Cable joint fault early diagnosis method and device based on edge calculation

Abstract

The invention discloses a cable joint fault early diagnosis method and device based on edge calculation, and relates to the technical field of cable joint early fault diagnosis, and the method comprises the steps: building an error function between a surface measurement temperature and a surface calculation temperature; introducing a dynamic weight factor into the heat source item; on the basis of the dynamic weight factors, the thermal parameters and boundary conditions of the heat flow balance relation are updated; determining a temperature field at the joint according to the updated heat conduction control equation, and establishing a target function by taking error function minimization as a target; based on the objective function, through the heat conduction control equation after the dynamic weight factor is updated, iterative inversion of a temperature field at the joint is carried out; performing early diagnosis on a cable joint fault by using the temperature field meeting an iteration ending condition; the temperature field in the cable connector is accurately inverted through the surface temperature, a basis is provided for judging whether the cable connector breaks down or not, and the accuracy and efficiency of early fault diagnosis of the cable connector are improved.

Description

Technical Field

[0001] This invention relates to the field of early fault diagnosis technology for cable joints, and more specifically, to a method and apparatus for early fault diagnosis of cable joints based on edge computing. Background Technology

[0002] Cable joints are common and critical components in cables. Factors such as aging of joint materials, improper installation, and changes in ambient temperature and humidity can all lead to damage to cable joints, resulting in phenomena such as partial discharge and overheating. This not only affects the normal operation of urban power lines but also easily causes major fire hazards. Therefore, it is crucial to reasonably and effectively assess the early fault status of cable joints. Currently, the true internal condition can be determined by measuring the surface temperature of the cable joint; however, this method requires a high level of personnel experience, and traditional single-point surface temperature measurement cannot reflect the true internal condition of the joint for most people, resulting in a high rate of misjudgment.

[0003] Therefore, this application is hereby submitted. Summary of the Invention

[0004] The purpose of this invention is to provide a method and apparatus for early diagnosis of cable joint faults based on edge computing. By accurately reproducing the internal temperature field of the cable joint through the surface temperature of the cable, it provides a basis and evidence for judging whether the cable joint is faulty, and improves the accuracy and efficiency of early fault diagnosis of cable joints.

[0005] The above-mentioned technical objective of the present invention is achieved through the following technical solution: Firstly, this application provides a method for early diagnosis of cable joint faults based on edge computing, including the following specific steps: An error function is established between the measured surface temperature and the calculated surface temperature determined by the cable temperature field. A dynamic weighting factor for the effect of load current on temperature is introduced into the heat source term of the preset heat conduction control equation. Based on the dynamic weighting factor, the thermal parameters in the heat conduction control equation and the boundary conditions of the heat flow balance relationship are updated. The temperature field at the joint is determined based on the updated heat conduction control equation, and an objective function is established with the goal of minimizing the error function. Based on the objective function and the updated heat conduction control equation through dynamic weighting factors, the temperature field at the joint is iteratively inverted until the preset iteration termination condition is met. The temperature field that meets the iteration termination condition is determined as the target temperature field distribution, and early diagnosis of cable joint faults is performed based on the target temperature field distribution.

[0006] Based on the above technical solution, the present invention can be further improved as follows.

[0007] Furthermore, the aforementioned error function is specifically as follows: ; In the formula, Represents the error function. This represents the surface temperature calculated in the t-th iteration. This represents the surface temperature measured in the t-th time. Represents the regularization parameter. To stabilize the functional.

[0008] Furthermore, the aforementioned heat conduction governing equation is as follows: ; In the formula, Indicates the density of the material. For specific heat capacity, Indicates thermal conductivity. For radial coordinates, in a two-dimensional axisymmetric model, they represent radial spatial variables in cylindrical coordinates. is the axial coordinate, representing the axial spatial variable in the cylindrical coordinate system in a two-dimensional axisymmetric model; This is a heat source term related to the load current.

