A MVB communication fault diagnosis system based on MVB online monitoring

Abstract

The present invention discloses an MVB communication fault diagnosis system based on MVB online monitoring, which relates to the technical field of communication network fault diagnosis. The MVB communication fault diagnosis system includes an isolation partition module, a data acquisition module, a relationship analysis module, a fault diagnosis analysis module, a fault location module, a fault alarm recording module and a fault handling suggestion module, wherein the modules are connected by electrical signals; the isolation partition module is used to divide the MVB network into multiple logical areas. The present invention monitors the data flow, signal quality and communication status on the vehicle bus, instantly discovers and locates communication faults, and feeds back the communication status in real time. Once an abnormality is detected, an alarm is immediately issued, and a fault diagnosis process is automatically started, thereby effectively preventing safety accidents caused by communication faults, ensuring the accuracy of fault diagnosis, reducing false alarms and missed alarms, and providing maintenance personnel with reliable fault information, which is convenient for rapid measures to be taken.

Description

Technical Field

[0001] The invention relates to the technical field of communication network fault diagnosis, and in particular to an MVB communication fault diagnosis system based on MVB online monitoring. Background Art

[0002] The multi-function vehicle bus (MVB) is responsible for data exchange between internal equipment in the vehicle and is an important part of the train communication network. The train communication network includes two-level buses: the wire train bus (WTB) and the multi-function vehicle bus (MVB). With the development of the rail transit industry, more and more train network systems have adopted the train communication network standard. As one of the standard media of the train communication network, the MVB network has achieved considerable market development. As the control systems and equipment inside the train become more and more complex, various devices and systems transmit and control data through the MVB bus. In order to ensure the safe, stable and efficient operation of the train, the health status of the MVB communication system must be monitored and diagnosed in real time.

[0003] In the prior art, since the MVB communication system involves multiple devices, nodes and connection links, when multiple subsystems fail at the same time, it is difficult to accurately locate the specific fault source, and it is easy to make misjudgments or missed judgments. For this reason, an MVB communication fault diagnosis system based on MVB online monitoring is proposed. By isolating the nodes of each subsystem and analyzing the relationships between different subsystems, the faulty nodes are located to solve the above problems. Summary of the invention

[0004] The present invention aims to provide an MVB communication fault diagnosis system based on MVB online monitoring to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] An MVB communication fault diagnosis system based on MVB online monitoring, the MVB communication fault diagnosis system comprises an isolation partition module, a data acquisition module, a relationship analysis module, a fault diagnosis analysis module, a fault location module, a fault alarm recording module and a fault handling suggestion module, wherein the modules are connected by electrical signals;

[0007] The isolation partition module is used to divide the MVB network into multiple logical areas to isolate each subsystem so as to independently analyze the operating status of each subsystem, which helps to reduce mutual interference between subsystems and improve the accuracy of fault location;

[0008] The data acquisition module is used to collect node data from each subsystem in the MVB network, including the communication signals and status information of each subsystem, and pre-process the collected node data to ensure the integrity and real-time nature of the data, providing a reliable data basis for subsequent fault analysis;

[0009] The relationship analysis module is used to analyze the mutual relationships between different subsystems, including communication protocols, data flows and dependencies, and to construct a relationship map between the subsystems;

[0010] The fault diagnosis and analysis module analyzes abnormal patterns in the node data based on the preprocessed node data and the relationship graph between the subsystems, determines whether a fault exists, and gives the fault type;

[0011] The fault alarm recording module, after determining the fault node, triggers the alarm mechanism, issues a fault alarm to the operator, and records the fault information, including the fault type, occurrence time, fault node, etc., to provide a basis for subsequent fault analysis and processing;

[0012] The fault handling suggestion module provides fault handling suggestions based on the fault location results and fault type, including fault repair steps, required tools or spare parts, preventive measures, etc. By providing specific handling suggestions, it helps operators quickly resolve faults and restore normal operation of the system.

[0013] A further improvement of the technical solution of the present invention is that the isolation partition module specifically includes:

[0014] According to the functional requirements, subsystem distribution and fault isolation requirements of the train, the overall topology of the MVB network is analyzed to identify all devices, nodes and their connection relationships;

[0015] Preset configuration files or user inputs define the boundaries and parameters of each subsystem, and divide the MVB network into multiple logical areas based on the network topology analysis results and actual application requirements. Each logical area contains a group of related devices and nodes.

[0016] The isolation partition module receives data frames from the MVB network, identifies the target address and type thereof, and determines a forwarding processing decision based on the target address and type of the data frame.

