Rail transit signal system reliability analysis method, device and electronic equipment

Abstract

The present application provides a kind of rail transit signal system reliability analysis method, device and electronic equipment, the method comprises: based on the task scene model and system function model of target signal system, determine task function relationship model;Based on task function relationship model and each system function failure mode, determine fault tree by failure analysis;Based on fault tree and the task reliability quantitative index of each task profile, determine the first failure probability control index of each system function failure mode.Through determining fault tree, can be based on fault tree and the task reliability quantitative index of each task profile, the task failure probability control index of each task profile is distributed to each system function failure mode, the first failure probability control index of each system function failure mode can be determined, the first failure probability control index can represent the reliability requirement of system function, realize the task reliability requirement of complex rail transit signal system is decomposed to each system function.

Description

Technical Field

[0001] This invention relates to the field of rail transit technology, and in particular to a method, apparatus, and electronic equipment for reliability analysis of rail transit signaling systems. Background Technology

[0002] Reliability is a crucial characteristic of engineering systems. For urban rail transit signaling control equipment, it directly impacts the equipment's ability to perform its functions and its total lifecycle costs. As next-generation equipment evolves towards high levels of informatization, networking, and intelligence, the functions, architectures, and behaviors of the equipment and its key components are becoming increasingly complex. The characteristics of complex systems, such as scalability, nonlinearity, emergent behavior, and uncertainty, are becoming more pronounced. Traditional reliability design and analysis methods are increasingly unable to meet the needs of developing complex engineering systems. How to decompose the reliability requirements of complex rail transit signaling systems into the functional breakdown of each system is a pressing issue that the industry needs to address. Summary of the Invention

[0003] To address the problems existing in the prior art, embodiments of the present invention provide a method, apparatus, and electronic device for reliability analysis of rail transit signaling systems.

[0004] In a first aspect, the present invention provides a reliability analysis method for a rail transit signaling system, comprising:

[0005] Based on the task scenario model and system function model of the target signal system, a task function relationship model is determined, which is used to represent the mapping relationship between each task profile and each system function.

[0006] Based on the task function relationship model and the failure modes of each system function, a fault tree is determined through failure analysis. The fault tree is used to represent the logical relationship between each system function failure mode and the task failure.

[0007] Based on the fault tree and the quantitative indicators of task reliability of each task profile, the first failure probability control index of each system functional failure mode is determined.

[0008] The task scenario model and the system function model are determined by modeling the target signal system using the model-based systems engineering (MBSE) modeling method. The task scenario model includes multiple task profiles, and the system function model includes multiple system functions.

[0009] Optionally, according to the reliability analysis method for a rail transit signaling system provided by the present invention, the step of determining the first failure probability control index for each system functional failure mode based on the fault tree and the quantitative indicators of task reliability for each task profile includes:

[0010] Based on the quantitative indicators of task reliability for each task profile, determine the task failure probability control indicators for each task profile.

[0011] Based on the fault tree and the task failure probability control index of each task profile, the failure probability control index analysis operation is performed on each task profile to determine the analysis results of each task profile. The analysis results include one or more second failure probability control indices corresponding to each target system functional failure mode. The target system functional failure mode is the system functional failure mode associated with the task profile determined based on the fault tree.

[0012] Based on the analysis results of each task profile, multiple second fault probability control indicators are classified and aggregated according to the system functional fault mode to determine the fault probability control indicator set for each system functional fault mode.

[0013] Based on the set of fault probability control indicators for each system's functional fault modes, a first fault probability control indicator for each system's functional fault mode is determined. The first fault probability control indicator is the one with the smallest probability value in the set of fault probability control indicators.

[0014] Optionally, according to the reliability analysis method for a rail transit signaling system provided by the present invention, the failure probability control index analysis operation includes:

[0015] Based on the fault tree, at least one minimal cut set of the task profile is obtained, and the minimal cut set includes one or more of the target system functional failure modes;

[0016] Based on the task failure probability control index and the first quantity, the failure probability control index of each minimum cut set is determined, where the first quantity is the total number of the at least one minimum cut set.

[0017] Based on the failure probability control index of each minimum cut set and the second quantity corresponding to each minimum cut set, the second failure probability control index of each target system functional failure mode in different minimum cut sets is determined, and the second quantity is the total number of target system functional failure modes in the minimum cut set.

[0018] The analysis results of the task profile are determined based on the second failure probability control index of each target system functional failure mode in different minimum cut sets.

[0019] Optionally, according to the reliability analysis method for a rail transit signaling system provided by the present invention, after determining the first failure probability control index of each system functional failure mode based on the fault tree and the quantitative index of task reliability of each task profile, the method further includes:

[0020] Based on the system architecture model of the target signal system, the fault modes of each system unit are determined, and the system unit fault modes are used to characterize the fault logic of the system unit.

[0021] Based on the failure modes of each system unit, the logical relationship between system unit failures and system function failures is analyzed to determine the system failure propagation model.

[0022] Based on the system fault propagation model and the first fault probability control index of each system functional fault mode, the third fault probability control index of each system unit fault mode is determined.

[0023] The system architecture model is determined by modeling the target signal system using the Model-Based Systems Engineering (MBSE) modeling method, and the system architecture model includes multiple system units.

[0024] Optionally, according to the reliability analysis method for a rail transit signaling system provided by the present invention, the system unit failure modes include state space variables, unit port variables, trigger event information, state transition functions, transfer functions, and port connection relationship functions.

