirement types are categorized according to the actual marshalling and demarcation functions performed by the station, ensuring a precise match between control requirements and station operation attributes, and avoiding conflicts between control commands and station functions.

[0048] For example, stations A, B, and C under RBC control are categorized and analyzed. Station A only undertakes "marching operations," with control requirements of "marching start control + intra-group sequence calibration," and associated train operation information of "trains 96001 (IG track), 96003 (IIG track), operation time window 08:00-08:30"; Station B undertakes "marching operations," with control requirements of "new train marshalling access + existing marshalling sequence maintenance," and associated train operation information of "..." Trains 96001 (IIG track), 96003 (IIG track), and 96005 (IG track) will have their operation window from 09:00 to 09:30. Station C will only be responsible for "marching and disassembly operations," with the control requirement type being "marching and disassembly control + train separation guidance after disassembly." The associated train operation information is "Trains 96001 (IG track), 96003 (IG track), and 96005 (IIG track) will have their operation window from 10:00 to 10:30."

[0049] Based on structured virtual train formation control data, the core matching dimensions among the plan, trains, and stations are identified, and key correlation information is extracted to form core correlation mapping information, providing a basis for the generation of subsequent control commands. The core extraction dimensions include the affiliation mapping between the plan and the train (which train belongs to which train formation plan), the operation mapping between the train and the station (the train formation and dismantling operations and tracks that the train needs to perform at the station), and the adaptation mapping between the plan and the station (the station operation sequence corresponding to the plan). Ultimately, three types of core information are obtained: mapping relationships, train affiliation, and station operation requirements.

[0050] Extract the core mapping information of plan number 0x00000001 from the structured control data: The mapping relationship is "Plan 0x00000001 → Station A → 96001 / 96003; Plan 0x00000001 → Station B → 96001 / 96003 / 96005; Plan 0x00000001 → Station C → 96001 / 96003 / 96005"; Train affiliation is "96001, 96003, and 96005 all belong to". Plan 0x00000001; Station operation requirements are: "Station A: 96001-IG-Marching-Sequence 1, 96003-IIG-Marching-Sequence 2; Station B: 96001-IIG-Marching-Sequence 1, 96003-IIG-Marching-Sequence 2, 96005-IG-Marching-Sequence 3; Station C: 96001-IG-Unmarching-Sequence 1, 96003-IG-Unmarching-Sequence 2, 96005-IIG-Unmarching-Sequence 3".

[0051] Combining real-time train operation status (such as positioning, speed, and current formation status) with a virtual formation state machine logic encompassing eight states: No Plan (NP), Waiting (WT), Establish Communication with Preceding Train (CT), Establish Formation with Preceding Train (GP), Deform from Preceding Train (UG), Train-to-Train Communication Anomaly (EC), Formation Anomaly (EG), and Deformation Anomaly (EU). Train formation status parameter values ​​are: Not Formed (0), Established Train-to-Train Communication (1), Formed (2), Deformation in Progress (3). This represents the core information associated with each type of station control requirement, and specific state transition judgment conditions are set. These conditions include AND / OR logical relationships and must satisfy three elements: train status, station conditions, and plan matching, ensuring that the timing of control command triggering aligns with the actual train operation. The core logic requires that the judgment conditions cover the three elements of "train status meets standards + station operation conditions meet requirements + plan matching," avoiding premature or erroneous triggering of control commands.

[0052] For the "train marshalling start control" requirement at Station A, the following state transition judgment conditions are set (WT→CT): ① The train is located within the station A entry signal (within the operating range); ② The train speed is ≤5km / h (marshalling preparation state); ③ The train marshalling status is "not marshalled" (parameter value 0); ④ The train level is "virtual marshalling level" and the mode is RBC control mode; ⑤ The plan affiliation is 0x00000001 (consistent with the station adaptation plan). For the "demarshalling operation" requirement at Station C, the following state transition judgment conditions are set (GP→UG): ① The train is located within the designated demarshalling track at Station C; ② The train speed is 0km / h (stationary state); ③ The preceding train has started demarshalling operation (e.g., 96001 has triggered the demarshalling command); ④ The train marshalling status is "marshalled" (parameter value 2).