[0017] A further improvement of the technical solution of the present invention is that the specific process of determining the forwarding processing decision includes:

[0018] The isolation partition module parses the data frames from the MVB network and extracts the data frame structure including the target address, source address, frame type (control frame, data frame, etc.) and priority, where the target address points to a specific subsystem, device or functional module;

[0019] Match the target address with the predefined logical area mapping table to determine whether the data frame belongs to a specific logical area. Based on the matching result and preset rules, output a forwarding processing decision to determine whether the data frame is forwarded to the target subsystem or isolated.

[0020] If the target address and type of the data frame meet the preset forwarding rules, the isolation partition module forwards the data frame to the corresponding target subsystem or the next logical area;

[0021] If the destination address or type of the data frame does not conform to the preset forwarding rules, or there is an abnormal situation such as the data frame is damaged or the destination address is invalid, the forwarding processing decision of the data frame isolation is executed to block it in the current logical area to prevent it from entering other subsystems, and record the isolation event, which helps to reduce mutual interference between subsystems and improve the stability and security of the system.

[0022] A further improvement of the technical solution of the present invention is that the data acquisition module specifically includes:

[0023] The data acquisition module is initialized after power-on, and the parameters of the communication rate and node address are configured. The connection parameters between the data acquisition module and the MVB network are set according to the preset configuration file or user input to ensure that the module can correctly access the network and identify each subsystem;

[0024] For each logical area, an independent data collection task is started. The data collection module identifies each node in the MVB network, including the communication devices and sensors of each subsystem, and periodically collects the node data of each subsystem, including communication signals and status information;

[0025] The collected raw node data is temporarily stored in a buffer for subsequent processing, and then the node data is preprocessed, including data integrity check, denoising, filtering and standardization operations to improve data quality;

[0026] Build a data warehouse and store the preprocessed node data in the data warehouse for subsequent analysis and processing.

[0027] A further improvement of the technical solution of the present invention is that the relationship analysis module specifically includes:

[0028] Initialize the parameters and settings of the relationship analysis module according to the system configuration file or user input, and obtain the node data of each subsystem from the data acquisition module, importing basic information including subsystem name, function description, interface definition, node address and communication protocol;

[0029] Analyze the communication protocols used between each subsystem, analyze the format, message structure and verification method of the communication protocol, clarify the data exchange process, and trace the flow of data by analyzing the communication protocols and data flow information between each subsystem, and determine the starting subsystem, intermediate links and end subsystem;

[0030] Identify direct dependencies between subsystems and infer indirect dependencies between subsystems by analyzing data flows and communication protocols;

[0031] Based on the analyzed communication protocols, data flows and dependencies, a relationship map is generated, and the locations of each subsystem, their relationships and the direction of data flow are displayed in the map. At the same time, the generated relationship map is optimized, including adjusting the layout, adding annotations and highlighting key nodes, to ensure that the map is clear and easy to understand for users to understand and analyze. The final relationship map is stored in the database and updated regularly to reflect changes in the system.

[0032] A further improvement of the technical solution of the present invention is that the fault diagnosis and analysis module specifically includes:

[0033] Import the preprocessed node data into the fault diagnosis and analysis module, and use the relationship map between the subsystems to clarify the connection relationship and communication path between the nodes;

[0034] According to the scope and path of the fault impact, locate the relevant nodes and connection lines in the relationship map, analyze the flow path of data between nodes, and determine the fault propagation path;

[0035] Perform feature extraction on the node data to extract the features of the communication signal and the state information, wherein the features of the communication signal are signal amplitude, signal phase and signal-to-noise ratio, and the features of the state information are temperature, current, voltage and response time;

[0036] Based on the fault management requirements of the MVB network, the abnormal thresholds of the communication signal characteristics and the status information characteristics are preset in combination with historical data to distinguish the abnormal characteristics in the communication signal characteristics and the status information characteristics. Among them, the abnormal communication signal characteristics are abnormal signal amplitude, signal phase distortion and signal-to-noise ratio reduction, and the abnormal status information characteristics are abnormal temperature, current fluctuation, voltage fluctuation and abnormal response time;

[0037] According to the collected real-time communication signal and status information data, each feature is compared with its abnormal threshold to obtain the abnormal identification value of each feature;

[0038] The abnormal identification value of the communication signal characteristics is obtained comprehensively, the signal abnormal identification index is calculated, and the abnormal trend of the communication signal is analyzed; the abnormal identification value of the state information characteristics is obtained comprehensively, the state abnormal identification index is calculated, and the abnormal trend of the state information is analyzed;

[0039] Combined with the signal anomaly identification index and the state anomaly identification index, the fault identification coefficient is calculated, the abnormal pattern in the node data is analyzed, and the specific fault type is determined based on the abnormal pattern identification result and the relationship map. The fault types include hardware failure, software failure and communication failure.

