Electric heat tracing control system and method based on intelligent temperature control and remote monitoring

Abstract

The application discloses an electric heat tracing control system based on intelligent temperature control and remote monitoring, and relates to the technical field of temperature control; a quantum dot sensor is used for high-precision temperature measurement and wireless transmission of a temperature monitoring module, MEMS sensors and gas sensors are used for an environmental parameter acquisition module, the environment is analyzed through machine learning, a control module uses a deep reinforcement learning algorithm, power is adjusted in combination with environmental parameters, a power management module uses a bidirectional DC-DC converter, wireless power supply and energy recovery, a remote communication module uses 6G and satellite communication, quantum encryption transmission data, a storage module uses a blockchain distributed storage, and an alarm module uses AR to prompt fault processing; the application can dynamically adjust power according to the environment and real-time temperature, has strong remote monitoring functions, can grasp the state in real time, has energy recovery and thermoelectric conversion, can quickly and accurately diagnose faults, and can improve system reliability and stability in combination with AR auxiliary processing.

Description

Technical Field

[0001] This invention relates to the field of remote temperature control system technology, and in particular to an electric heat tracing control system and method based on intelligent temperature control and remote monitoring. Background Technology

[0002] Electric heat tracing systems play a crucial role in numerous industrial production and daily life scenarios. They are primarily used to maintain the temperature of media within pipelines, tanks, and other equipment, preventing problems such as solidification and increased viscosity due to excessively low temperatures, thereby ensuring smooth production processes and normal equipment operation. In industries such as petroleum, chemical, and natural gas, electric heat tracing systems are indispensable key facilities.

[0003] Traditional electric heat tracing systems employ relatively simple temperature control methods, mostly relying on manual control or basic constant temperature control. Manual control depends on human experience, which is not only inefficient but also difficult to achieve real-time, precise adjustments, easily leading to excessively high or low temperatures, resulting in energy waste and equipment damage. While simple constant temperature control can maintain temperature stability to a certain extent, it cannot dynamically adjust to changes in environmental factors. When ambient temperature, humidity, pressure, and other conditions change, it fails to meet actual heat tracing requirements.

[0004] Furthermore, traditional electric heat tracing systems lack effective remote monitoring methods. Maintenance personnel need to conduct regular on-site inspections, which not only consumes significant manpower and time but also makes it difficult to promptly identify potential faults. If a system malfunction occurs, such as a short circuit or open circuit in the heat tracing cable, and cannot be addressed in a timely manner, it may lead to serious production accidents and economic losses. Simultaneously, traditional systems have limited data recording and analysis capabilities, making it difficult to comprehensively assess and optimize the system's operational status, which is detrimental to improving system reliability and energy efficiency. With the development of industrial automation and informatization, the limitations of traditional electric heat tracing systems are becoming increasingly apparent, urgently requiring a more intelligent, efficient, and reliable electric heat tracing control system and method to meet practical needs. Summary of the Invention

[0005] The present invention proposes an electric heat tracing control system and method based on intelligent temperature control and remote monitoring to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an electric heat tracing control system based on intelligent temperature control and remote monitoring, comprising:

[0007] Temperature monitoring module: A temperature sensor is installed at the location of the electric heat tracing system to collect real-time temperature data (T). real And it is transmitted wirelessly to the control module via a nano-antenna;

[0008] Environmental parameter acquisition module: Utilizes integrated microelectromechanical systems (MEMS) sensors to acquire ambient temperature (T). env Humidity H and pressure P of the medium inside the pipeline; simultaneously, a gas sensor is added to detect the concentration C of corrosive gases in the environment, according to the formula Calculate the absolute humidity, where D is the absolute humidity;

[0009] Control module: Receives data from the temperature monitoring module and the environmental parameter acquisition module. By constructing a deep neural network model based on policy gradients, the system learns the optimal control strategy through continuous interaction with the electric heating environment. When the real-time temperature is lower than the set lower limit T... min At the same time, taking into account environmental parameters, the optimized control algorithm formula is applied. Calculate and adjust the output power of the electric heat tracing system; where P out For output power, K p T i T d These are the proportional, integral, and differential coefficients, respectively, e = T set -T real T set T set To set the temperature, f(T) env H, P, C) is a power compensation function adjusted according to environmental parameters. When the temperature is higher than the set upper limit T max When necessary, reduce or cut off the output power.

