Heat tracing control system based on LoRa and 4G wireless technologies

Abstract

This invention belongs to the field of industrial temperature maintenance technology, specifically relating to a heat tracing control system based on LoRa and 4G wireless technologies. It consists of an integrated control system and an intelligent management system. The integrated control system is mainly used for real-time control: it uses high-precision sensors and a wireless communication architecture to achieve dynamic PID temperature adjustment of each heat tracing pipeline; it uses a zero-crossing detection circuit with a zero-crossing triggered solid-state relay to achieve zero-crossing triggered power control to suppress power system harmonics; the intelligent management system is mainly used to analyze global data, understand energy consumption, and provide risk warnings; it also visualizes the status, implements multi-dimensional parameter monitoring and fault location; and it configures remote strategies to achieve mode switching. This invention utilizes a closed-loop PID algorithm and stepless voltage regulation technology of a power adjustment device to achieve precise temperature control of ±1℃. Simultaneously, it uses wireless communication to transmit data in real time, dynamically matching heating demand and power output to construct a multi-coordinated intelligent thermal management network.

Description

Technical Field

[0001] This invention belongs to the field of industrial temperature maintenance technology, specifically relating to a heat tracing control system based on LoRa and 4G wireless technologies. Background Technology

[0002] Heat tracing control is an industrial temperature maintenance technology that actively heats and compensates for heat loss in equipment such as pipelines and storage tanks to ensure that the internal media (such as water, oil, chemical fluids, etc.) operate stably within a specific temperature range. Its essential purpose is to prevent the media from solidifying (such as antifreezing of crude oil), crystallizing, or increasing in viscosity, thus ensuring the continuity of the process. Typical applications include: anti-condensation of oil pipelines, heat preservation of pharmaceutical water, and constant temperature of chemical reaction vessels.

[0003] Problems with existing technology:

[0004] I. Energy waste and low temperature control accuracy

[0005] Traditional heat tracing control has the following drawbacks: heat tracing cables often operate at full power all day, resulting in serious energy waste; temperature control relies on mechanical temperature controllers, with an accuracy of only ±5℃ or more, and is prone to overshoot or response lag.

[0006] II. Inadequate security and power grid pollution

[0007] Traditional heat tracing control has drawbacks: phase power regulation generates high-order harmonics (THD > 30%), which interfere with the power grid;

[0008] III. Short communication distance and poor networking flexibility

[0009] Traditional heat tracing control has drawbacks: it relies on wired / short-range wireless technologies such as RS485 and ZigBee, resulting in limited coverage, typically <100m, requiring extensive cabling; expansion is difficult, and adding new nodes requires replanning the wiring, leading to high costs. Summary of the Invention

[0010] The purpose of this invention is to provide a heat tracing control system based on LoRa and 4G wireless technologies. It can achieve precise temperature control of ±1℃ by using a closed-loop PID algorithm and stepless voltage regulation technology of a power adjustment device. At the same time, it can transmit data in real time through wireless communication, dynamically match heating demand and power output, and build a multi-coordinated intelligent thermal management network.

[0011] The specific technical solution adopted by this invention is as follows:

[0012] A heat tracing control method based on LoRa and 4G wireless technologies, including a PID dynamic adjustment method, is described in the following steps:

[0013] Data acquisition and preprocessing;

[0014] PID calculation and decision-making are performed in the cloud. After the data is transmitted to the intelligent management system cloud platform, the platform calculates the target power in real time.

[0015] Command issuance and power execution: Control commands are issued to the target heat tracing base station, the heat tracing base station parses the commands, the PWM dynamically adjusts the duty cycle, controls the power of the heat tracing tape, and zero-crossing conduction achieves harmonic suppression;

[0016] Closed-loop calibration and safety protection.

[0017] In the data acquisition and preprocessing process, an industrial-grade digital temperature sensor is used, via SPI / I 2 The C interface connects to the MCU main control chip in the heat tracing base station, and collects temperature data every 10-30 seconds.

[0018] An RC low-pass filter is used to suppress high-frequency noise, and moving average filtering and Kalman filtering are used to eliminate environmental transient interference.

[0019] The MCU main control chip sends the filtered temperature data to the intelligent management system.

[0020] The temperature data is sent to the intelligent management system in three ways:

[0021] Method 1: The MCU main control chip packages the filtered temperature data into LoRa frames, sends them to the gateway LoRa via spread spectrum modulation, and then sends the data information to the intelligent management system via the gateway LoRa.

[0022] Method 2: The MCU main control chip sends the filtered temperature data to the built-in IoT card of the heat tracing base station, and the gateway uploads the data to the intelligent management system through the 4G module;

[0023] Method 3: The MCU main control chip packages the filtered temperature data into a data packet via the built-in Bluetooth module of the heat tracing base station and sends it to the smart device with the heat tracing smart control APP installed.

[0024] The steps for the platform to calculate the target power in real time include:

[0025] It quickly responds to the deviation between the measured temperature and the set value, and increases the weight of P when the temperature difference reaches the limit value;

[0026] Accumulate historical deviations to eliminate steady-state errors caused by differences in pipe materials;

[0027] It predicts temperature change trends, suppresses overshoot, and dynamically constrains the PID output range by combining current sensor data.

[0028] The steps for parsing the heat tracing base station instruction include:

[0029] After the MCU main control chip in the heat tracing base station parses the instructions, it drives the solid-state relay to conduct at the zero-crossing point of the AC power.