[0025] Optionally, according to the reliability analysis method for a rail transit signaling system provided by the present invention, after determining the third failure probability control index of each system unit failure mode based on the system failure propagation model and the first failure probability control index of each system functional failure mode, the method further includes:

[0026] Based on the third failure probability control index of each system unit failure mode and the failure mode of each hardware component, the fourth failure probability control index of each hardware component failure mode is determined by analyzing the logical relationship between the hardware component failure modes and the system unit failure.

[0027] Secondly, the present invention also provides a reliability analysis device for a rail transit signaling system, comprising:

[0028] The first determining module is used to determine the task function relationship model based on the task scenario model and system function model of the target signal system. The task function relationship model is used to represent the mapping relationship between each task profile and each system function.

[0029] The second determining module is used to determine a fault tree through failure analysis based on the task function relationship model and the failure modes of each system function. The fault tree is used to represent the logical relationship between the failure modes of each system function that lead to task failure.

[0030] The third determination module is used to determine the first failure probability control index of each system functional failure mode based on the fault tree and the quantitative indicators of task reliability of each task profile.

[0031] The task scenario model and the system function model are determined by modeling the target signal system using the model-based systems engineering (MBSE) modeling method. The task scenario model includes multiple task profiles, and the system function model includes multiple system functions.

[0032] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the reliability analysis method for rail transit signaling systems as described above.

[0033] Fourthly, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the reliability analysis method for rail transit signaling systems as described above.

[0034] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the reliability analysis method for rail transit signaling systems as described above.

[0035] The reliability analysis method, apparatus, and electronic equipment for rail transit signaling systems provided by this invention can model complex target signaling systems through MBSE (Mean Scale Analysis). This allows for the acquisition of task scenario models and system function models of the target signaling system. Based on these models, a task function relationship model can be determined. Furthermore, based on this model, the logical relationships leading to task failures due to system function failure modes can be analyzed to determine a fault tree. Then, based on the fault tree and quantitative reliability indicators for each task profile, task failure probability control indicators for each task profile can be allocated to each system function failure mode. This allows for the determination of a first failure probability control indicator for each system function failure mode. This first failure probability control indicator characterizes the reliability requirements of the system function, thus decomposing the task reliability requirements of complex rail transit signaling systems into individual system functions. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0037] Figure 1 This is one of the flowcharts illustrating the reliability analysis method for rail transit signaling systems provided by this invention;

[0038] Figure 2 This is the second flowchart of the reliability analysis method for rail transit signaling systems provided by the present invention;

[0039] Figure 3 This is the third flowchart of the reliability analysis method for rail transit signaling systems provided by this invention;

[0040] Figure 4 This is a schematic diagram of the system tasks of the interlocking system provided by the present invention;

[0041] Figure 5 This is a task decomposition diagram of the interlocking system provided by the present invention;

[0042] Figure 6 This is a schematic diagram showing the system function breakdown of the interlocking system provided by the present invention;

[0043] Figure 7 This is a schematic diagram of a system fault tree that leads to system task failure in the interlocking system provided by the present invention;

[0044] Figure 8 This is a schematic diagram of the system fault propagation model of the interlocking system provided by the present invention;

[0045] Figure 9 This is a schematic diagram of the structure of the reliability analysis device for rail transit signaling systems provided by the present invention;

[0046] Figure 10 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0047] To facilitate a clearer understanding of the various embodiments of the present invention, some relevant background knowledge will be introduced as follows.

[0048] In related technologies, the reliability requirement decomposition method takes the mean time between failures (MTBF) as the main line. It simply understands the reliability of the system as the sum of the reliability of the constituent units, ignoring the complex characteristics of the complex system itself, such as coupling and emergence. It is insufficient to characterize the reliability requirements of complex systems and cannot be implemented in the design process of complex systems and components.

[0049] Model-Based System Engineering (MBSE) is one of the design methodologies for complex systems. Compared to traditional systems engineering, MBSE places great emphasis on the application of "standardized" models throughout the entire equipment development process. On the one hand, it solves the problem of traditional systems engineering's inability to be implemented, and on the other hand, it compensates for the shortcomings of existing digital development technologies in requirements analysis and architecture design. It has been widely used in aerospace, rail transportation, and other fields.

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0051] Figure 1 This is one of the flowcharts illustrating the reliability analysis method for rail transit signaling systems provided by this invention, such as... Figure 1 As shown, the reliability analysis methods for rail transit signaling systems include:

[0052] Step 101: Based on the task scenario model and system function model of the target signal system, determine the task function relationship model, which is used to represent the mapping relationship between each task profile and each system function;

[0053] The task scenario model and the system function model are determined by modeling the target signal system using the model-based systems engineering (MBSE) modeling method. The task scenario model includes multiple task profiles, and the system function model includes multiple system functions.

[0054] Specifically, in order to decompose the mission reliability requirements of complex rail transit signaling systems into individual system functions, MBSE can be used to model complex target signaling systems. This allows for the acquisition of mission scenario models and system function models for the target signaling system. The mission scenario model can include multiple mission profiles and characterize the relationships between these profiles. Similarly, the system function model can include multiple system functions and characterize the relationships between these functions. Based on the mission scenario model and system function model, the mapping relationship between each mission profile and each system function can be analyzed (e.g., analyzing which system functions each mission profile depends on) to determine the mission function relationship model.

[0055] Step 102: Based on the task function relationship model and the failure modes of each system function, a fault tree is determined through failure analysis. The fault tree is used to represent the logical relationship between each system function failure mode and the task failure.

[0056] Specifically, after determining the task function relationship model, the logical relationship between system function failure modes leading to task failure can be analyzed based on the task function relationship model in order to determine the fault tree.

