An automatic construction method of fault relation visualization model for avionics communication electronic system

Abstract

The application discloses an automatic construction method of an aviation communication electronic system fault relation visual model, and the method comprises the following steps: acquiring an FMECA table of the aviation communication electronic system, performing knowledge extraction on the FMECA table, generating a fault knowledge graph object according to the extracted required knowledge, and extracting a corresponding object label, performing object matching on the FEMCA table, establishing the relation knowledge between objects according to the matched objects, establishing the relation connection between the graph objects according to the relation knowledge, and establishing the aviation communication electronic system fault relation visual model according to the generated objects, object labels and relation connection between the objects. The aviation communication electronic system FMECA table is taken as the input, the object knowledge and the relation knowledge required by the knowledge graph construction are automatically extracted, the visual aviation communication electronic system fault knowledge graph is automatically generated, and the ability foundation is laid for the intelligent mining of the fault correlation relation based on the high-dimensional knowledge graph required by the intelligent troubleshooting of the aviation communication electronic system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electronic system fault visualization, and particularly relates to an aviation communication electronic system fault relationship visualization model automatic construction method. BACKGROUND

[0002] The aviation communication electronic system is an important component subsystem of the aviation aircraft. Through the hierarchical distributed architecture system of the standard bus, according to the business requirements of the aviation aircraft, the aviation communication electronic system often has multiple functions such as flight control, navigation, communication, and the like, and the association relationship between the business modules is very complex. Association relationship analysis is a commonly used analysis technique, which is often used to analyze the association or correlation in a large amount of information, so as to help describe certain attributes or rules of the analyzed physical object. Aviation communication electronic system fault association relationship analysis is a method of analyzing the association relationship for the aviation communication electronic system fault. By analyzing the influence of the fault between the corresponding system levels, the causes, the test mechanism corresponding to the fault occurrence, the failure probability of the fault occurrence and the like, the purpose is to effectively help the fault analysis personnel to analyze and troubleshoot the fault more accurately and quickly. Knowledge graph is a new visualization technology that combines applied mathematics, graphics and information visualization. Since the knowledge graph adopts the method of constructing a high-latitude graph database to describe the object and the association relationship, the technology is mainly applied in the fields of intelligent search, text analysis, abnormal monitoring, relationship mining and the like, and can obtain higher efficiency compared with the method based on the database.

[0003] With the increase of the aviation communication electronic system functions, the system composition is becoming larger and larger, and the signal cross-linking relationship is becoming more and more complex, which makes the aviation communication system faults more and more. Once the fault occurs, there are many associated elements, and only relying on human experience to analyze and troubleshoot the fault, the execution process is complicated, time-consuming, and the accuracy of the execution result is low, which cannot meet the needs of the field rapid and effective troubleshooting. Therefore, for the field troubleshooting of the aviation communication electronic system, the introduction of the association relationship analysis modeling method represented by the knowledge graph can effectively support the improvement of the field troubleshooting ability.

[0004] The aviation communication electronic system fault association relationship modeling analysis method of the prior art mainly has three aspects of deficiencies:

[0005] First, the modeling method is inefficient. The current fault correlation relationship of the aviation communication electronic system is described by the failure mode and effects analysis method (hereinafter referred to as the FMECA method), and a failure mode and effects analysis table (hereinafter referred to as the FMECA table) is created by the method. This method is a database method, and when facing the current complex aviation communication electronic system, tens of thousands of text data are needed to describe the correlation relationship of different modules, different levels and different faults, and the search and application efficiency is low, which cannot meet the demand of rapid troubleshooting and fault analysis in the field troubleshooting process. The focus of the present application is to innovate the modeling method and support the improvement of troubleshooting analysis efficiency.

[0006] Second, the modeling is artificially dependent. The current fault correlation relationship model construction process of the aviation communication electronic system mainly relies on the experience analysis of the circuit principle by the circuit design personnel and the six personnel, and the correlation relationship model is manually created. The whole model construction is time-consuming and prone to errors. In the face of the troubleshooting of the aviation communication electronic system, it is difficult to quickly and accurately meet the demand of fault correlation relationship model construction, which is the key focus of the present application.

