Temperature control method and apparatus for cable heating; non-volatile storage medium

Abstract

This invention discloses a temperature control method, apparatus, and non-volatile storage medium for cable heating. The method includes: acquiring surface temperature data of the cable to be treated, wherein the surface temperature data includes the surface temperatures corresponding to multiple preset points on the cable; inputting the surface temperature data into a target neural network model to obtain conductor temperature data of the cable, wherein the target neural network model is trained based on an initial neural network model, which is constructed based on a temperature determination method corresponding to a thermal loop model, the thermal loop model describing the process of heat transfer from the conductor of the cable to the external environment; and adjusting the heating temperature of the cable based on the conductor temperature data. This invention solves the technical problems of inaccurate cable heating temperature control and low monitoring efficiency in traditional methods.

Description

Technical Field

[0001] This invention relates to the field of cable temperature monitoring technology, and more specifically, to a temperature control method, apparatus, and non-volatile storage medium for cable heating. Background Technology

[0002] In power systems, cables are a crucial component of power transmission, and their stability and reliability directly affect the safe operation of the power network. However, with the acceleration of urbanization and the increase in cable coverage, the failure rate at cable joints has also increased. Statistics show that up to 80% of cable failures originate from the joints, with human error and inaccurate time control in the cable heating and straightening process being among the main causes.

[0003] Traditional cable heating and straightening methods rely on manual operation, such as using heating belts for heating, followed by on-site monitoring of temperature and control of heating time by workers. This manual process is not only inefficient but also easily affected by the operator's experience and skill, leading to inconsistencies and uncontrollability in heating time, temperature control, and straightening and cooling steps. Furthermore, although various new cable heating methods exist, such as automatically regulating heating cables, constant power heating cables, mineral-insulated heating cables, and skin effect heating cables, these technologies still lack digital and intelligent processing of temperature probes and do not achieve fully automated monitoring and remote operation of the heating process. Insufficient temperature control precision during cable heating and straightening can easily lead to problems such as cable overheating or uneven heating, negatively impacting the long-term performance and service life of the cable, and also increasing the operational risks of the power system.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a temperature control method, apparatus, and non-volatile storage medium for cable heating, to at least solve the technical problems of inaccurate temperature control and low monitoring efficiency in traditional methods for cable heating.

[0006] According to one aspect of the present invention, a temperature control method for cable heating is provided, comprising: acquiring surface temperature data of a cable to be treated, wherein the surface temperature data includes the surface temperatures corresponding to multiple preset points of the cable to be treated; inputting the surface temperature data into a target neural network model to obtain conductor temperature data of the cable to be treated, wherein the target neural network model is trained based on an initial neural network model, the initial neural network model is constructed based on a temperature determination method corresponding to a thermal loop model, the thermal loop model is used to describe the process of heat transfer from the conductor of the cable to be treated to the external environment, and the conductor temperature data includes the conductor temperatures corresponding to multiple preset points of the cable to be treated; and adjusting the heating temperature of the cable to be treated based on the conductor temperature data.

[0007] Optionally, surface temperature data is acquired based on distributed fiber optic temperature sensors.

[0008] Optionally, the method for constructing the initial neural network model based on the temperature determination method corresponding to the thermal loop model includes: determining the temperature determination method of the thermal loop model according to the measurement principle of the distributed optical fiber temperature sensor; constructing the original neural network model based on the temperature determination method corresponding to the thermal loop model; determining the target delay order based on the preset autocorrelation function, wherein the target delay order characterizes the amount of historical surface temperature data required by the original neural network model to predict the conductor temperature; and adjusting the original neural network model based on the target delay order to obtain the initial neural network model.

[0009] Optionally, the temperature determination method corresponding to the thermal loop model is represented by a function expression, as follows:

[0010] ,

[0011] in, , , , , , These are the conductor temperature, insulation temperature, sheath temperature, outer sheath temperature, surface temperature, and ambient temperature of the cable to be treated. , , , , These are the conductor heat capacity, insulation heat capacity, sheath heat capacity, outer sheath heat capacity, and surface heat capacity of the cable to be treated. , , , , These are the conductor thermal resistance, insulation thermal resistance, sheath thermal resistance, outer sheath thermal resistance, and ambient thermal resistance of the cable to be treated. This represents the power consumption of the conductor in the cable to be processed.

[0012] Optionally, the expression for the preset autocorrelation function is as follows:

[0013] ,

[0014] in, The target delay order is The corresponding autocorrelation value at time, For time The measured surface temperature at that time. This is the average value of the measured surface temperature over a preset time period. This represents the total length of the time series within the preset time period.

[0015] Optionally, the initial neural network model is an exogenous nonlinear autoregressive neural network model.

[0016] Optionally, after obtaining the conductor temperature data of the cable to be processed, the method further includes: acquiring the topology of the cable to be processed; generating an initial three-dimensional model of the cable to be processed based on the topology; determining the color depth corresponding to each of the multiple preset points in the initial three-dimensional model based on the magnitude of the conductor temperature corresponding to each of the multiple preset points; and adjusting the initial three-dimensional model based on the color depth corresponding to each of the multiple preset points to obtain the target three-dimensional model corresponding to the cable to be processed.

[0017] According to another aspect of the present invention, a cable straightening heating system is also provided, employing any of the above-described temperature control methods for cable heating, comprising: a power supply module for providing power to the cable straightening heating system; an electric heating module for acquiring surface temperature data of the cable to be treated and adjusting the heating temperature of the cable to be treated based on the conductor temperature data; a pressure straightening module for applying pressure straightening treatment to the heated cable to be treated; an intelligent terminal control module for displaying the heating temperature and applied pressure in real time; a host computer system for inputting the surface temperature data into a target neural network model to obtain the conductor temperature data of the cable to be treated; and a wireless transmission module for transmitting data between the electric heating module, the pressure straightening module, the intelligent terminal control module, and the host computer system.