[0040] A further improvement of the technical solution of the present invention is that the calculation formula of the signal anomaly identification index is as follows:

[0041] ;

[0042] In the formula, is a signal anomaly identification indicator. is the current signal amplitude value, is the abnormal threshold of signal amplitude, is the current signal phase value, is the abnormal threshold of the signal phase, and are constants for adjusting the sensitivity of signal amplitude and signal phase, respectively. They take positive values ​​and are used to control the steepness of the exponential function. is the current signal-to-noise ratio value, is the abnormal threshold of the signal-to-noise ratio, The value range is between 0 and 1;

[0043] The calculation formula of the abnormal state identification index is as follows:

[0044] ;

[0045] In the formula, Identify indicators for abnormal status. is the current temperature value, is the abnormal temperature threshold, is the maximum allowable temperature, is the current value, is the abnormal threshold of current, is a constant for adjusting the current sensitivity, which takes a positive value and is used to control the steepness of the exponential function. is the current voltage value, is the voltage abnormality threshold, is the maximum allowable value of voltage, is the current response time value, is the abnormal threshold of response time, A constant that adjusts the sensitivity of the response time. It takes a positive value to control the steepness of the exponential function. The value range is between 0 and 1;

[0046] The calculation formula of the fault identification coefficient is as follows:

[0047] ;

[0048] In the formula, is the fault identification coefficient, is a signal anomaly identification indicator. Identify indicators for abnormal status. A constant for adjusting the sensitivity of the abnormal state identification indicator. It takes a positive value to control the steepness of the exponential function. The value range is between 0 and 1. When all features are within the normal range, Close to 0.

[0049] A further improvement of the technical solution of the present invention is that the fault type determination process includes:

[0050] The fault identification coefficient distinction threshold K is set according to historical data to distinguish between normal and abnormal situations. When the value of the fault identification coefficient is less than K, it indicates that an abnormal situation exists;

[0051] According to the size of the fault identification coefficient and the distinction threshold, a comparison is made to identify nodes with a higher degree of abnormality, and then the characteristics of the identified abnormal nodes are deeply analyzed to determine whether there is an abnormal pattern;

[0052] For abnormal patterns, further analyze the signal anomaly identification index and the state anomaly identification index to determine the specific abnormal characteristics, find the location of the abnormal node in the relationship map, and mark it;

[0053] Based on the connection relationship between the abnormal node and other nodes, the propagation path and impact range of the abnormal pattern in the MVB network are analyzed, and the specific fault type is determined according to the abnormal pattern, fault propagation path and node characteristics.

[0054] A further improvement of the technical solution of the present invention is that the fault alarm recording module specifically includes:

[0055] Through fault identification coefficient analysis, abnormal pattern detection and relationship graph analysis, the node where the fault occurs is determined, the specific location of the fault is clarified, and an accurate target is provided for subsequent alarm and processing;

[0056] When the fault node is determined, the fault alarm recording module automatically triggers the alarm mechanism, including sound and light alarm, SMS notification and email reminder. Then, through the preset alarm method, an alarm containing fault information is sent to the operator, so that the operator can quickly understand the fault situation and make the correct response decision;

[0057] When a fault alarm is triggered, the fault information is automatically recorded, including detailed information on the fault type, occurrence time, fault node and fault description, and a detailed fault report is generated based on the recorded fault information for subsequent fault analysis and processing. After the fault is processed, the operator will feed back the processing results to the fault alarm recording module, which will automatically update the fault status and record the processing results and time.

[0058] A further improvement of the technical solution of the present invention is that the fault handling suggestion module specifically includes:

[0059] According to the determined fault type, a corresponding fault handling suggestion is matched from a fault handling knowledge base containing various known fault handling suggestions;

[0060] Generate specific fault handling suggestions based on the matched related fault items, including fault repair steps, required tools or spare parts and preventive measures, and provide the generated handling suggestions to the operator, which lists in detail the steps required to repair the fault, including the necessary operation sequence and precautions, points out the specific tools, spare parts or test equipment required to complete the repair work, and provides suggestions to prevent similar faults from happening again;

[0061] Tracking the progress of troubleshooting allows operators to record the completion of each step, any problems encountered, and additional measures taken. After troubleshooting, operators provide feedback on the results and newly discovered information to update the troubleshooting knowledge base.

[0062] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0063] 1. The present invention provides an MVB communication fault diagnosis system based on MVB online monitoring. By monitoring the data flow, signal quality and communication status on the vehicle bus, the communication fault can be immediately discovered and located, and the communication status can be fed back in real time. Once an abnormality is detected, an alarm is immediately issued and a fault diagnosis process is automatically started, thereby effectively preventing safety accidents caused by communication failures, ensuring the accuracy of fault diagnosis, reducing false alarms and missed alarms, and providing maintenance personnel with reliable fault information, so as to facilitate rapid measures.

