Foster network model-based cable transient temperature prediction method and related device

Abstract

The invention discloses a Foster network model-based cable transient temperature prediction method and a related device. The method comprises the steps of obtaining target cable basic data, actual operation power data, actual environment temperature data and heat dissipation boundary conditions of a target cable; constructing a cable simulation model based on the basic data of the target cable; controlling the cable simulation model to perform transient simulation according to the actual operation power data, the actual environment temperature data and the heat dissipation boundary conditions to obtain a target simulation temperature curve; a target Foster network model is constructed; performing transient temperature prediction through the target Foster network model to obtain a target prediction temperature curve; target model precision is determined according to the target simulation temperature curve and the target prediction temperature curve; and when the target model precision is greater than the preset precision, determining the transient temperature of the target cable according to the target predicted temperature curve. By adopting the embodiment of the invention, the prediction error of the transient temperature is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of cable temperature prediction, and in particular to a cable transient temperature prediction method and related devices based on a Foster network model. Background Art

[0002] Cable transient temperature prediction is a key technology for safe power system operation and maintenance, as well as dynamic current-carrying capacity assessment. Traditional methods, primarily based on internationally standardized thermal circuit models, simplify the cable into a single-core equivalent structure and estimate temperature using steady-state or quasi-steady-state thermal resistance-capacitance (RC) networks. However, with increasing grid load fluctuations, the increasing proportion of renewable energy connected to the grid, and the increasing complexity of cable installation environments, the limitations of traditional methods are becoming increasingly apparent.

[0003] Currently, the problem with traditional methods is that the model is over-simplified and ignores the electromagnetic-thermal coupling effect and interlayer heat transfer differences of multi-core cables, resulting in large transient temperature prediction errors (especially under short-term overload conditions). Therefore, how to reduce the transient temperature prediction error has become an urgent problem to be solved. Summary of the Invention

[0004] The embodiments of the present application provide a cable transient temperature prediction method and related devices based on the Foster network model, which reduce the prediction error of transient temperature.

[0005] In a first aspect, an embodiment of the present application provides a cable transient temperature prediction method based on a Foster network model, comprising:

[0006] Obtain target cable basic data, actual operating power data, actual ambient temperature data and heat dissipation boundary conditions of the target cable;

[0007] Constructing a cable simulation model based on the target cable basic data;

[0008] Controlling the cable simulation model to perform transient simulation according to the actual operating power data, the actual ambient temperature data, and the heat dissipation boundary conditions to obtain a target simulation temperature curve; the abscissa of the target simulation temperature curve is time, and the ordinate is the temperature of the cable;

[0009] Constructing a target Foster network model according to the target simulation temperature curve and the target cable basic data;

[0010] Controlling the target Foster network model to perform transient temperature prediction according to the actual operating power data and the actual ambient temperature data to obtain a target predicted temperature curve; the abscissa of the target predicted temperature curve is time, and the ordinate is the temperature of the cable;

[0011] Determining a target model accuracy of the target Foster network model according to the target simulation temperature curve and the target predicted temperature curve;

[0012] When the target model accuracy is greater than a preset accuracy, the transient temperature of the target cable is determined according to the target predicted temperature curve.

[0013] In a second aspect, an embodiment of the present application provides a cable transient temperature prediction device based on a Foster network model, the device comprising: an acquisition unit and a model prediction unit, wherein:

[0014] The acquisition unit is used to acquire the target cable basic data, actual operating power data, actual ambient temperature data and heat dissipation boundary conditions of the target cable;

[0015] The model prediction unit is used to construct a cable simulation model based on the target cable basic data; control the cable simulation model to perform transient simulation according to the actual operating power data, the actual ambient temperature data and the heat dissipation boundary conditions to obtain a target simulation temperature curve; the horizontal coordinate of the target simulation temperature curve is time, and the vertical coordinate is the temperature of the cable; construct a target Foster network model according to the target simulation temperature curve and the target cable basic data; control the target Foster network model to perform transient temperature prediction according to the actual operating power data and the actual ambient temperature data to obtain a target predicted temperature curve; the horizontal coordinate of the target predicted temperature curve is time, and the vertical coordinate is the temperature of the cable; determine the target model accuracy of the target Foster network model according to the target simulation temperature curve and the target predicted temperature curve; when the target model accuracy is greater than the preset accuracy, determine the transient temperature of the target cable according to the target predicted temperature curve.

[0016] In a third aspect, an embodiment of the present application provides an electronic device comprising: a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps in the first aspect of the embodiment of the present application.

[0017] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the above-mentioned computer-readable storage medium stores a computer program for electronic data exchange, wherein the above-mentioned computer program enables a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application.

[0018] In a fifth aspect, embodiments of the present application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is operable to cause a computer to perform some or all of the steps described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package.

[0019] The implementation of this application has the following beneficial effects:

[0020] It can be seen that the cable transient temperature prediction method based on the Foster network model described in this application utilizes the high precision of the finite element simulation model and the rapidity of the Foster network model, and calibrates the Foster model parameters by comparing the temperature curves of the two to improve the prediction accuracy of the target Foster network model. Then, the transient temperature of the target cable is predicted by the target Foster network model, effectively reducing the prediction error of the transient temperature. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background technology, the drawings required for use in the embodiments of the present application or the background technology will be described below.

[0022] Figure 1 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application;

[0023] Figure 2 This is a scenario application diagram of an electronic device provided in an embodiment of the present application;

[0024] Figure 3 This is a flow chart of a cable transient temperature prediction method based on a Foster network model provided in an embodiment of the present application;

[0025] Figure 4 This is a schematic structural diagram of a target cable provided in an embodiment of the present application;

[0026] Figure 5 This is a schematic diagram of the structure of a target Foster network model provided in an embodiment of the present application;

[0027] Figure 6 1 is a schematic diagram comparing a target simulation temperature curve and a target prediction temperature curve provided in an embodiment of the present application;

[0028] Figure 7 This is a block diagram of the functional units of a cable transient temperature prediction device based on a Foster network model provided in an embodiment of the present application;

[0029] Figure 8It is a structural diagram of another electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0031] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0032] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document indicates that the associated objects are in an "or" relationship. The "plurality" appearing in the embodiments of this application refers to two or more.

[0033] In the embodiments of the present application, "at least one item" or similar expressions refers to any combination of these items, including any combination of single items or plural items, and refers to one or more, and multiple refers to two or more. For example, at least one item (item) of a, b, or c can represent the following seven situations: a, b, c, a and b, a and c, b and c, a, b, and c. Among them, each of a, b, and c can be an element or a set containing one or more elements.

[0034] The "connection" appearing in the embodiments of the present application refers to various connection methods such as direct connection or indirect connection to achieve communication between devices, and the embodiments of the present application do not impose any limitations on this.

[0035] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0036] The electronic devices described in the embodiments of the present application may include smart phones (such as Android phones, iOS phones, Windows Phone phones, etc.), tablet computers, PDAs, laptops, video matrices, monitoring platforms, mobile internet devices (MIDs) or wearable devices, etc. The above are only examples and not exhaustive, including but not limited to the above devices.

[0037] Of course, the above-mentioned electronic device can also be a server, for example, a cloud server.

[0038] The following describes the relevant contents, concepts, meanings, technical issues, technical solutions, beneficial effects, etc. involved in the embodiments of this application.

