Train data processing method and system based on dual-computer synchronous data

Abstract

The invention discloses a train data processing method and system based on dual-computer synchronous data, and relates to the technical field of data processing. Transmission data corresponding to a host is determined according to a data transmission list, the host and a master-slave relation; and the data transmission system between the host and the slave is determined based on the transmission data corresponding to the host, the current state of the host and the data load of the slave, so that the accuracy of the data transmission system between the host and the slave is improved. According to the to-be-synchronized data combination, the data transmission process of the host and the data receiving process of the slave, dual-computer synchronization data is determined, and a corresponding data processing mode is triggered; the plurality of sub-data processing items are determined based on the identification of the data processing mode, and the online data processing event is determined according to the plurality of sub-data processing items, the master-slave relationship between the host and the slave and the working state of the train, so that the accuracy of the online data processing event is improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a data processing method and system for trains based on dual-machine synchronous data. Background Technology

[0002] With the development of technology, trains are gradually being applied to people's lives as a means of transportation. Trains can transport goods or people. During operation, trains have multiple operational data and have master and slave units. In existing technologies, multiple operational data of the train are collected, the working status of the train is determined based on these multiple operational data, and the set of data to be transmitted is controlled. However, some data in the set of data to be transmitted needs to be synchronized. Existing trains manage the data set of data to be transmitted through master and slave units, ignoring the data volume and corresponding combination priority of each sub-data combination. This affects the accuracy of the data transmission system between master and slave units, resulting in low accuracy of online data processing events. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art. This invention provides a data processing method and system for trains based on dual-machine synchronous data.

[0004] This invention provides a train data processing method based on dual-machine synchronization data, comprising: Collect multiple operational data from the train, determine the corresponding master and slave units based on the multiple operational data, the train's operational status and overall shape, and mark the master-slave relationship between the master and slave units; A set of data to be transmitted is collected, and multiple sub-data combinations are determined based on the detection of the set of data to be transmitted. A data transmission list is determined according to the data volume, corresponding data content and corresponding combination priority of each sub-data combination. The data transmission list not only includes sorting, but also specifies in detail the transmission order of each data combination, the specific data items included, the optimized transmission bit length of each data item, the total bit length and the relevant combination priority flags. The transmission data corresponding to the host is determined based on the data transmission list, the host and the master-slave relationship. The data transmission system between the host and the slave is determined based on the transmission data corresponding to the host, the current state of the host and the data load of the slave. The data transmission system includes final path selection, protocol parameter configuration, flow control and congestion control strategies, and packet fragmentation strategy that is dynamically adjusted according to the minimum MTU of the path. In this data transmission system, the data combination to be synchronized is determined based on the identification of the data transmission system. The dual-machine synchronization data is determined according to the data combination to be synchronized, the data transmission history of the host and the data reception history of the slave, and the corresponding data processing method is triggered. Based on the identification of this data processing method, multiple sub-data processing items are determined. Online data processing events are determined according to the multiple sub-data processing items, the master-slave relationship between the master and slave, and the working status of the train, so as to optimize the data transmission efficiency of the train.

[0005] This invention provides a train data processing system based on dual-machine synchronization data, which is applied to the aforementioned train data processing method based on dual-machine synchronization data.

[0006] Compared with the prior art, the beneficial effects of the present invention are: (1) Collect multiple working data of the train, determine the corresponding master and slave based on the multiple working data, the working status and overall shape of the train, and mark the master-slave relationship between the master and slave; collect the data set to be transmitted, determine multiple sub-data combinations based on the detection of the data set to be transmitted, and determine the data transmission list based on the data volume, corresponding data content and corresponding combination priority of each sub-data combination. The master-slave relationship between the master and slave is introduced, which takes into account the data volume, corresponding data content and corresponding combination priority of each sub-data combination, and improves the accuracy of the data transmission list.

[0007] (2) Based on the data transmission list, host and master-slave relationship, determine the transmission data corresponding to the host, determine the data transmission system between the host and slave based on the transmission data corresponding to the host, the current status of the host and the data load of the slave, further control the transmission data corresponding to the host, realize the overall consideration of the transmission data corresponding to the host, the current status of the host and the data load of the slave, and improve the accuracy of the data transmission system between the host and slave.

[0008] (3) In this data transmission system, the data combination to be synchronized is determined based on the identification of the data transmission system. The dual-machine synchronization data is determined according to the data combination to be synchronized, the data transmission history of the host and the data reception history of the slave, and the corresponding data processing mode is triggered. Multiple sub-data processing items are determined based on the identification of the data processing mode. Online data processing events are determined according to the multiple sub-data processing items, the master-slave relationship between the host and the slave and the working status of the train. The dual-machine synchronization data is further controlled by taking into full account the data combination to be synchronized, the data transmission history of the host and the data reception history of the slave, and the accuracy of online data processing events is improved, so as to optimize the data transmission efficiency of the train. Attached Figure Description

[0009] Figure 1 This is a flowchart illustrating the train data processing method based on dual-machine synchronous data in an embodiment of the present invention. Figure 2This is a flowchart illustrating step S11 of the train data processing method based on dual-machine synchronization data in an embodiment of the present invention. Figure 3 This is a flowchart illustrating step S12 in the train data processing method based on dual-machine synchronization data in an embodiment of the present invention. Figure 4 This is a flowchart illustrating step S13 in the train data processing method based on dual-machine synchronization data in an embodiment of the present invention. Figure 5 This is a flowchart illustrating step S14 of the train data processing method based on dual-machine synchronization data in an embodiment of the present invention. Figure 6 This is a flowchart illustrating step S15 of the train data processing method based on dual-machine synchronization data in an embodiment of the present invention. Figure 7 This is a schematic diagram of the structural composition of a train data processing system based on dual-machine synchronous data in an embodiment of the present invention. Detailed Implementation

[0010] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0011] Please see Figures 1 to 7 A train data processing method based on dual-machine synchronous data is applied to a data processing scenario. The train data processing method based on dual-machine synchronous data includes: Step S11: Collect multiple working data of the train, determine the corresponding master and slave based on the multiple working data, the working status and overall shape of the train, and mark the master-slave relationship between the master and slave; Step S12: Collect the set of data to be transmitted, determine multiple sub-data combinations based on the detection of the set of data to be transmitted, and determine the data transmission list according to the data volume, corresponding data content and corresponding combination priority of each sub-data combination; Step S13: Determine the transmission data corresponding to the host based on the data transmission list, the host and the master-slave relationship, and determine the data transmission system between the host and the slave based on the transmission data corresponding to the host, the current status of the host and the data load of the slave. Step S14: In this data transmission system, the data combination to be synchronized is determined based on the identification of the data transmission system. The dual-machine synchronization data is determined according to the data combination to be synchronized, the data transmission history of the host and the data reception history of the slave, and the corresponding data processing method is triggered. Step S15: Based on the identification of this data processing method, multiple sub-data processing items are determined. Online data processing events are determined according to the multiple sub-data processing items, the master-slave relationship between the master and slave, and the working status of the train, so as to optimize the data transmission efficiency of the train.