[0030] The zero-crossing detection circuit generates a trigger pulse, and the PWM dynamically adjusts the duty cycle to precisely control the power of the heat tracing cable.

[0031] The steps for achieving harmonic suppression through zero-crossing conduction include zero-crossing point detection and command generation:

[0032] The mains voltage signal is input to the comparator through a voltage divider circuit, and a 5V TTL pulse signal is output when the AC voltage crosses zero.

[0033] The MCU main control chip has a built-in PLL algorithm that dynamically adjusts the zero-crossing trigger timing by sampling the power grid frequency in real time to compensate for power grid fluctuations.

[0034] The zero-crossing pulse triggers an external GPIO interrupt. After receiving the pulse, the MCU main control chip in the heat tracing base station immediately generates a zero-crossing trigger instruction for the solid-state relay in the interrupt service routine.

[0035] Furthermore, it also includes dynamic power adjustment and harmonic suppression:

[0036] The solid-state relay is turned on at the detected zero crossing point to synchronize the current waveform with the voltage phase, thus avoiding the 3rd / 5th / 7th harmonics caused by the step current.

[0037] The number of solid-state relay conduction cycles is set based on the PID calculation results;

[0038] The MCU main control chip analyzes the current waveform, calculates the total harmonic distortion rate, and automatically reduces the maximum duty cycle of the PWM when the limit is exceeded.

[0039] Furthermore, this also includes cloud-based collaborative optimization:

[0040] The system deploys an LSTM model in the cloud to analyze historical THD data and predict the harmonic risk level for the next 24 hours.

[0041] If the predicted THD > 5%, an automatic PWM duty cycle limit adjustment command is generated and sent to all heat tracing base station nodes;

[0042] When the cloud detects multi-node harmonic anomalies, it automatically triggers the power grid quality inspection process and pushes maintenance work orders.

[0043] A heat tracing control system based on LoRa and 4G wireless technologies, and a heat tracing control method based on LoRa and 4G wireless technologies, including:

[0044] Integrated control system:

[0045] The temperature of each heating pipeline is dynamically adjusted by PID control using high-precision sensors and a wireless communication architecture.

[0046] With the help of a zero-detection circuit and a zero-crossing trigger type solid-state relay, zero-crossing trigger power control is used to suppress power system harmonics;

[0047] It performs rapid power-off for overload / short circuits and also has an alarm function for situations affecting system stability;

[0048] Intelligent Management System:

[0049] Used to analyze global data, gain insights into energy consumption, and simultaneously provide risk warnings;

[0050] Enables visualization of status, multi-dimensional parameter monitoring, and fault location;

[0051] Configure remote policies to enable mode switching;

[0052] The integrated control system relies on the hardware layer to perform overload cutoff and temperature fine-tuning; the intelligent management system relies on cloud computing power to formulate energy efficiency strategies and analyze the root causes of failures; the integrated control system and the intelligent management system form a data flow closed loop, which combines safety and energy efficiency.

[0053] An electronic device, the electronic device comprising:

[0054] At least one processor;

[0055] and a memory communicatively connected to the at least one processor;

[0056] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform a heat tracing control method based on LoRa and 4G wireless technologies.

[0057] The technical effects achieved by this invention are as follows:

[0058] This invention uses a high-precision temperature sensor measurement module and PID algorithm to monitor equipment temperature in real time and dynamically adjust output power. The temperature control accuracy can reach ±1℃. Compared with other temperature controllers, it can save 10%-30% of electricity consumption per year, resulting in significant cost reduction and efficiency improvement. In addition, by using LoRa self-organizing network / 4G / Bluetooth communication to reduce wiring, maintenance costs are reduced by 70%.

[0059] This invention features multiple safety protections, real-time monitoring of voltage and current and automatic overload / short circuit cut-off, overheat alarm for power adjustment device, self-diagnosis of sensor faults, and temperature sensor measurement design, supporting temperature deviation alarm, ensuring safe and stable system operation.

[0060] This invention employs zero-crossing control of the power adjustment device to avoid harmonics in the power system and improve grid stability and system reliability.

[0061] When used with host computer software, this invention allows for free switching between three modes: constant temperature, industrial temperature control, and power limiting, according to actual needs, providing flexible control in multiple modes.

[0062] The present invention utilizes wireless communication to transmit data in real time, dynamically match heating demand with power output, and obtain timely warnings of abnormal power consumption, leakage or temperature control failure through AI diagnosis and local visualization monitoring, thereby constructing a multi-coordinated intelligent thermal management network.

[0063] This invention enables the entire communication process to have distributed control and flexible expansion features through a distributed expansion system. It supports multi-node distributed control and can expand the number of nodes and control range according to needs, flexibly adapting to projects of different scales. Attached Figure Description

[0064] Figure 1 This is a schematic diagram of the communication connection of the heat tracing base station provided in an embodiment of the present invention;

[0065] Figure 2 This is a system diagram of the heat tracing control system provided in an embodiment of the present invention. Detailed Implementation

[0066] To make the objectives and advantages of this invention clearer, the invention will be specifically described below with reference to embodiments. It should be understood that the following text is merely used to describe one or more specific embodiments of the invention and does not strictly limit the scope of protection specifically claimed by the invention.

[0067] like Figure 1 As shown, the heat tracing control system based on LoRa and 4G wireless technology includes a heat tracing control system, which consists of an integrated control system and an intelligent management system.

