Method and system for constructing digital twinborn model of power transmission line equipment based on improved IDEF

Abstract

The invention relates to an improved IDEF-based power transmission line equipment digital twinborn model construction method and system, and the method comprises the steps: dividing the construction stage of a power transmission line, building subsystems, adding an intermediate node between adjacent data nodes, setting a data exchange triggering mechanism, carrying out the data fusion based on a Kalman filtering fusion method, and carrying out the calculation of a twinborn model. Then, IDEF0, IDEF3, IDEF5 and modular IDEF1X are adopted, subsystems are integrated, a digital twin comprehensive model is jointly constructed, finally analogue simulation is conducted, and whether parameters are updated or not is judged based on the Euclidean distance; according to the method, different stages in power transmission line equipment construction are specifically considered, multi-angle system modeling is considered, the complexity of data communication is reduced through the intermediate node, the transmission efficiency is improved, meanwhile, a modular IDEF1X information modeling method is provided, the stability of the model is improved, whether parameters are updated or not is judged based on the Euclidean distance, and the method is suitable for being applied to power transmission line equipment construction. And the digital twinning evolution is intuitively and accurately completed, so that the model has relatively high reliability and accuracy.

Description

Technical Field

[0001] The present invention relates to a digital twin model method, belongs to the field of digital construction of power transmission lines, and in particular to a method and system for constructing a digital twin model of power transmission line equipment based on improved IDEF. Background Art

[0002] With the continuous development and modernization of the power system, transmission equipment plays a vital role in power transmission. In traditional power systems, the monitoring and maintenance of transmission equipment mainly rely on regular inspections and personnel experience judgment, and it is impossible to monitor the equipment status in real time. Moreover, due to the inconsistent experience of each person, the judgment and prediction of transmission line faults often deviate from the actual situation. Digital twin technology combines physical entities with virtual mappings, simulates and analyzes the operating conditions of actual equipment, and provides comprehensive monitoring, prediction and optimization functions for transmission line power equipment, thereby achieving smarter and more accurate management of the power system.

[0003] In the current research on digital twins, there are many construction methods, such as conceptual models for product lifecycle management, five-dimensional models, models based on meta-action theory, etc. However, these methods are often oriented to specific application scenarios, lacking consideration of the functional scalability of digital twins, resulting in the emergence of islands between multiple digital twins for different application scenarios. Considering the characteristics of large scale, multiple objects, complex functional structure, numerous elements, massive data, and multi-source heterogeneity of transmission lines, the construction process of digital twin models is complicated. The traditional modeling method of a single design structure lacks systematic and structured modeling analysis, which leads to content duplication or omissions when constructing digital twin models of transmission lines. The reliability and accuracy of the model make it difficult to achieve the systematic modeling requirements of digital twin models of transmission lines. Summary of the invention

[0004] The purpose of the present invention is to overcome the above-mentioned defects and problems existing in the prior art and to provide a method and system for constructing a digital twin model of power transmission line equipment based on improved IDEF with good reliability and accuracy.

[0005] To achieve the above objectives, the technical solution of the present invention is: a method for constructing a digital twin model of power transmission line equipment based on improved IDEF, comprising:

[0006] S1. Divide the construction phases of the transmission line equipment into the foundation construction phase, the tower assembly construction phase, and the line stringing construction phase, and establish respective subsystems for the above three phases; the subsystems include a three-dimensional static model subsystem, a three-dimensional visualization monitoring subsystem, and a simulation prediction subsystem;

[0007] S2. Add intermediate nodes between adjacent data nodes in the subsystem, set a trigger mechanism for data exchange between adjacent data nodes, and fuse multi-source heterogeneous data generated in the construction of transmission line equipment based on a distributed Kalman filter fusion method;

[0008] S3, use IDEF0 to build the functional model, IDEF3 to build the network workflow model, IDEF5 to build the ontology model, modular IDEF1X to build the data information model, and integrate the subsystems to jointly build a digital twin comprehensive model;

[0009] S4. Simulate the construction phase of the transmission line equipment based on the digital twin comprehensive model, and determine whether to update the parameters of the digital twin comprehensive model based on the Euclidean distance between the simulation results and the physical entity status monitoring data; if so, use the variational Bayesian model based on Euclidean distance to update the parameters of the digital twin comprehensive model.

[0010] The step S2 specifically includes:

[0011] S21, representing the state of each data node in the multi-source heterogeneous data through information pairs;

[0012] The information pair includes an information matrix and an information vector; the expression of the information matrix is ​​as follows:

[0013]

[0014] in: is the information matrix of the data node, is the estimated covariance at time k;

[0015] The expression of the information vector is as follows:

[0016]

[0017] in: is the information vector of the data node, is the estimated value of the state at time k;

[0018] S22. Add an intermediate node i between two adjacent data nodes, and introduce a binary variable for each data node during the data exchange process of each data node. The covariance value u is used as a threshold to determine whether to trigger data exchange;

[0019] The intermediate node i sends its estimated covariance to the adjacent data nodes and performs data fusion with the estimated covariance of the adjacent data nodes to obtain the fused information pair Then, the fusion of multi-source heterogeneous data is completed through loop iteration; the expression of the fusion information pair is as follows:

[0020]

[0021] in: is the information vector of the fused data node, is the information matrix of the fused data nodes;

[0022] The expression of the trigger mechanism is as follows:

[0023]

[0024] like The data exchange is triggered.

[0025] like No data exchange is triggered.

[0026] in: is the demand covariance of data node i at time k, is the updated value of data node i at time k, Indicates that data node i communicates with adjacent data nodes to exchange data at time k. It means that data node i does not communicate with adjacent data nodes to exchange data at time k.

[0027] In step S3, modular IDEF1X constructs a data information model, specifically including:

[0028] S31. Establish a DSM matrix for the construction equipment in the subsystem; the rows and columns in the matrix represent the construction equipment;

[0029] S32, based on the genetic algorithm, modularize the DSM matrix to obtain a DSM module;

[0030] S33. In the DSM module, the information with the same attributes of the construction equipment in the same module is grouped into the same set, represented by a box in IDEF1X, and then coupled through the connection between the construction equipment in the same module, the connection between the same modules is established to complete the construction of the data information model.

[0031] The step S32 specifically includes:

[0032] S321, define a fitness function and divide the DSM matrix into modules; the fitness function is expressed as follows:

[0033] max(F) = I / E;

[0034] Where: max(F) is the maximization of the value of the fitness function F, I is the average cohesion of the DSM module, and E is the average coupling of the DSM module;

[0035]

[0036] Where: N all is the total number of DSM modules, I m is the cohesion of module m, N m is the number of elements in module m, C m (i, j) is the size of the connection between element i and element j in module m, C max is the maximum value of the connection size between elements, E p，q is the coupling degree between module p and module q;

[0037] S322, mapping the DSM module division result to a two-dimensional coding matrix as a chromosome of a genetic algorithm for operation;

[0038] S323, based on the chromosome, perform population initialization and crossover mutation operations in sequence, and iterate the operations repeatedly to determine the optimal population of the genetic algorithm, and use the optimal population as the best partitioning scheme for the DSM module.