[0012] refer to Figure 2 In step S11, the specific steps are as follows: S111: The train is monitored in real time during operation and outputs multiple working data of the train. Multiple data combinations are constructed based on the multiple working data of the train. The working characteristics of the train are determined based on the identification of each data combination, and the working status of the train is marked. S112: Determine the corresponding working relationship based on the train's working status and multiple working characteristics. At the same time, collect the overall shape of the train, identify multiple data modules based on the identification of the overall shape of the train, determine the corresponding master and slave based on the working relationship between the multiple data modules and the train, and determine the corresponding master-slave relationship based on the matching of the master and slave in the data dimension, so as to mark the master-slave relationship between the master and slave.

[0013] In the embodiments of this application, the train is monitored in real time during operation and multiple working data of the train are output. Multiple data combinations are constructed based on the multiple working data of the train. The working characteristics of the train are determined based on the identification of each data combination, and the working status of the train is marked. This overall consideration of the identification of each data combination is compatible, ensuring the accuracy of the working characteristics of the train.

[0014] At this point, the distributed sensor network on the train is responsible for capturing analog or digital signals from the physical world. These raw signals are transmitted to the central data processing unit via the onboard network. The unit preprocesses the signals, including filtering to eliminate noise, calibrating to convert physical quantities into engineering units, and timestamping them, ultimately forming a structured working data stream. This process requires low latency and high reliability to ensure the real-time performance and validity of the data.

[0015] The system will preset or dynamically generate multiple data combination templates, each corresponding to a specific functional subsystem of the train. The construction process involves data aggregation, association and feature extraction. The system fills relevant data points into the corresponding data combination according to the timestamp and source of the data to form a data structure with specific semantics, thereby reducing the complexity of subsequent processing.

[0016] The system applies specific algorithms or rule engines to each data combination to identify the train's current operating characteristics in a specific functional dimension. This typically involves threshold judgment, state machine logic, or simple mathematical operations. Operating characteristics are a higher-level abstraction of the original data. The system takes all identified operating characteristics as input and uses a high-level decision logic (usually a priority-driven state machine) to comprehensively judge and ultimately determine one or more global operating states. This state is the highest-level description of the overall operation of the train. For example, the emergency braking characteristic has the highest priority. Once it occurs, it will override all other characteristics and directly mark the train's operating state as the emergency braking state.

[0017] Specifically, on the train, the speed sensor outputs pulse signals at a frequency of 100 times per second, which the central processing unit converts into speed values ​​in km / h; the traction inverter monitors and outputs the three-phase current values ​​and DC bus voltage values ​​of the traction motor in real time; the brake control unit collects the air pressure values ​​in the brake cylinders through pressure sensors; the onboard signaling system outputs the precise position coordinates of the current train and the status code of the forward signal; the door controller outputs the opening and closing status of each door; all these data are attached with precise system timestamps, forming a set of multi-dimensional, synchronous working data streams, for example: {timestamp: 1699123456.789, speed: 85.5, traction current: 315.2, brake pressure: 0.0, position: 10245.6, signal status: 'green light', door status: 'fully closed'}.

[0018] Based on the above working data, the system will construct the following data combinations: traction control data combination, including speed, acceleration, traction current, traction force and target speed; braking control data combination, including speed, deceleration, braking pressure and braking command; signal and safety data combination, including position, current speed, signal status and line speed limit; auxiliary system data combination, including car temperature, air conditioning set temperature, passenger compartment load rate and door status.

[0019] The system identifies the above data combination and derives the following operating characteristics: From the traction control data combination, the operating characteristic is identified as 'accelerating', due to traction current > 200A and acceleration > 0.5m / s². 2}; Identified from the brake control data combination: {Operating characteristic: 'Normal braking', reason: brake pressure between 50kPa and 300kPa}; Identified from the signal and safety data combination: {Operating characteristic: 'Approaching speed limit zone', reason: current speed > (road speed limit - 5km / h)}; Identified from the auxiliary system data combination: {Operating characteristic: 'Door open', reason: at least one of the door status variables is True}.

[0020] The decision-making logic integrates all operational characteristics: If the system simultaneously detects both {operational characteristic: 'door open'} and {operational characteristic: 'accelerating'}, according to the safety interlock rules, door opening conflicts with traction commands, and the safety state has higher priority. Therefore, the system ignores the acceleration characteristic and marks the train's operational state as the door safety interlock state, at which point the traction system will be suppressed; if there is no door open, but there is {operational characteristic: 'approaching speed limit zone'}, the operational state may be marked as the speed limit warning state; if everything is normal, the operational state is ultimately marked as the normal operation state.

[0021] Furthermore, based on the train's working status and multiple working characteristics, the corresponding working relationships are determined. At the same time, the overall shape of the train is collected, and multiple data modules are identified based on the recognition of the overall shape of the train. The corresponding master and slave are determined based on the working relationships of the multiple data modules and the train. The corresponding master-slave relationship is determined based on the matching of the master and slave in the data dimension, so as to mark the master-slave relationship between the master and slave. This overall consideration of matching the master and slave in the data dimension ensures the accuracy of the corresponding master-slave relationship.

[0022] At this point, a rule engine or state machine model is used to analyze the causal, temporal, or conditional dependencies between features and states. For example, an acceleration feature and a normal operating state together define the working relationship for traction control activation. This process is essentially a semantic description of the train's current operating intention. The working relationship defines which subsystems need to work together to achieve the current state. Meanwhile, the overall form of the train refers to the train's static, inherent physical and logical configuration information, which is usually stored in the train's configuration database. By parsing this configuration information, the system identifies which functionally independent data modules constitute the train. Each data module represents an independent functional domain or subsystem, with its own set of data interfaces and processing logic. This step is the foundation for establishing the system's hardware and software topology.

[0023] The system maps currently active working relationships to specific data modules and determines which module plays the dominant role (master) and which plays the follower or executor role (slave) in each active relationship. The decision is based on the module's functional attributes (such as control unit vs. execution unit), current health status, and computational load. A module may play different roles in different working relationships. The system analyzes the identified master-slave pairs, clarifies the data dimensions and content that need to be synchronized between them, including defining the synchronized data items, data format, synchronization frequency, and direction. The system creates a logical master-slave relationship marker for each master-slave pair and generates a corresponding data synchronization contract, which serves as the direct basis for subsequent data synchronization.

[0024] Specifically, assuming S111 outputs the working state as normal operation, and working characteristics such as {traction state: 'accelerating'}, {braking state: 'not activated'}, and {door state: 'fully closed'}; the system infers based on the preset rule base: if the state is normal operation and traction is accelerating, then the traction system master control relationship is established; if the doors are fully closed, then the door system locking relationship is established; the system determines the currently active working relationship set as: {'traction system master control', 'door system locked', 'signal system master control'}.

[0025] The system reads the train's configuration file and identifies the following data modules: onboard ATP / ATO module, traction control module (TCM), brake control module (BCM), door control module (DCM), auxiliary power module (APS), and train network management module (TCMS). These modules constitute the complete functional view of the train.

[0026] The system matches the working relationships with the data modules: for the traction system master control relationship, it matches the traction control module (TCM) and the auxiliary power supply module (APS), where the TCM is the master (issuing control commands) and the APS is the corresponding slave (responding to power demands); for the signal system master control relationship, it matches the on-board ATP / ATO module and the TCM, where the ATP / ATO module is the master (issuing speed commands) and the TCM is the slave (receiving and executing them); in this case, the TCM is both the master of the APS and the slave of the ATP / ATO.

