Method and system for constructing digital twinborn model of power distribution network

Abstract

The invention relates to the technical field of power distribution, in particular to a construction method and system for a digital twinborn model of a power distribution network. Establishing a digital element of the multi-physical field coupling characteristic of the power distribution network equipment based on quantum state space mapping, and simulating a collaborative degradation process of insulation aging and electromagnetic interference of the equipment; constructing a dynamic energy flow entanglement relationship among the nodes of the power distribution network according to the equipment degradation characteristic tensor; based on the quantum entanglement relationship between the device degradation feature tensor and the topology connection hypergraph, establishing a time-space coupling eigenmode of the device multi-physical field state and network energy flow distribution, and generating a quantum topology covariant tensor of a twin model; and carrying out dynamic fusion on real-time characteristic data and the quantum topology covariant tensor to form the digital twin model of the power distribution network. According to the method, the digital twin model capable of accurately reflecting the power distribution network equipment aging and energy transmission spatio-temporal evolution law is constructed, and a high-fidelity simulation platform is provided for power distribution network state evaluation, fault early warning and optimization regulation and control.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution, and particularly to a method and system for constructing a digital twin model of a distribution network. Background Art

[0002] With the development of the new power system, the complexity and dynamics of the distribution network have increased significantly. Traditional simulation methods are difficult to accurately depict the interaction between the multi-physical field coupling degradation of equipment and the spatio-temporal distribution of network energy flow. Existing digital twin technologies are mostly based on deterministic physical equations or data-driven models, which cannot effectively characterize non-linear co-degradation processes such as insulation aging and electromagnetic interference, and lack a quantization description of the dynamic energy flow entanglement relationship between nodes. Especially in the scenario of high penetration of distributed energy, the strong coupling effect between equipment states and network topologies makes it difficult for traditional graph models to capture the quantum dissipation characteristics of energy transmission. Although some studies have tried to introduce tensor networks or random walk theories, there are still problems such as inaccurate multi-scale spatio-temporal correlation modeling and insufficient fidelity of real-time data fusion. In addition, existing methods lack a dynamic calibration mechanism for quantum state offsets caused by environmental noise, resulting in a decrease in the synchronization between the twin model and the physical power grid. Therefore, there is an urgent need for a new construction method that integrates quantum state space mapping and topological covariance theory. Through the multi-level coupling of equipment degradation feature tensors, dynamic energy flow entanglement hypergraphs, and quantum topological covariance tensors, a high-fidelity dynamic mapping of the multi-physical field states and energy flow distribution of the distribution network can be realized, providing a theoretical basis for the precise regulation and fault prediction of intelligent distribution networks. Summary of the Invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a method and system for constructing a digital twin model of a distribution network.

[0004] The technical solution adopted by the present invention to achieve the above object is as follows: In the first aspect of the present invention, a method for constructing a digital twin model of a distribution network is disclosed, including the following steps: Based on quantum state space mapping, establish digital primitives of the multi-physical field coupling characteristics of distribution network equipment, simulate the co-degradation process of equipment insulation aging and electromagnetic interference, and generate equipment degradation feature tensors; According to the equipment degradation feature tensors, construct the dynamic energy flow entanglement relationship between distribution network nodes, and generate a topological connection hypergraph with quantum dissipation characteristics; Based on the quantum entanglement relationship between the equipment degradation feature tensors and the topological connection hypergraph, establish the spatio-temporal coupling eigenmode of the multi-physical field state of the equipment and the network energy flow distribution, and generate the quantum topological covariance tensor of the twin model; Obtain the real-time feature data of distribution network equipment, and dynamically fuse the real-time feature data with the quantum topological covariance tensor to form a digital twin model of the distribution network.

[0005] Preferably, a digital primitive of the multi-physical field coupling characteristics of distribution network equipment is established based on the quantum state space mapping, the co-degradation process of equipment insulation aging and electromagnetic interference is simulated, and a device degradation characteristic tensor is generated, specifically as follows: Input the microscopic defect distribution positions of the insulation materials of the distribution network equipment and the electromagnetic field strength gradient data into the quantum state space mapper, and generate a multi-physical field quantum state with superposition state characteristics through the eigenstate decomposition of the multi-physical field coupling equation; Perform time-evolution discretization processing on the multi-physical field quantum state, and use the quantum Monte Carlo method to generate a time-evolution sequence of the device degradation process on the asymmetric potential energy surface, forming a discretized quantum state set containing the probability weights of the aging paths; Insert a tunneling barrier layer modulated by electromagnetic interference parameters into the time-evolution sequence, and adjust the height and width of the barrier layer according to the local electric field distortion rate to generate a three-dimensional matrix of the tunneling barrier with time-varying characteristics; Obtain the tunneling probability amplitudes at each time slice in the time-evolution sequence, and perform quantum coherent superposition on the tunneling probability amplitudes of adjacent time slices to form an interference pattern of the tunneling probability amplitudes containing multi-path degradation correlations; Decouple the phase of the tunneling probability amplitude interference pattern through the quantum decoherence operator, and use the tensor network contraction algorithm to extract the characteristics and compress the dimensions of the multi-path interference fringes, and finally output a device degradation characteristic tensor with spatio-temporal correlations.

[0006] Preferably, a dynamic energy flow entanglement relationship between distribution network nodes is constructed according to the device degradation characteristic tensor, and a topological connection hypergraph with quantum dissipation characteristics is generated, specifically as follows: Decompose the local degradation sub-tensors corresponding to each distribution network node from the device degradation characteristic tensor. Each sub-tensor contains the tunneling probability amplitude and time-evolution correlation parameters of the node, where the tunneling probability amplitude is obtained by taking the modulus square after the eigenvalue decomposition of the tunneling probability amplitude interference pattern through the quantum decoherence operator; Based on the local degradation sub-tensors of each node, calculate the correlation degree of the degradation paths between the nodes. If the correlation degree exceeds the dynamic coupling threshold, establish a transient entanglement channel between the nodes, and its coupling strength is determined by the coherent superposition degree of the tunneling probability amplitudes; Superimpose the dissipation effect caused by environmental noise on the transient entanglement channel, and correct the energy flow exchange relationship between the nodes through the non-Hermitian Hamiltonian to generate a dynamic energy flow entanglement matrix with attenuation characteristics; Use the dynamic energy flow entanglement matrix to drive the quantum random walk. When the phase accumulation of the walking path reaches the preset hyper-edge formation threshold, aggregate the associated nodes into hyper-edges, and adjust the hyper-edge weights according to the dissipation coefficient of the entanglement matrix, and finally output a topological connection hypergraph with quantum dissipation characteristics.