[0018] According to another aspect of the present invention, a temperature control device for cable heating is also provided, comprising: an acquisition module for acquiring surface temperature data of a cable to be treated, wherein the surface temperature data includes the surface temperatures corresponding to multiple preset points of the cable to be treated; a prediction module for inputting the surface temperature data into a target neural network model to obtain conductor temperature data of the cable to be treated, wherein the target neural network model is trained based on an initial neural network model, the initial neural network model is constructed based on a temperature determination method corresponding to a thermal loop model, the thermal loop model is used to describe the process of heat transfer from the conductor of the cable to the external environment, and the conductor temperature data includes the conductor temperatures corresponding to multiple preset points of the cable to be treated; and an adjustment module for adjusting the heating temperature of the cable to be treated based on the conductor temperature data.

[0019] According to another aspect of the present invention, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored program, wherein, when the program is running, the device where the non-volatile storage medium is located is controlled to execute any of the above-described temperature control methods for cable heating.

[0020] According to another aspect of the present invention, a computer device is also provided, the computer device including a processor for running a program, wherein the program executes any of the above-described temperature control methods for cable heating.

[0021] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements any of the above-described temperature control methods for cable heating.

[0022] In this embodiment of the invention, a temperature control method for cable heating is employed. This method acquires surface temperature data of the cable to be treated, including the surface temperatures of multiple preset points on the cable. The surface temperature data is then input into a target neural network model to obtain conductor temperature data of the cable. This target neural network model is trained based on an initial neural network model, which is constructed using a temperature determination method corresponding to a thermal loop model. The thermal loop model describes the process of heat transfer from the conductor of the cable to the external environment. The conductor temperature data includes the conductor temperatures of multiple preset points on the cable. Based on the conductor temperature data, the heating temperature of the cable is adjusted, achieving precise temperature control during the cable heating process. This improves the accuracy of cable temperature measurement and the quality of cable heating and straightening, thereby solving the technical problems of inaccurate cable heating temperature control and low monitoring efficiency in traditional methods. Attached Figure Description

[0023] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0024] Figure 1 A hardware block diagram of a computer terminal for implementing a temperature control method for cable heating is shown.

[0025] Figure 2 This is a schematic flowchart of a temperature control method for cable heating provided according to an embodiment of the present invention;

[0026] Figure 3 This is a schematic diagram of the working principle of a distributed optical fiber sensor provided by an optional embodiment of the present invention;

[0027] Figure 4 This is a schematic diagram of the structure of a NARX neural network provided by an optional embodiment of the present invention;

[0028] Figure 5 This is a schematic diagram of a cable straightening heating system provided according to an optional embodiment of the present invention;

[0029] Figure 6 This is a flowchart illustrating the self-testing process of a cable straightening heater according to an optional embodiment of the present invention;

[0030] Figure 7 This is a flowchart of a digital cable heating straightener provided according to an optional embodiment of the present invention;

[0031] Figure 8 This is a schematic diagram of a cable straightening heater connected to a cloud platform according to an optional embodiment of the present invention;

[0032] Figure 9 This is a schematic diagram of the signal flow of a cable straightening heating tool controller according to an optional embodiment of the present invention;

[0033] Figure 10 This is a structural block diagram of a temperature control device for cable heating provided according to an embodiment of the present invention. Detailed Implementation

[0034] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0035] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0036] According to an embodiment of the present invention, a temperature control method for cable heating is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0037] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal for implementing a temperature control method for cable heating is shown. Figure 1 As shown, the computer terminal 10 may include one or more processors (shown as 102a, 102b, ..., 102n in the figure) (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0038] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10. As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0039] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the temperature control method for cable heating in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby implementing the temperature control method for cable heating described above. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0040] The display can be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10.

[0041] Figure 2 This is a schematic flowchart of a temperature control method for cable heating according to an embodiment of the present invention, as shown below. Figure 2 As shown, the method includes the following steps:

[0042] Step S201: Obtain the surface temperature data of the cable to be processed, wherein the surface temperature data includes the surface temperature of each of the multiple preset points of the cable to be processed.

[0043] In this step, the acquired "surface temperature data" is not just the temperature at a single point, but includes temperature data from multiple preset points on the cable. These preset points may be set according to the cable's length and structure to ensure comprehensive monitoring of the cable's temperature status and avoid localized overheating or uneven heating. By collecting temperature data from various points on the cable in real time, the system can more accurately reflect the cable's temperature distribution, thus providing data support for precise control of the cable heating process. The temperature data acquisition process is dynamic and continuous. Sensors monitor the temperature changes on the cable surface in real time and convert analog signals into digital signals through an analog-to-digital converter module, facilitating processing by the intelligent terminal control module and the host computer system. The intelligent terminal control module further processes these digital signals, using neural networks for temperature prediction and correction to compensate for temperature measurement deviations caused by changes in cable thermal parameters, environmental factors, and model errors.

[0044] Step S202: Input the surface temperature data into the target neural network model to obtain the conductor temperature data of the cable to be processed. The target neural network model is trained based on the initial neural network model. The initial neural network model is constructed based on the temperature determination method corresponding to the thermal loop model. The thermal loop model is used to describe the process of heat transfer from the conductor of the cable to be processed to the external environment. The conductor temperature data includes the conductor temperature corresponding to multiple preset points of the cable to be processed.