[0057] It is understandable that system function failure modes are the manifestations of system function failures. For example, a system function can be an external network communication function, and the failure mode of the external network communication function can be a network card failure or a communication timeout, etc.; for example, a system function can be a system initialization function, and the failure mode of the system initialization function can be a serial port failure or a CAN bus failure, etc.

[0058] Understandably, after determining the task function relationship model, system function failure modes can be taken as the bottom events and task profile failures as the top events. By analyzing the logical relationship between task profile failures and system function failure modes based on the task function relationship model, the fault tree can be determined.

[0059] Step 103: Based on the fault tree and the quantitative indicators of task reliability for each task profile, determine the first failure probability control index for each system functional failure mode.

[0060] Specifically, after determining the fault tree, based on the fault tree and the quantitative indicators of task reliability for each task profile, the task failure probability control indicators for each task profile can be allocated to each system functional failure mode. This allows for the determination of the first failure probability control indicator for each system functional failure mode, which can characterize the reliability requirements of the system function.

[0061] Optionally, based on the quantitative indicators of task reliability for each task profile, quantitative indicators of task unreliability (i.e., task failure probability control indicators) can be determined for each task profile.

[0062] The reliability analysis method for rail transit signaling systems provided by this invention can model complex target signaling systems using MBSE (Mean Squared Error Analysis). It can obtain task scenario models and system function models of the target signaling system. Based on these models, a task function relationship model can be determined. Furthermore, based on this model, the logical relationships between system function failure modes leading to task failures can be analyzed to determine the fault tree. Then, based on the fault tree and quantitative reliability indicators for each task profile, the task failure probability control indicators for each task profile can be allocated to each system function failure mode. This allows for the determination of the first failure probability control indicator for each system function failure mode. This first failure probability control indicator characterizes the reliability requirements of the system function, thus decomposing the task reliability requirements of complex rail transit signaling systems into individual system functions.

[0063] Optionally, according to the reliability analysis method for a rail transit signaling system provided by the present invention, the step of determining the first failure probability control index for each system functional failure mode based on the fault tree and the quantitative indicators of task reliability for each task profile includes:

[0064] Based on the quantitative indicators of task reliability for each task profile, determine the task failure probability control indicators for each task profile.

[0065] Based on the fault tree and the task failure probability control index of each task profile, the failure probability control index analysis operation is performed on each task profile to determine the analysis results of each task profile. The analysis results include one or more second failure probability control indices corresponding to each target system functional failure mode. The target system functional failure mode is the system functional failure mode associated with the task profile determined based on the fault tree.

[0066] Based on the analysis results of each task profile, multiple second fault probability control indicators are classified and aggregated according to the system functional fault mode to determine the fault probability control indicator set for each system functional fault mode.

[0067] Based on the set of fault probability control indicators for each system's functional fault modes, a first fault probability control indicator for each system's functional fault mode is determined. The first fault probability control indicator is the one with the smallest probability value in the set of fault probability control indicators.

[0068] Specifically, after determining the fault tree, quantitative indicators of task unreliability (i.e., task failure probability control indicators) can be determined based on quantitative indicators of task reliability for each task profile. Then, failure probability control indicator analysis operations can be performed for each task profile to determine the analysis results for each task profile.

[0069] It is understandable that performing failure probability control index analysis on a certain task profile may include: determining the system functional failure modes (i.e. target system functional failure modes) associated with the task profile based on the fault tree, and then allocating the task failure probability control index of the task profile to each target system functional failure mode based on the fault tree. After allocation, a target system functional failure mode may correspond to one or more second failure probability control indices, thereby determining the analysis results of the task profile.

[0070] Specifically, after determining the analysis results of each task profile, multiple second fault probability control indicators can be classified and aggregated according to the system functional fault modes to determine the fault probability control indicator set for each system functional fault mode.

[0071] It is understandable that for a certain system functional failure mode A, the analysis results of task profile B may include a second failure probability control index (named index 1) corresponding to system functional failure mode A, and the analysis results of task profile C may include two second failure probability control indices (named index 2 and index 3) corresponding to system functional failure mode A. Therefore, the failure probability control index set of system functional failure mode A includes index 1, index 2 and index 3.

[0072] Specifically, in determining the set of failure probability control indicators for each system functional failure mode, the indicators in the set of failure probability control indicators can be compared to determine the first failure probability control indicator for each system functional failure mode. The first failure probability control indicator is the one with the smallest probability value in the set of failure probability control indicators.

[0073] It is understandable that for a certain system functional failure mode A, the failure probability control index set of system functional failure mode A includes index 1, index 2 and index 3. If index 3 is the one with the smallest probability value among index 1, index 2 and index 3, then the first failure probability control index of failure mode A is index 3.

[0074] Therefore, by performing failure probability control index analysis on each task profile, the analysis results of each task profile can be determined, and then the failure probability control index set of each system functional failure mode can be determined, and the first failure probability control index of each system functional failure mode can be screened out. The first failure probability control index can characterize the reliability requirements of the system function.

[0075] Optionally, according to the reliability analysis method for a rail transit signaling system provided by the present invention, the failure probability control index analysis operation includes:

[0076] Based on the fault tree, at least one minimal cut set of the task profile is obtained, and the minimal cut set includes one or more of the target system functional failure modes;

[0077] Based on the task failure probability control index and the first quantity, the failure probability control index of each minimum cut set is determined, where the first quantity is the total number of the at least one minimum cut set.

[0078] Based on the failure probability control index of each minimum cut set and the second quantity corresponding to each minimum cut set, the second failure probability control index of each target system functional failure mode in different minimum cut sets is determined, and the second quantity is the total number of target system functional failure modes in the minimum cut set.