[0027] The system establishes a specific master-slave relationship for the established master-slave pair: For the master ATP / ATO module and the slave TCM module, an ATO-TCM_Control_Link flag is created, specifying that the synchronization data items are target speed, target acceleration, etc., the data type is UShort or enumeration, the synchronization direction is unidirectional, and the frequency is 100ms / time; For the master TCM module and the slave APS module, a TCM-APS_Power_Link flag is created, specifying that the synchronization data item is traction power demand, the data type is ULong, the synchronization direction is unidirectional, and the frequency is 50ms / time.

[0028] refer to Figure 3 In step S12, the specific steps are as follows: S121: The database of the marked train is used to determine the corresponding data space based on the identification of the train's database. The data set to be transmitted is determined according to the data space, the host and the slave, and the dynamic detection of the data set to be transmitted is triggered. Multiple sub-data combinations are determined according to the dynamic detection of the data set to be transmitted. Each sub-data combination has different content emphasis in the data dimension. S122: In multiple sub-data combinations, the data volume of each sub-data combination is determined based on the identification of each sub-data combination, and the data content of each sub-data combination is marked. The first layer of data transmission content is determined according to the data volume and corresponding data content of each sub-data combination. S123 determines the corresponding combination priority based on the comparison of each sub-data combination, determines the second layer of data transmission content based on the combination priority of each sub-data combination and the data volume of each sub-data combination, and determines the data transmission list based on the first layer of data transmission content and the second layer of data transmission content.

[0029] In the embodiments of this application, a database of marked trains is identified, and a corresponding data space is determined based on the identification of the train's database. A set of data to be transmitted is determined based on the data space, the host and the slave, and dynamic detection of the set of data to be transmitted is triggered. Multiple sub-data combinations are determined based on the dynamic detection of the set of data to be transmitted. Each sub-data combination has different content emphasis in the data dimension, which is compatible with the overall consideration of dynamic detection of the set of data to be transmitted and ensures the accuracy of multiple sub-data combinations.

[0030] At this point, the system accesses and logically marks the train's central configuration database, which contains complete data definitions for all electronic control units. Based on the determined master-slave roles, the system filters out all data items related to these roles from the database, forming a temporary, logical data space. This space is a subset of data defined by the currently active master-slave relationship, which greatly reduces the complexity of subsequent processing.

[0031] Within the defined data space, the system needs to further determine which data truly needs to be transmitted during the current operating cycle. The system will combine the real-time operating status of the train to activate data items within the data space, for example, only certain data needs to be transmitted under specific modes. Once the set of data to be transmitted is determined, the system will immediately start a dynamic detection process to continuously monitor the attribute changes of data items within the set, such as numerical updates or status changes. This is a proactive, event-driven monitoring mechanism.

[0032] Meanwhile, based on preset rules or models, the system clusters data items in the data set to be transmitted. The clustering is based on the functional correlation, temporal coupling, or semantic relevance of the data. Each sub-data set has its unique content emphasis, that is, which dimension of the system it mainly describes in terms of function. This makes the purpose of data transmission clearer and facilitates rapid parsing and processing by the receiver.

[0033] Specifically, the master-slave relationship between ATO and TCM_Control_Link (ATO master, TCM slave) has been established; the system accesses the train's central data dictionary, which defines thousands of data points; based on this master-slave relationship, the system filters out all data points output by the ATO module and input by the TCM module; the data space is defined as a set containing data items such as {target speed, target acceleration, operating mode, emergency braking command}, while irrelevant data such as door status or air conditioning temperature are excluded.

[0034] The system detects that the current train operating status is ATO mode; therefore, in the defined data space, {target speed, target acceleration, operating mode} is activated, forming a data set to be transmitted; the emergency braking command is in standby mode; the system then starts monitoring the set and detects that the target speed value has been updated from 80km / h to 85km / h, and the operating mode value has changed from cruising to acceleration. These change events are captured by the detection process.

[0035] Based on dynamically detected data changes, the system performs cluster analysis: the system detects that three data items—target speed, target acceleration, and operating mode (value is acceleration)—have changed simultaneously, and they collectively describe the train's driving intention; therefore, the system aggregates these three data items into a sub-data combination A, whose content focuses on driving instructions and control strategies; in contrast, if an emergency braking command is activated, it may form a separate sub-data combination B, whose content focuses on the highest priority safety interruption.

[0036] Furthermore, in multiple sub-data combinations, the data volume of each sub-data combination is determined based on the identification of each sub-data combination, and the data content of each sub-data combination is marked. The first layer of data transmission content is determined according to the data volume and corresponding data content of each sub-data combination, which takes into account the overall consideration of the data volume and corresponding data content of each sub-data combination and ensures the accuracy of the first layer of data transmission content.

[0037] At this point, the system identifies and classifies all data items within the combination, applying a dynamic bit allocation principle: for enumerated types, it calculates the number of binary bits required for its maximum value; for Boolean types, it fixes it at 1 bit; for basic types, it queries its real-time or preset boundary values ​​to calculate the effective number of bits. The optimal transmission lengths of all data items within the combination are added together to obtain the total data volume of the combination. Simultaneously, based on the name, module, and functional relevance of the data items, the system generates one or more descriptive tags for each sub-data combination, such as function, level, real-time performance, and security. These tags are attached to the metadata of the data combination, making it self-explanatory and used for subsequent priority determination.

[0038] Based on the quantitative (data volume) and qualitative (data content) analysis of the first two steps, a preliminary transmission strategy based on the intrinsic attributes of the data is formed. Its sorting logic is mainly based on the importance of the content and the transmission efficiency. Combinations with higher real-time performance or security are given higher initial weights. When the content weights are similar, combinations with smaller data volumes are given priority due to less bandwidth usage. The first layer of data transmission content generated is an ordered list that describes the ideal transmission order of data combinations without considering the external system status.

[0039] Specifically, there are two sub-data combinations: Sub-data combination A (driving instructions) includes target speed, target acceleration, and operating mode; Sub-data combination B (energy status) includes traction power requirement and auxiliary power requirement. The system calculates: the target speed (maximum speed limit of 120 km / h) requires 7 bits; the target acceleration (design maximum 1.5 m / s²) requires 7 bits. 2 The accuracy is 0.1), so 4 bits are needed for the operation mode (4 enumeration values); 2 bits are needed for the operation mode; therefore, the total data size of sub-data combination A is 13 bits; the traction power requirement (maximum power 2MW) requires 21 bits; the auxiliary power requirement (maximum 150kW) requires 18 bits; therefore, the total data size of sub-data combination B is 39 bits.

[0040] The system labels the above two sub-data combinations with their contents: Sub-data combination A, which contains target speed, acceleration, and operating mode, is labeled as {Function:'Driving Control', Level:'Command', Real-time:'High', Security:'Critical'}; Sub-data combination B, which contains power requirements, is labeled as {Function:'Energy Management', Level:'Status', Real-time:'Medium', Security:'Non-Critical'}.

[0041] The system comprehensively analyzes the two sub-data combinations: Sub-data combination A has a small data volume (13 bits) and high real-time performance and security; Sub-data combination B has a large data volume (39 bits) and lower real-time performance and security. Based on the priority of content importance and combined with transmission efficiency considerations, the system determines the order of the first-level data transmission content as: Sub-data combination A > Sub-data combination B. This indicates that, from the perspective of the data's inherent attributes, the driving instruction combination should be transmitted first.