[0039] First, some professional terms involved in this application are explained:

[0040] Cable simulation model: A virtual model constructed by abstracting and simplifying the cable's physical structure, material properties (such as conductor, insulation layer, and sheath), and heat conduction process through mathematical modeling or finite element analysis. It simulates the temperature distribution and variation of the cable under different operating conditions (such as current, ambient temperature, and heat dissipation), providing data support for cable thermal characteristic analysis.

[0041] Foster network model: An equivalent circuit model used for thermal system modeling. It compares the heat conduction process to the current and voltage in a circuit, and uses series or parallel networks of thermal resistance (R) and thermal capacitance (C) to represent the heat transfer path and energy storage characteristics. Common models include the Foster network (multi-order RC series structure) and the Cauer network (multi-order RC parallel structure). The former is more convenient for fitting transient thermal responses.

[0042] Cable transient temperature: The temperature value at each moment when the internal temperature of the cable changes dynamically over time when the operating conditions (such as load power, ambient temperature) change, which is different from the steady-state temperature during stable operation.

[0043] Thermal resistance: A physical quantity that measures the resistance to heat transfer, expressed in degrees Celsius per watt (°C / W). The greater the thermal resistance, the greater the temperature drop as heat passes through a material or interface, meaning the heat transfer efficiency is lower.

[0044] Heat capacity: The ability of an object to store heat, measured in J / °C (joules per degree Celsius), which represents the amount of heat required to raise the object's temperature by 1°C. The greater the heat capacity, the slower the object's temperature changes, i.e., the greater its thermal inertia.

[0045] Levenberg-Marquardt algorithm (LM algorithm): An iterative algorithm for nonlinear least squares optimization that combines the advantages of gradient descent method (steepest descent method) and Gauss-Newton method and is suitable for solving parameter fitting problems.

[0046] COMSOL Multiphysics software (COMSOL software): is a multiphysics simulation and analysis software widely used in engineering, scientific research, and other fields. It can simulate various physical phenomena and their interactions through numerical calculation methods, helping users solve complex physical field problems. In cable heat dissipation analysis, the software can couple "heat conduction" and "fluid flow" fields to simulate the effects of ambient temperature and heat dissipation boundary conditions.

[0047] See also Figure 1 , Figure 1 : is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application; it can be seen that the electronic device may include: a communication module, a control module, a prediction module, etc., which are not limited here, wherein:

[0048] The communication module is used for data acquisition. For example, it can obtain the target cable's basic data (such as structural parameters and material thermal conductivity), actual operating power data (current and voltage), actual ambient temperature data (real-time values ​​from environmental sensors), and heat dissipation boundary conditions (such as wind speed and soil thermal resistance) through Ethernet, serial ports (such as RS-485), or industrial buses (such as Modbus and CANopen). In addition, the communication module can synchronize multi-source data, connecting to the SCADA (Supervisory Control and Data Acquisition) system or IoT sensor network corresponding to the target cable, thereby ensuring the consistency of timestamps for real-time data such as power and temperature, and avoiding prediction errors caused by data delays.

[0049] The control module is used to build a cable simulation model and a target Foster network model based on the basic data of the target cable, laying the foundation for the subsequent prediction process.

[0050] The prediction module is used to predict the transient temperature of the target cable by using the target Foster network model to obtain a prediction result.

[0051] It should be explained that the above-mentioned electronic device can execute part or all of the steps of the cable transient temperature prediction method based on the Foster network model provided in the embodiment of the present application.

[0052] See also Figure 2 , Figure 2 This is a scenario application diagram of an electronic device provided in an embodiment of the present application. It can be seen that the electronic device can perform bidirectional communication with the target cable. When it is necessary to predict the transient temperature of the target cable, the electronic device can obtain various data of the target cable (for example, power data, temperature data, etc.), and based on these data, execute the cable transient temperature prediction method based on the Foster network model provided in an embodiment of the present application to predict the transient temperature of the target cable, as follows:

[0053] Obtain target cable basic data, actual operating power data, actual ambient temperature data and heat dissipation boundary conditions of the target cable;

[0054] Constructing a cable simulation model based on the target cable basic data;

[0055] Controlling the cable simulation model to perform transient simulation according to the actual operating power data, the actual ambient temperature data, and the heat dissipation boundary conditions to obtain a target simulation temperature curve; the abscissa of the target simulation temperature curve is time, and the ordinate is the temperature of the cable;

[0056] Constructing a target Foster network model according to the target simulation temperature curve and the target cable basic data;

[0057] Controlling the target Foster network model to perform transient temperature prediction according to the actual operating power data and the actual ambient temperature data to obtain a target predicted temperature curve; the abscissa of the target predicted temperature curve is time, and the ordinate is the temperature of the cable;

[0058] Determining a target model accuracy of the target Foster network model according to the target simulation temperature curve and the target predicted temperature curve;

[0059] When the target model accuracy is greater than a preset accuracy, the transient temperature of the target cable is determined according to the target predicted temperature curve.

[0060] See also Figure 3 , Figure 3 : This is a flow chart of a cable transient temperature prediction method based on a Foster network model provided in an embodiment of the present application. The method may include the following steps:

[0061] S301: Obtain target cable basic data, actual operating power data, actual ambient temperature data, and heat dissipation boundary conditions of a target cable.

[0062] In an embodiment of the present application, a cable design document of the target cable can be obtained, and basic cable data can be queried from the cable design document to obtain basic data of the target cable. Alternatively, the target cable can be disassembled and measured to obtain basic data of the target cable. For example, a vernier caliper can be used to measure the thickness of each layer, and the material composition of the cable can be determined by spectral analysis or material testing equipment. Then, a power monitoring device (for example, a current transformer, a voltage transformer) can be set on the target cable, and the current and voltage of the cable loop can be collected in real time by the power monitoring device to obtain current data and voltage data. Then, the power of the cable can be calculated based on the current data and voltage data to obtain actual operating power data. Similarly, the temperature of the target cable can be detected by a temperature monitoring device (for example, a temperature sensor) to obtain actual ambient temperature data. Finally, the environmental data around the target battery can be obtained, and the heat dissipation boundary conditions can be determined based on the basic data and environmental data of the target cable. For example, the heat dissipation boundary conditions can be: the boundary conditions at both ends of the cable are thermal insulation, and the boundary conditions of the cable skin are convection heat transfer.

[0063] S302: Construct a cable simulation model based on the target cable basic data.

[0064] In the embodiment of the present application, various characteristics of the target cable can be determined based on the basic data of the target cable, thereby constructing a cable simulation model of the target cable.

[0065] Optionally, the target cable includes an m-layer structure, where m is a positive integer, and the target cable basic data includes: target cable type, component material data of the target cable, and target structure data of the target cable; and constructing a cable simulation model based on the target cable basic data may include the following steps:

[0066] S21, determining the simulation boundary conditions of the target cable according to the target cable type;

[0067] S22, constructing a reference cable simulation model according to the target structure data;

[0068] S23, determining the material parameters corresponding to each structure in the m-layer structure according to the constituent material data, to obtain m material parameters;

[0069] S24. Adjust the reference cable simulation model according to the m material parameters and the simulation boundary conditions to obtain the cable simulation model.

