Digital reconstruction method for thermal characteristics of power transmission and transformation equipment

Abstract

The invention relates to a digital reconstruction method for thermal characteristics of power transmission and transformation equipment. The method comprises the following steps: acquiring a key variable data set of the power transmission and transformation equipment; the key variable data set comprises key variables of the power transmission and transformation equipment under multiple pieces of working condition information; obtaining a temperature field data set of a temperature field of the power transmission and transformation equipment under each key variable according to the multi-physical simulation model of the power transmission and transformation equipment; based on the temperature field data set, obtaining an external area temperature data set and an internal area temperature data set of the power transmission and transformation equipment; constructing a mapping relation between the key variable data set and the external region temperature data set to obtain a first agent model, and constructing a mapping relation between the key variable data set and the internal region temperature data set to obtain a second agent model; and obtaining target temperature distribution information of the power transmission and transformation equipment according to the first agent model and the second agent model. By adopting the method, the temperature analysis efficiency of the power transmission and transformation equipment can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of digitalization of electric power equipment, and in particular to a method, device, computer equipment, storage medium and computer program product for digitally reconstructing thermal characteristics of power transmission and transformation equipment. Background Art

[0002] The safe and stable operation of power transmission and transformation equipment is the guarantee of power system reliability assessment. With the continuous improvement of power grid voltage level and capacity, if the heating failure caused by the internal current-carrying structure of power transmission and transformation equipment occurs, it will seriously threaten the safe and stable operation of the converter station. Conventional detection methods such as on-site pressure detection, oil chromatography detection, and traditional infrared detection are difficult to timely and accurately evaluate the temperature status inside and outside the power transmission and transformation equipment.

[0003] Although the existing technology can analyze the temperature status of power transmission and transformation equipment through finite element simulation, the long solution process of the finite element simulation method makes it impossible to quickly see the temperature status information of the power transmission and transformation equipment. When a thermal failure occurs in the power transmission and transformation equipment, it may cause greater harm because it cannot be eliminated in time.

[0004] Therefore, how to shorten the temperature analysis time of power transmission and transformation equipment while ensuring the accuracy of the solution has become an urgent problem to be solved. Summary of the invention

[0005] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for digitally reconstructing the thermal characteristics of power transmission and transformation equipment, which can improve the temperature analysis efficiency of power transmission and transformation equipment, in order to address the above technical problems.

[0006] In a first aspect, the present application provides a method for digitally reconstructing thermal characteristics of power transmission and transformation equipment. The method comprises:

[0007] Acquire a key variable data set of a power transmission and transformation equipment; the key variable data set includes key variables of the power transmission and transformation equipment under multiple operating conditions; the key variables represent variables that affect the temperature distribution of the power transmission and transformation equipment;

[0008] According to the multi-physics simulation model of the power transmission and transformation equipment, a temperature field data set of the temperature field of the power transmission and transformation equipment under each of the key variables is obtained;

[0009] Based on the temperature field data set, obtaining an external area temperature data set and an internal area temperature data set of the power transmission and transformation equipment;

[0010] Constructing a mapping relationship between the key variable data set and the external area temperature data set to obtain a first proxy model, and constructing a mapping relationship between the key variable data set and the internal area temperature data set to obtain a second proxy model;

[0011] According to the first proxy model and the second proxy model, target temperature distribution information of the power transmission and transformation equipment is obtained.

[0012] In one embodiment, constructing a mapping relationship between the key variable data set and the external area temperature data set to obtain a first proxy model, and constructing a mapping relationship between the key variable data set and the internal area temperature data set to obtain a second proxy model, including:

[0013] Performing dimensionality reduction processing on the external area temperature data set to obtain external area temperature reconstructed data;

[0014] constructing the first proxy model according to an implicit relationship between the key variable data set and the external region temperature reconstruction data;

[0015] The key variable data set is upsampled according to the internal area temperature data set to obtain the second proxy model.

[0016] In one embodiment, obtaining target temperature distribution information of the power transmission and transformation equipment according to the first proxy model and the second proxy model includes:

[0017] Processing the first proxy model according to the key variable data set to obtain temperature information of an external area of ​​the power transmission and transformation equipment;

[0018] Processing the second agent model according to the key variable data set to obtain temperature information of the internal area of ​​the power transmission and transformation equipment;

[0019] The temperature information of the external area and the temperature information of the internal area are fused to obtain the target temperature distribution information; the target temperature distribution information is used to characterize the overall temperature distribution of the power transmission and transformation equipment.

[0020] In one embodiment, obtaining a key variable data set of a power transmission and transformation equipment includes:

[0021] Establishing a multi-physics simulation model of the power transmission and transformation equipment according to the size information and operating condition information of the power transmission and transformation equipment;

[0022] Performing equal-interval scanning processing on the operating condition range of the power transmission and transformation equipment to obtain a plurality of equal-interval operating condition information;

[0023] The key variable data set is obtained according to the values ​​of the key variables under each of the equally spaced operating condition information.

[0024] In one embodiment, the key variables include the ambient temperature, current carrying capacity and degree of degradation of the contact fingers of the power transmission and transformation equipment;

[0025] The degree of degradation of the contact finger is obtained according to the ratio between the actual conductivity of the power transmission and transformation equipment and the conductivity of the power transmission and transformation equipment when the contact finger is in a good state.

[0026] In one embodiment, obtaining an external area temperature dataset and an internal area temperature dataset of the power transmission and transformation equipment based on the temperature field dataset includes:

[0027] Determining temperature data belonging to an external area of ​​the power transmission and transformation equipment and determining temperature data belonging to an internal area of ​​the power transmission and transformation equipment from the temperature field data set;

[0028] Extracting and processing the temperature data of the external area according to the grid node information outside the multi-physics simulation model to obtain the temperature data set of the external area;

[0029] According to the grid node information inside the multi-physics simulation model, the temperature data of the internal area is interpolated to obtain the temperature data set of the internal area.