[0010] Power Management Module: Based on the instructions of the control module, it uses bidirectional DC-DC converter technology to adjust the power supply voltage and current of the electric heat tracing system; through PWM pulse width modulation technology, combined with soft switching control strategy, it reduces switching losses and introduces wireless power transmission technology to provide wireless power to some monitoring nodes or auxiliary equipment that are far from the power source without changing the existing wiring.

[0011] Remote communication module: Utilizing 6G communication technology and satellite communication as a backup link, it remotely transmits data from the temperature monitoring module, environmental parameter acquisition module, and control module's operating status to the monitoring center; it uses quantum encryption technology to encrypt the transmitted data, based on the principle of quantum key distribution, and simultaneously receives control commands from the monitoring center to achieve remote control functionality.

[0012] Furthermore, it also includes:

[0013] Fault Diagnosis Module: This module uses deep learning algorithms to analyze data from the temperature monitoring module, environmental parameter acquisition module, and the operating status of the control module; it uses a convolutional neural network (CNN) to extract waveform features from the temperature data, and combines a recurrent neural network (RNN) to process time series data to build a fault diagnosis model; by comparing the normal operating data with the current data, if the deviation exceeds a set threshold, it determines that the system has a fault, identifies the fault type, and transmits the fault information to the alarm module and storage module.

[0014] Self-optimization module: This module periodically collects system operation data and uses a genetic algorithm to optimize the parameters of the deep reinforcement learning model in the control module; it finds optimal model parameter combinations by simulating natural selection and genetic mutation processes; at the same time, it adopts an adaptive learning rate adjustment strategy to dynamically adjust the learning rate according to the convergence of model training.

[0015] Storage module: Employing blockchain-based distributed storage technology, temperature monitoring data, environmental parameter data, control module operation records, and historical system fault information are stored on nodes; hash algorithms are used to encrypt and store the data.

[0016] Alarm module: When the real-time temperature exceeds the set safety range T real <T min or T real >T max When environmental parameters are abnormal or the system malfunctions, an alarm signal will be issued through an audible and visual alarm.

[0017] Furthermore, the temperature monitoring module also adopts distributed optical fiber temperature sensing technology, laying special optical fibers along the pipeline and the heat tracing cable; utilizing the principle of combining stimulated Brillouin scattering (SBS) and Raman scattering, it realizes continuous temperature monitoring of the entire heat tracing area, and obtains the strain information of the heat tracing cable through spectral analysis of the scattered light from the optical fiber.

[0018] Furthermore, the power management module has an energy recovery function; when the electric heat tracing system is turned off or the output power is reduced, the remaining energy in the heat tracing tape is recovered and stored in the supercapacitor through a circuit composed of inductors and capacitors; at the same time, thermoelectric conversion technology is used to convert the waste heat generated during the operation of the heat tracing tape into electrical energy.

[0019] Furthermore, the remote communication module adopts software-defined networking (SDN) technology to achieve flexible configuration and management of the communication network; it schedules network traffic through a centralized controller, optimizes data transmission paths according to data priority and real-time requirements, and utilizes edge computing technology to perform preliminary processing and analysis of the collected data at edge nodes close to the electric heat tracing system.

[0020] A method for applying the aforementioned electric heat tracing control system based on intelligent temperature control and remote monitoring includes:

[0021] Data acquisition steps: Real-time temperature data is collected through the temperature monitoring module. Ambient temperature, humidity, pipeline medium pressure, and corrosive gas concentration are obtained using the environmental parameter acquisition module, and absolute humidity is calculated. The collected data is denoised using a Kalman filter algorithm, and features are extracted using wavelet transform.

[0022] Data analysis and processing steps: The control module receives the collected data, analyzes the deviation e between the real-time temperature and the set temperature, calculates the output power according to the deep reinforcement learning algorithm formula, and uses machine learning algorithms to analyze environmental parameters and predict environmental change trends.

[0023] Control execution steps: According to the instructions of the control module, the power management module adjusts the power supply voltage and current of the electric heat tracing system through a bidirectional DC-DC converter and PWM technology, combined with a soft-switching control strategy, so that the output power of the heat tracing cable reaches the calculated value; at the same time, it uses wireless power transmission technology to power some equipment.