[0064] 2. The present invention provides an MVB communication fault diagnosis system based on MVB online monitoring, which accurately locates the fault node and its impact range through relationship graph analysis, greatly improves the accuracy and efficiency of fault diagnosis, and reduces the possibility of false alarms and missed alarms. By combining the fault identification coefficient, abnormal pattern detection and relationship graph analysis, the specific fault type and location can be accurately determined, providing a clear goal for subsequent processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0066] Figure 1 It is a schematic diagram of the functional modules of the system of the present invention;

[0067] Figure 2 Schematic diagram of the working process of the fault diagnosis and analysis module of the present invention. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] Embodiment 1, as Figure 1 As shown, the present invention provides an MVB communication fault diagnosis system based on MVB online monitoring, the MVB communication fault diagnosis system includes an isolation partition module, a data acquisition module, a relationship analysis module, a fault diagnosis analysis module, a fault location module, a fault alarm recording module and a fault processing suggestion module, wherein the electrical signals between the modules are connected;

[0070] The isolation partition module is used to divide the MVB network into multiple logical areas to isolate each subsystem so that the operating status of each subsystem can be analyzed independently, which helps to reduce mutual interference between subsystems and improve the accuracy of fault location. According to the functional requirements of the train, the distribution of subsystems and the requirements of fault isolation, the overall topology of the MVB network is analyzed to identify all devices, nodes and their connection relationships. Among them, all devices in the network are identified, including the central control unit, various sensors, actuators, displays, door controllers, braking systems, etc. The unique node address of each device on the MVB network is identified. The node is a master node (such as CCU, responsible for management and coordination of network communications) or a slave node (such as sensors and actuators, responding to the command of the master node), and the physical and Logical connection: In the bus topology, all devices are connected through a shared communication line. In the star topology, each device is connected to the central switch through a separate line. The preset configuration file or user input defines the boundaries and parameters of each subsystem. According to the network topology analysis results and the actual application requirements, the MVB network is divided into multiple logical areas. Each logical area contains a group of related devices and nodes to achieve logical isolation of subsystems, reduce the mutual influence between different subsystems, and facilitate independent monitoring and analysis. Among them, the subsystems include power control, braking system, door control, lighting system, passenger information system, etc. The isolation partition module receives data frames from the MVB network, identifies their target address and type, and determines the forwarding processing decision based on the target address and type of the data frame;

[0071] Furthermore, the specific process of determining the forwarding processing decision includes:

[0072] The isolation partition module parses the data frames from the MVB network, extracts the data frame structure including the target address, source address, frame type (control frame, data frame, etc.) and priority, where the target address points to a specific subsystem, device or functional module, matches the target address with the predefined logical area mapping table, determines whether the data frame belongs to a specific logical area, outputs a forwarding processing decision based on the matching result and preset rules, and determines whether the data frame is forwarded to the target subsystem or isolated. If the target address and type of the data frame meet the preset forwarding rules, the isolation partition module forwards the data frame to the corresponding target subsystem or the next logical area. If the target address or type of the data frame does not meet the preset forwarding rules, or there is an abnormal situation such as the data frame is damaged or the target address is invalid, the forwarding processing decision of the data frame isolation is executed to block it in the current logical area to prevent it from entering other subsystems, and record the isolation event, which helps to reduce mutual interference between subsystems and improve the stability and security of the system.

[0073] The data acquisition module is used to collect node data from each subsystem in the MVB network, including the communication signals and status information of each subsystem, and pre-process the collected node data to ensure the integrity and real-time nature of the data, providing a reliable data basis for subsequent fault analysis. The data acquisition module is initialized after power-on, configures the communication rate and node address parameters, and sets the connection parameters between the data acquisition module and the MVB network according to the preset configuration file or user input to ensure that the module can correctly access the network and identify each subsystem. For each logical area, an independent data acquisition task is started. The data acquisition module identifies each node in the MVB network, including the communication equipment and sensors of each subsystem, and periodically collects the data of each subsystem. The node data, including communication signals and status information, is temporarily stored in the buffer for subsequent processing, and then the node data is preprocessed, including data integrity check, denoising, filtering and standardization operations to improve data quality. Among them, an integrity check is performed to ensure that the data is not lost or damaged. If the data is found to be incomplete, try to re-collect or record error logs, filter and clean the collected data, remove invalid data, noise data and abnormal data, and unify the data format to ensure that all collected data have a unified timestamp, so that data from different sources can be accurately aligned in subsequent analysis. A data warehouse is built and the preprocessed node data is stored in the data warehouse for subsequent analysis and processing;