[0030] In a second aspect, the present application also provides a device for digitally reconstructing thermal characteristics of power transmission and transformation equipment. The device comprises:

[0031] A variable acquisition module is used to acquire a key variable data set of a power transmission and transformation equipment; the key variable data set includes key variables of the power transmission and transformation equipment under multiple operating conditions; the key variables represent variables that affect the temperature distribution of the power transmission and transformation equipment;

[0032] A temperature acquisition module, used to obtain a temperature field data set of the temperature field of the power transmission and transformation equipment under each of the key variables according to the multi-physics simulation model of the power transmission and transformation equipment;

[0033] A temperature reconstruction module, used to obtain an external area temperature data set and an internal area temperature data set of the power transmission and transformation equipment based on the temperature field data set;

[0034] A temperature mapping module is used to construct a mapping relationship between the key variable data set and the external area temperature data set to obtain a first proxy model, and to construct a mapping relationship between the key variable data set and the internal area temperature data set to obtain a second proxy model;

[0035] The distribution acquisition module is used to obtain the target temperature distribution information of the power transmission and transformation equipment according to the first proxy model and the second proxy model.

[0036] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0037] Acquire a key variable data set of a power transmission and transformation equipment; the key variable data set includes key variables of the power transmission and transformation equipment under multiple operating conditions; the key variables represent variables that affect the temperature distribution of the power transmission and transformation equipment;

[0038] According to the multi-physics simulation model of the power transmission and transformation equipment, a temperature field data set of the temperature field of the power transmission and transformation equipment under each of the key variables is obtained;

[0039] Based on the temperature field data set, obtaining an external area temperature data set and an internal area temperature data set of the power transmission and transformation equipment;

[0040] Constructing a mapping relationship between the key variable data set and the external area temperature data set to obtain a first proxy model, and constructing a mapping relationship between the key variable data set and the internal area temperature data set to obtain a second proxy model;

[0041] According to the first proxy model and the second proxy model, target temperature distribution information of the power transmission and transformation equipment is obtained.

[0042] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0043] Acquire a key variable data set of a power transmission and transformation equipment; the key variable data set includes key variables of the power transmission and transformation equipment under multiple operating conditions; the key variables represent variables that affect the temperature distribution of the power transmission and transformation equipment;

[0044] According to the multi-physics simulation model of the power transmission and transformation equipment, a temperature field data set of the temperature field of the power transmission and transformation equipment under each of the key variables is obtained;

[0045] Based on the temperature field data set, obtaining an external area temperature data set and an internal area temperature data set of the power transmission and transformation equipment;

[0046] Constructing a mapping relationship between the key variable data set and the external area temperature data set to obtain a first proxy model, and constructing a mapping relationship between the key variable data set and the internal area temperature data set to obtain a second proxy model;

[0047] According to the first proxy model and the second proxy model, target temperature distribution information of the power transmission and transformation equipment is obtained.

[0048] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0049] Acquire a key variable data set of a power transmission and transformation equipment; the key variable data set includes key variables of the power transmission and transformation equipment under multiple operating conditions; the key variables represent variables that affect the temperature distribution of the power transmission and transformation equipment;

[0050] According to the multi-physics simulation model of the power transmission and transformation equipment, a temperature field data set of the temperature field of the power transmission and transformation equipment under each of the key variables is obtained;

[0051] Based on the temperature field data set, obtaining an external area temperature data set and an internal area temperature data set of the power transmission and transformation equipment;

[0052] Constructing a mapping relationship between the key variable data set and the external area temperature data set to obtain a first proxy model, and constructing a mapping relationship between the key variable data set and the internal area temperature data set to obtain a second proxy model;

[0053] According to the first proxy model and the second proxy model, target temperature distribution information of the power transmission and transformation equipment is obtained.

[0054] The above-mentioned method, device, computer equipment, storage medium and computer program product for digital reconstruction of thermal characteristics of power transmission and transformation equipment obtain key variable data sets of power transmission and transformation equipment; the key variable data sets include key variables of power transmission and transformation equipment under multiple working conditions; the key variables represent variables that affect the temperature distribution of power transmission and transformation equipment; according to the multi-physics simulation model of the power transmission and transformation equipment, a temperature field data set of the temperature field of the power transmission and transformation equipment under each key variable is obtained; based on the temperature field data set, an external area temperature data set and an internal area temperature data set of the power transmission and transformation equipment are obtained; a mapping relationship between the key variable data set and the external area temperature data set is constructed to obtain a first proxy model, and a mapping relationship between the key variable data set and the internal area temperature data set is constructed to obtain a second proxy model; according to the first proxy model and the second proxy model, the target temperature distribution information of the power transmission and transformation equipment is obtained.

[0055] By adopting this method, by establishing a mapping relationship between key variables and temperature data of power transmission and transformation equipment under various operating conditions, relevant staff can directly obtain temperature distribution information of power transmission and transformation equipment by inputting actual operating conditions, thereby realizing rapid analysis of internal and external temperature states of power transmission and transformation equipment, while ensuring the accuracy of temperature analysis, it also effectively shortens the time of temperature analysis, making it convenient for staff to accurately and quickly locate the location of thermal faults inside power transmission and transformation equipment, thereby improving the operating safety of power transmission and transformation equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a schematic flow chart of a method for digitally reconstructing thermal characteristics of power transmission and transformation equipment in one embodiment;

[0057] Figure 2 A schematic flow chart of steps for obtaining a first proxy model and a second proxy model in one embodiment;

[0058] Figure 3 A schematic diagram of a BP neural network structure used in an external area of ​​a power transmission and transformation device in one embodiment;