[0053] The station control demand classification results, associated mapping core information, and state transition judgment conditions are integrated and processed to generate control command information and state transition instruction information that meet the requirements of virtual train formation control, including demand type, mapping rules, and judgment criteria. The control command information is added to message packet 44 of "M24 General Message" in the CTCS-3 train control system and is issued at 2-second intervals, including the train's own identifier, the preceding train's identifier, the operation instruction, and the anomaly identifier. The station control demand classification results, associated mapping core information, and state transition judgment conditions are integrated to clarify the mapping rules (train-station-plan matching logic) and judgment criteria (state transition trigger thresholds) corresponding to each demand type, ultimately generating control command information and state transition instruction information that meet the requirements of virtual train formation control. The core logic is to achieve a closed loop of "demand-mapping-judgment-instruction" after integration, ensuring that instructions can be directly called and issued to the train by the RBC.

[0054] The integrated information is as follows: ① Requirement type: A-station marshalling start control; Mapping rule: 96001 (IG track) and 96003 (IIG track) under plan 0x00000001 are adapted to A-station marshalling operation; Judgment criterion: Meets WT→CT migration conditions; Control command: "Establish communication with the preceding train" (parameter value 1); State transition instruction: NP→WT→CT. ② Requirement type: C-station demarcation control; Mapping rule: 96001 / 96003 / 96005 under plan 0x00000001 are adapted to C-station demarcation operation; Judgment criterion: Meets GP→UG migration conditions; Control command: "Demarcate with the preceding train" (parameter value 3); State transition instruction: GP→UG→WT. ③ Requirement type: B-station adds new train formation access; Mapping rule: 96005 under plan 0x0000001 is adapted to B-station formation operation and merged into the existing formation sequence; Judgment criteria: 96005 is located on B-station IG track, speed ≤ 5km / h, status is "unformed", and 96001 / 96003 is in "formed" status; Control command: "form with the preceding train" (parameter value 2); Status transition instruction: NP→WT→CT→GP.

[0055] S105 processes the train formation execution status and vehicle-to-ground communication feedback information, optimizes the state transition conditions and associated judgment weights of the virtual formation state machine, and generates a dynamic formation control optimization model.

[0056] In one implementation, the train formation execution status and train-to-ground communication feedback information are categorized and organized according to preset virtual formation control rules, generating execution data and associated feedback information corresponding to each status. The classification dimension is "status type + communication quality," where train status includes normal / abnormal, and communication feedback includes normal, interrupted, packet loss, and delayed. According to preset virtual formation control rules (the core rule being "classified by train formation status type + communication feedback result"), the train-reported formation execution status (not formed, train-to-train communication established, formed, disforming, various abnormal statuses) and train-to-ground communication feedback information (communication normal, communication interrupted, data packet loss, feedback delay) are systematically categorized and organized, clarifying the specific execution data (such as status parameter values, triggering timing, and involved trains) and associated feedback information (such as communication link status and data transmission quality) corresponding to each status, generating a dataset specific to each status. The classification dimension covers "status type + communication quality," ensuring that the data can directly support subsequent state machine optimization and avoiding confusion between different types of data.

[0057] The following data related to trains in the classification and organization plan 0x0000001 are as follows: ① When the status is "Grouped (GP, parameter value 2)", the executed data includes "Triggering time: 96003 and 96001 complete train-to-train communication at station A and there are no hidden trains" and "Trains involved: 96001, 96003". The associated feedback information is "Train-to-ground communication is normal, data transmission delay ≤ 0.5s"; ② When the status is "Train-to-train communication abnormal (EC)", the executed data includes "Triggering time: 96005 reports 'establish train-to-train communication' without a grouping plan" and "Trains involved: 96005". The associated feedback information is "Train-to-ground communication is normal, but the feedback data does not match the plan"; ③ When the status is "Grouping and disbanding abnormal (EU)", the executed data includes "Triggering time: 96003 reports 'disbanding grouping' at station B (grouping command station)" and "Trains involved: 96003". The associated feedback information is "Train-to-ground communication is normal, but the feedback data conflicts with the station's operation requirements".