[0070] In the embodiment of the present application, the target cable type may include one of the following: a directly buried cable, a pipe-laid cable, an air-laid cable, etc., which is not limited here.

[0071] In a specific embodiment, the simulation boundary conditions of the target cable can be determined according to the target cable type. Specifically, a mapping relationship between preset cable types and boundary conditions can be pre-stored, and the simulation boundary conditions corresponding to the target cable type can be determined based on the mapping relationship. For example, assuming that the target cable type is a pipeline-laid cable, the corresponding simulation boundary conditions are: the thermal resistance of the pipeline material, the convection heat dissipation coefficient of the air in the pipeline, and the heat exchange efficiency between the pipeline and the surrounding environment.

[0072] Optional, see Figure 4 , Figure 4 It is a structural schematic diagram of a target cable provided in an embodiment of the present application; it can be seen that the target cable includes: a conductor, a conductor shielding layer, an insulating layer, an insulating shielding layer, a filling layer, an inner sheath, an armor layer, and an outer sheath; among them, since the main function of the conductor is to transmit current, its heat generation can be regarded as an overall heat source in thermal analysis. Although the conductor has a certain heat capacity and thermal resistance, its thermal characteristics are relatively concentrated and simple compared with other layers. Therefore, it is not considered as a separate layer in this application, that is, m=7, and the m-layer structures are: a conductor shielding layer, an insulating layer, an insulating shielding layer, a filling layer, an inner sheath, an armor layer, and an outer sheath.

[0073] Then, a reference cable simulation model can be constructed according to the target structure data. Specifically, the reference cable simulation model can be constructed according to the target structure data by COMSOL software. For example, the thickness, diameter and cross-sectional layout of each layer of the target cable (such as three cores arranged in an equilateral triangle or in a line) can be determined according to the target structure data. For example, assuming that the conductor diameter is 10 mm, the conductor shielding layer thickness is 0.5 mm, the insulation layer thickness is 5 mm, the insulation shielding layer thickness is 0.3 mm, the filling layer outer diameter is 35 mm, the inner sheath thickness is 2 mm, the armor layer thickness is 1.5 mm, and the outer sheath thickness is 3 mm, COMSOL software can generate a reference cable simulation model based on these data; then, the material parameters corresponding to each structure in the m-layer structure can be determined according to the constituent material data to obtain m material parameters. For example, the material parameters corresponding to the conductor are copper / aluminum, the material parameters corresponding to the conductor shielding layer are semiconductor water-resistant tape, etc., which are not specifically limited here.

[0074] The reference cable simulation model can be adjusted according to m material parameters and simulation boundary conditions to obtain a cable simulation model. Specifically, the COMSOL software has an "Add Material" function key, through which materials or customized properties can be specified for each layer structure in the reference cable simulation model. For example, the conductor layer can be selected and "copper" or "aluminum" material can be added. In addition, property parameters such as thermal conductivity, specific heat capacity, and density can also be customized to obtain a cable simulation model.

[0075] It should be explained that, in the embodiment of the present application, the target cable may be a three-core cable, and the cable simulation model is a three-dimensional geometric model of the three-core cable including the above-mentioned m-layer structure.

[0076] In this way, the boundary conditions are locked by "cable type" (for example, overhead cables need to consider air convection, and direct buried cables need to consider soil thermal resistance), and then the geometric model is built based on the "structural data". Finally, the physical properties are filled with "material parameters" - this "boundary → structure → material" sequence conforms to the analysis logic of engineering problems from macroscopic scenarios to microscopic parameters, avoiding the disconnect between requirements and implementation during the modeling process. For example, if the target cable is a high-voltage submarine cable, its boundary conditions must include seawater convection heat dissipation (boundary conditions), the armor layer's water pressure resistance structure (structural data), and the outer sheath's seawater corrosion resistance material (material parameters). The layered design ensures that each link focuses on the constraints of a specific dimension to avoid confusion.

[0077] S303. Control the cable simulation model to perform transient simulation according to the actual operating power data, the actual ambient temperature data and the heat dissipation boundary conditions to obtain a target simulation temperature curve; the abscissa of the target simulation temperature curve is time, and the ordinate is the cable temperature.

[0078] In an embodiment of the present application, actual operating power data, actual ambient temperature data and heat dissipation boundary conditions can be input into a cable simulation model to perform transient simulation, thereby obtaining a target simulation temperature curve.

[0079] Optionally, step S303, controlling the cable simulation model to perform transient simulation according to the actual operating power data, the actual ambient temperature data, and the heat dissipation boundary condition to obtain a target simulation temperature curve, may include the following steps:

[0080] A1. Determine the target convective heat transfer coefficient according to the heat dissipation boundary conditions;

[0081] A2. Obtain the preset step size;

[0082] A3. Inputting the actual operating power data, the actual ambient temperature data, and the target convective heat transfer coefficient into the cable simulation model, driving the cable simulation model to perform transient simulation, and collecting temperature values ​​of the cable simulation model according to the preset step size to obtain multiple temperature values;

[0083] A4. Perform curve fitting according to each temperature value of the multiple temperature values ​​and the acquisition time corresponding to each temperature value to obtain the target simulation temperature curve.

[0084] In the embodiment of the present application, the preset step size may be preset in advance or defaulted, for example, the preset step size may be 0.1 seconds.

[0085] In a specific embodiment, the target convective heat transfer coefficient can be determined first according to the heat dissipation boundary conditions; then, a preset step size can be obtained; further, the actual operating power data, the actual ambient temperature data and the target convective heat transfer coefficient can be input into the cable simulation model, the cable simulation model can be driven to perform transient simulation, and the temperature value of the cable simulation model can be collected according to the preset step size to obtain multiple temperature values. Specifically, a transient solver can be selected in the COMSOL software, and the total simulation time (for example, 24 hours) can be determined according to the simulation requirements. Then, the actual operating power data (such as the current curve that changes with time) can be imported into the COMSOL software, and the power-time relationship can be defined by the "function" function of the COMSOL software to obtain the power function P(t). For example, if the cable is lightly loaded (50A) from 0 to 20 hours and overloaded (100A) from 20 to 24 hours, it needs to be segmented. Define the current curve; similarly, the actual ambient temperature data and the target convective heat transfer coefficient can be imported to drive the cable simulation model for transient simulation. During the simulation process, the transient solver can discretize the equations through the finite element method, iteratively solve the temperature value of the cable in each time step, and obtain multiple temperature values. Alternatively, the transient solver can use an automatic time step (such as the BDF method) instead of a fixed step size (i.e., a preset step size). The COMSOL software will automatically adjust the step size according to the temperature change gradient and encrypt the calculation when the temperature changes drastically. For example, if the temperature changes drastically, a short time step (such as 1 second) is used to capture the temperature transient at the moment of load mutation (such as the temperature rise under the starting current shock) to avoid losing peak data due to too large a sampling interval; if the temperature change is not large, a long time step (such as 30 minutes) is used to monitor the temperature in the steady state stage to reduce redundant calculations.

[0086] Finally, curve fitting can be performed based on each temperature value in the multiple temperature values ​​and the acquisition time corresponding to each temperature value to obtain the target simulation temperature curve. Specifically, each temperature value and its corresponding acquisition time can be combined to obtain multiple coordinate points. Then, a curve fitting method (for example, polynomial fitting method, spline method, etc.) can be used to fit these multiple coordinate points to obtain the target simulation temperature curve.