[0007] Preferably, based on the quantum entanglement relationship between the device degradation feature tensor and the topological connection hypergraph, a spatio-temporal coupling eigenmode of the device multi-physical field state and the network energy flow distribution is established, and a quantum topological covariant tensor of the twin model is generated, specifically as follows: Extract the tunneling probability amplitude interference pattern of each node from the device degradation feature tensor, and combine it with the dynamic energy flow entanglement matrix of the topological connection hypergraph to calculate the quantum coherence degree of the multi-physical field coupling between nodes; When the quantum coherence degree exceeds the preset eigenmode generation threshold, perform a tensor product operation on the corresponding tunneling probability amplitude interference pattern and the energy flow entanglement matrix to generate a multi-field coupling quantum state containing spatio-temporal correlation characteristics; Perform non-adiabatic evolution processing on the multi-field coupling quantum state, and adjust the phase accumulation rate of the evolution path according to the hyperedge weight of the topological connection hypergraph to form a spatio-temporal evolution trajectory with dynamic correlation; Superimpose the phase diffusion caused by the quantum decoherence effect on the spatio-temporal evolution trajectory, solve the steady-state solution of the multi-physical field state of each node through the quantum master equation, and extract the eigenvalue distribution of the solution as the spatio-temporal coupling eigenmode; Based on the hyperedge connection relationship between the spatio-temporal coupling eigenmode and the topological connection hypergraph, use the covariant derivative operation under the constraint of quantum entanglement entropy to perform local-global correlation mapping of the eigenmode along the hyperedge direction, and finally output a quantum topological covariant tensor reflecting the multi-scale dynamic characteristics of the distribution network.

[0008] Preferably, obtain the real-time characteristic data of the distribution network equipment, and dynamically fuse the real-time characteristic data with the quantum topological covariant tensor to form a distribution network digital twin model, specifically as follows: Real-time collect the real-time insulation state parameters and electromagnetic interference intensity of the distribution network equipment, and convert them into real-time characteristic vectors encoded by quantum bits; Perform a quantum state superposition operation on the real-time characteristic vector and the quantum topological covariant tensor. When the fidelity of the superposition state exceeds the preset fusion threshold, trigger the dynamic calibration mechanism; Based on the spatio-temporal coupling eigenmode in the quantum topological covariant tensor, perform phase alignment processing on the real-time characteristic vector to eliminate the quantum state offset caused by environmental noise; Use the dynamic energy flow entanglement matrix to assign weights to the calibrated real-time characteristic vector to generate a fused quantum state with spatio-temporal consistency; Map the fused quantum state to the distribution network topological space through quantum Fourier transform, and adjust the state parameters of each node according to the hyperedge connection relationship of the topological connection hypergraph, and finally output a distribution network digital twin model that synchronously evolves with the physical power grid.

[0009] Preferably, perform a quantum state superposition operation on the real-time feature vector and the quantum topological covariant tensor. When the fidelity of the superposition state exceeds a preset fusion threshold, trigger the dynamic calibration mechanism, specifically: Perform quantum state purification on the real-time feature vector to eliminate the mixed state components introduced by measurement noise and obtain a standardized real-time quantum state; Perform a quantum interference operation on the real-time quantum state and the space-time coupling eigenmodes in the quantum topological covariant tensor to generate a superposition state containing the correlation between the old and new states; Calculate the fidelity of the superposition state and the reference quantum state in the quantum topological covariant tensor. When the fidelity exceeds the fusion threshold, obtain the fidelity deviation value; Determine the calibration intensity coefficient based on the fidelity deviation value and perform progressive adjustment on the phase and amplitude of the real-time quantum state through quantum controlled gate operations; Use the hyperedge weight distribution of the topological connection hypergraph to perform regional smoothing on the calibrated quantum state to ensure the state continuity of adjacent nodes; Recode the calibrated quantum state into an updated real-time feature vector to complete the dynamic calibration process.

[0010] The second aspect of the present invention discloses a construction system for a digital twin model of a distribution network. The construction system for the digital twin model of the distribution network includes a memory and a processor. A program for the construction method of the digital twin model of the distribution network is stored in the memory. When the program for the construction method of the digital twin model of the distribution network is executed by the processor, the steps of the construction method of the digital twin model of the distribution network described in any one of the above are implemented.

[0011] The present invention solves the technical defects in the background art and has the following beneficial effects: By constructing a device degradation feature tensor through quantum state space mapping and establishing a topological connection relationship with quantum dissipation characteristics, it realizes cross-scale unified modeling from microscopic device states to macroscopic network energy flows, constructs a digital twin model that can accurately reflect the aging of distribution network devices and the spatio-temporal evolution law of energy transmission, and provides a high-fidelity simulation platform for distribution network state assessment, fault warning, and optimal regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0013] Figure 1 It is the overall method flow chart of the construction method of this digital twin model of the distribution network; Figure 2 This is a partial method flowchart of the construction method of the digital twin model of the distribution network; Figure 3 This is a system block diagram of the construction system of the digital twin model of the distribution network. Detailed implementation manners

[0014] In order to more clearly understand the above objects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific implementation manners. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.

[0015] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0016] As Figure 1 shown, the first aspect of the present invention discloses a construction method of a digital twin model of a distribution network, including the following steps: S102. Based on the quantum state space mapping, establish digital primitives of the multi-physical field coupling characteristics of distribution network equipment, simulate the collaborative degradation process of equipment insulation aging and electromagnetic interference, and generate equipment degradation characteristic tensors; S104. According to the equipment degradation characteristic tensors, construct the dynamic energy flow entanglement relationship between the nodes of the distribution network, and generate a topological connection hypergraph with quantum dissipation characteristics; S106. Based on the quantum entanglement relationship between the equipment degradation characteristic tensors and the topological connection hypergraph, establish the spatio-temporal coupling eigenmode of the equipment multi-physical field state and the network energy flow distribution, and generate the quantum topological covariant tensor of the twin model; S108. Obtain the real-time characteristic data of the distribution network equipment, and dynamically fuse the real-time characteristic data with the quantum topological covariant tensor to form a digital twin model of the distribution network.

[0017] It should be noted that the present invention solves the problems in traditional digital twin modeling of distribution networks that it is difficult to accurately characterize the multi-physical field coupling degradation characteristics of equipment and the network dynamic energy flow correlation. By constructing equipment degradation characteristic tensors through quantum state space mapping and establishing topological connection relationships with quantum dissipation characteristics, cross-scale unified modeling from microscopic equipment states to macroscopic network energy flows is realized, and a digital twin model that can accurately reflect the spatio-temporal evolution laws of distribution network equipment aging and energy transmission is constructed, providing a high-fidelity simulation platform for distribution network state assessment, fault warning and optimal control.