[0068] The integrated control system, designed for rapid edge execution, focuses on real-time response and relies on the hardware layer to perform overload cutoff and temperature fine-tuning; the intelligent management system, designed for intelligent decision-making in the cloud, focuses on long-term optimization and relies on cloud computing power to formulate energy efficiency strategies and analyze the root causes of failures.

[0069] The integrated control system and intelligent management system form a closed loop of data flow, and at the same time, they are linked to safety and energy efficiency.

[0070] The integrated control system employs a dynamic temperature control system, a multi-layer protection system, a power grid stabilization system, and an interactive management system.

[0071] (I) Dynamic temperature control system: Through a high-precision temperature sensor measurement module and PID algorithm, the system monitors the equipment temperature in real time and dynamically adjusts the output power, achieving a temperature control accuracy of ±1℃. The core technologies required are as follows:

[0072] High-precision temperature sensing module: used to collect temperature data from temperature sensors located at each heat tracing pipeline;

[0073] Technical prerequisites: Industrial-grade digital temperature sensor (such as LMT85 or similar device) is used, supporting a measurement accuracy of ±0.3℃. The sensor integrates hardware and software filtering design and eliminates environmental noise interference through MCU unit; Installation method: Directly attached to the surface of the heat tracing cable or key nodes of the pipeline to collect temperature data in real time.

[0074] PID control algorithm:

[0075] Dynamic optimization mechanism: Proportional term (P): Quickly responds to temperature deviations (such as the difference between the set value and the measured value); Integral term (I): Accumulates historical deviations and eliminates steady-state errors (such as long-term temperature drift); Differential term (D): Predicts temperature change trends and suppresses overshoot;

[0076] Parameter self-adaptation: The PID coefficients are dynamically adjusted based on the thermal inertia model (such as pipe material and environmental heat dissipation coefficient) to adapt to different operating conditions.

[0077] Dynamic power adjustment module:

[0078] Execution unit: Controls solid-state relays or IGBT modules via PWM (Pulse Width Modulation) to adjust the power supply voltage (DC12-24V) of the heating pipeline.

[0079] Energy efficiency optimization: Combine current sensing magnetic sensors (such as RSCM17100KP101) to monitor load current in real time, avoid overload and reduce energy consumption.

[0080] Wireless communication architecture:

[0081] Local transmission layer (LoRa): Employs LoRa / 4G version heat tracing base stations, utilizes modules such as SX1278 to build a Mesh network, and connects the heat tracing base station nodes to the gateway LoRa via wireless communication, with a transmission distance of 3-8km. Finally, data is transmitted to the intelligent management system via the gateway LoRa; Data encryption: End-to-end encryption based on AES-128 to prevent tampering.

[0082] Remote transmission layer (4G): The heat tracing base station adopts LoRa / 4G version. The heat tracing base station node has a built-in IoT card. The gateway uploads data to the intelligent management system through the 4G module (supporting MQTT protocol) to realize cross-regional monitoring.

[0083] Mobile transmission layer (Bluetooth): The heat tracing base station adopts Bluetooth version. The heat tracing base station node communicates with the smart device with the heat tracing smart control APP installed through the built-in Bluetooth module. The heat tracing smart control APP is the smart device operation terminal of the smart management system.

[0084] Technical prerequisites: The heat tracing base station integrates a power adjustment device module, an MCU main control chip, a LoRa or 4G wireless communication module (the Bluetooth version has a built-in Bluetooth module), a temperature sensor interface, a current and voltage acquisition unit, and a high-brightness LCD display module. The entire circuit board adopts surge protection and electromagnetic compatibility suppression design to ensure stable operation of the circuit system under harsh working conditions. Specifically, the Bluetooth version has a built-in Bluetooth communication module and requires a dedicated explosion-proof mobile phone with a built-in APP to scan the device's QR code for device control and parameter viewing / modification. The LoRa / 4G version has an antenna on the casing and can be used with a host computer for device control and parameter viewing / modification. It also has a built-in Bluetooth chip, supporting standalone Bluetooth communication.

[0085] The specific operation process for output power adjustment is as follows:

[0086] Step 1: Data Acquisition and Preprocessing

[0087] The temperature sensor collects data every 10-30 seconds, which is then filtered by the MCU and transmitted via SPI / I 2 Send the data via the C interface to the heat tracing base station node;

[0088] The node packages data (including device ID, temperature value, and timestamp) in three ways: Method 1: Send the data to the gateway LoRa in low power mode (DR=5), and then the gateway transmits it to the intelligent management system. Method 2: The built-in gateway uploads the data to the cloud of the intelligent management system via the 4G module. Method 3: Transmit the data to the built-in APP on a dedicated mobile phone via the built-in Bluetooth communication module.

[0089] Step 2: Cloud-based PID calculation and decision-making

[0090] After the data is transmitted to the cloud platform, the platform calls the PID algorithm library to calculate the target power in real time;

[0091] The PID algorithm formula is: Where P is the heat dissipation power, K is the thermal conductivity of the insulation layer, A is the average heat dissipation area of ​​the insulation layer, δ is the average thickness of the insulation layer, and ΔT is the temperature difference between the two sides of the insulation layer.