[0042] Therefore, the corresponding combination priority is determined based on the comparison of each sub-data combination. The second layer of data transmission content is determined according to the combination priority and data volume of each sub-data combination. The data transmission list is determined based on the first and second layer of data transmission content. This approach takes into account both the first and second layer of data transmission content, ensuring the accuracy of the data transmission list. At the same time, the master-slave relationship between the master and slave is introduced, taking into account the data volume, corresponding data content, and corresponding combination priority of each sub-data combination, thus improving the accuracy of the data transmission list.

[0043] At this point, the system will conduct a horizontal comparison of all sub-data combinations, evaluating dimensions including safety criticality, real-time requirements, functional importance, and correlation with current system events. The system typically uses a weighted scoring model or a rule-based decision tree to transform these multi-dimensional qualitative assessments into a quantitative combination priority, which represents the urgency and importance of the data combination's transmission at the current moment.

[0044] By combining business value (priority) with transmission cost (data volume), a final, comprehensive transmission prioritization strategy is formed. The prioritization logic is based primarily on the combination priority, sorting from high to low. When two or more data combinations have the same priority, the data volume is used as a secondary reference. Combinations with smaller data volumes will be transmitted first to achieve higher bandwidth utilization. This prioritization result constitutes the second layer of data transmission content, which is a transmission plan that best meets the current system operation requirements after comprehensive consideration.

[0045] The system compares the first level of content based on the inherent attributes of the data with the second level of content based on business value, and uses the priority of the second level of content as the basis for the final decision. Based on the final determined transmission order, the system generates a structured data transmission list. This data transmission list not only includes the sorting, but also specifies in detail the transmission order of each data combination, the specific data items included, the optimized transmission bit length of each data item, the total bit length, and the relevant combination priority flags.

[0046] Specifically, the system evaluates the driving command combination (A) and the energy status combination (B): Combination A directly controls the train speed, so its safety criticality, real-time requirements and functional importance are the highest, and it is strongly dependent on the current ATO mode. The system assigns it a combination priority of 1 (highest level); Combination B mainly affects energy efficiency, does not directly involve safety, and has medium real-time requirements. The system assigns it a combination priority of 3 (normal level).

[0047] The system combines priority and data volume for final sorting; the main sorting criterion is combination priority, with combination A (priority 1) being higher than combination B (priority 3); therefore, the sorting of the second layer of data transmission content is: sub-data combination A > sub-data combination B; in this case, the difference in data volume (A is 13 bits, B is 39 bits) confirms this sorting, but it is not the decisive factor.

[0048] The system integrates the two sets of data to generate the final data transmission list. Since the first sorting (A>B) and the second sorting (A>B) result are consistent, the decision is strengthened. The final list generated by the system is as follows: Transmission item 1: Sequence is 1, data combination is driving command combination, including target speed (7 bits), target acceleration (4 bits), and operating mode (2 bits), total length 13 bits, priority is 1; Transmission item 2: Sequence is 2, data combination is energy status combination, including traction power requirement (21 bits) and auxiliary power requirement (18 bits), total length 39 bits, priority is 3.

[0049] refer to Figure 4 In step S13, the specific steps are as follows: S131: Collect the host and master-slave relationship, determine the data control content of the host based on the active relationship, determine the transmission data corresponding to the host according to the data transmission list and the data control content of the host, and determine the current status of the host based on the host status detection, and determine the corresponding data transmission framework according to the current status of the host and the transmission data corresponding to the host. S132: Determine the data transmission route of the data transmission framework based on the detection of the data transmission framework; determine multiple data transmission nodes based on the identification of the data transmission route of the data transmission framework; and determine the data transmission system between the master and slave based on the node location, corresponding node shape, and slave data load of each data transmission node.

[0050] In the embodiments of this application, the host and master-slave relationship are collected, the data management content of the host is determined based on the active relationship, the transmission data corresponding to the host is determined according to the data transmission list and the data management content of the host, and the current state of the host is determined based on the host's state detection. The corresponding data transmission framework is determined according to the current state of the host and the transmission data corresponding to the host. This overall consideration of the current state of the host and the transmission data corresponding to the host ensures the accuracy of the corresponding data transmission framework.

[0051] At this point, the system identifies the host entity currently being operated on and its master-slave relationship, and queries the configuration database based on this relationship to determine the logical data scope of the host, i.e., the data control content. The system performs an intersection operation between the global data transmission list and the host's own data control content, and filters out data items that are both in the global transmission list and within the host's control scope, forming the transmission data that the host actually needs to execute, ensuring clear responsibilities for data transmission.

[0052] The system uses a built-in monitoring agent to sample key performance indicators of the host in real time, including computing resources (CPU, memory), network resources (queue length, bandwidth utilization) and system health (error count, hardware status). The system quantifies and weights these multi-dimensional raw detection values ​​to form one or a set of current status descriptions that can comprehensively reflect the current operating status of the host. These can be discrete status labels (such as: health, overload) or continuous numerical scores.

[0053] The system has a built-in rule base that maps the host's current state to different transmission strategies. For example, a healthy state matches the real-time optimal strategy, while an overloaded state matches the degradation survival strategy. Based on the matched strategy, the system generates a data transmission framework that defines the basic parameters of transmission behavior, such as the packet encapsulation format, transmission cycle, packet size, and reliability mechanism.

[0054] Specifically, the system identifies the current host as the ATO module, with a master-slave relationship of ATO-TCM_Control_Link. Based on this relationship, the system determines that the data control content of the ATO module is {target speed, target acceleration, operating mode, emergency braking command}. This is compared with the data transmission list generated by S12. The driving command combination is completely within the control range, while the energy status combination is not. Therefore, the system ultimately determines that the transmission data corresponding to the ATO module is the specific data item in the driving command combination.

[0055] The system performs a status check on the ATO module, and the results show: CPU utilization 35%, memory usage 50%, average network transmission queue length 2, and error count 0 in the last 5 minutes. Based on the above indicators, the system determines the current status of the ATO module as: {Status label: 'Healthy', Overall score: 95 / 100}, indicating that it has sufficient resources to perform high-frequency data transmission tasks.

[0056] The system matches the real-time optimal strategy based on the health status of the ATO module and the high-priority driving command data to be transmitted. Based on this strategy, the system determines the data transmission framework as follows: it uses a compact bit stream format for encapsulation; the transmission cycle is once every 100 milliseconds; the data packet size is strictly packed according to the minimum bit length (13 bits) calculated by S122; the acknowledgment and retransmission (ARQ) mechanism is enabled to ensure reliability; and a low-priority MVB bus is activated as a backup channel.

[0057] Furthermore, the data transmission route of the data transmission framework is determined based on the detection of the data transmission framework; multiple data transmission nodes are identified based on the identification of the data transmission route of the data transmission framework; and the data transmission system between the master and slave is determined based on the node location, corresponding node shape, and slave data load of each data transmission node. This comprehensive consideration of the node location, corresponding node shape, and slave data load ensures the accuracy of the data transmission system between the master and slave. At the same time, the transmission data corresponding to the master is further controlled, realizing the comprehensive consideration of the transmission data corresponding to the master, the current state of the master, and the slave data load, thus improving the accuracy of the data transmission system between the master and slave.

[0058] At this point, the system parses the data transmission framework and extracts key network parameters such as communication protocols, QoS requirements, expected latency, and bandwidth. The system accesses the configuration database that stores the complete network topology of the train, and performs path search in the topology map based on the framework's protocols and QoS requirements to find one or more physical links that meet the transmission requirements and take into account the real-time load conditions.