[0018] Preferably, a digital primitive of the multi-physical field coupling characteristics of distribution network equipment is established based on the quantum state space mapping, the co-degradation process of equipment insulation aging and electromagnetic interference is simulated, and a device degradation characteristic tensor is generated, such as Figure 2 shown, specifically as follows: S202. Input the microscopic defect distribution positions of the insulation materials of the distribution network equipment and the electromagnetic field strength gradient data into the quantum state space mapper, and generate a multi-physical field quantum state with superposition state characteristics through the eigenstate decomposition of the multi-physical field coupling equation; Among them, the quantum state space mapper refers to a system device that converts device physical field data (such as electric field, material defects) into quantum state superposition characteristics; the multi-physical field coupling equation refers to a partial differential equation system that simultaneously describes the electromagnetic field-thermal field-material characteristics (such as the Maxwell-Schrödinger equation).

[0019] S204. Perform time-evolution discretization processing on the multi-physical field quantum state, and use the quantum Monte Carlo method to generate a time-evolution sequence of the device degradation process on the asymmetric potential energy surface, forming a discretized quantum state set containing the probability weights of the aging paths; S206. Insert a tunneling barrier layer modulated by electromagnetic interference parameters into the time-evolution sequence, and adjust the height and width of the barrier layer according to the local electric field distortion rate to generate a three-dimensional matrix of the tunneling barrier with time-varying characteristics; Among them, the local electric field distortion rate refers to the deviation ratio of the electric field strength at a specific position to the reference field strength.

[0020] S208. Obtain the tunneling probability amplitudes at each time slice in the time-evolution sequence, and perform quantum coherent superposition on the tunneling probability amplitudes of adjacent time slices to form an interference pattern of the tunneling probability amplitudes containing multi-path degradation correlation; S210. Decouple the phase of the tunneling probability amplitude interference pattern through the quantum decoherence operator, and use the tensor network contraction algorithm to extract the characteristics and compress the dimensions of the multi-path interference fringes, and finally output a device degradation characteristic tensor with spatio-temporal correlation.

[0021] Among them, the quantum decoherence operator refers to a mathematical operator that eliminates the phase correlation between quantum states (such as the Lindblad operator).

[0022] Taking a 10 kV cross-linked polyethylene cable joint as an example, the microscopic defect distribution of its insulation layer is obtained through X-ray tomography. The defect position coordinates (such as (2.3 μm, 1.7 μm, 5.4 μm)) and the local electric field strength gradient (such as the non-uniform distribution from 12 kV / mm to 18 kV / mm) are used as input data. The quantum state space mapper uses a multi-physics coupling equation to correlate the electric field distortion at the defect with the material dielectric constant, generating a multi-physics quantum state that is a superposition of the defect state (|0>) and the breakdown state (|1>) (such as 0.7|0> + 0.3|1>). By discretizing the time step Δt = 1 hour, the aging process is simulated on an asymmetric potential energy surface (the potential well depth is 1.2 eV, and the potential barrier height is 0.8 eV), and a sequence of quantum states at 8-hour intervals within 30 days is output (such as the nth step is 0.65|0> + 0.35|1>). According to the real-time electromagnetic interference spectrum (such as the 150 kHz pulse amplitude is 1.2 kV), the parameters of the tunneling barrier layer are dynamically adjusted to generate a tunneling barrier matrix with a size of 50×50×30 (the potential barrier height fluctuation range is from 0.5 eV to 1.1 eV). The tunneling probability amplitudes of adjacent time slices (such as the 5th and 6th steps) are extracted (such as 0.32 and 0.38), and after coherent superposition, an interference pattern (the fringe spacing is 0.15π) is formed, and then it is contracted to a 4D eigen-tensor (the size is 8×8×8×8) through a tensor network, and finally an eigen-tensor characterizing the spatio-temporal degradation characteristics of the cable joint is output.

[0023] In summary, through quantum state space mapping and time evolution modeling, combined with the tunneling barrier modulated by electromagnetic interference and quantum coherent superposition, the interaction between insulation aging and electromagnetic interference can be accurately characterized, and finally a high-precision spatio-temporal correlation device degradation eigen-tensor is generated, thereby improving the accuracy and dynamic adaptability of the modeling of the degradation state of distribution network equipment, and providing a reliable multi-physics coupling degradation characteristic data basis for subsequent digital twin analysis.

[0024] Preferably, a dynamic energy flow entanglement relationship between distribution network nodes is constructed based on the device degradation eigen-tensor, and a topological connection hypergraph with quantum dissipation characteristics is generated, specifically: The local degradation sub-tensors corresponding to each distribution network node are decomposed from the device degradation eigen-tensor. Each sub-tensor contains the tunneling probability amplitude and the time evolution correlation parameters of the node. The tunneling probability amplitude is obtained by taking the modulus square of the eigenvalue decomposition of the tunneling probability amplitude interference pattern through a quantum decoherence operator; the time evolution correlation parameter is a dynamic correlation index obtained by performing time series decomposition on the device degradation eigen-tensor and extracting the state evolution correlation coefficient (such as the peak interval of the autocorrelation function τ = 12 hours) of each node's quantum state at discrete time steps (such as Δt = 1 hour). Among them, the distribution network node refers to the basic unit that constitutes the topological structure of the distribution network (such as equipment connection points like transformers and cable joints); the local degradation sub-tensor is a multi-dimensional data set extracted from the global degradation feature tensor, which characterizes the aging state of a single node (including tunneling probability amplitude and time evolution parameters).

[0025] Based on the local degradation sub-tensors of each node, calculate the correlation degree of the degradation paths between nodes. If the correlation degree exceeds the dynamic coupling threshold, establish a transient entanglement channel between the nodes. The coupling strength of the transient entanglement channel is determined by the coherent superposition degree of the tunneling probability amplitudes; It should be noted that the specific steps for calculating the correlation degree of the degradation paths between nodes are as follows: First, extract the tunneling probability amplitude and time evolution parameters of the node at the current moment from the local degradation sub-tensor of each node; then, perform a product operation on the tunneling probability amplitudes of the two nodes, and at the same time compare the similarity degree of their time evolution parameters; next, perform a weighted sum on these two results according to the preset weight ratio; finally, through a normalization process, convert the calculation result into a correlation degree value between 0 and 1.