[0074] The relationship analysis module is used to analyze the relationships between different subsystems, including communication protocols, data flows and dependencies, and to build a relationship map between subsystems. According to the system configuration file or user input, the relationship analysis module parameters and settings are initialized, and the node data of each subsystem is obtained from the data acquisition module. The basic information including subsystem name, function description, interface definition, node address and communication protocol is imported to analyze the communication protocol used between each subsystem, parse the format, message structure and verification method of the communication protocol, clarify the data exchange process, and track the flow of data by analyzing the communication protocol and data flow information between each subsystem, determine the starting subsystem, intermediate links and end subsystem, and identify the direct dependency between subsystems. , and infer the indirect dependency between subsystems by analyzing the data flow and communication protocol. If subsystem A directly calls the interface or function of subsystem B, then the two subsystems are determined to have a direct dependency relationship. If subsystem A communicates with subsystem C through subsystem B, then it is determined that there is an indirect dependency relationship between A and C. Based on the analyzed communication protocol, data flow and dependency relationship, a relationship map is generated, and the location, relationship and data flow direction of each subsystem are displayed in the map. At the same time, the generated relationship map is optimized, including adjusting the layout, adding annotations and highlighting key nodes, etc., to ensure that the map is clear and easy to understand, and is convenient for users to understand and analyze. The final relationship map is stored in the database and updated regularly to reflect changes in the system.

[0075] The fault diagnosis and analysis module analyzes the abnormal patterns in the node data based on the preprocessed node data and the relationship graph between each subsystem, determines whether there is a fault, and gives the fault type;

[0076] The fault alarm recording module triggers the alarm mechanism after determining the fault node, issues a fault alarm to the operator, and records the fault information, including the fault type, occurrence time, fault node, etc., to provide a basis for subsequent fault analysis and processing;

[0077] The fault handling suggestion module provides fault handling suggestions based on the fault location results and fault type, including fault repair steps, required tools or spare parts, preventive measures, etc. By providing specific handling suggestions, it helps operators quickly resolve faults and restore normal operation of the system.

[0078] Embodiment 2, as Figure 2 As shown, based on Example 1, the present invention provides a technical solution: preferably, the fault diagnosis and analysis module specifically includes:

[0079] The preprocessed node data is imported into the fault diagnosis and analysis module, and the relationship map between the subsystems is used to clarify the connection relationship and communication path between the nodes. According to the scope and path of the fault impact, the relevant nodes and connection lines are located in the relationship map, the data flow path between the nodes is analyzed, the fault propagation path is determined, and the node data is feature extracted to extract the characteristics of the communication signal and status information. Among them, the characteristics of the communication signal are signal amplitude, signal phase and signal-to-noise ratio, and the characteristics of the status information are temperature, current, voltage and response time. Based on the fault management needs of the MVB network, the abnormal thresholds of the communication signal characteristics and the status information characteristics are preset in combination with historical data to distinguish the abnormal characteristics in the communication signal characteristics and the status information characteristics. Among them, the abnormal communication signal characteristics are abnormal signal amplitude, Signal phase distortion and signal-to-noise ratio decrease. The abnormal status information features are temperature anomaly, current fluctuation, voltage fluctuation and abnormal response time. According to the collected real-time communication signal and status information data, compare each feature with its abnormal threshold, obtain the abnormal identification value of each feature, synthesize the abnormal identification value of the communication signal feature, calculate the signal abnormal identification index, and analyze the abnormal trend of the communication signal; synthesize the abnormal identification value of the status information feature, calculate the status abnormal identification index, analyze the abnormal trend of the status information, combine the signal abnormal identification index and the status abnormal identification index, calculate the fault identification coefficient, analyze the abnormal pattern in the node data, and judge the specific fault type according to the abnormal pattern identification result and the relationship map, where the fault types include hardware fault, software fault and communication fault;

[0080] Furthermore, the calculation formula of the signal anomaly identification index is as follows:

[0081] ;

[0082] In the formula, is a signal anomaly identification indicator. is the current signal amplitude value, is the abnormal threshold of signal amplitude, is the current signal phase value, is the abnormal threshold of the signal phase, and are constants for adjusting the sensitivity of signal amplitude and signal phase, respectively. They take positive values ​​and are used to control the steepness of the exponential function. is the current signal-to-noise ratio value, is the abnormal threshold of the signal-to-noise ratio, The value range is between 0 and 1. When all features are within the normal range, Close to 0, when one or some features exceed their abnormal threshold, Close to 1, if ,but Close to 0, if ,but Close to 1, as As the value increases, the value of this part gradually increases, and the rate of change is Control, if ,but Close to 0, if ,but Close to 1, as As the value increases, the value of this part gradually increases, and the rate of change is Control, if ,but Close to 0, if ,but Close to 1, as As it increases, the value of this part gradually increases;

[0083] The calculation formula of the status abnormality identification index is as follows:

[0084] ;

