Tire vulcanizer hot plate electromagnetic induction heating equipment and intelligent temperature control method

Abstract

The invention relates to the technical field of tire production and manufacturing, in particular to electromagnetic induction heating equipment for a hot plate of a tire vulcanizing machine and an intelligent temperature control method. The equipment comprises an intelligent electromagnetic heating controller, an intelligent thermal management optimization module, an aluminum plate, an electromagnetic coil, a hot plate and thermal resistors, the output end of the intelligent electromagnetic heating controller is connected to the electromagnetic coil, and the intelligent thermal management optimization module is connected to the thermal resistors and used for receiving hot plate temperature values measured by the thermal resistors; the temperature values are input into an intelligent electromagnetic heating controller to be processed, a coil groove is formed in the back face of the working face of the hot plate, an electromagnetic coil is laid on the back face of the working face of the hot plate, and the hot plate is directly heated under the control of the intelligent electromagnetic heating controller. According to the method, the intelligent electromagnetic heating controller is provided with a self-learning PI (Proportional Integral) algorithm module and is matched with a high-precision ADC (Analog to Digital Converter) to convert a temperature signal acquired by a thermal resistor and input by an intelligent thermal management optimization module into a digital signal and process the digital signal, so that an electromagnetic coil is intelligently controlled.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tire production and manufacturing, in particular to a tire vulcanizing machine hot plate electromagnetic induction heating equipment and a method for intelligently controlling temperature by using the equipment. BACKGROUND

[0002] The steam heating method widely used in the traditional tire vulcanization process needs to rely on a boiler to generate high-pressure steam and deliver it to the hot plate through a pipeline. The integrated control of its temperature and pressure involves multiple links such as steam generation, delivery, pressure stabilization, and temperature control. The process chain is long and the regulation and control are complex. Not only does it need to accurately match the corresponding relationship between steam pressure and temperature, but it also needs to deal with parameter fluctuations caused by problems such as steam leakage and pipeline fouling, which further affects the vulcanization stability. At the same time, steam heating has the obvious shortcomings of high energy consumption and high pollution. The boiler burning fuel (coal, natural gas, etc.) will produce a large amount of CO2, SO2, and other pollutants, and the heat loss rate of steam during delivery and heat exchange is as high as more than 30%, resulting in low energy utilization efficiency. More importantly, the heat transfer of steam through the internal flow channel of the hot plate is easily affected by the flow channel layout and the degree of fouling, resulting in a temperature uniformity error of the working surface of the hot plate usually exceeding ±3℃, which directly affects the crosslinking uniformity of the tire rubber and reduces the product quality. The traditional resistance wire heating method also has obvious defects. The resistance wire conducts heat to the hot plate by itself, but the external insulation layer and installation gap will cause a lot of heat loss, and the thermal efficiency is generally less than 60%, resulting in serious energy waste. Moreover, the resistance wire is easily oxidized and aged in the high-temperature vulcanization environment, and after long-term use, problems such as wire breakage and power attenuation will occur, which not only increases the maintenance cost and the risk of production interruption due to frequent shutdown and replacement, but also causes uneven distribution of heating power of the hot plate due to local damage of the resistance wire, further aggravating temperature fluctuations and making it difficult to meet the process requirements of high-precision tire vulcanization. SUMMARY

[0003] To achieve the above-mentioned purpose, the present application provides a tire vulcanizing machine hot plate electromagnetic induction heating equipment and an intelligent temperature control method to realize "zero steam" supply of vulcanization heat and energy-saving manufacturing of tires, and to greatly improve the uniformity of the surface temperature of the hot plate while improving the heating efficiency and the vulcanization effect.

[0004] To achieve the above-mentioned purpose, the technical solution adopted by the present application is as follows: a tire vulcanizing machine hot plate electromagnetic induction heating equipment, comprising an intelligent electromagnetic heating controller, an intelligent thermal management optimization module, an aluminum plate, an electromagnetic coil, a hot plate, and a thermal resistor,

[0005] The output end of the intelligent electromagnetic heating controller is connected to the electromagnetic coil;

[0006] The intelligent thermal management optimization module is connected to the thermal resistances, the number of the thermal resistances is multiple, and the multiple thermal resistances are respectively inserted into the multiple temperature measuring probe holes in the side wall of the hot plate.

[0007] The working surface of the hot plate is close to the surface of the mold, the other surface of the working surface is provided with a coil groove, the electromagnetic coil is arranged in the coil groove, the number of the electromagnetic coils is multiple, and the multiple electromagnetic coils are respectively connected to the intelligent electromagnetic heating controller, and the aluminum plate is fixedly connected to the hot plate through bolts, and the electromagnetic coil is fixed in the coil groove of the hot plate.

