A method for evaluating the fidelity of digital twin models of aircraft engine assembly accuracy

Abstract

The present invention provides a method for evaluating the fidelity of an aero-engine assembly precision digital twin model, comprising: constructing an assembly precision directed weighted network based on assembly precision characteristics; introducing an assembly precision prediction model used in the digital twin model to construct a prediction model fidelity transfer network; identifying the fidelity input nodes of the transfer network and their lowest level, constructing a set of fidelity node weight matrices, a fidelity input matrix, and a set of prediction model weight matrices; establishing a fidelity transfer and evolution model; using the fidelity input matrix, the set of prediction model weight matrices, and the set of fidelity node weight matrices as inputs to drive the fidelity transfer and evolution model, solving the fidelity evaluation response using the MC method, and fitting the fidelity response data set to obtain a fidelity CDF curve and a PDF curve. The present invention can evaluate the fidelity of an aero-engine assembly precision digital twin model in real time, facilitating the selection of high-precision and high-consistency digital twin models.

Description

Technical Field

[0001] The present invention belongs to the technical field of aero-engine assembly and digital twin technology, and specifically relates to a method for evaluating the fidelity of a digital twin model of aero-engine assembly accuracy. Background Art

[0002] Aircraft engine assembly accuracy has a significant impact on its aerodynamic performance and safety. Given the advantages of digital twin technology, such as real-time interaction and dynamic updates, it is increasingly being used in aircraft engine assembly precision control. This control method primarily involves constructing a digital twin model of aircraft engine assembly accuracy, updating it in real time based on measurement data from the assembly process, and optimizing process parameters to achieve assembly precision control. However, there are numerous digital twin models for assembly accuracy, and currently, there is a lack of evaluation metrics for assessing the accuracy and consistency of these digital twin models. This makes it difficult to select a model with high accuracy and consistency, which seriously hinders the application of digital twins in aircraft engine assembly. Summary of the Invention

[0003] The purpose of the present invention is to solve the problem in the prior art that it is difficult to select a high-precision and high-consistency digital twin model of aircraft engine assembly precision, and to provide a fidelity evaluation method for a digital twin model of aircraft engine assembly precision, which can evaluate the accuracy and consistency of the assembly precision digital twin model during the aircraft engine assembly process in real time, and can promote the application of digital twins in aircraft engine assembly.

[0004] To achieve the above objectives, the technical solutions provided by the present invention are:

[0005] The present invention provides a method for evaluating the fidelity of a digital twin model of aircraft engine assembly accuracy, comprising the following steps:

[0006] Step 1: Construct a fidelity transfer network, which includes the following sub-steps:

[0007] Step 1.1: Extract and abstract the precision features of aircraft engine parts and components into network nodes. Determine the direction of each node edge through the assembly relationship. Calculate the weight of each node through sensitivity analysis. Construct an assembly precision directed weighted network and obtain the corresponding adjacency matrix.

[0008] In step 1.2, based on the obtained adjacency matrix, the assembly accuracy prediction model used by the assembly accuracy digital twin model is introduced, and the assembly accuracy nodes are replaced with fidelity nodes to construct the assembly accuracy prediction model fidelity transfer network;

[0009] Step 2: Construct a fidelity transfer and evolution model based on the assembly accuracy prediction model and the fidelity transfer network. The model inputs are the fidelity input matrix, the fidelity node weight matrix set, and the prediction model weight matrix set. The output is the fidelity response dataset. The model includes the following sub-steps:

[0010] Step 2.1: Divide the assembly hierarchy according to the distribution of the assembly accuracy prediction model, and construct a set of fidelity node weight matrices by combining the assembly hierarchy and the identity matrix;

[0011] Step 2.2: Identify the fidelity input nodes and the lowest level of the fidelity input nodes of the fidelity transfer network at each assembly time, evaluate the accuracy of the corresponding input data, and construct a fidelity input matrix based on the evaluation results of the fidelity input nodes and input data.