[0024] Remote communication and monitoring steps: The remote communication module transmits the collected data and system operating status to the monitoring center via 6G communication technology, uses quantum encryption technology to ensure data security, and simultaneously receives control commands from the monitoring center to achieve remote monitoring and control; SDN and edge computing technologies are used to optimize data transmission and processing;

[0025] Fault handling and recording steps: If the alarm module issues an alarm, staff will handle the fault according to the AR visualization prompts. The handling process and results are recorded in the blockchain-based distributed storage module for subsequent analysis and experience summarization. The deep learning model of the fault diagnosis module is used to quickly locate and diagnose the fault.

[0026] Furthermore, the data acquisition process also includes sensor fusion processing of the acquired data; using DS evidence theory, data from temperature sensors, humidity sensors, pressure sensors, and gas sensors are fused; and sensor self-calibration technology is used to periodically calibrate the sensors.

[0027] Furthermore, in the data analysis and processing steps, when the system detects the ambient temperature T... env Below a certain low temperature threshold T env-low Furthermore, the concentration of corrosive gas C exceeds a certain danger threshold C. danger When the set temperature T is reached, it will automatically increase. set The value of is adjusted, and the output power compensation coefficient of the tracing cable is increased.

[0028] Furthermore, in the fault handling and recording steps, if the fault diagnosis module determines that the fault is due to the aging of the heat tracing cable, the control module notifies the staff to replace the heat tracing cable through the remote communication module, and provides the staff with suggestions and selection references for replacing the heat tracing cable based on historical data and prediction models; at the same time, the storage module records the fault occurrence time, fault type, handling process and related suggestions.

[0029] Compared with existing technologies, the beneficial effects of this invention are:

[0030] In terms of temperature control, high-precision temperature sensors and advanced control algorithms are used to achieve precise temperature control of the electric heat tracing system. The output power of the heat tracing cable can be dynamically adjusted according to real-time temperature and environmental parameters to avoid excessively high or low temperatures, ensuring that the medium inside the pipeline is always within a suitable temperature range, thus improving production stability and product quality.

[0031] The system boasts powerful remote monitoring capabilities, utilizing 6G communication and quantum encryption technology to transmit system operation data securely and in real-time to the monitoring center. Maintenance personnel can monitor the system's operational status anytime without being on-site, promptly identifying and addressing potential faults, significantly improving operational efficiency and reducing labor costs and failure risks.

[0032] In terms of energy utilization, the power management module's energy recovery and thermoelectric conversion functions can convert the residual energy and waste heat of the heat tracing cable into electrical energy, achieving secondary energy utilization. Simultaneously, the intelligent control algorithm can precisely adjust the power according to actual needs, avoiding energy waste and significantly reducing operating costs.

[0033] The fault diagnosis module employs deep learning algorithms to quickly and accurately locate and diagnose fault types. Combined with augmented reality technology, it provides on-site maintenance personnel with a visual fault-handling guide, improving the efficiency and accuracy of fault handling.

[0034] The self-optimization module continuously optimizes the control strategy through genetic algorithms and adaptive learning techniques, enabling the system to better adapt to different environments and operating conditions. This significantly improves the system's reliability and stability, reduces losses caused by downtime due to failures, and provides strong support for the company's production and operations. Attached Figure Description

[0035] Figure 1 This is a schematic block diagram of the electric heat tracing control system based on intelligent temperature control and remote monitoring proposed in this invention;

[0036] Figure 2 This is a schematic block diagram of the electric heat tracing control method based on intelligent temperature control and remote monitoring proposed in this invention;

[0037] Figure 3This is a schematic block diagram illustrating the temperature monitoring data trend of the electric heat tracing control system based on intelligent temperature control and remote monitoring proposed in this invention.

[0038] Figure 4 This is a schematic block diagram comparing the energy utilization rate of the electric heat tracing control system based on intelligent temperature control and remote monitoring proposed in this invention.