[0008] The intelligent electromagnetic heating controller is internally provided with a self-control temperature module to realize the self-control temperature function, and is provided with a self-learning proportional-integral-derivative closed loop control algorithm module, is matched with a high-precision analog-digital conversion unit, can quickly convert the temperature signal collected by the thermal resistance input by the intelligent thermal management optimization module into a digital signal and process the temperature signal, and then controls the electromagnetic coil.

[0009] The technical scheme for realizing the object of the application further comprises that the intelligent coil matching element is internally integrated with a programmable inductance capacitance matching network, a real-time inductance detection unit and a microprocessor, can automatically identify the actual inductance parameters of different coil layouts, different numbers of turns or different diameters of coils, and quickly calculates optimal matching parameters through the microprocessor, dynamically adjusts the inductance and capacitance values of the internal matching network, so that the impedance matching rate of the electromagnetic coil and the intelligent electromagnetic heating controller is always maintained above 95%.

[0010] The application further provides a method for intelligently controlling temperature by using the above-mentioned hot plate electromagnetic induction heating equipment of the tire vulcanizing machine, and the method is as follows: after the self-learning system of the intelligent electromagnetic heating controller is started, three input modules firstly complete the transmission of basic parameters, provide clear basis for subsequent control, and form a collaborative input link: first, an operator inputs a target temperature in the form of a "temperature setting value" according to the tire vulcanizing process requirement, and the parameter is directly transmitted to the core control unit of the intelligent electromagnetic heating controller as the core reference of the whole temperature control process; at the same time, the temperature sensor installed on the mold is started in real time, collects initial temperature data and generates a "temperature feedback" signal, and synchronously transmits the signal to the core control unit to establish an initial temperature deviation judgment; in addition, the state monitoring unit built in the system automatically collects "working condition data", including power grid voltage fluctuation, equipment heat dissipation condition, current vulcanizing workpiece specification and material information and the like, and these data are used as key basis for working condition adaptation to assist the core control unit in accurately identifying the working environment, and the three input signals are transmitted in parallel to jointly build an "target clear and state clear" initial working scene for the core control unit.

[0011] Furthermore, the intelligent electromagnetic heating controller incorporates an intelligent proportional-integral-derivative closed-loop control algorithm module and a self-learning module. These two modules combine to form a dynamic linkage control method: During system startup or in scenarios involving changes in operating conditions such as workpiece or mold replacement, the self-learning module combines the input "operating condition data" with the temperature response curve fed back by the intelligent closed-loop control algorithm module to automatically identify the system's core thermal characteristics, including the workpiece's heat capacity and the equipment's heat loss rate. It then establishes a dedicated mathematical model and generates a set of optimal initial parameters for the intelligent closed-loop control algorithm module based on this model, avoiding the tedious manual debugging. During normal operation, the self-learning module continuously records the power output curve, temperature feedback curve, and operating condition change data of the intelligent closed-loop control algorithm module, comparing the actual temperature control effect with the ideal target in real time. If performance fluctuations are detected, the proportional-integral-derivative parameters are automatically fine-tuned and synchronously transmitted to the intelligent closed-loop control algorithm module to ensure it remains in optimal operating condition. This integration achieves a unity of "real-time control" and "continuous optimization," enabling the system to cope with both immediate temperature deviations and long-term operating condition changes.

[0012] Furthermore, the intelligent electromagnetic heating controller is used in conjunction with an intelligent thermal management optimization module. The intelligent thermal management optimization module integrates machine learning algorithms and multi-channel sensor access units, enabling stable access of multiple thermal resistors. The thermal resistors are evenly distributed inside the hot plate, forming a full-area temperature sensing network. The intelligent thermal management optimization module flexibly sets the sampling interval according to actual temperature control requirements. During the heating process, it drives a channel timing switching mechanism through a built-in machine learning algorithm, sequentially selecting a single thermal resistor as the current temperature control reference. The intelligent thermal management optimization module continuously collects temperature data from each sensor node and uses algorithms to dynamically optimize the switching timing and temperature control parameters, gradually reducing the temperature deviation between each thermal resistor, ultimately achieving high-precision temperature control and improving the uniformity of temperature distribution on the hot plate surface.

