Fault propagation diagnosis method for comprehensive monitoring system under distributed avionics architecture

Abstract

The invention provides a fault propagation diagnosis method for an integrated monitoring system under a distributed avionics architecture, belongs to the technical field of aircraft environment integrated monitoring systems, and specifically constructs a task-function-resource cross-layer formalized model oriented to the integrated monitoring system of the distributed avionics architecture, carries out coupling correlation analysis of the integrated monitoring system, and provides a fault propagation diagnosis method for the integrated monitoring system of the distributed avionics architecture. On this basis, a system coupling association risk correction matrix is introduced from three dimensions of system task priority, system function failure state influence level and fault propagation time factor, a coupling association risk coefficient of the system is quantified, and a fault propagation path of the system is combined to calculate a fault propagation probability and fault propagation intensity of the comprehensive monitoring system; the fault propagation key risk path of the comprehensive monitoring system is identified, the comprehensive fault propagation intensity of each fault node is analyzed and compared, the fault diagnosis efficiency and accuracy of the comprehensive monitoring system are improved, and the fault diagnosis workload of the comprehensive monitoring system is reduced.

Description

Technical Field

[0001] This application relates to the field of integrated environmental monitoring systems for aircraft, and in particular to a method for diagnosing fault propagation in an integrated monitoring system under a distributed avionics architecture. Background Technology

[0002] Distributed Integrated Modular Avionics (DIMA) is a current trend in civil aircraft avionics system architecture. The DIMA architecture allows computing resources and actuators to be distributed across different parts of the aircraft. Its design philosophy is more flexible than the IMA architecture, placing greater emphasis on the scalability, adaptability, and interchangeability of system modules. Under the open system architecture of DIMA, the functional divisions of aircraft electromechanical, control, and avionics systems are gradually merging and permeating each other. Traditional integrated environmental surveillance systems will also be gradually incorporated into the DIMA platform, leading to the development of distributed avionics architecture integrated surveillance systems based on the DIMA platform. For specific systems with high real-time requirements, such as Traffic Collision Avoidance Systems (TCAS), low latency, low jitter, and high deterministic data transmission are essential. With the introduction of the DIMA shared computing resource platform, powerful computing resources can be provided to meet the high real-time performance requirements of integrated surveillance systems.

[0003] Conventional fault diagnosis methods can be divided into manual analysis and artificial intelligence-based diagnostic analysis. The former relies on the operator's subjective judgment and work experience; the latter mainly covers fault diagnosis methods based on information monitoring and training models, enabling automated analysis and evaluation of fault propagation behavior. With the introduction of the DIMA shared computing resource platform, the high coupling between various general processing modules (GPMs) must be considered. Furthermore, sensors themselves are susceptible to environmental influences, leading to deviations in the acquired signals and consequently, inaccurate analysis results. Additionally, the establishment of fault model libraries is complex, and model training is costly. Currently, no fault propagation diagnosis method has been developed specifically for distributed integrated surveillance systems. The aforementioned methods lack quantitative analysis of the coupling relationships between modules within the system and are not suitable for fault diagnosis in distributed avionics architecture integrated surveillance systems. Summary of the Invention

[0004] In view of this, this application provides a fault propagation diagnosis method for an integrated surveillance system under a distributed avionics architecture, which solves the problems in the prior art, improves the efficiency and accuracy of fault diagnosis of the integrated surveillance system, and reduces the workload of fault diagnosis of the integrated surveillance system.

[0005] This application provides a fault propagation diagnosis method for an integrated surveillance system under a distributed avionics architecture, which adopts the following technical solution: A method for diagnosing fault propagation in an integrated surveillance system under a distributed avionics architecture includes: Step 1: Construct a cross-layer formal model of the integrated monitoring system and output the cross-layer mapping correlation matrix of the system; Step 2: Conduct mapping coupling correlation analysis between cross-layer resources of the integrated monitoring system, calculate the fault propagation coupling matrix and fault propagation routing matrix of the integrated monitoring system, and output the cross-layer fault propagation path set of the integrated monitoring system; Step 3: Construct the failure severity matrix of the integrated monitoring system, calculate the coupling correlation risk coefficient, output the corrected failure distribution function of the integrated monitoring system, and calculate the corrected failure probability of the integrated monitoring system. Step 4: Calculate the propagation probability of each fault path in the integrated monitoring system. Step 5: Quantify the flow frequency in the cross-layer fault propagation path set of the integrated monitoring system, and calculate the fault propagation intensity of each fault propagation path in the integrated monitoring system. Step 6: Based on the fault propagation intensity of the integrated monitoring system, identify the critical path of fault propagation and obtain the fault diagnosis result of the integrated monitoring system.