[0087] In this way, by performing curve fitting on the collected temperature-time discrete points, the numerical calculation noise (such as temperature fluctuations caused by grid iteration errors) can be effectively eliminated, and a smooth target simulation temperature curve can be obtained, which is convenient for the subsequent model accuracy evaluation of the target Foster network model.

[0088] Optionally, step A1, determining the target convective heat transfer coefficient according to the heat dissipation boundary condition, may include the following steps:

[0089] B1. determining a first convection heat transfer coefficient corresponding to the target cable according to basic data of the target cable;

[0090] B2. Obtaining a target laying method corresponding to the target cable;

[0091] B3. Determine a calculation formula for the convective heat transfer coefficient based on the target installation method and the heat dissipation boundary conditions;

[0092] B4. Calculating according to the target cable basic data and the convection heat transfer coefficient calculation formula to obtain a second convection heat transfer coefficient;

[0093] B5. Acquire the target position of the target cable;

[0094] B6. Determine heat sources within a preset distance around the target location to obtain a heat sources, where a is a natural number;

[0095] B7. Determine the target radiation heat transfer coefficient corresponding to the a heat source;

[0096] B8. determining a third convective heat transfer coefficient based on the target radiation heat transfer coefficient and the second convective heat transfer coefficient;

[0097] B9. Determine the target convective heat transfer coefficient based on the first convective heat transfer coefficient and the third convective heat transfer coefficient.

[0098] In an embodiment of the present application, the target laying method may include one of the following: underground laying, overhead laying, laying in a special environment (for example, underwater laying), etc., which are not limited here; the preset distance can be preset or defaulted in advance. For example, according to the cable heat dissipation analysis requirements, the preset distance is usually 1 to 10 meters (for example, assuming that the underground cable is buried at a depth of 1.2 meters, a radius of 5 meters can be set).

[0099] In a specific embodiment, the first convective heat transfer coefficient corresponding to the target cable can be determined based on the basic data of the target cable. Specifically, the basic data of the target cable may include the target cable type. The first convective heat transfer coefficient can be determined based on the target cable type. For example, a mapping relationship between a preset cable type and a convective heat transfer coefficient can be pre-stored, and the first convective heat transfer coefficient corresponding to the target cable type is determined based on the mapping relationship. Then, the target laying method corresponding to the target cable can be obtained. Specifically, the target usage scenario of the target cable can be obtained. Then, the target laying method can be determined based on the target usage scenario. For example, a mapping relationship between a preset usage scenario and a laying method can be pre-stored, and the target laying method corresponding to the target usage scenario is determined based on the mapping relationship. Alternatively, the target cable can be photographed by a camera to obtain a cable image, and the cable image can be subjected to image recognition to thereby determine the target laying method.

[0100] Next, the convective heat transfer coefficient calculation formula can be determined according to the target laying method and the heat dissipation boundary conditions. Specifically, the reference convective heat transfer coefficient calculation formula can be determined according to the target laying method. Then, the reference convective heat transfer coefficient calculation formula can be modified based on the heat dissipation boundary conditions to obtain the convective heat transfer coefficient calculation formula. For example, assuming that the target laying method is underground laying, the cable can be regarded as an infinitely long cylinder, and the reference convective heat transfer coefficient calculation formula is as follows:

[0101]

[0102] Among them, h ref Expressed as the convection heat transfer coefficient, h is the cable buried depth, the cable outer radius is r, λ soil is the thermal conductivity of soil;

[0103] Assume that the heat dissipation boundary condition is: the boundary condition of the cable skin is convective heat transfer; the reference convective heat transfer coefficient calculation formula assumes that the cable is in perfect contact with the soil (no contact thermal resistance), and does not consider the contact thermal resistance (caused by micro-roughness). The preset thermal resistance correction factor β can be introduced into the reference convective heat transfer coefficient calculation formula to obtain the convective heat transfer coefficient calculation formula, which is as follows:

[0104]

[0105] Furthermore, the second convective heat transfer coefficient can be obtained by calculation based on the basic data of the target cable and the calculation formula of the convective heat transfer coefficient. Specifically, multiple parameters required for the calculation of the convective heat transfer coefficient can be extracted from the basic data of the target cable, and these multiple parameters can be brought into the calculation formula of the convective heat transfer coefficient for calculation to obtain the second convective heat transfer coefficient. For example, the cable burial depth and the outer radius of the target cable can be obtained from the basic data of the target cable, and these data can be brought into the calculation formula of the convective heat transfer coefficient for calculation to obtain the second convective heat transfer coefficient; then, the target position of the target cable can be obtained. Specifically, a position monitoring sensor can be set on the target cable, and the position of the target cable can be monitored in real time by the position monitoring sensor, thereby obtaining the target position.

[0106] Then, the heat sources within a preset distance around the target location can be determined to obtain a heat sources. Specifically, the power system design diagram around the target location can be obtained, and all possible heat sources can be marked in the power system design diagram to obtain a heat sources. For example, in a power plant, transformers, air-conditioning outdoor units, heating equipment, etc. near the target cable can be identified; then, the target radiation heat transfer coefficient corresponding to the a heat source can be determined; then, the third convective heat transfer coefficient can be determined based on the target radiation heat transfer coefficient and the second convective heat transfer coefficient. Specifically, the target radiation heat transfer coefficient and the second convective heat transfer coefficient can be directly added to obtain the third convective heat transfer coefficient; finally, the target convective heat transfer coefficient can be determined based on the first convective heat transfer coefficient and the third convective heat transfer coefficient.

[0107] In this way, by screening surrounding heat sources at a preset distance, the qualitative heat source impact is converted into a quantitative radiation heat transfer coefficient, breaking the simplified assumption of ignoring surrounding heat sources in traditional calculations. This is particularly suitable for complex scenarios such as industrial plants and urban pipeline corridors. For example, the radiation heat dissipation of steam pipes next to cables will increase the surface temperature of the cables. By calculating its radiation heat transfer coefficient, the contribution of this impact to the overall convective heat transfer coefficient can be quantified.

[0108] Optionally, step B9, determining the target convective heat transfer coefficient according to the first convective heat transfer coefficient and the third convective heat transfer coefficient, may include the following steps:

[0109] C1. determining a deviation between the first convective heat transfer coefficient and the third convective heat transfer coefficient to obtain a target deviation;

[0110] C2. When the target deviation is less than a preset deviation, determining a first weight corresponding to the first convective heat transfer coefficient and a second weight corresponding to the third convective heat transfer coefficient; the sum of the first weight and the second weight is 1; and determining the target convective heat transfer coefficient based on the first weight, the second weight, the first convective heat transfer coefficient, and the third convective heat transfer coefficient;

[0111] C3. When the target deviation is not less than the preset deviation, determine the target convective heat transfer coefficient range corresponding to the target cable according to the basic data of the target cable; determine the middle value of the target convective heat transfer coefficient range; determine a first deviation between the first convective heat transfer coefficient and the middle value; determine a second deviation between the third convective heat transfer coefficient and the middle value; determine the smaller deviation between the first deviation and the second deviation to obtain a third deviation; determine the convective heat transfer coefficient corresponding to the third deviation as the target convective heat transfer coefficient.