[0039] In step S4, determining whether to update the parameters of the digital twin comprehensive model specifically includes:

[0040] S41. Compare the Euclidean distance between the simulation results of the digital twin comprehensive model and the physical entity state monitoring data under the same working conditions to obtain the Euclidean distance of the historical data set;

[0041] The simulation result is X=(x 1 , x 2 , x 3 ……x n ), the state monitoring data is Y=(y 1 ,y 2 ,y 3 ……y n ); The Euclidean distance between the simulation result and the state monitoring data is as follows:

[0042]

[0043] S42, setting the maximum Euclidean distance As as the update trigger threshold, then adding a new sample set for simulation, and comparing the simulation results of the new sample set with the historical state monitoring data to obtain the Euclidean distance of the new sample set;

[0044] S43, comparing the Euclidean distance of the historical data set with the Euclidean distance of the new sample set;

[0045] If the Euclidean distance of the historical data set and the Euclidean distance of the new sample set are both greater than the maximum Euclidean distance As, the parameters of the digital twin comprehensive model are updated.

[0046] In step S4, updating the parameters of the digital twin comprehensive model specifically includes:

[0047] S44, constructing the input of Bayesian inference based on likelihood function and prior distribution;

[0048] The real-time status data of the physical entity is X=[x 1 , x 2 , ..., x n ], the simulation data of the digital twin comprehensive model is X(θ i )=[x 1 (θ i ), x 2 (θ i ), ..., x n (θ i )], real-time state data X and simulation data X(θ i ) is X=X(θ i )+ε; where: ε=[ε 1 , ε 2 , ...ε m ] is the m-dimensional vector of error;

[0049] Then the expression of the likelihood function P(x|θ) is as follows:

[0050]

[0051] Where: μ i , σ i are the mean and standard deviation of the errors respectively;

[0052] S45. Beta distribution is used to describe the change process of physical entity parameters; the expression of Beta distribution is as follows:

[0053] f(θ;α,β)=θ α-1 (1-θ) β-1 ;

[0054] Among them: θ is the parameter to be updated, α and β are parameters greater than zero;

[0055] S46, calculating the values ​​of α and β according to the prior distribution change data of the parameters; the calculation formula is as follows:

[0056]

[0057] in: is the parameter mean, s θis the parameter standard deviation;

[0058] The output of Bayesian inference is as follows:

[0059]

[0060] Among them: p(θ|x) is the posterior distribution of parameter θ, that is, the parameter of the updated digital twin comprehensive model; p(x|θ) is the likelihood function, which represents the probability of observing data x when the parameter value is θ; p(θ) is the prior distribution of parameter θ, which is the same as the prior probability distribution

[0061] S47. Approximate the posterior distribution p(θ|x) to a simple distribution q(θ)=q φ (θ);

[0062] S48. Use KL divergence to quantify the posterior distribution p(θ|x) and the simple distribution q φ The mismatch between (θ); the expression of the KL divergence is as follows:

[0063]

[0064] Where: E φ is the expectation of the logarithmic function in brackets;

[0065] S49, transform the problem of solving the posterior distribution into the problem of solving the ELBO maximum value of the evidence lower bound, confirm the objective function, and complete the parameter update of the digital twin comprehensive model;

[0066] The expression of the evidence lower bound ELBO is as follows:

[0067]

[0068] Where: p(x|θ)p(θ) is the joint distribution of θ and x;

[0069] The objective function is as follows:

[0070] F(θ)=max[ELBO];

[0071] Where: F(θ) is the initial value of the objective function.

[0072] A digital twin model construction system for power transmission line equipment based on improved IDEF, the system comprising:

[0073] The digital twin subsystem construction module is used to divide the construction phase of the transmission line equipment into the foundation construction phase, the tower assembly construction phase, and the line stringing construction phase, and establish respective subsystems for the above three phases; the subsystems include a three-dimensional static model subsystem, a three-dimensional visualization monitoring subsystem, and a simulation prediction subsystem;

[0074] The digital twin data fusion module is used to add intermediate nodes between adjacent data nodes in the subsystem, set a trigger mechanism for data exchange between adjacent data nodes, and fuse multi-source heterogeneous data generated during the construction of transmission line equipment based on the distributed Kalman filter fusion method;

[0075] The digital twin model construction module is used to build the functional model using IDEF0, the network workflow model using IDEF3, the ontology model using IDEF5, and the data information model using modular IDEF1X, and integrate subsystems to jointly build a digital twin comprehensive model;

[0076] The digital twin model update module is used to simulate the construction phase of the transmission line equipment based on the digital twin comprehensive model, and determine whether to update the parameters of the digital twin comprehensive model based on the Euclidean distance between the simulation results and the physical entity status monitoring data; if so, the variational Bayesian model based on Euclidean distance is used to update the parameters of the digital twin comprehensive model.

[0077] The digital twin data fusion module performs data fusion according to the following steps:

[0078] S21, representing the state of each data node in the multi-source heterogeneous data through information pairs;

[0079] The information pair includes an information matrix and an information vector; the expression of the information matrix is ​​as follows:

[0080]

[0081] in: is the information matrix of the data node, is the estimated covariance at time k;

[0082] The expression of the information vector is as follows:

[0083]

[0084] in: is the information vector of the data node, is the estimated value of the state at time k;

[0085] S22. Add an intermediate node i between two adjacent data nodes, and introduce a binary variable for each data node during the data exchange process of each data node. The covariance value u is used as a threshold to determine whether to trigger data exchange;

[0086] The intermediate node i sends its estimated covariance to the adjacent data nodes and performs data fusion with the estimated covariance of the adjacent data nodes to obtain the fused information pair Then, the fusion of multi-source heterogeneous data is completed through loop iteration; the expression of the fusion information pair is as follows:

[0087]

[0088]

[0089] in: is the information vector of the fused data node, is the information matrix of the fused data nodes;

[0090] The expression of the trigger mechanism is as follows:

[0091]

[0092] like The data exchange is triggered.

[0093] like No data exchange is triggered.

[0094] in: is the demand covariance of data node i at time k, is the updated value of data node i at time k, Indicates that data node i communicates with adjacent data nodes to exchange data at time k. It means that data node i does not communicate with adjacent data nodes to exchange data at time k.