[0009] Furthermore, the heat source term after introducing the dynamic weighting factor is specifically as follows: ; In the formula, This indicates the heat source term related to the load current. This represents a dynamic weighting factor related to the load current. For the current load current, This represents the AC resistance as a function of temperature; it is the resistance of a conductor under AC conditions, and its value varies with temperature.

[0010] Furthermore, the aforementioned dynamic weighting factors are specifically as follows: ; In the formula, This represents the basic weighting coefficient, which adjusts the weighting benchmark under normal current conditions. Its value ranges from 0.8 to 1.2 and is dimensionless. This represents the nonlinear adjustment index, which controls the degree of nonlinearity of the weight change with current. Its value range is 1.5–2.5, dimensionless. This is an overload compensation coefficient used to enhance heat generation when the current exceeds the threshold; it is dimensionless. The slope parameter of the transition region is used to determine the rate at which the weighting factor increases with the current after the current exceeds the threshold; it is dimensionless. This indicates the cable's rated current, which is the maximum current at which the conductor can operate safely for extended periods. This represents the overload threshold current, which is the critical current that triggers overload compensation.

[0011] Furthermore, the thermal parameters in the heat conduction governing equation and the boundary conditions of the heat flow balance relationship are updated. The thermal parameters include thermal conductivity, specifically: ; In the formula, For current I and temperature T The function represents the corrected effective thermal conductivity; Represents the reference thermal conductivity, which is the thermal conductivity of the material under reference conditions; The temperature sensitivity coefficient represents the degree to which thermal conductivity changes with temperature. As a dynamic weighting factor, Indicates the current temperature. This indicates the reference temperature, which serves as the benchmark for temperature changes. The updated boundary conditions are as follows: ; In the formula, Thermal conductivity, The convective heat transfer coefficient is used to characterize the intensity of convective heat transfer. For measuring surface temperature, Indicates ambient temperature. Indicates the emissivity of the connector surface. The Stefan-Boltzmann constant is... As a dynamic weighting factor, This is a correction term for radiative heat flux density. The normal temperature gradient is used to reflect the rate of temperature change in the direction perpendicular to the surface. Location of the cable wall. The boundary condition constraint symbol indicates that the expression applies to the radius. It is established at the surface.

[0012] Furthermore, the iterative inversion of the temperature field at the joint is performed as follows: ; In the formula, , They represent the first sequence Temperature field of the next iteration Represents the iteration step size factor. Let be the second derivative of the objective function, and let be the objective function. exist The Hessian matrix at that location, Denotes the regularization parameter, where It is the identity matrix. Describe the objective function exist The gradient vector at that point.

[0013] Secondly, this application provides an edge computing-based cable joint fault early diagnosis device, applied to any of the edge computing-based cable joint fault early diagnosis methods in the first aspect, comprising: The error function establishment module is used to establish an error function between the measured surface temperature and the calculated surface temperature based on the surface temperature of the cable joint and the surface temperature determined by the cable temperature field. The weighting module is used to introduce a dynamic weighting factor that affects the temperature of the load current into the heat source term of the preset heat conduction control equation. The equation update module is used to update the thermal parameters and boundary conditions of the heat flow balance relationship in the heat conduction control equation based on the dynamic weighting factor. The objective function establishment module is used to determine the temperature field at the joint based on the updated heat conduction control equations, and to establish an objective function with the goal of minimizing the error function. The temperature field inversion module is used to iteratively invert the temperature field at the joint based on the objective function and the updated heat conduction control equation through dynamic weighting factors, until the preset iteration termination condition is met. The iterative solution module is used to determine the temperature field that meets the iteration termination condition as the target temperature field distribution, and to perform early diagnosis of cable joint faults based on the target temperature field distribution.

[0014] Thirdly, this application provides an electronic device, including: at least one processor, at least one memory, and a data bus; In this system, the processor and memory communicate with each other via a data bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute an edge computing-based early diagnosis method for cable joint faults, as described in any of the first aspects.

[0015] Fourthly, this application provides a non-transitory computer-readable storage medium that stores computer instructions that cause a computer to execute any one of the methods in the first aspect: an edge computing-based method for early diagnosis of cable joint faults.