[0039] Figure 5 This is a schematic block diagram representing the beneficial effects of the electric heat tracing control system based on intelligent temperature control and remote monitoring proposed in this invention. Detailed Implementation

[0040] 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0042] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0043] Reference Figure 1-5 An electric heat tracing control system based on intelligent temperature control and remote monitoring includes the following modules:

[0044] Temperature monitoring module: High-precision temperature sensors are deployed at key locations in the electric heat tracing system, such as pipe surfaces and heat tracing cable joints. Quantum dot sensor technology, for example, utilizes the unique optical and electrical properties of quantum dots to achieve ultra-high resolution temperature measurement with an accuracy of ±0.1℃. The sensors collect real-time temperature data T at a frequency of no less than 10 seconds. real The data is transmitted wirelessly to the control module via a nano-antenna, which effectively reduces signal transmission loss and ensures stable data transmission. When deploying quantum dot temperature sensors at key locations in the electric heating system, ensure the sensors are securely installed and in good contact with the monitored area to guarantee measurement accuracy. When setting a sampling frequency of at least 10 seconds, consider both the data volume and system processing capacity to avoid data redundancy that could strain transmission and storage. While nano-antennas reduce signal loss, it is crucial to protect against external electromagnetic interference and regularly check the antenna status to ensure stable data transmission.

[0045] Environmental parameter acquisition module: Utilizes integrated microelectromechanical systems (MEMS) sensors to acquire ambient temperature T. env The humidity (H) and the pressure of the medium inside the pipeline (P) are also considered. Simultaneously, a gas sensor is added to detect the concentration (C) of corrosive gases in the environment, such as sulfur dioxide and hydrogen sulfide. According to the formula... (Where D represents absolute humidity) Calculate absolute humidity and comprehensively analyze the impact of environmental factors on the electric heat tracing system. Introduce machine learning algorithms to preprocess and extract features from the collected environmental parameters, establish a correlation model between environmental factors and electric heat tracing requirements, and predict potential environmental risks. When using MEMS sensors to collect environmental parameters, pay attention to the accuracy calibration and service life of the sensors to prevent data deviation due to sensor performance degradation. When using gas sensors to detect corrosive gas concentrations, select sensors with appropriate range and sensitivity based on environmental characteristics and take protective measures to avoid sensor corrosion damage. When constructing the correlation model between environmental factors and electric heat tracing requirements, continuously update the data to adapt to environmental changes under different operating conditions.

[0046] Control module: Receives data from the temperature monitoring module and the environmental parameter acquisition module, and incorporates a deep reinforcement learning algorithm. By constructing a deep neural network model based on policy gradients, the system learns the optimal control strategy through continuous interaction with the electric heating environment. When the real-time temperature is lower than the set lower limit T... min At the same time, taking into account environmental parameters, the optimized control algorithm formula is applied. Calculate and adjust the output power of the electric heat tracing system; where P out For output power, K p T i T d These are the proportional, integral, and differential coefficients, respectively, e = Tset -T real T set T set To set the temperature, f(T) env H, P, C) is a power compensation function adjusted according to environmental parameters. When the temperature is higher than the set upper limit T max When necessary, reduce or cut off the output power. When building the neural network model using the built-in deep reinforcement learning algorithm, a large amount of historical data covering different operating conditions should be used for training to prevent overfitting or underfitting. When calculating and adjusting the output power of the electric heat tracing system, changes in environmental parameters must be monitored in real time, and the power compensation function updated promptly. Furthermore, the effectiveness of the control strategy should be evaluated regularly, and algorithm parameters optimized based on actual operating results to ensure efficient and stable system operation.

[0047] Power Management Module: Based on instructions from the control module, this module uses bidirectional DC-DC converter technology to adjust the supply voltage and current of the electric heat tracing system. Through PWM (Pulse Width Modulation) technology combined with a soft-switching control strategy, switching losses are reduced, and power conversion efficiency is improved. Simultaneously, wireless power transfer technology is introduced to wirelessly power some monitoring nodes or auxiliary equipment located far from the power source without altering existing wiring, ensuring stable system operation and comprehensive monitoring. When adjusting the power supply using bidirectional DC-DC converter technology, the adjustment range of voltage and current must be strictly controlled to avoid exceeding the rated operating parameters of the electric heat tracing equipment and damaging it. Using PWM technology combined with a soft-switching control strategy requires precise setting of relevant parameters to effectively reduce switching losses. When introducing wireless power transfer technology, the transmission distance and power must be rationally planned to ensure stable power supply to equipment located far from the power source, while preventing electromagnetic radiation from interfering with surrounding equipment.