[0026] Superimpose the dissipation effect caused by environmental noise on the transient entanglement channel, and correct the energy flow exchange relationship between nodes through the non-Hermitian Hamiltonian to generate a dynamic energy flow entanglement matrix with attenuation characteristics; among them, the non-Hermitian Hamiltonian is a mathematical operator that describes the energy dissipation characteristics in an open quantum system, and its non-Hermiticity reflects the energy exchange between the system and the environment; Use the dynamic energy flow entanglement matrix to drive quantum random walks. When the phase accumulation of the walking path reaches the preset hyperedge formation threshold, aggregate the associated nodes into hyperedges, and adjust the hyperedge weights according to the dissipation coefficient of the entanglement matrix, and finally output a topological connection hypergraph with quantum dissipation characteristics.

[0027] Taking a distribution network node with three 10 kV cable joints as an example, local degradation sub-tensors (size 4×4×4) corresponding to nodes A, B, and C are decomposed from the device degradation feature tensor (size 8×8×8×8). The sub-tensor of node A contains a tunneling probability amplitude of 0.38 and a time evolution parameter τ = 12 hours. By calculating the correlation degree of the degradation paths between nodes A and B (such as 0.72), a transient entanglement channel is established when it exceeds the dynamic coupling threshold (such as 0.6), and its coupling strength is determined by the degree of coherent superposition of the tunneling probability amplitudes of the two (0.38 and 0.42) (coupling coefficient 0.35). After superimposing environmental noise (signal-to-noise ratio 18 dB), the non-Hermitian Hamiltonian (imaginary part coefficient -0.15) is used to correct the energy flow exchange relationship, and a 3×3 dynamic energy flow entanglement matrix is generated (main diagonal attenuation coefficient 0.85). When the phase accumulation of the quantum random walk on the path of node A→B→C reaches π / 2 (hyperedge formation threshold), the three nodes are aggregated into a hyperedge, and the hyperedge weight is set to 0.78 according to the dissipation coefficient of the entanglement matrix (0.15), and finally a topological connection hypergraph containing one ternary hyperedge is formed.

[0028] In summary, this method solves the problem that the traditional distribution network topology model is difficult to characterize the dynamic energy flow correlation and energy dissipation characteristics between nodes. By establishing a transient channel and a dynamic energy flow matrix based on quantum entanglement theory, combined with the quantum random walk mechanism, it can accurately reflect the influence of the device degradation state on the network energy flow distribution, realizing a high-precision unified modeling of the distribution network topology structure and dynamic energy transmission characteristics, and providing a network connection relationship model for the digital twin system that can characterize the quantum dissipation characteristics.

[0029] Preferably, based on the quantum entanglement relationship between the device degradation feature tensor and the topological connection hypergraph, a spatio-temporal coupling eigenmode of the device multi-physical field state and the network energy flow distribution is established, and a quantum topological covariant tensor of the twin model is generated, specifically: Extract the interference pattern of the tunneling probability amplitude of each node from the device degradation feature tensor, and combine it with the dynamic energy flow entanglement matrix of the topological connection hypergraph to calculate the quantum coherence degree of the multi-physical field coupling between nodes; It should be noted that the specific steps for calculating the quantum coherence degree of the multi-physical field coupling between nodes are as follows: First, extract the amplitude and phase information of the characteristic fringes from the interference pattern of the tunneling probability amplitude of each node; then, determine the energy flow coupling strength between the corresponding nodes according to the element values of the dynamic energy flow entanglement matrix in the topological connection hypergraph; then, perform a normalized product operation on the characteristic parameters of the node interference pattern and the energy flow coupling strength; finally, obtain a quantum coherence degree value between 0 and 1 through integral operation, which comprehensively reflects the quantum state correlation degree between nodes under the action of the multi-physical field.

[0030] When the quantum coherence degree exceeds the preset eigenmode generation threshold, perform a tensor product operation on the corresponding tunneling probability amplitude interference pattern and the energy flux entanglement matrix to generate a multi-field coupled quantum state containing spatio-temporal correlation characteristics; It should be noted that the specific steps for performing the tensor product operation are as follows: Expand the tunneling probability amplitude interference pattern of the node into a multi-dimensional array form (such as a 16×16 matrix of node A), and at the same time keep the dynamic energy flux entanglement matrix in its original dimension (such as a 3×3 matrix); then, according to the mathematical definition of the tensor product, multiply each element of the interference pattern array by the entire energy flux entanglement matrix, so as to combine the dimensions of the two arrays (such as obtaining a tensor of 16×16×3×3); then perform a dimension rearrangement operation on the generated tensor to merge the relevant dimensions (such as adjusting to a structure of 16×16×9); finally, perform a normalization process on the tensor to ensure that the numerical values of each dimension are within a unified dimension range, forming the final multi-field coupled quantum state containing spatio-temporal correlation characteristics.

[0031] Perform non-adiabatic evolution processing on the multi-field coupled quantum state, and adjust the phase accumulation rate of the evolution path according to the hyper-edge weight of the topological connection hypergraph to form a spatio-temporal evolution trajectory with dynamic correlation; It should be noted that the steps of non-adiabatic evolution processing are as follows: First, based on the initial parameters of the multi-field coupled quantum state (such as a tensor structure of 16×16×3), set the evolution time step Δt = 0.5 hours and the total evolution duration of 8 hours; then dynamically adjust the Hamiltonian parameters of each evolution path according to the hyper-edge weight of the topological connection hypergraph (such as the weight of node A-B is 0.7), where the larger the hyper-edge weight, the smaller the phase accumulation rate adjustment coefficient (such as the weight of 0.7 corresponds to the adjustment coefficient of 0.85); then solve the time-dependent Schrödinger equation within each time step and record the instantaneous changes of the quantum state parameters; finally, integrate the evolution results of each time step in time series to form a spatio-temporal evolution trajectory containing 24 state points, where each state point retains the quantum state characteristic parameters at the current moment (such as phase angle, amplitude, etc.).

[0032] Superimpose the phase diffusion caused by the quantum decoherence effect on the spatio-temporal evolution trajectory, solve the steady-state solution of the multi-physical field state of each node through the quantum master equation, and extract the eigenvalue distribution of the solution as the spatio-temporal coupling eigenmode; Among them, the quantum master equation is a differential equation used to describe the state evolution of an open quantum system, and the energy exchange and decoherence effect between the system and the environment are characterized by introducing dissipation terms and noise terms.

[0033] Based on the hyper-edge connection relationship between the spatio-temporal coupling eigenmode and the topological connection hypergraph, use the covariant derivative operation under the constraint of quantum entanglement entropy to perform a local-global correlation mapping of the eigenmode along the hyper-edge direction, and finally output a quantum topological covariant tensor reflecting the multi-scale dynamic characteristics of the distribution network.