[0012] Step 2.3: Analyze the assembly accuracy prediction model called by the fidelity transfer network at each moment and evaluate the accuracy of the prediction model. Combine the unit matrix, the input-output relationship of the prediction model, and the prediction model accuracy evaluation results to construct a prediction model weight matrix set.

[0013] Step 2.4: Construct a digital twin model fidelity transfer and evolution model based on the lowest level of the fidelity input node, the fidelity input matrix, the fidelity node weight matrix set, and the prediction model weight matrix set.

[0014] In step 3, the MC sampling method is used to sample the accuracy evaluation results of the input data at each moment, and the fidelity transfer and evolution model is driven to fit the output fidelity response data set to obtain the fidelity CDF curve and fidelity PDF curve, thereby realizing the evaluation of the fidelity of the digital twin model in the aircraft engine assembly process.

[0015] Furthermore, in step 1.2, the fidelity transfer network G at time T1 is constructed first. t1 , and then G t1 Extended to all assembly moments T, the assembly accuracy prediction model fidelity transfer network G is obtained t .

[0016] Furthermore, in step 2.2, the fidelity node weight matrix D at time T1 is first constructed. Gω,1 , then the fidelity node weight matrix set is obtained as: D Gω ={D Gω,1 ,D Gω,2 ,…,D Gω,T}.

[0017] Furthermore, step 2.2 includes:

[0018] Step 2.2.1, identify the fidelity input nodes of the fidelity transfer network at each assembly moment, determine the source of the input nodes, and divide the input data into measured data and theoretical data;

[0019] Step 2.2.2, evaluate the accuracy of the input data, including:

[0020] Step 2.2.2.1, express the evaluation method of measurement data accuracy as:

[0021] T md =ω me ·T me +ω md ·T md ·δ

[0022] Where, T md Indicates the accuracy of measurement data, T me Indicates the accuracy of the measuring device, ω me represents the measurement equipment accuracy weight, T md represents the impact of data density, ω md represents the data density accuracy weight, and δ represents the data density indicator parameter;

[0023] Among them, the measurement equipment accuracy T me Calculated by the following formula: Where mv represents the measured value and te represents the measurement error;

[0024] Data density affects T md Calculated by the following formula: Where md is the data density, ns represents the standard size, a and c are the evaluation parameters;

[0025] Step 2.2.2.2, setting the accuracy of the theoretical data to a predetermined value;

[0026] Step 2.2.3, combine the fidelity input nodes and input data evaluation results at each moment to construct the fidelity input vector at each moment and summarize it into the fidelity input matrix

[0027] Furthermore, step 2.3 includes:

[0028] Step 2.3.1: Analyze the number of prediction models called by the fidelity transfer network at each moment and the input-output relationship of the prediction models;

[0029] Step 2.3.2, the following formula is used by R 2 Methods to evaluate the accuracy of prediction models:

[0030]

[0031] In the formula, ntest represents the number of test points, y i represents the true measurement value, represents the predicted value, represents the mean of the measured values;

[0032] Step 2.3.3, combining the unit matrix, the input-output relationship of the prediction model and the accuracy evaluation results of the prediction model, the prediction model weight matrix set is constructed as follows: D pm ={D pm,1 ,D pm,2 ,…,D pm,T}.

[0033] Furthermore, step 2.4 includes:

[0034] Step 2.4.1: Express the digital twin model fidelity transfer model as:

[0035]

[0036] Where, F 1 represents the fidelity at time T1, represents the fidelity input vector at time T1, D Gω,j and D pm,j Respectively represent the fidelity node weight matrix and prediction model weight matrix to be called;

[0037] In step 2.4.2, the digital twin model fidelity evolution model is expressed as:

[0038]

[0039] Where, F i is the fidelity at time i, is the fidelity input vector at time i, L i is the lowest level of input nodes at time i.

[0040] Furthermore, step 3 includes:

[0041] Step 3.1: Set the assembly time t = T1 and sample the accuracy evaluation results of the input data at time t. MC sampling is used for random variables, and MC sampling is used for interval variables, which is equivalent to uniform distribution. The sampling dimension is q.