[0092] Example: Suppose a section of pipeline needs to be kept at a minimum temperature of 10℃ to prevent freezing. When the ambient temperature is -10℃, the temperature measurement accuracy of the explosion-proof heat tracing intelligent control base station of this solution reaches 0.1℃, and the temperature control accuracy is less than 1℃. That is, the explosion-proof heat tracing intelligent control base station of this solution can be set to 10℃ to ensure that the pipeline will not freeze. Traditional products have a differential on / off temperature of ±10℃. In order to ensure that the minimum temperature is not lower than 10℃, the temperature setting value should be 10+10=20℃. If this solution and a traditional product are used to insulate the same pipeline, K, A, and δ are all the same.

[0093] Calculate based on the above conditions:

[0094] The traditional average heating power is: ;

[0095] The average heating power of this scheme is: ;

[0096] Based on the above formula, we can conclude that: ;

[0097] Therefore, it can be calculated that under specific environments, the energy-saving effect of this explosion-proof heat tracing intelligent control base station can reach 10%-30%, which greatly saves users' electricity consumption.

[0098] Step 3: Command Issuance and Power Execution

[0099] Taking the above method one as an example, the control command is returned to the gateway LoRa, and then broadcast to the target heat tracing base station. The heat tracing base station parses the command and dynamically adjusts the duty cycle of PWM (e.g., from 50% to 70%) to drive the heat tracing pipeline to heat up.

[0100] Step 4: Closed-loop calibration and safety protection

[0101] Real-time feedback: A new round of temperature data is uploaded to verify the temperature control effect; if the deviation continues to be greater than 1℃, the PID parameters will be automatically retuned.

[0102] Multiple protections: The current sensor detects short circuits / overcurrents and triggers the air switch to cut off power; when the temperature exceeds the limit, the platform pushes an alarm to the APP and starts the backup heating unit.

[0103] According to this solution: by using PID dynamic compensation and sensor hardware filtering, temperature fluctuations can be controlled within ±1℃, ensuring temperature control accuracy; by using dynamic power adjustment to reduce ineffective heating, energy consumption is reduced by about 30%, improving energy efficiency; and by using LoRa self-organizing network / 4G / Bluetooth communication to reduce wiring, maintenance costs are reduced by 70%.

[0104] (II) A multi-protection system is used to monitor voltage and current in real time and automatically cut off overload / short circuit. It has over-temperature alarm for power regulation device, self-diagnosis of sensor faults and temperature sensor temperature measurement design, supports temperature deviation alarm, and ensures safe and stable operation of the system. The core technologies required are as follows:

[0105] Electrical parameter precision monitoring module:

[0106] Current / voltage detection: A magnetic sensor (such as RSCM17100KP101) is used to acquire the load current in real time, and a high-precision ADC (16 bits or more) is used to measure voltage fluctuations, with a detection accuracy of ±0.5%.

[0107] Overload / short circuit protection: When the current exceeds the limit, the circuit is instantly cut off by a solid-state relay (SSR) or IGBT module with a response time of <20ms; integrated resettable fuse (PTC) prevents repeated impacts.

[0108] Over-temperature protection of the power regulating device:

[0109] Temperature monitoring point: Embed a digital temperature sensor (such as DS18B20) in the heat sink of the IGBT / PWM module to monitor the temperature of the power device in real time;

[0110] Tiered alarm mechanism: Level 1 warning (>80℃): Reduce PWM duty cycle and output power; Level 2 protection (>100℃): Force power off and push alarm to APP.

[0111] Sensor fault self-diagnosis:

[0112] Dual-channel verification: The main sensor (such as LMT85) and the redundant backup sensor acquire data synchronously. If the data difference is >1℃, a diagnosis is triggered. A self-test signal is embedded in the current loop to detect open / short circuit faults in the sensor.

[0113] Fault location: Transmit the fault code to the heat tracing base station (e.g., “ERR101” indicates that the temperature sensor has failed), and the system platform will automatically generate a maintenance work order.

[0114] Dynamic alarm for temperature deviation:

[0115] Intelligent threshold algorithm: Set dynamic alarm thresholds (e.g., ±2℃ fluctuation range) based on historical temperature curves; trigger emergency state by detecting sudden changes (>5℃ / min);

[0116] Multi-level linkage: If the deviation persists for 2 minutes without being eliminated, the backup heating unit will be activated and maintenance personnel will be notified.

[0117] The specific operation process of the system is as follows:

[0118] Step 1: Real-time monitoring and local decision-making

[0119] Various types of data are collected in a regular manner, pre-processed locally, and then subjected to security analysis. If the data is deemed safe, the heat tracing base station uploads it to the intelligent management system. If the data is deemed abnormal, protective actions are executed.

[0120] Step 2: Cloud-based intelligent diagnosis and alerts

[0121] The heat tracing base station uploads data to the intelligent management system, and the system platform performs the following analyses: temperature trend prediction of the power adjustment device (based on LSTM model) and sensor health assessment (verifying data volatility and correlation).

[0122] The diagnostic results are pushed to the APP, and the location of the faulty equipment is marked (e.g., "Temperature sensor abnormal at heat tracing node of pipe No. 3").

[0123] Step 3: Closed-loop protection and recovery

[0124] After the fault is cleared, the system automatically detects the line and gradually restores power supply after confirming safety (PWM increases in 10% increments); Backup strategy: When the main sensor fails, switch to the redundant sensor and calibrate the reading deviation.

[0125] According to this solution: the overload cut-off response time is ≤50ms, which is several times faster than traditional thermal relays, greatly improving safety; the sensor fault self-diagnosis accuracy is >95%, the maintenance response time is shortened by 70%, and operation and maintenance are optimized. The system achieves full coverage protection from electrical safety to sensor health through a four-layer closed loop of "sensing-decision-execution-feedback".