[0006] Optionally, step 1 specifically includes: Combining the architectural features and standards of integrated monitoring systems under the distributed DIMA architecture, this paper analyzes the cross-layer mapping mechanism of tasks, functions, and resources of integrated monitoring systems for formal requirements, completes the layered architecture decomposition of integrated monitoring systems under the DIMA platform, constructs a cross-layer formal model of the system from the system task layer, function layer, and resource layer, and improves the mapping relationship between different layers of the integrated monitoring system. System task layer element T = {t1, t2, ... t k}, where k is the number of tasks; system function layer element F = {f1,f2,…f m}, where m is the number of system functions; system resource layer element D = {d1,d2,…d} n}, where n is the number of resource layer processing modules; The mapping and correlation relationships between different levels of the integrated monitoring system are defined, and the cross-level mapping and correlation matrix M is defined. tf To represent the mapping relationship between system task layer and functional layer elements, define the functional-resource layer mapping association matrix M. fd To represent the mapping relationship between system functional layer and resource layer elements, define the task-resource layer mapping association matrix M. td The mapping relationship between system task layer and resource layer elements is represented by the cross-layer mapping correlation matrix M = [M tf Mfd M td ].

[0007] Optionally, step 2 specifically includes: In the system cross-layer mapping correlation matrix, the Floyd algorithm is introduced to carry out mapping coupling correlation analysis between the system task layer, functional layer and resource layer, calculate the system fault propagation coupling matrix and fault propagation routing matrix, and output the cross-layer fault propagation path set of the integrated monitoring system.

[0008] Optionally, step 3 specifically includes: By combining task scenario identification system task priority, a system task priority quantification matrix is ​​constructed based on the task-resource cross-layer correlation matrix of the comprehensive monitoring system. By introducing the functional hazard assessment method in SAE ARP4761, the failure states of each function of the system are identified, the impact level of each functional failure state is analyzed and quantified, and a system function impact level matrix is ​​constructed based on the cross-layer correlation matrix of the integrated monitoring system function-resource. Based on a large number of fault injection simulation experiments, the direct propagation time matrix of the integrated monitoring system is output, and the fault propagation time matrix of the integrated monitoring system is constructed by combining the cross-layer fault propagation path set of the integrated monitoring system. By combining the system task priority quantification matrix, the system function impact level matrix, and the system fault propagation time matrix, a failure severity matrix of the integrated monitoring system is constructed. The coupling correlation risk between the system fault propagation path sets is quantified. By introducing membership functions, a mapping relationship between the coupling degree between the system fault propagation path sets and the system failure severity matrix is ​​established. The corrected coupling correlation risk coefficient is calculated, the corrected failure distribution function of each level element node of the integrated monitoring system is output, and the corrected failure probability of the integrated monitoring system is calculated.

[0009] Optionally, step 4 specifically includes: Based on the integrated failure distribution function and integrated failure probability of the integrated monitoring system, considering the degree of failure impact of each GPM node in the system resource layer and the directed edges of the failure path, the node failure impact coefficient and coupling function are introduced. Combined with the cross-layer failure propagation path set of the integrated monitoring system, the propagation probability of each failure path of the integrated monitoring system is calculated and output.

[0010] Optionally, step 5 specifically includes: The flow frequency of faults in the fault propagation path set is quantified by introducing an edge betweenness correction factor; The edge betweenness correction factor is the flow frequency of the fault in the fault propagation path set, which is calculated by the ratio of the number of paths through directed edges of any node in the fault propagation path set to the total number of paths between any node. The fault propagation intensity of each fault propagation path in the integrated monitoring system is calculated by combining the propagation probability of each fault path.

[0011] In summary, this application includes the following beneficial technical effects: The fault propagation diagnosis method for integrated surveillance systems under a distributed avionics architecture provided in this application adopts a hierarchical formal modeling technique to construct a task-function-resource hierarchical model for integrated surveillance systems, which effectively reduces the complexity of system modeling. The fault propagation diagnosis method for integrated surveillance systems under a distributed avionics architecture provided in this application conducts coupling correlation analysis based on system hierarchical modeling. It calculates the fault propagation strength of the integrated surveillance system from two dimensions: system fault propagation probability and system fault propagation structure, and identifies the critical path of fault propagation. This method solves the problems of traditional fault propagation diagnosis methods, such as reliance on human experience which is prone to errors and lack of consideration and proof of quantitative analysis of the coupling relationship between various modules within the system. It improves the fault diagnosis efficiency of integrated surveillance systems and reduces the workload of fault diagnosis for integrated surveillance systems under a distributed avionics architecture. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart illustrating the implementation of the fault propagation diagnosis method for the integrated surveillance system under the distributed avionics architecture of this application. Figure 2 This is a schematic diagram of the task-function-resource hierarchical mapping mechanism of the integrated surveillance system under the distributed avionics architecture fault propagation diagnosis method of this application. Figure 3 This is a schematic diagram of the layered architecture of the integrated surveillance system for the fault propagation diagnosis method of the integrated surveillance system under the distributed avionics architecture of this application. Figure 4 This is a schematic diagram of the cross-layer fault propagation path of the integrated surveillance system under the distributed avionics architecture in an embodiment of this application. Detailed Implementation

