Digital twinning method and system for power transmission and transformation equipment

Abstract

The invention discloses a digital twinning method and system for power transmission and transformation equipment, and relates to the technical field of digital twinning of the power transmission and transformation equipment, and the method comprises the following steps: constructing a multi-physical field coupling model of an electromagnetic field, a thermal field and a fluid dynamic field of the power transmission and transformation equipment; collecting multi-dimensional data of the equipment in real time, and preprocessing the multi-dimensional data; constructing and updating a digital twinborn model in real time, and predicting temperature rise and heat dissipation characteristics of the equipment; and obtaining fault prediction and life evaluation of the equipment. Through the innovative multi-physics field coupling and deep learning combined method, the digital twin system can reflect the real-time state of the equipment more accurately and support interaction and feedback of multi-field information, so that the real-time monitoring and service life management capabilities of the equipment are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of digital twin of power transmission and transformation equipment, and in particular to a digital twin method and system for power transmission and transformation equipment. Background Art

[0002] Digital twin technology refers to a virtual digital model built through physical equipment, sensors, simulation models and data processing technology, which can reflect the state changes of the physical system in real time. In the power transmission and transformation system, the operating state of the equipment will be affected by the combined effects of electromagnetic fields, thermal fields and fluid fields, resulting in complex multi-physical field coupling phenomena inside the equipment. These coupling effects will affect the performance, life and reliability of the equipment. For example, power transmission and transformation equipment will generate a large amount of electromagnetic loss during operation, causing the temperature of the equipment to rise. High temperature will cause material aging and change the fluid dynamics, thereby affecting the heat dissipation and electromagnetic characteristics of the equipment. In order to ensure the long-term stable operation of the equipment, it is particularly important to monitor, simulate, predict and optimize its operation in real time.

[0003] In this process, data collection is crucial. By obtaining the operating parameters of the equipment (such as current, voltage, temperature, pressure, etc.) through sensors, the status of the equipment can be monitored in real time. However, the existing single physical field simulation technology cannot fully reflect the actual working status of the power transmission and transformation equipment, especially under the coupling of multiple physical fields. The traditional simulation method cannot capture its complex internal mechanism, resulting in insufficient accuracy of the digital twin model. In addition, the real-time collection of data from power transmission and transformation equipment also faces many challenges, especially data noise and model real-time problems, which may lead to distortion of monitoring results, thereby affecting the accuracy of decision-making and the predictive ability of the model. Therefore, the digital twin model of power transmission and transformation equipment requires high-precision multi-physical field coupling simulation to achieve joint modeling and dynamic simulation of the electromagnetic, thermal and fluid dynamics fields inside the equipment, so as to ensure that the model reflects the actual working status with high accuracy.

[0004] At present, although there are modeling methods for single physical fields, such as finite element method for electromagnetic simulation and heat transfer simulation, these methods have limited application effects in multi-physical field coupling analysis and it is difficult to effectively capture the complex electromagnetic-thermal-fluid coupling effects. In addition, traditional numerical simulation methods have the problem of insufficient efficiency in real-time data processing and cannot adapt to the needs of high dynamic changes in power transmission and transformation equipment. Therefore, it is of great practical significance to build a digital twin model that can effectively simulate the coupling of multiple physical fields. Summary of the invention

[0005] In view of the problems existing in the existing digital twins and systems of power transmission and transformation equipment, the present invention is proposed.

[0006] Therefore, the problem to be solved by the present invention is that the accuracy of equipment status monitoring and fault prediction is relatively poor.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides a digital twin method for power transmission and transformation equipment, which comprises the following steps:

[0009] Construct a multi-physics coupling model of electromagnetic field, thermal field and fluid dynamics field of power transmission and transformation equipment;

[0010] Collect multi-dimensional data of equipment in real time and perform pre-processing;

[0011] Build and update the digital twin model in real time, and predict the temperature rise and heat dissipation characteristics of the equipment;

[0012] Obtain failure predictions and lifetime estimates for equipment.