[0045] In this step, the initial neural network model is constructed based on the temperature determination method of the thermal loop model. The thermal loop model is a mathematical expression that concretizes the process of heat transfer from the cable conductor to the external environment. It considers heat exchange mechanisms such as heat conduction, heat convection, and heat radiation between the cable conductor, insulation layer, outer sheath, and surrounding environment. Based on the constructed thermal loop model, a neural network is used to learn the heat transfer process from the cable conductor to the external environment. The input variables of the neural network can be defined as the real-time temperature data of the cable surface (temperature at all points across the entire width) and the ambient temperature. These data can reflect the external heat exchange environment during the cable heating process. The output variable is a more accurate real-time cable conductor temperature, that is, the temperature change of the internal conductor of the cable during the heating process. The key parameter for network adjustment is the temperature difference between the input and output.

[0046] Step S203: Adjust the heating temperature of the cable to be processed based on the conductor temperature data.

[0047] In this step, after obtaining the conductor temperature data, it can be compared with a pre-set heating temperature target. Based on the comparison result, the output power of the heating module can be dynamically adjusted. If the conductor temperature is lower than the target temperature, the system will increase the power of the heating module to accelerate the heating process; conversely, if the conductor temperature is close to or exceeds the target temperature, the system will reduce the heating power or stop heating to avoid overheating or uneven heating of the cable, thereby protecting the electrical performance and physical integrity of the cable.

[0048] Through the above steps, the goal of precise temperature control during cable heating is achieved, thereby improving the accuracy of cable temperature measurement and the quality of cable heating and straightening. This solves the technical problems of inaccurate cable heating temperature control and low monitoring efficiency in traditional methods.

[0049] As an optional embodiment, surface temperature data is acquired based on distributed fiber optic temperature sensors.

[0050] Optionally, a distributed fiber optic temperature sensor is a device capable of continuous temperature measurement along the length of a distributed fiber, and its working principle is based on the stimulated Raman scattering (SRS) effect. Figure 3 This is a schematic diagram of the working principle of a distributed optical fiber sensor according to an optional embodiment of the present invention, such as... Figure 3 As shown, due to nonlinear effects, when high-power optical radiation propagates in an optical fiber waveguide, the emission spectrum exhibits local maxima of Stokes and anti-Stokes spectral densities, relative to the laser frequency. Stokes displacement The value of Stokes scattering frequency Anti-Stokes Stokes and Anti-Stokes The intensity correlation of the components is as follows:

[0051]

[0052] in, Absolute temperature Let be Planck's constant. is the Boltzmann constant. By feeding a radiation pulse into an optical fiber and further utilizing the intensity of the Stokes and anti-Stokes components of Raman scattering at different time points, the temperature distribution along the fiber length can be determined.

[0053] Traditional temperature sensors mainly include thermal sensors, resistance temperature detectors (RTDs), and some special semiconductor sensors. These sensors are not very safe or stable, are susceptible to electromagnetic interference, and are also flammable, explosive, and have poor corrosion resistance. Distributed fiber optic sensors, on the other hand, effectively overcome these weaknesses. They feature continuous distributed measurement, resistance to electromagnetic interference, suitability for remote monitoring, and high sensitivity and measurement accuracy.

[0054] As an optional embodiment, the method for constructing the initial neural network model based on the temperature determination method corresponding to the thermal loop model includes: determining the temperature determination method of the thermal loop model according to the measurement principle of the distributed optical fiber temperature sensor; constructing the original neural network model based on the temperature determination method corresponding to the thermal loop model; determining the target delay order based on the preset autocorrelation function, wherein the target delay order characterizes the amount of historical surface temperature data required by the original neural network model to predict the conductor temperature; and adjusting the original neural network model based on the target delay order to obtain the initial neural network model.

[0055] Optionally, the thermal loop model describes the heat transfer process between the conductor and the cable's insulation layer, outer sheath, and surrounding environment during cable heating. In this embodiment, the thermal loop model can be established based on the measurement principle of distributed fiber optic temperature sensors, considering the thermal resistance, thermal capacity, and ambient temperature of each layer of the cable. The thermal loop model is the foundation for constructing the original neural network model, used to describe the nonlinear thermal dynamic characteristics during cable heating. The construction of the original neural network model considers surface temperature data and ambient temperature as inputs, and uses a neural network architecture to predict the temperature of the cable conductor. To improve the accuracy and robustness of the neural network model in predicting the cable conductor temperature, it is necessary to determine important hyperparameters, such as the number of hidden layer neurons and the delay order. The number of hidden layer neurons can typically be selected using the following empirical formula:

[0056]

[0057] in, This indicates the number of neurons in the hidden layer. Indicates the number of nodes in the input layer. This represents the number of nodes in the output layer. In practical applications, the approximate number of neurons in the hidden layer can generally be determined first using an empirical formula, and then adjusted according to the actual training situation.

[0058] As an optional embodiment, the temperature determination method corresponding to the thermal loop model is represented by a function expression, as follows:

[0059] ,

[0060] in, , , , , , These are the conductor temperature, insulation temperature, sheath temperature, outer sheath temperature, surface temperature, and ambient temperature of the cable to be treated. , , , , These are the conductor heat capacity, insulation heat capacity, sheath heat capacity, outer sheath heat capacity, and surface heat capacity of the cable to be treated. , , , , These are the conductor thermal resistance, insulation thermal resistance, sheath thermal resistance, outer sheath thermal resistance, and ambient thermal resistance of the cable to be treated. This represents the power consumption of the conductor in the cable to be processed.

[0061] Optionally, the temperature change during cable heating is quantified by calculating the heat exchange between the cable conductor and the insulation layer, outer sheath, and surrounding environment. Specifically, the process of heat transfer from the conductor to the insulation layer, then from the insulation layer to the outer sheath, and finally dissipation from the outer sheath into the environment is considered during cable heating. Since the thermal resistance of each layer of the cable and the conductor power dissipation change over time, this expression can predict the temperature change trend of the cable conductor based on real-time temperature data and environmental conditions.