[0014] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0015] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. This application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0016] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this application, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0017] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. The illustrations only show the components related to this application and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0018] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0019] This application provides a method for diagnosing fault propagation in an integrated surveillance system under a distributed avionics architecture.

[0020] like Figure 1 As shown, a fault propagation diagnosis method for an integrated surveillance system under a distributed avionics architecture includes: Step 1: Construct a cross-layer formal model of the integrated monitoring system and output the system's cross-layer mapping and correlation matrix. Combining the architectural characteristics of a distributed integrated monitoring system and relevant standards, analyze the task-function-resource layered mapping mechanism of the demand-oriented integrated monitoring system. Complete the layered architecture decomposition of the integrated monitoring system under a distributed architecture. Based on this, construct a system hierarchical model from the system task layer, function layer, and resource layer, outputting the layered mapping and correlation matrix of the integrated monitoring system, and improving the mapping and correlation relationships between different layers of the integrated monitoring system.

[0021] Step 2: In the system cross-layer mapping correlation matrix, the Floyd algorithm is introduced to carry out mapping coupling correlation analysis between the system task layer, functional layer and resource layer, calculate the system fault propagation coupling matrix and fault propagation routing matrix, and output the cross-layer fault propagation path set of the integrated monitoring system.

[0022] Step 3: Construct a failure severity matrix for the integrated monitoring system, calculate the coupling correlation risk coefficient, output the corrected failure distribution function for the integrated monitoring system, and calculate the corrected failure probability of the integrated monitoring system. Specifically, a failure severity matrix is ​​constructed by introducing a system coupling correlation risk correction matrix from three dimensions: system task priority, the impact level of system functional failure status, and fault propagation time. The corrected coupling correlation risk coefficient is then calculated by introducing a membership function to quantify the calculation. The corrected failure distribution function for each level of element nodes in the integrated monitoring system is output, and the corrected failure probability of the integrated monitoring system is calculated.

[0023] Step 4: Calculate the propagation probability of each fault path in the integrated monitoring system. Specifically, based on the integrated failure distribution function and integrated failure probability of the integrated monitoring system, considering the fault impact of each GPM node in the system resource layer and the directed edges of the fault paths, a node failure impact coefficient and a coupling degree function are introduced. Combined with the cross-layer fault propagation path set of the integrated monitoring system, the propagation probability of each fault path in the integrated monitoring system is calculated. Here, GPM stands for General Processing Module.

[0024] Step 5: Quantify the flow frequency of faults in the cross-layer fault propagation path set of the integrated monitoring system, and calculate the fault propagation intensity of each fault propagation path in the integrated monitoring system. Specifically, the flow frequency of faults in the fault propagation path set is quantified by introducing an edge betweenness correction factor; the edge betweenness correction factor is the flow frequency of faults in the fault propagation path set, calculated by the ratio of the number of paths through directed edges from any node to the total number of paths between any node; the fault propagation intensity of each fault path in the integrated monitoring system is calculated by combining the propagation probability of each fault path.

[0025] Step 6: Based on the fault propagation intensity of the integrated monitoring system, identify the critical path of fault propagation and obtain the fault diagnosis result of the integrated monitoring system. The critical path of fault propagation refers to the path that is most likely to cause the fault to spread rapidly and cause serious consequences. The higher the fault propagation intensity value, the higher the fault propagation priority. The comprehensive fault propagation intensity for a certain node is calculated by comprehensively calculating the fault propagation intensity of each path based on identifying all paths that take that node as the source of fault propagation.