[0079] The analysis results of the task profile are determined based on the second failure probability control index of each target system functional failure mode in different minimum cut sets.

[0080] Specifically, to determine the analysis results of the task profile, at least one minimal cut set of the task profile can be obtained based on the fault tree. The minimal cut set can be the minimum cut set necessary for the occurrence of the event at the top of the fault tree. A minimal cut set can be one or more target system functional failure modes. Then, based on the task failure probability control index and the total number of minimal cut sets (i.e., the first quantity), the failure probability control index of each minimal cut set can be determined. Then, based on the failure probability control index of each minimal cut set and the second quantity corresponding to each minimal cut set (i.e., the total number of target system functional failure modes in the minimal cut set), the second failure probability control index of each target system functional failure mode in different minimal cut sets can be determined, and thus the analysis results of the task profile can be determined.

[0081] Alternatively, based on the fault tree, at least one minimal cut set of the task profile can be obtained using the determinant method or Boolean algebra method.

[0082] Optionally, the quantitative index of mission reliability in the mission profile can be R. i Then, the quantitative index of task unreliability (i.e., the task failure probability control index) in the task profile can be Q. i =1-R i , where i represents the number of the task profile.

[0083] Based on the fault tree, obtain at least one minimal cut set of the task profile. The total number of cut sets of at least one minimal cut set (i.e., the first quantity) can be C. i The failure probability control index for each minimal cut set can be P. cij =Q i / C i , where j represents the number of the minimal cut set.

[0084] For a given minimal cut set, if the total number (second quantity) of the target system functional failure modes included in the minimal cut set is N cij For this minimal cut set, the second failure probability control index for the functional failure modes of the target system can be P. Fij m =P cij / N cij , where m represents the number of the target system functional failure mode under the minimum cut set.

[0085] Therefore, by obtaining at least one minimal cut set of the task profile based on the fault tree, the task failure probability control index can be assigned to the functional failure modes of each target system. The analysis results of the task profile can be determined. By determining the analysis results of each task profile, the set of failure probability control indices for each system functional failure mode can be determined, and the first failure probability control index for each system functional failure mode can be selected. The first failure probability control index can characterize the reliability requirements of the system function.

[0086] Optionally, according to the reliability analysis method for rail transit signaling systems provided by the present invention, Figure 2 This is the second flowchart of the reliability analysis method for rail transit signaling systems provided by this invention, as shown below. Figure 2 As shown, after determining the first failure probability control index for each system functional failure mode based on the fault tree and the quantitative indicators of task reliability for each task profile, the method further includes:

[0087] Step 201: Based on the system architecture model of the target signal system, determine the fault modes of each system unit. The system unit fault modes are used to characterize the fault logic of the system unit.

[0088] Step 202: Based on the failure modes of each system unit, determine the system failure propagation model by analyzing the logical relationship between system unit failures and system function failures;

[0089] Step 203: Based on the system fault propagation model and the first fault probability control index of each system functional fault mode, determine the third fault probability control index of each system unit fault mode.

[0090] The system architecture model is determined by modeling the target signal system using the Model-Based Systems Engineering (MBSE) modeling method, and the system architecture model includes multiple system units.

[0091] Specifically, in order to decompose the reliability requirements of complex rail transit signaling systems into individual system units, MBSE can be used to model the complex target signaling system and obtain its system architecture model. This model can characterize the functional characteristics of each system unit and the relationships between them. After determining the first failure probability control index for each system's functional failure mode, based on the system architecture model of the target signaling system, failure logic can be defined for each system unit to determine its failure mode. This allows for analysis of the logical relationships between system unit failures and system function failures, leading to the determination of the system failure propagation model. Based on this model, the first failure probability control index for each system's functional failure mode can be allocated to each system unit's failure mode, thus determining the third failure probability control index for each system unit's failure mode. This third failure probability control index characterizes the reliability requirements of the system unit.

[0092] It is understandable that by modeling the target signal system based on MBSE modeling, elements such as activity diagrams, timing diagrams, state diagrams, and internal block diagrams (including functional operations, functional timing, and system composition) can be determined. These elements can be extracted through neutral file interfaces such as XMI, thereby establishing a multi-level normal function model, that is, a system architecture model. The system architecture model can include logical architecture model, physical architecture model, and component architecture model.

[0093] Understandably, for relatively simple systems, the failures of their constituent units can be considered approximately independent, and system failures are often caused by the failure of a single component. However, for complex systems, not only may unit failures be dependent, logically related, or temporally related, but the system's operating state and configuration also frequently change depending on the task scenario. By analyzing the logical relationships between system unit failures and system functional failures based on the failure modes of each system unit, a system failure propagation model can be constructed. This model can describe the propagation and patterns of system failures, allowing failure control to be used as the core of reliability analysis.

[0094] Therefore, by determining the system fault propagation model, the first fault probability control index of each system functional fault mode can be allocated to the fault modes of each system unit, and the third fault probability control index of each system unit fault mode can be determined. The third fault probability control index can characterize the reliability requirements of the system unit, and can decompose the task reliability requirements of the complex rail transit signaling system to each system unit.

[0095] Optionally, according to the reliability analysis method for a rail transit signaling system provided by the present invention, the system unit failure modes include state space variables, unit port variables, triggering events, state transition functions, transfer functions, and port connection relationship functions.