[0085] In the formula, Identify indicators for abnormal status. is the current temperature value, is the abnormal temperature threshold, is the maximum allowable temperature, is the current value, is the abnormal threshold of current, is a constant for adjusting the current sensitivity, which takes a positive value and is used to control the steepness of the exponential function. is the current voltage value, is the voltage abnormality threshold, is the maximum allowable value of voltage, is the current response time value, is the abnormal threshold of response time, A constant that adjusts the sensitivity of the response time. It takes a positive value to control the steepness of the exponential function. The value range is between 0 and 1. When all features are within the normal range, Close to 0, when one or some features exceed their abnormal threshold, Close to 1, if ,but Close to 0, if ,but along with Increases gradually, and the rate of change is controlled by the square root function. If ,but Close to 0, if ,but Close to 1, as As the value increases, the value of this part gradually increases, and the rate of change is Control, if ,but Close to 0, if ,but along with The increase gradually increases, and the rate of change is controlled by a linear function. ,but Close to 0, if ,but Close to 1, as As the value increases, the value of this part gradually increases, and the rate of change is control;

[0086] The calculation formula of the fault identification coefficient is as follows:

[0087] ;

[0088] In the formula, is the fault identification coefficient, is a signal anomaly identification indicator. Identify indicators for abnormal status. A constant for adjusting the sensitivity of the abnormal state identification indicator. It takes a positive value to control the steepness of the exponential function. The value range is between 0 and 1. When all features are within the normal range, Close to 0, if is close to 0, then Close to 0, if is close to 1, then along with Increases gradually, and the rate of change is controlled by the square root function. If ,but Close to 0, if ,but Close to 1, as As the value increases, the value of this part gradually increases, and the rate of change is control;

[0089] Furthermore, the fault type determination process includes:

[0090] According to historical data, the fault identification coefficient distinction threshold K is set to distinguish between normal and abnormal situations. When the value of the fault identification coefficient is less than K, it indicates that an abnormal situation exists. According to the size of the fault identification coefficient and the distinction threshold, a comparison is made to identify nodes with a higher degree of abnormality, and then the characteristics of the identified abnormal nodes are deeply analyzed to determine whether there is an abnormal pattern. For the abnormal pattern, the signal abnormality identification index and the state abnormality identification index are further analyzed to determine the specific abnormal characteristics, and the position of the abnormal node is found in the relationship map and marked. Based on the connection relationship between the abnormal node and other nodes, the propagation path and impact range of the abnormal pattern in the MVB network are analyzed, and the specific fault type is determined according to the abnormal pattern, the fault propagation path and the node characteristics. Among them, hardware failures are manifested as damage or performance degradation of physical components, such as aging of circuit boards, poor interface contact, etc. In the relationship map, hardware failures are related to specific hardware nodes. Software failures are manifested as program errors, improper configurations or virus infections, etc. In the relationship map, software failures are related to software nodes or related configuration nodes. Communication failures are manifested as signal interference, communication protocol errors or network failures, etc. In the relationship map, communication failures are usually related to communication nodes or network nodes.

[0091] The fault alarm recording module specifically includes:

[0092] By means of fault identification coefficient analysis, abnormal pattern detection and relationship map analysis, the node where the fault occurs is determined, the specific location of the fault is clarified, and an accurate target is provided for subsequent alarm and processing. When the fault node is determined, the fault alarm recording module automatically triggers the alarm mechanism, including multiple methods such as sound and light alarm, SMS notification and email reminder, and then sends an alarm containing fault information to the operator through the preset alarm method, so that the operator can quickly understand the fault situation and make correct response decisions. At the same time as the fault alarm, the fault information is automatically recorded, including detailed information on the fault type, occurrence time, fault node and fault description, and a detailed fault report is generated based on the recorded fault information for subsequent fault analysis and processing. After the fault processing is completed, the operator will feedback the processing results to the fault alarm recording module, and the fault alarm recording module will automatically update the fault status and record the processing results and processing time;

[0093] The troubleshooting suggestion module specifically includes:

[0094] According to the determined fault type, the corresponding fault handling suggestion is matched from the fault handling knowledge base containing various known fault handling suggestions. According to the matched related fault entries, specific fault handling suggestions are generated, including fault repair steps, required tools or spare parts and preventive measures. The generated handling suggestions are provided to the operator, wherein the steps required to repair the fault are listed in detail, including the necessary operation sequence and precautions, and the specific tools, spare parts or test equipment required to complete the repair work are pointed out. Suggestions for preventing similar faults from happening again are provided, and the progress of fault handling is tracked, allowing the operator to record the completion of each step, any problems encountered and additional measures taken. After handling the fault, the operator feedbacks the handling results and newly discovered information to update the fault handling knowledge base.