[0112] In the embodiment of the present application, the preset deviation can be preset in advance or defaulted.

[0113] In a specific embodiment, the deviation between the first convective heat transfer coefficient and the third convective heat transfer coefficient may be calculated as follows:

[0114] Target deviation = |first convection heat transfer coefficient - third convection heat transfer coefficient| / third convection heat transfer coefficient × 100%;

[0115] According to the above formula, the target deviation can be obtained; when the target deviation is less than the preset deviation, the first weight corresponding to the first convective heat transfer coefficient (for example, 0.4) and the second weight corresponding to the third convective heat transfer coefficient (for example, 0.6) can be determined; then, a weighted operation can be performed based on the first weight, the second weight, the first convective heat transfer coefficient and the third convective heat transfer coefficient to obtain the target convective heat transfer coefficient.

[0116] When the target deviation is not less than the preset deviation, the target convective heat transfer coefficient range corresponding to the target cable can be determined based on the basic data of the target cable. Specifically, the component material data of the target cable can be determined based on the basic data of the target cable, and the target convective heat transfer coefficient range can be determined based on the component material data. For example, a mapping relationship between preset material data and convective heat transfer coefficient ranges can be pre-stored, and the target convective heat transfer coefficient range corresponding to the component material data can be determined based on the mapping relationship; then, the middle value of the target convective heat transfer coefficient range can be determined; further, a first deviation between the first convective heat transfer coefficient and the middle value can be determined, specifically as follows:

[0117] First deviation = |first convective heat transfer coefficient - middle value| / middle value × 100%;

[0118] According to the above formula, the first deviation can be obtained; then, the second deviation of the third convective heat transfer coefficient from the intermediate value can be calculated, and the calculation method is the same as the calculation method of the first deviation; then, the smaller deviation between the first deviation and the second deviation can be found to obtain the third deviation; the convective heat transfer coefficient corresponding to the third deviation is determined as the target convective heat transfer coefficient.

[0119] In this way, when the target deviation is less than the preset deviation, the two results are combined through weights to avoid amplifying errors in a single data point and improve calculation stability through weighted averaging. When the target deviation is not less than the preset deviation, the reasonable range determined by the basic cable data is used as a benchmark. By comparing the median value with the deviation, the coefficient closer to the theoretical range is selected to prevent abnormal data from dominating the results.

[0120] Optionally, step B7, determining the target radiation heat transfer coefficient corresponding to the a heat sources, may include the following steps:

[0121] D1. Determine the radiation heat transfer coefficient of each of the a heat sources to obtain a radiation heat transfer coefficients;

[0122] D2. Obtaining a target dirt area on the target cable surface;

[0123] D3. Determine the target fine-tuning coefficient corresponding to the target dirt area;

[0124] D4. Obtain the position of each of the a heat sources to obtain a positions;

[0125] D5. Determine the distance between each of the a positions and the target position to obtain a distances;

[0126] D6. Determine the weight corresponding to each of the a distances to obtain a weights;

[0127] D7. Determine a reference radiation heat transfer coefficient based on the a weights and the a radiation heat transfer coefficients;

[0128] D8. Adjust the reference radiation heat transfer coefficient according to the target fine-tuning coefficient to obtain the target radiation heat transfer coefficient.

[0129] In an embodiment of the present application, the radiation heat transfer coefficient of each of a heat sources can be determined to obtain a radiation heat transfer coefficients. Specifically, a preset radiation heat transfer coefficient calculation formula can be obtained, and the radiation heat transfer coefficients of a heat sources can be calculated based on the radiation heat transfer coefficient calculation formula to obtain a radiation heat transfer coefficients. Then, the target contamination area on the surface of the target cable can be obtained. Specifically, the surface of the target cable can be detected by an infrared sensor to obtain temperature distribution data. Since contaminants (such as dust and oil) usually cause abnormal surface temperature due to their thermal resistance characteristics, the temperature abnormality area of ​​the target cable can be identified based on the temperature distribution data, and the area of ​​the temperature abnormality area, that is, the target contamination area, can be determined.

[0130] Next, the target fine-tuning coefficient corresponding to the target dirt area can be determined. For example, a mapping relationship between a preset dirt area and the fine-tuning coefficient can be pre-stored, and the target fine-tuning coefficient corresponding to the target dirt area can be determined based on the mapping relationship, wherein the value range of the target fine-tuning coefficient can be -0.25 to 0.25; further, the position of each heat source in the a heat sources can be obtained to obtain a positions. Specifically, the positions of the a heat sources can be found from the power system design diagram to obtain a positions.

[0131] Then, the distance between each of the a positions and the target position can be calculated to obtain a distance; then, the weight corresponding to each of the a distances can be determined to obtain a weight. Specifically, a mapping relationship between preset distances and weights can be pre-stored, and the a weights corresponding to the a distances can be determined based on the mapping relationship; then, a weighted operation can be performed based on the a weights and the a radiation heat transfer coefficients to obtain a reference radiation heat transfer coefficient; finally, the reference radiation heat transfer coefficient can be adjusted according to the target fine-tuning coefficient, as follows:

[0132] Target radiation heat transfer coefficient = reference radiation heat transfer coefficient × (1 + target fine-tuning coefficient);

[0133] According to the above formula, the target radiation heat transfer coefficient can be obtained.

[0134] In this way, by assigning weights based on the distance between the heat source and the target position (the closer the distance, the higher the weight), the physical law of radiation heat transfer that "the closer the distance, the greater the impact" is complied with, avoiding treating far-field heat sources and near-field heat sources equally, making the calculation more in line with the actual scenario.

[0135] S304: Construct a target Foster network model according to the target simulation temperature curve and the target cable basic data.

[0136] Optionally, constructing a target Foster network model according to the target simulation temperature curve and the target cable basic data may include the following steps:

[0137] S41, determining the network order corresponding to the target Foster network model according to the m-layer structure, and obtaining a target network order n, where n=m+1;

[0138] S42: Construct a thermal response expression of the target Foster network model based on the target network order; the thermal response expression is as follows:

[0139]

[0140] Wherein, T(t) represents the predicted temperature value of the target Foster network model at time t, T0 represents the initial ambient temperature value of the target cable; P represents the input power of the target cable; R i represents the thermal resistance of the i-th order in the target Foster network model; C i represents the heat capacity of the i-th order in the target Foster network model; e represents a natural constant; t represents time;

[0141] S43, constructing an objective function according to the target simulation temperature curve; the objective function is specifically as follows:

[0142]

[0143] Wherein, F(x) represents the value of the objective function, x represents the parameter vector to be optimized, and the parameter vector to be optimized includes all thermal resistances and all thermal capacities in the target Foster network model; y j represents the temperature value corresponding to the jth time point in the target simulation temperature curve; f(x,t j ) represents the calculated value of the thermal response expression T(t) at the jth time point; p represents the total number of time points corresponding to the target simulation temperature curve;

[0144] S44, solving the objective function according to a preset optimization target and a preset solution algorithm to obtain n groups of model parameters; each group of model parameters includes a thermal resistance and a heat capacity;

[0145] S45. Determine the target Foster network model according to the n groups of model parameters and the thermal response expression.