[0042] Step 3.2: Drive the fidelity transfer and evolution model, calculate the fidelity evaluation response at time t, with dimension q, and fit the CDF curve and PDF curve of the fidelity at time t;

[0043] Step 3.3, repeat steps 3.1 and 3.2 until the assembly time t = T, and output the fidelity CDF curve and PDF curve at each time.

[0044] Furthermore, in step 1.1, the adjacency matrix is ​​constructed as follows:

[0045]

[0046] Where, ω ij Calculated by sensitivity analysis, it represents the weight of the directed edge from node i to j. A value of 0 indicates that there is no direct connection between the corresponding nodes.

[0047] Furthermore, in step 2.2.2.1, ω me and ω md Calculated by analytic hierarchy process.

[0048] The advantages of the present invention are:

[0049] The fidelity evaluation method of the digital twin model of aircraft engine assembly accuracy provided by the present invention first constructs an assembly accuracy directed weighted network based on the accuracy characteristics of assembly parts and components; then introduces the assembly accuracy prediction model used by the digital twin model to construct an assembly accuracy prediction model fidelity transfer network; divides the assembly hierarchy according to the prediction model, and constructs a fidelity node weight matrix set; identifies the fidelity input nodes and their lowest levels at each moment, evaluates the accuracy of the input data, and constructs a fidelity input matrix; evaluates the accuracy of the prediction model, and constructs a prediction model weight matrix set; then establishes a fidelity transfer and evolution model; then uses the fidelity input matrix, the prediction model weight matrix set, and the fidelity node weight matrix set as input to drive the fidelity transfer and evolution model, obtains the fidelity evaluation response by solving according to the MC method, and fits the fidelity response data set to obtain the fidelity CDF curve and PDF curve to achieve fidelity evaluation. Therefore, the present invention can evaluate the fidelity of the digital twin model of aircraft engine assembly accuracy in real time. This method not only helps to discover the limitations and potential improvements of the digital twin model of aircraft engine assembly accuracy, but also can be used as an evaluation indicator to provide a basis for the selection of twin models, which helps to select high-precision and high-consistency assembly accuracy digital twin models. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The above and / or other features and advantages of the present invention will become more readily understood through the following description with reference to the accompanying drawings, in which:

[0051] Figure 1 It is a flow chart of the method for evaluating the fidelity of the digital twin model of the aircraft engine assembly accuracy of the present invention;

[0052] Figure 2This is the process of constructing the fidelity transfer network at time T1 in the present invention;

[0053] Figure 3 It is the fidelity transfer network of the assembly accuracy prediction model in the present invention;

[0054] Figure 4 It is the fidelity MC evaluation process in the present invention;

[0055] Figure 5 is the casing concentricity directed weighted network adjacency matrix obtained in the example of the present invention;

[0056] Figure 6 It is the fidelity transfer network of the casing concentricity model at time T1 obtained in the example of the present invention;

[0057] Figure 7 This is the fidelity evaluation result of the casing concentricity model obtained in the example of the present invention. DETAILED DESCRIPTION

[0058] The present invention will be described in detail below with reference to the accompanying drawings by means of exemplary embodiments of the present invention. It should be noted that the following detailed description of the present invention is only for the purpose of illustration and is not intended to limit the present invention.

[0059] The present invention provides a fidelity evaluation method for a digital twin model of aircraft engine assembly accuracy, which is used to evaluate the accuracy and consistency of the digital twin model of aircraft engine assembly accuracy in real time. It not only helps to discover the limitations and potential improvements of the digital twin model of aircraft engine assembly accuracy, but also can serve as an evaluation indicator to provide a basis for the selection of twin models.

[0060] First, the overall reference Figure 1 As an exemplary embodiment of the present invention, the fidelity evaluation method of the digital twin model of the assembly accuracy of an aircraft engine generally includes: constructing a fidelity network and a set of fidelity node weight matrices based on sensitivity analysis and combined with the time-varying nature of digital twins; on the basis of the fidelity network, analyzing the fidelity input nodes at each moment, evaluating the accuracy of the input data, and constructing a fidelity input matrix; on the basis of the fidelity network, analyzing the relationship between the assembly accuracy prediction model called at each moment and its input and output, evaluating the accuracy of the prediction model, and constructing a set of prediction model weight matrices; constructing a fidelity transfer and evolution model; and evaluating the fidelity through the MC method.