[0096] Specifically, MBSE can be used to model complex target signal systems and obtain a system architecture model of the target signal system. The system architecture model can represent the functional characteristics of each system unit and the relationships between them. By analyzing the system architecture model, the normal mode of each system unit can be determined. Then, by referring to the normal mode of each system unit, fault logic can be defined for each system unit to determine the fault mode of each system unit. The fault mode of the system unit includes state space variables, unit port variables, trigger event information, state transition function, transfer function, and port connection relationship function.

[0097] Understandably, state-space variables can represent the normal and / or fault states of a system unit; unit port variables can represent the input and output port values ​​of a system unit; trigger event information can represent the trigger events that cause state transitions within a system unit and the corresponding event distribution types; state transition functions can represent the logical relationships for state transitions within a system unit; transfer functions can represent the logical relationships for determining output port values ​​based on input port values ​​and state-space variables; and port connection relationship functions can represent the connection relationships between the ports of this system unit and the ports of other system units.

[0098] It is understandable that by referring to the normal mode of each system unit and defining the fault logic for each system unit, the fault mode of each system unit can be determined. The transfer function of the fault mode of the system unit can represent the logical relationship of determining the output port value based on the input port value and the state space variable. The port connection relationship function of the fault mode of the system unit can represent the connection relationship between the port of this system unit and the port of other system units. Therefore, based on the fault modes of each system unit, by analyzing the logical relationship of the system unit fault leading to the failure of system function, a system fault propagation model can be constructed.

[0099] It is understood that the reliability analysis method for rail transit signaling systems provided by this invention utilizes the ability of the MBSE design model to describe the functions, behaviors, and states of complex systems, achieving functional and reliability modeling from the same source. This overcomes the shortcomings of traditional methods in describing the fault propagation behavior of complex systems such as polymorphism and fault correlation.

[0100] Therefore, by determining the state space variables, unit port variables, trigger event information, state transition functions, transfer functions, and port connection relationship functions for each system unit, the fault modes of each system unit can be determined, and the fault modes of each system unit can be used to construct a system fault propagation model.

[0101] Optionally, according to the reliability analysis method for a rail transit signaling system provided by the present invention, after determining the third failure probability control index of each system unit failure mode based on the system failure propagation model and the first failure probability control index of each system functional failure mode, the method further includes:

[0102] Based on the third failure probability control index of each system unit failure mode and the failure mode of each hardware component, the fourth failure probability control index of each hardware component failure mode is determined by analyzing the logical relationship between the hardware component failure modes and the system unit failure.

[0103] Specifically, in order to decompose the reliability requirements of complex rail transit signaling systems to each hardware component, after determining the third failure probability control index of each system unit failure mode, the logical relationship between the hardware component failure modes and system unit failures can be analyzed based on the failure modes of each hardware component. Then, based on this logical relationship, the third failure probability control index of each system unit failure mode can be allocated to each hardware component failure mode to determine the fourth failure probability control index of each hardware component failure mode. The fourth failure probability control index can characterize the reliability requirements of the hardware components.

[0104] Therefore, by analyzing the logical relationship between hardware component failure modes and system unit failures, the fourth failure probability control index of each hardware component failure mode can be determined. The fourth failure probability control index can characterize the reliability requirements of hardware components and can decompose the task reliability requirements of complex rail transit signaling systems to each hardware component.

[0105] Optionally, after the preliminary design of the target signal system is completed, a multi-physics simulation model of the target signal system can be established. Fault simulation injection and formal verification methods can be used to virtually verify the reliability requirements of the system (including the reliability requirements of each system function, each system unit, and each hardware component). The design can be improved and optimized based on the verification results.

[0106] Understandably, multiphysics modeling is currently a technical means for virtual verification of complex systems. It mainly uses mathematical equations to describe the physical laws and phenomena of subsystems in different domains. Based on the topology of the physical system and the connection mechanism of each subsystem, it integrates multi-domain models. Finally, it calculates the response values ​​of each parameter of the system by solving differential equations. It is also called a virtual prototype model of the system.

[0107] Therefore, relying on the integrated simulation model provided by MBSE, a multi-physics simulation model that closely approximates real-world usage scenarios and environmental conditions can be constructed. This allows for thorough verification of system reliability before physical construction, further improving all aspects of system reliability design.

[0108] Optionally, Figure 3 This is the third flowchart of the reliability analysis method for rail transit signaling systems provided by this invention, as shown below. Figure 3 As shown, the rail transit signaling system can be modeled using the MBSE modeling method. This modeling process can include the requirements analysis stage, system function analysis stage, logical architecture analysis stage, physical architecture analysis stage, component architecture analysis stage, and verification stage.

[0109] like Figure 3 As shown, the reliability analysis method for rail transit signaling systems provided by this invention can be applied to each stage of the MBSE modeling process:

[0110] (1) The reliability requirements of the task can be determined during the requirements analysis phase;

[0111] (2) During the system function analysis phase, the failure modes of each system function can be determined, and the fault tree can be determined. Based on the fault tree, the reliability requirements of the task can be allocated to each system function to determine the reliability requirements of each system function.

[0112] (3) During the logical architecture analysis phase, the failure modes of each system logical unit can be determined, as well as the failure propagation model corresponding to the logical architecture. Based on the failure propagation model, the reliability requirements of each system function can be allocated to each system logical unit to determine the reliability requirements of each system logical unit.

[0113] (4) During the physical architecture analysis phase, the failure modes of each system physical unit can be determined, as well as the failure propagation model corresponding to the physical architecture. Based on the failure propagation model, the reliability requirements of each system logical unit can be allocated to each system physical unit to determine the reliability requirements of each system physical unit.