[0058] Based on optimization requirements, the arrangement and design of state transition conditions and associated judgment weights for the virtual train formation state machine were carried out. Adjustment standards, judgment dimensions, and weight assignment information were obtained to generate state machine optimization design information. The judgment dimensions were plan matching degree (0.4), positioning accuracy (0.3), communication stability (0.2), and trackside status (0.1). The overall weight for abnormal state judgment was increased by 20%. Based on the optimization requirements of virtual train formation control (the core requirement being "improving state transition accuracy and reducing the probability of false triggering of abnormalities"), the arrangement and design of transition conditions and associated judgment weights for the eight states (NP, WT, CT, GP, UG, EC, EG, EU) of the virtual train formation state machine were carried out. Adjustment standards (such as the quantification threshold of transition conditions and weight assignment rules), judgment dimensions (such as train positioning accuracy, communication stability, and plan matching degree), and specific weight assignment information were clarified to form state machine optimization design information. Weight assignment is positively correlated with state importance and control risk; the judgment weight of critical safety states (such as abnormal states) is higher than that of ordinary states.

[0059] To address the state machine optimization requirements, the following optimization information is designed: ① Adjusted standards: In the "CT→GP" migration conditions, the threshold for determining "no hidden trains" is refined from "no trackside occupancy alarm" to "no trackside occupancy alarm and vehicle-to-vehicle communication interaction ≥ 3 times"; ② Judgment dimensions: covering "plan matching degree (weight 0.4), train positioning accuracy (weight 0.3), communication stability (weight 0.2), and trackside status (weight 0.1)"; ③ Weight assignment: The overall migration judgment weight for abnormal states (EC, EG, EU) is increased by 20%, with the communication stability weight for "EC→WT" adjusted to 0.5 to ensure that abnormal states can quickly revert to a safe state; ④ For the "WT→CT" migration, a quantitative condition of "train speed ≤ 5km / h" is added to avoid erroneous triggering of communication establishment commands during high-speed travel.

[0060] Based on train operating conditions and virtual formation control requirements, corresponding dynamic adaptation nodes are set for each type of optimized design information to ensure that the optimized state machine matches the actual train formation operation needs. Combining actual train operating conditions (such as different station operation types, train speed, and track complexity) and virtual formation control requirements (such as formation safety and timely de-formation), corresponding dynamic adaptation nodes are set for each type of state machine optimized design information to ensure that the optimized state machine can be flexibly adjusted according to actual operating conditions to match train formation operation requirements. Dynamic adaptation nodes need to be associated with "operating condition parameters + control objectives" to achieve a closed loop of "operating condition change → node trigger → state machine adjustment".

[0061] To optimize design information, dynamic adaptation nodes are set: ① For the "A-station train formation operation" scenario, the adaptation node is set as "the train is positioned within the track range of A-station (longitude XXX, latitude XXX to XXX) and the speed is ≤5km / h", triggering the adjustment of the accuracy verification threshold for the "WT→CT" migration condition (the allowable positioning error range is tightened from ±10m to ±5m); ② For the "B-station new train formation" scenario, the adaptation node is set as "the distance between 96005 and the preceding train 96003 is ≤100m", triggering the weight adjustment for the "CT→GP" migration (the trackside state weight is temporarily increased to 0.2); ③ For the "train-to-ground communication delay ≥3s" condition, the adaptation node is set as "the communication delay is ≥3s for 3 consecutive cycles", triggering "the communication stability weight for all state migrations is increased to 0.6", prioritizing reliable communication before executing state transitions.