[0061] Specifically, the method for evaluating the fidelity of a digital twin model of aircraft engine assembly accuracy provided by the present invention includes: step S1, constructing a fidelity transfer network, which specifically includes the following sub-steps:

[0062] Step S1.1: Extract and abstract the precision features of aircraft engine parts and components into network nodes. Determine the direction of each node edge through assembly relationships. Calculate the weight of each node through sensitivity analysis. Construct an assembly precision directed weighted network and obtain the corresponding adjacency matrix.

[0063] In step S1.2, based on the obtained adjacency matrix, the assembly accuracy prediction model used by the assembly accuracy digital twin model is introduced, and the assembly accuracy nodes are replaced with fidelity nodes to construct the assembly accuracy prediction model fidelity transfer network.

[0064] The fidelity evaluation method of the present invention further includes: Step S2, constructing a fidelity transfer and evolution model based on the assembly accuracy prediction model and the fidelity transfer network. The model inputs are the fidelity input matrix, the fidelity node weight matrix set, and the prediction model weight matrix set, and the output is a fidelity response dataset. Step S2 specifically includes the following sub-steps:

[0065] Step S2.1, dividing the assembly level according to the distribution of the assembly accuracy prediction model, and constructing a set of fidelity node weight matrices by combining the assembly level and the identity matrix;

[0066] Step S2.2: Identify the fidelity input nodes and the lowest level of the fidelity input nodes of the fidelity transfer network at each assembly time, evaluate the accuracy of the corresponding input data, and construct a fidelity input matrix based on the evaluation results of the fidelity input nodes and input data.

[0067] Step S2.3: Analyze the assembly accuracy prediction model called by the fidelity transfer network at each moment and evaluate the accuracy of the prediction model. Combine the unit matrix, the input-output relationship of the prediction model, and the prediction model accuracy evaluation results to construct a prediction model weight matrix set.

[0068] Step S2.4, constructing a digital twin model fidelity transfer and evolution model based on the lowest level of the fidelity input node and the fidelity input matrix, the fidelity node weight matrix set, and the prediction model weight matrix set.

[0069] The fidelity evaluation method provided by the present invention also includes: step S3, using the MC sampling method to sample the accuracy evaluation results of the input data at each moment, and driving the fidelity transfer and evolution model to fit the output fidelity response data set to obtain the fidelity CDF curve and the fidelity PDF curve, thereby realizing the evaluation of the fidelity of the digital twin model during the assembly process of the aircraft engine.

[0070] For step S1, Figure 2 As shown in the figure, the connection relationship between the assembly accuracy nodes is determined according to the assembly relationship, and the weights between the nodes are determined through sensitivity analysis. The adjacency matrix of the assembly accuracy in this assembly process is constructed as G:

[0071]

[0072] Where, ω ij Calculated by sensitivity analysis, it represents the weight of the directed edge from node i to j. A value of 0 indicates that there is no direct connection between the corresponding nodes.

[0073] Secondly, based on G, the assembly accuracy prediction model used in the digital twin model of aircraft engine assembly accuracy is introduced, and the assembly accuracy node is replaced by the fidelity node to construct the assembly accuracy model fidelity transfer network G at time T1. t1 The sources of fidelity nodes are described by nodes of different shapes. Triangular nodes indicate that the fidelity is determined by measured data, pentagonal nodes indicate that the fidelity is determined by predictions, and parallelogram nodes indicate that the fidelity is determined by theoretical data. Figure 2 In the embodiment of , there are 8 fidelity nodes, the superscript of the node indicates the assembly time, and the subscript indicates the assembly precision number, such as: Represents the fidelity of assembly accuracy a1 at time T1. The fidelity node weight inherits the assembly accuracy node weight. Figure 2 There are three prediction model nodes in the network, namely PM1, PM2 and PM3. The weight of the prediction model node is determined by the accuracy of the prediction model.