[0114] (5) During the component architecture analysis phase, the failure modes of each system component unit can be determined, as well as the failure propagation model corresponding to the component architecture. Based on the failure propagation model, the reliability requirements of each system physical unit can be allocated to each system component unit to determine the reliability requirements of each system component unit.

[0115] (6) During the verification phase, based on the multi-physics simulation model of the rail transit signaling system, fault simulation injection and formal verification methods can be used to virtually verify the reliability requirements of the system (including the reliability requirements of each system function, the reliability requirements of each system unit and the reliability requirements of each hardware component).

[0116] The following is an optional example of the present invention, but it is not intended to limit the present invention.

[0117] This example takes the interlocking system in an urban rail transit signaling system as an example. Combining the technical processes of system MBSE requirements analysis, functional analysis and architecture design, it performs multi-level fault mode identification and reliability requirement analysis to illustrate the application process of the technical solution and key technologies of this invention.

[0118] (1) Reliability requirements of interlocking systems;

[0119] The basic reliability of an interlocking system is generally characterized by the mean time between failures (MTBF), while the task reliability is generally characterized by the task reliability, with a value generally not lower than 0.99. At the same time, as a safety-critical system, the interlocking system is generally required to have "fail-safe" capability.

[0120] (2) Determine the reliability requirements of the interlocking system's functions;

[0121] Based on the task scenario model and system function model of the interlocking system, the top-level functions of the system are identified, and a task function relationship model is constructed to represent the mapping relationship between each task profile and each system function.

[0122] Figure 4 This is a schematic diagram of the system tasks of the interlocking system provided by the present invention, as shown below. Figure 4 As shown, the task scenario model includes several types of tasks such as system power-on startup, system platform initialization, input processing, application business processing, and output processing.

[0123] Figure 5 This is a task decomposition diagram of the interlocking system provided by the present invention, as shown below. Figure 5 As shown, for different types of tasks, multi-level task decomposition is performed. The system platform initialization task is decomposed into various task profiles, and the specific inputs, outputs and influencing factors under each task profile are defined.

[0124] Figure 6 This is a schematic diagram of the system function breakdown of the interlocking system provided by the present invention, as shown below. Figure 6 As shown, the system functional model describes the interaction and interdependence between the top-level functions of the system, including external network communication function, internal network communication function, input multiplication / take-two function, equipment status acquisition and processing function, route control function, equipment control function, equipment drive processing function, and output multiplication / take-two function, etc.

[0125] After obtaining the task scenario model and system function model, a task function relationship model is constructed by analyzing the mapping relationship between each task profile and each system function.

[0126] Figure 7 This is a schematic diagram of a system fault tree leading to system task failure in the interlocking system provided by the present invention, as shown below. Figure 7As shown, based on the task function relationship model, a fault tree is determined through failure analysis. The fault tree is used to represent the logical relationship between the failure modes of each system function that lead to task failure.

[0127] Based on the fault tree and quantitative indicators of task reliability for each task profile, the first failure probability control index for each system functional failure mode can be determined.

[0128] (3) Determine the reliability requirements of the system units of the interlocking system;

[0129] Figure 8 This is a schematic diagram of the system fault propagation model of the interlocking system provided by the present invention, as shown below. Figure 8 As shown, after determining the first failure probability control index for each system function failure mode, based on the system architecture model of the interlocking system (including the logical architecture model, physical architecture model, and component architecture model of the interlocking system), failure logic can be defined for each system unit to determine the failure mode of each system unit. This allows for analysis of the logical relationship between system unit failures and system function failures, thus determining the system failure propagation model. Here, SF represents system function, LU represents logical unit, PU represents physical unit, and CU represents component unit.

[0130] After determining the system fault propagation model of the interlocking system, the first fault probability control index of each system functional fault mode can be allocated to each system unit fault mode based on the system fault propagation model. The third fault probability control index of each system unit fault mode can be determined, and the third fault probability control index can characterize the reliability requirements of the system unit.

[0131] (4) Determine the reliability requirements of the hardware components of the interlocking system;

[0132] After determining the third failure probability control index for each system unit failure mode, the logical relationship between the hardware component failure modes and the system unit failure can be analyzed based on the failure modes of each hardware component. Then, based on this logical relationship, the third failure probability control index of each system unit failure mode can be assigned to each hardware component failure mode to determine the fourth failure probability control index of each hardware component failure mode. The fourth failure probability control index can characterize the reliability requirements of the hardware components.

[0133] (5) System reliability simulation verification;

[0134] After the preliminary design of the interlocking system is completed, a physical-level simulation model of the interlocking system can be established. Using fault simulation injection and formal verification methods, the reliability requirements of the system (including the reliability requirements of each system function, each system unit, and each hardware component) can be virtually verified. The design can be improved and optimized based on the verification results.

[0135] The reliability analysis method for rail transit signaling systems provided by this invention can model complex target signaling systems using MBSE (Mean Squared Error Analysis). It can obtain task scenario models and system function models of the target signaling system. Based on these models, a task function relationship model can be determined. Furthermore, based on this model, the logical relationships between system function failure modes leading to task failures can be analyzed to determine the fault tree. Then, based on the fault tree and quantitative reliability indicators for each task profile, the task failure probability control indicators for each task profile can be allocated to each system function failure mode. This allows for the determination of the first failure probability control indicator for each system function failure mode. This first failure probability control indicator characterizes the reliability requirements of the system function, thus decomposing the task reliability requirements of complex rail transit signaling systems into individual system functions.

[0136] The reliability analysis device for rail transit signaling systems provided by the present invention is described below. The reliability analysis device for rail transit signaling systems described below and the reliability analysis method for rail transit signaling systems described above can be referred to in correspondence.