[0095] The digital twin model construction module constructs the digital twin model according to the following steps:

[0096] S31. Establish a DSM matrix for the construction equipment in the subsystem; the rows and columns in the matrix represent the construction equipment;

[0097] S32, based on the genetic algorithm, modularize the DSM matrix to obtain a DSM module;

[0098] S33, in the DSM module, the information of the construction equipment with the same attributes in the same module is classified into the same set, which is represented by a box in IDEF1X, and then coupled through the connection of the construction equipment in the same module, the connection between the same modules is established, and the construction of the data information model is completed;

[0099] The step S32 specifically includes:

[0100] S321, define a fitness function and divide the DSM matrix into modules; the fitness function is expressed as follows:

[0101] max(F) = I / E;

[0102] Where: max(F) is the maximization of the value of the fitness function F, I is the average cohesion of the DSM module, and E is the average coupling of the DSM module;

[0103]

[0104]

[0105] Where: N all is the total number of DSM modules, I m is the cohesion of module m, N m is the number of elements in module m, C m (i, j) is the size of the connection between element i and element j in module m, C max is the maximum value of the connection size between elements, E p，q is the coupling degree between module p and module q;

[0106] S322, mapping the DSM module division result to a two-dimensional coding matrix as a chromosome of a genetic algorithm for operation;

[0107] S323, based on the chromosome, perform population initialization and crossover mutation operations in sequence, and iterate the operations repeatedly to determine the optimal population of the genetic algorithm, and use the optimal population as the best partitioning scheme for the DSM module.

[0108] The digital twin model update module updates the parameters of the model according to the following steps:

[0109] S44, constructing the input of Bayesian inference based on likelihood function and prior distribution;

[0110] The real-time status data of the physical entity is X=[x 1 , x 2 , ..., x n ], the simulation data of the digital twin comprehensive model is X(θ i )=[x 1 (θ i ), x 2 (θ i ), ..., x n (θ i )], real-time state data X and simulation data X(θ i ) is X=X(θ i )+ε; where: ε=[ε 1 ,ε2 ,...,ε m ] is the m-dimensional vector of error;

[0111] Then the expression of the likelihood function p(x|θ) is as follows:

[0112]

[0113] Where: μ i , σ i are the mean and standard deviation of the errors respectively;

[0114] S45. Beta distribution is used to describe the change process of physical entity parameters; the expression of Beta distribution is as follows:

[0115] f(θ;α,β)=θ α-1 (1-θ) β-1 ;

[0116] Among them: θ is the parameter to be updated, α and β are parameters greater than zero;

[0117] S46, calculating the values ​​of α and β according to the prior distribution change data of the parameters; the calculation formula is as follows:

[0118]

[0119] in: is the parameter mean, s θ is the parameter standard deviation;

[0120] The output of Bayesian inference is as follows:

[0121]

[0122] Among them: p(θ|x) is the posterior distribution of parameter θ, that is, the parameter of the updated digital twin comprehensive model; p(θ|x) is the likelihood function, which represents the probability of observing data x when the parameter value is θ; p(θ) is the prior distribution of parameter θ, which is the same as the prior probability distribution

[0123] S47. Approximate the posterior distribution p(θ|x) to a simple distribution q(θ)=q φ (θ);

[0124] S48. Use KL divergence to quantify the posterior distribution p(θ|x) and the simple distribution q φ The mismatch between (θ); the expression of the KL divergence is as follows:

[0125]

[0126] Where: E φis the expectation of the logarithmic function in brackets;

[0127] S49, transform the problem of solving the posterior distribution into the problem of solving the ELBO maximum value of the evidence lower bound, confirm the objective function, and complete the parameter update of the digital twin comprehensive model;

[0128] The expression of the evidence lower bound ELBO is as follows:

[0129]

[0130] Where: p(x|θ)p(θ) is the joint distribution of θ and x;

[0131] The objective function is as follows:

[0132] F(θ)=max[ELBO];

[0133] Where: F(θ) is the initial value of the objective function.

[0134] Compared with the prior art, the present invention has the following beneficial effects:

[0135] The present invention discloses a method and system for constructing a digital twin model of power transmission line equipment based on improved IDEF. The method first divides the construction phases of the power transmission line equipment and respectively establishes respective subsystems, then adds intermediate nodes between adjacent data nodes, and sets up a data exchange trigger mechanism, then performs data fusion based on a Kalman filter fusion method, then uses IDEF0 to construct functions, IDEF3 to construct network workflows, IDEF5 to construct entities, and modular IDEF1X to construct data information, and integrates subsystems to jointly construct a digital twin comprehensive model, and finally performs simulation, and determines whether to update parameters based on the Euclidean distance between the simulation results and the state monitoring data; in the application of the present invention, different stages in the construction process of the power transmission line equipment are specifically considered, multi-angle system modeling is taken into account, and the complexity of data communication is reduced by adding intermediate nodes, and the data transmission efficiency is improved, and a modular IDEF1X information modeling method is proposed to improve the stability of the digital twin model, and finally determines whether to update parameters based on the Euclidean distance, and intuitively and accurately completes the digital twin evolution, so that the digital twin model of the power transmission line equipment has high reliability and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0136] Figure 1 It is a flow chart of the method steps in the present invention.

[0137] Figure 2 It is a schematic diagram of the digital twin comprehensive model in the present invention.

[0138] Figure 3It is an update flow chart of the digital twin comprehensive model in the present invention.

[0139] Figure 4 It is a modeling flow chart of the digital twin comprehensive model in the present invention.

[0140] Figure 5 It is a real-time monitoring data diagram of the ambient temperature of the power transmission line in the present invention.

[0141] Figure 6 It is the hidden danger area identification map in the present invention.

[0142] Figure 7 It is a schematic diagram of the system structure in the present invention.

[0143] Figure 8 It is a schematic diagram of the device structure in the present invention.

[0144] In the figure: digital twin stage division module 1, digital twin data fusion module 2, digital twin model construction module 3, digital twin model update module 4, processor 5, memory 6, computer program code 61. DETAILED DESCRIPTION

[0145] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0146] Embodiment 1:

[0147] See also Figure 1 , a method for constructing a digital twin model of transmission line equipment based on improved IDEF, comprising:

[0148] S1. Divide the construction phases of the transmission line equipment into the foundation construction phase, the tower assembly construction phase, and the line stringing construction phase, and establish respective subsystems for the above three phases; the subsystems include a three-dimensional static model subsystem, a three-dimensional visualization monitoring subsystem, and a simulation prediction subsystem;

[0149] See also Figure 2 The three-dimensional static model subsystem refers to: mapping the physical properties of the physical entities in the subsystem into a virtual environment to construct a virtual model, forming a three-dimensional static model subsystem; the physical entities include poles and towers, construction machinery, and construction tools, and the virtual model includes pole and tower models, construction machinery models, and construction tool models;

[0150] The three-dimensional visualization monitoring subsystem refers to: integrating the storage function, analysis function, transmission function, and display function when processing digital twin data in the subsystem to form a three-dimensional visualization monitoring subsystem; the digital twin data includes physical entity data and operator data;

[0151] The simulation prediction subsystem refers to: simulating and analyzing the state of the physical entity in the subsystem, simulating and predicting the state change of the physical entity in the virtual environment, forming a simulation prediction subsystem; the simulation prediction includes physical entity fault warning and remaining working time prediction.