[0059] Figure 4 A schematic diagram of a residual deep convolutional neural network structure used in an internal area of ​​a power transmission and transformation device in one embodiment;

[0060] Figure 5 A schematic diagram of the internal structure of a residual deep convolutional neural network upsampling block in one embodiment;

[0061] Figure 6 It is a flowchart of a method for digitally reconstructing thermal characteristics of power transmission and transformation equipment in another embodiment;

[0062] Figure 7 A schematic flow chart of a method for digitally reconstructing thermal characteristics of power transmission and transformation equipment in yet another embodiment of an embodiment;

[0063] Figure 8 is a schematic diagram of the overall structure of a proxy model in one embodiment;

[0064] Fig. 9 A schematic diagram of a comparison result of the power transmission and transformation equipment temperature reconstructed by the proxy model in one embodiment and the power transmission and transformation equipment temperature calculated by the existing finite element method;

[0065] Fig.10 A schematic diagram of error analysis between a proxy model and a temperature result of a power transmission and transformation equipment calculated by an existing finite element method in one embodiment;

[0066] Fig.11 It is a structural block diagram of a device for digitally reconstructing thermal characteristics of power transmission and transformation equipment in one embodiment;

[0067] Fig.12 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0069] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0070] In one embodiment, Figure 1 As shown, a method for digitally reconstructing the thermal characteristics of power transmission and transformation equipment is provided. This embodiment uses the method applied to a terminal as an example for illustration. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0071] Step S101, obtaining a key variable data set of power transmission and transformation equipment; the key variable data set includes key variables of the power transmission and transformation equipment under multiple working conditions; the key variables represent variables that affect the temperature distribution of the power transmission and transformation equipment.

[0072] In practical applications, the power transmission and transformation equipment may be a valve-side bushing of a UHV converter transformer.

[0073] Among them, the UHV converter transformer valve side bushing, also referred to as bushing, is an important component in power equipment and is part of the converter transformer valve. The UHV converter transformer valve side bushing is a protective device used to protect other equipment from electromagnetic interference and short-circuit damage. The UHV converter transformer valve side bushing is usually a tubular object made of insulating material, used to cover and protect other equipment to prevent short circuits and other damage.

[0074] Specifically, a multi-physics simulation model of the power transmission and transformation equipment can be built in the terminal, and one or more key variables that affect the temperature distribution of the power transmission and transformation equipment can be determined. The operating condition range of the power transmission and transformation equipment can be processed by scanning at equal intervals, and then the values ​​of the key variables under each scanned condition information can be obtained, and a key variable data set can be constructed based on the values, which is recorded as key variable data V={v1, v2,…, v i ,…,v n}, where vi represents the value of the key variable under the i-th operating condition information; n represents the number of operating condition information.

[0075] Step S102, obtaining a temperature field data set of the temperature field of the power transmission and transformation equipment under various key variables according to the multi-physics simulation model of the power transmission and transformation equipment.

[0076] Specifically, the terminal can calculate the temperature field of the power transmission and transformation equipment at each key variable v according to the multi-physics simulation model of the power transmission and transformation equipment. i Temperature field T i , and then obtain the temperature field data set Y={T1, T2,…, T n}. The temperature field data is exported in the order of the grid nodes of the multi-physics simulation model.

[0077] Step S103, based on the temperature field data set, obtaining an external area temperature data set and an internal area temperature data set of the power transmission and transformation equipment.

[0078] The external area temperature data set may be data describing the external area temperature of the power transmission and transformation equipment.

[0079] The internal area temperature data set may be data describing the internal area temperature of the power transmission and transformation equipment.

[0080] Specifically, the terminal can extract the external area temperature data in the temperature field data set according to the grid nodes of the multi-physics simulation model to obtain the external area temperature data set Y e ={T e1 , T e2 , …, T en The terminal can interpolate the internal area temperature data in the temperature field data set according to the structured grid in the grid form of the multi-physics simulation model to obtain the internal area temperature data Y under the grid node. i ={T i1 ,T i2 , …, T in}. The grid-form structured grid refers to a regular quadrilateral grid.

[0081] Step S104, constructing a mapping relationship between the key variable data set and the external area temperature data set to obtain a first proxy model, and constructing a mapping relationship between the key variable data set and the internal area temperature data set to obtain a second proxy model.

[0082] Specifically, the terminal constructs the key variable V and the external area temperature dataset Y of the power transmission and transformation equipment respectively. e and the internal area temperature dataset Y iThe mapping relationship between them gives two proxy models, namely the first proxy model G e and the second proxy model G i .

[0083] Step S105: obtaining target temperature distribution information of the power transmission and transformation equipment according to the first proxy model and the second proxy model.

[0084] Specifically, for the main values ​​of key variables within the actual operating conditions of the UHV converter transformer side, the terminal uses the proxy model G e and G i The temperature of the external area and the temperature of the internal area of ​​the power transmission and transformation equipment can be reconstructed separately, and finally the overall temperature distribution of the power transmission and transformation equipment can be reconstructed by adding the two together to obtain the target temperature distribution information of the power transmission and transformation equipment. ,Right now .