[0137] Figure 9 This is a schematic diagram of the structure of the reliability analysis device for rail transit signaling systems provided by the present invention, as shown below. Figure 9 As shown, the device includes: a first determining module 901, a second determining module 902, and a third determining module 903, wherein:

[0138] The first determining module is used to determine the task function relationship model based on the task scenario model and system function model of the target signal system. The task function relationship model is used to represent the mapping relationship between each task profile and each system function.

[0139] The second determining module is used to determine a fault tree through failure analysis based on the task function relationship model and the failure modes of each system function. The fault tree is used to represent the logical relationship between the failure modes of each system function that lead to task failure.

[0140] The third determination module is used to determine the first failure probability control index of each system functional failure mode based on the fault tree and the quantitative indicators of task reliability of each task profile.

[0141] The task scenario model and the system function model are determined by modeling the target signal system using the Model-Based Systems Engineering (MBSE) modeling method.

[0142] The task scenario model includes multiple task profiles, and the system function model includes up to five system functions.

[0143] The rail transit signaling system reliability analysis device provided by this invention can model complex target signaling systems through MBSE (Mean Squared Error Analysis), obtain the task scenario model and system function model of the target signaling system, and based on the task scenario model and system function model, can...

[0144] By defining a task function relationship model, and then analyzing the logical relationships leading to task failures due to system 0-function failure modes based on this model, a fault tree can be determined, which in turn allows for further analysis.

[0145] Based on the fault tree and quantitative indicators of task reliability for each task profile, the task failure probability control indicators for each task profile are assigned to each system functional failure mode. This allows for the determination of the first failure probability control indicator for each system functional failure mode, which characterizes...

[0146] The reliability requirements of system functions are implemented by decomposing the reliability requirements of complex rail transit signaling systems into the functions of each system.

[0147] Figure 10 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 10 As shown, the electronic device may include: a processor 1010, a communications interface 1020, a memory 1030, and a communication interface.

[0148] Bus 1040, in which processor 1010, communication interface 1020, and memory 1030 communicate with each other via communication bus 1040. Processor 1010 can access memory 1030.

[0149] The logical instructions in the code are used to execute a reliability analysis method for a rail transit signaling system, the method including:

[0150] Based on the task scenario model and system function model of the target signal system, determine the task function.

[0151] The task function relationship model is used to represent the mapping relationship between each task profile and each system function.

[0152] Based on the task function relationship model and the failure modes of each system function, a fault tree is determined through failure analysis. The fault tree is used to represent the logical relationship between each system function failure mode and the task failure.

[0153] Based on the fault tree and the quantitative indicators of task reliability of each task profile, the first failure probability control index of each system functional failure mode is determined.

[0154] The task scenario model and the system function model are determined by modeling the target signal system using the model-based systems engineering (MBSE) modeling method. The task scenario model includes multiple task profiles, and the system function model includes multiple system functions.

[0155] Furthermore, the logical instructions in the aforementioned memory 1030 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0156] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the reliability analysis method for rail transit signaling systems provided by the above methods, the method including:

[0157] Based on the task scenario model and system function model of the target signal system, a task function relationship model is determined, which is used to represent the mapping relationship between each task profile and each system function.

[0158] Based on the task function relationship model and the failure modes of each system function, a fault tree is determined through failure analysis. The fault tree is used to represent the logical relationship between each system function failure mode and the task failure.

[0159] Based on the fault tree and the quantitative indicators of task reliability of each task profile, the first failure probability control index of each system functional failure mode is determined.

[0160] The task scenario model and the system function model are determined by modeling the target signal system using the model-based systems engineering (MBSE) modeling method. The task scenario model includes multiple task profiles, and the system function model includes multiple system functions.

[0161] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the reliability analysis method for a rail transit signaling system provided by the methods described above, the method comprising:

[0162] Based on the task scenario model and system function model of the target signal system, a task function relationship model is determined, which is used to represent the mapping relationship between each task profile and each system function.

[0163] Based on the task function relationship model and the failure modes of each system function, a fault tree is determined through failure analysis. The fault tree is used to represent the logical relationship between each system function failure mode and the task failure.

[0164] Based on the fault tree and the quantitative indicators of task reliability of each task profile, the first failure probability control index of each system functional failure mode is determined.

[0165] The task scenario model and the system function model are determined by modeling the target signal system using the model-based systems engineering (MBSE) modeling method. The task scenario model includes multiple task profiles, and the system function model includes multiple system functions.

[0166] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0167] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus necessary general-purpose hardware platforms, and of course, it can also be implemented using hardware. Based on this understanding, the above technical solutions are essentially, or rather, improvements upon existing...

[0168] The technological contributions can be embodied in the form of software products, which can be stored in computer-readable storage media such as ROM / RAM, magnetic disks, and optical discs.

[0169] Disks, etc., including a number of instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the methods described in various embodiments or some portions of embodiments.

[0170] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, the present invention is not intended to limit it.