[0152] S2. Add intermediate nodes between adjacent data nodes in the subsystem, set a trigger mechanism for data exchange between adjacent data nodes, and fuse multi-source heterogeneous data generated in the construction of transmission line equipment based on a distributed Kalman filter fusion method;

[0153] Further, it specifically includes:

[0154] S21, representing the state of each data node in the multi-source heterogeneous data through information pairs;

[0155] The information pair includes an information matrix and an information vector; the expression of the information matrix is ​​as follows:

[0156]

[0157] in: is the information matrix of the data node, is the estimated covariance at time k;

[0158] The expression of the information vector is as follows:

[0159]

[0160]

[0161] in: is the information vector of the data node, is the estimated value of the state at time k;

[0162] S22. Add an intermediate node i between two adjacent data nodes, and introduce a binary variable for each data node during the data exchange process of each data node. The covariance value u is used as a threshold to determine whether to trigger data exchange;

[0163] The intermediate node i sends its estimated covariance to the adjacent data nodes and performs data fusion with the estimated covariance of the adjacent data nodes to obtain the fused information pair Then, the fusion of multi-source heterogeneous data is completed through loop iteration; the expression of the fusion information pair is as follows:

[0164]

[0165] in: is the information vector of the fused data node, is the information matrix of the fused data nodes;

[0166] The expression of the trigger mechanism is as follows:

[0167]

[0168] like The data exchange is triggered.

[0169] like No data exchange is triggered.

[0170] in: is the demand covariance of data node i at time k, is the updated value of data node i at time k, Indicates that data node i communicates with adjacent data nodes to exchange data at time k. It means that data node i does not communicate with adjacent data nodes to exchange data at time k.

[0171] S3, use IDEF0 to build the functional model, IDEF3 to build the network workflow model, IDEF5 to build the ontology model, modular IDEF1X to build the data information model, and integrate the subsystems to jointly build a digital twin comprehensive model;

[0172] See also Figure 3 ,In this embodiment, IDEF is used with the top-level design as a guide to ,implement a systematic and structured modeling and analysis of the ,transmission line construction equipment, including IDEF0 functional modeling, IDEF3 network ,workflow modeling and IDEF5 ontology modeling.

[0173] The IDEF0 functional modeling means that when constructing a transmission line equipment model, the functional role of the subsystem, the relationship between functions, etc., must be strictly analyzed from the top layer to the bottom layer, from the system to the equipment, to form a digital twin model of the transmission line equipment. Through functional modeling, the functions and structural composition of the transmission line equipment can be deeply analyzed, the main equipment categories, including poles, construction machinery, tools, etc., can be determined, and the model library can be built. Finally, the IDEF0 model is constructed to clarify the hierarchical structure of the functional block diagram and the direction of information flow.

[0174] The IDEF3 network workflow modeling refers to: using process modeling to decompose the workflow of each stage of transmission line construction equipment, using the PFN model diagram of the IDEF3 method to construct the flow process of data in the digital twin model of transmission line construction equipment, and using a graphical language to record the priority and causal relationship of events and state changes in the actual production process of manufacturing transmission line equipment.

[0175] The IDEF5 ontology modeling refers to: through ontology modeling, the conceptual model of the digital twin model of transmission line construction equipment is constructed using the IDEF5 graphical ontology acquisition method, providing an interactive, shared, structured and consistent conceptual framework for the development of the digital twin model ontology of transmission line construction equipment.

[0176] The modular IDEF1X data information model construction refers to: establishing a design structure matrix (DSM) of construction equipment, dividing the equipment into modules with "high cohesion and low coupling", and using genetic algorithms to solve the DSM module division problem. Finally, a modular IDEF1X information model is established on this basis, which is conducive to improving the stability of the digital twin model.

[0177] Further, it specifically includes:

[0178] S31. For the construction equipment in the subsystem, an n×n DSM matrix is ​​established; the rows and columns in the matrix represent the construction equipment.

[0179] S32, based on the genetic algorithm, modularize the DSM matrix to obtain a DSM module;

[0180] S33. In the DSM module, the information with the same attributes of the construction equipment in the same module is grouped into the same set, represented by a box in IDEF1X, and then coupled through the connection between the construction equipment in the same module, the connection between the same modules is established to complete the construction of the data information model.

[0181] Furthermore, the step S32 specifically includes:

[0182] S321, define a fitness function and divide the DSM matrix into modules; the fitness function is expressed as follows:

[0183] max(F) = I / E;

[0184] Where: max(F) is the maximization of the value of the fitness function F, I is the average cohesion of the DSM module, and E is the average coupling degree of the DSM module;

[0185]

[0186] Where: N all is the total number of DSM modules, I m is the cohesion of module m, N m is the number of elements in module m, C m (i, j) is the size of the connection between element i and element j in module m, C max is the maximum value of the connection size between elements, Ep，q is the coupling degree between module p and module q;

[0187] S322, mapping the DSM module division result to a two-dimensional coding matrix as a chromosome of a genetic algorithm for operation;

[0188] The two-dimensional coding matrix is ​​an n×m Boolean matrix, where n is the number of modules into which the elements in the DSM matrix are divided, m is the dimension of the DSM matrix, and the matrix element a i,j The value of is "1" or "0". The value "1" represents that the part element j is divided into the jth module, and the value "0" represents that the part element j is not divided into the i-th module.

[0189] S323, based on the chromosome, perform population initialization and crossover mutation operations in sequence, and iterate the operations repeatedly to determine the optimal population of the genetic algorithm, and use the optimal population as the best partitioning scheme for the DSM module.

[0190] S4. Simulate the construction phase of the transmission line equipment based on the digital twin comprehensive model, and determine whether to update the parameters of the digital twin comprehensive model based on the Euclidean distance between the simulation results and the physical entity status monitoring data; if so, use the variational Bayesian model based on Euclidean distance to update the parameters of the digital twin comprehensive model.

[0191] Furthermore, it is determined whether to update the parameters of the digital twin comprehensive model, including:

[0192] S41. Compare the Euclidean distance between the simulation results of the digital twin comprehensive model and the physical entity state monitoring data under the same working conditions to obtain the Euclidean distance of the historical data set;

[0193] The simulation result is X=(x 1 ,x 2 ,x 3 ……x n ), the state monitoring data is Y=(y 1 ,y 2 ,y 3 ……y n ); The Euclidean distance between the simulation result and the state monitoring data is as follows:

[0194]

[0195] S42, setting the maximum Euclidean distance As as the update trigger threshold, then adding a new sample set for simulation, and comparing the simulation results of the new sample set with the historical state monitoring data to obtain the Euclidean distance of the new sample set;

[0196] S43, comparing the Euclidean distance of the historical data set with the Euclidean distance of the new sample set;

[0197] If the Euclidean distance of the historical data set and the Euclidean distance of the new sample set are both greater than the maximum Euclidean distance As, the parameters of the digital twin comprehensive model are updated.

