A fuselage skin AO planning method, system, device and medium based on typical features

Abstract

The present invention relates to the technical field of aircraft manufacturing, and more specifically, to a fuselage skin AO planning method, system, device, and medium based on typical features. The method first obtains fuselage skin data based on acquired three-dimensional model data and constructs a fuselage skin library. Secondly, based on the aircraft three-dimensional model, a fuselage skin typical planning feature library is extracted and constructed. Then, the fuselage skin typical planning feature library is analyzed, and combined with knowledge graph technology, the relationship between the features and the skin is clarified, the fuselage skin AO is inferred, and a full-aircraft fuselage skin assembly content set is formed. Finally, a graph traversal algorithm is called to check the completeness of the full-aircraft fuselage skin assembly content set. This improves the structured expression of the fuselage skin AO planning process and effectively enhances the planning efficiency and accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of aircraft manufacturing, and in particular to a fuselage skin AO planning method, system, equipment and medium based on typical features. Background Art

[0002] With the rapid development of information technologies such as knowledge graphs and machine learning, aircraft manufacturing is shifting from two-dimensional drawings to a three-dimensional model-based manufacturing model. The fuselage skin, also known as the fuselage shell, is a crucial component of the aircraft structure. It covers the aircraft's skeleton, forming its outer shape. Its primary functions include providing aerodynamic shape, protecting internal structures and systems, acting as a load-bearing structure, and providing shock absorption. The fuselage skin is distributed throughout the aircraft and is characterized by its large number and complex assembly. The assembly of the fuselage skin is primarily carried out in the form of an assembly outline (AO), which primarily combines human experience with the characteristics of the fuselage skin of various aircraft sections to form the final assembly content. This experience-based approach to fuselage skin planning presents challenges such as low efficiency, difficulty ensuring completeness, and a lack of accumulated process knowledge. Summary of the Invention

[0003] In response to the problems of low efficiency, difficulty in ensuring completeness, and inability to accumulate process knowledge in fuselage skin, the present invention proposes a fuselage skin AO planning method, system, equipment and medium based on typical features; this method constructs a fuselage skin typical structural feature library, a fuselage skin object library, and a fuselage skin typical planning feature library, and builds a fuselage skin planning graph network based on various parameter libraries to perform structured graph expression on its planning process and results, thereby effectively improving the planning efficiency and accuracy.

[0004] A fuselage skin AO planning method based on typical features specifically includes the following steps:

[0005] Step S1: obtaining fuselage skin data based on the acquired 3D model data and constructing a fuselage skin library;

[0006] Step S2: extracting and constructing a typical planning feature library of the fuselage skin based on the aircraft 3D model;

[0007] Step S3: Analyze the typical planning feature library of the fuselage skin, combine it with knowledge graph technology, clarify the relationship between the features and the skin, infer the fuselage skin AO, and form the full fuselage skin assembly content set;

[0008] Step S4: Call the graph traversal algorithm to check the completeness of the entire fuselage skin assembly content set.

[0009] In order to better implement the present invention, further, the specific operation of step S1 is: according to the appearance characteristics of the aircraft skin, the fuselage skin data is obtained, and the fuselage skin data set is defined as the full-aircraft fuselage skin library D{D1, D2, D3...Dt}, wherein Dt represents the t-th specific fuselage skin object; the typical structural characteristics of the fuselage skin object are {T1, T2, T3...Tm}, wherein Tm represents the m-th typical feature of the fuselage skin.