[0062] The classification and organization results, state machine optimization design information, and dynamic adaptation nodes are processed to generate state machine adjustment information and a dynamic grouping control optimization model that meet the requirements of virtual grouping control, including state classification information, optimization schemes, and adaptation standards. A weighted decision tree algorithm is used to integrate the classified and organized state dataset, state machine optimization design information, and dynamic adaptation nodes: first, the priority of each optimization design information is quantified by the algorithm; then, combined with the triggering conditions of the dynamic adaptation nodes, the specific content of state machine adjustment (such as supplementing transition conditions and updating weight assignments) is determined. Finally, state machine adjustment information containing state classification information (corresponding to the original state type), optimization schemes (specific adjustment measures), and adaptation standards (applicable operating conditions) is generated, and a dynamic grouping control optimization model is constructed. The weighted decision tree algorithm uses "state priority + operating condition adaptability" as the decision basis to output the optimal state machine adjustment strategy, ensuring that the model can dynamically adapt to different operating scenarios.

[0063] After integration and processing, the following information is generated: ① State machine adjustment information: The optimization scheme for state "CT→GP" is "supplementing the migration condition 'vehicle-to-vehicle communication interaction times ≥ 3 times' + adjusting the judgment weights to plan matching degree 0.4, positioning accuracy 0.3, communication stability 0.2, and trackside status 0.1", with the adaptation standard being "all marshalling operation stations, train speed ≤ 10km / h"; the optimization scheme for state "EC→WT" is "simplifying the migration condition to 'normal communication and no effective marshalling plan' + increasing the communication stability weight to 0.5", with the adaptation standard being "all scenarios without a marshalling plan or where associated objects do not match". ② Dynamic marshalling control optimization model: The model has a built-in 12-classified state dataset, integrating the above optimization schemes and dynamic adaptation nodes. It can receive train operation data (positioning, speed, communication status) in real time. When an adaptation node is triggered, the model automatically adjusts the state transition conditions and weights. For example, after the train enters the A station track, the model automatically tightens the positioning accuracy threshold to improve the accuracy of marshalling state transitions.

[0064] S106 processes real-time train operation data and train formation plan information updated by the centralized dispatching system based on the dynamic formation control optimization model, and generates dynamic control results and anomaly protection instructions for virtual train formation by combining the train-to-ground communication and train-to-train communication status.

[0065] In one implementation, based on the dynamic formation control optimization model and virtual formation control rules (the core rules being "plan matching + reliable communication + status compliance"), targeted processing is performed on the real-time train operation data and the formation plan information updated by the centralized dispatching system (CTC). ① Data Analysis: Extract core train operation data, including positioning information (transponders the train passes and distance offset from transponders), speed data (real-time speed), and train formation status (parameter values ​​0-3 correspond to unformed / communication established / formed / deformation in progress); ② Plan Matching Verification: Combine the updated train formation plan content from the CTC (such as adjusted formation / deformation stations and order within the group) to verify the matching of the train's current operating status with the plan (such as whether it is at the station specified in the plan and whether the order within the group is consistent); ③ Communication Status Verification: Verify the stability of the train-to-ground communication connection (communication delay threshold ≤ 0.5s, data packet loss rate 0) and the train-to-train communication interaction status (adjacent train information exchange success rate ≥ 99%, communication link quality meets standards). The threshold for train-to-ground communication interruption is 6s; if no train data is received for 6 consecutive seconds, it is considered a communication interruption; ④ Multi-Source Data Fusion Verification: Perform comprehensive verification of the above data through a fusion algorithm to ensure the accuracy and reliability of the input data. For example, regarding plan 0x00000001, the real-time data of train 96003 is analyzed: it is located on track IIG at station B (the planned designated marshalling station), with a speed of 5 km / h (meeting the speed requirements for marshalling preparation), and the marshalling status is "establishing train-to-train communication" (parameter value 1); the plan updated by CTC still maintains the core requirement of "marshalling at station B and dismounting at station C", and the train operation matches the plan; the train-to-ground communication delay is 0.3s with no packet loss, and the information exchange success rate between train-to-train communication and the preceding train 96001 is 100%, and the data is verified to be valid.

[0066] For plan 0x00000001, the real-time data of train 96003 was analyzed: it was located on the IIG track at station B (the planned designated marshalling station), with a speed of 5 km / h (meeting the marshalling preparation speed requirements), and the marshalling status was "establishing train-to-train communication" (parameter value 1). The CTC-updated plan still maintains the core requirement of "marshalling at station B and dismounting at station C," and the train operation matches the plan. The train-to-ground communication delay was 0.3s (≤0.5s threshold), with no packet loss. The information exchange success rate with the preceding train 96001 in train-to-train communication was 100%. After verification by the multi-source data fusion algorithm, the data was determined to be valid and meet the subsequent control requirements.