[0013] Furthermore, the intelligent thermal management optimization module employs a "single-point dominant temperature control + multi-point data reference" method during the heating process. Instead of simply switching sequentially, the module dynamically allocates temperature control priorities based on the temperature deviations of each measuring point. Specifically, the system presets a temperature threshold. When the temperature of a measuring point falls below the threshold, that point automatically gains temperature control priority. The intelligent thermal management optimization module uses the temperature of that measuring point as a feedback signal to adjust the output power of the electromagnetic heating controller in the corresponding area. If multiple measuring points simultaneously exceed the threshold, the system prioritizes the measuring point with the lowest temperature as the dominant temperature control point, while simultaneously referencing the temperature deviations of other measuring points. An algorithm compensates for the heating power of adjacent areas, preventing localized temperature imbalances caused by single-point temperature control.

[0014] Compared with the prior art, the advantages of this invention are as follows:

[0015] 1. The electromagnetic induction heating technology of this invention has no heat conduction loss due to self-heating, has high thermal efficiency, significant energy-saving effect, and is green, environmentally friendly, pollution-free and has a long lifespan.

[0016] 2. The electromagnetic induction heating technology of this invention replaces traditional steam heating to achieve "zero steam" heating. Furthermore, the coils are directly distributed inside the hot plate, which is easier to process than steam pipes, reduces maintenance costs, and minimizes modifications to the hot plate structure.

[0017] 3. The electromagnetic coils of this invention are distributed radially and circumferentially, and can be distributed in one or more groups of coils respectively. The distribution density can be adjusted according to different hot plates, which greatly improves the overall temperature uniformity of the hot plate surface.

[0018] 4. This invention adopts a combination of intelligent electromagnetic heating controller and intelligent thermal management optimization module or PLC for temperature control, which solves the problem of inaccurate temperature measurement caused by the processing precision of hot plate, and greatly improves the overall temperature control accuracy and surface temperature uniformity.

[0019] 5. This invention uses an aluminum plate to shield excess magnetic fields to the maximum extent, so that the effective magnetic field acts entirely on the hot plate, avoiding interference and other problems caused by the metal around the hot plate.

[0020] 6. The present invention adopts the "full coverage measurement point layout + dynamic priority switching" mode, which not only ensures the comprehensiveness of temperature monitoring, but also solves the problem of decreased temperature control accuracy caused by signal conflict from multiple measurement points through adaptive switching cycle and priority allocation.

[0021] 7. This invention constructs a "temperature difference-power" linkage model, which dynamically adjusts the heating power of the corresponding area through an algorithm, rather than simply relying on the temperature feedback of a single measuring point. This effectively compensates for local temperature deviations caused by factors such as differences in hot plate structure and coil layout avoidance, and significantly improves the temperature uniformity of the entire area.

[0022] 8. This invention optimizes the installation structure of the temperature sensing element, uses shielded cables and ceramic protective tubes to build a comprehensive anti-interference system, avoids interference from high-frequency electromagnetic fields on temperature signals, and ensures the accuracy of temperature data acquisition. At the same time, it combines a multi-point data cross-validation algorithm to eliminate abnormal data and further improve the reliability of the temperature control system.

[0023] 9. The intelligent thermal management optimization module of this invention supports the connection of two types of temperature sensors: resistance temperature detectors (RTDs) and thermocouples. The sensor type can be selected through a DIP switch or software settings to adapt to vulcanization processes with different accuracy requirements. At the same time, the module reserves standardized communication interfaces (RS485, Modbus TCP, etc.) to seamlessly connect with electromagnetic heating controllers and vulcanizing machine host computer systems of different brands and models. It has strong versatility and is convenient for upgrading and modifying existing equipment. Attached Figure Description

[0024] Figure 1 This is a general structural diagram of the electromagnetic induction heating equipment for the hot plate of the tire vulcanizing machine described in this invention;

[0025] Figure 2 The diagram shows the assembly structure of the electromagnetic coil described in the invention.

[0026] Figure 3 A structural diagram of an embodiment of the electromagnetic coil described in the invention;

[0027] Figure 4 A structural diagram of the electromagnetic coil in Embodiment 2 of the invention;

[0028] Figure 5 A structural diagram of the electromagnetic coil of the invention in Embodiment 3;

[0029] Figure 6 The diagram shows the structure of the electromagnetic coil in Embodiment 4 of the invention.

[0030] Figure 7 A structural diagram of the fifth embodiment of the electromagnetic coil described in the invention;

[0031] Figure 8 The intelligent electromagnetic heating controller described in the invention provides an intelligent heating and temperature control process. Figure 1 ;

[0032] Figure 9 The intelligent electromagnetic heating controller described in the invention provides an intelligent heating and temperature control process. Figure 2 ;

[0033] Figure 10 The intelligent heating and temperature control flowchart of the intelligent thermal management optimization module described in the invention.