[0059] After determining the transmission route, the system needs to identify all logical and physical entities involved in data processing along the path. The system traverses the determined data transmission route, defines each link as a data transmission node, and classifies them as the data initiator (source node), the intermediate device responsible for forwarding (relay node), and the final data receiver (destination node).

[0060] The system comprehensively analyzes the physical distribution (affecting latency), technical specifications (such as port rate and whether hardware QoS is supported) of all nodes, as well as the real-time receiving load and processing capacity of the slave devices. Based on the above analysis, the system finally determines a comprehensive and dynamic data transmission system, which includes final path selection, protocol parameter configuration, flow control and congestion control strategies, and packet fragmentation strategies that are dynamically adjusted according to the minimum MTU of the path.

[0061] Specifically, the system analyzes the data transmission framework generated by the ATO-TCM link, extracts the protocol as Train Ethernet (ETB), sets QoS as the highest priority, and requires extremely high reliability. The system queries the train's network topology map, plans a gigabit Ethernet main path from the ATO module of car Mc1 to the TCM module of car M1, and determines a low-bandwidth path via the MVB bus as a hot backup, ultimately forming a redundant architecture.

[0062] The system identifies four data transmission nodes along the main path: the source node is the ATO module located in the Mc1 vehicle; relay node 1 is the Ethernet switch in the Mc1 vehicle; relay node 2 is the Ethernet switch in the M1 vehicle; and the target node is the TCM module located in the M1 vehicle.

[0063] The system performs a comprehensive analysis of all nodes: both switches have gigabit ports and support hardware QoS, and the distance between nodes is compliant; at the same time, the data load of the TCM module is only 15%, indicating sufficient receiving capacity; based on this, the system generates the final data transmission architecture: the primary path is activated for data transmission, while heartbeat packets are sent through the backup path to monitor availability; the VLAN ID of the data stream is set to 10, and the DSCP priority is set to the highest value; due to the low load on the slave devices, flow control is not enabled to ensure the fastest speed; since the data packets are much smaller than the Ethernet MTU, fragmentation is not required.

[0064] refer to Figure 5 In step S14, the specific steps are as follows: S141: Monitor the data transmission system in real time, dynamically identify the data transmission system, determine the data already transmitted by the host during the dynamic identification process, determine multiple data to be synchronized based on the data already transmitted by the host, the data load of the slave and the corresponding data control content, and determine the combination of data to be synchronized based on the multiple data to be synchronized and the corresponding data content. S142: Determine the data transmission history of the host based on the host identification, determine the data transmission history of the slave based on the slave identification, and determine the dual-machine synchronization data based on the data combination to be synchronized, the data transmission history of the host, and the data reception history of the slave. S143: Based on the identification of dual-machine synchronous data, multiple data synchronization projects are determined. According to the project content, corresponding project execution order and data transmission system of each data synchronization project, the corresponding data processing method is determined to trigger the corresponding data processing method.

[0065] In the embodiments of this application, the data transmission system is monitored in real time, dynamically identified, and the data already transmitted by the host is determined during the dynamic identification process. Based on the data already transmitted by the host, the data load of the slave, and the corresponding data management content, multiple data to be synchronized are determined. Based on the multiple data to be synchronized and the corresponding data content, a combination of data to be synchronized is determined, which takes into account the overall consideration of the multiple data to be synchronized and the corresponding data content, and ensures the accuracy of the combination of data to be synchronized.

[0066] At this time, the system continuously collects performance indicators and events related to data transmission through probes and agents deployed on the host, slave, and intermediate network nodes. The system performs real-time analysis and correlation of the massive amount of monitoring data collected, and dynamically evaluates and identifies the health status of the transmission system based on preset rules or state machine models. By analyzing the host's sending records and the transmission logs of the network protocol stack, the system can accurately reconstruct the set of all data items that the host has attempted to send in one or more recent synchronization cycles.

[0067] The system employs a weighted decision model to calculate a synchronization risk score for each transmitted data item. The risk score calculation takes into account whether the data item was sent during a period of high load on the slave device, its importance level in the data management content, and whether there is clear negative feedback (such as not receiving an ACK). The system filters out all data items whose risk scores exceed a preset threshold, forming multiple data lists to be synchronized.

[0068] The scattered data items to be synchronized are reorganized into structured units with business meaning and transmission efficiency; the system analyzes the inherent relationships between the data items to be synchronized, such as whether they belong to the same control command or describe the same device state; based on the correlation analysis, the system re-aggregates the data items to be synchronized into one or more data combinations to be synchronized, and assigns a temporary, high-priority tag to each newly formed combination, such as a state recovery combination, so that it can be given priority in subsequent steps.

[0069] Specifically, in the train, the system monitors the ATO-TCM data transmission system and detects that the ATO module has sent two data frames in the past 100 milliseconds. Through dynamic identification, the system confirms that these two frames correspond to the updates of the target speed and the operating mode, respectively. At the same time, it detects that the TCM module's receive queue depth jumps to the maximum value instantly and triggers a buffer overflow interrupt. Based on the host's transmission record, the system determines that the host has transmitted the following data: {Target speed: 85km / h, Operating mode: Cruise}.

[0070] The system makes decisions based on the following information: the transmitted data is {target speed, operating mode}; the CPU utilization of the slave TCM module spikes to 95% when the data arrives, causing the receive buffer to overflow; according to the business contract, the operating mode is key control data, and the target speed is key status data; the decision-making process considers that both of these data items are sent when the slave is overloaded and are of high importance, with an extremely high synchronization risk score; the system determines the multiple data items to be synchronized as: {target speed, operating mode}.

[0071] The system reassembles the data to be synchronized: Analyzing {target speed, operating mode}, it is found that they jointly describe the complete driving instructions from ATO to TCM and have a strong logical correlation; therefore, the system aggregates these two data items into a new data combination to be synchronized; since the purpose of this combination is to correct the slave's state and restore it to consistency with the master, the system marks it as the driving instruction state recovery combination and assigns it the highest synchronization priority.

[0072] Furthermore, the data transmission history of the host is determined based on the identification of the host, and the data transmission history of the slave is determined based on the identification of the slave. The dual-machine synchronization data is determined based on the data combination to be synchronized, the data transmission history of the host, and the data reception history of the slave. This approach takes into account the overall consideration of the data combination to be synchronized, the data transmission history of the host, and the data reception history of the slave, thus ensuring the accuracy of the dual-machine synchronization data.

[0073] At this point, the system collects data from multiple levels, including application layer logs, transport / network layer logs, and network interface driver statistics, and integrates this raw data into a structured event stream ordered by time. Each event includes at least a timestamp, data item ID, data content, sending status, sequence number, and ACK status, providing factual basis for subsequent synchronization decisions.

[0074] The system collects data from the slave device's network interface driver statistics, protocol stack logs, and application layer processing logs, and integrates them into a structured data reception process. Each event includes a timestamp, packet sequence number, reception status, whether it is delivered to the application layer, and the final processing result, which are used to verify whether the data has been successfully received and processed by the slave device.

[0075] The system compares the transmission history of the master and the reception history of the slave, and accurately locates which data packets were sent by the master but not received / processed by the slave based on sequence number and timestamp. According to the comparison results and the importance of the combination to be synchronized, the system selects the most suitable strategy from simple retransmission, state compensation or state query and differential synchronization. Based on the selected strategy, the system generates dual-machine synchronization data containing data content, synchronization strategy and encapsulation format, forming a complete execution plan.