[0067] Based on the state transition logic and weight configuration of the optimization model, the virtual train formation control strategy is calculated. State machine adjustment operations are executed to adapt to the actual train operating conditions, generating the processed formation control strategy and communication status verification result elements. Based on the state transition logic (including jump rules for 8 states) and weight configuration (plan matching degree 0.4, communication stability 0.3, trackside status 0.2, positioning accuracy 0.1) of the dynamic formation control optimization model, the virtual train formation control strategy is calculated, clarifying the current formation operation to be executed (establish communication / formation / deformation) and the direction of state machine adjustment. State machine adjustment operations are then performed to adapt to the actual train operating conditions, generating the processed formation control strategy and communication status verification result elements.

[0068] Based on the above verification results, the model determines that train 96003 meets the state transition conditions of "establishing communication → grouping (CT→GP)": the planned matching degree meets the standard (0.4 points full marks), the communication stability meets the standard (0.3 points full marks), the trackside equipment feedback shows no abnormal occupation of the IIG track at station B (0.2 points full marks), and the positioning accuracy error is ≤3m (0.1 points full marks). The calculated control strategy is "execute the grouping operation with the preceding train 96001". At the same time, the state machine parameters are adjusted, and the trigger threshold of "CT→GP" is optimized to "the number of train-to-train communication interactions is ≥3 times". The generated processing result elements include "grouping control command: grouping with the preceding train (parameter value 2)" and "communication status: both train-to-ground and train-to-train communication are normal".

[0069] The processed train formation control strategy and communication status verification results are collaboratively optimized to ensure the rationality of the control strategy and the effectiveness of communication status monitoring. This optimization is performed from two dimensions: "control rationality" and "communication reliability." On one hand, the control strategy is verified to ensure it aligns with the actual train operating conditions (e.g., whether the formation operation matches the current station's operational type and whether the speed is suitable for the formation action). If strategy conflicts exist, dynamic adjustments are made (e.g., changing "immediate formation" to "formation after the preceding train is ready"). On the other hand, the communication status monitoring logic is optimized. For cases with slightly high communication latency, the data transmission cycle is adjusted (e.g., shortening it from 2 seconds to 1 second) to ensure that the communication status can support the execution of the control strategy in real time and prevent control failure due to communication problems.

[0070] The train formation control strategy for train 96003 was optimized: the original strategy was "immediately form a train with 96001", but the verification found that the preceding train 96001 was still completing the information synchronization with 96005, so it was adjusted to "wait 2 seconds before forming the train". At the same time, the train-to-train communication delay was monitored to increase from 0.3s to 0.45s, so the data transmission cycle was adjusted to 1.5s to ensure real-time synchronization of information between trains during the formation process. The optimized control strategy meets the requirements for rationality and communication monitoring effectiveness.

[0071] The integrated processing and optimization results generate dynamic control results and anomaly protection instructions for virtual train formation, encompassing a complete virtual formation control scheme and anomaly protection requirements. The control scheme clearly defines the specific operational instructions, execution timing, and associated train objects for the train. The anomaly protection instructions address potential risk scenarios (such as communication interruptions, scheduling conflicts, and trackside anomalies), specifying protective measures (such as formation interruption, emergency disassembly, and speed reduction protection) to ensure the safety and stability of the virtual formation process.

[0072] The integrated processing and optimization results generate dynamic control results and anomaly protection instructions for train virtual formation, which include a complete virtual formation control scheme and anomaly protection requirements. The dynamic control results clearly define the specific operation instructions, execution timing, and associated train objects of the train. For example: "Train 96003 is on track IIG at station B. After waiting for 2 seconds, it will perform a formation operation with the preceding train 96001. The order within the formation will remain at 2. After the formation is completed, it will run to station C as planned."