[0007] Third, the visualization effect is poor. The current fault correlation relationship of the aviation communication electronic system is reflected in the FMECA table and stored in the database in the form of text. This presentation method is abstract and not intuitive, and cannot effectively support the rapid analysis and judgment of the troubleshooting personnel. SUMMARY

[0008] The main purpose of the present application is to provide an aviation communication electronic system fault relationship visualization model automatic construction method, which aims to solve the technical problems of low accuracy of the current aviation communication electronic system fault correlation relationship analysis and poor visualization presentation effect.

[0009] To achieve the above purpose, the present application provides an aviation communication electronic system fault relationship visualization model automatic construction method, which comprises the following steps:

[0010] Obtain the FMECA table of the aviation communication electronic system;

[0011] Perform knowledge extraction on the FMECA table, generate fault knowledge graph objects according to the extracted required knowledge, and extract corresponding object labels;

[0012] Perform object matching on the FEMCA table, establish the relationship knowledge between objects according to the matched objects, and establish the relationship connection between the graph objects according to the relationship knowledge;

[0013] According to the generated objects, object labels and relationship connections between objects, the aviation communication electronic system fault relationship visualization model is established.

[0014] Optionally, the FMECA table comprises a system FMECA table and a module FMECA table; wherein:

[0015] The system FMECA table records system FMECA information, which comprises a system name, system failure modes and system failure numbers;

[0016] The module FMECA table comprises a module FMECA table of a module class and a module FMECA table of a unit class, the module FMECA table of the module class comprises a product list table, a device-level FMECA table, a circuit-level FMECA table and a module-level FMECA table, and the module FMECA table of the unit class comprises a product list table, a device-level FMECA table, a circuit-level FMECA table, a module-level FMECA table and a unit-level FMECA table.

[0017] Optionally, the module FMECA table comprises:

[0018] The product list table records comprise a device name, a device number, a device failure rate, a circuit name, a circuit number, a circuit failure rate, a module name, a module number and a module failure rate;

[0019] The device-level FMECA table records comprise a device name, a device failure name, a device failure number, an influence on a circuit, a failure rate and a hazard level;

[0020] The circuit-level FMECA table records comprise a circuit name, a circuit failure name, a circuit failure number, an influence on a module, a hazard level, a test opportunity and an on-board test point;

[0021] The module-level FMECA table records comprise a module name, a module failure name, a module failure number, an influence on a system / unit, a hazard level, a test opportunity and an on-board test point;

[0022] The unit-level FMECA table records comprise a unit name, a unit failure name, a unit failure number, an influence on a system, a hazard level, a test opportunity and an on-board test point.

[0023] Optionally, the knowledge extraction comprises reading knowledge extraction and calculation knowledge extraction; wherein:

[0024] The reading knowledge extraction is used for reading cell information of each sub-table in the FMECA table, and corresponding object and object label are generated;

[0025] The calculation knowledge extraction is used for reading cell information of each sub-table in the FMECA table, and object and object label are generated after calculation.

[0026] Optionally, the calculating knowledge extraction specifically includes numbered calculating knowledge extraction and failure rate calculating knowledge extraction; wherein:

[0027] The numbered calculating knowledge extraction is used for reading the original number, splitting the number of the corresponding object, and generating the corresponding object label.

[0028] The failure rate calculating knowledge extraction is used for reading the failure rate of each cell, and generating the corresponding object label by calculating the total.

[0029] Optionally, the generating fault knowledge graph object specifically includes reading the system FMECA table to generate object knowledge, reading the module class FMECA table to generate object knowledge, and reading the unit class FMECA table to generate object knowledge.

[0030] Optionally, the performing object matching specifically includes: according to the information of each cell in the FMECA table, according to the information searched out by different cells, and matching according to a preset matching relationship.

[0031] Optionally, the establishing relationship knowledge between objects specifically includes: according to the matched objects, generating corresponding relationship knowledge according to a preset generation relationship.

[0032] Optionally, the establishing relationship knowledge specifically includes: generating relationship knowledge according to the system FMECA table, generating relationship knowledge according to the product list table, generating relationship knowledge according to the unit level FMECA table, generating relationship knowledge according to the module level FMECA table, generating relationship knowledge according to the circuit level FMECA table, and generating relationship knowledge according to the device level FMECA table.

[0033] Optionally, the established aviation communication electronic system fault relationship visualization model includes objects, relationships and labels; wherein:

[0034] The objects include system faults, unit faults, module faults, circuit faults, device faults, systems, units, modules, circuits, devices, and airborne test points.