[0126] (III) The power grid stability system employs zero-crossing control via a power regulating device to avoid harmonic generation in the power system, thereby improving grid stability and system reliability. The core technologies required are as follows:

[0127] Zero-crossing detection circuit:

[0128] Technical prerequisites: Use a high-precision voltage comparator (such as LM393) or optocoupler isolation device (such as H11AA1) to monitor the AC voltage waveform in real time. When the voltage zero crossing point is detected (i.e., the instantaneous voltage value is 0V), a pulse signal is generated as a trigger reference.

[0129] Anti-interference design: Hardware filtering circuit (RC low-pass filter) combined with software debouncing algorithm to avoid false triggering due to power grid noise.

[0130] Zero-crossing triggered solid-state relays (SSRs):

[0131] Execution mechanism: The SSR has a built-in zero-voltage switch (ZVS), which only conducts the load circuit at the zero-crossing point of the AC current, ensuring that the current waveform is synchronized with the voltage and avoiding harmonics caused by phase change; Advantages: Response time ≤10ms, no mechanical contacts, lifespan of more than 10^7 cycles, which is superior to traditional electromagnetic relays.

[0132] Adaptive phase synchronization algorithm:

[0133] Dynamic compensation: The MCU acquires the power grid frequency (50 / 60Hz) in real time through the ADC and dynamically adjusts the zero-crossing detection timing in combination with phase-locked loop (PLL) technology to adapt to power grid fluctuations;

[0134] Harmonic suppression: Zero-crossing conduction makes the current waveform close to a sine wave, controlling the total harmonic distortion (THD) to <5% (traditional phase control can achieve THD of 30%).

[0135] LoRa command coordination mechanism:

[0136] Low-latency transmission: Utilizing LoRa spread spectrum technology (SF=7, BW=125kHz), the communication latency between the gateway and the node is <200ms, ensuring that power adjustment commands are synchronized with the grid cycle;

[0137] Conflict avoidance: TDMA time-division multiple access scheduling sends control commands in a time-division manner, avoiding command collisions caused by multiple nodes responding at the same time.

[0138] The specific operation process of zero-crossing control is as follows:

[0139] Step 1: Zero-crossing detection and instruction generation

[0140] Zero-crossing pulse generation: The mains voltage signal is input to the comparator through a voltage divider circuit, and a 5V TTL pulse signal is output when the AC voltage crosses zero (0V);

[0141] Phase-locked loop (PLL) compensation: The MCU main control chip has a built-in PLL algorithm that dynamically adjusts the zero-crossing trigger timing by sampling the power grid frequency in real time (40-70Hz adaptive) to compensate for power grid fluctuations;

[0142] Interrupt response mechanism: The zero-crossing pulse triggers an external interrupt via GPIO. After receiving the pulse, the MCU main control chip in the heat tracing base station immediately generates a zero-crossing triggered solid-state relay (SSR) trigger instruction in the interrupt service routine.

[0143] Example: When the set temperature needs to be increased, the MCU turns on the SSR at the next zero-crossing point, so that the heating tape starts at the zero voltage point.

[0144] Step 2: Dynamic Power Adjustment and Harmonic Suppression

[0145] Solid State Relay (SSR) Driver: The SSR is turned on at the detected zero crossing point to synchronize the current waveform with the voltage phase, thus avoiding the 3rd / 5th / 7th harmonics caused by step current.

[0146] Multi-cycle PWM power regulation: The number of SSR conduction cycles is set according to the PID calculation results. For example, in a 50Hz power grid, a 20% duty cycle corresponds to 10 half-waves being conducted per second, reducing high-frequency noise caused by current discontinuity.

[0147] Harmonic closed-loop suppression: The MCU main control chip analyzes the current waveform, calculates the total harmonic distortion (THD), and automatically reduces the maximum duty cycle of PWM when the limit is exceeded.

[0148] Step 3: Cloud-based collaborative optimization

[0149] Harmonic trend prediction: The system deploys an LSTM model in the cloud to analyze historical THD data and predict the harmonic risk level for the next 24 hours;

[0150] Dynamic strategy delivery: If the predicted THD > 5%, automatically generate a PWM duty cycle limit adjustment command and send it to all heat tracing base station nodes;

[0151] Fault linkage response: When the cloud detects multi-node harmonic anomalies, it automatically triggers the power grid quality inspection process and pushes maintenance work orders.

[0152] According to this plan: by reducing reactive power loss through zero-crossing control, the overall energy saving rate reaches 18% (compared to phase voltage regulation), significantly improving energy efficiency; in the pipeline heat tracing system, power grid harmonic interference accidents are reduced by 90%, ensuring stability.

[0153] (iv) Interactive management system: Real-time temperature, power, alarm status, etc., can be observed on-site or remotely via a high-brightness LCD screen / APP / host computer, and parameters can be modified. The specific operation process is as follows:

[0154] Step 1: Data Acquisition and Local Display

[0155] After the temperature sensor and current detection module data are processed by the MCU, they are sent to the LCD screen driver through the SPI interface and displayed in the form of dynamic curves / numerical values. When on-site personnel touch to input parameters (such as setting a temperature value), the LCD screen generates an operation log and stores it in Flash to prevent data loss in case of power failure.