[0073] The anomaly protection instructions target four core anomaly scenarios, clearly defining the anomaly status identifiers and corresponding protection operations, as follows:

[0074] Unplanned communication / train formation establishment: The train has no train formation plan but reports "establishing train-to-train communication" (parameter value 1) or "train formation already established" (parameter value 2). The train is issued with the "train-to-train communication abnormal (0x1)" or "train formation abnormal (0x2)" flag, and the train is instructed to disconnect train-to-train communication or cancel train formation.

[0075] Incorrect train formation: If a train has a formation plan, but the train number corresponding to the reported preceding / following train ID is inconsistent with the plan, a corresponding abnormality flag will be issued, and the train will be instructed to interrupt the current train-to-train communication or de-form.

[0076] Abnormal occupation of the train compartment: After the train is formed according to the plan, if the trackside equipment detects that there is an unplanned train or abnormal occupation in the train compartment, it will immediately issue a "decoupling instruction (parameter value 3)" to instruct the train to perform emergency decoupling.

[0077] Train-to-ground communication interruption: If no train data is received for 6 consecutive seconds, the corresponding abnormality flag is sent to the train ahead and behind the train with communication failure, instructing them to interrupt train-to-train communication or decouple from the train with communication failure.

[0078] like Figure 2 As shown, a planned-driven radio block center virtual grouping control system includes:

[0079] Data acquisition module 201 is used by the radio block center to receive and acquire the train formation plan, train parameters and real-time operation data, trackside equipment and interlocking route information of the centralized dispatching system, so as to provide complete data input for subsequent virtual formation control;

[0080] The planning association and verification module 202 is used to associate the marshalling plan of the centralized dispatching system with the basic data of the line and station configured by the radio block center, establish a cross-source association mapping between the marshalling plan information and the line topology and station tracks, add an initial verification tag for the legality of the plan to each associated data, and generate target associated plan data.

[0081] The topology generation and caching module 203 is used to split the target associated plan data according to the structure of plan number-train information-station operation-verification result, introduce virtual grouping topology relationship generation logic to realize the unification of multi-source plan data features, and establish a priority execution caching mechanism for legal and executable grouping plans to generate structured virtual grouping control data.

[0082] The control command generation module 204 is used to generate targeted virtual train formation / deformation control commands and state transition instructions based on the plan-train-station association mapping in the structured virtual train formation control data and the real-time operation status of the train and the formation state machine logic, in response to the virtual train formation / deformation control requirements of different stations.

[0083] The state machine optimization module 205 is used to process the train formation execution state and vehicle-to-ground communication feedback information, optimize the state transition conditions and associated judgment weights of the virtual formation state machine, and generate a dynamic formation control optimization model.

[0084] The dynamic control and protection module 206 is used to process the real-time train operation data and the train formation plan information updated by the centralized dispatching system based on the dynamic formation control optimization model, and generate the dynamic control results and abnormal protection instructions for the virtual train formation by combining the train-to-ground communication and train-to-train communication status.

[0085] A computing device includes a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein when the computer program instructions are executed by the processor, the device is triggered to execute any planned radio block center virtual grouping control method.

[0086] The methods and / or embodiments in this application can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. When the computer program is executed by a processing unit, it performs the functions defined in the methods of this application.

[0087] It should be noted that the computer-readable medium described in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0088] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0089] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be regarded as exemplary and non-limiting in all respects, and the scope of this application is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within this application.