[0034] 1. Intelligent electromagnetic heating controller, 2. Intelligent thermal management optimization module, 3. Insulation plate, 4. Aluminum plate, 5. Electromagnetic coil, 6. Hot plate, 7. Resistance temperature detector, 8. Intelligent coil matching element. Detailed Implementation

[0035] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. The invention will be further described in detail below with reference to the accompanying drawings.

[0036] The purpose of this invention is to address the various drawbacks of existing hot plate heating systems by proposing an electromagnetic induction heating device for a tire vulcanizing machine. This device solves the problem of uniform surface temperature of the hot plate by optimizing the hot plate structure design, coil design, and parameter optimization.

[0037] like Figure 1As shown, the electromagnetic induction heating equipment for a tire vulcanizing machine hot plate according to the present invention includes an intelligent electromagnetic heating controller 1, an intelligent thermal management optimization module 2, an aluminum plate 4, an electromagnetic coil 5, a hot plate 6, and a thermal resistor 7.

[0038] The output terminal of the intelligent electromagnetic heating controller 1 is connected to the electromagnetic coil 5;

[0039] The intelligent thermal management optimization module 2 is connected to the thermal resistor 7. There are multiple thermal resistors 7, which are respectively inserted into multiple temperature measuring holes on the side wall of the hot plate 6. The intelligent thermal management optimization module 2 receives the temperature values ​​of the hot plate 6 measured by multiple thermal resistors 7 and inputs each temperature value into the intelligent electromagnetic heating controller 1 for processing.

[0040] The working surface of the hot plate 6 is in close contact with the mold surface, and a coil groove is opened on the other side of the working surface. The electromagnetic coil 5 is laid in the coil groove. There are multiple sets of electromagnetic coils 5, which are respectively connected to the intelligent electromagnetic heating controller 1. The aluminum plate 4 is fixedly connected to the hot plate 6 by bolts, and the electromagnetic coil 5 is fixed in the coil groove of the hot plate 6.

[0041] The intelligent electromagnetic heating controller 1 has a built-in self-temperature control module to realize the self-temperature control function, and has a self-learning proportional-integral-derivative closed-loop control algorithm module. Combined with a high-precision analog-to-digital converter, it can quickly convert the temperature signal collected by the thermal resistor 7 input by the intelligent thermal management optimization module 2 into a digital signal and process it, thereby controlling the electromagnetic coil 5.

[0042] The intelligent electromagnetic heating controller 1 iteratively optimizes heating parameters based on a self-learning algorithm, while the intelligent thermal management optimization module 2 predicts temperature change trends through multi-channel temperature acquisition and machine learning models. The electromagnetic coil 5 is wound with high-temperature resistant copper wire and externally covered with a high-temperature resistant mica insulation layer. Based on the structural characteristics of the hot plate 6 and the tire vulcanization temperature requirements, it is modularly laid in dedicated coil slots inside the hot plate in a uniform or non-uniform spacing manner. The coil slots are precision-machined and insulated to ensure electrical isolation and tight fit between the electromagnetic coil and the hot plate. At least one set of electromagnetic coils is provided. Each set or multiple sets of electromagnetic coils connected in series establish an independent control link between the intelligent electromagnetic heating controller and the intelligent thermal management optimization module. The two work together bidirectionally to achieve precise power adjustment in each coil area. Combined with the rapid heat conduction of the high thermal conductivity aluminum plate 4 and the heat loss blocking of the high-efficiency insulation plate, the overall temperature uniformity of the hot plate is improved, significantly enhancing vulcanization efficiency and tire quality.

[0043] The hot plate 6 adopts an integrated layered structure design of "working surface-coil slot-insulation layer". Its working surface, which is in contact with the mold, is precision polished to ensure a tight fit with the mold surface and improve heat conduction efficiency. The other side of the working surface is machined with several U-shaped or spiral coil slots adapted to the layout of the electromagnetic coils through five-axis linkage milling. The slot walls are coated with a high-temperature resistant insulating coating. After the electromagnetic coils 5 are laid in the slots according to the preset partitioning scheme (radial / circumferential composite partitioning), the coils are pressed and fixed by bolts using a high thermal conductivity aluminum plate 4. Thermally conductive silicone grease is filled between the aluminum plate, the coil slot wall, and the coil surface to ensure the stability of the coil installation and enhance heat conduction. The high-efficiency insulation plate 3 is tightly attached to the bottom of the aluminum plate 4 to form a closed structure of "hot plate-coil-aluminum plate-insulation plate". This design, through the metal shielding of the aluminum plate and the physical barrier of the insulation plate, completely confines the high-frequency alternating magnetic field generated by the electromagnetic coil during operation within the hot plate, achieving zero magnetic field divergence. Tested and verified by a third-party testing agency, it is far below the national electromagnetic radiation safety standard, completely eliminating the interference of the magnetic field on the vulcanizing machine control system, sensors and peripheral equipment, while maximizing the utilization rate of electromagnetic energy. It is the ultimate solution for hot plate heating that balances heating efficiency, temperature control accuracy and electromagnetic compatibility.