[0016] Compared with the prior art, the present invention has at least the following beneficial effects: In this application, an objective function is first established with the goal of minimizing the error function between the measured surface temperature and the calculated surface temperature. A dynamic weighting factor is introduced into the heat source term in the heat conduction control equation, and the thermal parameters and boundary conditions of the heat flow balance relationship in the heat conduction control equation are updated through the dynamic weighting factor. Secondly, the updated heat conduction control equation is discretized using methods such as the finite element method, finite difference method, or finite volume method, transforming the continuous partial differential equation into a discrete algebraic equation system. The numerical solution of the temperature field is then obtained through iterative solving, yielding the calculated surface temperature, which in turn allows the determination of the objective function value. Subsequently, based on the second and first derivatives of the objective function with respect to temperature, the temperature field of the cable joint is continuously updated using iterative inversion formulas until the iteration termination condition is met. Finally, the temperature field satisfying the iteration termination condition is defined as the target temperature field distribution, thus obtaining the accurate temperature field within the cable joint. This enables early fault diagnosis based on precise temperature data, providing a basis and evidence for determining whether a cable joint is faulty and improving the accuracy and efficiency of early fault diagnosis for cable joints. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of the diagnostic method in an embodiment of the present invention; Figure 2 This is a connection diagram of the diagnostic system in an embodiment of the present invention; Figure 3 This is a schematic diagram of the connection of an electronic device in an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0019] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0020] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0021] In the description of the embodiments of the present invention, "multiple" means at least two.

[0022] Example 1: To address the problems of high false positive rates caused by relying on surface temperature measurements to determine the internal condition of cable joints, which requires extensive human experience and suffers from significant discrepancies between the measured temperature and the actual temperature, this example provides an early fault diagnosis method for cable joints based on edge computing. This method utilizes reinforcement learning for early fault diagnosis. Figure 1 As shown, the specific steps include the following: S1 establishes an error function between the measured surface temperature and the calculated surface temperature by using the surface temperature of the cable joint and the surface temperature determined by the cable temperature field.

[0023] Specifically, the error function mentioned above is as follows: ; In the formula, Represents the error function. This represents the surface temperature calculated in the t-th iteration. This represents the surface temperature measured in the t-th time. Represents the regularization parameter. To stabilize the functional.

[0024] S2 introduces a dynamic weighting factor that accounts for the influence of load current on temperature into the heat source term of the preset heat conduction control equation.

[0025] Specifically, the aforementioned heat conduction governing equation is as follows: ; In the formula, Indicates the density of the material. For specific heat capacity, Indicates thermal conductivity. For radial coordinates, in a two-dimensional axisymmetric model, they represent radial spatial variables in cylindrical coordinates. is the axial coordinate, representing the axial spatial variable in the cylindrical coordinate system in a two-dimensional axisymmetric model; This is a heat source term related to the load current.

[0026] The calculated surface temperature is determined during the process of solving the heat conduction forward problem. In cable temperature field analysis, the forward problem refers to solving the temperature field distribution based on known material properties, boundary conditions, initial conditions, and heat source terms, which then determines the calculated surface temperature.

[0027] Specifically, the heat source term after introducing the dynamic weighting factor is as follows: ; In the formula, This indicates the heat source term related to the load current. This represents a dynamic weighting factor related to the load current. For the current load current, This represents the AC resistance as a function of temperature; it is the resistance of a conductor under AC conditions, and its value varies with temperature.

[0028] Optionally, the above dynamic weighting factors are specifically as follows: ; In the formula, This represents the basic weighting coefficient, which adjusts the weighting benchmark under normal current conditions. Its value ranges from 0.8 to 1.2 and is dimensionless. This represents the nonlinear adjustment index, which controls the degree of nonlinearity of the weight change with current. Its value range is 1.5–2.5, dimensionless. This is an overload compensation coefficient used to enhance heat generation when the current exceeds the threshold; it is dimensionless. The slope parameter of the transition region is used to determine the rate at which the weighting factor increases with the current after the current exceeds the threshold; it is dimensionless. This indicates the cable's rated current, which is the maximum current at which the conductor can operate safely for extended periods. This represents the overload threshold current, which is the critical current that triggers overload compensation.