Claims

1. A plan-driven virtual grouping control method for radio block centers, characterized in that, include: The wireless block center receives and acquires train formation plans, train parameters and real-time operating data, trackside equipment and interlocking route information from the centralized dispatching system. The system performs correlation processing between the marshalling plan of the centralized dispatching system and the basic data of lines and stations configured by the radio block center, establishes a cross-source correlation mapping between marshalling plan information and line topology and station tracks, adds an initial verification tag for the legality of the plan to each piece of correlation data, and generates target correlation plan data. The target-related planning data is split based on the structure of plan number-train information-station operation-verification result. Virtual formation topology relationship generation logic is introduced to achieve unified features of multi-source planning data. At the same time, a priority execution caching mechanism is established for legal and executable formation plans to generate structured virtual formation control data. To meet the virtual train formation / deformation control requirements of different stations, based on the plan-train-station association mapping in the structured virtual formation control data, and combined with the real-time train operation status and formation state machine logic, targeted virtual formation control commands and state transition instructions are generated. The train formation execution status and vehicle-to-ground communication feedback information are processed to optimize the state transition conditions and associated judgment weights of the virtual formation state machine and generate a dynamic formation control optimization model. The dynamic train formation control optimization model processes real-time train operation data and train formation plan information updated by the centralized dispatching system. Combined with the train-to-ground communication and train-to-train communication status, it generates dynamic control results and anomaly protection instructions for virtual train formation.

2. The plan-driven virtual grouping control method for radio block centers according to claim 1, characterized in that, The system correlates the marshalling plans of the centralized dispatching system with the basic line and station data configured by the radio block center, establishing a cross-source correlation mapping between marshalling plan information and line topology and station tracks. An initial verification tag for plan validity is added to each piece of correlated data, generating target correlated plan data, including: The train operation control multi-dimensional collection of marshalling plan data is preprocessed to generate line and station feature data adapted to the virtual marshalling control of the radio block center. The marshalling plan data includes plan identification number, number of trains, train number, number of stations passed through, station number, marshalling order, track number, marshalling and demarcation plan instructions. The associated data are the line basic data and station basic data pre-configured by the radio block center. The adapted line and station feature data are analyzed in a targeted manner to generate a list of core calculation and control variables, including line topology mapping variables for virtual train formation control, station track matching variables, numbering determination variables for plan legality verification, train information verification variables, station operation compliance judgment variables, and cross-source data association identification variables. The adapted line and station feature data, core calculation and control variable list, original marshalling plan data and radio block center associated data are synchronously processed and accurately calculated. Multi-dimensional line and station data are matched in real time, and the mapping and matching results of marshalling plan information with line topology and station tracks are quickly calculated. Then, based on the initial verification rules of plan legality, legality judgment planning is carried out. Subsequently, based on the data analysis results of mapping and matching, the initial verification label for the legality of the plan is determined. Finally, combined with the basic control requirements of the virtual grouping of the radio block center, the target-related plan data is output.

3. The plan-driven virtual grouping control method for radio block centers according to claim 2, characterized in that, The target-related planning data is split based on the structure of plan number-train information-station operation-verification result. Virtual formation topology generation logic is introduced to unify the characteristics of multi-source planning data. Simultaneously, a priority execution caching mechanism is established for legally executable formation plans, generating structured virtual formation control data, including: Based on the structured characteristics of target-related plan data and the control requirements of virtual grouping, the data splitting module and the topology generation engine negotiate to determine the data splitting and feature unification strategy, and determine the data splitting dimensions and virtual grouping topology construction standards through multi-dimensional plan decomposition and topology relationship mapping. A two-stage processing mechanism of splitting and unifying is adopted to process the target-related plan data. First, the target-related plan data is split and analyzed in all dimensions according to the structure of plan number-train information-station operation-verification result. Then, based on the virtual grouping topology relationship generation logic, the split data is unified to achieve the multi-source plan data characteristics. The core requirements of accuracy and control efficiency of the associated virtual grouping are met, and standardized virtual grouping basic data is generated. The standardized virtual grouping basic data is screened for validity. The plan data that fails the verification is removed. The redundant data after feature unification is simplified. The legal and executable grouping plan is cached first. The preprocessed virtual grouping data scheme is generated, which includes data removal method, redundancy simplification strategy and priority caching mechanism. The preprocessed virtual train formation data scheme is integrated and optimized. Fine-tuning is completed by combining the feedback information of line topology, station tracks, train operation and formation / deformation operations. At the same time, a data correction feedback system is built based on the configuration data of the radio block center. By dynamically improving the topology relationship, supplementing the train and station association information, and combining the association weight of planning data and virtual train formation control, the data structure is optimized to generate structured virtual train formation control data that is adapted to the virtual train formation control of the radio block center.