[0146] In the embodiment of the present application, the preset optimization target and the preset solution algorithm can be preset or defaulted in advance.

[0147] In a specific embodiment, the network order corresponding to the target Foster network model may be determined according to the m-layer structure to obtain the target network order n, where n=m+1.

[0148] It should be explained that because the target cable has an m-layer structure, the target Foster network model uses an "RC network" (resistance-capacitance series branch) to characterize the distribution characteristics of the system's thermal resistance and heat capacity. For multi-layer structures, the thermal resistance and heat capacity of each layer usually correspond to an RC branch. However, in actual modeling, boundary conditions and the integrity of the overall heat transfer path must be considered. When the cable has clear "heat input boundaries" and "heat output boundaries," the model order is often "the number of physical layers of the cable + 1." For example, the seven-layer structure of the cable can be considered as the heat transfer path from the conductor (heat source) to the external environment, and the additional order can correspond to the convection / radiation heat transfer boundary between the outermost layer (outer sheath) and the environment, or be used to characterize the equivalent boundary heat capacity in the overall heat transfer process. Therefore, n = m + 1.

[0149] Next, the thermal response expression of the target Foster network model can be constructed based on the target network order; the thermal response expression is as follows:

[0150]

[0151] Where T(t) represents the predicted temperature value of the target Foster network model at time t, T0 represents the initial ambient temperature value of the target cable; P represents the input power of the target cable; Ri represents the thermal resistance of the i-th order in the target Foster network model; C i represents the heat capacity of the i-th order in the target Foster network model; e represents a natural constant; t represents time;

[0152] Next, the objective function can be constructed based on the target simulation temperature curve; the objective function is as follows:

[0153]

[0154] Where F(x) represents the value of the objective function, x represents the parameter vector to be optimized, and the parameter vector to be optimized includes all thermal resistances and all thermal capacities in the target Foster network model; y j represents the temperature value corresponding to the target simulation temperature curve at the jth time point; f(x,t j ) represents the calculated value of the thermal response expression T(t) at the jth time point; p represents the total number of time points corresponding to the target simulation temperature curve;

[0155] It needs to be explained that x=(R1,C1,…,R k ,C k ,…,R n ,C n ); where R k Indicates the kth order thermal resistance, C k represents the kth-order heat capacity, where k is a positive integer less than n.

[0156] Then, the objective function can be solved according to the preset optimization goal and the preset solution algorithm to obtain n groups of model parameters, wherein the preset optimization goal can be: making the predicted temperature value of the target Foster network model as close as possible to the simulated temperature value of the cable simulation model, or ensuring that the fitting residual of the target Foster network model is less than 1°C; the preset solution algorithm can be the LM algorithm; finally, the target Foster network model can be determined according to the n groups of model parameters and the thermal response expression. Specifically, the n groups of model parameters can be substituted into the thermal response expression to obtain the target Foster network model.

[0157] Optional, see Figure 5 , Figure 5 is a structural diagram of a target Foster network model provided in an embodiment of the present application, Figure 5 A network structure consisting of multiple RC (thermal resistance-heat capacity) units in series is shown in FIG. In this application, there are 8 RC units. Figure 5 C2 and C nThe dotted line between the two indicates that there are other RC units between them. For simplicity, not all of them are drawn. Each RC unit represents a step in the heat conduction process. By combining multiple such units, complex thermal dynamic characteristics can be simulated more accurately. The heat flow enters from the left side, passes through multiple RC units, and then exchanges heat with the surrounding environment of the cable.

[0158] The ambient temperature represents the temperature of the external environment in which the model is located. It is a boundary condition in the heat conduction process and affects the thermal balance of the entire system.

[0159] θ1: Initial core temperature, that is, the core temperature of the target cable when heat conduction begins. It is the starting state parameter of the model calculation.

[0160] θ n : Represents the temperature of the nth-order node in the Foster network model. It reflects the temperature state at that node after the effects of the previous n-1-order thermal resistance and n-1-order heat capacitance during heat conduction. It is an important parameter for analyzing temperature distribution and changes during heat conduction. For example, θ2 represents the temperature of θ1 after the effects of the first-order thermal resistance and first-order heat capacitance.

[0161] P1: is heat flow, which represents the amount of heat transferred per unit time. It is the driving force of the heat conduction process and determines the transfer rate and total amount of heat in the network.

[0162] T n : The thermal resistance of the nth order of the Foster network model. The thermal resistance reflects the obstruction of the material or structure to heat transfer. The greater the thermal resistance, the more difficult the heat transfer. For example, T1 represents the first-order thermal resistance.

[0163] C n : The nth-order heat capacity of the Foster network model. Heat capacity represents an object's ability to store heat, that is, the amount of heat absorbed or released per unit temperature change. The larger the heat capacity, the smaller the temperature change for the same amount of heat absorbed or released. For example, C1 represents the first-order heat capacity.

[0164] θ amb : Indicates the ambient temperature around the target cable.

[0165] In this way, by adopting the rule of "network order n = number of structural layers m + 1", the additional order can characterize the boundary thermal effect or global thermal coupling. For example, in the seven-layer structure of a three-core cable (such as the conductor shielding layer, the insulation layer, etc.), the low thermal resistance characteristics of the metal shielding layer will lead to uneven heat conduction between layers. The additional order can simulate the accumulation or dissipation of heat at the boundary between layers, avoiding the loss of boundary effects caused by the strict correspondence of the number of layers.

[0166] S305 , controlling the target Foster network model to perform transient temperature prediction according to the actual operating power data and the actual ambient temperature data to obtain a target predicted temperature curve; the abscissa of the target predicted temperature curve is time, and the ordinate is the temperature of the cable.

[0167] In an embodiment of the present application, actual operating power data and actual ambient temperature data may be input into a target Foster network model, so that the target Foster network model performs transient temperature prediction, thereby obtaining a target predicted temperature curve.

[0168] S306 : Determine a target model accuracy of the target Foster network model according to the target simulation temperature curve and the target predicted temperature curve.

[0169] In an embodiment of the present application, the target fitting residual between the target simulation temperature curve and the target predicted temperature curve can be calculated, and the target model accuracy can be determined based on the target fitting residual. For example, the temperature difference at each time point in the target simulation temperature curve and the target predicted temperature curve can be calculated to obtain multiple temperature differences. Then, the root mean square error of these multiple temperature differences can be calculated, that is, the target fitting residual. The target model accuracy is determined based on the target fitting residual. The smaller the fitting residual, the higher the model accuracy.

[0170] See also Figure 6 , Figure 6 This is a schematic diagram comparing a target simulation temperature curve and a target prediction temperature curve provided in an embodiment of the present application, wherein curve L1 represents the target simulation temperature curve, and curve L2 represents the target prediction temperature curve. The target model accuracy of the target Foster network model can be determined by calculating the target fitting residuals of these two curves.

[0171] S307 : When the target model accuracy is greater than a preset accuracy, determine the transient temperature of the target cable according to the target predicted temperature curve.

[0172] In the embodiment of the present application, the preset accuracy can be preset in advance or defaulted.

[0173] In a specific embodiment, when the target model accuracy is greater than the preset accuracy, it indicates that the prediction of the target Foster network model is accurate, and the target predicted temperature curve can be determined as the transient temperature of the target cable.