[0095] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An MVB communication fault diagnosis system based on MVB online monitoring, characterized in that: The MVB communication fault diagnosis system includes an isolation partition module, a data acquisition module, a relationship analysis module, a fault diagnosis and analysis module, a fault location module, a fault alarm recording module and a fault handling suggestion module, wherein the electrical signals between the modules are connected; The isolation partition module is used to divide the MVB network into multiple logical areas. The isolation partition module specifically includes: According to the functional requirements, subsystem distribution and fault isolation requirements of the train, the overall topology of the MVB network is analyzed to identify all devices, nodes and their connection relationships; Preset configuration files or user inputs define the boundaries and parameters of each subsystem, and divide the MVB network into multiple logical areas based on the network topology analysis results and actual application requirements. Each logical area contains a group of related devices and nodes. The isolation partition module receives data frames from the MVB network, identifies the target address and type thereof, and determines a forwarding processing decision based on the target address and type of the data frame; The data acquisition module is used to collect node data from each subsystem in the MVB network and pre-process the collected node data; The relationship analysis module is used to analyze the mutual relationships between different subsystems, including communication protocols, data flows and dependencies, and to construct a relationship map between the subsystems; The fault diagnosis and analysis module analyzes abnormal patterns in the node data based on the preprocessed node data and the relationship graph between the subsystems, determines whether a fault exists, and gives the fault type; The fault alarm recording module, after determining the fault node, triggers the alarm mechanism, issues a fault alarm to the operator, and records the fault information; The fault handling suggestion module provides fault handling suggestions based on the fault location result and the fault type.

2. The MVB communication fault diagnosis system based on MVB online monitoring according to claim 1, characterized in that: The specific process of determining the forwarding processing decision includes: The isolation partition module parses the data frame from the MVB network and extracts the data frame structure including the target address, source address, frame type and priority, wherein the target address points to a specific subsystem, device or functional module; Match the target address with the predefined logical area mapping table to determine whether the data frame belongs to a specific logical area. Based on the matching result and preset rules, output a forwarding processing decision to determine whether the data frame is forwarded to the target subsystem or isolated. If the target address and type of the data frame meet the preset forwarding rules, the isolation partition module forwards the data frame to the corresponding target subsystem or the next logical area; If the destination address or type of the data frame does not conform to the preset forwarding rules, or there is an abnormal situation such as the data frame is damaged or the destination address is invalid, the forwarding processing decision of the data frame isolation is executed to block it in the current logical area to prevent it from entering other subsystems, and the isolation event is recorded.

3. The MVB communication fault diagnosis system based on MVB online monitoring according to claim 2 is characterized in that: The data acquisition module specifically includes: The data acquisition module is initialized after power-on, configures the parameters of the communication rate and node address, and sets the connection parameters between the data acquisition module and the MVB network according to the preset configuration file or user input; For each logical area, an independent data collection task is started. The data collection module identifies each node in the MVB network, including the communication devices and sensors of each subsystem, and periodically collects the node data of each subsystem, including communication signals and status information; The collected raw node data is temporarily stored in a buffer, and then the node data is preprocessed, including data integrity check, denoising, filtering and standardization operations; Build a data warehouse and store the preprocessed node data in the data warehouse.

4. The MVB communication fault diagnosis system based on MVB online monitoring according to claim 3 is characterized in that: The relationship analysis module specifically includes: Initialize the parameters and settings of the relationship analysis module according to the system configuration file or user input, and obtain the node data of each subsystem from the data acquisition module, importing basic information including subsystem name, function description, interface definition, node address and communication protocol; Analyze the communication protocols used between each subsystem, analyze the format, message structure and verification method of the communication protocol, clarify the data exchange process, and trace the flow of data by analyzing the communication protocols and data flow information between each subsystem, and determine the starting subsystem, intermediate links and end subsystem; Identify direct dependencies between subsystems and infer indirect dependencies between subsystems by analyzing data flows and communication protocols; Based on the analyzed communication protocols, data flows and dependencies, a relationship map is generated, and the location of each subsystem, the relationship between them and the direction of data flow are displayed in the map. At the same time, the generated relationship map is optimized, and the final relationship map is stored in the database and updated regularly to reflect changes in the system.