[0198] See also Figure 4 ,Furthermore, update the parameters of the digital twin comprehensive model, including:

[0199] S44, constructing the input of Bayesian inference based on likelihood function and prior distribution;

[0200] The real-time status data of the physical entity is X=[x 1 ,x 2 ,…,x n ], the simulation data of the digital twin comprehensive model is X(θ i )=[x 1 (θ i ),x 2 (θ i ),…,x n (θ i )], real-time state data X and simulation data X(θ i ) is X=X(θ i )+ε; where: ε=[ε 1 , ε 2 , ..., ε m ] is the m-dimensional vector of error;

[0201] Then the expression of the likelihood function p(x|θ) is as follows:

[0202]

[0203] Where: μ i , σ i are the mean and standard deviation of the errors respectively;

[0204] S45. Beta distribution is used to describe the change process of physical entity parameters; the expression of Beta distribution is as follows:

[0205] f(θ;α,β)=θ α-1 (1-θ) β-1 ;

[0206] Among them: θ is the parameter to be updated, α and β are parameters greater than zero;

[0207] S46, calculating the values ​​of α and β according to the prior distribution change data of the parameters; the calculation formula is as follows:

[0208]

[0209] in: is the parameter mean, S θ is the parameter standard deviation;

[0210] The output of Bayesian inference is as follows:

[0211]

[0212] Among them: p(θ|x) is the posterior distribution of parameter θ, that is, the parameter of the updated digital twin comprehensive model; p(x|θ) is the likelihood function, which represents the probability of observing data x when the parameter value is θ; p(θ) is the prior distribution of parameter θ, which is the same as the prior probability distribution

[0213] S47. Approximate the posterior distribution p(θ|x) to a simple distribution q(θ)=q φ (θ);

[0214] S48. Use KL divergence to quantify the posterior distribution p(θ|x) and the simple distribution q φ The mismatch between (θ); the expression of the KL divergence is as follows:

[0215]

[0216] Where: E φ is the expectation of the logarithmic function in brackets;

[0217] S49, transform the problem of solving the posterior distribution into the problem of solving the ELBO maximum value of the evidence lower bound, confirm the objective function, and complete the parameter update of the digital twin comprehensive model;

[0218] The expression of the evidence lower bound ELBO is as follows:

[0219]

[0220] Where: p(x|θ)p(θ) is the joint distribution of θ and x;

[0221] The objective function is as follows:

[0222] F(θ)=max[ELBO];

[0223] Where: F(θ) is the initial value of the objective function.

[0224] The problem of solving the posterior distribution is converted into solving the ELBO maximum value problem. According to the real-time state data and the parameter prior distribution, the parameter posterior distribution can be calculated to complete a parameter update. Combined with multiple real-time state data, the entire twin model can be updated.

[0225] See also Figure 5 In this embodiment, the real-time and reliability of the digital twin model data interaction are verified through examples. It can be seen from the figure that the line temperature prediction error is controlled within ±0.4℃, and the maximum value appears in the 21st group. The actual ambient temperature is 44.3℃, the lower limit of the monitoring temperature is 44.1℃, and the upper limit is 44.7℃. This is mainly due to the significant change in wind speed at that time, and it is difficult for the model to fully adapt. But overall, the prediction accuracy is high, which proves that the visual monitoring of the adaptive transmission line equipment digital twin model based on the improved IDEF is effective, and the modeling accuracy can be further improved through incremental learning in the future.

[0226] See also Figure 6 In this embodiment, the accuracy of the digital twin model in predicting the hidden danger area is verified by examples. The simulation results in the figure show that the misdiagnosis numbers of the safe area, machine hidden danger area, floating object hidden danger area, ice area, dancing area, pollution area, mine area and bird damage area (area categories are numbered from 1 to 8) are 15, 15, 6, 11, 12, 13, 12, 13 respectively; the recognition accuracy of the eight areas is also above 85%, and the overall recognition accuracy reaches 88%, which verifies the model's ability to identify hidden danger areas and confirms that the hidden danger area identification model of the adaptive transmission line equipment digital twin model based on the improved IDEF is reliable.

[0227] Embodiment 2:

[0228] See also Figure 7 , a digital twin model construction system for power transmission line equipment based on improved IDEF, the system comprising:

[0229] The digital twin subsystem construction module 1 is used to divide the construction phase of the transmission line equipment into the foundation construction phase, the tower assembly construction phase, and the line stringing construction phase, and establish respective subsystems for the above three phases; the subsystems include a three-dimensional static model subsystem, a three-dimensional visualization monitoring subsystem, and a simulation prediction subsystem;

[0230] Furthermore, the subsystems divided by the digital twin subsystem building module 1 are as follows:

[0231] The three-dimensional static model subsystem refers to: mapping the physical properties of the physical entities in the subsystem into a virtual environment to construct a virtual model, forming a three-dimensional static model subsystem; the physical entities include poles and towers, construction machinery, and construction tools, and the virtual model includes pole and tower models, construction machinery models, and construction tool models;

[0232] The three-dimensional visualization monitoring subsystem refers to: integrating the storage function, analysis function, transmission function, and display function when processing digital twin data in the subsystem to form a three-dimensional visualization monitoring subsystem; the digital twin data includes physical entity data and operator data;

[0233] The simulation prediction subsystem refers to: simulating and analyzing the state of the physical entity in the subsystem, simulating and predicting the state change of the physical entity in the virtual environment, forming a simulation prediction subsystem; the simulation prediction includes physical entity fault warning and remaining working time prediction.

[0234] Digital twin data fusion module 2 is used to add intermediate nodes between adjacent data nodes in the subsystem, set a trigger mechanism for data exchange between adjacent data nodes, and fuse multi-source heterogeneous data generated during the construction of transmission line equipment based on the distributed Kalman filter fusion method;

[0235] Furthermore, the digital twin data fusion module 2 performs data fusion according to the following steps:

[0236] S21, representing the state of each data node in the multi-source heterogeneous data through information pairs;

[0237] The information pair includes an information matrix and an information vector; the expression of the information matrix is ​​as follows:

[0238]

[0239]

[0240] in: is the information matrix of the data node, is the estimated covariance at time k;

[0241] The expression of the information vector is as follows:

[0242]

[0243] in: is the information vector of the data node, is the estimated value of the state at time k;

[0244] S22. Add an intermediate node i between two adjacent data nodes, and introduce a binary variable for each data node during the data exchange process of each data node. The covariance value u is used as a threshold to determine whether to trigger data exchange;

[0245] The intermediate node i sends its estimated covariance to the adjacent data nodes and performs data fusion with the estimated covariance of the adjacent data nodes to obtain the fused information pair Then, the fusion of multi-source heterogeneous data is completed through loop iteration; the expression of the fusion information pair is as follows:

[0246]

[0247] in: is the information vector of the fused data node, is the information matrix of the fused data nodes;

[0248] The expression of the trigger mechanism is as follows:

[0249]

[0250] like The data exchange is triggered.