[0048] Remote Communication Module: Utilizing 6G communication technology and satellite communication as a backup link, this module remotely transmits data from the temperature monitoring module, environmental parameter acquisition module, and control module's operational status to the monitoring center. Quantum encryption technology is used to encrypt the transmitted data, ensuring absolute security during transmission based on the principle of quantum key distribution. Simultaneously, it receives control commands from the monitoring center, enabling remote control and ensuring real-time system monitoring and management. When using 6G communication technology for data transmission, attention must be paid to network coverage and signal strength, especially in remote or complex terrain areas. Combining this with a satellite communication backup link can ensure continuous data transmission. When using quantum encryption technology, the quantum key distribution equipment must be protected to prevent key leakage. Furthermore, received control commands must be rigorously verified to ensure accuracy and prevent system malfunctions due to incorrect commands.

[0049] Storage Module: Employing blockchain-based distributed storage technology, temperature monitoring data, environmental parameter data, control module operation records, and historical system fault information are stored across multiple nodes. Hash algorithms are used to encrypt and store the data, ensuring its immutability and traceability. Storage time is no less than 5 years, facilitating subsequent retrieval, analysis, and fault tracing.

[0050] Alarm module: When the real-time temperature exceeds the set safe range (T) real <T min or T real >T max When environmental parameters are abnormal (such as excessive pressure, excessive humidity, or excessive concentration of corrosive gases) or when the system malfunctions, an alarm signal will be issued via an audible and visual alarm. Simultaneously, augmented reality (AR) technology will be used to provide on-site personnel with visual prompts indicating the location of the fault and instructions for handling it, thereby improving the efficiency of fault handling.

[0051] This invention also includes:

[0052] Fault Diagnosis Module: This module utilizes deep learning algorithms to analyze data from the temperature monitoring module, environmental parameter acquisition module, and the operating status of the control module. It employs a Convolutional Neural Network (CNN) to extract waveform features from the temperature data and combines this with a Recurrent Neural Network (RNN) to process time-series data, constructing a fault diagnosis model. By comparing normal operating data with current data, if the deviation exceeds a set threshold, a system fault is identified, and the fault type is determined, such as short circuit, open circuit, or aging of the heating cable. Simultaneously, the fault information is transmitted to the alarm module and storage module. Transfer learning technology is used to transfer model parameters trained on similar electric heating systems to the current system, reducing model training time and data requirements, and improving the accuracy and efficiency of fault diagnosis.

[0053] Self-optimization module: This module periodically collects system operation data and uses a genetic algorithm to optimize the parameters of the deep reinforcement learning model in the control module. By simulating natural selection and genetic mutation processes, it seeks the optimal combination of model parameters to improve the system's control accuracy and response speed. Simultaneously, an adaptive learning rate adjustment strategy is employed, dynamically adjusting the learning rate based on the model's training convergence to accelerate convergence. Furthermore, an exploration-utilization balance mechanism from reinforcement learning is introduced, allowing the system to continuously explore new control strategies while fully utilizing existing successful experiences, enabling the electric heat tracing system to better adapt to different environments and operating conditions.

[0054] In this invention, the temperature monitoring module also employs distributed fiber optic temperature sensing technology, with special optical fibers laid along the pipeline and the heat tracing cable. Utilizing the combined principles of stimulated Brillouin scattering (SBS) and Raman scattering, continuous temperature monitoring of the entire heat tracing area is achieved with a positioning accuracy of ±0.5 meters, enabling timely detection of localized overheating or overcooling issues. Furthermore, spectral analysis of the scattered light from the optical fibers can also obtain strain information of the heat tracing cable, allowing for early prediction of mechanical failures.

[0055] In this invention, the power management module has an energy recovery function. When the electric heat tracing system is shut down or its output power is reduced, the remaining energy in the heat tracing cable is recovered and stored in a supercapacitor through a circuit composed of an inductor and a capacitor. Simultaneously, thermoelectric conversion technology is used to convert waste heat generated during the operation of the heat tracing cable into electrical energy, further improving energy utilization efficiency. The maximum power point tracking (MPPT) algorithm is used to optimize the energy recovery and conversion process, ensuring efficient energy utilization under different operating conditions.