[0010] In order to better implement the present invention, further, step S2 specifically includes the following steps:

[0011] Step S21: extracting the fuselage skin AO planned by the recognition process experience by using semantic recognition, obtaining the main influencing factors affecting the fuselage skin AO, and recording them as Q{Q1, Q2, Q3...QN}, where QN is the Nth main influencing factor affecting the fuselage skin AO;

[0012] Step S22: Obtain the frequency of occurrence of the impact factor QJ and record it as PJ;

[0013] Step S23: The frequency of occurrence in the assembly content is recorded as the important occurrence frequency and as Ej;

[0014] Step S24: The n features with the highest frequencies are taken as typical planning features of the fuselage skin and recorded as F{F1, F2, F3...Fn};

[0015] Step S25: Call the BERT model to extract the relationship type between typical planning features of the fuselage skin and use it as a triple<Fi,Rk,Fj> A typical planning feature graph network of the fuselage skin is constructed in the form of ; where: Fi represents the i-th feature of the fuselage skin planning feature, Fj represents the j-th feature of the fuselage skin planning feature, and Rk represents the relationship type between the two.

[0016] In order to better implement the present invention, further, step S3 specifically includes the following steps:

[0017] Step S31: Based on the attention mechanism and weight mechanism, analyze the importance value T of each node and the importance weight of its neighboring nodes to it, which is recorded as W{W1, W2…W i}, where W i is the importance weight of the i-th neighboring node to it;

[0018] Step S32: Taking the fuselage skin object as input, semantic matching and logical verification are performed on it in the fuselage skin typical planning feature graph network to form the final AO assembly content.

[0019] In order to better implement the present invention, further, step S32 specifically includes the following steps:

[0020] Step S321: Obtain the fuselage skin object Di and its typical structural features in the fuselage skin library;

[0021] Step S322: semantically matching the local network whose structural features are satisfied in the typical planning feature network;

[0022] Step S323: forming the final AO assembly content based on the screened local network;

[0023] Step S324: loop through the fuselage skin library objects.

[0024] In order to better implement the present invention, further, the generation logic of the final AO assembly content is specifically as follows: using a depth traversal algorithm, find the root node, calculate the importance weight value of the root node's child nodes, obtain the nodes and node information on the route with the maximum weight as the assembly content of a single AO, and if there are h root nodes, generate h AO assembly contents.

[0025] In order to better implement the present invention, further, the specific operation of step S4 is: using the graph depth traversal and breadth traversal algorithms to search whether there are "unique points" or "breakpoints" in the full fuselage skin instance assembly content, determine whether all the fuselage skin structural features have nodes and relationships in the instance graph, and form an evaluation conclusion based on the results.

[0026] Based on the above-mentioned typical feature-based fuselage skin AO planning method, in order to better implement the present invention, a typical feature-based fuselage skin AO planning system is further proposed, which is used to execute the above-mentioned typical feature-based fuselage skin AO planning method; the system includes a construction unit, an inference unit, and a planning unit;

[0027] The construction unit is used to obtain fuselage skin data based on the acquired three-dimensional model data and construct a fuselage skin library; based on the aircraft three-dimensional model, extract and construct a fuselage skin typical planning feature library;

[0028] The reasoning unit is used to analyze the typical planning feature library of the fuselage skin, combine the knowledge graph technology, clarify the relationship between the features and the skin, infer the fuselage skin AO, and form the full fuselage skin assembly content set;

[0029] The planning unit is used to call a graph traversal algorithm to check the completeness of the entire fuselage skin assembly content set.

[0030] Based on the above-mentioned typical feature-based fuselage skin AO planning method, in order to better realize the present invention, further, an electronic device is proposed, including a memory and a processor; a computer program is stored on the memory; when the computer program is executed on the processor, the above-mentioned typical feature-based fuselage skin AO planning method is implemented.

[0031] Based on the above-mentioned typical feature-based fuselage skin AO planning method, in order to better realize the present invention, a computer-readable storage medium is further proposed, on which computer instructions are stored; when the computer instructions are executed on the above-mentioned electronic device, the above-mentioned typical feature-based fuselage skin AO planning method is implemented.

[0032] The present invention has the following beneficial effects:

[0033] The present invention realizes the construction of a fuselage skin typical structural feature library, a fuselage skin object library, a fuselage skin typical planning feature library, and a fuselage skin AO assembly content set network, thereby improving the structured expression of the fuselage skin AO planning process and effectively improving the efficiency and accuracy of planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a schematic flowchart of the fuselage skin AO planning method based on typical features provided by the present invention.