[0251] like No data exchange is triggered.

[0252] in: is the demand covariance of data node i at time k, is the updated value of data node i at time k, Indicates that data node i communicates with adjacent data nodes to exchange data at time k. It means that data node i does not communicate with adjacent data nodes to exchange data at time k.

[0253] Digital twin model construction module 3 is used to build a functional model using IDEF0, a network workflow model using IDEF3, an ontology model using IDEF5, and a data information model using modular IDEF1X, and integrate subsystems to jointly build a digital twin comprehensive model;

[0254] Furthermore, the digital twin model construction module 3 constructs the digital twin model according to the following steps:

[0255] S31. Establish a DSM matrix for the construction equipment in the subsystem; the rows and columns in the matrix represent the construction equipment;

[0256] S32, based on the genetic algorithm, modularize the DSM matrix to obtain a DSM module;

[0257] S33, in the DSM module, the information of the construction equipment with the same attributes in the same module is classified into the same set, which is represented by a box in IDEF1X, and then coupled through the connection of the construction equipment in the same module, the connection between the same modules is established, and the construction of the data information model is completed;

[0258] The step S32 specifically includes:

[0259] S321, define a fitness function and divide the DSM matrix into modules; the fitness function is expressed as follows:

[0260] max(F) = I / E;

[0261] Where: max(F) is the maximization of the value of the fitness function F, I is the average cohesion of the DSM module, and E is the average coupling of the DSM module;

[0262]

[0263] Where: N all is the total number of DSM modules, I m is the cohesion of module m, N m is the number of elements in module m, C m (i, j) is the size of the connection between element i and element j in module m, C max is the maximum value of the connection size between elements, E p，q is the coupling degree between module p and module q;

[0264] S322, mapping the DSM module division result to a two-dimensional coding matrix as a chromosome of a genetic algorithm for operation;

[0265] S323, based on the chromosome, perform population initialization and crossover mutation operations in sequence, and iterate the operations repeatedly to determine the optimal population of the genetic algorithm, and use the optimal population as the best partitioning scheme for the DSM module.

[0266] The digital twin model updating module 4 is used to simulate the construction phase of the transmission line equipment based on the digital twin comprehensive model, and determine whether to update the parameters of the digital twin comprehensive model based on the Euclidean distance between the simulation results and the physical entity state monitoring data; if so, the variational Bayesian model based on the Euclidean distance is used to update the parameters of the digital twin comprehensive model;

[0267] Furthermore, the digital twin model updating module 4 updates the parameters of the model according to the following steps:

[0268] S44, constructing the input of Bayesian inference based on likelihood function and prior distribution;

[0269] The real-time status data of the physical entity is X=[x 1 , x 2 , ..., x n ], the simulation data of the digital twin comprehensive model is X(θ i )=[x 1 (θ), x 2 (θ i ), ..., x n (θ i )], real-time state data X and simulation data X(θ i ) is X=X(θ i )+ε; where: ε=[ε 1 , ε 2 , ...ε m ] is the m-dimensional vector of error;

[0270] Then the expression of the likelihood function P(x|θ) is as follows:

[0271]

[0272] Where: μ i , σ i are the mean and standard deviation of the errors respectively;

[0273] S45. Beta distribution is used to describe the change process of physical entity parameters; the expression of Beta distribution is as follows:

[0274] f(θ;α,β)=θ α-1 (1-θ) β-1 ;

[0275] Among them: θ is the parameter to be updated, α and β are parameters greater than zero;

[0276] S46, calculating the values ​​of α and β according to the prior distribution change data of the parameters; the calculation formula is as follows:

[0277]

[0278] in: is the parameter mean, S θ is the parameter standard deviation;

[0279] The output of Bayesian inference is as follows:

[0280]

[0281] Among them: p(θ|x) is the posterior distribution of parameter θ, that is, the parameter of the updated digital twin comprehensive model; p(x|θ) is the likelihood function, which represents the probability of observing data x when the parameter value is θ; p(θ) is the prior distribution of parameter θ, which is the same as the prior probability distribution

[0282] S47. Approximate the posterior distribution p(θ|x) to a simple distribution q(θ)=q φ (θ);

[0283] S48. Use KL divergence to quantify the posterior distribution p(θ|x) and the simple distribution q φ The mismatch between (θ); the expression of the KL divergence is as follows:

[0284]

[0285] Where: E φ is the expectation of the logarithmic function in brackets;

[0286] S49, transform the problem of solving the posterior distribution into the problem of solving the ELBO maximum value of the evidence lower bound, confirm the objective function, and complete the parameter update of the digital twin comprehensive model;

[0287] The expression of the evidence lower bound ELBO is as follows:

[0288]

[0289] Where: p(x|θ)p(θ) is the joint distribution of θ and x;

[0290] The objective function is as follows:

[0291] F(θ)=max[ELBO];

[0292] Where: F(θ) is the initial value of the objective function.

[0293] Embodiment 3:

[0294] See also Figure 8 , a device for constructing a digital twin model of a power transmission line device based on improved IDEF, the device comprising a processor 5 and a memory 6;

[0295] The memory 6 is used to store the computer program code 61 and transmit the computer program code 61 to the processor 5;

[0296] The processor 5 is used to execute the method for constructing a digital twin model of transmission line equipment based on improved IDEF described in Example 1 according to the instructions in the computer program code 61.

[0297] This embodiment also includes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed on a computer, the method for constructing a digital twin model of transmission line equipment based on improved IDEF described in Example 1 is implemented.

[0298] Although the embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for constructing a digital twin model of power transmission line equipment based on improved IDEF. It is characterized in that include: S1. Divide the construction phases of the transmission line equipment into the foundation construction phase, the tower assembly construction phase, and the line stringing construction phase, and establish respective subsystems for the above three phases; the subsystems include a three-dimensional static model subsystem, a three-dimensional visualization monitoring subsystem, and a simulation prediction subsystem; S2. Add intermediate nodes between adjacent data nodes in the subsystem, set a trigger mechanism for data exchange between adjacent data nodes, and fuse multi-source heterogeneous data generated in the construction of transmission line equipment based on a distributed Kalman filter fusion method; S3. Use IDEF0 to build the functional model of the subsystem, use IDEF3 to build the network workflow model of the subsystem, use IDEF5 to build the ontology model of the subsystem, and use modular IDEF1X to build the data information model of the subsystem. Associate and map the functional model, network workflow model, ontology model, data information model with the subsystem to jointly build a digital twin comprehensive model. S4. Based on the digital twin comprehensive model and the integrated multi-source heterogeneous data, simulate the construction phase of the transmission line equipment, and based on the Euclidean distance between the simulation results and the physical entity status monitoring data, determine whether to update the parameters of the digital twin comprehensive model; if so, use the variational Bayesian model based on Euclidean distance to update the parameters of the digital twin comprehensive model.