[0085] In the above-mentioned digital reconstruction method of the thermal characteristics of the power transmission and transformation equipment, a key variable data set of the power transmission and transformation equipment is obtained; the key variable data set includes key variables of the power transmission and transformation equipment under multiple working conditions; the key variables represent variables that affect the temperature distribution of the power transmission and transformation equipment; according to the multi-physics simulation model of the power transmission and transformation equipment, a temperature field data set of the temperature field of the power transmission and transformation equipment under each key variable is obtained; based on the temperature field data set, an external area temperature data set and an internal area temperature data set of the power transmission and transformation equipment are obtained; a mapping relationship between the key variable data set and the external area temperature data set is constructed to obtain a first proxy model, and a mapping relationship between the key variable data set and the internal area temperature data set is constructed to obtain a second proxy model; according to the first proxy model and the second proxy model, the target temperature distribution information of the power transmission and transformation equipment is obtained. By adopting this method, by establishing a mapping relationship between key variables and temperature data of power transmission and transformation equipment under various operating conditions, relevant staff can directly obtain temperature distribution information of power transmission and transformation equipment by inputting actual operating conditions, thereby realizing rapid analysis of internal and external temperature states of power transmission and transformation equipment, while ensuring the accuracy of temperature analysis, it also effectively shortens the time of temperature analysis, making it convenient for staff to accurately and quickly locate the location of thermal faults inside power transmission and transformation equipment, thereby improving the operating safety of power transmission and transformation equipment.

[0086] In one embodiment, Figure 2 As shown, the above step S104, constructing a mapping relationship between the key variable data set and the external area temperature data set to obtain a first proxy model, and constructing a mapping relationship between the key variable data set and the internal area temperature data set to obtain a second proxy model, specifically includes the following contents:

[0087] Step S201 , performing dimensionality reduction processing on the external area temperature data set to obtain external area temperature reconstructed data.

[0088] Step S202: construct a first proxy model according to the implicit relationship between the key variable data set and the external area temperature reconstruction data.

[0089] Specifically, the terminal obtains the external area temperature data set Y e Dimensionality reduction is performed to obtain the reconstructed data of the external area temperature. In practical applications, the external area temperature dataset Y can be decomposed by POD e Perform dimensionality reduction:

[0090] First, the external area temperature dataset Y e Decentralize and calculate the external area temperature data set Y e The mean vector of ,Right now:

[0091]

[0092] The external area temperature dataset Y e Subtracting the mean vector gives the decentralized data:

[0093]

[0094] Structuring Data The covariance matrix X of is:

[0095]

[0096] Perform eigendecomposition on the covariance matrix X and obtain the eigenvalues and the corresponding eigenvector , and Also known as POD characteristic coefficients and modes. In order to ensure the accuracy of calculation and prediction model, singular values The following conditions need to be met:

[0097]

[0098] in, It represents the ratio of the first r-order eigenvalues ​​to the sum of all eigenvalues, also known as the first r-order modal contribution rate. ε is selected as 99.99%. According to the contribution rate, the first r eigenvalues ​​are taken. and the corresponding modal As the basis of the subspace, the external area temperature data can be reconstructed through the subspace. External temperature reconstruction data of power transmission and transformation equipment under the kth working condition information It can be expressed as:

[0099]

[0100] Furthermore, for a given temperature matrix, the r-order POD mode is certain, but the characteristic coefficient Varies with the CFX input conditions. For a given input condition v * , constructing the implicit relationship f between the characteristic coefficient and the input condition, we can get the first proxy model that characterizes the mapping relationship between the key variable data set and the external area temperature data set:

[0101]

[0102] In practical applications, BP deep neural network can be used to construct the implicit relationship f between feature coefficients and input conditions. The structure of BP neural network is as follows: Figure 3 As shown. The BP deep neural network is a multi-layer feedforward neural network that uses the error back propagation algorithm to train the network. The network is divided into an input layer, an output layer, and a hidden layer. The number of neurons in the input layer and the output layer are the number of input and output parameters, respectively. The number of neurons in the hidden layer and the number of hidden layers are reasonably selected according to the degree of nonlinearity of the data. The input data of the BP deep neural network is the working condition, that is, the key variable: ambient temperature (T out )、current carrying capacity(I c ) and the degradation degree of the finger (k), and its output data are the characteristic coefficients (α1, α2, α3) corresponding to each key variable.

[0103] Step S203: up-sample the key variable data set according to the internal area temperature data set to obtain a second proxy model.

[0104] Specifically, a residual deep convolutional neural network can be used to construct a data set Y containing key variables V and the internal temperature of the power transmission and transformation equipment. i The second proxy model G of the mapping relationship i . Figure 4 Schematic diagram of the structure of the residual deep convolutional neural network. For example, the size of the key variable V is first changed through the full convolution layer, and then the key variable V after the size change is gradually upsampled through the upsampling block of the residual deep convolutional neural network, and finally a two-dimensional temperature matrix Y representing the temperature of the internal area is obtained. i The upsampling block is implemented by a transposed convolution layer, a BN layer (Batch Normalization layer), and an activation function. The internal structure diagram of the upsampling block is shown in Figure 5 As shown in Figure 2, the upsampling block enables the residual deep convolutional neural network to train deeper models by introducing residual connections.

[0105] In this embodiment, by performing dimensionality reduction processing on the external area temperature data set, the dimension of the data can be effectively compressed while retaining key information features, thereby effectively improving the efficiency of temperature analysis of power transmission and transformation equipment. The first proxy model established by the implicit relationship between the key variable data set and the external area temperature reconstruction data helps to deeply understand the impact of external temperature on key variables. By upsampling the internal area temperature data set to obtain the second proxy model, the generalization ability of the second-generation model can be enhanced, thereby improving the accuracy of the analysis of the internal area temperature distribution.

[0106] In one embodiment, the above step S105 obtains target temperature distribution information of the power transmission and transformation equipment according to the first proxy model and the second proxy model, and specifically includes the following contents: according to the key variable data set, the first proxy model is processed to obtain temperature information of the external area of ​​the power transmission and transformation equipment; according to the key variable data set, the second proxy model is processed to obtain temperature information of the internal area of ​​the power transmission and transformation equipment; the temperature information of the external area and the temperature information of the internal area are fused to obtain target temperature distribution information; the target temperature distribution information is used to characterize the overall temperature distribution of the power transmission and transformation equipment.