[0056] In this invention, the remote communication module employs Software-Defined Networking (SDN) technology to achieve flexible configuration and management of the communication network. A centralized controller intelligently schedules network traffic, optimizing data transmission paths based on data priority and real-time requirements to ensure rapid transmission of critical data. Simultaneously, edge computing technology is utilized to perform preliminary processing and analysis of the collected data at edge nodes close to the electric heat tracing system, reducing data transmission volume, lowering communication latency, and improving system response speed.

[0057] This invention also discloses an electric heat tracing control method based on intelligent temperature control and remote monitoring, comprising the following steps:

[0058] Data acquisition steps: Real-time temperature data is collected through the temperature monitoring module. Ambient temperature, humidity, pipeline medium pressure, and corrosive gas concentration are obtained using the environmental parameter acquisition module, and absolute humidity is calculated. The collected data is denoised using a Kalman filter algorithm, and features are extracted using wavelet transform to improve data quality and effectiveness.

[0059] Data analysis and processing steps: The control module receives the collected data, analyzes the deviation 'e' between the real-time temperature and the set temperature, and calculates the output power using a deep reinforcement learning algorithm. Machine learning algorithms are then used to analyze environmental parameters, predict environmental change trends, and adjust the control strategy in advance.

[0060] Control execution steps: Following instructions from the control module, the power management module adjusts the supply voltage and current of the electric heat tracing system using a bidirectional DC-DC converter and PWM technology, combined with a soft-switching control strategy, to ensure the output power of the heat tracing cable reaches the calculated value. Simultaneously, wireless power transfer technology is used to power some devices, ensuring stable system operation.

[0061] Remote communication and monitoring steps: The remote communication module transmits the collected data and system operating status to the monitoring center via 6G communication technology (combined with satellite communication backup), using quantum encryption technology to ensure data security. Simultaneously, it receives control commands from the monitoring center to achieve remote monitoring and control. SDN and edge computing technologies are used to optimize data transmission and processing, improving system response speed.

[0062] Fault Handling and Recording Steps: If the alarm module issues an alert, staff will handle the fault according to the AR visual prompts. The handling process and results are recorded in a blockchain-based distributed storage module for subsequent analysis and experience summarization. The deep learning model of the fault diagnosis module is used to quickly locate and diagnose faults, improving fault handling efficiency.

[0063] In this invention, the data acquisition step also includes multi-sensor fusion processing of the acquired data. DS evidence theory is employed to fuse data from multiple sources, such as temperature, humidity, pressure, and gas sensors, improving data reliability and accuracy. Simultaneously, sensor self-calibration technology is used to periodically calibrate the sensors, reducing the impact of sensor errors on the system. When performing multi-sensor fusion processing in the data acquisition step, attention must be paid to the time synchronization of different sensors to avoid data fusion deviations caused by differences in acquisition time. When using DS evidence theory to fuse data, the basic probability allocation of each piece of evidence must be reasonably set, fully considering the characteristics and historical performance of the sensors. When using sensor self-calibration technology, the calibration cycle should be set comprehensively considering the stability of the sensors and the operating environment. Data recording and backup during the calibration process must be performed to prevent calibration errors from affecting system operation.

[0064] In this invention, during the data analysis and processing step, when the system detects the ambient temperature T... env Below a certain low temperature threshold T env-low Furthermore, the concentration of corrosive gas C exceeds a certain danger threshold C. danger When the set temperature T is reached, it will automatically increase. set The value of the set temperature and the output power compensation coefficient of the heating cable should be adjusted to enhance the heating effect and prevent corrosion, ensuring the normal operation of the medium in the pipeline. During data analysis and processing, the thresholds for ambient temperature and corrosive gas concentration should be accurately determined based on the characteristics of the medium in the pipeline, the material and performance of the heating cable, and other factors to avoid misjudgment or delayed response due to improper threshold settings. When automatically increasing the set temperature and the output power compensation coefficient, the working status of the heating cable should be monitored to prevent excessive power from causing safety problems such as overheating or short circuits.