2. According to the method for constructing a digital twin model of power transmission line equipment based on improved IDEF according to claim 1, Features: The step S2 specifically includes: S21, representing the state of each data node in the multi-source heterogeneous data through information pairs; The information pair includes an information matrix and an information vector; the expression of the information matrix is ​​as follows: in: is the information matrix of the data node, is the estimated covariance at time k; The expression of the information vector is as follows: in: is the information vector of the data node, is the estimated value of the state at time k; S22. Add an intermediate node i between two adjacent data nodes, and introduce a binary variable for each data node during the data exchange process of each data node. The covariance value u is used as a threshold to determine whether to trigger data exchange; The expression of the trigger mechanism is as follows: like The data exchange is triggered. like No data exchange is triggered. in: is the demand covariance of data node i at time k, is the updated value of data node i at time k, Indicates that data node i communicates with adjacent data nodes to exchange data at time k. Indicates that data node i does not communicate with adjacent data nodes for data exchange at time k; S23, based on the distributed Kalman filter fusion method, the intermediate node i sends its own estimated covariance to the adjacent data nodes, and performs data fusion with the estimated covariance of the adjacent data nodes to obtain the fusion information pair The fusion of multi-source heterogeneous data is completed through loop iteration; the expression of the fusion information pair is as follows: in: is the information vector of the fused data node, is the information matrix of the fused data nodes.

3. According to the method for constructing a digital twin model of power transmission line equipment based on improved IDEF according to claim 1, Features: In step S3, the data information model of the subsystem is constructed using modular IDEF1X, which specifically includes: S31, establishing a DSM matrix; the rows and columns in the matrix represent the construction equipment in the construction stage; S32, based on the genetic algorithm, modularize the DSM matrix to obtain a DSM module; S33. In the DSM module, the information with the same attributes of the construction equipment in the same module is grouped into the same set, represented by a box in IDEF1X, and then coupled through the connection between the construction equipment in the same module, the connection between the same modules is established to complete the construction of the data information model.

4. According to the method for constructing a digital twin model of power transmission line equipment based on improved IDEF according to claim 3, Features: The step S32 specifically includes: S321, define a fitness function and divide the DSM matrix into modules; the fitness function is expressed as follows: max(F)=I / ∑; Where: max(F) is the maximization of the value of the fitness function F, I is the average cohesion of the DSM module, and E is the average coupling of the DSM module; Where: N all is the total number of DSM modules, I m is the cohesion of module n, N m is the number of elements in module m, C m (i, j) is the size of the connection between element i and element j in module m, C max is the maximum value of the connection size between elements, E p，q is the coupling degree between module p and module q; S322, based on the genetic algorithm, mapping the DSM module division result to a two-dimensional coding matrix as a chromosome of the genetic algorithm to participate in the operation; S323, performing population initialization and crossover mutation operations on chromosomes in sequence, and repeatedly iterating operations to determine the optimal population of the genetic algorithm, and using the optimal population as the best partitioning scheme for the DSM module.

5. According to the method for constructing a digital twin model of power transmission line equipment based on improved IDEF according to claim 1, Features: In step S4, determining whether to update the parameters of the digital twin comprehensive model specifically includes: S41, comparing the Euclidean distance between the simulation results of the digital twin comprehensive model and the physical entity state monitoring data to obtain the Euclidean distance of the historical data set; The simulation result is X=(x 1 , x 2 , x 3 ……x n ), the state monitoring data is Y=(y 1 ,y 2 ,y 3 ……y n ); The Euclidean distance between the simulation result and the state monitoring data is as follows: S42, setting the maximum Euclidean distance As as the update trigger threshold, then selecting new data samples from multi-source heterogeneous data as new sample sets for simulation, and comparing the simulation results of the new sample sets with the historical state monitoring data to obtain the Euclidean distance of the new sample sets; S43, comparing the Euclidean distance of the historical data set with the Euclidean distance of the new sample set; If the Euclidean distance of the historical data set and the Euclidean distance of the new sample set are both greater than the maximum Euclidean distance As, the parameters of the digital twin comprehensive model are updated.

6. According to claim 1, a method for constructing a digital twin model of power transmission line equipment based on improved IDEF, Features: In step S4, updating the parameters of the digital twin comprehensive model specifically includes: S44, constructing the input of Bayesian inference based on likelihood function and prior distribution; Assume that the real-time state data of the physical entity is X = [x 1 , x 2 , ..., x n ], the simulation data of the digital twin comprehensive model is X(θ i )=[x 1 (θ i ), x 2 (θ i ), ..., x n (θ i )], real-time state data X and simulation data X(θ i ) is X=X(θ i )+ε; where: ε=[ε 1 , ε 2 , ..., ε m ] is the m-dimensional vector of error; Then the expression of the likelihood function P(x|θ) is as follows: Where: μ i , σ i are the mean and standard deviation of the errors respectively; S45. Beta distribution is used to describe the change process of physical entity parameters; the expression of Beta distribution is as follows: f(θ;α,β)=θ α-1 (1-θ) β-1 ; Where: θ is the physical entity parameter to be updated, α and β are both physical entity parameters greater than zero; S46. Calculate the values ​​of α and β according to the prior distribution change data of the physical entity parameters; the calculation formula is as follows: in: is the mean value of the physical entity parameter, S θ is the parameter standard deviation; The output of Bayesian inference is as follows: Among them: p(θ|x) is the posterior distribution of θ, that is, the parameters of the updated digital twin comprehensive model; p(x|θ) is the likelihood function, which represents the probability of observing data x when the parameter value is θ; p(θ) is the prior distribution of θ, which is the same as the prior probability distribution S47. Approximate the posterior distribution p(θ|x) to a simple distribution q(θ)=q φ (θ); S48. Use KL divergence to quantify the posterior distribution p(θ|x) and the simple distribution q φ The mismatch between (θ); the expression of the KL divergence is as follows: Where: E φ is the expectation of the logarithmic function in brackets; S49, transform the problem of solving the posterior distribution into the problem of solving the ELBO maximum value of the evidence lower bound, confirm the objective function, and complete the parameter update of the digital twin comprehensive model; The expression of the evidence lower bound ELBO is as follows: Where: p(x|θ)p(θ) is the joint distribution of θ and x; The objective function is as follows: F(θ)=max[ELBO]; Where: F(θ) is the initial value of the objective function.