[0029] S3 updates the thermal parameters and boundary conditions of the heat flow balance relationship in the heat conduction control equation based on the dynamic weighting factor.

[0030] Specifically, the thermal parameters in the heat conduction governing equation and the boundary conditions of the heat flow balance relationship are updated as described above. The thermal parameters include thermal conductivity, specifically: ; In the formula, For current I and temperature T The function represents the corrected effective thermal conductivity; Represents the reference thermal conductivity, which is the thermal conductivity of the material under reference conditions; The temperature sensitivity coefficient represents the degree to which thermal conductivity changes with temperature. As a dynamic weighting factor, Indicates the current temperature. This indicates the reference temperature, which serves as the benchmark for temperature changes. Optionally, the updated boundary conditions are as follows: ; In the formula, Thermal conductivity, The convective heat transfer coefficient is used to characterize the intensity of convective heat transfer. For measuring surface temperature, Indicates ambient temperature. Indicates the emissivity of the connector surface. The Stefan-Boltzmann constant is... As a dynamic weighting factor, This is a correction term for radiative heat flux density. The normal temperature gradient is used to reflect the rate of temperature change in the direction perpendicular to the surface. Location of the cable wall. The boundary condition constraint symbol indicates that the expression applies to the radius. It is established at the surface.

[0031] S4. Determine the temperature field at the joint based on the updated heat conduction control equation, and establish the objective function with the goal of minimizing the error function.

[0032] The objective function is to minimize The process of estimating the internal temperature distribution is a mathematical optimization problem, the goal of which is to minimize the difference between the calculated surface temperature and the measured surface temperature. Specifically, estimating the internal temperature distribution from the measured surface temperature is an inverse problem of heat conduction. In this process, the calculated surface temperature is used as one of the inputs to the solution of the inverse problem, and together with the measured value, it is used to adjust the model parameters to more accurately invert the internal temperature distribution.

[0033] S5, based on the objective function and the updated heat conduction control equation through dynamic weighting factors, performs iterative inversion of the temperature field at the joint until the preset iteration termination condition is met; inversion is performed based on strategy optimization, imitation learning, etc.

[0034] Optionally, the above iterative inversion of the temperature field at the joint is performed as follows: ; In the formula, , They represent the first sequence Temperature field of the next iteration Represents the iteration step size factor. Let be the second derivative of the objective function, and let be the objective function. exist The Hessian matrix at that location, Denotes the regularization parameter, where It is the identity matrix. Describe the objective function exist The gradient vector at that point.

[0035] The introduction of the regularization term ensures the stability of the iterative process and prevents non-convergence or oscillations caused by ill-posedness of the problem (such as small perturbations in the measurement data leading to large changes in the solution). The regularization parameter can be adjusted according to the specific problem. Specifically, the above iteration continuously utilizes the gradient and curvature information (Hessian matrix) of the objective function, combined with regularization techniques, to stabilize the iterative process, gradually approximating the internal temperature distribution that satisfies the heat conduction equation and measurement conditions, and finally obtaining a temperature field that is closest to the true temperature distribution.

[0036] S6. The temperature field that meets the iteration termination condition is determined as the target temperature field distribution, and early diagnosis of cable joint faults is performed based on the target temperature field distribution.

[0037] The accuracy of the calculated surface temperature directly affects the inversion accuracy of the internal temperature distribution. At the same time, the inversion results of the internal temperature distribution can also be used to verify and adjust the process of obtaining the calculated surface temperature. That is, obtaining the calculated surface temperature is a complex process based on mathematical models, dynamic weight factor design, adaptive parameter adjustment, and numerical solution. Furthermore, it is closely related to the deduction of the internal temperature distribution from the surface temperature measurement. The two are interdependent and together constitute the complete framework for cable temperature field inversion.