[0035] Figure 2 This is a schematic block diagram of the reasoning process provided by the present invention. DETAILED DESCRIPTION

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. It should be understood that the described embodiments are only part of the embodiments of the present invention, not all of the embodiments, and therefore should not be regarded as limiting the scope of protection. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technical personnel in this field without making creative work are within the scope of protection of the present invention.

[0037] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "disposed," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections, electrical connections; direct connections, indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0038] Example 1:

[0039] This embodiment proposes a fuselage skin AO planning method based on typical features, which specifically includes the following steps:

[0040] Step S1: obtaining fuselage skin data based on the acquired 3D model data and constructing a fuselage skin library;

[0041] The specific operation of step S1 is: according to the appearance characteristics of the aircraft skin, fuselage skin data is obtained, and the fuselage skin data set is defined as a full-aircraft fuselage skin library D{D1, D2, D3...Dt}, where Dt represents the t-th specific fuselage skin object; the typical structural characteristics of the fuselage skin object are {T1, T2, T3...Tm}, where Tm represents the m-th typical feature of the fuselage skin.

[0042] Step S2: extracting and constructing a typical planning feature library of the fuselage skin based on the aircraft 3D model;

[0043] The step S2 specifically includes the following steps:

[0044] Step S21: extracting the fuselage skin AO planned by the recognition process experience by using semantic recognition, obtaining the main influencing factors affecting the fuselage skin AO, and recording them as Q{Q1, Q2, Q3...QN}, where QN is the Nth main influencing factor affecting the fuselage skin AO;

[0045] Step S22: Obtain the frequency of occurrence of the impact factor QJ and record it as PJ;

[0046] Step S23: The frequency of occurrence in the assembly content is recorded as the important occurrence frequency and as Ej;

[0047] Step S24: The n features with the highest frequencies are taken as typical planning features of the fuselage skin and recorded as F{F1, F2, F3...Fn};

[0048] Step S25: Call the BERT model to extract the relationship type between typical planning features of the fuselage skin and use it as a triple<Fi,Rk,Fj> A typical planning feature graph network of the fuselage skin is constructed in the form of .

[0049] Step S3: Analyze the typical planning feature library of the fuselage skin, combine it with knowledge graph technology, clarify the relationship between the features and the skin, infer the fuselage skin AO, and form the full fuselage skin assembly content set;

[0050] Furthermore, the step S3 specifically includes the following steps:

[0051] Step S31: Based on the attention mechanism and weight mechanism, analyze the importance value T of each node and the importance weight of its neighboring nodes to it, which is recorded as W{W1, W2…W i}, where W i is the importance weight of the i-th neighboring node to it;

[0052] Step S32: Taking the fuselage skin object as input, semantic matching and logical verification are performed on it in the fuselage skin typical planning feature graph network to form the final AO assembly content.

[0053] In order to better implement the present invention, further, step S32 specifically includes the following steps:

[0054] Step S321: Obtain the fuselage skin object Di and its typical structural features in the fuselage skin library;

[0055] Step S322: semantically matching the local network whose structural features are satisfied in the typical planning feature network;

[0056] Step S323: forming the final AO assembly content based on the screened local network;

[0057] The generation logic for forming the final AO assembly content is specifically as follows: using a depth traversal algorithm to find the root node, calculating the importance weight values of the root node's child nodes, obtaining the nodes and node information on the route with the maximum weight as the assembly content of a single AO, and if there are h root nodes, then generating h AO assembly contents.

[0058] Step S324: loop through the fuselage skin library objects.

[0059] Step S4: Call the graph traversal algorithm to check the completeness of the entire fuselage skin assembly content set.

[0060] The specific operation of step S4 is: using the graph depth traversal and breadth traversal algorithms to search whether there are "unique points" or "breakpoints" in the full fuselage skin instance assembly content, determine whether all the fuselage skin structural features have nodes and relationships in the instance graph, and form an evaluation conclusion based on the results.