[0026] The fault propagation diagnosis method for an integrated surveillance system under a distributed avionics architecture provided in this application can be divided into three parts: the construction of a cross-layer formal model of the integrated surveillance system, the calculation of the fault propagation path set and fault propagation probability, and the identification of critical fault propagation paths. To establish the cross-layer formal model of the integrated surveillance system, it is necessary to first obtain the relevant system design documents and system functional requirements documents, clarify the configuration of the task layer, functional layer, and resource layer of the integrated surveillance system, and on this basis, construct the cross-layer mapping correlation matrix of the integrated surveillance system to improve the mapping correlation relationship of resources between different layers of the integrated surveillance system under the distributed avionics architecture, serving as input for subsequent mapping coupling correlation analysis. Secondly, the Floyd algorithm and membership functions are introduced to conduct mapping coupling correlation analysis between the system task layer, functional layer, and resource layer, calculate the system fault propagation coupling matrix and fault propagation routing matrix, and identify the cross-layer fault propagation path set of the integrated surveillance system. Based on this, a failure severity matrix is ​​constructed by introducing a system coupling correlation risk correction matrix from three dimensions: system task priority, system function failure status impact level, and failure propagation time. The corrected coupling correlation risk coefficient is calculated by introducing a membership function, and the corrected failure distribution function of each level element node of the integrated surveillance system is output to calculate the corrected failure probability of the integrated surveillance system. Finally, the failure propagation intensity of the integrated surveillance system is calculated from two dimensions: system failure propagation probability and system failure propagation structure. The critical path of failure propagation is identified, and the comprehensive failure propagation intensity of each failure node is analyzed and compared to obtain the failure diagnosis results of the integrated surveillance system under the distributed avionics architecture.

[0027] This application utilizes a formalized cross-layer modeling mechanism for integrated surveillance systems to construct a task-function-resource cross-layer formal model for a distributed avionics architecture integrated surveillance system. It then conducts coupling correlation analysis of the integrated surveillance system and introduces a system coupling correlation risk correction matrix based on three dimensions: system task priority, the impact level of system functional failure states, and fault propagation time. This matrix quantifies the system's coupling correlation risk coefficients and calculates the fault propagation probability and intensity of the integrated surveillance system by combining the system's fault propagation paths. It identifies key risk paths for fault propagation in the integrated surveillance system and analyzes and compares the overall fault propagation intensity of each fault node. This approach solves the problems of traditional fault diagnosis methods, such as complex modeling processes, high model training costs, reliance on human experience leading to errors, and a lack of quantitative analysis and proof of the coupling relationships between various modules within the system. This improves the efficiency and accuracy of fault diagnosis for integrated surveillance systems and reduces the workload of fault diagnosis.

[0028] like Figure 2 and Figure 3As shown, in one embodiment, the layered architecture of the integrated surveillance system for distributed avionics includes three layers: a mission layer, a functional layer, and a resource layer. The mapping relationships between different layers in this embodiment include one-to-one, one-to-many, many-to-one, and many-to-many relationships. Furthermore, the main sources of system fault propagation include the failure of system components themselves and the failure coupling between modules. Under the distributed avionics architecture, the functional interactions between modules in the resource layer of the integrated surveillance system are complex, and system faults propagate from lower module-level hierarchies to higher hierarchies. From the perspective of integrated surveillance system design, this embodiment mainly involves information display and alarm functions, weather detection functions, traffic collision avoidance, terrain collision avoidance, wind shear detection functions, and communication and navigation functions. The above functions are achieved using the General Processing Module (GPM) at the resource layer of the distributed avionics architecture. This embodiment configures nine GPMs, each residing in a single application, for the integrated surveillance system resource layer. These GPMs are configured according to application partitions, including main processing and graphics generation applications, WXR weather applications, TCAS traffic collision avoidance applications, TAWS terrain collision avoidance applications, alarm applications, control command output, communication processing applications, ADS-B applications, and RWS applications. All GPMs are fully interconnected, meaning it is conservatively assumed that there is a path for fault propagation between any two GPMs at the mission resource layer. Specifically, WXR stands for Weather Radar; TCAS stands for Traffic Collision Avoidance System; TAWS stands for Terrain Awareness and Warning System; ADS-B stands for Automatic Dependent Surveillance-Broadcast; and RWS stands for Radar Warning System.

[0029] The system resource layer general processing module (GPM) converts data from the remote distributed data acquisition unit (RDC) to ARINC664 format. When the RDC acts as an output device, it can convert ARINC664 format to analog, discrete, and other data bus formats, thus connecting various subsystem sensors, actuators, and other data buses to the resource layer GPM module of the AFDX network. Furthermore, the RDC can be configured to host relatively simple control command output applications in partitions, namely the integrated remote data acquisition unit (IRDU), serving as a backup redundancy for some core critical GPMs. This facilitates rapid configuration for dynamic reconfiguration and improves system security. The RDC stands for Remote Data Concentrator. RDCs communicate with each other via avionics full-duplex interactive Ethernet (AFDX). The distributed data acquisition unit (RDC) is an LRU (Local Runner), located remotely from the main aircraft computing resources but close to the connected subsystem devices. It possesses scalable remote interfaces and processing capabilities, providing data conversion input / output functions for the integrated surveillance system. As an input device, the RDC can convert analog, discrete, and other data buses such as ARINC429. IRDC stands for Integrated Remote Data Concentrator.