[0074] Finally, if Figure 3 As shown, G t1 Extended to all assembly moments, the assembly process in this embodiment is completed through 4 assembly moments, and the assembly accuracy prediction model fidelity transfer network G is constructed t It should be understood that Figure 2 and Figure 3 The assembly accuracy nodes and assembly time used in the fidelity network are only examples for the purpose of convenience of explanation and are not intended to limit the present invention.

[0075] In step S2, step S2.1 first constructs the fidelity node weight matrix D at time T1 Gω,1 :

[0076]

[0077] Then the fidelity node weight matrix set is obtained as: D Gω ={D Gω,1 ,D Gω,2 ,…,D Gω,T}, each element represents the fidelity node weight matrix at each assembly moment.

[0078] Step S2.2 analyzes the fidelity input nodes at each moment:

[0079] by Figure 3 For example, we first identify the fidelity input nodes of the fidelity transfer network at each assembly moment and the lowest level of the fidelity input nodes, and determine the source of the input nodes. The input data is divided into measurement data and theoretical data. The fidelity input nodes at time T1 include and The first three are determined by measurement data, and the last two are determined by theoretical tolerance data. According to this method, the fidelity input node is analyzed from the fidelity network at each moment.

[0080] Then evaluate the accuracy of the input data:

[0081] The evaluation method of measurement data accuracy is expressed as:

[0082] T md =ω me ·T me +ω md ·T md ·δ

[0083] Where, T md Indicates the accuracy of measurement data, T me Indicates the accuracy of the measuring device, ω me represents the measurement equipment accuracy weight, T md represents the impact of data density, ω md represents the data density accuracy weight, δ represents the data density indicator parameter, if the measurement data accuracy is related to the data density, then δ = 1, otherwise, δ = 0. In some embodiments, ω me and ω md It can be calculated by the analytic hierarchy process (AHP).

[0084] Among them, the measurement equipment accuracy T me Calculated by the following formula:

[0085]

[0086] Where mv represents the measured value, te represents the measurement error, and te may be expressed in the form of interval or random distribution.

[0087] Data density affects T md Calculated by the following formula:

[0088]

[0089] Where md is the data density, ns represents the standard size, a and c are evaluation parameters, preferably, a=0.15, c=1.

[0090] Theoretical data are usually tolerance data, design data, etc., and their accuracy is Ttd Can be set to a predetermined value, such as T td =0.75.

[0091] Then, the fidelity input nodes at each moment are combined with the input data evaluation results to construct the fidelity input vector at each moment and summarize it into the fidelity input matrix Taking the above embodiment with 4 assembly moments as an example, the fidelity input matrix is ​​as follows:

[0092]

[0093] Where each element is the evaluation result of the input data.

[0094] Step S2.3 may include: Step S2.3.1, analyzing the number of prediction models called by the fidelity transfer network at each moment and the input-output relationship of the prediction model. Figure 2 For example, there are three prediction models, which are called by three different assembly moments. The input and output relationships of each prediction model are analyzed. Taking the prediction model PM1 as an example, its input node is and The output node is

[0095] Step S2.3 also includes: Step S2.3.2, the following formula is obtained by R 2 Methods to evaluate the accuracy of prediction models:

[0096]

[0097] In the formula, ntest represents the number of test points, y i represents the true measurement value, represents the predicted value, Represents the mean of the measurements.

[0098] Then, in step S2.3.3, the prediction model weight matrix at each moment is constructed by combining the unit matrix, the input-output relationship of the prediction model, and the accuracy evaluation results of the prediction model. Figure 2 Taking time T1 as an example, the prediction model weight matrix D pm,1 as follows:

[0099]

[0100] According to this method, the prediction model weight matrix at each moment is constructed, and the prediction model weight matrix set is constructed as: D pm ={D pm,1 ,D pm,2 ,…,D pm,T}, where each element represents the prediction model weight matrix at each assembly moment. It should be noted that Figure 2In the embodiment, the prediction model is not called at time T4, so D pm,4 It is the identity matrix. By definition, when the prediction model is not called at this moment, its prediction model weight matrix is ​​the identity matrix.