[0034] It should be noted that the steps for performing the covariant derivative operation are as follows: First, based on the eigenvalue distribution of the spatiotemporal coupled eigenmodes (such as the main peak of 1.2 eV at node A), the local state manifolds of each node are constructed. At the same time, according to the hyperedge connection relationship of the topological connection hypergraph (such as the hyperedge of node A - B - C), the connection topology between the manifolds is determined. Then, using the quantum entanglement entropy (such as the entropy value of 0.75 between node A and B) as a constraint condition, the local change rate of the state parameters in the hyperedge direction (such as the gradient change along the A - B direction) is calculated. Next, taking the hyperedge weight (such as 0.7) as the connection coefficient, the traditional derivative operation is corrected to ensure the continuity of the quantum state at the hyperedge connection during the differentiation process. Finally, a covariant derivative tensor (such as a 16×16×16 structure) reflecting the cooperative evolution of multiple nodes is output, where each element value represents the correlation change intensity of the quantum state in the corresponding topological dimension (such as the correlation intensity gradient of 0.12 / eV along the hyperedge direction at node A).

[0035] Taking the nodes of a 10 kV distribution network containing 3 distribution transformers as an example, the tunneling probability amplitude interference patterns (with fringe spacings of 0.12π, 0.15π, and 0.18π respectively) are extracted from the device degradation feature tensors (size 8×8×8) of nodes A, B, and C. Combining with the dynamic energy flow entanglement matrix (a 3×3 matrix with element values ranging from 0.2 to 0.8), the quantum coherence degree between node A and B is calculated to be 0.75 (with a threshold set at 0.6). The interference pattern of node A and the energy flow entanglement matrix are subjected to a tensor product operation to generate a multi - field coupled quantum state of 16×16×3. Based on the hyperedge weight (the weight of node A - B - C is 0.7), the evolution step size Δt = 0.5 hours is adjusted. After 8 - hour evolution, a spatiotemporal trajectory containing 24 state points is formed. After superimposing the phase diffusion caused by the ambient temperature fluctuation (diffusion coefficient 0.05 / hour), the eigenvalue distribution of the steady - state solution of node A is obtained (the main peak is located at 1.2 eV). Finally, a quantum topological covariant tensor with a size of 16×16×16 is generated through the covariant derivative operation, where the correlation intensity gradient of node A along the hyperedge direction is 0.12 / eV.

[0036] In summary, this step solves the problem that traditional digital twin models are difficult to accurately represent the spatiotemporal coupling relationship between the state of distribution network equipment and the network energy flow. By establishing a multi - field coupled quantum state driven by quantum entanglement relationships and integrating non - adiabatic evolution and quantum decoherence effects, the dynamic correlation modeling between the microscopic degradation characteristics of equipment and the macroscopic operating state of the power grid is realized, generating a quantum topological covariant tensor that can simultaneously reflect the evolution of multiple physical field states and the spatiotemporal characteristics of energy transmission in the distribution network, providing a key multi - scale dynamic correlation data basis for constructing a high - fidelity digital twin model.

[0037] Preferably, real-time characteristic data of distribution network equipment is obtained, and the real-time characteristic data is dynamically fused with the quantum topological covariant tensor to form a digital twin model of the distribution network. Specifically: Real-time insulation state parameters and electromagnetic interference intensity of distribution network equipment are collected in real time and converted into real-time feature vectors encoded by quantum bits; Perform a quantum state superposition operation on the real-time feature vector and the quantum topological covariant tensor. When the fidelity of the superposition state exceeds a preset fusion threshold, trigger the dynamic calibration mechanism; Based on the space-time coupling eigenmode in the quantum topological covariant tensor, perform phase alignment processing on the real-time feature vector to eliminate the quantum state offset caused by environmental noise; Use the dynamic energy flow entanglement matrix to assign weights to the calibrated real-time feature vector to generate a fusion quantum state with space-time consistency; Map the fusion quantum state to the distribution network topology space through quantum Fourier transform, and adjust the state parameters of each node according to the hyperedge connection relationship of the topological connection hypergraph, and finally output a digital twin model of the distribution network that synchronously evolves with the physical power grid.

[0038] Taking the 10kV distribution transformer node A as an example, its insulation resistance value (such as 850MΩ) and partial discharge amplitude (such as 25dBmV) are collected in real time, and converted into a 2-qubit real-time feature vector (amplitude of the |10> state is 0.82) through a quantum encoder. Perform a superposition operation on this vector and the quantum topological covariant tensor corresponding to node A (16×16×16 structure, main peak 1.2eV). After measuring the fidelity of 0.91 (threshold 0.85), trigger calibration. Use the space-time coupling eigenmode (phase reference 1.05π) to perform phase correction on the real-time vector (phase difference after adjustment <0.02π). Assign a weight coefficient of 0.68 through the dynamic energy flow entanglement matrix (weight of node A-B is 0.7) to generate a fusion quantum state (amplitude of the |10> state is 0.79). After mapping to the topological space through quantum Fourier transform, update the state parameters of node A according to the hyperedge A-B-C connection relationship to: insulation degradation index 0.38±0.02, and finally generate a digital twin of node A containing 16 synchronous state parameters.

[0039] In summary, through quantum state superposition operation and dynamic calibration mechanism, the accurate fusion of real-time monitoring data and the twin model is realized, and a high-fidelity digital twin model that can adapt to environmental changes and maintain real-time synchronization with the physical power grid is constructed, thus providing a reliable basis for the intelligent operation and maintenance decision-making of the distribution network system.

[0040] Preferably, perform a quantum state superposition operation on the real-time feature vector and the quantum topological covariant tensor. When the fidelity of the superposition state exceeds a preset fusion threshold, trigger the dynamic calibration mechanism. Specifically: Perform quantum state purification on the real-time feature vector to eliminate the mixed state components introduced by measurement noise and obtain a standardized real-time quantum state; It should be noted that the execution method steps of the quantum state purification are as follows: First, perform projective measurement on the noisy quantum state collected in real time (such as the |01> state amplitude 0.68 ± 0.05 of node B), and reconstruct its density matrix through quantum state tomography technology; then use the principal component analysis method to identify and separate the non-diagonal mixed state components caused by measurement noise (such as the noise components in the amplitude fluctuation of ±0.05); then retain the main eigenstate (|01> state) through quantum filtering operation, and use the quantum purification gate to perform amplitude normalization on the retained state (adjust the amplitude to 0.71); finally, verify the fidelity of the purified quantum state (such as verifying that its similarity with the ideal state reaches more than 95%), and output the standardized real-time quantum state (|01> state amplitude 0.71).