[0030] The specific steps of the fault propagation diagnosis method for the integrated surveillance system under the distributed avionics architecture in this embodiment include: Step 1: Combining the characteristics of the distributed integrated monitoring system architecture and relevant standards, analyze the task-function-resource hierarchical mapping mechanism of the demand-oriented integrated monitoring system, complete the hierarchical architecture decomposition of the integrated monitoring system under the distributed architecture, and on this basis, construct a system hierarchical model from the system task layer, function layer and resource layer, output the hierarchical mapping association matrix of the integrated monitoring system, and improve the mapping association relationship between different layers of the integrated monitoring system.

[0031] The hierarchical model of the integrated monitoring system described in this embodiment includes: The system mission layer, which is based on civil aircraft flight missions with specific requirements, defines the integrated surveillance system mission layer T = {t1, t2, ... t}. k}, where t is an element of the task layer of the integrated monitoring system, and k is the number of tasks.

[0032] The system functional layer consists of the functions of the corresponding subsystems, defining the integrated monitoring system function F = {f1, f2, ... f m}, where f is an element of the integrated monitoring system's functional layer, and m is the number of system functions.

[0033] The system resource layer, through powerful shared computing resources, enables rapid loading of application software or control algorithms. The integrated monitoring system resource layer is defined as D = {d1, d2, ... d...}. n}, where d is an element of the resource layer of the integrated monitoring system, and n is the number of resource layer processing modules.

[0034] To address the mapping relationships between different levels of the integrated monitoring system, a cross-level mapping relationship matrix M is defined. tf This represents the mapping relationship between system task layer and functional layer elements: (1); In equation (1), Represents task t i With function f j There is a mapping relationship. This indicates that there is no mapping relationship.

[0035] Similarly, the function-resource layer mapping correlation matrix M can be obtained. fd as follows: (2); In equation (2), Indicates function f i With resource d j There is a mapping relationship. This indicates that there is no mapping relationship.

[0036] Based on matrix operations Define the task-resource layer mapping correlation matrix of the integrated monitoring system. : (3); In equation (3), Represents task t i With function d j There is a mapping relationship. This indicates no mapping relationship. Based on this, output the hierarchical mapping correlation matrix [M]. tf M fd M td ].

[0037] Step 2: Based on the cross-layer mapping and correlation matrix of the integrated monitoring system constructed in the previous steps, the Floyd algorithm is introduced to conduct mapping coupling and correlation analysis between the system task layer, functional layer, and resource layer. The system fault propagation coupling matrix and fault propagation routing matrix are calculated to identify the cross-layer fault propagation path set of the integrated monitoring system, such as... Figure 4As shown. Based on the hierarchical mapping association matrix constructed in this embodiment, information transmission between different levels introduces coupling relationships between different levels. The coupling forms between the levels of the integrated monitoring system include direct coupling and indirect coupling. Direct coupling indicates direct information interaction between element nodes in a certain level or mapping by the same element in the upper level. Indirect coupling is an extension of direct coupling, representing the indirect coupling relationship between elements in different levels, that is, information transmission through several intermediate element nodes. The quantification of the coupling relationship between each level is reflected by the following coupling matrix: (4); In equation (4), C represents the coupling correlation matrix of each level of the integrated monitoring system, which is derived from the direct coupling matrix C. d and indirect coupling matrix C u composition; Represents the direct coupling matrix C d The element takes a value of 0 or 1, where a value of 0 indicates node d. i With d j There is no coupling relationship, and a value of 1 indicates that node d has no coupling relationship. i With d j There is a direct coupling relationship, i.e., the coupling degree is 1, and fault propagation requires 1 step; Represents the indirect coupling matrix C u The element takes the value of a positive integer greater than 1, where a value of n represents node d. i With d j There is an indirect coupling relationship, i.e., the coupling degree is n, and the node is d. i to d j The fault propagation requires n steps; using the above coupling matrix as input, the Floyd algorithm is introduced to calculate the fault propagation path of each level element of the integrated monitoring system, and n is recursively updated. 3 Next, based on this, further selection from node d i to node d j The shortest path for fault propagation is found, and a set of cross-layer fault propagation paths for the integrated monitoring system is constructed, G = {(D t E t ), (D f E f ), (D d E d )}, where D is the node set of the corresponding level D = {D i1 D i2 ,…D in}, where E is the set of directed edges E = {E} that minimizes the shortest path for fault propagation at the corresponding level. i1→2 E i2→3 ,…E im→n} represents the fault propagation relationship between element nodes at each level, where D tE represents the task layer node set of the integrated monitoring system. t E represents the directed edge set of the task layer of the integrated monitoring system. f D represents the set of functional layer nodes of the integrated monitoring system. d E represents the resource layer node set of the integrated monitoring system. d This represents the directed edge set of the resource layer of the integrated monitoring system.