[0061] Working principle: This embodiment first obtains fuselage skin data based on the acquired 3D model data and constructs a fuselage skin library; secondly, based on the aircraft 3D model, extracts and constructs a fuselage skin typical planning feature library; then, the fuselage skin typical planning feature library is analyzed, and combined with knowledge graph technology, the relationship between features and skin is clarified, the fuselage skin AO is inferred, and the full-aircraft fuselage skin assembly content set is formed; finally, a graph traversal algorithm is called to check the completeness of the full-aircraft fuselage skin assembly content set; thus, the structured expression of the fuselage skin AO planning process is improved, and the efficiency and accuracy of planning are effectively improved.

[0062] Example 2:

[0063] This embodiment is based on the above embodiment 1. Figure 1 、 Figure 2 As shown, a specific embodiment is described in detail, which specifically includes the following steps.

[0064] Step S1: Build the fuselage skin library.

[0065] The fuselage skin of the entire aircraft has different shapes and is distributed in various locations. Based on the data of the three-dimensional model, the appearance characteristics of the skin are defined. It is the outermost layer of the fuselage structure. Based on this typical characteristic, the fuselage skin data is obtained, mainly obtaining the appearance feature information, drawing number, name, etc. The fuselage skin data set is defined as the full-aircraft fuselage skin library D{D1,D2,D3...Dt}, where Dt represents the t-th specific fuselage skin object; the typical structural characteristics of the fuselage skin object are {T1,T2,T3...Tm}, where Tm represents the m-th typical characteristic of the fuselage skin.

[0066] Taking a certain aircraft model as an example, there are more than 500 fuselage skin objects in total. The typical structural features extracted mainly include T{structural parts, fuel tank area, rib type, material type, name, drawing number, and belonging component} and other information.

[0067] Step S2: Based on the aircraft 3D model, extract and construct a typical planning feature library of the fuselage skin.

[0068] Obtaining and defining the fuselage skin library and its three-dimensional model features is only to extract the three-dimensional digital model into two-dimensional vector feature data to support the subsequent data feature processing. Data feature processing is strongly related to the area where the fuselage skin is located, the structural characteristics of the fuselage skin, the structural characteristics of the aircraft, etc. Its traditional approach is based on the experience of process personnel. By adopting semantic recognition and other methods, the fuselage skin AO previously planned based on process experience is extracted and identified, and the main influencing factors affecting the fuselage skin AO planning are obtained and recorded as Q{Q1, Q2, Q3...QN}, where QN represents the Nth influencing factor. And for each influencing factor, its frequency of occurrence is counted and recorded as P J The frequency of occurrence in the assembly content is determined, and the important frequency is recorded as E j The 10 features with the highest frequency are obtained as the typical planning features of the fuselage skin, and their features are defined as F{F1, F2, F3…Fn}.

[0069] The optimized BERT algorithm is used to extract the relationship types between the typical planning features of the fuselage skin and the relationship types are expressed as triples.<Fi,Rk,Fj> Where Fi represents the i-th feature of the fuselage skin planning feature, Fj represents the j-th feature of the fuselage skin planning feature, and Rk represents the relationship type between the two. Based on the relationship of the triples, a typical fuselage skin planning feature graph network is constructed.

[0070] By extracting existing fuselage cover skin AO data, we identified key influencing factors, including the connected object (structural component or internal skin), double-thread bolts, support plate nuts, tank interior and exterior, and rib type. Relationships included whether the object was inside or outside the tank, whether there were ribs, and whether the object was connected to the frame. Seven root node types were defined, allowing a single skin to generate up to seven AOs.

[0071] Step S3: Analyze the typical feature library of the fuselage skin, and combine it with knowledge graph technology to clarify the relationship between the features and the skin, infer the fuselage skin AO, and form the full fuselage skin assembly content set.