[0156] Step 2: Remote monitoring and command issuance

[0157] APP / Host computer operation: Users log in to the cloud platform to view the device status in real time (e.g., "Node 3 power 72%, temperature 45℃"); when modifying parameters, the platform generates a command packet after verifying permissions; low latency response: the gateway LoRa processes commands through a priority queue, and critical alarm commands are transmitted in the queue (latency <500ms).

[0158] Step 3: Multi-device status synchronization

[0159] After the parameters are modified locally on the LCD screen, they are immediately reported to the system cloud, triggering an automatic refresh of the APP / host computer interface; the cloud records all operation logs (including operator IP / time), supporting audit traceability.

[0160] The interactive management technologies mentioned above are all existing technologies and will not be elaborated on here.

[0161] The intelligent management system employs a mode switching system, an energy consumption management system, an intelligent early warning system, a multi-dimensional state perception system, and a distributed expansion system.

[0162] (I) The mode switching system is used in conjunction with host computer software, which can freely switch between three modes: constant temperature, industrial temperature control, and power limiting, according to actual needs. The core technologies required are as follows:

[0163] Host computer software architecture:

[0164] Multi-mode strategy engine: A control logic library developed based on C# / Python, encapsulating three algorithm modules: constant temperature (PID closed loop), industrial temperature control (multi-segment temperature curve), and power limiting (threshold priority); Visual configuration interface: Supports drag-and-drop setting of temperature curves, power limits, and switching of trigger conditions (such as time / event driven).

[0165] Data synchronization mechanism: The MQTT / Modbus TCP protocol is used to synchronize parameter configurations in real time with the cloud / local server through the heat tracing base station.

[0166] Edge computing control unit:

[0167] Dynamic algorithm loading: The controller (such as the STM32F4 series MCU) pre-stores the core code of three modes and switches to run the algorithm after receiving instructions from the host computer; Example: The constant temperature mode enables the PID library (proportional coefficient Kp=0.8), and the industrial temperature control mode calls the preset temperature curve table (such as the stepped temperature rise required by the pharmaceutical process).

[0168] Local policy cache: Stores the last valid instruction and maintains the current mode of operation when communication is interrupted (to prevent loss of control).

[0169] Power dynamic execution module:

[0170] Multi-mode adapter: Constant temperature mode: PID output PWM duty cycle, accuracy ±1%; Power limiting mode: Hard cut-off of over-limit power (e.g., forced reduction of duty cycle to 50% when >2kW); Industrial temperature control mode: Automatic switching of PID parameter groups according to time axis (e.g., increasing the weight of integral term in high temperature range); Zero-crossing trigger protection: SSR zero-crossing switch avoids harmonics during mode switching.

[0171] The specific operation process for mode switching is as follows:

[0172] Step 1: Mode Configuration and Command Issuance

[0173] Users select a mode (such as "industrial temperature control") on the host computer, set the temperature curve (0~60 minutes to 90℃), and click "send". Taking the first method mentioned above as an example, the parameter package is uploaded to the system cloud, encrypted, and then pushed to the LoRa gateway in the target area.

[0174] Step 2: Controller Dynamic Switching

[0175] The controller parses the instructions and loads the corresponding algorithms: Constant temperature mode: Initializes PID parameters (Kp / Ki / Kd); Industrial temperature control mode: Reads the preset curve table from Flash and starts the timer for segmented temperature control; Power limiting mode: Activates the current monitoring interrupt and forces power reduction when the limit is exceeded.

[0176] Status synchronization: Immediately report the switching result to the system cloud (e.g., "Mode switching successful").

[0177] Step 3: Perform closed-loop verification

[0178] The controller outputs a PWM signal to drive the heating tape, and simultaneously collects real-time temperature / power.

[0179] Abnormal handling: If the temperature rise of the industrial temperature control section exceeds the timeout, the PID parameters will be automatically readjusted; if the power approaches the threshold, the load will be reduced by 10% in advance (to prevent instantaneous overshoot).

[0180] After the data is transmitted back, the host computer displays the dynamic curve and alarm status.

[0181] According to the content of this solution, it can be flexibly adapted to various scenarios, such as: constant temperature mode: used for pharmaceutical warehousing (±1℃ precise temperature control); industrial temperature control: meets the needs of thermal power plant pipeline cascading thawing process; power limitation: safe operation in explosion-proof areas of chemical plants; in addition, through remote switching mode via host computer, on-site commissioning time is reduced by 70%, improving maintenance efficiency.

[0182] (ii) The energy management system is equipped with an energy consumption data storage and modeling module, an intelligent analysis engine module, a visualization and strategy configuration module, and a dynamic optimization execution module.

[0183] The energy consumption data storage and modeling module is used to build a time-series database to store historical energy consumption data and establish dynamic energy consumption models (such as power prediction models based on pipe material and ambient temperature). It also supports data compression storage and fast retrieval, and provides API interfaces for analysis modules to call.

[0184] The intelligent analysis engine module can analyze historical data through machine learning algorithms (such as LSTM) to generate baseline energy consumption curves under different operating conditions; and compare the actual energy consumption with the baseline value in real time to identify abnormal scenarios such as overload and ineffective heating (such as sudden current exceeding 15A).

[0185] The visualization and strategy configuration module provides a web / app interface to display real-time energy consumption heat maps, equipment energy efficiency rankings, energy-saving suggestions, etc., and supports custom strategies (such as time-sharing temperature control and power limits).