[0013] As a preferred solution of the digital twin method of power transmission and transformation equipment of the present invention, wherein: the behavior of the power transmission and transformation equipment in this case is expressed as,

[0014] Δ·D=ρ;

[0015] Δ·B=0;

[0016]

[0017] Where D is electric displacement, B is magnetic induction intensity, E is electric field intensity, H is magnetic field intensity, ρ is charge density, and J is current density;

[0018] The electromagnetic field model is used to simulate the current density and magnetic field distribution of the equipment under different working voltage and load conditions. The electromagnetic loss generated by the current density J will be input into the subsequent thermal field model as a heat source, thereby affecting the temperature rise calculation of the equipment. Among them, the Fourier heat conduction equation is used to simulate the thermal field distribution as follows:

[0019]

[0020] Where T is temperature, α is thermal diffusivity, ρ is density, and c p is the specific heat capacity, Q is the heat source term;

[0021] The thermal field model is based on the output of the electromagnetic field model. It converts the electromagnetic loss into the heat source inside the device to simulate the temperature rise of the device under different working conditions. The obtained temperature field distribution will be used in the fluid dynamics model. The Navier-Stokes equation is used to describe the fluid convection heat dissipation characteristics as follows:

[0022]

[0023] In the formula, u is the fluid velocity, p is the fluid pressure, μ is the fluid viscosity, and g is the gravitational acceleration. The temperature distribution output by the thermal field model is input into the fluid dynamics model as a boundary condition to simulate the convective heat dissipation of the fluid on the device surface.

[0024] As a preferred solution of the digital twin method for power transmission and transformation equipment of the present invention, wherein: the multi-dimensional data includes current, voltage, temperature and flow rate;

[0025] The multidimensional data is used to verify the multi-physics field coupling model in real time.

[0026] As a preferred solution of the digital twin method for power transmission and transformation equipment of the present invention, wherein: preprocessing the multidimensional data includes standardization and normalization processing;

[0027] Set the temperature data to T i , expressed as:

[0028]

[0029] In the formula, is the mean of the temperature data, σ T is the standard deviation.

[0030] As a preferred solution of the digital twin method for power transmission and transformation equipment described in the present invention, the step of constructing and updating the digital twin model in real time includes:

[0031] Input multi-dimensional data in real time;

[0032] Extract spatial features through convolutional neural networks;

[0033] Time series forecasting using long short-term memory networks;

[0034] Real-time status updates and prediction output.

[0035] As a preferred solution of the digital twin method for power transmission and transformation equipment of the present invention, the step of predicting the temperature rise and heat dissipation characteristics of the equipment includes:

[0036] Assuming the heat source intensity of the equipment is Q, the temperature rise equation is expressed as:

[0037]

[0038] In the formula, T0 is the initial temperature and t is the time. The temperature rise prediction result is used to determine whether the equipment has an overheating risk under high load.

[0039] Use the Navier-Stokes equation to evaluate the convective heat dissipation characteristics of the device surface:

[0040] q=h(T s -T ∞ );

[0041] In the formula, q is the heat dissipation, h is the convective heat transfer coefficient, T s is the surface temperature of the equipment, T ∞ is the ambient fluid temperature.

[0042] As a preferred solution of the digital twin method for power transmission and transformation equipment of the present invention, the steps of obtaining equipment fault prediction and life assessment include:

[0043] Set a temperature rise threshold ΔT threshold As a fault warning standard, when the actual temperature rise exceeds this threshold, a fault warning is triggered, and the conditions are:

[0044] |T predicted -T normal |>ΔT threshold ;

[0045] Where, T predicted is the temperature rise value predicted by the model, T normal is the temperature rise under normal working conditions;

[0046] If the conditions are met, the system determines that the equipment may have the risk of abnormal heat dissipation or excessive electromagnetic loss, and issues a warning signal in time. Fault prediction can detect equipment operation problems in advance and effectively avoid losses caused by sudden failures;

[0047] Based on long-term monitoring data of temperature rise and load changes, estimate the cumulative fatigue damage of equipment under different working conditions and predict its remaining life;

[0048] Assuming the cumulative damage rate is D(t), it is expressed using the Miner linear damage accumulation model as follows:

[0049]

[0050] Where N i is the number of operating cycles under the i-th condition, N f is the number of failure cycles of the equipment under this working condition;

[0051] When D(t) ≥ 1, the equipment is considered to have reached its fatigue life limit and needs to be replaced or maintained.