[0038] Step 3: Construct a failure severity matrix by introducing a system coupling correlation risk correction matrix from three dimensions: system task priority, system function failure status impact level, and failure propagation time. Then, quantify and calculate the corrected coupling correlation risk coefficient by introducing a membership function, output the corrected failure distribution function of each level element node of the integrated monitoring system, and calculate the corrected failure probability of the integrated monitoring system.

[0039] First, by combining the task scenario with the task priority of the system, a priority sequence T for the integrated monitoring system task set T is output. s = {t S1 ,t S2 ,…t Sk}, where t Sk Represents the task layer element t of the integrated surveillance system k The priority is determined by k, where k is a positive integer greater than 1; a task priority quantification matrix for the integrated monitoring system is constructed based on the task-resource cross-layer correlation matrix of the integrated monitoring system. ; Secondly, the functional hazard assessment method from SAE ARP4761 is introduced to identify the failure states of each function of the system and analyze and quantify the impact level of each functional failure state. The failure state impact level includes five levels: catastrophic, hazardous, significant, minor, and no impact, with severity decreasing sequentially. Therefore, these levels are mapped sequentially from 5 to 1, defining the failure state level F for each function of the integrated monitoring system. c = {f c1 ,f c2 ,…f cm}, f cm f represents the impact level of the failure state of the integrated monitoring system function m. cm A system function impact level matrix is ​​constructed based on the cross-layer correlation matrix of integrated monitoring system functions and resources, with values ​​ranging from positive integers 1 to 5. .

[0040] Meanwhile, based on a large number of fault injection simulation experiments, the direct propagation time matrix of the fault in the integrated monitoring system is output. : (5); In equation (5), n represents the number of element nodes in the resource layer of the integrated monitoring system. Represents node d i With d j There is no direct coupling, and the direct fault propagation time is 0. Represents resource layer node d i With d j There is a direct coupling relationship, i.e., node d i to d j The direct fault propagation time is x seconds. Based on this, and combined with the cross-layer fault propagation path set G of the integrated monitoring system, the fault propagation time matrix of the integrated monitoring system is calculated. : (6); In equation (6), This represents an element in the set G of cross-layer fault propagation paths in the integrated monitoring system, representing node d. i to d j Fault propagation path, where n represents the number of resource layer element nodes in the integrated monitoring system. Represents node d i to d j Fault propagation time along the fault propagation path.

[0041] Finally, combined with the system task priority quantization matrix System Function Impact Level Matrix and system fault propagation time matrix Construct a comprehensive monitoring system failure severity matrix Quantify the coupling and correlation risk between sets of system fault propagation paths: (7); In equation (7), T represents the matrix transpose operation, and n represents the number of element nodes in the resource layer of the integrated monitoring system. This represents the Hadamard product of two matrices of the same dimension. To quantify the initial failure severity, a membership function is introduced to map the initial failure severity of the integrated monitoring system to the interval [1-5]. The following membership function is given: (8); In equation (8), The degree of indirect coupling is represented by ; a and c are correction factors, which can be quantified using the indirect coupling matrix. In this embodiment, the severity of failure in the integrated monitoring system decreases non-linearly with the degree of coupling between different levels of the system. That is, the lower the coupling between two points, the simpler the fault propagation pattern, and the higher the severity of the resulting failure. Therefore, when there is a direct coupling relationship between the GPMs in the resource layer, i.e., the degree of coupling... At that time, the severity of the failure has the greatest impact, and correspondingly When the coupling degree is At that time, the severity of the failure has the least impact, so take Substituting these values ​​into equation (8) yields the values ​​of the correction factors a and c. Combining this with equation (7), the calculation... Based on the severity matrix, the modified integrated monitoring system coupling risk coefficient is calculated: (9); In equation (9), Let n be the number of GPMs in the resource layer of the integrated monitoring system. Based on this, and considering the coupling failure effects of nodes at each level, the corrected failure distribution function for each element node of the integrated monitoring system is calculated and output. Calculate the probability of failure of the integrated monitoring system correction.