5. The MVB communication fault diagnosis system based on MVB online monitoring according to claim 4 is characterized in that: The fault diagnosis and analysis module specifically includes: Import the preprocessed node data into the fault diagnosis and analysis module, and use the relationship map between the subsystems to clarify the connection relationship and communication path between the nodes; According to the scope and path of the fault impact, locate the relevant nodes and connection lines in the relationship map, analyze the flow path of data between nodes, and determine the fault propagation path; Perform feature extraction on the node data to extract the features of the communication signal and the state information, wherein the features of the communication signal are signal amplitude, signal phase and signal-to-noise ratio, and the features of the state information are temperature, current, voltage and response time; Based on the fault management requirements of the MVB network, the abnormal thresholds of the communication signal characteristics and the status information characteristics are preset in combination with historical data to distinguish the abnormal characteristics in the communication signal characteristics and the status information characteristics. Among them, the abnormal communication signal characteristics are abnormal signal amplitude, signal phase distortion and signal-to-noise ratio reduction, and the abnormal status information characteristics are abnormal temperature, current fluctuation, voltage fluctuation and abnormal response time; According to the collected real-time communication signal and status information data, each feature is compared with its abnormal threshold to obtain the abnormal identification value of each feature; The abnormal identification value of the communication signal characteristics is obtained comprehensively, the signal abnormal identification index is calculated, and the abnormal trend of the communication signal is analyzed; the abnormal identification value of the state information characteristics is obtained comprehensively, the state abnormal identification index is calculated, and the abnormal trend of the state information is analyzed; Combined with the signal anomaly identification index and the state anomaly identification index, the fault identification coefficient is calculated, the abnormal pattern in the node data is analyzed, and the specific fault type is determined based on the abnormal pattern identification result and the relationship map. The fault types include hardware failure, software failure and communication failure.

6. The MVB communication fault diagnosis system based on MVB online monitoring according to claim 5 is characterized in that: The calculation formula of the signal anomaly identification index is as follows: ； In the formula, is a signal anomaly identification indicator. is the current signal amplitude value, is the abnormal threshold of signal amplitude, is the current signal phase value, is the abnormal threshold of the signal phase, and are constants for adjusting the sensitivity of signal amplitude and signal phase, respectively. is the current signal-to-noise ratio value, is the abnormal threshold of the signal-to-noise ratio, The value range is between 0 and 1; The calculation formula of the abnormal state identification index is as follows: ； In the formula, Identify indicators for abnormal status. is the current temperature value, is the abnormal temperature threshold, is the maximum allowable temperature, is the current value, is the abnormal threshold of current, is the constant for adjusting the sensitivity of the current, is the current voltage value, is the voltage abnormality threshold, is the maximum allowable value of voltage, is the current response time value, is the abnormal threshold of response time, The constant that adjusts the sensitivity for the response time, The value range is between 0 and 1; The calculation formula of the fault identification coefficient is as follows: ； In the formula, is the fault identification coefficient, is a signal anomaly identification indicator. Identify indicators for abnormal status. A constant for adjusting the sensitivity of the status anomaly identification indicator, The value range is between 0 and 1.

7. The MVB communication fault diagnosis system based on MVB online monitoring according to claim 6 is characterized in that: The fault type determination process includes: The fault identification coefficient distinction threshold K is set according to historical data to distinguish between normal and abnormal situations. When the value of the fault identification coefficient is less than K, it indicates that an abnormal situation exists; According to the size of the fault identification coefficient and the distinction threshold, a comparison is made to identify nodes with a higher degree of abnormality, and then the characteristics of the identified abnormal nodes are deeply analyzed to determine whether there is an abnormal pattern; For abnormal patterns, further analyze the signal anomaly identification index and the state anomaly identification index to determine the specific abnormal characteristics, find the location of the abnormal node in the relationship map, and mark it; Based on the connection relationship between the abnormal node and other nodes, the propagation path and impact range of the abnormal pattern in the MVB network are analyzed, and the specific fault type is determined according to the abnormal pattern, fault propagation path and node characteristics.

8. The MVB communication fault diagnosis system based on MVB online monitoring according to claim 7 is characterized in that: The fault alarm recording module specifically includes: Through fault identification coefficient analysis, abnormal pattern detection and relationship graph analysis, the faulty node is determined and the specific location of the fault is clarified; When the fault node is determined, the fault alarm recording module automatically triggers the alarm mechanism, including sound and light alarm, SMS notification and email reminder, and then sends an alarm containing fault information to the operator through the preset alarm method; When a fault alarm is triggered, the fault information is automatically recorded, including detailed information on the fault type, occurrence time, fault node and fault description, and a detailed fault report is generated based on the recorded fault information. After the fault is handled, the operator will feed back the handling results to the fault alarm recording module, which will automatically update the fault status and record the handling results and time.

9. The MVB communication fault diagnosis system based on MVB online monitoring according to claim 8, characterized in that: The fault handling suggestion module specifically includes: According to the determined fault type, a corresponding fault handling suggestion is matched from a fault handling knowledge base containing various known fault handling suggestions; Generate specific fault handling suggestions based on the matched related fault items, including fault repair steps, required tools or spare parts, and preventive measures, and provide the generated handling suggestions to the operator; Tracking the progress of troubleshooting allows operators to record the completion of each step, any problems encountered, and additional measures taken. After troubleshooting, operators provide feedback on the results and newly discovered information to update the troubleshooting knowledge base.

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