[0107] Specifically, the target value of the key variable can be obtained based on the actual operating conditions of the power transmission and transformation equipment, or it can be selected from the key variable data set. The target value is calculated using the first proxy model to obtain the temperature information of the external area of ​​the power transmission and transformation equipment. The target value is calculated using the second proxy model to obtain the temperature information of the internal area of ​​the power transmission and transformation equipment. Then the temperature information of the external area and the temperature information of the internal area are added, that is, , to obtain the overall temperature distribution of the power transmission and transformation equipment, the terminal obtains the target temperature distribution information .

[0108] In this embodiment, by combining the first proxy model and the second proxy model to obtain target temperature distribution information that can comprehensively describe the overall temperature distribution of the power transmission and transformation equipment, a rapid and accurate analysis of the internal and external temperature states of the power transmission and transformation equipment is achieved. While ensuring the accuracy of the temperature analysis, the time of the temperature analysis is effectively shortened, which facilitates the staff to accurately and quickly locate the location of the internal thermal fault of the power transmission and transformation equipment, thereby improving the operating safety of the power transmission and transformation equipment.

[0109] In one embodiment, the above step S101, obtaining a key variable data set of the power transmission and transformation equipment, specifically includes the following contents: establishing a multi-physics simulation model of the power transmission and transformation equipment according to the size information and operating condition information of the power transmission and transformation equipment; performing equal-interval scanning processing on the operating condition range of the power transmission and transformation equipment to obtain a plurality of equally-interval operating condition information; and obtaining a key variable data set according to the values ​​of the key variables under each equally-interval operating condition information.

[0110] Among them, the multi-physics simulation model can be an electromagnetic-temperature (heat)-fluid (flow) calculation simulation model of power transmission and transformation equipment.

[0111] The size information may be information describing the structural size of the power transmission and transformation equipment.

[0112] The operating condition information may be information describing the operating conditions experienced by the power transmission and transformation equipment during operation.

[0113] Specifically, the terminal can construct an electromagnetic-thermal-flow calculation simulation model of the power transmission and transformation equipment based on the size information and actual operating condition information of the power transmission and transformation equipment. The terminal then obtains a multi-physics simulation model and then determines the key variable v that affects the temperature distribution of the power transmission and transformation equipment. Within the actual operating condition range of the power transmission and transformation equipment, a data set of the key variable v is obtained by scanning at equal intervals. For example, the terminal can determine the upper and lower limits of the operating condition information according to the operating condition range, and then set the scanning interval of the operating condition range by a fixed value, and then start taking values ​​from the upper limit of the operating condition range according to the scanning interval until the lower limit of the operating condition range, so as to obtain a plurality of equally spaced operating condition information, and finally obtain the values ​​of the key variables under each equally spaced operating condition information to obtain a key variable data set.

[0114] In this embodiment, by establishing a multi-physics simulation model of the power transmission and transformation equipment, the performance changes of the power transmission and transformation equipment under different operating conditions can be analyzed, and then the key variable data sets under each equally spaced operating condition information can be obtained, providing a reliable processing basis for the subsequent temperature distribution analysis.

[0115] In one embodiment, the key variables include the ambient temperature, current carrying capacity and degradation degree of the contact fingers of the power transmission and transformation equipment; wherein the degradation degree of the contact fingers is obtained based on the ratio between the actual conductivity of the power transmission and transformation equipment and the conductivity of the power transmission and transformation equipment when the contact fingers are in good condition.

[0116] In this embodiment, the monitoring of ambient temperature and current carrying capacity is set as key variables, which can determine the working status of the power transmission and transformation equipment in real time, discover potential problems in time and perform preventive maintenance. In addition, setting the degradation degree of the contact finger as a key variable can make the temperature status analysis of the power transmission and transformation equipment more accurate, and also help to evaluate the health status of the power transmission and transformation equipment, ensure the reliability of the power transmission and transformation equipment under different working conditions, reduce the failure rate, and extend the service life of the equipment.

[0117] In one embodiment, the above step S103 obtains an external area temperature data set and an internal area temperature data set of the power transmission and transformation equipment based on the temperature field data set, and specifically includes the following contents: determining the temperature data of the external area belonging to the power transmission and transformation equipment, and determining the temperature data of the internal area belonging to the power transmission and transformation equipment from the temperature field data set; extracting and processing the temperature data of the external area according to the grid node information outside the multi-physics simulation model to obtain the external area temperature data set; interpolating the temperature data of the internal area according to the grid node information inside the multi-physics simulation model to obtain the internal area temperature data set.

[0118] The grid node information refers to the nodes when the multi-physics simulation model is gridded. When extracting temperature data, it can be extracted according to the node order.

[0119] Specifically, the terminal can determine the temperature data of the external area of ​​the power transmission and transformation equipment from the temperature field data set, wherein the external area of ​​the power transmission and transformation equipment can be the upper and lower terminal structures, composite insulation sleeves, oil tanks, transformer oil and flanges of the power transmission and transformation equipment. Then, the temperature data of the external area is extracted and processed according to the grid node information to obtain the temperature data set of the external area.

[0120] Furthermore, the terminal can determine the temperature data of the internal area of ​​the power transmission and transformation equipment from the temperature field data set; wherein the internal area of ​​the power transmission and transformation equipment can be a current-carrying conductor structure, a capacitor core, SF6, and an epoxy glass hollow insulator. Then, the temperature data of the internal area is interpolated according to a structured grid in the form of a grid to obtain the temperature data set of the internal area.

[0121] In this embodiment, by extracting the external area temperature data set of the power transmission and transformation equipment and interpolating the internal area temperature data set, detailed temperature information inside and outside the power transmission and transformation equipment can be obtained, providing reliable data support for subsequent temperature distribution analysis, and effectively improving the accuracy of temperature status analysis of the power transmission and transformation equipment.