[0041] Perform a quantum interference operation on the real-time quantum state and the space-time coupling eigenmode in the quantum topological covariant tensor to generate a superposition state containing the correlation between the new and old states; It should be noted that the steps of the quantum interference operation are as follows: Load the standardized real-time quantum state (such as the |01> state amplitude 0.71 of node B) and the eigenmode reference quantum state (such as the |01> state with a phase of 1.15π) into the two input channels of the quantum interferometer; then make the two quantum states undergo coherent superposition at a 50:50 beam splitting ratio through a controllable beam splitter to generate an interference pattern containing the correlation between the new and old states (such as a fringe spacing of 0.25π); then measure the probability amplitude distribution of each ground state (|01> and |10>) at the interference output end (such as measuring the |01> state amplitude of 0.69); finally, extract the interference phase difference parameter (such as 0.03π) and the relative amplitude change (-0.02) to generate a superposition state output that simultaneously carries real-time measurement information and historical state characteristics.

[0042] Calculate the fidelity between the superposition state and the reference quantum state in the quantum topological covariant tensor. When the fidelity exceeds the fusion threshold, obtain the fidelity deviation value; It should be noted that the steps for calculating the fidelity between the superposition state and the reference quantum state are as follows: extract the reference quantum state density matrix corresponding to the node from the quantum topological covariant tensor (such as the 2×2 matrix corresponding to the amplitude of the |01> state of 0.72 at node B), and at the same time convert the superposition state generated by interference into a density matrix (such as the matrix corresponding to the amplitude of the |01> state of 0.69); then calculate the square root of the product of the two matrices to obtain the original fidelity value between 0 and 1 (such as the initial calculation result of 0.86); then introduce the neighborhood correction factor of the topological connection hypergraph (such as the correction coefficient of 1.05 corresponding to the neighborhood weight of 0.65 at node B) to weight and adjust the original value; finally, output the topologically calibrated final fidelity value (such as 0.89), which comprehensively reflects the similarity of the superposition state and the reference state in terms of the quantum state after considering the network topological relationship.

[0043] Determine the calibration intensity coefficient based on the fidelity deviation value, and perform progressive adjustment on the phase and amplitude of the real-time quantum state through quantum controlled gate operations; Use the hyperedge weight distribution of the topological connection hypergraph to perform regional smoothing on the calibrated quantum state to ensure the state continuity of adjacent nodes; Recode the calibrated quantum state into an updated real-time feature vector to complete the dynamic calibration process.

[0044] Taking the 10 kV cable joint node B as an example, after removing the mixed state components from its noisy real-time feature vector (amplitude of the |01> state 0.68±0.05) through projective measurement, a purified quantum state (amplitude of the |01> state 0.71) is obtained. Interfere this state with the spatio-temporal coupled eigenmode corresponding to node B (reference phase 1.15π) to generate a superposition state (amplitude of the |01> state 0.69, phase difference 0.03π). Calculate its fidelity of 0.89 (threshold 0.85) with the reference quantum state (amplitude 0.72) to obtain a deviation value of 0.04. Set the calibration intensity coefficient to 0.3 according to the deviation value, and perform three-step adjustment through a controlled phase gate (rotation angle 0.12π) and an amplitude adjustment gate (scaling factor 1.05) to obtain a calibrated state (amplitude of the |01> state 0.715, phase difference <0.01π). After neighborhood smoothing in combination with the weight distribution of hypergraph A - B - C (weight of node B 0.65), finally output a standardized feature vector (amplitude of the |01> state 0.72±0.005) to complete the state calibration of node B.

[0045] In summary, through the quantum state purification and dynamic calibration mechanisms, this method realizes the precise matching and adaptive adjustment between real-time monitoring data and the twin model, improves the state update accuracy of the digital twin model and the parameter consistency between adjacent nodes, and ensures a high degree of synchronization between the model output result and the actual operation state of the physical power grid.

[0046] In this embodiment, the method for constructing the digital twin model of the distribution network further includes the following steps: Separate the active tensor component induced by electromagnetic interference from the device degradation feature tensor, and extract the coupling correlation degree between the space-time torsion parameter and the electromagnetic field strength gradient through the quantum gravitational field equation; Based on the distribution characteristics of the space-time torsion parameter, combined with the dielectric anisotropy of the device material, construct an asymmetric connection coefficient matrix with electromagnetic-gravitational coupling characteristics, and its components characterize the torsional effect of electromagnetic distortion on the space-time curvature; Use the asymmetric connection coefficient matrix to calculate the torsion curvature component of the space-time curvature tensor, and perform dynamic covariance analysis with the geometric distortion amount of the degradation feature tensor. When the absolute value of the covariance between the torsion curvature and the distortion amount exceeds the preset curvature threshold, it is determined that there is non-Euclidean geometric distortion; Apply the covariant differential operator based on the asymmetric connection coefficient to the non-Euclidean geometric distortion region, and reconstruct the affine connection structure of the degradation feature tensor by adjusting the phase synchronization between the torsion curvature component and the electromagnetic field gradient; Input the corrected connection coefficient into the quantum gravitational field evolution equation, and iteratively optimize the geometric manifold curvature of the degradation feature tensor through the dynamic balance constraint of the torsion curvature flow until it satisfies the local gauge invariance condition of the electromagnetic-gravitational coupling field.

[0047] Taking the 10 kV cable joint node C as an example, separate the active tensor component induced by electromagnetic interference (the main diagonal element fluctuation range is ±0.15) from the device degradation feature tensor (size 8×8×8), and extract the coupling correlation degree (0.78) between the space-time torsion parameter (amplitude 0.25) and the local field strength gradient (18 kV / mm) through the quantum gravitational field equation. Based on the dielectric anisotropy of the epoxy resin material at node C (the dielectric constant ratio in the x-y-z axes is 1:1.2:0.9), construct a 3×3 asymmetric connection coefficient matrix (the maximum non-diagonal element is 0.35), and its z-axis component (-0.22) characterizes the torsional effect of electromagnetic distortion on the space-time curvature. Calculate the covariance (0.85) between the torsion curvature component (0.19) of the space-time curvature tensor and the geometric distortion amount (0.21) of the degradation tensor. When it exceeds the curvature threshold of 0.8, it is determined that there is non-Euclidean geometric distortion in the z-axis direction. Apply the covariant differential operator (rotation angle π / 6) to the distortion region, and adjust the phase difference between the torsion curvature component and the field strength gradient from 0.35π to 0.05π. The reconstructed connection structure shows that the z-axis affine parameter is corrected from 0.48 to 0.52. Input the new connection coefficient into the quantum gravitational field equation until it satisfies the local gauge invariance condition of the electromagnetic-gravitational coupling field.