[0035] The labels corresponding to the system faults, unit faults, module faults and circuit faults include names, numbers, failure rates, hazard levels and test opportunities; the label corresponding to the device fault includes names, numbers, failure rates and hazard levels; the labels corresponding to the system, unit, module, circuit and device include names, numbers and failure rates; and the label corresponding to the airborne test point includes a number.

[0036] The belonging section and the initial end of the relationship are connected to two different objects, respectively.

[0037] The present application has the following beneficial effects:

[0038] The aviation communication electronic system fault relation visual model automatic construction method provided by the application can take a standard aviation communication electronic system FMECA table as input, automatically extract object knowledge and relation knowledge required for knowledge graph construction, automatically generate a visual aviation communication electronic system fault knowledge graph, realize automatic construction of a fault correlation relation visual representation model suitable for aviation communication electronic system fault analysis, and lay a foundation for intelligent mining of fault correlation relations based on a high-dimensional knowledge graph required for intelligent fault analysis of an aviation communication electronic system.

[0039] 1. Automatic construction of an aviation communication electronic system fault knowledge graph, automatic extraction of object and relation knowledge from an FMECA table, automatic construction of a knowledge graph to replace a traditional manual model construction method, reduction of errors caused by manual operation, and effective improvement of modeling efficiency

[0040] 2. Visual representation of aviation communication electronic system fault correlation relations, replacement of a traditional fault correlation relation representation method through an FMECA table, visual representation through a visual graph, realization of visual display of fault correlation relations, and facilitation of intelligent mining of correlation relations. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 FIG. 1 is a flowchart of the aviation communication electronic system fault relation visual model automatic construction method of the application.

[0042] Figure 2 FIG. 2 is a schematic diagram of the principle of the aviation communication electronic system fault relation visual model automatic construction method of the application.

[0043] Figure 3 FIG. 3 is an input composition block diagram of the aviation communication electronic system FMECA table of the application.

[0044] Figure 4 FIG. 4 is a schematic diagram of the fault knowledge graph object automatic generation method based on the FMECA table of the application.

[0045] Figure 5 FIG. 5 is a schematic diagram of the fault knowledge graph relation automatic generation method based on the FMECA table of the application.

[0046] Figure 6 FIG. 6 is a schematic diagram of the fault knowledge graph relation automatic generation method based on the FMECA table of the application.

[0047] Figure 7 FIG. 7 is a schematic diagram of the aviation communication electronic system fault correlation relation visual modeling method based on the knowledge graph of the application.

[0048] The object, features and advantages of the present application will be further illustrated in conjunction with the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0049] It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

[0050] At present, in the related technical field, the existing aviation communication electronic system fault correlation relationship modeling analysis method has low modeling efficiency, is artificially dependent, and has poor visualization effect.

[0051] In order to solve this problem, various embodiments of the aviation communication electronic system fault relationship visualization model automatic construction method of the present application are proposed. The aviation communication electronic system fault relationship visualization model automatic construction method provided by the present application automatically extracts object knowledge and relationship knowledge required for knowledge graph construction by taking a standard aviation communication electronic system FMECA table as input, automatically generates a visual aviation communication electronic system fault knowledge graph, and realizes automatic construction of a fault correlation relationship visualization representation model suitable for aviation communication electronic system troubleshooting analysis, thereby laying a foundation for intelligent mining of fault correlation relationship based on high-dimensional knowledge graph required for intelligent troubleshooting of aviation communication electronic system.

[0052] The embodiment of the present application provides an aviation communication electronic system fault relationship visualization model automatic construction method, referring to Figure 1 , Figure 1 The flowchart of the aviation communication electronic system fault relationship visualization model automatic construction method embodiment of the present application.

[0053] In the embodiment, the aviation communication electronic system fault relationship visualization model automatic construction method includes the following steps:

[0054] S100: acquiring an FMECA table of an aviation communication electronic system;

[0055] S200: performing knowledge extraction on the FMECA table, generating fault knowledge graph objects according to the extracted required knowledge, and extracting corresponding object labels;

[0056] S300: performing object matching on the FEMCA table, establishing relationship knowledge between objects according to the matched objects, and establishing relationship connections between graph objects according to the relationship knowledge;

[0057] S400: establishing an aviation communication electronic system fault relationship visualization model according to the generated objects, object labels and relationship connections between objects.