[0076] Specifically, the system constructs a data transmission history for the ATO host, recording transmission events related to {target speed, operating mode}: Event 1 (10:30:05.000) shows that the data packet (sequence number 500) was successfully submitted to the network stack; Event 2 (10:30:05.100) shows that the packet did not receive an ACK and triggered a retransmission; Event 3 (10:30:05.150) shows that the retransmitted packet (sequence number 501) was successfully submitted; Event 4 (10:30:05.250) shows that the packet with sequence number 501 still did not receive an ACK and was marked as suspected loss. This history indicates that the host has attempted to send twice but has not received an acknowledgment.

[0077] The system establishes a data reception history for the TCM slave device: Event 1 (10:30:05.050) shows that the previous data packet (sequence number 499) was successfully processed; Event 2 (10:30:05.250) records that the system status is a receive buffer overflow, and subsequent data packets are discarded by the driver layer; Event 3 (10:30:05.300) records that the system status returns to normal. This history reveals a key issue: within the time window when the master sends critical data packets, the slave device experiences a buffer overflow, causing the data packets to be directly discarded by the underlying layer.

[0078] The system makes the following decision: Through process comparison, it is confirmed that the data packets with sequence numbers 500 and 501 were completely lost during the slave buffer overflow. Since the lost data is a strongly related control command, simple retransmission may not be sufficient to restore the slave state. Therefore, the system selects a state compensation strategy. The final determined dual-machine synchronization data is a newly constructed, high-priority state compensation data packet, which contains {target speed: 85km / h, operating mode: cruise}, adopts a compact format with special identifiers, and is marked as needing to be sent immediately and await confirmation in order to force a refresh of the slave state.

[0079] Therefore, multiple data synchronization projects are identified based on the recognition of dual-machine synchronous data. The corresponding data processing method is determined according to the project content, corresponding project execution order and data transmission system of each data synchronization project to trigger the corresponding data processing method. This method takes into account the overall consideration of the project content, corresponding project execution order and data transmission system of each data synchronization project, and ensures the accuracy of the corresponding data processing method.

[0080] At this point, the system identifies the attributes of the synchronized data and decomposes all the operations required to complete the task according to its type (such as retransmission packets and compensation packets). Each decomposed operation is defined as an independent data synchronization project. Typical projects include data encapsulation projects, data sending projects, timer start projects, status query projects, log recording projects, and alarm triggering projects.

[0081] The system specifies the concrete parameters (such as data values ​​and protocol versions) for each project and defines a strict execution order based on the logical dependencies between projects. At the same time, the system extracts relevant parameters from the data transmission architecture determined in S13, such as network interface, VLAN ID, and QoS priority. Combining the above three points, the system generates a clear data processing method for each project that includes all execution instructions and parameters.

[0082] The system's task scheduler or state machine calls the functions or services corresponding to the data processing methods of each project in sequence according to the determined execution order. For projects that can be executed in parallel, the system will use concurrent or asynchronous mechanisms to improve efficiency. The execution result (success, failure, timeout) of each data processing method will serve as feedback, which may trigger other subsequent synchronous projects or change the running state of the system, forming a closed-loop control.

[0083] Specifically, in S142, the system decides to send a state compensation data packet; based on this, the system determines the following data synchronization items: Item A, construct the state compensation data packet; Item B, send the data packet through a high-priority queue; Item C, start a 50-millisecond ACK waiting timer; Item D, if the timer expires, record the synchronization failure event; Item E, if more than 3 consecutive failures occur, trigger a serious fault alarm.

[0084] The system determines the specific data processing methods for the above projects: For project A, the processing method is to call the data packaging library function, pass in parameters such as target speed and operating mode, and generate a 12-bit compact data stream; For project B, the processing method is to call the Ethernet driver sending function after project A is completed, and send the data packet using the VLAN ID and QoS priority configured in S13; For project C, the processing method is to start a 50-millisecond timer in parallel with project B, and bind a timeout callback function.

[0085] The system triggers data processing sequentially: the data packaging function is called, successfully generating a compensation data stream; then, the Ethernet driver sending function is called, the data packet is sent, and a 50-millisecond timer begins counting down; the system enters a waiting state, listening for ACKs from the slave device or timer timeout events; if an ACK is received, synchronization is successful; if the timer times out, the processing method of item D will be triggered, logging will be recorded, and the next round of retransmission or alarm procedures may be initiated.

[0086] refer to Figure 6 In step S15, the specific steps are as follows: S151: Collect the data processing method, determine the corresponding data processing path based on the identification of the data processing method, determine multiple data processing nodes based on the detection of the data processing path, determine the corresponding sub-data processing items based on the identification of each data processing node, and collect multiple sub-data processing items. S152: Collect the master-slave relationship between the master and slave devices, determine the online data processing framework based on multiple sub-data processing projects and the master-slave relationship between the master and slave devices, and present the data processing process in the online data processing framework; S153: Based on the data processing history and the train's operating status, determine the online data processing event, and based on the identification of the online data processing event, determine the corresponding data optimization event to optimize the train's data transmission efficiency.

[0087] In the embodiments of this application, the data processing method is collected, the corresponding data processing path is determined based on the identification of the data processing method, multiple data processing nodes are determined based on the detection of the data processing path, and the corresponding sub-data processing items are determined based on the identification of each data processing node, so as to collect multiple sub-data processing items, which takes into account the overall consideration of the identification of each data processing node and ensures the accuracy of the corresponding sub-data processing items.

[0088] At this point, the system captures and records all the data processing methods that are ultimately triggered; through a built-in knowledge base or rule engine, it identifies the underlying implementations corresponding to these high-level methods and connects the relevant modules to build a complete data processing path from the data source to the final physical output.

[0089] Each functional entity or software module along the path is concretized into a monitorable and analyzable node. The system traverses the data processing path determined in the previous step, analyzes the function and implementation method of each link in the path, and defines each independent processing unit (which can be a software module, system service, hardware driver, or specific hardware peripheral) as a data processing node to discretize the continuous process and facilitate more refined performance analysis. The system conducts in-depth analysis of the internal functions of each data processing node, identifies a series of continuous operations that must be performed to complete its function, and defines each such operation as a sub-data processing project. The system collects performance data such as execution time and call frequency of these sub-processing projects in real time through performance analysis tools or hardware counters.

[0090] Specifically, the system collected the key method triggered in S14: sending state compensation data packets through a high-priority queue; the system's knowledge base identified that this method involves the ATO application layer, the operating system network protocol stack, the network card driver, and the network hardware interface; based on this, the system determined the data processing path corresponding to this data processing method to be: ATO application layer > operating system network protocol stack > network card driver > network hardware interface.

[0091] The system detected the above path and identified six data processing nodes: Node 1 is the ATO control task (application layer); Node 2 is the Socket interface layer; Node 3 is the TCP / IP protocol processing module; Node 4 is the flow control module (the above three belong to the protocol stack); Node 5 is the e1000e network card driver; and Node 6 is the Intel I210 Gigabit network card (hardware).

[0092] The system decomposes and collects data for some key nodes: For node 1 (ATO control task), the sub-project is identified as calling the data packaging API, and its execution time is collected as 10 microseconds; For node 3 (TCP / IP protocol processing module), the sub-projects are identified as allocating memory buffer, constructing Ethernet frame header, and performing CRC check calculation, and their execution times are collected as 5, 2, and 15 microseconds respectively; For node 5 (network card driver), the sub-projects are identified as mapping DMA address, filling transmit descriptor, and writing to control register, and their execution times are collected as 3, 1, and 0.5 microseconds respectively.