[0062] As an optional embodiment, the expression for the preset autocorrelation function is as follows:

[0063] ,

[0064] in, The target delay order is The corresponding autocorrelation value at time, For time The measured surface temperature at that time. This is the average value of the measured surface temperature over a preset time period. This represents the total length of the time series within the preset time period.

[0065] Optionally, by using a preset autocorrelation function, this embodiment can scientifically determine the delay order of the neural network, thereby constructing a more accurate neural network model to predict the temperature of the cable conductor. This is of great significance for improving the temperature control accuracy during the cable straightening heating process, avoiding uneven heating or overheating, and protecting the electrical performance and physical integrity of the cable.

[0066] As an optional embodiment, the initial neural network model is an exogenous nonlinear autoregressive neural network model.

[0067] Optionally, in this embodiment, the initial neural network model used can be an exogenous nonlinear autoregressive neural network (NARX) model. The NARX neural network performs machine learning on the heat transfer process from the cable conductor to the external environment to improve the calculation accuracy of the cable conductor temperature prediction. Since the cable temperature, cable thermal parameters, and environmental thermal parameters change over time, time series modeling and prediction are required. Therefore, a memory function is introduced into the network, making the network itself a dynamic system. Figure 4 This is a schematic diagram of the structure of a NARX neural network provided by an optional embodiment of the present invention, such as... Figure 4 As shown, the NARX regression neural network includes an input layer, hidden layers, an output layer, and input and output delays. Considering that the NARX network contains multi-step delayed inputs and multi-step delayed network output feedback, it exhibits excellent performance in reflecting the historical information and dynamic characteristics of the system. The inputs to the NARX neural network are as follows:

[0068]

[0069] in, express Cable surface temperature data at any given time. , ... This represents the surface temperature at each point on the cable obtained by the distributed fiber optic sensor. Determined by the spatial resolution of the fiber optic sensor. express The temperature of the surrounding environment at the cable site at that moment. The NARX neural network output is as follows:

[0070]

[0071] in, It is the output of the hidden layer. It is the weight matrix from the hidden layer to the output layer. It is the bias term of the output layer.

[0072] As an optional embodiment, after obtaining the conductor temperature data of the cable to be processed, the method further includes: acquiring the topology of the cable to be processed; generating an initial three-dimensional model of the cable to be processed based on the topology; determining the color depth corresponding to each of the multiple preset points in the initial three-dimensional model based on the magnitude of the conductor temperature corresponding to each of the multiple preset points; and adjusting the initial three-dimensional model based on the color depth corresponding to each of the multiple preset points to obtain the target three-dimensional model corresponding to the cable to be processed.

[0073] Optionally, topology refers to the cable's geometry, length, diameter, and the layout and material properties of its internal layers (such as conductors, insulation, and shielding). Using the cable's topology data, an initial 3D model of the cable can be generated through 3D modeling software or a pre-designed algorithm. This model is a digital reproduction of the cable's shape and structure, clearly showing its outer contour and internal structure. The choice of color depth typically follows a temperature gradient, with cool colors indicating increasing temperature. For example, lower temperatures might correspond to blue or green, while higher temperatures might correspond to orange or red; the color depth represents the temperature level, with darker colors indicating higher temperatures and lighter colors indicating lower temperatures. By applying different color depths in the 3D model, it is possible to visually identify which parts are hotter, which parts are colder, and whether there are any issues such as uneven temperature distribution.

[0074] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0075] Through the above description of the embodiments, those skilled in the art can clearly understand that the temperature control method for cable heating according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0076] According to an embodiment of the present invention, a system applying the above-described temperature control method for cable heating is also provided. The system includes: a power supply module for providing power to the cable straightening heating system; an electric heating module for acquiring surface temperature data of the cable to be treated and adjusting the heating temperature of the cable to be treated based on the conductor temperature data; a pressure straightening module for applying pressure to straighten the heated cable to be treated; an intelligent terminal control module for displaying the heating temperature and applied pressure in real time; a host computer system for inputting the surface temperature data into a target neural network model to obtain the conductor temperature data of the cable to be treated; and a wireless transmission module for transmitting data between the electric heating module, the pressure straightening module, the intelligent terminal control module, and the host computer system.

[0077] Optionally, Figure 5 This is a schematic diagram of a cable straightening and heating system according to an optional embodiment of the present invention, such as... Figure 5 As shown, the temperature heating module, also known as the electric heating module, can consist of a distributed fiber optic temperature sensor integrated between the cable and the heating strip, and an automated electric heating strip, used for real-time temperature monitoring and continuous heating of the entire cable. The pressure straightening module consists of a three-pronged gripper, an automatic turntable, and a pressure sensor, used for pressure straightening of the cable. The intelligent terminal control module is a modification of commercially available products, adding a digital display screen to the operation panel to show the heating temperature and applied pressure. It can also add indicator lights and a buzzer to provide guidance and warnings during the heating and straightening process. The wireless transmission module interacts with the host computer system via a 4G network. The host computer system receives data and performs further recording, analysis, storage, and management.

[0078] By comprehensively measuring and digitally visualizing temperature changes during the cable heating and straightening process on the intelligent terminal control module, monitoring the cable heating process becomes more intuitive and convenient. Simultaneously, the introduction of a 4G wireless transmission module enables communication with a cloud server on the host computer, further enhancing the system's intelligence and automation level and providing possibilities for remote monitoring and management. Furthermore, the system's design considers compatibility and scalability with existing equipment, allowing it to flexibly adapt to different application scenarios and needs, facilitating future technology upgrades and functional expansion. Compared to existing power cable temperature monitoring technologies, this provides a more comprehensive and in-depth monitoring solution, contributing to further improvements in power system operation and maintenance strategies and efficiency.