[0122] In one embodiment, Figure 6 As shown, another method for digitally reconstructing thermal characteristics of power transmission and transformation equipment is provided, and the method is applied to a terminal as an example for explanation, and includes the following steps:

[0123] Step S601: establishing a multi-physics simulation model of the power transmission and transformation equipment according to the size information and operation condition information of the power transmission and transformation equipment.

[0124] Step S602, performing equal-interval scanning processing on the operating condition range of the power transmission and transformation equipment to obtain a plurality of equal-interval operating condition information.

[0125] Step S603, obtaining a key variable data set according to the values ​​of the key variables under each equally spaced operating condition information.

[0126] Step S604: obtaining a temperature field data set of the temperature field of the power transmission and transformation equipment under various key variables according to the multi-physics simulation model of the power transmission and transformation equipment.

[0127] Step S605: determining the temperature data of the external area of ​​the power transmission and transformation equipment and determining the temperature data of the internal area of ​​the power transmission and transformation equipment from the temperature field data set.

[0128] Step S606: extract and process the temperature data of the external area according to the grid node information outside the multi-physics simulation model to obtain the temperature data set of the external area.

[0129] Step S607 , interpolating the temperature data of the internal area according to the grid node information inside the multi-physics simulation model to obtain a temperature data set of the internal area.

[0130] Step S608, constructing a mapping relationship between the key variable data set and the external area temperature data set to obtain a first proxy model, and constructing a mapping relationship between the key variable data set and the internal area temperature data set to obtain a second proxy model.

[0131] Step S609: obtaining target temperature distribution information of the power transmission and transformation equipment according to the first proxy model and the second proxy model.

[0132] The above-mentioned digital reconstruction method of the thermal characteristics of the power transmission and transformation equipment can achieve the following beneficial effects: by establishing a mapping relationship between the key variables and temperature data of the power transmission and transformation equipment under various operating conditions, relevant staff can directly obtain the temperature distribution information of the power transmission and transformation equipment by inputting the actual operating conditions, thereby realizing rapid analysis of the internal and external temperature states of the power transmission and transformation equipment, while ensuring the accuracy of the temperature analysis, it also effectively shortens the time of the temperature analysis, making it easier for staff to accurately and quickly locate the location of thermal faults inside the power transmission and transformation equipment, thereby improving the operating safety of the power transmission and transformation equipment.

[0133] In order to more clearly illustrate the digital reconstruction method of the thermal characteristics of the power transmission and transformation equipment provided by the embodiment of the present disclosure, the digital reconstruction method of the thermal characteristics of the power transmission and transformation equipment is specifically described below with a specific embodiment. Figure 7 and Figure 8 As shown, another digital reconstruction method of thermal characteristics of power transmission and transformation equipment is provided, which can be applied to terminals. Taking the power transmission and transformation equipment as the UHV converter transformer valve side bushing (referred to as bushing) as an example, the specific contents include the following:

[0134] (1) Temperature field simulation calculation: First determine the key variables that affect the casing temperature and the selection range of the key variables. Then, use the finite element simulation software to simulate the casing under different working conditions to obtain the key variable data set.

[0135] (2) Data preprocessing: Generate a temperature field data set and extract the node temperatures of the outer and inner areas of the casing. The terminal then obtains the outer and inner area temperature data sets of the casing.

[0136] (3) Proxy model neural network training: POD decomposition is performed on the external area temperature data set to obtain the temperature characteristic coefficient; then a BP neural network is constructed to learn the mapping relationship between the temperature characteristic coefficient and the key variables. In addition, a residual deep convolutional neural network can also be constructed to learn the mapping relationship between the internal temperature data set and the key variables.

[0137] (4) Reconstructing the casing temperature: The terminal can use the two mapping relationships obtained in step (3) to construct a data-driven casing temperature reconstruction agent model, and input the key variable parameters in the actual operation of the casing into the data-driven casing temperature reconstruction agent model, so as to quickly reconstruct the internal temperature of the casing through the data-driven casing temperature reconstruction agent model. Figure 8 It is a schematic diagram of the overall structure of the proxy model. Figure 8 As shown in the figure, in actual applications, the terminal can use the external environment temperature, current carrying capacity and sliding contact resistance degradation multiple of the bushing as key variables, and then the terminal inputs the external environment temperature, current carrying capacity and sliding contact resistance degradation multiple of the bushing during operation as key variables into the data-driven bushing temperature reconstruction agent model. Specifically: the three key variables are input into the BP neural network to obtain the first three-order temperature characteristic coefficients, and the first three-order temperature characteristic coefficients are multiplied by the characteristic vectors of the key variables to obtain the node temperature of the outer area of ​​the bushing; the three key variables are also input into the residual deep convolutional neural network to obtain the node temperature of the inner area of ​​the bushing. Finally, the node temperatures of the outer and inner areas of the bushing are merged to obtain the overall temperature distribution.

[0138] In addition, the temperature of the power transmission and transformation equipment reconstructed by the proxy model in this application is compared with the temperature of the power transmission and transformation equipment obtained by the existing finite element calculation method. The comparison results are as follows: Fig. 9 As shown. The error analysis was performed on the temperature of the power transmission and transformation equipment reconstructed by the proxy model and the temperature of the power transmission and transformation equipment obtained by the existing finite element calculation method. The error analysis results are shown in Fig.10 shown.