[0044] Naturally, the above-mentioned closed structure of "hot plate-coil-aluminum plate-insulation plate" can also be replaced by a closed structure of "hot plate-coil-insulation plate-aluminum plate". The insulation plate 3 is in close contact with the hot plate coil groove on one side to fix the electromagnetic coil, and in close contact with the aluminum plate on the other side. The heat is reduced by the insulation plate to reduce the heat transfer to the vulcanizing machine body, and the aluminum plate below the insulation plate also plays the role of shielding the magnetic field.

[0045] The aforementioned resistance temperature detector (RTD) 7 can also be a thermocouple, installed in an embedded manner along the radial-circumferential layout of the hot plate 6: radially divided into three zones—inner, middle, and outer—with 2-4 measuring points in each zone; circumferentially divided into zones of equal or unequal proportion, symmetrically distributed, with at least one measuring point in each zone, ensuring coverage of key areas on the hot plate's working surface. The total number of measuring points can be configured from 1 to 16 depending on the hot plate size and temperature control requirements, achieving comprehensive temperature monitoring without blind spots. The measuring end of the RTD or thermocouple is tightly fitted to the measuring hole inside the hot plate, with the hole wall coated with thermal grease to reduce thermal resistance. A high-temperature resistant protective tube is fitted around the temperature sensing element to prevent electromagnetic interference with the electromagnetic coil and to prevent corrosion from high-temperature gases during the sulfidation process. The intelligent thermal management optimization module is connected to the RTD or thermocouple using a shielded cable, with the cable wrapped in a metal braided mesh shielding layer to effectively resist electromagnetic interference generated by the high-frequency intelligent electromagnetic heating controller, ensuring the stability and accuracy of temperature signal transmission.

[0046] The intelligent electromagnetic heating controller 1 is connected to the electromagnetic coil 5 via an intelligent coil matching element 8. One end of the intelligent coil matching element 8 is reliably connected to the intelligent electromagnetic heating controller 1 through a terminal block, and the other end adopts a modular interface design, which can be directly connected to a set of independent electromagnetic coils or multiple sets of electromagnetic coil units connected in series or in parallel.

[0047] While the aluminum plate fixing scheme described above can achieve zero magnetic field divergence through the metal shielding effect, the aluminum plate, being a highly conductive metal, will generate electromagnetic coupling when in close contact with the electromagnetic coil. This causes a significant shift in the coil's equivalent inductance, disrupting the preset matching relationship between the coil and the controller in conventional electromagnetic heating schemes. Directly using traditional matching methods would result in problems such as heating power attenuation, abnormal high-frequency oscillations, or even controller malfunctions. Therefore, conventional electromagnetic heating schemes are completely unsuitable for the structural design of this equipment. To address this, this equipment specifically adds an intelligent coil matching element 8, typically a transformer or isolation transformer element. Internally, it integrates a programmable inductor-capacitor matching network, a real-time inductance detection unit, and a microprocessor. It can automatically identify the actual inductance parameters of coils with different layouts (such as radial partitioning or circumferential partitioning), different numbers of turns, or different wire diameters. The microprocessor quickly calculates the optimal matching parameters and dynamically adjusts the inductance and capacitance values ​​of the internal matching network, ensuring that the impedance matching rate between the electromagnetic coil 5 and the intelligent electromagnetic heating controller 1 is always maintained above 95%. Meanwhile, the intelligent coil matching element 8 supports a wide range of inductance adaptation, which can flexibly cope with the coil layout adjustment corresponding to different tire models, greatly expanding the coil inductance adaptation range in practical applications, effectively compensating for the influence of aluminum plate on electromagnetic coil inductance, and ultimately ensuring that electromagnetic energy is efficiently converted into heat energy, so as to improve the overall heating efficiency to more than 90%, avoiding energy waste and equipment failure caused by matching imbalance.

[0048] The electromagnetic coil 5 is divided into different control zones according to the different structures of the hot plate 6, avoiding structures such as the mold-locking hole and the hot plate fixing hole, for example... Figures 2-7 As shown,

[0049] The electromagnetic coil 5 can be distributed in three different ways in different areas: radially as a whole in one area, inner and outer in two areas, and inner, middle and outer in three areas, to adapt to different mold sizes.