[0052] In a second aspect, an embodiment of the present invention provides a digital twin system for power transmission and transformation equipment, which includes a model building module, a data acquisition module, an update module, and an evaluation and prediction module;

[0053] The model building module is used to build a multi-physics field coupling model of the electromagnetic field, thermal field and fluid dynamics field of the power transmission and transformation equipment;

[0054] The data acquisition module is responsible for real-time acquisition of multi-dimensional data of the device and pre-processing;

[0055] The update module constructs and updates the digital twin model in real time, inputs multidimensional data in real time, extracts spatial features through convolutional neural networks, and uses long short-term memory networks to perform time series predictions to achieve real-time status updates and prediction outputs;

[0056] The evaluation and prediction module sets a temperature rise threshold as a fault warning standard. When the actual temperature rise exceeds the threshold, a fault warning is triggered. Based on the long-term monitoring data of temperature rise and load changes, the cumulative fatigue damage of the equipment under different working conditions is estimated, and its remaining life is predicted.

[0057] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, it implements any step of the above-mentioned digital twin method for power transmission and transformation equipment.

[0058] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored, wherein: when the computer program is executed by a processor, any step of the above-mentioned digital twin method for power transmission and transformation equipment is implemented.

[0059] The beneficial effects of the present invention are as follows: through multi-physical field coupling modeling, synchronous simulation of electromagnetic, thermal and fluid dynamics fields can be achieved, and the complex physical processes inside the power transmission and transformation equipment can be effectively reflected, thereby improving the accuracy of equipment status monitoring and fault prediction; through multi-dimensional and multi-granular data fusion, the model can dynamically adapt to changes in the operating state of the equipment, overcoming the shortcomings of the traditional single physical field model in terms of accuracy; by introducing a deep learning algorithm, a model update mechanism capable of processing large-scale data in real time is constructed, which significantly improves the data processing efficiency and the response speed of the model, and ensures the monitoring accuracy and real-time performance of the equipment under high dynamic conditions; through the interaction and feedback of the multi-physical field information of the equipment, the accuracy of equipment fault prediction and life assessment is optimized, providing reliable support for the stable operation of the power transmission and transformation system.

[0060] Through innovative methods combining multi-physics field coupling and deep learning, the digital twin system can more accurately reflect the real-time status of the equipment and support the interaction and feedback of multi-field information, thereby enhancing the real-time monitoring and life management capabilities of the equipment; it is not only suitable for the monitoring and management of power transmission and transformation systems, but can also be promoted to other fields that require multi-physics field coupling simulation, such as aerospace, intelligent manufacturing, etc., and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0062] Figure 1 Flowchart of the digital twin method for power transmission and transformation equipment.

[0063] Figure 2 Schematic diagram of the multi-physics field coupling model of the digital twin method for power transmission and transformation equipment.

[0064] Figure 3 The digital twin model flow chart of the digital twin method for power transmission and transformation equipment. DETAILED DESCRIPTION

[0065] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0066] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0067] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0068] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0069] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0070] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0071] Example 1

[0072] Reference Figure 1 to Figure 3 , which is the first embodiment of the present invention, and provides a digital twin method for power transmission and transformation equipment, comprising the following steps:

[0073] S1. Construct a multi-physics field coupling model of electromagnetic field, thermal field and fluid dynamic field of power transmission and transformation equipment. The behavior of the power transmission and transformation equipment is expressed as:

[0074] Δ·D=ρ;

[0075] Δ·B=0;

[0076]

[0077] Where D is electric displacement, B is magnetic induction intensity, E is electric field intensity, H is magnetic field intensity, ρ is charge density, and J is current density;

[0078] The electromagnetic field model is used to simulate the current density and magnetic field distribution of the equipment under different working voltage and load conditions. The electromagnetic loss generated by the current density J will be input into the subsequent thermal field model as a heat source, thereby affecting the temperature rise calculation of the equipment. Among them, the Fourier heat conduction equation is used to simulate the thermal field distribution as follows:

[0079]

[0080] Where T is temperature, α is thermal diffusivity, ρ is density, and c p is the specific heat capacity, Q is the heat source term;

[0081] The thermal field model is based on the output of the electromagnetic field model. It converts the electromagnetic loss into the heat source inside the device to simulate the temperature rise of the device under different working conditions. The obtained temperature field distribution will be used in the fluid dynamics model. The Navier-Stokes equation is used to describe the fluid convection heat dissipation characteristics as follows:

[0082]

[0083] In the formula, u is the fluid velocity, p is the fluid pressure, μ is the fluid viscosity, and g is the gravitational acceleration. The temperature distribution output by the thermal field model is input into the fluid dynamics model as a boundary condition to simulate the convective heat dissipation of the fluid on the device surface. The simulation accuracy of the device heat dissipation characteristics is further improved through the coupling relationship between temperature and flow rate. The schematic diagram of the multi-physics field coupling model is shown in Figure 2 shown.