4. The plan-driven virtual grouping control method for radio block centers according to claim 1, characterized in that, To address the virtual train formation / deformation control requirements of different stations, based on the plan-train-station association mapping in the structured virtual formation control data, and combined with the real-time train operation status and formation state machine logic, targeted virtual formation control commands and state transition instructions are generated, including: Based on the train marshalling and demarcation operation types of each station, the virtual train marshalling / demarshalling control requirements of each station within the control range of the radio block center are classified and sorted out, generating the corresponding control requirement types and associated train operation information for each station. Based on structured virtual train formation control data, the core matching dimensions of the plan-train-station association mapping are sorted out and extracted to obtain mapping relationships, train affiliation and station operation requirements information, and generate core association mapping information; By combining the real-time operation status of the train with the logic of the virtual train formation state machine, corresponding state transition judgment conditions are set for the core information of the association mapping of various types of control requirements to ensure that the triggered control commands match the actual operating conditions of the train. The station control demand classification results, associated mapping core information, and state transition judgment conditions are integrated and processed to generate control command information and state transition instruction information that meet the requirements of virtual grouping control, including demand type, mapping rules, and judgment criteria.

5. The plan-driven virtual grouping control method for radio block centers according to claim 1, characterized in that, The train formation execution status and vehicle-to-ground communication feedback information are processed to optimize the state transition conditions and correlation decision weights of the virtual formation state machine, generating a dynamic formation control optimization model, including: The train formation execution status and train-to-ground communication feedback information are classified and organized according to the preset virtual formation control rules, and the execution data and related feedback information corresponding to each status are generated. Based on optimization requirements, the arrangement and design of the state transition conditions and associated judgment weights of the virtual grouping state machine are carried out, and the adjustment criteria, judgment dimensions and weight assignment information are obtained to generate state machine optimization design information. Based on the train operation conditions and virtual formation control requirements, corresponding dynamic adaptation nodes are set for each type of optimized design information to ensure that the optimized state machine matches the actual train formation operation requirements. The classification and sorting results, state machine optimization design information, and dynamic adaptation nodes are processed to generate state machine adjustment information and dynamic grouping control optimization model that meet the requirements of virtual grouping control, including state classification information, optimization schemes, and adaptation standards.

6. The plan-driven virtual grouping control method for radio block centers according to claim 1, characterized in that, Based on the dynamic train formation control optimization model, the system processes real-time train operation data and train formation plan information updated by the centralized dispatching system. Combining the train-to-ground communication and train-to-train communication status, it generates dynamic control results and anomaly protection commands for virtual train formation, including: Based on the dynamic formation control optimization model and virtual formation control rules, targeted processing is performed on the real-time train operation data and the formation plan information updated by the centralized dispatching system. Core data such as train positioning, speed and formation status are extracted through parsing operations. The matching between train operation and plan is verified by combining the updated content of the centralized dispatching system, and the stability of the train-to-ground communication connection and the train-to-train communication interaction status are verified to complete the multi-source data fusion verification. Based on the state transition logic and weight configuration of the optimization model, the train virtual formation control strategy is calculated, the state machine adjustment operation is executed to adapt to the actual train operating conditions, and the processed formation control strategy and communication status verification result elements are generated. The processed grouping control strategy and communication status verification results are collaboratively optimized to ensure the rationality of the control strategy and the effectiveness of communication status monitoring. The integrated processing and optimization results generate dynamic control results and anomaly protection instructions for train virtual formation, which include a complete virtual formation control scheme and anomaly protection requirements.

7. A plan-driven radio block center virtual grouping control system, characterized in that, The system is used to execute executable instructions to perform the plan-driven radio block center virtual grouping control method according to any one of claims 1 to 6.

8. An electronic device, characterized in that, include: First processor; and memory for storing executable instructions of the first processor; The first processor is configured to execute the plan-driven radio block center virtual grouping control method according to any one of claims 1 to 6 by executing the executable instructions.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the second processor, it implements the plan-driven virtual grouping control method for radio block centers as described in any one of claims 1 to 6.

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