[0050] The hot plate 6 is radially distributed in one area, with the electromagnetic coils 5 wound in an arc-shaped S-shape, U-shape, or spiral shape from the outside to the inside, and controlled by the intelligent electromagnetic heating controller 1. The hot plate 6 is radially distributed in two areas, with the electromagnetic coils 5 divided into inner and outer regions, wound in an arc-shaped S-shape, and controlled by two sets of intelligent electromagnetic heating controllers 1. The hot plate 6 is radially distributed in three areas, with the electromagnetic coils 5 divided into inner, middle, and outer regions, with the inner electromagnetic coil 51, middle electromagnetic coil 52, and outer electromagnetic coil 53 each wound in an arc-shaped S-shape, and controlled by three sets of intelligent electromagnetic heating controllers 1.

[0051] The electromagnetic coil 5 can be divided into different regions, such as the hot plate 6, which is distributed circumferentially in equal or unequal proportions, to accommodate different types of molds.

[0052] The electromagnetic coils 5 are distributed proportionally into several regions along the circumference based on the position of the mold-locking holes in the vulcanizing machine. These regions are typically divided into two, four, six, or eight equal parts. The electromagnetic coils in each region are identically distributed with the same coil density.

[0053] The electromagnetic coils 5 are distributed in unequal proportions, divided into several unequal regions according to the special position of the mold-locking hole of the vulcanizing machine. Therefore, the electromagnetic coils are divided into regions of different sizes along the circumference, and the coil density is different.

[0054] This invention also provides a method for intelligent temperature control using the electromagnetic induction heating equipment for the hot plate of a tire vulcanizing machine described above. The method involves the intelligent electromagnetic heating controller's self-learning system starting up, with the three main input modules first transmitting basic parameters to provide a clear basis for subsequent control, forming a collaborative input link: First, the operator inputs the target temperature into the system as a "temperature setpoint" according to the tire vulcanizing process requirements. This parameter serves as the core benchmark for the entire temperature control process and is directly transmitted to the core control unit of the intelligent electromagnetic heating controller. Simultaneously, the temperature sensor installed on the mold starts in real time, collecting initial temperature data and generating a "temperature feedback" signal, which is synchronously transmitted to the core control unit for establishing initial temperature deviation judgment. Furthermore, the system's built-in status monitoring unit automatically collects "operating condition data," including grid voltage fluctuations, equipment heat dissipation conditions, and the specifications and material information of the current vulcanized workpiece. This data serves as a key basis for operating condition adaptation, assisting the core control unit in accurately identifying the working environment. The three main input signals are transmitted in parallel, jointly constructing an initial working scenario for the core control unit that is "clear in its target and status," such as... Figure 8 .

[0055] The core control unit, acting as the system's "brain," receives input signals and, through the coordinated operation of the intelligent PID controller and the self-learning module, completes the core processing flow of "deviation analysis - parameter calculation - command output." These two functions are deeply integrated and mutually supportive: the intelligent PID module prioritizes calculating the difference between the "temperature setpoint" and the "temperature feedback" to obtain temperature deviation data. Based on this deviation, the module automatically matches the control strategy—if the deviation is large (e.g., in the initial stage of heating), it increases the power output gain to achieve rapid heating and shorten the preheating time; if the deviation is small (e.g., in the heat preservation stage near the target temperature), it switches to a fine-tuning mode, optimizing integral and derivative parameters to suppress temperature overshoot and fluctuations, ensuring temperature control accuracy.

[0056] The power adjustment command generated by the core control unit is converted and executed by the power drive module, and then the actual state is fed back through the feedback link, forming a complete closed loop: After receiving the electrical signal command from the core control unit, the power drive module converts it into actual power output that can drive the heating mechanism, and controls the electromagnetic coil to generate a high-frequency alternating magnetic field of corresponding intensity; the heating mechanism, i.e., the electromagnetic coil, generates heat through the eddy current effect under power drive and transfers it to the mold, pushing the mold temperature closer to the set value; during this process, the temperature sensor continuously collects the real-time temperature of the mold and continuously sends the updated "temperature feedback" signal back to the core control unit, so that the core control unit can grasp the temperature control effect in real time and adjust subsequent commands according to the new temperature deviation. This closed loop process of "command output - power execution - temperature change - status feedback" is repeated to ensure that the temperature is always stable within the set range.

[0057] The self-learning system of the intelligent electromagnetic heating controller adopts a proportional-integral-differential closed-loop control algorithm, i.e., PID control mode, and has dual regulation capabilities of "dynamic parameter optimization + historical data iteration".