[0079] The proposed temperature control method for cable heating, when applied to the aforementioned system, enables precise temperature monitoring, achieving a cable temperature control accuracy of 0.1℃. This effectively prevents safety accidents such as overheating and insufficient heating during cable heating, thereby extending cable lifespan and improving the safety and reliability of the power system. Furthermore, through real-time monitoring and data analysis, the system can promptly detect and warn of potential fault risks, providing strong support for the stable operation of the power system. In contrast, traditional monitoring methods may not provide sufficient data to prevent and respond promptly to cable faults, resulting in relatively lower safety and reliability of cable operation.

[0080] As an optional embodiment, the above system can be used to perform power emergency repair tasks, and the specific steps are as follows:

[0081] S11: Place the cable straightening heater based on the above system settings at the designated emergency repair location, and connect the power supply and 4G wireless communication module to ensure smooth communication between the equipment and the host computer system.

[0082] S12: Figure 6 This is a flowchart illustrating the self-test process of a cable straightening heater according to an optional embodiment of the present invention, as shown below. Figure 6 As shown, when the power switch of the cable straightening heater is turned on, the intelligent terminal control module starts a self-test program to check the status of each module. After the self-test is completed, the intelligent terminal control module emits a prompt tone through a buzzer and sends the equipment status information to the host computer system through the wireless transmission module. The host computer system confirms the equipment status and returns a confirmation command.

[0083] S13: The operator should tightly connect both ends of the cable to be straightened to the rotary joint of the automatic winding machine, ensuring a secure connection. Distributed fiber optic sensors are evenly arranged along the length of the cable and are tightly fitted to the cable insulation layer.

[0084] S14: The host computer system calculates the heating temperature and time based on the initial temperature of the cable and sends the heating command to the intelligent terminal control module via the wireless transmission module. Upon receiving the command, the intelligent terminal control module activates the electric heating module and heats the cable according to preset parameters. Distributed fiber optic sensors collect real-time temperature data from the cable surface and convert the analog signals into digital signals via a temperature acquisition device, feeding this data back to the intelligent terminal control module and the host computer system.

[0085] S15: The host computer system uses the NARX neural network model to accurately calculate the temperature of the cable conductor and dynamically adjusts the heating strategy based on the calculation results. For example, first, the number of hidden layer neurons is determined. Assuming the number of input layer nodes is 10 and the number of output layer nodes is 1, the number of hidden layer neurons is calculated to be 6. Next, by calculating the autocorrelation function, significantly non-zero lags of 1 and 3 are selected. Then, the NARX neural network uses the temperature data of the last three time points at a unified location: 29.5℃, 30℃, and 31℃ as input, and the network outputs a temperature value of 32℃. Finally, by comparing the temperature predicted by the NARX neural network with the actual measured temperature, the heating power is dynamically adjusted to ensure that the cable surface temperature is uniform and within the preset range. The pressure straightening module applies uniform pressure to the cable according to the instructions of the host computer system to perform a straightening operation. The pressure sensor monitors the pressure on the cable surface in real time and feeds the data back to the intelligent terminal control module and the host computer system. The intelligent terminal control module dynamically adjusts the pressure intensity based on the feedback pressure data to ensure the straightening effect.

[0086] S16: After straightening is completed, the intelligent terminal control module stops heating and pressurizing operations and starts the circulating airflow system to cool the cable. The temperature sensor continues to monitor the cable surface temperature and feeds the data back to the intelligent terminal control module and the host computer system in real time. When the cable surface temperature drops to a safe range, the host computer system issues a completion command, and the intelligent terminal control module prompts the operator to remove the straightened cable via a buzzer.

[0087] S17: The operator removes the straightened cable, performs a visual inspection and electrical performance test to ensure the cable meets safety operation standards. After completing the emergency repair, the equipment power is turned off, the site is cleaned up, and the equipment is moved back to the transport vehicle for future use.

[0088] Through this optional embodiment, the cable straightening heater demonstrates its high efficiency and intelligence in the application of power emergency repair sites. The comprehensive temperature monitoring via distributed fiber optic sensors and the conductor temperature calculation method based on the NARX neural network model ensure precise control of the cable heating process. Simultaneously, the combination of the intelligent terminal control module and the 4G wireless transmission module enables remote monitoring and automated operation, significantly improving repair efficiency and cable straightening quality.

[0089] As an optional embodiment, the cable straightening heater of the above-mentioned cable straightening heating system can also be used in a cable manufacturing production line, and the specific steps are as follows:

[0090] S21: Install cable straightening heaters at the end of the production line in the cable manufacturing plant and perform necessary commissioning. Ensure the equipment is connected to the production line's automated control system to enable data exchange and remote control. Simultaneously, check the communication status of the wireless transmission module to ensure unimpeded communication with the host computer system.

[0091] S22: The production planning department uploads the daily production plan to the cloud server, including parameters such as cable type, specifications, straightening temperature, and time. The host computer system retrieves the production plan from the cloud server and automatically adjusts the operating parameters of the cable straightening heater according to the plan. This process is achieved through a wireless transmission module, ensuring the real-time nature and accuracy of the data.