[0139] It should be noted that the internal temperature field of the power transmission and transformation equipment is fluid-solid coupled heat transfer, the heat transfer mechanism is complex, and the nonlinearity is high. The deep convolutional neural network can effectively fit the mapping relationship between the key variables and the temperature field. Due to the complex structure and shape of the sheath and terminal parts of the power transmission and transformation equipment, the unstructured meshing results are difficult to use as the output of the convolutional neural network. Therefore, a reduced-order model based on deep learning is selected to fit the relationship between the external temperature of the power transmission and transformation equipment and the key variables. In addition, except for the transformer oil, which is an adiabatic area (set to 60°C), the rest of these areas are solid areas, which is conducive to improving the order reduction efficiency of POD and the reconstruction performance of the proxy model. According to the embodiment, when the external area of ​​the power transmission and transformation equipment is reduced in order, the contribution rate of the first three order eigenvalues ​​reaches 99.99%; while the first 7 orders are required to achieve the same contribution rate for the overall calculation domain of the power transmission and transformation equipment. This is because the internal gas has a large degree of nonlinearity, and the first three order information is difficult to describe the distribution characteristics of the entire temperature field. Increasing the order will also affect the model reconstruction performance. Therefore, the use of regional proxy models can effectively solve the problem that the temperature data inside the power transmission and transformation equipment is difficult to fit due to the strong nonlinearity, and the convolutional neural network cannot be applied due to the complex shape and structure outside. This method can effectively improve the temperature reconstruction performance.

[0140] In this embodiment, the problem that the internal temperature of the power transmission and transformation equipment cannot be quickly visualized and analyzed is solved, and a reference is provided for the digital twin and thermal fault diagnosis of power transmission and transformation equipment such as power transmission and transformation equipment. On the one hand, by using deep learning and data dimensionality reduction technology, the implicit mapping relationship between the operating conditions (current carrying capacity, external ambient temperature, finger degradation multiple) and the overall temperature distribution of the power transmission and transformation equipment is obtained. The internal temperature distribution of the power transmission and transformation equipment can be directly obtained through the input conditions of the operating conditions, and the internal state of the power transmission and transformation equipment can be visualized and quickly analyzed and thermal fault diagnosis can be performed. On the other hand, by using deep learning technology, a residual deep convolutional neural network is used inside the power transmission and transformation equipment to learn the mapping relationship between the input conditions and the temperature field. The introduction of residual connections enables the network to train a deeper model, avoiding the fitting difficulties caused by the strong nonlinearity of the fluid-solid coupling temperature inside the power transmission and transformation equipment. On the other hand, when constructing the proxy model of the input variables and the temperature of the power transmission and transformation equipment, a method of construction by region and method is adopted. A reduced-order model based on a BP neural network is used in the external area of ​​the power transmission and transformation equipment, and a residual deep convolutional neural network is used inside the power transmission and transformation equipment. It effectively solves the problem that the external structures of power transmission and transformation equipment, such as sheaths, terminals, flanges, etc., are difficult to build models through convolutional neural networks due to their complex shapes, and facilitates the accurate construction of the mapping relationship between input operating conditions and the temperature of power transmission and transformation equipment.

[0141] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0142] Based on the same inventive concept, the embodiment of the present application also provides a device for digitally reconstructing the thermal characteristics of power transmission and transformation equipment for implementing the method for digitally reconstructing the thermal characteristics of power transmission and transformation equipment involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of the device for digitally reconstructing the thermal characteristics of one or more power transmission and transformation equipment provided below can refer to the limitations of the method for digitally reconstructing the thermal characteristics of power transmission and transformation equipment above, and will not be repeated here.

[0143] In one embodiment, Fig.11As shown, a digital reconstruction device 1100 for thermal characteristics of power transmission and transformation equipment is provided, comprising: a variable acquisition module 1101, a temperature acquisition module 1102, a temperature reconstruction module 1103, a temperature mapping module 1104 and a distribution acquisition module 1105, wherein:

[0144] The variable acquisition module 1101 is used to acquire a key variable data set of the power transmission and transformation equipment; the key variable data set includes key variables of the power transmission and transformation equipment under multiple working conditions; the key variables represent variables that affect the temperature distribution of the power transmission and transformation equipment.

[0145] The temperature acquisition module 1102 is used to obtain a temperature field data set of the temperature field of the power transmission and transformation equipment under various key variables according to the multi-physics simulation model of the power transmission and transformation equipment.

[0146] The temperature reconstruction module 1103 is used to obtain an external area temperature dataset and an internal area temperature dataset of the power transmission and transformation equipment based on the temperature field dataset.

[0147] The temperature mapping module 1104 is used to construct a mapping relationship between the key variable data set and the external area temperature data set to obtain a first proxy model, and to construct a mapping relationship between the key variable data set and the internal area temperature data set to obtain a second proxy model.

[0148] The distribution acquisition module 1105 is used to obtain target temperature distribution information of the power transmission and transformation equipment according to the first proxy model and the second proxy model.

[0149] In one embodiment, the temperature mapping module 1104 is also used to perform dimensionality reduction processing on the external area temperature data set to obtain external area temperature reconstruction data; construct a first proxy model based on the implicit relationship between the key variable data set and the external area temperature reconstruction data; and upsample the key variable data set based on the internal area temperature data set to obtain a second proxy model.

[0150] In one embodiment, the distribution acquisition module 1105 is also used to process the first proxy model according to the key variable data set to obtain the temperature information of the external area of ​​the power transmission and transformation equipment; process the second proxy model according to the key variable data set to obtain the temperature information of the internal area of ​​the power transmission and transformation equipment; fuse the temperature information of the external area and the temperature information of the internal area to obtain the target temperature distribution information; the target temperature distribution information is used to characterize the overall temperature distribution of the power transmission and transformation equipment.

[0151] In one embodiment, the variable acquisition module 1101 is also used to establish a multi-physics simulation model of the power transmission and transformation equipment based on the size information and operating condition information of the power transmission and transformation equipment; perform equal-interval scanning processing on the operating condition range of the power transmission and transformation equipment to obtain multiple equally-interval operating condition information; and obtain a key variable data set based on the values ​​of the key variables under each equally-interval operating condition information.