[0058] Specifically, as Figure 2As shown, the embodiment is realized through four steps: first, constructing and inputting the FMECA table; second, automatically generating the object of the knowledge graph through the fault knowledge graph object automatic generation method based on the FMECA table; third, automatically generating the object relationship of the knowledge graph through the fault knowledge graph relationship automatic generation method based on the FMECA table; fourth, completing the visual fault knowledge graph presentation through the aviation communication electronic system fault correlation relationship visual modeling method based on the knowledge graph.

[0059] With reference to Figure 3 The first step of the method of the present application is to construct and input the FMECA table, which consists of two parts. The first part is the input of the system FMECA table, which needs to include the system name, system failure mode, system failure number; the second part is the module FMECA table of each module, including the module FMECA table of the module class and the module FMECA table of the unit class. The module FMECA table of the module class is divided into a product list table, a device level FMECA table, a circuit level FMECA table and a module level FMECA table; the module FMECA table of the unit class is divided into a product list table, a device level FMECA table, a circuit level FMECA table, a module level FMECA table and a unit level FMECA table. The input information of the product list table includes the device name, the device number, the device failure rate, the circuit name, the circuit number, the circuit failure rate, the module name, the module number and the module failure rate; the input information of the device level FMECA table includes the device name, the device failure name, the device failure number, the influence on the circuit, the failure rate, the hazard level; the input information of the circuit level FMECA table includes the circuit name, the circuit failure name, the circuit failure number, the influence on the module, the hazard level, the test timing, the airborne test point; the input information of the module level FMECA table includes the module name, the module failure name, the module failure number, the influence on the system / unit, the hazard level, the test timing, the airborne test point, wherein the input information of the module FMECA table of the unit class is the influence on the unit, and the input information of the module FMECA table of the module class is the influence on the system; the input information of the unit level FMECA table includes the unit name, the unit failure name, the unit failure number, the influence on the system, the hazard level, the test timing, the airborne test point.

[0060] With reference to Figure 4 The second step of the method of the present application is the fault knowledge graph object automatic generation method based on the FMECA table, which consists of two kinds of knowledge extraction methods and three generated object knowledge parts.

[0061] The first knowledge extraction method reads knowledge extraction by reading the cell information of each sub-table in the FMECA table to generate objects and labels; the second knowledge extraction method calculates knowledge extraction by reading the cell information of each sub-table in the FMECA table to generate objects and labels after calculation. Among them, the calculation knowledge extraction includes two types of numbering and failure rate, the numbering calculation knowledge extraction is to read the original numbering, split the corresponding object numbering, and generate the corresponding object label; the failure rate calculation knowledge extraction is to read the failure rate of each cell, and generate the corresponding object label by calculating the total.

[0062] The first part of generating object knowledge is to read the system FMECA table to generate object knowledge, the second part is to read the module class FMECA table to generate object knowledge, and the third part is to read the unit class FMECA table to generate object knowledge.

[0063] The first part of generating object knowledge is to read the system FMECA table to generate object knowledge, the second part is to read the module class FMECA table to generate object knowledge, and the third part is to read the unit class FMECA table to generate object knowledge.

[0064] The second part of the reading unit class FMECA table generation object knowledge is divided into four subparts of reading product list generation object knowledge, reading module level FMECA table generation object knowledge, reading circuit level FMECA table generation object knowledge and reading device level FMECA table generation object knowledge. The reading product list generation object knowledge is that firstly, the device object is generated, different device objects are generated by reading the device name, the device name, device number and device failure rate under each device are read, and the name, number and failure rate tags corresponding to the device object are extracted by reading the knowledge; then, the circuit object is generated, different circuit objects are generated by reading the circuit name, the circuit name, circuit number and circuit failure rate under each circuit are read, and the name, number and failure rate tags corresponding to the circuit object are extracted by reading the knowledge; finally, the module object is generated, different module objects are generated by reading the module name, the module name, module number and module failure rate under each module are read, and the name, number and failure rate tags corresponding to the module object are extracted by reading the knowledge. The reading module level FMECA table generation object knowledge is that firstly, the module fault name is read, the module fault object is generated according to each module fault name, then the module fault name, module fault number, failure rate, test opportunity and airborne test point are read, the name, number, failure rate, hazard level and test opportunity tags corresponding to the module fault object are extracted by reading the knowledge, finally, the airborne test point object and number tags are generated by reading the airborne test point and extracting the knowledge. The reading circuit level FMECA table generation object knowledge is similar to the reading module level FMECA table generation object knowledge, which will not be repeated here. The reading device level FMECA table generation object knowledge only generates the device fault object, the tags include name, number, failure rate and hazard level, and the method is similar to the reading module level FMECA table generation object knowledge, which will not be repeated here.