[0084] S2. Collect multi-dimensional data of the equipment in real time and perform pre-processing.

[0085] In this step, sensors are used to collect multidimensional data of the equipment in real time, and the data is standardized and normalized for use in subsequent models. The multi-physics field coupling model calculated in the first step provides reference boundary conditions and data verification standards for data acquisition. Sensors are arranged to obtain real-time data of electromagnetic fields, temperature fields, and fluid dynamics fields, including current, voltage, temperature, flow rate and other data. The results of data acquisition are used to verify the accuracy of the multi-physics field coupling model in the first step in real time to ensure the adaptability of the model under real working conditions.

[0086] The multi-dimensional data includes current, voltage, temperature and flow rate;

[0087] The multidimensional data is used to verify the multi-physics field coupling model in real time.

[0088] Preprocessing of multidimensional data includes standardization and normalization;

[0089] Set the temperature data to T i , expressed as:

[0090]

[0091] In the formula, is the mean of the temperature data, σ T is the standard deviation.

[0092] Through denoising (such as filters), random noise and environmental noise in the data are removed to ensure the quality of data input to the model. Table 1 shows the effects of different preprocessing methods, including the comparison of the advantages and disadvantages of standardization, denoising, and normalization.

[0093] Table 1 Comparison of data preprocessing methods:

[0094]

[0095] S3. Build and update the digital twin model in real time, and predict the temperature rise and heat dissipation characteristics of the equipment.

[0096] The steps to build and update the digital twin model in real time include:

[0097] Input multi-dimensional data in real time;

[0098] Extract spatial features through convolutional neural networks;

[0099] Time series forecasting using long short-term memory networks;

[0100] Real-time status updates and prediction output.

[0101] Multi-physics coupling simulation. The obtained electromagnetic, thermal, and fluid coupling model data and real-time monitoring data are input into the deep learning model to achieve real-time simulation of the device status. Based on the convolutional neural network (CNN), spatial features such as temperature rise and current density distribution inside the device are extracted:

[0102] Y CNN =f CNN (X);

[0103] Among them, X is the input multidimensional data, Y CNN is the output feature map.

[0104] Time series feature extraction and prediction. Use long short-term memory network (LSTM) to perform time series analysis to capture the state change trend of the equipment during long-term operation and predict future temperature rise and electromagnetic field characteristics:

[0105] h t =LSTM(x t ,h t-1 );

[0106] Among them, h t is the hidden state of LSTM, reflecting the changes of device characteristics over time.

[0107] Through continuous data input and the model's self-learning ability, the digital twin model is updated in real time to reflect the latest status of the equipment. The data preprocessing results are used as the input update standard of the model to ensure that the data meets the requirements before entering the model, thereby improving the accuracy of the simulation results.

[0108] The steps to predict the temperature rise and heat dissipation characteristics of the equipment include:

[0109] Assuming the heat source intensity of the equipment is Q, the temperature rise equation is expressed as:

[0110]

[0111] In the formula, T0 is the initial temperature and t is the time. The temperature rise prediction result is used to determine whether the equipment has an overheating risk under high load.

[0112] Use the Navier-Stokes equation to evaluate the convective heat dissipation characteristics of the device surface:

[0113] q=h(T s -T ∞ );

[0114] In the formula, q is the heat dissipation, h is the convective heat transfer coefficient, T s is the surface temperature of the equipment, T ∞ is the ambient fluid temperature.

[0115] S4. Obtain equipment failure prediction and life assessment.

[0116] The steps to obtain equipment failure prediction and life assessment include:

[0117] Set a temperature rise threshold ΔT threshold As a fault warning standard, when the actual temperature rise exceeds this threshold, a fault warning is triggered, and the conditions are:

[0118] |T predicted -T normal |>ΔT threshold ;

[0119] Where, T predicted is the temperature rise value predicted by the model, T normal is the temperature rise under normal working conditions;

[0120] If the conditions are met, the system determines that the equipment may have the risk of abnormal heat dissipation or excessive electromagnetic loss, and issues a warning signal in time. Fault prediction can detect equipment operation problems in advance and effectively avoid losses caused by sudden failures;

[0121] Based on long-term monitoring data of temperature rise and load changes, estimate the cumulative fatigue damage of equipment under different working conditions and predict its remaining life;

[0122] Assuming the cumulative damage rate is D(t), it is expressed using the Miner linear damage accumulation model as follows:

[0123]

[0124] Where N i is the number of operating cycles under the i-th condition, N f is the number of failure cycles of the equipment under this working condition;

[0125] When D(t) ≥ 1, the equipment is considered to have reached its fatigue life limit and needs to be replaced or maintained.