[0152] In one embodiment, the key variables include the ambient temperature, current carrying capacity and degradation degree of the contact fingers of the power transmission and transformation equipment; wherein the degradation degree of the contact fingers is obtained based on the ratio between the actual conductivity of the power transmission and transformation equipment and the conductivity of the power transmission and transformation equipment when the contact fingers are in good condition.

[0153] In one embodiment, the temperature reconstruction module 1103 is also used to determine the temperature data of the external area of ​​the power transmission and transformation equipment and the temperature data of the internal area of ​​the power transmission and transformation equipment from the temperature field data set; extract and process the temperature data of the external area according to the grid node information outside the multi-physics simulation model to obtain the external area temperature data set; interpolate the temperature data of the internal area according to the grid node information inside the multi-physics simulation model to obtain the internal area temperature data set.

[0154] Each module in the above-mentioned digital reconstruction device of thermal characteristics of power transmission and transformation equipment can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0155] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Fig.12As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. 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 input / output interface of the computer device is used to exchange information between the processor and the external device. 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 realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a digital reconstruction method of the thermal characteristics of a power transmission and transformation equipment is realized. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0156] Those skilled in the art will understand that Fig.12 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0157] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0158] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0159] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0160] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0161] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0162] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for digitally reconstructing thermal characteristics of power transmission and transformation equipment, characterized in that: The method comprises: Acquire a key variable data set of a power transmission and transformation equipment; the key variable data set includes key variables of the power transmission and transformation equipment under multiple operating conditions; the key variables represent variables that affect the temperature distribution of the power transmission and transformation equipment; According to the multi-physics simulation model of the power transmission and transformation equipment, a temperature field data set of the temperature field of the power transmission and transformation equipment under each of the key variables is obtained; Based on the temperature field data set, obtaining an external area temperature data set and an internal area temperature data set of the power transmission and transformation equipment; Constructing a mapping relationship between the key variable data set and the external area temperature data set to obtain a first proxy model, and constructing a mapping relationship between the key variable data set and the internal area temperature data set to obtain a second proxy model; According to the first proxy model and the second proxy model, target temperature distribution information of the power transmission and transformation equipment is obtained.

2. The method according to claim 1, characterized in that The step of constructing a mapping relationship between the key variable data set and the external area temperature data set to obtain a first proxy model, and constructing a mapping relationship between the key variable data set and the internal area temperature data set to obtain a second proxy model, includes: Performing dimensionality reduction processing on the external area temperature data set to obtain external area temperature reconstructed data; constructing the first proxy model according to an implicit relationship between the key variable data set and the external region temperature reconstruction data; The key variable data set is upsampled according to the internal area temperature data set to obtain the second proxy model.

3. The method according to claim 1, characterized in that The step of obtaining target temperature distribution information of the power transmission and transformation equipment according to the first proxy model and the second proxy model includes: Processing the first proxy model according to the key variable data set to obtain temperature information of an external area of ​​the power transmission and transformation equipment; Processing the second agent model according to the key variable data set to obtain temperature information of the internal area of ​​the power transmission and transformation equipment; The temperature information of the external area and the temperature information of the internal area are fused to obtain the target temperature distribution information; the target temperature distribution information is used to characterize the overall temperature distribution of the power transmission and transformation equipment.

4. The method according to claim 1, characterized in that: The step of obtaining a key variable data set of power transmission and transformation equipment includes: Establishing a multi-physics simulation model of the power transmission and transformation equipment according to the size information and operating condition information of the power transmission and transformation equipment; Performing equal-interval scanning processing on the operating condition range of the power transmission and transformation equipment to obtain a plurality of equal-interval operating condition information; The key variable data set is obtained according to the values ​​of the key variables under each of the equally spaced operating condition information.

5. The method according to claim 4, characterized in that The key variables include the ambient temperature, current carrying capacity and deterioration degree of the contact fingers of the power transmission and transformation equipment; The degree of degradation of the contact finger is obtained according to the ratio between the actual conductivity of the power transmission and transformation equipment and the conductivity of the power transmission and transformation equipment when the contact finger is in a good state.

6. The method according to claim 1, characterized in that The step of obtaining the external area temperature data set and the internal area temperature data set of the power transmission and transformation equipment based on the temperature field data set includes: Determining temperature data belonging to an external area of ​​the power transmission and transformation equipment and determining temperature data belonging to an internal area of ​​the power transmission and transformation equipment from the temperature field data set; Extracting and processing the temperature data of the external area according to the grid node information outside the multi-physics simulation model to obtain the temperature data set of the external area; According to the grid node information inside the multi-physics simulation model, the temperature data of the internal area is interpolated to obtain the temperature data set of the internal area.

7. A digital reconstruction device for thermal characteristics of power transmission and transformation equipment, characterized in that: The device comprises: A variable acquisition module is used to acquire a key variable data set of a power transmission and transformation equipment; the key variable data set includes key variables of the power transmission and transformation equipment under multiple operating conditions; the key variables represent variables that affect the temperature distribution of the power transmission and transformation equipment; A temperature acquisition module, used to obtain a temperature field data set of the temperature field of the power transmission and transformation equipment under each of the key variables according to the multi-physics simulation model of the power transmission and transformation equipment; A temperature reconstruction module, used to obtain an external area temperature data set and an internal area temperature data set of the power transmission and transformation equipment based on the temperature field data set; A temperature mapping module is used to construct a mapping relationship between the key variable data set and the external area temperature data set to obtain a first proxy model, and to construct a mapping relationship between the key variable data set and the internal area temperature data set to obtain a second proxy model; The distribution acquisition module is used to obtain the target temperature distribution information of the power transmission and transformation equipment according to the first proxy model and the second proxy model.

8. 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 method according to any one of claims 1 to 6 are implemented.

9. 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 method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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