[0038] Specifically, by following the steps described above, a precise temperature field within the cable joint can be obtained, enabling early fault diagnosis based on accurate temperature data. This provides a basis and foundation for determining whether a cable joint is faulty, and improves the accuracy and efficiency of early fault diagnosis for cable joints.

[0039] Example 2: This application provides an edge computing-based early fault diagnosis device for cable joints, applied to the edge computing-based early fault diagnosis method for cable joints in Example 1, such as... Figure 2 As shown, it includes: The error function establishment module is used to establish an error function between the measured surface temperature and the calculated surface temperature based on the surface temperature of the cable joint and the surface temperature determined by the cable temperature field. The weighting module is used to introduce a dynamic weighting factor that affects the temperature of the load current into the heat source term of the preset heat conduction control equation. The equation update module is used to update the thermal parameters and boundary conditions of the heat flow balance relationship in the heat conduction control equation based on the dynamic weighting factor. The objective function establishment module is used to determine the temperature field at the joint based on the updated heat conduction control equations, and to establish an objective function with the goal of minimizing the error function. The temperature field inversion module is used to iteratively invert the temperature field at the joint based on the objective function and the updated heat conduction control equation through dynamic weighting factors, until the preset iteration termination condition is met. The iterative solution module is used to determine the temperature field that meets the iteration termination condition as the target temperature field distribution, and to perform early diagnosis of cable joint faults based on the target temperature field distribution.

[0040] Example 3: This application provides an electronic device, such as... Figure 3 As shown, it includes: at least one processor, at least one memory, and a data bus; In this system, the processor and memory communicate with each other via a data bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute an early diagnosis method for cable joint faults based on edge computing, as described in Example 1.

[0041] Example 4: This application provides a non-transitory computer-readable storage medium that stores computer instructions, which cause a computer to execute an edge computing-based early diagnosis method for cable joint faults according to Example 1.

[0042] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0043] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0044] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0045] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0046] Those skilled in the art will understand that all or part of the steps in the above facts and methods can be implemented by a program instructing related hardware. The program or the program described therein can be stored in a computer-readable storage medium. When the program is executed, it includes the following steps: at this time, the corresponding method steps are introduced. The storage medium can be ROM / RAM, magnetic disk, optical disk, etc.

[0047] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for early diagnosis of cable joint faults based on edge computing, characterized in that, The specific steps include the following: An error function is established between the measured surface temperature and the calculated surface temperature determined by the cable temperature field. A dynamic weighting factor for the effect of load current on temperature is introduced into the heat source term of the preset heat conduction control equation. Based on the dynamic weighting factor, the thermal parameters in the heat conduction control equation and the boundary conditions of the heat flow balance relationship are updated. The temperature field at the joint is determined based on the updated heat conduction control equation, and an objective function is established with the goal of minimizing the error function. Based on the objective function and the updated heat conduction control equation with dynamic weighting factors, the temperature field at the joint is iteratively inverted until the preset iteration termination condition is met. The temperature field that meets the iteration termination condition is determined as the target temperature field distribution, and early diagnosis of cable joint faults is performed based on the target temperature field distribution.

2. The method for early diagnosis of cable joint faults based on edge computing according to claim 1, characterized in that, The error function is specifically as follows: ； In the formula, Represents the error function. This represents the surface temperature calculated in the t-th iteration. This represents the surface temperature measured in the t-th time. Represents the regularization parameter. To stabilize the functional.

3. The method for early diagnosis of cable joint faults based on edge computing according to claim 1, characterized in that, The governing equation for heat conduction is as follows: ； In the formula, Indicates the density of the material. For specific heat capacity, Indicates thermal conductivity. For radial coordinates, in a two-dimensional axisymmetric model, they represent radial spatial variables in cylindrical coordinates. is the axial coordinate, representing the axial spatial variable in the cylindrical coordinate system in a two-dimensional axisymmetric model; This is a heat source term related to the load current.

4. The method for early diagnosis of cable joint faults based on edge computing according to claim 1, characterized in that, The heat source term after introducing the dynamic weighting factor is as follows: ； In the formula, This indicates the heat source term related to the load current. This represents a dynamic weighting factor related to the load current. For the current load current, This represents the AC resistance as a function of temperature; it is the resistance of a conductor under AC conditions, and its value varies with temperature.