[0072] Taking the fuselage skin object as input, it is semantically matched and logically verified in the fuselage skin typical planning feature graph network to form the final AO assembly content. The main steps are as follows:

[0073] First, obtain the fuselage skin object Di and its typical structural features in the fuselage skin library;

[0074] Secondly, in the typical planning feature network, the semantics matches the local network whose structural features are satisfied;

[0075] The final AO assembly content is then generated based on the filtered local network. The generation logic uses a depth-based traversal algorithm to find the root node, the nodes along the route, and their information as the assembly content of a single AO. If there are h (up to 7) root nodes, then h AO assembly contents are generated.

[0076] Finally loop through the fuselage skin library objects.

[0077] Through the above steps, the AO of the fuselage skin cover can be generated.

[0078] Step S4: Use a graph traversal algorithm to check the completeness of the entire fuselage skin assembly content set.

[0079] Using graph depth and breadth traversal algorithms, the entire fuselage skin instance assembly is searched for unique points or breakpoints. All fuselage skin structural features are determined to have nodes and relationships within the instance graph. The results are then used to form an evaluation conclusion.

[0080] Through analysis, taking a certain aircraft as an example, it is possible to accurately determine whether it is missing equipment, thereby improving the efficiency and accuracy of AO planning.

[0081] Working principle: In order to solve the problems of low efficiency of fuselage skin, difficulty in ensuring completeness, and inability to accumulate process technology knowledge, this embodiment combines a three-dimensional model to propose a fuselage skin AO planning method based on typical features to improve the efficiency of fuselage skin planning and carry process planning knowledge; it realizes the construction of a fuselage skin typical structural feature library, a fuselage skin object library, a fuselage skin typical planning feature library, and a fuselage skin AO assembly content set network, and builds a fuselage skin planning graph network based on various parameter libraries to perform structured graph expression of its planning process and results, thereby improving the structured expression of the fuselage skin AO planning process and effectively improving the planning efficiency.

[0082] The rest of this embodiment is the same as that of the above-mentioned embodiment 1, and therefore will not be described in detail.

[0083] Example 3:

[0084] This embodiment, based on any one of the above embodiments 1-2, proposes a fuselage skin AO planning system based on typical features, which is used to execute the above fuselage skin AO planning method based on typical features; it includes a construction unit, an inference unit, and a planning unit;

[0085] The construction unit is used to obtain fuselage skin data based on the acquired three-dimensional model data and construct a fuselage skin library; based on the aircraft three-dimensional model, extract and construct a fuselage skin typical planning feature library;

[0086] The reasoning unit is used to analyze the typical planning feature library of the fuselage skin, combine the knowledge graph technology, clarify the relationship between the features and the skin, infer the fuselage skin AO, and form the full fuselage skin assembly content set;

[0087] The planning unit is used to call a graph traversal algorithm to check the completeness of the entire fuselage skin assembly content set.

[0088] This embodiment also proposes an electronic device, including a memory and a processor; the memory stores a computer program; when the computer program is executed on the processor, the above-mentioned fuselage skin AO planning method based on typical features is implemented.

[0089] This embodiment further proposes a computer-readable storage medium having computer instructions stored thereon. When the computer instructions are executed on the above-mentioned electronic device, the above-mentioned fuselage skin AO planning method based on typical features is implemented.

[0090] The rest of this embodiment is the same as any of the above-mentioned embodiments 1 and 2, and thus will not be described in detail.

[0091] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any simple modification or equivalent change made to the above embodiment based on the technical essence of the present invention shall fall within the scope of protection of the present invention.