[0048] In summary, to solve the problem of non-Euclidean geometric distortion of the equipment degradation feature tensor in the distribution network under extreme electromagnetic interference. In this embodiment, the electromagnetic-gravitational coupling parameters are extracted through the quantum gravitational field equation, and an asymmetric connection coefficient matrix is constructed to achieve the accurate characterization and dynamic correction of the space-time curvature torsion effect caused by electromagnetic distortion, and a device degradation feature correction mechanism that can adapt to the change of electromagnetic interference intensity is established, ensuring that the digital twin model can still maintain the physical accuracy of the geometric structure in a strong electromagnetic environment, providing a reliable theoretical basis for the equipment state evaluation under extreme working conditions.

[0049] In this embodiment, the method for constructing the distribution network digital twin model further includes the following steps: Extract the degradation feature quantum state from the equipment degradation feature tensor of the distributed nodes, establish an initial entanglement channel between the geographically isolated distribution network nodes through the quantum teleportation protocol, and generate a set of entangled pairs with spatial correlation; Perform Bell state measurement on the initial entangled pairs, screen out the entangled pairs with entanglement purity lower than the preset threshold, and condense multiple low-purity entangled pairs into a high-purity entangled state through entanglement swapping operation to generate a condensed entanglement purity factor carrying the node degradation features; Input the condensed entanglement purity factor into the quantum teleportation channel, and collapse the quantum state (such as the insulation aging phase parameter) of the source node degradation feature tensor to the target node through quantum measurement operation to generate the teleportation fidelity of the degradation features across geographical regions; Perform phase compensation on the target node degradation feature tensor according to the teleportation fidelity, and establish a time evolution synchronization constraint condition for the cross-node degradation features by using the correlation of the condensed entanglement purity factor to generate a degradation feature space-time correlation tensor with non-local consistency; Embed quantum error correction codes in the space-time correlation tensor, obtain the non-local correlation error of the degradation features of each node through local measurement, and trigger the dynamic reconstruction of the condensed entanglement purity factor when the error exceeds the synchronization threshold until the degradation feature space-time correlation tensors of all geographically isolated nodes meet the quantum state synchronization convergence condition.

[0050] Taking the 10 kV cable joint nodes D (urban area) and E (suburban area) 50 km apart as an example, the insulation aging quantum state (phase parameter 1.25π) is extracted from the degradation feature tensor (size 8×8×8) of node D. Three groups of initial entangled pairs (entanglement purities 0.68, 0.72, 0.65) are established between the two nodes through the quantum teleportation protocol. Two groups of entangled pairs with purities lower than the threshold of 0.7 are selected through Bell state measurement, and the concentrated entanglement purity factor (purity increased to 0.82) is generated through entanglement swapping operation. After inputting this factor into the teleportation channel, the insulation aging phase parameter (1.25π) of node D collapses to node E, and the teleportation fidelity across regions is measured to be 0.85. The phase of the degradation tensor of node E is compensated according to the fidelity (adjustment amount 0.03π), and the time synchronization constraint (time deviation < 0.5 hours) is established by combining the correlation of the concentration factor, generating the spatio-temporal correlation tensor (size 16×16). After embedding the quantum error correction code (Shor code), the local measurement shows that the non-local correlation error is 0.12 (threshold 0.15), triggering a dynamic reconstruction to reduce the phase synchronization deviation of the spatio-temporal correlation tensor of node E to 0.08, meeting the quantum state synchronization convergence condition.

[0051] In summary, in the process of constructing the digital twin model of the distribution network, it is usually difficult to synchronize and model the degradation characteristics between distributed distribution network nodes with geographical isolation in real time. In view of this, in this embodiment, a quantum correlation channel for degradation characteristics across regions is established through quantum entanglement concentration and teleportation technologies, realizing the long-distance transmission and collaborative modeling of the aging states between nodes.

[0052] As Figure 3 shown, the second aspect of the present invention discloses a construction system 8 of a digital twin model of a distribution network. The construction system of the digital twin model of the distribution network includes a memory 60 and a processor 80. A program for the construction method of the digital twin model of the distribution network is stored in the memory 60. When the program for the construction method of the digital twin model of the distribution network is executed by the processor 80, the steps of the construction method of the digital twin model of the distribution network described in any one of the above are realized.

[0053] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the couplings, direct couplings, or communication connections between the components shown or discussed with each other can be through some interfaces, and the indirect couplings or communication connections of devices or units can be electrical, mechanical, or other forms.

[0054] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units; and some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0055] In addition, in each embodiment of the present invention, each functional unit may be fully integrated into one processing unit, or each unit may be separately regarded as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0056] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including the above method embodiments; and the foregoing storage medium includes: removable storage devices, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks or optical disks and other various media that can store program codes.

[0057] Alternatively, if the above-mentioned integrated units of the present invention are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the various embodiments of the present invention. And the foregoing storage medium includes: removable storage devices, ROM, RAM, magnetic disks or optical disks and other various media that can store program codes.

[0058] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention.

Claims

1. A method for constructing a digital twin model of a distribution network, characterized in that It includes the following steps: Based on the quantum state space mapping, establish the digital primitive of the multi-physical field coupling characteristics of the distribution network equipment, simulate the collaborative degradation process of equipment insulation aging and electromagnetic interference, and generate the equipment degradation characteristic tensor; According to the equipment degradation characteristic tensor, construct the dynamic energy flow entanglement relationship between the nodes of the distribution network, and generate the topological connection hypergraph with quantum dissipation characteristics; Based on the quantum entanglement relationship between the equipment degradation characteristic tensor and the topological connection hypergraph, establish the spatio-temporal coupling eigenmode of the equipment multi-physical field state and the network energy flow distribution, and generate the quantum topological covariant tensor of the twin model; Obtain the real-time characteristic data of the distribution network equipment, and dynamically fuse the real-time characteristic data with the quantum topological covariant tensor to form the distribution network digital twin model.

2. The construction method of a digital twin model of a distribution network according to claim 1, characterized in that, Based on the quantum state space mapping, establish the digital primitive of the multi-physical field coupling characteristics of the distribution network equipment, simulate the collaborative degradation process of equipment insulation aging and electromagnetic interference, and generate the equipment degradation characteristic tensor. Specifically: Input the microscopic defect distribution position of the distribution network equipment insulation material and the electromagnetic field strength gradient data into the quantum state space mapper, and generate the multi-physical field quantum state with superposition state characteristics through the eigenstate decomposition of the multi-physical field coupling equation; Perform time evolution discretization processing on the multi-physical field quantum state, and use the quantum Monte Carlo method to generate the time evolution sequence of the equipment degradation process on the asymmetric potential energy surface, forming a discretized quantum state set containing the probability weights of the aging paths; Insert the tunneling barrier layer modulated by the electromagnetic interference parameters into the time evolution sequence, and adjust the height and width of the barrier layer according to the local electric field distortion rate to generate the three-dimensional matrix of the time-varying tunneling barrier; Obtain the tunneling probability amplitudes at each time slice in the time evolution sequence, and perform quantum coherent superposition on the tunneling probability amplitudes of adjacent time slices to form the interference pattern of the tunneling probability amplitudes containing the multi-path degradation correlation; Decouple the phase of the tunneling probability amplitude interference pattern through the quantum decoherence operator, and use the tensor network contraction algorithm to extract the characteristics and compress the dimensions of the multi-path interference fringes, and finally output the equipment degradation characteristic tensor with spatio-temporal correlation.