[0174] When the target model accuracy is not greater than the preset accuracy, it means that the prediction of the target Foster network model is inaccurate and the target Foster network model needs to be readjusted, or a new target Foster network model is constructed until the model accuracy of the target Foster network model is greater than the preset accuracy. The target Foster network model is used for prediction to obtain the transient temperature of the target cable.

[0175] In summary, the cable transient temperature prediction method based on the Foster network model described in this application utilizes the high precision of the finite element simulation model and the rapidity of the Foster network model, and calibrates the Foster model parameters by comparing the temperature curves of the two to improve the prediction accuracy of the target Foster network model. Then, the transient temperature of the target cable is predicted by the target Foster network model, effectively reducing the prediction error of the transient temperature.

[0176] See also Figure 7 , Figure 7 This is a functional unit block diagram of a cable transient temperature prediction device 700 based on a Foster network model provided in an embodiment of the present application. The cable transient temperature prediction device 700 based on a Foster network model includes: an acquisition unit 701 and a model prediction unit 702, wherein:

[0177] The acquisition unit 701 is used to acquire the target cable basic data, actual operating power data, actual ambient temperature data and heat dissipation boundary conditions of the target cable;

[0178] The model prediction unit 702 is used to construct a cable simulation model based on the target cable basic data; control the cable simulation model to perform transient simulation according to the actual operating power data, the actual ambient temperature data and the heat dissipation boundary conditions to obtain a target simulation temperature curve; the horizontal coordinate of the target simulation temperature curve is time, and the vertical coordinate is the temperature of the cable; construct a target Foster network model according to the target simulation temperature curve and the target cable basic data; control the target Foster network model to perform transient temperature prediction according to the actual operating power data and the actual ambient temperature data to obtain a target predicted temperature curve; the horizontal coordinate of the target predicted temperature curve is time, and the vertical coordinate is the temperature of the cable; determine the target model accuracy of the target Foster network model according to the target simulation temperature curve and the target predicted temperature curve; when the target model accuracy is greater than the preset accuracy, determine the transient temperature of the target cable according to the target predicted temperature curve.

[0179] In a specific implementation, the cable transient temperature prediction device 700 based on the Foster network model described in the embodiment of the present invention can also execute other implementations described in the cable transient temperature prediction method based on the Foster network model provided in the above embodiment of the present invention, which will not be repeated here.

[0180] See also Figure 8 , Figure 8 : is a structural diagram of another electronic device provided in an embodiment of the present application. The electronic device may include a processor, a memory, a communication interface, and one or more programs. The processor, memory, and communication interface may be interconnected via a bus. The one or more programs are stored in the memory and configured to be executed by the processor. In the embodiment of the present application, the program includes instructions for performing the following steps:

[0181] Obtain target cable basic data, actual operating power data, actual ambient temperature data and heat dissipation boundary conditions of the target cable;

[0182] Constructing a cable simulation model based on the target cable basic data;

[0183] Controlling the cable simulation model to perform transient simulation according to the actual operating power data, the actual ambient temperature data, and the heat dissipation boundary conditions to obtain a target simulation temperature curve; the abscissa of the target simulation temperature curve is time, and the ordinate is the temperature of the cable;

[0184] Constructing a target Foster network model according to the target simulation temperature curve and the target cable basic data;

[0185] Controlling the target Foster network model to perform transient temperature prediction according to the actual operating power data and the actual ambient temperature data to obtain a target predicted temperature curve; the abscissa of the target predicted temperature curve is time, and the ordinate is the temperature of the cable;

[0186] Determining a target model accuracy of the target Foster network model according to the target simulation temperature curve and the target predicted temperature curve;

[0187] When the target model accuracy is greater than a preset accuracy, the transient temperature of the target cable is determined according to the target predicted temperature curve.

[0188] In specific implementation, the electronic device described in the embodiment of the present invention may also execute other implementations described in the above-mentioned method embodiment of the present invention, which will not be described in detail here.

[0189] An embodiment of the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0190] The present application also provides a computer program product comprising a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer may comprise an electronic device.

[0191] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0192] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0193] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0194] Those skilled in the art will appreciate that all or part of the process steps in the above-described method embodiments can be implemented by a computer program instructing the relevant hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can include the process steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

[0195] The steps of the method or algorithm described in the embodiments of the present application can be implemented in hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, which can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs, or any other form of storage medium well known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and storage medium can be located in an ASIC. In addition, the ASIC can be located in a terminal device or a management device. Of course, the processor and storage medium can also be present in a terminal device or a management device as discrete components.

[0196] Those skilled in the art will appreciate that in one or more of the above examples, the functions described in the embodiments of the present application can be implemented in whole or in part through software, hardware, firmware, or any combination thereof. When implemented using software, they can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part.

[0197] The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated therein.

[0198] The available media may be magnetic media (eg, floppy disks, hard disks, magnetic tapes), optical media (eg, digital video discs (DVDs)), or semiconductor media (eg, solid state disks (SSDs)).

[0199] The modules / units included in the devices and products described in the above embodiments may be software modules / units, hardware modules / units, or partly software modules / units and partly hardware modules / units. For example, for the devices and products applied to or integrated in the chip, the modules / units included therein may all be implemented in the form of hardware such as circuits, or at least part of the modules / units may be implemented in the form of software programs, which run on the processor integrated inside the chip, and the remaining (if any) modules / units may be implemented in the form of hardware such as circuits; for the devices and products applied to or integrated in the chip module, the modules / units included therein may all be implemented in the form of hardware such as circuits, and different modules / units may be located in the same component (such as chip, circuit module, etc.) or different components of the chip module, or at least part of the modules / units may be It is implemented in the form of a software program, which runs on the processor integrated inside the chip module, and the remaining (if any) modules / units can be implemented in the form of hardware such as circuits; for various devices and products applied to or integrated in the terminal equipment, the various modules / units contained therein can be implemented in the form of hardware such as circuits, and different modules / units can be located in the same component (for example, chip, circuit module, etc.) or different components in the terminal equipment, or, at least some modules / units can be implemented in the form of a software program, which runs on the processor integrated inside the terminal equipment, and the remaining (if any) modules / units can be implemented in the form of hardware such as circuits.

[0200] The specific implementation methods described above further illustrate the purpose, technical solutions and beneficial effects of the embodiments of the present application. It should be understood that the above description is only a specific implementation method of the embodiments of the present application and is not intended to limit the scope of protection of the embodiments of the present application. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solutions of the embodiments of the present application should be included in the scope of protection of the embodiments of the present application.

Claims

1. A cable transient temperature prediction method based on Foster network model, characterized in that: include: Obtain target cable basic data, actual operating power data, actual ambient temperature data and heat dissipation boundary conditions of the target cable; Constructing a cable simulation model based on the target cable basic data; Controlling the cable simulation model to perform transient simulation according to the actual operating power data, the actual ambient temperature data, and the heat dissipation boundary conditions to obtain a target simulation temperature curve; The horizontal coordinate of the target simulation temperature curve is time, and the vertical coordinate is the temperature of the cable; Constructing a target Foster network model according to the target simulation temperature curve and the target cable basic data; Controlling the target Foster network model to perform transient temperature prediction according to the actual operating power data and the actual ambient temperature data to obtain a target predicted temperature curve; the abscissa of the target predicted temperature curve is time, and the ordinate is the temperature of the cable; Determining a target model accuracy of the target Foster network model according to the target simulation temperature curve and the target predicted temperature curve; When the target model accuracy is greater than a preset accuracy, the transient temperature of the target cable is determined according to the target predicted temperature curve.