[0065] In this invention, during the fault handling and recording steps, if the fault diagnosis module determines that the heat tracing cable is aging, the control module notifies the staff to replace the heat tracing cable via the remote communication module. Based on historical data and predictive models, the control module provides the staff with optimal timing suggestions and selection references for replacing the heat tracing cable. Simultaneously, the storage module records the fault occurrence time, fault type, handling process, and related suggestions to facilitate subsequent maintenance and system optimization. During the fault handling and recording steps, the fault diagnosis module considers multiple characteristics and indicators when determining if the heat tracing cable is aging to avoid misjudgment. When the control module notifies the staff to replace the heat tracing cable, it ensures the stability and accuracy of remote communication to prevent information transmission errors or loss. Providing optimal replacement timing suggestions and selection references should consider actual operating conditions and inventory status, while the information recorded by the storage module should ensure completeness and accuracy to facilitate subsequent analysis and optimization.

[0066] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An electric heat tracing control system based on intelligent temperature control and remote monitoring, characterized in that, include: Temperature monitoring module: A temperature sensor is installed at the location of the electric heat tracing system to collect real-time temperature data (T). real And it is transmitted wirelessly to the control module via a nano-antenna; Environmental parameter acquisition module: Utilizes integrated microelectromechanical systems (MEMS) sensors to acquire ambient temperature (T). env Humidity H and pressure P of the medium inside the pipeline; simultaneously, a gas sensor is added to detect the concentration C of corrosive gases in the environment, according to the formula Calculate the absolute humidity, where D is the absolute humidity; Control module: Receives data from the temperature monitoring module and the environmental parameter acquisition module. By constructing a deep neural network model based on strategy gradients, the system optimizes its control strategy through continuous interaction with the electric heating environment. When the real-time temperature is lower than the set lower limit T... min At the same time, taking into account environmental parameters, the optimized control algorithm formula is applied. Calculate and adjust the output power of the electric heat tracing system; where P out For output power, K p T i T d These are the proportional, integral, and differential coefficients, respectively, e = T set -T real T set T set To set the temperature, f(T) env H, P, C) is a power compensation function adjusted according to environmental parameters; when the temperature is higher than the set upper limit T max When necessary, reduce or cut off the output power; Power Management Module: Based on the instructions of the control module, it uses bidirectional DC-DC converter technology to adjust the power supply voltage and current of the electric heat tracing system; through PWM pulse width modulation technology, combined with soft switching control strategy, it reduces switching losses and introduces wireless power transmission technology to provide wireless power to monitoring nodes or auxiliary equipment that are far from the power source without changing the existing wiring. Remote communication module: Utilizing 6G communication technology and satellite communication as a backup link, it remotely transmits data from the temperature monitoring module, environmental parameter acquisition module, and control module's operating status to the monitoring center; it uses quantum encryption technology to encrypt the transmitted data, based on the principle of quantum key distribution, and simultaneously receives control commands from the monitoring center to achieve remote control functionality.

2. The electric heat tracing control system based on intelligent temperature control and remote monitoring according to claim 1, characterized in that, Also includes: Fault Diagnosis Module: This module uses deep learning algorithms to analyze data from the temperature monitoring module, environmental parameter acquisition module, and the operating status of the control module; it uses a convolutional neural network (CNN) to extract waveform features from the temperature data, and combines a recurrent neural network (RNN) to process time series data to build a fault diagnosis model; by comparing the normal operating data with the current data, if the deviation exceeds a set threshold, it determines that the system has a fault, identifies the fault type, and transmits the fault information to the alarm module and storage module.

3. The electric heat tracing control system based on intelligent temperature control and remote monitoring according to claim 1, characterized in that, Also includes: Self-optimization module: This module periodically collects system operation data and uses a genetic algorithm to optimize the parameters of the deep reinforcement learning model in the control module; it finds the optimal combination of model parameters by simulating the process of natural selection and genetic mutation; at the same time, it adopts an adaptive learning rate adjustment strategy to dynamically adjust the learning rate according to the convergence of model training. Storage module: Employing blockchain-based distributed storage technology, temperature monitoring data, environmental parameter data, control module operation records, and historical system fault information are stored on nodes; hash algorithms are used to encrypt and store the data. Alarm module: When the real-time temperature exceeds the set safety range T real <T min or T real >T max When environmental parameters are abnormal or the system malfunctions, an alarm signal will be issued through an audible and visual alarm.