7. A digital twin model construction system for power transmission line equipment based on improved IDEF, It is characterized in that The system comprises: A digital twin subsystem construction module (1) is used to divide the construction phase of the power transmission line equipment into a foundation construction phase, a tower assembly construction phase, and a line stringing construction phase, and to establish respective subsystems for the above three phases; the subsystems include a three-dimensional static model subsystem, a three-dimensional visualization monitoring subsystem, and a simulation prediction subsystem; A digital twin data fusion module (2) is used to add intermediate nodes between adjacent data nodes in the subsystem, set a trigger mechanism for data exchange between adjacent data nodes, and fuse multi-source heterogeneous data generated during the construction of transmission line equipment based on a distributed Kalman filter fusion method; A digital twin model construction module (3) is used to construct a functional model of the subsystem using IDEF0, a network workflow model of the subsystem using IDEF3, an ontology model of the subsystem using IDEF5, and a data information model of the subsystem using modular IDEF1X, and associate and map the functional model, network workflow model, ontology model, data information model and subsystem to jointly construct a digital twin comprehensive model; The digital twin model update module (4) is used to simulate the construction phase of the transmission line equipment based on the digital twin comprehensive model and the integrated multi-source heterogeneous data, and determine whether to update the parameters of the digital twin comprehensive model based on the Euclidean distance between the simulation results and the physical entity status monitoring data; if so, a variational Bayesian model based on the Euclidean distance is used to update the parameters of the digital twin comprehensive model.

8. According to the improved IDEF-based digital twin model construction system for power transmission line equipment according to claim 7, Features: The digital twin data fusion module (2) performs data fusion according to the following steps: S21, representing the state of each data node in the multi-source heterogeneous data through information pairs; The information pair includes an information matrix and an information vector; the expression of the information matrix is ​​as follows: in: is the information matrix of the data node, is the estimated covariance at time k; The expression of the information vector is as follows: in: is the information vector of the data node, is the estimated value of the state at time k; S22. Add an intermediate node i between two adjacent data nodes, and introduce a binary variable for each data node during the data exchange process of each data node. The covariance value u is used as a threshold to determine whether to trigger data exchange; The expression of the trigger mechanism is as follows: like The data exchange is triggered. like No data exchange is triggered. in: is the demand covariance of data node i at time k, is the updated value of data node i at time k, Indicates that data node i communicates with adjacent data nodes to exchange data at time k. Indicates that data node i does not communicate with adjacent data nodes for data exchange at time k; S23, based on the distributed Kalman filter fusion method, the intermediate node sends its own estimated covariance to the adjacent data node, and performs data fusion with the estimated covariance of the adjacent data node to obtain a fusion information pair The fusion of multi-source heterogeneous data is completed through loop iteration; the expression of the fusion information pair is as follows: in: is the information vector of the fused data node, is the information matrix of the fused data nodes.

9. The digital twin model construction system for power transmission line equipment based on improved IDEF according to claim 8, Features: The digital twin model construction module (3) constructs the digital twin model according to the following steps: S31, establishing a DSM matrix; the rows and columns in the matrix represent the construction equipment in the construction stage; S32, based on the genetic algorithm, modularize the DSM matrix to obtain a DSM module; S33, in the DSM module, the information of the construction equipment with the same attributes in the same module is classified into the same set, which is represented by a box in IDEF1X, and then coupled through the connection of the construction equipment in the same module, the connection between the same modules is established, and the construction of the data information model is completed; The step S32 specifically includes: S321, define a fitness function and divide the DSM matrix into modules; the fitness function is expressed as follows: max(F) = I / E; Where: max(F) is the maximization of the value of the fitness function F, I is the average cohesion of the DSM module, and E is the average coupling of the DSM module; Where: N all is the total number of DSM modules, I m is the cohesion of module m, N m is the number of elements in module m, C m (i, j) is the size of the connection between element i and element j in module m, C max is the maximum value of the connection size between elements, E p，q is the coupling degree between module P and module q; S322, based on the genetic algorithm, mapping the DSM module division result to a two-dimensional coding matrix as a chromosome of the genetic algorithm to participate in the operation; S323, performing population initialization and crossover mutation operations on chromosomes in sequence, and repeatedly iterating operations to determine the optimal population of the genetic algorithm, and using the optimal population as the best partitioning scheme for the DSM module.

10. The digital twin model construction system for power transmission line equipment based on improved IDEF according to claim 8, Features: The digital twin model updating module (4) determines and updates the parameters of the model according to the following steps: S41, comparing the Euclidean distance between the simulation results of the digital twin comprehensive model and the physical entity state monitoring data to obtain the Euclidean distance of the historical data set; The simulation result is X=(x 1 , x 2 , x 3 ……x n ), the state monitoring data is Y=(y 1 ,y 2 ,y 3 ……y n ); The Euclidean distance between the simulation result and the state monitoring data is as follows: S42, setting the maximum Euclidean distance As as the update trigger threshold, then selecting new data samples from multi-source heterogeneous data as new sample sets for simulation, and comparing the simulation results of the new sample sets with the historical state monitoring data to obtain the Euclidean distance of the new sample sets; S43, comparing the Euclidean distance of the historical data set with the Euclidean distance of the new sample set; If the Euclidean distance of the historical data set and the Euclidean distance of the new sample set are both greater than the maximum Euclidean distance As, the parameters of the digital twin comprehensive model are updated; S44, constructing the input of Bayesian inference based on likelihood function and prior distribution; Assume that the real-time state data of the physical entity is X = [x 1 , x 2 , ..., x n ], the simulation data of the digital twin comprehensive model is X(θ i )=[x 1 (θ i ), x 2 (θ i ), ..., x n (θ i )], real-time state data X and simulation data X(θ i ) is X=X(θ i )+ε; where: ε=[ε 1 , ε 2 , ..., ε m ] is the m-dimensional vector of error; Then the expression of the likelihood function P(x|θ) is as follows: Where: μ i , σ i are the mean and standard deviation of the errors respectively; S45. Beta distribution is used to describe the change process of physical entity parameters; the expression of Beta distribution is as follows: f(θ;α,β)=θ α-1 (1-θ) β-1 ; Where: θ is the physical entity parameter to be updated, α and β are both physical entity parameters greater than zero; S46. Calculate the values ​​of α and β according to the prior distribution change data of the physical entity parameters; the calculation formula is as follows: in: is the mean value of the physical entity parameter, s θ is the parameter standard deviation; The output of Bayesian inference is as follows: Among them: p(θ|x) is the posterior distribution of θ, that is, the parameters of the updated digital twin comprehensive model; p(x|θ) is the likelihood function, which represents the probability of observing data x when the parameter value is θ; p(θ) is the prior distribution of θ, which is the same as the prior probability distribution S47. Approximate the posterior distribution p(θ|x) to a simple distribution q(θ)=q φ (θ); S48. Use KL divergence to quantify the mismatch between the posterior distribution p(θ|x) and the simple distribution qφ(θ); the expression of the KL divergence is as follows: Where: E φ is the expectation of the logarithmic function in brackets; S49, transform the problem of solving the posterior distribution into the problem of solving the ELBO maximum value of the evidence lower bound, confirm the objective function, and complete the parameter update of the digital twin comprehensive model; The expression of the evidence lower bound ELBO is as follows: Where: p(x|θ)p(θ) is the joint distribution of θ and x; The objective function is as follows: F(θ)=max[ELBO]; Where: F(θ) is the initial value of the objective function.

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