2. The method according to claim 1, wherein The target cable includes an m-layer structure, where m is a positive integer. The target cable basic data includes: target cable type, component material data of the target cable, and target structure data of the target cable. The cable simulation model is constructed based on the target cable basic data, including: Determining simulation boundary conditions of the target cable according to the target cable type; constructing a reference cable simulation model according to the target structure data; Determine the material parameters corresponding to each structure in the m-layer structure according to the constituent material data to obtain m material parameters; The reference cable simulation model is adjusted according to the m material parameters and the simulation boundary conditions to obtain the cable simulation model.

3. The method according to claim 2, wherein The target Foster network model is constructed according to the target simulation temperature curve and the target cable basic data, comprising: Determine the network order corresponding to the target Foster network model according to the m-layer structure to obtain a target network order n, where n=m+1; The thermal response expression of the target Foster network model is constructed based on the target network order; the thermal response expression is specifically as follows: Wherein, T(t) represents the predicted temperature value of the target Foster network model at time t, T o represents the initial ambient temperature of the target cable; P represents the input power of the target cable; R i represents the thermal resistance of the i-th order in the target Foster network model; C i represents the heat capacity of the i-th order in the target Foster network model; e represents a natural constant; t represents time; An objective function is constructed according to the target simulation temperature curve; the objective function is specifically as follows: Wherein, F(x) represents the value of the objective function, x represents the parameter vector to be optimized, and the parameter vector to be optimized includes all thermal resistances and all thermal capacities in the target Foster network model; y j represents the temperature value corresponding to the jth time point in the target simulation temperature curve; f(x,t j ) represents the calculated value of the thermal response expression T(t) at the jth time point; p represents the total number of time points corresponding to the target simulation temperature curve; Solving the objective function according to a preset optimization goal and a preset solution algorithm to obtain n groups of model parameters; each group of model parameters includes a thermal resistance and a heat capacity; The target Foster network model is determined according to the n groups of model parameters and the thermal response expression.

4. The method according to any one of claims 1 to 3, wherein The step of controlling the cable simulation model to perform transient simulation according to the actual operating power data, the actual ambient temperature data, and the heat dissipation boundary condition to obtain a target simulation temperature curve includes: determining a target convective heat transfer coefficient according to the heat dissipation boundary condition; Get the preset step size; Inputting the actual operating power data, the actual ambient temperature data, and the target convection heat transfer coefficient into the cable simulation model, driving the cable simulation model to perform transient simulation, and collecting temperature values ​​of the cable simulation model according to the preset step size to obtain multiple temperature values; Curve fitting is performed according to each temperature value of the multiple temperature values ​​and the acquisition time corresponding to each temperature value to obtain the target simulation temperature curve.

5. The method according to claim 4, wherein Determining the target convective heat transfer coefficient according to the heat dissipation boundary condition includes: Determining a first convection heat transfer coefficient corresponding to the target cable according to the basic data of the target cable; Obtaining a target laying method corresponding to the target cable; Determine a calculation formula for the convective heat transfer coefficient according to the target installation method and the heat dissipation boundary conditions; Calculating according to the target cable basic data and the convection heat transfer coefficient calculation formula to obtain a second convection heat transfer coefficient; Obtaining a target position of the target cable; Determine heat sources within a preset distance around the target location to obtain a heat sources, where a is a natural number; Determining a target radiation heat transfer coefficient corresponding to the a heat sources; determining a third convective heat transfer coefficient according to the target radiation heat transfer coefficient and the second convective heat transfer coefficient; The target convective heat transfer coefficient is determined according to the first convective heat transfer coefficient and the third convective heat transfer coefficient.

6. The method according to claim 5, wherein The determining the target convective heat transfer coefficient according to the first convective heat transfer coefficient and the third convective heat transfer coefficient includes: determining a deviation between the first convective heat transfer coefficient and the third convective heat transfer coefficient to obtain a target deviation; When the target deviation is less than a preset deviation, determining a first weight corresponding to the first convective heat transfer coefficient and a second weight corresponding to the third convective heat transfer coefficient; the sum of the first weight and the second weight is 1; and determining the target convective heat transfer coefficient based on the first weight, the second weight, the first convective heat transfer coefficient, and the third convective heat transfer coefficient; When the target deviation is not less than the preset deviation, the target convective heat transfer coefficient range corresponding to the target cable is determined according to the basic data of the target cable; the middle value of the target convective heat transfer coefficient range is determined; a first deviation between the first convective heat transfer coefficient and the middle value is determined; a second deviation between the third convective heat transfer coefficient and the middle value is determined; the smaller deviation between the first deviation and the second deviation is determined to obtain a third deviation; and the convective heat transfer coefficient corresponding to the third deviation is determined to be the target convective heat transfer coefficient.

7. The method according to claim 5, wherein Determining the target radiation heat transfer coefficient corresponding to the a heat sources includes: Determining the radiation heat transfer coefficient of each of the a heat sources to obtain a radiation heat transfer coefficients; Obtaining a target dirt area on the target cable surface; Determining a target fine-tuning coefficient corresponding to the target dirt area; Obtaining the position of each of the a heat sources to obtain a positions; Determine the distance between each of the a positions and the target position to obtain a distances; Determine a weight corresponding to each of the a distances to obtain a weights; Determining a reference radiation heat transfer coefficient according to the a weights and the a radiation heat transfer coefficients; The reference radiation heat transfer coefficient is adjusted according to the target fine-tuning coefficient to obtain the target radiation heat transfer coefficient.

8. A cable transient temperature prediction device based on Foster network model, characterized in that: The device comprises: an acquisition unit and a model prediction unit, wherein: The acquisition unit is used to acquire target cable basic data, actual operating power data, actual ambient temperature data and heat dissipation boundary conditions of the target cable; The model prediction unit is used to construct a cable simulation model based on the target cable basic data; control the cable simulation model to perform transient simulation according to the actual operating power data, the actual ambient temperature data and the heat dissipation boundary conditions to obtain a target simulation temperature curve; the horizontal coordinate of the target simulation temperature curve is time, and the vertical coordinate is the temperature of the cable; construct a target Foster network model according to the target simulation temperature curve and the target cable basic data; control the target Foster network model to perform transient temperature prediction according to the actual operating power data and the actual ambient temperature data to obtain a target predicted temperature curve; the horizontal coordinate of the target predicted temperature curve is time, and the vertical coordinate is the temperature of the cable; determine the target model accuracy of the target Foster network model according to the target simulation temperature curve and the target predicted temperature curve; when the target model accuracy is greater than the preset accuracy, determine the transient temperature of the target cable according to the target predicted temperature curve.

9. An electronic device, characterized in that: include: a processor, a memory, a communication interface, and one or more programs; The one or more programs are stored in the memory and configured to be executed by the processor, wherein the programs include instructions for executing the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that A computer program for electronic data exchange is stored, wherein the computer program enables a computer to execute the method according to any one of claims 1 to 7.

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