[0065] The third part of the reading unit class FMECA table generation object knowledge is similar to the second part, the difference is that the unit object and the corresponding tag are generated, and the reading unit level FMECA table generation object is generated, which will not be repeated here.

[0066] Referring to Figure 5 and Figure 6The third step of the method is a failure knowledge graph relationship automatic generation method based on the FMECA table, which is divided into two types of unit-oriented and module-oriented. The two methods are similar, and here the unit-oriented FMECA table failure knowledge graph relationship automatic generation method is taken as an example for description. The failure knowledge graph relationship automatic generation method includes two processes of searching FMECA table matching knowledge labels and generating relationship knowledge, which can be divided into six types of system FMECA table generating relationship knowledge, product list table generating relationship knowledge, unit-level FMECA table generating relationship knowledge, module-level FMECA table generating relationship knowledge, circuit-level FMECA table generating relationship knowledge and device-level FMECA table generating relationship knowledge.

[0067] The first process of the failure knowledge graph relationship automatic generation method is to search the FMECA table matching knowledge labels, that is, according to the information of each cell in the FMECA table, according to the information searched out of different cells, and according to the matching relationship in the FMECA table, the matching is performed. Figure 5 The second process of generating relationship knowledge is to establish the corresponding relationship knowledge according to the matched objects and according to the relationship generation in the FMECA table. Figure 5

[0068] The first system FMECA table generating relationship knowledge of the failure knowledge graph relationship automatic generation method includes system fault belonging to system relationship knowledge generation and airborne test point belonging to system fault belonging relationship knowledge generation. The system fault belonging to system relationship knowledge generation first reads the system name and the system fault number, then analyzes the system number through the system fault number, matches the system object name and number label through the system name and the system number, finds out the corresponding relationship belonging object, then reads the system fault object name and number label through the system fault number and the system fault mode, finds out the corresponding relationship object, and finally generates the belonging end and the initial end of the system fault belonging to system relationship according to the found relationship belonging object and the relationship object. The airborne test point belonging to system fault belonging relationship first reads the system fault mode and the system fault number, matches the system fault object name and number label, finds out the corresponding relationship belonging object, then reads the airborne test point, matches the airborne test point object number label, finds out the corresponding relationship object, and finally generates the belonging end and the initial end of the airborne test point belonging to system relationship according to the found relationship belonging object and the relationship object.

[0069] ​The second product list table generates relationship knowledge, including device belonging to circuit relationship knowledge generation, circuit belonging to module relationship knowledge generation and module belonging to unit relationship knowledge generation. The device belonging to circuit relationship knowledge generation, firstly reads the device name and device number, matches the name and number label of the device object, finds the corresponding device relationship object, then reads the circuit name and circuit number, matches the name and number label of the circuit object, finds the corresponding circuit belonging relationship object, and finally generates the belonging end and initial end of the device belonging to circuit relationship according to the found relationship belonging object and relationship object. The circuit belonging to circuit relationship knowledge generation, firstly reads the circuit name and circuit number, matches the name and number label of the circuit object, finds the corresponding circuit relationship object, then reads the module name and module number, matches the name and number label of the module object, finds the corresponding module belonging relationship object, and finally generates the belonging end and initial end of the circuit belonging to module relationship according to the found relationship belonging object and relationship object. The module belonging to unit relationship knowledge generation, firstly reads the module name and module number, matches the name and number label of the module circuit object, finds the corresponding module relationship object, then reads the unit name and module number, matches the name and number label of the unit object, finds the corresponding unit belonging relationship object, and finally generates the belonging end and initial end of the module belonging to unit relationship according to the found relationship belonging object and relationship object. The unit belonging to system relationship knowledge generation, firstly reads the unit name and unit number, the name and number label of the unit circuit object, finds the corresponding unit relationship object, then reads the system name and module number, matches the name and number label of the system object, finds the corresponding system belonging relationship object, and finally generates the belonging end and initial end of the unit belonging to system relationship according to the found relationship belonging object and relationship object.