[0101] In step S2.4, the digital twin model fidelity transfer model can be first constructed based on the fidelity input matrix, the fidelity node weight matrix set, and the prediction model weight matrix set. Then, the digital twin model fidelity evolution model can be constructed based on the fidelity input matrix, the fidelity node weight matrix set, and the prediction model weight matrix set using the lowest level of the fidelity input node. Specifically, the model includes the following sub-steps:

[0102] Step S2.4.1, express the digital twin model fidelity transfer model as:

[0103]

[0104] Where, F 1 represents the fidelity at time T1, represents the fidelity input vector at time T1, D Gω,j and D pm,j Respectively represent the fidelity node weight matrix and prediction model weight matrix to be called;

[0105] In step S2.4.2, the digital twin model fidelity evolution model is expressed as:

[0106]

[0107] Where, F i is the fidelity at time i, is the fidelity input vector at time i, L i is the lowest level of input nodes at time i, obtained in step S2.2.

[0108] As for step S3, from the input data evaluation process, we know that the evaluation result of the input data may be in the form of probability distribution or interval. Therefore, the fidelity evaluation process involves the problem of random-interval uncertainty transmission. The evaluation process can be as follows: Figure 4 The specific steps are as follows:

[0109] Step S3.1: Set the assembly time t = T1 and sample the accuracy evaluation results of the input data at time t. MC sampling is used for random variables, and MC sampling is used for interval variables, which is equivalent to uniform distribution. The sampling dimension is q.

[0110] Step S3.2: Drive the fidelity transfer and evolution model, calculate the fidelity evaluation response at time t, with dimension q, and fit the probability distribution function (CDF) curve and probability density function (PDF) curve of the fidelity at time t;

[0111] Step S3.3, repeat the above steps until the assembly time t=T, output the CDF curve and PDF curve of the fidelity at each time, thereby realizing the evaluation of the model fidelity during the assembly process.

[0112] As described above, the fidelity evaluation method of the digital twin model of aircraft engine assembly accuracy of the present invention first constructs an assembly accuracy directed weighted network based on the accuracy characteristics of assembly parts and components; then introduces the assembly accuracy prediction model used by the digital twin model to construct an assembly accuracy prediction model fidelity transfer network; divides the assembly hierarchy according to the prediction model, and constructs a fidelity node weight matrix set; identifies the fidelity input nodes and their lowest levels at each moment, evaluates the accuracy of the input data, and constructs a fidelity input matrix; evaluates the accuracy of the prediction model, and constructs a prediction model weight matrix set; then establishes a fidelity transfer and evolution model; thereafter, the fidelity input matrix, the prediction model weight matrix set, and the fidelity node weight matrix set are used as input to drive the fidelity transfer and evolution model, and the fidelity evaluation response is obtained by solving the problem according to the MC method, and the fidelity response data set is fitted to obtain a fidelity CDF curve and a PDF curve to achieve fidelity evaluation. Therefore, the present invention can evaluate the fidelity of the digital twin model of aircraft engine assembly accuracy in real time. This method not only helps to discover the limitations and potential improvements of the digital twin model of aircraft engine assembly accuracy, but also can be used as an evaluation indicator to provide a basis for the selection of twin models, which helps to select high-precision and high-consistency assembly accuracy digital twin models.

[0113] Next, the fidelity evaluation method of the digital twin model of aircraft engine assembly accuracy provided by the present invention is further explained with reference to examples.

[0114] This example uses the fidelity of the digital twin model of the concentricity of the aircraft engine core casing as the object. Technical personnel in this field can evaluate the fidelity of other assembly accuracy models, such as the rotor blade tip assembly clearance and the concentricity of multi-stage rotors.

[0115] First, a directed weighted network of casing concentricity is constructed, which has 26 nodes in total, and the adjacency matrix of the directed weighted network of casing concentricity is obtained as follows: Figure 5 shown.

[0116] The concentricity prediction model takes into account the actual contact of the surface, and its accuracy is 0.9649. The fidelity transfer network of the casing concentricity model at time T1 is constructed as follows: Figure 6 shown.