[0186] The dynamic optimization execution module is used to send the energy-saving strategy generated in the cloud (such as reducing the pipe insulation temperature by 2°C at night) to the terminal via 4G / LoRa / Bluetooth link, and dynamically adjust the PID parameters or PWM duty cycle.

[0187] The aforementioned technologies offer energy consumption insights and precise management, enabling real-time energy consumption monitoring and data analysis. They support the development of energy-saving strategies and help reduce operating costs and improve energy efficiency.

[0188] (III) The intelligent early warning system is equipped with a multi-source anomaly detection module, an intelligent analysis and decision-making module, a dynamic hierarchical alarm mechanism, a rapid response and location module, and a visual fault tracing system.

[0189] The multi-source anomaly detection module includes an electrical parameter monitoring unit, which uses a high-precision current sensor (such as RSCM17100KP101) and voltage detection circuit to collect load current and voltage data in real time and identify anomalies such as overload (>15A) and short circuit; it also includes an environmental and equipment status sensing unit, which monitors over-temperature risks by embedding a digital temperature sensor in the heat sink of the power device; and it uses a triaxial accelerometer to detect abnormal equipment vibration.

[0190] The intelligent analysis and decision-making module contains a multi-dimensional diagnostic engine for data verification and trend prediction. It uses cross-validation with primary and backup sensors (such as LMT85) to trigger fault diagnosis when the difference is >1℃. It can also analyze historical temperature / current curves based on LSTM models to predict over-limit risks (such as temperature >100℃ trends).

[0191] A dynamic hierarchical alarm mechanism is used to execute differentiated responses based on risk levels: Level 1 warning (e.g., temperature > 80℃): automatically reduce power and push notification to the APP; Level 2 protection (e.g., short circuit): immediately cut off power and generate a maintenance work order.

[0192] The rapid response and positioning module includes communication alarm distribution. In local response mode, the gateway broadcasts fault codes to the heat tracing base station via LoRa or directly sends fault codes to the heat tracing base station with a delay of <200ms. In remote push mode, the 4G gateway sends location information (such as "Node 3 overload") to the APP / host computer via the MQTT protocol.

[0193] The visual fault tracing system integrates device IDs, timestamps, and sensor data through a cloud platform to generate fault maps, and marks abnormal points on a host computer map.

[0194] The aforementioned technologies feature intelligent alarms and risk warnings. The system can automatically analyze operational anomalies, provide early warnings, quickly locate problems, and reduce maintenance risks and response time.

[0195] (iv) The internal configuration of the state multidimensional perception system includes a state visualization module and a state synchronization and security module.

[0196] Multi-dimensional status visualization module: via a high-brightness LCD screen (500 cd / m²) 2 The system displays current, voltage, and temperature curves and line status on-site (brightness), and supports touch operation to modify parameters; it also provides a "one-screen overview" via APP / host computer, supports map positioning of devices, color marking of abnormal lines (red indicates faults), and allows clicking to view details.

[0197] Status synchronization and safety module: Assigns a unique identifier to each device to ensure accurate correspondence between data and physical location and avoid confusion among multiple nodes; it can also automatically execute the last valid instruction in the event of communication interruption to maintain safe power output and prevent loss of control.

[0198] The above-mentioned technologies feature equipment status and multi-dimensional sensing, enabling comprehensive monitoring of equipment operation status, real-time display of data such as current, voltage, and temperature, and quick viewing of the status of each line. All of the above are existing technologies and will not be elaborated further here.

[0199] (v) The distributed expansion system is internally equipped with a dynamic networking communication module, an edge computing control module, and a cloud elastic expansion module.

[0200] The dynamic networking communication module adopts LoRa Mesh self-organizing networking technology, supports new nodes to automatically register and join the network and be assigned a unique device ID, and realizes star / relay hybrid topology connection of multiple nodes (≥100). It uses TDMA (Time Division Multiple Access) mechanism to schedule node communication in a time-division manner to avoid data collisions and ensure channel stability during large-scale deployment.

[0201] Edge computing control module: Each heat tracing base station node has a built-in MCU that executes basic control logic such as PID algorithm and overload protection locally, reducing reliance on the cloud; it supports policy caching, so that when the 4G network is down, it continues to run according to the last valid instruction, ensuring system robustness.

[0202] The cloud-based elastic scaling module, based on a microservices architecture cloud platform, dynamically allocates computing resources (such as Kubernetes clusters) through containerization technology to cope with a surge in the number of nodes; it provides a visual configuration interface that supports drag-and-drop addition of new nodes and definition of control rules without modifying the underlying code.

[0203] The node expansion process is as follows: After the new node is powered on, it sends a network access request to the intelligent management system. After authentication by the gateway, if the authentication is successful, an ID is assigned and the configuration is distributed. If the authentication fails, the node is stored locally and will be retried. Authentication successful requests are added to the TDMA scheduling queue, and finally, data can be transmitted normally. The above technology features distributed control and flexible expansion. It supports multi-node distributed control and can expand the number of nodes and control range according to needs, flexibly adapting to projects of different scales.

[0204] The above description is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained in this invention are implemented according to conventional methods in the art unless otherwise specified or limited.