[0070] The third unit-level FMECA table generating relationship knowledge is similar to the fourth module-level FMECA table generating relationship knowledge, the fifth circuit-level FMECA table generating relationship knowledge and the sixth device-level FMECA table generating relationship knowledge. The unit-level FMECA table generating relationship knowledge includes unit fault attribution unit relationship knowledge generation, unit fault attribution system fault relationship knowledge generation and airborne test point attribution unit fault relationship knowledge generation. The unit fault attribution unit relationship knowledge generation firstly reads the unit fault name and unit fault number, matches the name and number tags of the unit fault object, finds the corresponding unit fault relationship object, then reads the unit name and unit number, matches the name and number tags of the unit object, finds the corresponding system attribution relationship object, and finally generates the attribution end and initial end of the unit fault attribution unit relationship according to the found relationship attribution object and relationship object. The unit fault attribution system fault knowledge generation firstly reads the unit fault name and unit fault number, matches the name and number tags of the unit fault object, finds the corresponding unit fault relationship object, then reads the influence on the system, matches the name tag of the system fault, finds the corresponding system attribution relationship object, and finally generates the attribution end and initial end of the unit fault attribution system fault relationship according to the found relationship attribution object and relationship object. The airborne test point attribution unit fault knowledge generation firstly reads the airborne test point number in the unit-level FMECA table, matches the number tag of the airborne test point object, finds the corresponding airborne test point relationship object, then reads the unit fault name and unit fault number, matches the name and number tags of the unit fault object, finds the corresponding system attribution relationship object, and finally generates the attribution end and initial end of the airborne test point attribution unit relationship according to the found relationship attribution object and relationship object. In addition, the sixth device-level FMECA table generating relationship knowledge is similar to the above method, and will not be repeated here.

[0071] Referring to Figure 7The fourth step of the method of the present application is based on the aviation communication electronic system fault correlation relationship visual modeling method of the knowledge graph, which is composed of three parts of objects, relationships and labels. The objects are composed of circles of different colors and the same size, and the circles are divided according to the different categories of the objects. The categories of the objects are consistent with the categories of the objects generated in the second step, including system fault, unit fault, module fault, circuit fault, device fault, system, unit, module, circuit, device, and airborne test point. The labels are displayed in the form of text in the circles. By default, the name label is displayed. If there is no name label such as the label of the airborne test point, the label is displayed in the circle. The categories of the labels are consistent with the categories of the object labels generated in the second step, wherein the labels of the system fault, unit fault, module fault and circuit fault are name, number, failure rate, hazard level and test opportunity, the labels of the device fault are name, number, failure rate and hazard level, the labels of the system, unit, module, circuit and device are name, number and failure rate, and the label of the airborne test point is number. The relationships are divided according to different categories and represented in the form of arrows. The attribution end and the initial end of the arrow are connected to two different objects, respectively. The attribution end and the initial end of the relationship generated according to the relationship knowledge generation method in the third step are connected by an arrow, and the category is consistent with the relationship object generated according to the relationship knowledge generation method in the third step. The relationship arrow is divided into unit attribution system, module attribution system, module attribution unit, circuit attribution module, device attribution circuit, system fault attribution system, unit fault attribution unit, module fault attribution module, circuit fault attribution circuit, device fault attribution device, module fault attribution system fault, unit fault attribution system fault, module fault attribution unit fault, circuit fault attribution module fault, device fault attribution circuit fault, test point attribution system fault, test point attribution unit fault, test point attribution module fault, and test point attribution circuit fault.

[0072] In the embodiment, an aviation communication electronic system fault relationship visual model automatic construction method is provided. The method can improve the aviation communication electronic system fault correlation relationship modeling efficiency and automation degree, effectively avoid the problem of accuracy decline caused by human dependence on complex models, quickly realize the visual model display of the fault correlation relationship, and provide a technical basis for intelligent analysis of the correlation relationship of the aviation communication electronic system fault.

[0073] The above is only a preferred embodiment of the application, and does not limit the patent scope of the application. Any equivalent structure or equivalent flow transformation obtained by using the content of the application specification and drawings, or direct or indirect application in other related technical fields, is also included in the patent protection scope of the application.