[0058] In the initial heating stage, the intelligent electromagnetic heating controller automatically records key parameters such as the temperature oscillation amplitude ΔT, the heating rate v, and the steady-state time t during the preceding heating process. It analyzes the temperature fluctuation pattern using a built-in algorithm model and dynamically adjusts the proportional coefficient K. p Integral coefficient K i and differential coefficient K d This enables adaptive matching of PID parameters;

[0059] When the temperature fluctuation amplitude increases due to the adjustment of the vulcanization process (such as the change of the temperature setpoint) or changes in the external environment (such as power grid voltage fluctuations or mold replacement), the self-learning system of the intelligent electromagnetic heating controller can respond quickly within 3 to 5 control cycles. It suppresses overshoot by strengthening the differential action and eliminates steady-state error by optimizing the integral action, so that the temperature fluctuation quickly converges to the set range.

[0060] During long-term operation, the intelligent electromagnetic heating controller automatically stores the optimal PID parameter combinations corresponding to different tire models and vulcanization processes, forming a parameter database. This database can be directly accessed under similar operating conditions, further shortening the control response time and improving temperature control stability. Through this technology, the controller's temperature control accuracy can stably reach ±0.5℃, meeting the temperature requirements of high-precision tire vulcanization processes.

[0061] The intelligent electromagnetic heating controller adopts a standardized communication protocol and interface definition, enabling seamless integration with tire vulcanizing machine main units of different brands and models without the need for additional adapter modules or communication protocol modifications, simplifying equipment installation and commissioning. After interconnection, the following collaborative functions can be achieved: the vulcanizing machine main unit sends temperature setpoints, heating rates, and other control parameters to the electromagnetic heating controller based on the tire vulcanization process curve; the controller precisely executes heating control according to the instructions; the controller feeds back the actual temperature of the hot plate and heating status to the vulcanizing machine main unit in real time; the main unit integrates temperature data with parameters such as pressure and time to achieve closed-loop control of the vulcanization process; when the controller malfunctions, the vulcanizing machine main unit can respond quickly, automatically pausing the vulcanization process and locking related operations to prevent the production of defective tires, while simultaneously prompting operators to handle the fault.

[0062] The intelligent electromagnetic heating controller incorporates an intelligent proportional-integral-derivative closed-loop control algorithm (intelligent PID) module and a self-learning module. These two modules combine to form a dynamic linkage control method: During system startup or in scenarios involving changes in operating conditions such as workpiece or mold replacement, the self-learning module combines the input "operating condition data" with the temperature response curve (e.g., temperature rise slope) fed back by the intelligent closed-loop control algorithm (intelligent PID) module. It automatically identifies the system's core thermal characteristics, including the workpiece's heat capacity and the equipment's heat loss rate. This allows it to establish a dedicated mathematical model and generate a set of optimal initial parameters for the intelligent closed-loop control algorithm (intelligent PID) module based on this model, avoiding manual intervention. The debugging process is cumbersome; during normal operation, the self-learning module continuously records the power output curve, temperature feedback curve, and operating condition change data of the intelligent closed-loop control algorithm (intelligent PID) module, and compares the actual temperature control effect with the ideal target (such as a temperature control accuracy of ±0.5℃) in real time. If performance fluctuations are detected (such as accelerated heat dissipation due to workpiece oxidation), the proportional-integral-derivative parameters are automatically fine-tuned and synchronously transmitted to the intelligent closed-loop control algorithm (intelligent PID) module to ensure that it is always in the optimal working state. The connection between the two achieves the unity of "real-time control" and "continuous optimization", enabling the system to cope with both immediate temperature deviations and long-term operating condition changes. Figure 9 As shown.

[0063] The intelligent electromagnetic heating controller 1 is used in conjunction with the intelligent thermal management optimization module 2. The intelligent thermal management optimization module 2 integrates machine learning algorithms and multi-channel sensor access units, enabling stable access of multiple resistance thermometers 7 (or thermocouples). The resistance thermometers 7 are evenly distributed inside the hot plate 6, forming a full-area temperature sensing network. The intelligent thermal management optimization module 2 flexibly sets the sampling interval according to actual temperature control requirements. During the heating process, it drives a channel timing switching mechanism through a built-in machine learning algorithm, sequentially selecting a single resistance thermometer 7 as the current temperature control reference. By continuously collecting temperature data from each sensor node, the intelligent thermal management optimization module 2 dynamically optimizes the switching timing and temperature control parameters using algorithms, gradually reducing the temperature deviation between each resistance thermometer 7, ultimately achieving high-precision temperature control and improving the uniformity of temperature distribution on the surface of the hot plate 6.