5. The method for early diagnosis of cable joint faults based on edge computing according to claim 4, characterized in that, The dynamic weighting factor is specifically: ； In the formula, This represents the basic weighting coefficient, which adjusts the weighting benchmark under normal current conditions. Its value ranges from 0.8 to 1.2 and is dimensionless. This represents the nonlinear adjustment index, which controls the degree of nonlinearity of the weight change with current. Its value range is 1.5–2.5, dimensionless. This is an overload compensation coefficient used to enhance heat generation when the current exceeds the threshold; it is dimensionless. The slope parameter of the transition region is used to determine the rate at which the weighting factor increases with the current after the current exceeds the threshold; it is dimensionless. This indicates the cable's rated current, which is the maximum current at which the conductor can operate safely for extended periods. This represents the overload threshold current, which is the critical current that triggers overload compensation.

6. The method for early diagnosis of cable joint faults based on edge computing according to claim 1, characterized in that, The process involves updating the thermal parameters in the heat conduction control equation and the boundary conditions of the heat flow balance relationship. The thermal parameters include thermal conductivity, specifically: ； In the formula, For current I and temperature T The function represents the corrected effective thermal conductivity; Represents the reference thermal conductivity, which is the thermal conductivity of the material under reference conditions; The temperature sensitivity coefficient represents the degree to which thermal conductivity changes with temperature. As a dynamic weighting factor, Indicates the current temperature. This indicates the reference temperature, which serves as the benchmark for temperature changes. The updated boundary conditions are as follows: ； In the formula, Thermal conductivity, The convective heat transfer coefficient is used to characterize the intensity of convective heat transfer. For measuring surface temperature, Indicates ambient temperature. Indicates the emissivity of the connector surface. The Stefan-Boltzmann constant is... As a dynamic weighting factor, This is a correction term for radiative heat flux density. The normal temperature gradient is used to reflect the rate of temperature change in the direction perpendicular to the surface. Location of the cable wall. The boundary condition constraint symbol indicates that the expression applies to the radius. It is established at the surface.

7. The method for early diagnosis of cable joint faults based on edge computing according to claim 1, characterized in that, The iterative inversion of the temperature field at the joint specifically involves: ； In the formula, , They represent the first sequence Temperature field of the next iteration Represents the iteration step size factor. Let be the second derivative of the objective function, and let be the objective function. exist The Hessian matrix at that location, Denotes the regularization parameter, where It is the identity matrix. Describe the objective function exist The gradient vector at that point.

8. A cable joint fault early diagnosis device based on edge computing, characterized in that, include: The error function establishment module is used to establish an error function between the measured surface temperature and the calculated surface temperature based on the surface temperature of the cable joint and the surface temperature determined by the cable temperature field. The weighting module is used to introduce a dynamic weighting factor that affects the temperature of the load current into the heat source term of the preset heat conduction control equation. The equation update module is used to update the thermal parameters and boundary conditions of the heat flow balance relationship in the heat conduction control equation based on the dynamic weighting factor. The objective function establishment module is used to determine the temperature field at the joint based on the updated heat conduction control equation, and to establish an objective function with the goal of minimizing the error function. The temperature field inversion module is used to perform iterative inversion of the temperature field at the joint based on the objective function and the updated heat conduction control equation through dynamic weighting factors, until the preset iteration termination condition is met. The iterative solution module is used to determine the temperature field that meets the iteration termination condition as the target temperature field distribution, and to perform early diagnosis of cable joint faults based on the target temperature field distribution.

9. An electronic device, characterized in that, include: At least one processor, at least one memory, and a data bus; The processor and the memory communicate with each other via the data bus. The memory stores program instructions that can be executed by the processor, which invokes the program instructions to perform the method as claimed in any one of claims 1-7.

10. A non-transitory computer-readable storage medium, characterized in that, A non-transitory computer-readable storage medium stores computer instructions that cause a computer to perform the method of any one of claims 1-7.

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