Claims

1. A fuselage skin AO planning method based on typical features, characterized in that: The specific steps include: Step S1: obtaining fuselage skin data based on the acquired 3D model data and constructing a fuselage skin library; Step S2: extracting and constructing a typical planning feature library of the fuselage skin based on the aircraft 3D model; Step S21: extracting the fuselage skin AO planned by the recognition process experience by using semantic recognition, obtaining the main influencing factors affecting the fuselage skin AO, and recording them as Q{Q1, Q2, Q3...QN}, where QN is the Nth main influencing factor affecting the fuselage skin AO; Step S22: Obtain the frequency of occurrence of the impact factor QJ and record it as PJ; Step S23: The frequency of occurrence in the assembly content is recorded as the important occurrence frequency and as Ej; Step S24: The n features with the highest frequencies are taken as typical planning features of the fuselage skin and recorded as F{F1, F2, F3...Fn}; Step S25: Call the BERT model to extract the relationship type between typical planning features of the fuselage skin and use it as a triple<Fi,Rk,Fj> The typical planning feature graph network of the fuselage skin is constructed in the form of ; where: Fi represents the i-th feature of the fuselage skin planning feature, Fj represents the j-th feature of the fuselage skin planning feature, and Rk represents the relationship type between the two; Step S3: Analyze the typical planning feature library of the fuselage skin, combine it with knowledge graph technology, clarify the relationship between the features and the skin, infer the fuselage skin AO, and form the full fuselage skin assembly content set; Step S31: Based on the attention mechanism and weight mechanism, analyze the importance value T of each node and the importance weight of its neighboring nodes to it, which is recorded as W{W1, W2…W i }, where W i is the importance weight of the i-th neighboring node to it; Step S32: Taking the fuselage skin object as input, semantic matching and logic verification are performed on it in the fuselage skin typical planning feature graph network to form the final AO assembly content; Step S321: Obtain the fuselage skin object Di and its typical structural features in the fuselage skin library; Step S322: semantically matching the local network whose structural features are satisfied in the typical planning feature network; Step S323: forming the final AO assembly content based on the screened local network; Step S324: loop through the fuselage skin library objects; Step S4: Call the graph traversal algorithm to check the completeness of the entire fuselage skin assembly content set.

2. The fuselage skin AO planning method based on typical features according to claim 1, characterized in that: The specific operation of step S1 is: according to the appearance characteristics of the aircraft skin, fuselage skin data is obtained, and the fuselage skin data set is defined as a full-aircraft fuselage skin library D{D1, D2, D3...Dt}, where Dt represents the t-th specific fuselage skin object; the typical structural characteristics of the fuselage skin object are {T1, T2, T3...Tm}, where Tm represents the m-th typical feature of the fuselage skin.

3. The fuselage skin AO planning method based on typical features according to claim 2, characterized in that: The generation logic of the final AO assembly content is as follows: using the depth traversal algorithm, find the root node, calculate the importance weight value of the root node's child nodes, obtain the nodes and node information on the route with the maximum weight as the assembly content of a single AO, if there is h root nodes, then generate h AO assembly content.

4. The fuselage skin AO planning method based on typical features according to claim 2, characterized in that: The specific operation of step S4 is: using the graph depth traversal and breadth traversal algorithms to search whether there are "unique points" or "breakpoints" in the full fuselage skin instance assembly content, determine whether all the fuselage skin structural features have nodes and relationships in the instance graph, and form an evaluation conclusion based on the results.

5. A fuselage skin AO planning system based on typical features, used to execute the fuselage skin AO planning method based on typical features according to claim 1; characterized in that: It includes construction unit, reasoning unit and planning unit; The construction unit is used to obtain fuselage skin data based on the acquired three-dimensional model data and construct a fuselage skin library; Extract and construct a typical planning feature library of fuselage skin based on the aircraft 3D model; The reasoning unit is used to analyze the typical planning feature library of the fuselage skin, combine the knowledge graph technology, clarify the relationship between the features and the skin, infer the fuselage skin AO, and form the full fuselage skin assembly content set; The planning unit is used to call a graph traversal algorithm to check the completeness of the entire fuselage skin assembly content set.

6. An electronic device, characterized in that: The method comprises a memory and a processor; a computer program is stored in the memory; when the computer program is executed on the processor, the fuselage skin AO planning method based on typical features as described in any one of claims 1 to 4 is implemented.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions; when the computer instructions are executed on the electronic device according to claim 6, the fuselage skin AO planning method based on typical features according to any one of claims 1 to 4 is implemented.

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