[0126] The equipment is considered to have reached its fatigue life limit and needs to be replaced or maintained. During the life assessment process, the calculated temperature rise characteristics will provide long-term thermal stress change data of the equipment to support the calculation of the cumulative damage rate and improve the accuracy of life prediction. Life assessment provides a scientific basis for equipment maintenance planning and resource allocation, helping to extend the service life of the equipment and reduce operating costs.

[0127] In summary, through multi-physics field coupling modeling, synchronous simulation of electromagnetic, thermal and fluid dynamics fields can be achieved, effectively reflecting the complex physical processes inside the power transmission and transformation equipment, thereby improving the accuracy of equipment status monitoring and fault prediction; through multi-dimensional and multi-granular data fusion, the model can dynamically adapt to changes in the operating status of the equipment, overcoming the lack of accuracy of the traditional single physical field model; by introducing deep learning algorithms, a model update mechanism capable of processing large-scale data in real time is constructed, which significantly improves data processing efficiency and model response speed, ensuring the monitoring accuracy and real-time performance of the equipment under highly dynamic conditions; through the interaction and feedback of multi-physics field information of the equipment, the accuracy of equipment fault prediction and life assessment is optimized, providing reliable support for the stable operation of the power transmission and transformation system.

[0128] Through innovative methods combining multi-physics field coupling and deep learning, the digital twin system can more accurately reflect the real-time status of the equipment and support the interaction and feedback of multi-field information, thereby enhancing the real-time monitoring and life management capabilities of the equipment; it is not only suitable for the monitoring and management of power transmission and transformation systems, but can also be promoted to other fields that require multi-physics field coupling simulation, such as aerospace, intelligent manufacturing, etc., and has broad application prospects.

[0129] Example 2

[0130] On the basis of the first embodiment, this embodiment further provides a digital twin system for power transmission and transformation equipment, including a model building module, a data acquisition module, an update module, and an evaluation and prediction module;

[0131] The model building module is used to build a multi-physics field coupling model of the electromagnetic field, thermal field and fluid dynamics field of the power transmission and transformation equipment;

[0132] The data acquisition module is responsible for real-time acquisition of multi-dimensional data of the device and pre-processing;

[0133] The update module constructs and updates the digital twin model in real time, inputs multidimensional data in real time, extracts spatial features through convolutional neural networks, and uses long short-term memory networks to perform time series predictions to achieve real-time status updates and prediction outputs;

[0134] The evaluation and prediction module sets a temperature rise threshold as a fault warning standard. When the actual temperature rise exceeds the threshold, a fault warning is triggered. Based on the long-term monitoring data of temperature rise and load changes, the cumulative fatigue damage of the equipment under different working conditions is estimated, and its remaining life is predicted.

[0135] This embodiment also provides a computer device, which is suitable for the digital twin method of power transmission and transformation equipment, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the digital twin method of power transmission and transformation equipment proposed in the above embodiment.

[0136] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0137] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for realizing the digital twin of power transmission and transformation equipment proposed in the above embodiment is implemented.

[0138] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0139] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the present invention.

Claims

1. A digital twin method for power transmission and transformation equipment, characterized in that: The following steps are included: Construct a multi-physics coupling model of electromagnetic field, thermal field and fluid dynamics field of power transmission and transformation equipment; Collect multi-dimensional data of equipment in real time and perform pre-processing; Build and update the digital twin model in real time, and predict the temperature rise and heat dissipation characteristics of the equipment; Obtain failure predictions and lifetime estimates for equipment.