3. The construction method of a digital twin model of a distribution network according to claim 1, characterized in that, According to the equipment degradation characteristic tensor, construct the dynamic energy flow entanglement relationship between the nodes of the distribution network, and generate the topological connection hypergraph with quantum dissipation characteristics. Specifically: Decompose the local degradation sub-tensors corresponding to each distribution network node from the equipment degradation characteristic tensor. Each sub-tensor contains the tunneling probability amplitude and the time evolution correlation parameters of the node, where the tunneling probability amplitude is obtained by taking the modulus square after the eigenvalue decomposition of the tunneling probability amplitude interference pattern through the quantum decoherence operator; Based on the local degradation sub-tensors of each node, calculate the correlation degree of the degradation paths between the nodes. If the correlation degree exceeds the dynamic coupling threshold, establish a transient entanglement channel between the nodes, and its coupling strength is determined by the coherent superposition degree of the tunneling probability amplitudes; Superimpose the dissipation effect caused by the environmental noise on the transient entanglement channel, and correct the energy flow exchange relationship between the nodes through the non-Hermitian Hamiltonian to generate the dynamic energy flow entanglement matrix with attenuation characteristics; Drive quantum random walk using a dynamic energy flow entanglement matrix. When the phase accumulation of the walking path reaches the preset hyperedge formation threshold, aggregate associated nodes into hyperedges and adjust the hyperedge weights according to the dissipation coefficient of the entanglement matrix, and finally output a topological connection hypergraph with quantum dissipation characteristics.

4. The construction method of a digital twin model of a distribution network according to claim 1, characterized in that, Based on the quantum entanglement relationship between the device degradation feature tensor and the topological connection hypergraph, establish a spatio-temporal coupled eigenmode of the device multi-physical field state and the network energy flow distribution, and generate a quantum topological covariant tensor of the twin model, specifically: Extract the tunneling probability amplitude interference pattern of each node from the device degradation feature tensor, and combine it with the dynamic energy flow entanglement matrix of the topological connection hypergraph to calculate the quantum coherence degree of the multi-physical field coupling between nodes; When the quantum coherence degree exceeds the preset eigenmode generation threshold, perform a tensor product operation on the corresponding tunneling probability amplitude interference pattern and the energy flow entanglement matrix to generate a multi-field coupling quantum state containing spatio-temporal correlation characteristics; Perform non-adiabatic evolution processing on the multi-field coupling quantum state, and adjust the phase accumulation rate of the evolution path according to the hyperedge weight of the topological connection hypergraph to form a spatio-temporal evolution trajectory with dynamic correlation; Superimpose the phase diffusion caused by the quantum decoherence effect on the spatio-temporal evolution trajectory, solve the steady-state solution of the multi-physical field state of each node through the quantum master equation, and extract the eigenvalue distribution of the solution as the spatio-temporal coupled eigenmode; Based on the spatio-temporal coupled eigenmode and the hyperedge connection relationship of the topological connection hypergraph, use the covariant derivative operation under the constraint of quantum entanglement entropy to perform local-global correlation mapping of the eigenmode along the hyperedge direction, and finally output a quantum topological covariant tensor reflecting the multi-scale dynamic characteristics of the distribution network.

5. The construction method of a digital twin model of a distribution network according to claim 1, wherein, Obtain the real-time characteristic data of the distribution network equipment, and dynamically fuse the real-time characteristic data with the quantum topological covariant tensor to form a digital twin model of the distribution network, specifically: Real-time collect the real-time insulation state parameters and electromagnetic interference intensity of the distribution network equipment, and convert them into real-time characteristic vectors encoded by quantum bits; Perform a quantum state superposition operation on the real-time characteristic vector and the quantum topological covariant tensor. When the fidelity of the superposition state exceeds the preset fusion threshold, trigger the dynamic calibration mechanism; Based on the spatio-temporal coupled eigenmode in the quantum topological covariant tensor, perform phase alignment processing on the real-time characteristic vector to eliminate the quantum state shift caused by environmental noise; Use the dynamic energy flow entanglement matrix to assign weights to the calibrated real-time characteristic vector to generate a fusion quantum state with spatio-temporal consistency; Map the fusion quantum state to the distribution network topological space through quantum Fourier transform, and adjust the state parameters of each node according to the hyperedge connection relationship of the topological connection hypergraph, and finally output a digital twin model of the distribution network that synchronously evolves with the physical power grid.

6. The construction method of a digital twin model of a distribution network according to claim 5, characterized in that, Perform a quantum state superposition operation on the real-time characteristic vector and the quantum topological covariant tensor. When the fidelity of the superposition state exceeds the preset fusion threshold, trigger the dynamic calibration mechanism, specifically: Perform quantum state purification processing on the real-time characteristic vector to eliminate the mixed state components introduced by measurement noise, and obtain a standardized real-time quantum state; Perform a quantum interference operation on the real-time quantum state and the spatio-temporal coupled eigenmode in the quantum topological covariant tensor to generate a superposition state containing the correlation between the new and old states; Calculate the fidelity between the superposition state and the reference quantum state in the quantum topological covariant tensor. When the fidelity exceeds the fusion threshold, obtain the fidelity deviation value; Determine the calibration intensity coefficient based on the fidelity deviation value, and perform progressive adjustment on the phase and amplitude of the real-time quantum state through quantum controlled gate operations; Use the hyperedge weight distribution of the topological connection hypergraph to perform regional smoothing on the calibrated quantum state to ensure the state continuity of adjacent nodes; Recode the calibrated quantum state into an updated real-time feature vector to complete the dynamic calibration process.

7. A construction system for a digital twin model of a distribution network, characterized in that, The construction system of the distribution network digital twin model includes a memory and a processor. The construction method program of the distribution network digital twin model is stored in the memory. When the construction method program of the distribution network digital twin model is executed by the processor, the steps of the construction method of the distribution network digital twin model according to any one of claims 1 to 6 are implemented.

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