[0092] S23: Figure 7 This is a flowchart of a digital cable heating straightener provided according to an optional embodiment of the present invention, such as... Figure 7 As shown, after the production line starts, the cable straightening heater automatically initiates the heating and straightening process according to the task sent by the host computer system. Distributed fiber optic sensors are evenly arranged along the length of the cable to collect cable surface temperature data in real time. The temperature acquisition device converts the collected analog signals into digital signals through an analog-to-digital signal converter, and the intelligent terminal control module displays the temperature and pressure in real time. As the control core of the system, the intelligent terminal control module is not only responsible for data acquisition and processing, but also dynamically adjusts the working status of the heating module and the pressure application module according to the preset control logic and the instructions from the host computer, ensuring the automation and intelligence of the entire straightening process.

[0093] S24: During the heating process, the operator monitors the cable surface temperature and pressure through the intelligent terminal control module to ensure they remain within the set range. After heating is complete, the equipment automatically switches to the pressure straightening module, where pressure sensors monitor the cable surface pressure and display it in real time on the intelligent terminal control module. This process, controlled by the intelligent terminal control module, ensures the accuracy and consistency of the straightening process.

[0094] S25: After straightening, the equipment automatically stops heating and pressurizing, and the circulating airflow system begins to cool the cable. Operators monitor the cable surface temperature via an intelligent terminal control module until it drops to a safe range. After cooling, the cable enters the finished product inspection area for visual inspection and electrical performance testing. This process not only ensures cable quality but also reduces cooling time and improves production efficiency through intelligent control.

[0095] S26: Figure 8 This is a schematic diagram of a cable straightening heater connected to a cloud platform according to an optional embodiment of the present invention, as shown below. Figure 8As shown, temperature, pressure, and time data throughout the straightening process are uploaded to a cloud server in real time for production data analysis and quality control. Production managers can view the straightening records of each cable through the cloud platform to analyze production efficiency and product quality. This process is achieved through a wireless transmission module, ensuring the real-time nature and traceability of the data. Figure 9 This is a schematic diagram of the signal flow of a cable straightening heating tool controller according to an optional embodiment of the present invention, such as... Figure 9 As shown, the wireless transmission module utilizes the 4G network protocol to efficiently and stably transmit the collected data to the host computer system. Operators can remotely view the device status, adjust parameters, and receive real-time alarm information through the cloud platform, significantly improving the convenience and security of operation.

[0096] According to an embodiment of the present invention, an apparatus for implementing the above-described temperature control method for cable heating is also provided. Figure 10 This is a structural block diagram of a temperature control device for cable heating according to an embodiment of the present invention, such as... Figure 10 As shown, the device includes an acquisition module 1001, a prediction module 1002, and an adjustment module 1003. The device will be described below.

[0097] The acquisition module 1001 is used to acquire the surface temperature data of the cable to be processed, wherein the surface temperature data includes the surface temperature corresponding to multiple preset points of the cable to be processed.

[0098] The prediction module 1002, connected to the acquisition module 1001, is used to input surface temperature data into the target neural network model to obtain conductor temperature data of the cable to be processed. The target neural network model is trained based on the initial neural network model, which is constructed based on the temperature determination method corresponding to the thermal loop model. The thermal loop model is used to describe the process of heat transfer from the conductor of the cable to be processed to the external environment. The conductor temperature data includes the conductor temperature corresponding to multiple preset points of the cable to be processed.

[0099] The adjustment module 1003, connected to the prediction module 1002, is used to adjust the heating temperature of the cable to be processed based on the conductor temperature data.

[0100] It should be noted that the acquisition module 1001, prediction module 1002, and adjustment module 1003 correspond to steps S201 to S203 in the embodiments. Multiple modules implement the same instances and application scenarios as their corresponding steps, but are not limited to the content disclosed in the above embodiments. It should also be noted that the above modules, as part of the device, can run on the computer terminal 10 provided in the embodiments.

[0101] Embodiments of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one of a plurality of network devices in a computer network. The computer device includes a memory and a processor.

[0102] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the temperature control method and device for cable heating in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the aforementioned temperature control method for cable heating. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to a computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0103] The processor can access information and applications stored in the memory via a transmission device to perform the following steps: acquiring surface temperature data of the cable to be processed, wherein the surface temperature data includes the surface temperatures corresponding to multiple preset points of the cable to be processed; inputting the surface temperature data into a target neural network model to obtain conductor temperature data of the cable to be processed, wherein the target neural network model is trained based on an initial neural network model, the initial neural network model is constructed based on the temperature determination method corresponding to the thermal loop model, the thermal loop model is used to describe the process of heat transfer from the conductor of the cable to the external environment, and the conductor temperature data includes the conductor temperatures corresponding to multiple preset points of the cable to be processed; and adjusting the heating temperature of the cable to be processed based on the conductor temperature data.

[0104] Optionally, the processor may also execute program code that performs the following steps: surface temperature data is acquired based on a distributed fiber optic temperature sensor.

[0105] Optionally, the processor may also execute program code for the following steps: The method for constructing the initial neural network model based on the temperature determination method corresponding to the thermal loop model includes: determining the temperature determination method of the thermal loop model according to the measurement principle of the distributed optical fiber temperature sensor; constructing the original neural network model based on the temperature determination method corresponding to the thermal loop model; determining the target delay order based on the preset autocorrelation function, wherein the target delay order characterizes the amount of historical surface temperature data required by the original neural network model to predict the conductor temperature; and adjusting the original neural network model based on the target delay order to obtain the initial neural network model.

[0106] Optionally, the processor can also execute program code that performs the following steps: The temperature determination method corresponding to the thermal loop model is represented by a function expression, as follows:

[0107] ,

[0108] in, , , , , , These are the conductor temperature, insulation temperature, sheath temperature, outer sheath temperature, surface temperature, and ambient temperature of the cable to be treated. , , , , These are the conductor heat capacity, insulation heat capacity, sheath heat capacity, outer sheath heat capacity, and surface heat capacity of the cable to be treated. , , , , These are the conductor thermal resistance, insulation thermal resistance, sheath thermal resistance, outer sheath thermal resistance, and ambient thermal resistance of the cable to be treated. This represents the power consumption of the conductor in the cable to be processed.