[0093] Furthermore, the master-slave relationship between the host and slave is collected. Based on multiple sub-data processing projects and the master-slave relationship between the host and slave, an online data processing framework is determined. The online data processing framework presents the data processing process and takes into account the overall consideration of multiple sub-data processing projects and the master-slave relationship between the host and slave, ensuring the accuracy of the online data processing framework.

[0094] At this point, the system directly obtains the master-slave relationship corresponding to the current data transmission from the configuration. This relationship is not only a simple pairing, but also contains rich semantics such as relationship type (such as control, status reporting), business domain, and service level agreement (SLA). The collected master-slave relationship will be used as a metadata tag and attached to the data processing framework to be built next, ensuring that all analysis can return to the essence of the business.

[0095] The system uses all collected sub-data processing items as basic building blocks, with the master-slave relationship as the starting and ending point, and places all sub-items on a unified time axis. Based on the dependencies of data flow and control flow, these items are connected with directed edges to form a complete online data processing framework that spans the master and slave, which is essentially a directed acyclic graph (DAG). During the construction process, master-slave relationship information is injected into the framework, such as marking key delay threshold lines on the time axis according to SLA requirements.

[0096] The static structure is transformed into a dynamic and analyzable processing flow. The completed framework fully presents the entire process of data generation from the host application layer to consumption by the slave application layer, and integrates multi-dimensional information such as execution time, resource consumption, queue depth and error codes of each sub-project. This processing flow is usually visualized in the form of timeline charts, Gantt charts or flame charts. Analysts can click on any node to view its detailed parameters and trace upstream and downstream dependencies.

[0097] Specifically, in the train, the system performed a compensation data synchronization from the ATO module to the TCM module; the system queried the configuration and obtained the master-slave relationship on which this synchronization operation was based: ATO-TCM_Control_Link; this relationship was marked as a control relationship, belonging to the traction control domain, and its SLA requirement was a delay of less than 10ms.

[0098] The system constructs an online data processing framework based on the ATO-TCM_Control_Link relationship and the sub-projects of S151. The system aggregates all sub-projects on the host side (such as calling the packaged API, CRC calculation, DMA writing) and sub-projects on the slave side (such as network card receive interrupt, driver parsing, application layer update), and places them precisely on the timeline according to timestamps, connecting them with arrows to show the data flow. The framework is named the ATO-TCM_Control_Link online data processing framework, and a red delay threshold line is drawn at the 10ms position.

[0099] The system presents the data processing history in the form of a timeline graph. Users can clearly see that the CRC calculation sub-item takes 15 microseconds, which is the most time-consuming operation on the host side. The timeline graph also shows that after the data packet arrives at the TCM network card, the driver parsing item is delayed by 50 milliseconds before it begins to execute. Clicking on this area pops up the message: TCM receive buffer overflow, data packet processing is delayed. The total duration line of the timeline graph shows that the total time for this synchronization is 65 milliseconds, which clearly exceeds the 10ms red threshold line, intuitively indicating that this synchronization did not meet the SLA requirements.

[0100] Therefore, online data processing events are determined based on the data processing history and the train's operating status. Corresponding data optimization events are then identified based on the recognition of these online data processing events to optimize the train's data transmission efficiency. This approach incorporates overall considerations for identifying online data processing events, ensuring the accuracy of the corresponding data optimization events. Furthermore, it fully considers the data combination to be synchronized, the master's data transmission history, and the slave's data reception history, further controlling the dual-machine synchronization data and improving the accuracy of online data processing events to optimize the train's data transmission efficiency.

[0101] At this point, the system performs multi-dimensional analysis on each sub-project in the data processing framework, including threshold detection, trend analysis, and anomaly pattern recognition. Simultaneously, the system correlates the analyzed performance anomalies with the train's current operating status to distinguish between performance fluctuations under normal operating conditions and genuine performance problems. When a performance anomaly is confirmed to be not reasonably explainable by the current operating status, the system generates a structured online data processing event that includes the event type, occurrence time, affected nodes, and severity.

[0102] The system maintains an event-optimization strategy mapping knowledge base. When a data processing event is identified, the system queries this knowledge base to find a matching optimization strategy. Based on the mapping result, the system generates a data optimization event that includes the optimization type, target node, optimization parameters, and expected effect. The system transforms the optimization event into specific control instructions and sends them to the target data processing node through the management channel. After receiving the instructions, the node applies the new configuration or algorithm to automatically complete the optimization, forming a continuous optimization closed loop of monitoring, analysis, decision-making, and execution.

[0103] Specifically, the system analyzed the processing history of ATO-TCM_Control_Link and found that the average time for the CRC calculation sub-item had continuously increased from the usual 5 microseconds to 15 microseconds, exceeding the 10 microsecond threshold. The system checked the current train's operating status and found it to be operating normally, ruling out the possibility that this was a normal operating condition. Therefore, the system identified an online data processing event: {Event Type: 'Computational Performance Degradation', Occurrence Time: 'Current Time', Affected Node: 'TCP / IP Protocol Processing Module', Related Indicator: 'Average CRC Calculation Time', Severity: 'Medium'}.

[0104] The system optimizes based on the events identified in the previous step: After querying the knowledge base, the system checks for the CRC calculation performance degradation event and finds that the ATO module's CPU supports the hardware CRC acceleration instruction set. Therefore, the system identifies a data optimization event: {Optimization type: 'Enable hardware CRC offloading', Target node: 'ATO module's TCP / IP protocol stack', Optimization parameters: 'Switch the CRC check algorithm from software implementation to hardware implementation', Expected effect: 'CRC calculation time reduced by more than 80%'}. The system generates a kernel module parameter configuration instruction and sends it to the ATO module's operating system through a secure management channel. After receiving the instruction, the operating system dynamically loads the new configuration, enabling the protocol stack to automatically call the hardware CRC instruction in subsequent processing, thereby optimizing data transmission efficiency.

[0105] Please see Figure 7 , Figure 7 This is a schematic diagram of the structural composition of a train data processing system based on dual-machine synchronous data in an embodiment of the present invention; the train data processing system based on dual-machine synchronous data includes: The marking module 21 is used to collect multiple working data of the train, determine the corresponding master and slave based on the multiple working data, the working status and overall shape of the train, and mark the master-slave relationship between the master and slave. The data transmission list module 22 is used to collect the data set to be transmitted, determine multiple sub-data combinations based on the detection of the data set to be transmitted, and determine the data transmission list according to the data volume, corresponding data content and corresponding combination priority of each sub-data combination. The data transmission system module 23 is used to determine the transmission data corresponding to the host based on the data transmission list, the host and the master-slave relationship, and to determine the data transmission system between the host and the slave based on the transmission data corresponding to the host, the current status of the host and the data load of the slave. The dual-machine synchronization data module 24 is used to determine the data combination to be synchronized based on the identification of the data transmission system, determine the dual-machine synchronization data according to the data combination to be synchronized, the data transmission history of the master and the data reception history of the slave, and trigger the corresponding data processing mode. The online data processing event module 25 is used to identify multiple sub-data processing items based on the identification of the data processing method, and to determine online data processing events according to the multiple sub-data processing items, the master-slave relationship between the master and slave, and the working status of the train, so as to optimize the data transmission efficiency of the train.