[0117] The core engine casing assembly involves the front casing, compressor casing and rear casing assembly. The assembly can be divided into two moments. The measurement data density is 30. The accuracy of the measurement equipment is shown in Table 1. In the table, L represents the theoretical length of the quantity to be measured.

[0118] Table 1 Measurement equipment accuracy

[0119]

[0120] The data measurement at the two moments is shown in Table 2.

[0121] Table 2 Data measurement at each time

[0122]

[0123] According to AHP, select the measurement equipment accuracy weight ω me =0.4, data density accuracy weight ω md =0.6.

[0124] Then, based on the measurement data accuracy evaluation results and the concentricity model fidelity transfer network, the concentricity model fidelity at two moments is evaluated according to the fidelity evaluation process, and the concentricity model fidelity CDF curve and PDF curve at two moments are obtained as follows: Figure 7 As shown in Figure 3, the fidelity of the casing concentricity digital twin model is evaluated in real time. Therefore, this example verifies the effectiveness of the fidelity evaluation method of the present invention.

[0125] Finally, it should be noted that the features mentioned and / or illustrated in the above description of the exemplary embodiments of the present invention may be incorporated into one or more other embodiments in the same or similar manner, combined with features in other embodiments, or substituted for corresponding features in other implementations. The technical solutions obtained by such combination or substitution shall also be deemed to be included in the scope of protection of the present invention.

Claims

1. A method for evaluating the fidelity of a digital twin model of aircraft engine assembly accuracy, characterized by: The following steps are involved: Step 1: Construct a fidelity transfer network, which includes the following sub-steps: Step 1.1: Extract and abstract the precision features of aerospace engine parts and components into network nodes. The direction of each node edge is determined by the assembly relationship, the weight of each node is calculated through sensitivity analysis, and the assembly accuracy directed weighted network is constructed to obtain the corresponding adjacency matrix; In step 1.2, based on the obtained adjacency matrix, the assembly accuracy prediction model used by the assembly accuracy digital twin model is introduced, and the assembly accuracy nodes are replaced with fidelity nodes to construct the assembly accuracy prediction model fidelity transfer network; Step 2: Construct a fidelity transfer and evolution model based on the assembly accuracy prediction model and the fidelity transfer network. The model inputs are the fidelity input matrix, the fidelity node weight matrix set, and the prediction model weight matrix set. The output is the fidelity response dataset. The model includes the following sub-steps: Step 2.1: Divide the assembly hierarchy according to the distribution of the assembly accuracy prediction model, and construct a set of fidelity node weight matrices by combining the assembly hierarchy and the identity matrix; Step 2.2: Identify the fidelity input nodes and the lowest level of the fidelity input nodes of the fidelity transfer network at each assembly time, evaluate the accuracy of the corresponding input data, and construct a fidelity input matrix based on the evaluation results of the fidelity input nodes and input data. Step 2.3: Analyze the assembly accuracy prediction model called by the fidelity transfer network at each moment and evaluate the accuracy of the prediction model. Combine the unit matrix, the input-output relationship of the prediction model, and the prediction model accuracy evaluation results to construct a prediction model weight matrix set. Step 2.4: Construct a digital twin model fidelity transfer and evolution model based on the lowest level of the fidelity input node, the fidelity input matrix, the fidelity node weight matrix set, and the prediction model weight matrix set. In step 3, the MC sampling method is used to sample the accuracy evaluation results of the input data at each moment, and the fidelity transfer and evolution model is driven to fit the output fidelity response data set to obtain the fidelity CDF curve and fidelity PDF curve, thereby realizing the evaluation of the fidelity of the digital twin model in the aircraft engine assembly process.

2. The method for evaluating the fidelity of a digital twin model of aircraft engine assembly accuracy according to claim 1, characterized in that: In step 1.2, first construct the fidelity transfer network G at time T1 t1 , and then G t1 Extended to all assembly moments T, the assembly accuracy prediction model fidelity transfer network G is obtained t .