[0109] Optionally, the processor may also execute program code with the following steps: The expression for the preset autocorrelation function is as follows:

[0110] ,

[0111] in, The target delay order is The corresponding autocorrelation value at time, For time The measured surface temperature at that time. This is the average value of the measured surface temperature over a preset time period. This represents the total length of the time series within the preset time period.

[0112] Optionally, the processor may also execute program code for the following steps: the initial neural network model is an exogenous nonlinear autoregressive neural network model.

[0113] Optionally, the processor may also execute program code for the following steps: after obtaining the conductor temperature data of the cable to be processed, it further includes: acquiring the topology of the cable to be processed; generating an initial three-dimensional model of the cable to be processed based on the topology; determining the color depth corresponding to each of the multiple preset points in the initial three-dimensional model based on the magnitude of the conductor temperature corresponding to each of the multiple preset points; and adjusting the initial three-dimensional model based on the color depth corresponding to each of the multiple preset points to obtain the target three-dimensional model corresponding to the cable to be processed.

[0114] This invention provides a temperature control method for cable heating. By acquiring surface temperature data of the cable to be treated, including the surface temperatures of multiple preset points on the cable, the surface temperature data is input into a target neural network model to obtain conductor temperature data of the cable. The target neural network model is trained based on an initial neural network model, which is constructed based on a temperature determination method corresponding to a thermal loop model. The thermal loop model describes the process of heat transfer from the conductor of the cable to the external environment. The conductor temperature data includes the conductor temperatures of multiple preset points on the cable. Based on the conductor temperature data, the heating temperature of the cable is adjusted, achieving precise temperature control during the cable heating process. This improves the accuracy of cable temperature measurement and the quality of cable heating and straightening, thus solving the technical problems of inaccurate cable heating temperature control and low monitoring efficiency in traditional methods.

[0115] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a non-volatile storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0116] Embodiments of the present invention also provide a non-volatile storage medium. Optionally, in this embodiment, the aforementioned non-volatile storage medium can be used to store the program code executed by the temperature control method for cable heating provided in the above embodiments.

[0117] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0118] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: acquiring surface temperature data of the cable to be processed, wherein the surface temperature data includes the surface temperature corresponding to each of multiple preset points of the cable to be processed; inputting the surface temperature data into a target neural network model to obtain conductor temperature data of the cable to be processed, wherein the target neural network model is trained based on an initial neural network model, the initial neural network model is constructed based on the temperature determination method corresponding to the thermal loop model, the thermal loop model is used to describe the process of heat transfer from the conductor of the cable to be processed to the external environment, and the conductor temperature data includes the conductor temperature corresponding to each of multiple preset points of the cable to be processed; and adjusting the heating temperature of the cable to be processed based on the conductor temperature data.

[0119] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: surface temperature data is acquired based on a distributed fiber optic temperature sensor.

[0120] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: The method for constructing an initial neural network model based on the temperature determination method corresponding to the thermal loop model includes: determining the temperature determination method of the thermal loop model according to the measurement principle of the distributed optical fiber temperature sensor; constructing an original neural network model based on the temperature determination method corresponding to the thermal loop model; determining the target delay order based on a preset autocorrelation function, wherein the target delay order characterizes the amount of historical surface temperature data required by the original neural network model to predict the conductor temperature; and adjusting the original neural network model based on the target delay order to obtain the initial neural network model.

[0121] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: the temperature determination method corresponding to the thermal loop model is represented by a function expression, as follows:

[0122] ,

[0123] in, , , , , , These are the conductor temperature, insulation temperature, sheath temperature, outer sheath temperature, surface temperature, and ambient temperature of the cable to be treated. , , , , These are the conductor heat capacity, insulation heat capacity, sheath heat capacity, outer sheath heat capacity, and surface heat capacity of the cable to be treated. , , , , These are the conductor thermal resistance, insulation thermal resistance, sheath thermal resistance, outer sheath thermal resistance, and ambient thermal resistance of the cable to be treated. This represents the power consumption of the conductor in the cable to be processed.

[0124] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: the expression of the preset autocorrelation function is as follows:

[0125] ,

[0126] in, The target delay order is The corresponding autocorrelation value at time, For time The measured surface temperature at that time. This is the average value of the measured surface temperature over a preset time period. This represents the total length of the time series within the preset time period.

[0127] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: the initial neural network model is an exogenous nonlinear autoregressive neural network model.

[0128] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: after obtaining the conductor temperature data of the cable to be processed, the method further includes: acquiring the topology of the cable to be processed; generating an initial three-dimensional model of the cable to be processed based on the topology; determining the color depth corresponding to each of the multiple preset points in the initial three-dimensional model based on the magnitude of the conductor temperature corresponding to each of the multiple preset points; and adjusting the initial three-dimensional model based on the color depth corresponding to each of the multiple preset points to obtain a target three-dimensional model corresponding to the cable to be processed.

[0129] Embodiments of the present invention also provide a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it can: acquire surface temperature data of the cable to be processed, wherein the surface temperature data includes the surface temperature corresponding to multiple preset points of the cable to be processed; input the surface temperature data into a target neural network model to obtain conductor temperature data of the cable to be processed, wherein the target neural network model is trained based on an initial neural network model, the initial neural network model is constructed based on the temperature determination method corresponding to the thermal loop model, the thermal loop model is used to describe the process of heat transfer from the conductor of the cable to be processed to the external environment, and the conductor temperature data includes the conductor temperature corresponding to multiple preset points of the cable to be processed; and adjust the heating temperature of the cable to be processed based on the conductor temperature data.