[0171] Those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A reliability analysis method for a rail transit signaling system, characterized in that, include: Based on the task scenario model and system function model of the target signal system, a task function relationship model is determined, which is used to represent the mapping relationship between each task profile and each system function. Based on the task function relationship model and the failure modes of each system function, a fault tree is determined through failure analysis. The fault tree is used to represent the logical relationship between each system function failure mode and the task failure. Based on the fault tree and the quantitative indicators of task reliability of each task profile, the first failure probability control index of each system functional failure mode is determined. The task scenario model and the system function model are determined by modeling the target signal system using the model-based systems engineering (MBSE) modeling method. The task scenario model includes multiple task profiles, and the system function model includes multiple system functions. The step of determining the first failure probability control index for each system functional failure mode based on the fault tree and the quantitative indicators of task reliability for each task profile includes: determining the task failure probability control index for each task profile based on the quantitative indicators of task reliability for each task profile; performing failure probability control index analysis operations on each task profile based on the fault tree and the task failure probability control index for each task profile to determine the analysis results for each task profile, wherein the analysis results include one or more second failure probability control indices corresponding to each target system functional failure mode, wherein the target system functional failure mode is the system functional failure mode associated with the task profile determined based on the fault tree; classifying and aggregating multiple second failure probability control indices according to the system functional failure mode based on the analysis results of each task profile to determine the failure probability control index set for each system functional failure mode; and determining the first failure probability control index for each system functional failure mode based on the failure probability control index set for each system functional failure mode, wherein the first failure probability control index is the item with the smallest probability value in the failure probability control index set. The failure probability control index analysis operation includes: obtaining at least one minimum cut set of the task profile based on the fault tree, wherein the minimum cut set includes one or more target system functional failure modes; determining the failure probability control index of each minimum cut set based on the task failure probability control index and a first quantity, wherein the first quantity is the total number of the at least one minimum cut set; determining the second failure probability control index of each target system functional failure mode in different minimum cut sets based on the failure probability control index of each minimum cut set and a second quantity corresponding to each minimum cut set, wherein the second quantity is the total number of target system functional failure modes in the minimum cut sets; and determining the analysis result of the task profile based on the second failure probability control index of each target system functional failure mode in different minimum cut sets.

2. The reliability analysis method for rail transit signaling systems according to claim 1, characterized in that, After determining the first failure probability control index for each system functional failure mode based on the quantitative indicators of task reliability of the fault tree and each task profile, the method further includes: Based on the system architecture model of the target signal system, the fault modes of each system unit are determined, and the system unit fault modes are used to characterize the fault logic of the system unit. Based on the failure modes of each system unit, the logical relationship between system unit failures and system function failures is analyzed to determine the system failure propagation model. Based on the system fault propagation model and the first fault probability control index of each system functional fault mode, the third fault probability control index of each system unit fault mode is determined. The system architecture model is determined by modeling the target signal system using the Model-Based Systems Engineering (MBSE) modeling method, and the system architecture model includes multiple system units.

3. The reliability analysis method for rail transit signaling systems according to claim 2, characterized in that, The system unit fault modes include state space variables, unit port variables, trigger event information, state transition functions, transfer functions, and port connection relationship functions.

4. The reliability analysis method for rail transit signaling systems according to claim 2 or 3, characterized in that, After determining the third fault probability control index for each system unit fault mode based on the system fault propagation model and the first fault probability control index for each system functional fault mode, the method further includes: Based on the third failure probability control index of each system unit failure mode and the failure mode of each hardware component, the fourth failure probability control index of each hardware component failure mode is determined by analyzing the logical relationship between the hardware component failure modes and the system unit failure.

5. A reliability analysis device for a rail transit signaling system, characterized in that, include: The first determining module is used to determine the task function relationship model based on the task scenario model and system function model of the target signal system. The task function relationship model is used to represent the mapping relationship between each task profile and each system function. The second determining module is used to determine a fault tree through failure analysis based on the task function relationship model and the failure modes of each system function. The fault tree is used to represent the logical relationship between the failure modes of each system function that lead to task failure. The third determination module is used to determine the first failure probability control index of each system functional failure mode based on the fault tree and the quantitative indicators of task reliability of each task profile. The task scenario model and the system function model are determined by modeling the target signal system using the model-based systems engineering (MBSE) modeling method. The task scenario model includes multiple task profiles, and the system function model includes multiple system functions. The step of determining the first failure probability control index for each system functional failure mode based on the fault tree and the quantitative indicators of task reliability for each task profile includes: determining the task failure probability control index for each task profile based on the quantitative indicators of task reliability for each task profile; performing failure probability control index analysis operations on each task profile based on the fault tree and the task failure probability control index for each task profile to determine the analysis results for each task profile, wherein the analysis results include one or more second failure probability control indices corresponding to each target system functional failure mode, wherein the target system functional failure mode is the system functional failure mode associated with the task profile determined based on the fault tree; classifying and aggregating multiple second failure probability control indices according to the system functional failure mode based on the analysis results of each task profile to determine the failure probability control index set for each system functional failure mode; and determining the first failure probability control index for each system functional failure mode based on the failure probability control index set for each system functional failure mode, wherein the first failure probability control index is the item with the smallest probability value in the failure probability control index set. The failure probability control index analysis operation includes: obtaining at least one minimum cut set of the task profile based on the fault tree, wherein the minimum cut set includes one or more target system functional failure modes; determining the failure probability control index of each minimum cut set based on the task failure probability control index and a first quantity, wherein the first quantity is the total number of the at least one minimum cut set; determining the second failure probability control index of each target system functional failure mode in different minimum cut sets based on the failure probability control index of each minimum cut set and a second quantity corresponding to each minimum cut set, wherein the second quantity is the total number of target system functional failure modes in the minimum cut sets; and determining the analysis result of the task profile based on the second failure probability control index of each target system functional failure mode in different minimum cut sets.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the reliability analysis method for rail transit signaling systems as described in any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the reliability analysis method for rail transit signaling systems as described in any one of claims 1 to 4.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the reliability analysis method for rail transit signaling systems as described in any one of claims 1 to 4.

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