2. The digital twin method for power transmission and transformation equipment according to claim 1, characterized in that: The behavior of the power transmission and transformation equipment in this case is expressed as: Δ·D=ρ; Δ·B=0; Where D is electric displacement, B is magnetic induction intensity, E is electric field intensity, H is magnetic field intensity, ρ is charge density, and J is current density; The electromagnetic field model is used to simulate the current density and magnetic field distribution of the equipment under different working voltage and load conditions. The electromagnetic loss generated by the current density J will be input into the subsequent thermal field model as a heat source, thereby affecting the temperature rise calculation of the equipment. Among them, the Fourier heat conduction equation is used to simulate the thermal field distribution as follows: Where T is temperature, α is thermal diffusivity, ρ is density, and c p is the specific heat capacity, Q is the heat source term; The thermal field model is based on the output of the electromagnetic field model. It converts the electromagnetic loss into the heat source inside the device to simulate the temperature rise of the device under different working conditions. The obtained temperature field distribution will be used in the fluid dynamics model. The Navier-Stokes equation is used to describe the fluid convection heat dissipation characteristics as follows: In the formula, u is the fluid velocity, p is the fluid pressure, μ is the fluid viscosity, and g is the gravitational acceleration. The temperature distribution output by the thermal field model is input into the fluid dynamics model as a boundary condition to simulate the convective heat dissipation of the fluid on the device surface.

3. The digital twin method for power transmission and transformation equipment according to claim 2, characterized in that: The multidimensional data includes current, voltage, temperature and flow rate; The multi-dimensional data is used to verify the multi-physics field coupling model in real time.

4. The digital twin method for power transmission and transformation equipment according to claim 3, characterized in that: Preprocessing of multidimensional data includes standardization and normalization; Set the temperature data to T i , expressed as: In the formula, is the mean of the temperature data, σ T is the standard deviation.

5. The digital twin method for power transmission and transformation equipment according to claim 4, characterized in that: The steps to build and update the digital twin model in real time include: Input multi-dimensional data in real time; Extract spatial features through convolutional neural networks; Time series forecasting using long short-term memory networks; Real-time status updates and prediction output.

6. The digital twin method for power transmission and transformation equipment according to claim 5, characterized in that: The steps to predict the temperature rise and heat dissipation characteristics of the equipment include: Assuming the heat source intensity of the equipment is Q, the temperature rise equation is expressed as: In the formula, T0 is the initial temperature and t is the time. The temperature rise prediction result is used to determine whether the equipment has an overheating risk under high load. Use the Navier-Stokes equation to evaluate the convective heat dissipation characteristics of the device surface: q=h(T s -T ∞ ); In the formula, q is the heat dissipation, h is the convective heat transfer coefficient, T s is the surface temperature of the equipment, T ∞ is the ambient fluid temperature.

7. The digital twin method for power transmission and transformation equipment according to claim 6, characterized in that: The steps to obtain equipment failure prediction and life assessment include: Set a temperature rise threshold ΔT threshold As a fault warning standard, when the actual temperature rise exceeds this threshold, a fault warning is triggered, and the conditions are: |T predicted -T normal |>ΔT threshold ; Where, T predicted is the temperature rise value predicted by the model, T normal is the temperature rise under normal working conditions; If the conditions are met, the system determines that the equipment may have the risk of abnormal heat dissipation or excessive electromagnetic loss, and issues a warning signal in time. Fault prediction can detect equipment operation problems in advance and effectively avoid losses caused by sudden failures; Based on long-term monitoring data of temperature rise and load changes, estimate the cumulative fatigue damage of equipment under different working conditions and predict its remaining life; Assuming the cumulative damage rate is D(t), it is expressed using the Miner linear damage accumulation model as follows: Where N i is the number of operating cycles under the i-th condition, N f is the number of failure cycles of the equipment under this working condition; When D(t) ≥ 1, the equipment is considered to have reached its fatigue life limit and needs to be replaced or maintained.

8. A digital twin system for power transmission and transformation equipment, based on the digital twin method for power transmission and transformation equipment according to any one of claims 1 to 7, characterized in that: It includes model building module, data collection module, update module, and evaluation and prediction module; The model building module is used to build a multi-physics field coupling model of the electromagnetic field, thermal field and fluid dynamics field of the power transmission and transformation equipment; The data acquisition module is responsible for real-time acquisition of multi-dimensional data of the device and pre-processing; The update module constructs and updates the digital twin model in real time, inputs multidimensional data in real time, extracts spatial features through convolutional neural networks, and uses long short-term memory networks to perform time series predictions to achieve real-time status updates and prediction outputs; The evaluation and prediction module sets a temperature rise threshold as a fault warning standard. When the actual temperature rise exceeds the threshold, a fault warning is triggered. Based on the long-term monitoring data of temperature rise and load changes, the cumulative fatigue damage of the equipment under different working conditions is estimated, and its remaining life is predicted.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the digital twin method for power transmission and transformation equipment described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the digital twin method for power transmission and transformation equipment described in any one of claims 1 to 7 are implemented.

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