[0130] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0131] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0132] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0133] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0134] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0135] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0136] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A temperature control method for cable heating, characterized in that, include: Obtain surface temperature data of the cable to be processed, wherein the surface temperature data includes the surface temperature corresponding to each of multiple preset points of the cable to be processed; The surface temperature data is input into the target neural network model to obtain the conductor temperature data of the cable to be processed. The target neural network model is trained based on the initial neural network model, which is constructed based on the temperature determination method corresponding to the thermal loop model. The thermal loop model is used to describe the process of heat transfer from the conductor of the cable to be processed to the external environment. The conductor temperature data includes the conductor temperature corresponding to multiple preset points of the cable to be processed. Based on the conductor temperature data, the heating temperature of the cable to be processed is adjusted; The surface temperature data is acquired based on a distributed fiber optic temperature sensor. The temperature determination method for the thermal loop model is determined according to the measurement principle of the distributed fiber optic temperature sensor. The temperature determination method corresponding to the thermal loop model is represented by a function expression, which is as follows: ， in, , , , , , These are the conductor temperature, insulation layer temperature, sheath temperature, outer sheath temperature, surface temperature, and ambient temperature of the cable to be processed. , , , , These are the conductor heat capacity, insulation heat capacity, sheath heat capacity, outer sheath heat capacity, and surface heat capacity of the cable to be processed. , , , , These are the conductor thermal resistance, insulation thermal resistance, sheath thermal resistance, outer sheath thermal resistance, and ambient thermal resistance of the cable to be processed. The power consumption of the conductor of the cable to be processed.

2. The method according to claim 1, characterized in that, The method for constructing the initial neural network model based on the temperature determination method corresponding to the thermal loop model includes: Based on the temperature determination method corresponding to the thermal circuit model, an original neural network model is constructed. Based on a preset autocorrelation function, a target delay order is determined, wherein the target delay order characterizes the amount of historical surface temperature data required by the original neural network model to predict the conductor temperature; Based on the target delay order, the original neural network model is adjusted to obtain the initial neural network model.

3. The method according to claim 2, characterized in that, The expression for the preset autocorrelation function is as follows: ， in, The target delay order is The corresponding autocorrelation value at time, For time The measured surface temperature at that time. This is the average value of the measured surface temperature over a preset time period. The total length of the time series within the preset time period.

4. The method according to claim 1, characterized in that, The initial neural network model is an exogenous nonlinear autoregressive neural network model.

5. The method according to any one of claims 1 to 4, characterized in that, After obtaining the conductor temperature data of the cable to be processed, the process further includes: Obtain the topology of the cable to be processed; Based on the aforementioned topology, an initial three-dimensional model of the cable to be processed is generated; Based on the conductor temperature corresponding to each of the multiple preset points, the color depth corresponding to each of the multiple preset points in the initial three-dimensional model is determined. Based on the color depth corresponding to each of the multiple preset points, the initial three-dimensional model is adjusted to obtain the target three-dimensional model corresponding to the cable to be processed.

6. A cable straightening heating system, characterized in that, The temperature control method for cable heating according to any one of claims 1 to 5 includes: The power supply module is used to provide power to the cable straightening heating system; An electric heating module is used to acquire surface temperature data of the cable to be processed, and to adjust the heating temperature of the cable to be processed based on the conductor temperature data; The pressure straightening module is used to apply pressure and straighten the heated cable to be processed. The intelligent terminal control module is used to display the heating temperature and applied pressure in real time; The host computer system is used to input the surface temperature data into the target neural network model to obtain the conductor temperature data of the cable to be processed; The wireless transmission module is used to transmit data between the electric heating module, the pressure straightening module, the intelligent terminal control module, and the host computer system.

7. A temperature control device for cable heating, characterized in that, include: The acquisition module is used to acquire surface temperature data of the cable to be processed, wherein the surface temperature data includes the surface temperature corresponding to each of multiple preset points of the cable to be processed. The prediction module is used to input the surface temperature data into the target neural network model to obtain the conductor temperature data of the cable to be processed. The target neural network model is trained based on the initial neural network model, which is constructed based on the temperature determination method corresponding to the thermal loop model. The thermal loop model is used to describe the process of heat transfer from the conductor of the cable to be processed to the external environment. The conductor temperature data includes the conductor temperature corresponding to multiple preset points of the cable to be processed. An adjustment module is used to adjust the heating temperature of the cable to be processed based on the conductor temperature data; The surface temperature data is acquired based on a distributed fiber optic temperature sensor. The temperature determination method for the thermal loop model is determined according to the measurement principle of the distributed fiber optic temperature sensor. The temperature determination method corresponding to the thermal loop model is represented by a function expression, which is as follows: ， in, , , , , , These are the conductor temperature, insulation layer temperature, sheath temperature, outer sheath temperature, surface temperature, and ambient temperature of the cable to be processed. , , , , These are the conductor heat capacity, insulation heat capacity, sheath heat capacity, outer sheath heat capacity, and surface heat capacity of the cable to be processed. , , , , These are the conductor thermal resistance, insulation thermal resistance, sheath thermal resistance, outer sheath thermal resistance, and ambient thermal resistance of the cable to be processed. The power consumption of the conductor of the cable to be processed.

8. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the non-volatile storage medium to perform the temperature control method for cable heating as described in any one of claims 1 to 5.

9. A computer device, characterized in that, include: Memory and processor The memory stores computer programs; The processor is configured to execute a computer program stored in the memory, wherein when the computer program is executed, the processor performs the temperature control method for cable heating as described in any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the temperature control method for cable heating as described in any one of claims 1 to 5.

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