[0106] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A data processing method for trains based on dual-machine synchronous data, characterized in that, include: Collect multiple operational data from the train, determine the corresponding master and slave units based on the multiple operational data, the train's operational status and overall shape, and mark the master-slave relationship between the master and slave units; A set of data to be transmitted is collected, and multiple sub-data combinations are determined based on the detection of the set of data to be transmitted. A data transmission list is determined according to the data volume, corresponding data content and corresponding combination priority of each sub-data combination. The data transmission list not only includes sorting, but also specifies in detail the transmission order of each data combination, the specific data items included, the optimized transmission bit length of each data item, the total bit length and the relevant combination priority flags. Based on the data transmission list, the host and the master-slave relationship, determine the transmission data corresponding to the host, and determine the data transmission system between the host and the slave based on the transmission data corresponding to the host, the current status of the host and the data load of the slave. The data transmission system includes final path selection, protocol parameter configuration, flow control and congestion control strategies, and packet fragmentation strategies that are dynamically adjusted based on the path's minimum MTU. In this data transmission system, the data combination to be synchronized is determined based on the identification of the data transmission system. The dual-machine synchronization data is determined according to the data combination to be synchronized, the data transmission history of the host and the data reception history of the slave, and the corresponding data processing method is triggered. Based on the identification of this data processing method, multiple sub-data processing items are determined. Online data processing events are determined according to the multiple sub-data processing items, the master-slave relationship between the master and slave, and the working status of the train, so as to optimize the data transmission efficiency of the train.

2. The train data processing method based on dual-machine synchronous data according to claim 1, characterized in that, The system collects multiple operational data points from the train, determines the corresponding master and slave units based on these data points, the train's operational status, and overall configuration, and marks the master-slave relationship between them, including: The train is monitored in real time during operation and outputs multiple operational data. Multiple data combinations are constructed based on these data, and the operational characteristics of the train are determined by identifying each data combination, and the operational status of the train is marked. The corresponding working relationships are determined based on the train's working status and multiple working characteristics. At the same time, the overall shape of the train is collected, and multiple data modules are determined based on the recognition of the overall shape of the train. The corresponding master and slave are determined based on the working relationships of multiple data modules and the train. The master-slave relationship is determined based on the matching of the master and slave in the data dimension, so as to mark the master-slave relationship between the master and slave.

3. The train data processing method based on dual-machine synchronous data according to claim 1, characterized in that, The collected data set to be transmitted is used to determine multiple sub-data combinations based on the detection of the data set. A data transmission list is then determined according to the data volume, corresponding data content, and corresponding combination priority of each sub-data combination, including: The database of the marked train is used to determine the corresponding data space based on the identification of the train's database. The set of data to be transmitted is determined according to the data space, the host and the slave, and the dynamic detection of the set of data to be transmitted is triggered. Multiple sub-data combinations are determined according to the dynamic detection of the set of data to be transmitted, and each sub-data combination has different content emphasis in the data dimension. In multiple sub-data combinations, the data volume of each sub-data combination is determined based on the identification of each sub-data combination, and the data content of each sub-data combination is marked. The first layer of data transmission content is determined according to the data volume and corresponding data content of each sub-data combination. The corresponding combination priority is determined by comparing each sub-data combination. The second layer of data transmission content is determined based on the combination priority and the data volume of each sub-data combination. The data transmission list is determined based on the first layer of data transmission content and the second layer of data transmission content.

4. The train data processing method based on dual-machine synchronous data according to claim 1, characterized in that, The step of determining the transmission data corresponding to the host based on the data transmission list, the host, and the master-slave relationship, and determining the data transmission system between the host and slave based on the transmission data corresponding to the host, the current state of the host, and the data load of the slave, includes: The system collects the host and master-slave relationship data, determines the host's data management content based on this relationship, determines the corresponding transmission data of the host based on the data transmission list and the host's data management content, determines the host's current state based on the host's status detection, and determines the corresponding data transmission framework based on the host's current state and the corresponding transmission data of the host.

5. The train data processing method based on dual-machine synchronous data according to claim 4, characterized in that, The step of determining the transmission data corresponding to the host based on the data transmission list, the host, and the master-slave relationship, and determining the data transmission system between the host and the slave based on the transmission data corresponding to the host, the current state of the host, and the data load of the slave, further includes: The data transmission route of the data transmission framework is determined based on the detection of the data transmission framework; multiple data transmission nodes are determined based on the identification of the data transmission route of the data transmission framework; and the data transmission system between the master and slave is determined based on the node location, corresponding node shape and slave data load of each data transmission node.

6. The train data processing method based on dual-machine synchronous data according to claim 1, characterized in that, In this data transmission system, the data combination to be synchronized is determined based on the identification of the data transmission system. The dual-machine synchronization data is determined according to the data combination to be synchronized, the data transmission history of the host, and the data reception history of the slave, and the corresponding data processing method is triggered, including: The data transmission system is monitored in real time, dynamically identified, and the data already transmitted by the host is determined during the dynamic identification process. Based on the data already transmitted by the host, the data load of the slave, and the corresponding data management content, multiple data to be synchronized are determined. Based on the multiple data to be synchronized and their corresponding data content, the combination of data to be synchronized is determined.

7. The train data processing method based on dual-machine synchronous data according to claim 6, characterized in that, In this data transmission system, the data combination to be synchronized is determined based on the identification of the data transmission system. The dual-machine synchronization data is determined according to the data combination to be synchronized, the data transmission history of the host, and the data reception history of the slave, and the corresponding data processing method is triggered. The system also includes: The master's data transmission history is determined based on the master's identification, and the slave's data transmission history is determined based on the slave's identification. The dual-machine synchronization data is determined based on the data combination to be synchronized, the master's data transmission history, and the slave's data reception history. Based on the identification of dual-machine synchronous data, multiple data synchronization projects are determined. According to the project content, corresponding project execution order and data transmission system of each data synchronization project, the corresponding data processing method is determined to trigger the corresponding data processing method.

8. The train data processing method based on dual-machine synchronous data according to claim 1, characterized in that, The identification based on this data processing method determines multiple sub-data processing items, and online data processing events are determined according to these sub-data processing items, the master-slave relationship between the master and slave, and the train's operating status, in order to optimize the train's data transmission efficiency, including: The data processing method is collected, and the corresponding data processing path is determined based on the identification of the data processing method. Multiple data processing nodes are determined based on the detection of the data processing path, and the corresponding sub-data processing items are determined based on the identification of each data processing node, so as to collect multiple sub-data processing items.

9. The train data processing method based on dual-machine synchronous data according to claim 8, characterized in that, The method of identifying multiple sub-data processing items based on this data processing method, and determining online data processing events based on these sub-data processing items, the master-slave relationship between the master and slave devices, and the train's operating status to optimize train data transmission efficiency, further includes: The master-slave relationship between the master and slave is collected. Based on multiple sub-data processing projects and the master-slave relationship between the master and slave, an online data processing framework is determined. The online data processing framework presents the data processing process. Based on the data processing history and the train's operating status, online data processing events are identified. Based on the identification of these online data processing events, corresponding data optimization events are determined to optimize the train's data transmission efficiency.

10. A train data processing system based on dual-machine synchronous data, characterized in that, The train data processing system based on dual-machine synchronization data is applied to the train data processing method based on dual-machine synchronization data as described in any one of claims 1-9.

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