Claims

1. An automatic construction method of an aviation communication electronic system fault relationship visualization model, characterized in that, The method comprises the following steps: An FMECA table of an aviation communication electronic system is acquired; the FMECA table comprises a system FMECA table and a module FMECA table; wherein: the system FMECA table records system FMECA information, and the system FMECA information comprises a system name, system failure modes and system failure numbers; the module FMECA table comprises a module FMECA table of a module class and a module FMECA table of a unit class, the module FMECA table of the module class comprises a product list table, a device-level FMECA table, a circuit-level FMECA table and a module-level FMECA table, and the module FMECA table of the unit class comprises a product list table, a device-level FMECA table, a circuit-level FMECA table, a module-level FMECA table and a unit-level FMECA table; Knowledge extraction is performed on the FMECA table, failure knowledge graph objects are generated according to the extracted required knowledge, and corresponding object labels are extracted; the knowledge extraction is specifically reading knowledge extraction and calculation knowledge extraction; wherein: the reading knowledge extraction is used for reading cell information of each sub-table in the FMECA table, and corresponding objects and object labels are generated; the calculation knowledge extraction is used for reading cell information of each sub-table in the FMECA table, and objects and object labels are generated after calculation; the calculation knowledge extraction specifically comprises numbered calculation knowledge extraction and failure rate calculation knowledge extraction; wherein: the numbered calculation knowledge extraction is used for splitting the number of the corresponding object after reading the original number, and generating the corresponding object label; the failure rate calculation knowledge extraction is used for reading the failure rate of each cell, and generating the corresponding object label by calculating the sum; Object matching is performed on the FEMCA table, relationship knowledge between objects is established according to the matched objects, and relationship connections between graph objects are established according to the relationship knowledge; According to the generated objects, object labels and relationship connections between objects, an aviation communication electronic system failure relationship visualization model is established.

2. The method of claim 1, wherein the method further comprises: In the module FMECA table: The product list table records include device names, device numbers, device failure rates, circuit names, circuit numbers, circuit failure rates, module names, module numbers and module failure rates; The device-level FMECA table records include device names, device failure names, device failure numbers, effects on circuits, failure rates and hazard levels; The circuit-level FMECA table records include circuit names, circuit failure names, circuit failure numbers, effects on modules, hazard levels, test opportunities and airborne test points; The module-level FMECA table records include module names, module failure names, module failure numbers, effects on systems / units, hazard levels, test opportunities and airborne test points; The unit-level FMECA table records include unit names, unit failure names, unit failure numbers, effects on systems, hazard levels, test opportunities and airborne test points.

3. The method of claim 1, wherein the method further comprises: The generating fault knowledge graph object specifically comprises reading system FMECA table generating object knowledge, reading module class FMECA table generating object knowledge and reading unit class FMECA table generating object knowledge.

4. The method of claim 1, wherein the method further comprises: The executing object matching specifically comprises: according to the information of each cell in the FMECA table, according to the information searched out by different cells, and according to a preset matching relationship, the information is matched.

5. The method of claim 4, wherein the method further comprises: The establishing object and object relationship knowledge specifically comprises: according to the matched object, a corresponding relationship knowledge is established according to a preset generating relationship.

6. The method of claim 5, wherein the method further comprises: The establishing corresponding relationship knowledge specifically comprises: system FMECA table generating relationship knowledge, product list table generating relationship knowledge, unit level FMECA table generating relationship knowledge, module level FMECA table generating relationship knowledge, circuit level FMECA table generating relationship knowledge and device level FMECA table generating relationship knowledge.

7. The method of claim 1, wherein the method further comprises: The established aviation communication electronic system fault relationship visual model comprises objects, relationships and labels. The objects comprise system fault, unit fault, module fault, circuit fault, device fault, system, unit, module, circuit, device and airborne test point. The labels corresponding to the system fault, unit fault, module fault and circuit fault comprise name, number, failure rate, hazard level and test opportunity; the label corresponding to the device fault comprises name, number, failure rate and hazard level; the labels corresponding to the system, unit, module, circuit and device comprise name, number and failure rate; the label corresponding to the airborne test point comprises number. The belonging section and initial end of the relationship are connected with two different objects respectively.

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