[0042] (10); In equation (10), λ i The initial failure rate of each level of node. The revised integrated monitoring system coupling correlation risk coefficient, where t represents time. express .

[0043] Step 4: Based on the integrated failure distribution function and integrated failure probability of the integrated monitoring system, considering the failure impact of each GPM node in the system resource layer and the directed edges of the failure path, a node failure impact coefficient and a coupling degree function are introduced. Combined with the cross-layer failure propagation path set of the integrated monitoring system, the propagation probability of each failure path in the integrated monitoring system is calculated and output. In this embodiment, the propagation probability of each failure path in the integrated monitoring system is calculated from two dimensions: the failure impact of each element node at each level of the system and the failure impact of directed edges. The failure impact of a node is determined by the node failure impact coefficient and the node's modified failure distribution function. The node failure impact coefficient is quantified by the influence of the node failure probability change rate on the overall system failure rate change. Directed edges, as the transmission medium between two nodes, mainly characterize the failure impact of upstream nodes on downstream nodes at each level of the integrated monitoring system. Therefore, a coupling degree function is introduced to calculate the failure propagation probability of directed edges. The impact of directed edge faults is quantified, and the propagation probability of each fault path in the integrated monitoring system is calculated and output by combining the cross-layer fault propagation path set of the integrated monitoring system. : (11); In equation (11), The node d represents the centralized node of the cross-layer fault propagation path in the integrated monitoring system. a to d b The probability of fault propagation between them Indicates time, node failure impact coefficient F i (t) is the corrected failure distribution function in step three. To provide the overall failure distribution function of the system resource layer of the comprehensive monitoring system, Let f be the failure propagation probability of a directed edge. The node d represents the centralized node of the cross-layer fault propagation path in the integrated monitoring system. j The corrected failure distribution function, The calculation method is based on formula (10).

[0044] Step 5: The flow frequency of faults in the cross-layer fault propagation path set of the integrated monitoring system is quantified by introducing an edge betweenness correction factor. Based on this, the fault propagation intensity of each fault propagation path in the integrated monitoring system is calculated by combining the propagation probability of each fault path. In this embodiment, the edge betweenness correction factor is the flow frequency B(e) of the fault in the fault propagation path set. i→j ), from any node d in the fault propagation path set x and d y The space is connected by a directed edge e. i→j The number of paths and any node d x and d y The ratio of the number of all paths is used to represent the degree of control that directed edges of the fault propagation path set have over surrounding nodes. The greater the degree of control, the easier it is for the fault to propagate rapidly, and the higher the fault flow frequency. Taking into account both the propagation probability of the fault path and the fault flow frequency, the fault propagation intensity of each fault propagation path in the integrated monitoring system is calculated. : (12); In equation (12), Let be the probability of fault propagation from node a to node b. Indicates time, B(e) i→j ) represents the fault flow frequency on this path, and θ represents the number of directed edges on this path.

[0045] (13); In equation (13), Represents any node and The number of paths between; Represents any node and Between directed edges The path, Indicates a resource node.

[0046] Step 6: Based on the calculation formula (12) for the fault propagation intensity of the integrated monitoring system, calculate the fault propagation intensity of all paths in the cross-layer fault propagation path set of the integrated monitoring system and the comprehensive fault propagation intensity of each node, identify the critical path of fault propagation, and obtain the fault diagnosis result of the integrated monitoring system. In this embodiment, the critical path of fault propagation refers to the path that is most likely to cause the fault to spread rapidly and cause serious consequences. The greater its fault propagation intensity value, the higher the fault propagation priority. The comprehensive fault propagation intensity of a certain node is calculated by comprehensively calculating the fault propagation intensity of each path based on identifying all paths that take that node as the source of fault propagation, analyzing and comparing the comprehensive fault propagation intensity of each fault node, and obtaining the fault diagnosis result of the integrated monitoring system.

[0047] Through the above methods, the embodiments of this application form a complete process for fault diagnosis of the integrated surveillance system under the distributed avionics architecture. This solves the problems of complex modeling process, high model training and propagation cost, reliance on human experience and easy error, and lack of consideration and proof of quantitative analysis of the coupling relationship between various modules within the system in traditional fault propagation diagnosis methods. This improves the fault diagnosis efficiency of the integrated surveillance system and reduces the workload of fault propagation diagnosis of the integrated surveillance system.