Claims

1. A heat tracing control method based on LoRa and 4G wireless technologies, characterized in that, This includes the PID dynamic adjustment method, with the specific steps as follows: Step 1: Data Acquisition and Preprocessing; Step 2: Cloud-based PID calculation and decision-making. After the data is transmitted to the intelligent management system cloud platform, the platform calculates the target power in real time. The steps for the platform to calculate the target power in real time include: After the data is transmitted to the cloud platform, the platform calls the algorithm library to calculate the target power in real time; The algorithm formula is: Where P is the heat dissipation power, K is the thermal conductivity of the insulation layer, A is the average heat dissipation area of ​​the insulation layer, δ is the average thickness of the insulation layer, and ΔT is the temperature difference between the two sides of the insulation layer. Step 3: Command issuance and power execution. The control command is issued to the target heat tracing base station. The heat tracing base station parses the command, dynamically adjusts the duty cycle of PWM, controls the power of the heat tracing cable, and achieves harmonic suppression by zero-crossing conduction. The steps for parsing the heat tracing base station instruction include: After the MCU main control chip in the heat tracing base station parses the instructions, it drives the solid-state relay to conduct at the zero-crossing point of the AC power. The zero-crossing detection circuit generates a trigger pulse, and the PWM dynamically adjusts the duty cycle to precisely control the power of the heating tape. Step 4: Closed-loop calibration and safety protection.

2. The heat tracing control method based on LoRa and 4G wireless technology according to claim 1, characterized in that: In the data acquisition and preprocessing process, an industrial-grade digital temperature sensor is used, via SPI / I 2 The C interface connects to the MCU main control chip in the heat tracing base station, and collects temperature data every 10-30 seconds. An RC low-pass filter is used to suppress high-frequency noise, and moving average filtering and Kalman filtering are used to eliminate environmental transient interference. The MCU main control chip sends the filtered temperature data to the intelligent management system.

3. The heat tracing control method based on LoRa and 4G wireless technology according to claim 2, characterized in that, The temperature data is sent to the intelligent management system via local transmission: The MCU main control chip packages the filtered temperature data into LoRa frames, sends them to the gateway LoRa via spread spectrum modulation, and then sends the data information to the intelligent management system via the gateway LoRa.

4. The heat tracing control method based on LoRa and 4G wireless technology according to claim 2, characterized in that, The temperature data is sent to the intelligent management system via remote transmission. The MCU main control chip sends the filtered temperature data to the IoT card built into the heat tracing base station, and the gateway uploads the data to the intelligent management system via the 4G module.

5. The heat tracing control method based on LoRa and 4G wireless technology according to claim 2, characterized in that, The temperature data is sent to the intelligent management system via mobile transmission. The MCU main control chip packages the filtered temperature data into a data packet via the built-in Bluetooth module of the heat tracing base station and sends it to a smart device equipped with a heat tracing smart control APP.

6. The heat tracing control method based on LoRa and 4G wireless technology according to claim 1, characterized in that, The steps for achieving harmonic suppression through zero-crossing conduction include zero-crossing point detection and command generation: The mains voltage signal is input to the comparator through a voltage divider circuit, and a 5V TTL pulse signal is output when the AC voltage crosses zero. The MCU main control chip has a built-in PLL algorithm that dynamically adjusts the zero-crossing trigger timing by sampling the power grid frequency in real time to compensate for power grid fluctuations. The zero-crossing pulse triggers an external GPIO interrupt. After receiving the pulse, the MCU main control chip in the heat tracing base station immediately generates a zero-crossing trigger instruction for the solid-state relay in the interrupt service routine.

7. The heat tracing control method based on LoRa and 4G wireless technology according to claim 6, characterized in that, It also includes dynamic power adjustment and harmonic suppression: The solid-state relay is turned on at the detected zero crossing point to synchronize the current waveform with the voltage phase, thus avoiding the 3rd / 5th / 7th harmonics caused by the step current. The number of solid-state relay conduction cycles is set based on the PID calculation results; The MCU main control chip analyzes the current waveform, calculates the total harmonic distortion rate, and automatically reduces the maximum duty cycle of the PWM when the limit is exceeded.

8. The heat tracing control method based on LoRa and 4G wireless technology according to claim 6, characterized in that, It also includes cloud-based collaborative optimization: The system deploys an LSTM model in the cloud to analyze historical THD data and predict the harmonic risk level for the next 24 hours. If the predicted THD is greater than 5%, an automatic PWM duty cycle limit adjustment command will be generated and sent to all heat tracing base station nodes. When the cloud detects multi-node harmonic anomalies, it automatically triggers the power grid quality inspection process and pushes maintenance work orders.

9. A heat tracing control system based on LoRa and 4G wireless technologies, characterized in that, The heat tracing control method based on LoRa and 4G wireless technology according to any one of claims 1 to 8 includes: Integrated control system: The temperature of each heating pipeline is dynamically adjusted by PID control using high-precision sensors and a wireless communication architecture. With the help of a zero-detection circuit and a zero-crossing trigger type solid-state relay, zero-crossing trigger power control is used to suppress power system harmonics; It performs rapid power-off for overload / short circuits and also has an alarm function for situations affecting system stability; Intelligent Management System: Used to analyze global data, gain insights into energy consumption, and simultaneously provide risk warnings; Enables visualization of status, multi-dimensional parameter monitoring, and fault location; Configure remote policies to enable mode switching; The integrated control system relies on the hardware layer to perform overload cutoff and temperature fine-tuning; the intelligent management system relies on cloud computing power to formulate energy efficiency strategies and analyze the root causes of failures; the integrated control system and the intelligent management system form a data flow closed loop, which combines safety and energy efficiency linkage.

10. An electronic device, characterized in that: The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the heat tracing control method based on LoRa and 4G wireless technology as described in any one of claims 1 to 8.

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