3. The method for evaluating the fidelity of a digital twin model of aircraft engine assembly accuracy according to claim 2, characterized in that: In step 2.1, first construct the fidelity node weight matrix D at time T1 Gω,1 , then the fidelity node weight matrix set is obtained as: D Gω ={D Gω,1 ,D Gω,2 ,…,D Gω,T }.

4. The method for evaluating the fidelity of a digital twin model of aircraft engine assembly accuracy according to claim 3 is characterized in that: Step 2.2 includes: Step 2.2.1, identify the fidelity input nodes of the fidelity transfer network at each assembly moment, determine the source of the input nodes, and divide the input data into measured data and theoretical data; Step 2.2.2, evaluate the accuracy of the input data, including: Step 2.2.2.1, express the evaluation method of measurement data accuracy as: T md =ω me ·T me +oh md ·T md ·d Where, T md Indicates the accuracy of measurement data, T me Indicates the accuracy of the measuring device, ω me represents the measurement equipment accuracy weight, T md represents the impact of data density, ω md represents the data density accuracy weight, and δ represents the data density indicator parameter; Among them, the measurement equipment accuracy T me Calculated by the following formula: Where mv represents the measured value and te represents the measurement error; Data density affects T md Calculated by the following formula: Where md is the data density, ns represents the standard size, a and c are the evaluation parameters; Step 2.2.2.2, setting the accuracy of the theoretical data to a predetermined value; Step 2.2.3, combine the fidelity input nodes and input data evaluation results at each moment to construct the fidelity input vector at each moment and summarize it into the fidelity input matrix 5. The method for evaluating the fidelity of a digital twin model of aircraft engine assembly accuracy according to claim 4, characterized in that: Step 2.3 includes: Step 2.3.1: Analyze the number of prediction models called by the fidelity transfer network at each moment and the input-output relationship of the prediction models; Step 2.3.2, the following formula is used by R 2 Methods to evaluate the accuracy of prediction models: In the formula, ntest represents the number of test points, y i represents the true measurement value, represents the predicted value, represents the mean of the measured values; Step 2.3.3, combining the unit matrix, the input-output relationship of the prediction model and the accuracy evaluation results of the prediction model, the prediction model weight matrix set is constructed as follows: D pm ={D pm,1 ,D pm,2 ,…,D pm,T }.

6. The method for evaluating the fidelity of a digital twin model of aircraft engine assembly accuracy according to claim 5, characterized in that: Step 2.4 includes: Step 2.4.1: Express the digital twin model fidelity transfer model as: Where, F 1 represents the fidelity at time T1, represents the fidelity input vector at time T1, D Gω,j and D pm,j Respectively represent the fidelity node weight matrix and prediction model weight matrix to be called; In step 2.4.2, the digital twin model fidelity evolution model is expressed as: Where, F i is the fidelity at time i, is the fidelity input vector at time i, L i is the lowest level of input nodes at time i.

7. The method for evaluating the fidelity of a digital twin model of aircraft engine assembly accuracy according to claim 6, characterized in that: Step 3 includes: Step 3.1: Set the assembly time t = T1 and sample the accuracy evaluation results of the input data at time t. MC sampling is used for random variables, and MC sampling is used for interval variables, which is equivalent to uniform distribution. The sampling dimension is q. Step 3.2: Drive the fidelity transfer and evolution model, calculate the fidelity evaluation response at time t, with dimension q, and fit the CDF curve and PDF curve of the fidelity at time t; Step 3.3, repeat steps 3.1 and 3.2 until the assembly time t = T, and output the fidelity CDF curve and PDF curve at each time.

8. The method for evaluating the fidelity of a digital twin model of aircraft engine assembly accuracy according to claim 1 or 2, characterized in that: In step 1.1, the adjacency matrix is ​​constructed as follows: Where, ω ij Calculated by sensitivity analysis, it represents the weight of the directed edge from node i to j. A value of 0 indicates that there is no direct connection between the corresponding nodes.

9. The method for evaluating the fidelity of a digital twin model of aircraft engine assembly accuracy according to any one of claims 3 to 7, characterized in that: In step 2.2.2.1, ω me and ω md Calculated by analytic hierarchy process.

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