[0048] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for diagnosing fault propagation of an integrated monitoring system under a distributed avionics architecture, characterized in that, The method comprises the following steps: Step 1, constructing a cross-layer formalization model of the integrated monitoring system, and outputting a system cross-layer mapping correlation matrix; Step 2, carrying out mapping coupling correlation analysis between cross-layer resources of the integrated monitoring system, calculating a fault propagation coupling matrix and a fault propagation routing matrix of the integrated monitoring system, and outputting a cross-layer fault propagation path set of the integrated monitoring system; Step 3, constructing a failure severity matrix of the integrated monitoring system, calculating a coupling correlation risk coefficient, outputting a modified failure distribution function of the integrated monitoring system, and calculating a modified failure probability of the integrated monitoring system; Step 4, calculating the propagation probability of each fault path of the integrated monitoring system; Step 5, quantifying the flow frequency in the cross-layer fault propagation path set of the integrated monitoring system, and calculating the fault propagation intensity of each fault propagation path of the integrated monitoring system; Step 6, identifying the fault propagation critical path according to the fault propagation intensity of the integrated monitoring system, and obtaining the fault diagnosis result of the integrated monitoring system.

2. The method of claim 1, wherein, The step 1 specifically comprises: In combination with the architecture characteristics and standards of the integrated monitoring system under the distributed DIMA architecture, the task, function and resource cross-layer mapping mechanism of the integrated monitoring system facing formalization requirements are analyzed, the hierarchical architecture decomposition of the integrated monitoring system under the DIMA platform is completed, the cross-layer formalization model is constructed from the system task layer, the function layer and the resource layer, and the mapping correlation relationship between different layers of the integrated monitoring system is perfected. System task layer elements T = {t1, t2, … t k k}, where k is the number of tasks; system function layer elements F = {f1, f2, … f m m}, where m is the number of system functions; system resource layer elements D = {d1, d2, … d n n}, where n is the number of resource layer processing modules; The mapping and association relationship between different levels of the integrated monitoring system is mapped and associated, and a cross-layer mapping and association matrix M is defined tf The mapping relationship between the system task layer and the functional layer elements is represented, and a functional-resource layer mapping and association matrix M is defined fd The mapping relationship between the system functional layer and the resource layer elements is represented, and a task-resource layer mapping and association matrix M is defined td The mapping relationship between the system task layer and the resource layer elements is represented, and the integrated monitoring system cross-layer mapping and association matrix M = [M tf , M fd , M td ] is defined.

3. The method of claim 2, wherein, The step 2 specifically comprises: In the system cross-layer mapping correlation matrix, the Floyd algorithm is introduced to carry out mapping coupling correlation analysis between the system task layer, the function layer and the resource layer, calculate the system fault propagation coupling matrix and the fault propagation routing matrix, and output the cross-layer fault propagation path set of the integrated monitoring system.

4. The method of claim 1, wherein the method further comprises: The step 3 specifically comprises: In combination with the task scenario identification system task priority, a system task priority quantization matrix is constructed on the basis of the integrated monitoring system task-resource cross-layer correlation matrix; By introducing the function hazard assessment method in SAE ARP4761, the failure state of each function of the system is identified, the influence level of each function failure state is analyzed and quantified, and a system function influence level matrix is constructed on the basis of the integrated monitoring system function-resource cross-layer correlation matrix; Based on the fault injection simulation test of large sample times, the fault direct propagation time matrix of the integrated monitoring system is output, and the integrated monitoring system fault propagation time matrix is constructed in combination with the cross-layer fault propagation path set of the integrated monitoring system; In combination with the system task priority quantization matrix, the system function influence level matrix and the system fault propagation time matrix, a failure severity matrix of the integrated monitoring system is constructed, the coupling correlation risk degree between the system fault propagation path set is quantified, the mapping relationship between the coupling degree between the system fault propagation path set and the system failure severity matrix is established through the introduction of a membership function, a modified coupling correlation risk coefficient is calculated, a modified failure distribution function of each hierarchical element node of the integrated monitoring system is output, and a modified failure probability of the integrated monitoring system is calculated.

5. The method of claim 1, wherein, The step 4 specifically comprises: On the basis of the integrated failure distribution function and the integrated failure probability of the integrated monitoring system, the failure influence coefficient of a node and a coupling degree function are introduced by considering the failure influence degree of each GPM node and the directed edge of the fault path in the system resource layer, and the propagation probability of each fault path of the integrated monitoring system is calculated by combining the cross-layer fault propagation path set of the integrated monitoring system.

6. The method of claim 1, wherein, The step 5 specifically comprises: The edge betweenness correction factor is the flow frequency of the fault in the fault propagation path set, and is calculated by the ratio of the path number of the directed edge passing through any node in the fault propagation path set to the path number between all nodes. The fault propagation strength of each fault propagation path of the integrated monitoring system is calculated by combining the propagation probability of each fault path of the integrated monitoring system. ​

Images