4. The electric heat tracing control system based on intelligent temperature control and remote monitoring according to claim 1, characterized in that, The temperature monitoring module also employs distributed optical fiber temperature sensing technology, laying special optical fibers along the pipeline and the heat tracing cable; utilizing the principle of combining stimulated Brillouin scattering (SBS) and Raman scattering, it achieves continuous temperature monitoring of the entire heat tracing area, and obtains strain information of the heat tracing cable through spectral analysis of the scattered light from the optical fiber.

5. The electric heat tracing control system based on intelligent temperature control and remote monitoring according to claim 1, characterized in that, The power management module has an energy recovery function; when the electric heat tracing system is turned off or the output power is reduced, the remaining energy in the heat tracing tape is recovered and stored in the supercapacitor through a circuit composed of inductors and capacitors; at the same time, thermoelectric conversion technology is used to convert the waste heat generated during the operation of the heat tracing tape into electrical energy.

6. The electric heat tracing control system based on intelligent temperature control and remote monitoring according to claim 1, characterized in that, The remote communication module adopts software-defined networking (SDN) technology to achieve flexible configuration and management of the communication network. It schedules network traffic through a centralized controller, optimizes data transmission paths according to data priority and real-time requirements, and utilizes edge computing technology to process and analyze the collected data at edge nodes close to the electric heat tracing system.

7. A method for using the electric heat tracing control system based on intelligent temperature control and remote monitoring as described in any one of claims 1-6, characterized in that, include: Data acquisition steps: Real-time temperature data is collected through the temperature monitoring module; ambient temperature, humidity, pipeline medium pressure, and corrosive gas concentration are obtained through the environmental parameter acquisition module, and absolute humidity is calculated; Kalman filtering algorithm is used to denoise the collected data, and wavelet transform is used to extract features from the data; Data analysis and processing steps: The control module receives the collected data, analyzes the deviation e between the real-time temperature and the set temperature, calculates the output power according to the deep reinforcement learning algorithm formula, and uses machine learning algorithms to analyze environmental parameters and predict environmental change trends. Control execution steps: According to the instructions of the control module, the power management module adjusts the power supply voltage and current of the electric heat tracing system through a bidirectional DC-DC converter and PWM technology, combined with a soft-switching control strategy, so that the output power of the heat tracing cable reaches the calculated value; at the same time, it uses wireless power transmission technology to power some equipment. Remote communication and monitoring steps: The remote communication module transmits the collected data and system operating status to the monitoring center via 6G communication technology, uses quantum encryption technology to ensure data security, and simultaneously receives control commands from the monitoring center to achieve remote monitoring and control; SDN and edge computing technologies are used to optimize data transmission and processing; Fault handling and recording steps: If the alarm module issues an alarm, staff will handle the fault according to the AR visualization prompts. The handling process and results are recorded in the blockchain-based distributed storage module. The deep learning model in the fault diagnosis module is used to locate and diagnose faults.

8. The electric heat tracing control method based on intelligent temperature control and remote monitoring according to claim 7, characterized in that, The data acquisition process also includes sensor fusion processing of the acquired data; using DS evidence theory, data from temperature sensors, humidity sensors, pressure sensors, and gas sensors are fused; and sensor self-calibration technology is used to periodically calibrate the sensors.

9. The electric heat tracing control method based on intelligent temperature control and remote monitoring according to claim 7, characterized in that, In the data analysis and processing steps, when the system detects the ambient temperature T env Below a certain low temperature threshold T env-low Furthermore, the concentration of corrosive gas C exceeds a certain danger threshold C. danger When the set temperature T is reached, it will automatically increase. set The value of is adjusted, and the output power compensation coefficient of the tracing cable is increased.

10. The electric heat tracing control method based on intelligent temperature control and remote monitoring according to claim 7, characterized in that, In the fault handling and recording steps, if the fault diagnosis module determines that the fault is caused by the aging of the heat tracing cable, the control module will notify the staff to replace the heat tracing cable through the remote communication module, and provide the staff with suggestions and selection references for replacing the heat tracing cable based on historical data and prediction models; at the same time, the storage module records the time of occurrence of the fault, the fault type, the handling process and related suggestions.

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