[0064] The intelligent thermal management optimization module has a built-in programmable timer, and the cycle switching period is dynamically adjusted according to the vulcanization stage: a short cycle switching is used during the heating stage to quickly collect temperature data at each measuring point and provide real-time feedback on the heating rate of the hot plate to avoid local overheating; a long cycle switching is used during the constant temperature stage to ensure the stability of temperature data at each measuring point and accurately maintain the set temperature. Users can preset the switching period through the touch interface of the intelligent thermal management optimization module or the host computer software, or enable "automatic mode". The system automatically shortens the switching period according to the real-time temperature fluctuation range, and automatically extends the switching period when the temperature is stable within ±1℃, balancing the control response speed and stability.

[0065] The intelligent thermal management optimization module employs a "single-point dominant temperature control + multi-point data reference" method during heating. Instead of simply switching sequentially, the module dynamically allocates temperature control priority based on the temperature deviation of each measuring point. Specifically, the system presets a temperature threshold. When the temperature of a measuring point falls below the threshold, that point automatically gains temperature control priority. The intelligent thermal management optimization module uses the temperature of that measuring point as a feedback signal to adjust the output power of the electromagnetic heating controller in the corresponding area. If multiple measuring points simultaneously exceed the threshold, the system prioritizes the measuring point with the lowest temperature as the dominant temperature control point, while also referencing the temperature deviations of other measuring points. An algorithm compensates for the heating power of adjacent areas, preventing localized temperature imbalances caused by single-point temperature control.

[0066] The intelligent thermal management optimization module control module has a built-in temperature difference compensation model. By calculating the temperature difference between each measuring point in real time (ΔT = Ti - Tj, where i and j are any two measuring points), when the temperature difference exceeds a set threshold (e.g., ΔT > 1℃), the system automatically activates a compensation mechanism: for areas with lower temperatures, the output power of the corresponding electromagnetic heating controller is increased (the power adjustment range is proportional to the temperature difference, with a maximum adjustment range not exceeding 20% ​​of the rated power); for areas with higher temperatures, the power is appropriately reduced or maintained. Through multiple rounds of cyclic switching and power adjustment, the temperature difference between each measuring point is gradually reduced, ultimately improving the temperature uniformity of the hot plate working surface. Figure 10 .

[0067] Furthermore, from the perspective of temperature control alone, the intelligent electromagnetic heating controller can also be used with a temperature controller or PLC. The machine learning algorithm learns autonomously and iterates parameters based on real-time collected temperature data, continuously correcting the temperature control strategy to reduce the temperature difference between each sensing node, ultimately achieving intelligent control that is "unmanned, adaptable to changes in working conditions, and stable and accurate in temperature control". This solves the problem of inaccurate temperature measurement caused by the processing precision of the hot plate, and greatly improves the overall temperature control accuracy and surface temperature uniformity.

Claims

1. An electromagnetic induction heating device for a tire vulcanizing machine hot plate, characterized in that, It includes an intelligent electromagnetic heating controller (1), an intelligent thermal management optimization module (2), an aluminum plate (4), an electromagnetic coil (5), a hot plate (6), and a resistance temperature detector (RTD) (7). The output terminal of the intelligent electromagnetic heating controller (1) is connected to the electromagnetic coil (5); The intelligent thermal management optimization module (2) is connected to the thermal resistor (7). There are multiple thermal resistors (7), which are respectively inserted into multiple temperature measuring holes on the side wall of the hot plate (6). The intelligent thermal management optimization module (2) receives the temperature values ​​of the hot plate (6) measured by multiple thermal resistors (7) and inputs each temperature value into the intelligent electromagnetic heating controller (1) for processing. The working surface of the hot plate (6) is in close contact with the mold surface, and a coil groove is opened on the other side of the working surface. The electromagnetic coil (5) is laid in the coil groove. There are multiple sets of electromagnetic coils (5), which are respectively connected to the intelligent electromagnetic heating controller (1). The aluminum plate (4) is fixedly connected to the hot plate (6) by bolts, and the electromagnetic coil (5) is fixed in the coil groove of the hot plate (6). The intelligent electromagnetic heating controller (1) has a built-in self-temperature control module to realize the self-temperature control function, and is equipped with a self-learning proportional-integral-derivative closed-loop control algorithm module. Combined with a high-precision analog-to-digital conversion unit, it can quickly convert the temperature signal collected by the thermal resistor (7) input by the intelligent thermal management optimization module (2) into a digital signal and process it, thereby controlling the electromagnetic coil (5).

2. The electromagnetic induction heating equipment for the hot plate of the tire vulcanizing machine according to claim 1, characterized in that, The intelligent electromagnetic heating controller (1) is connected to the output end of the electromagnetic coil (5) by an intelligent coil matching element (8). One end of the intelligent coil matching element (8) is reliably connected to the intelligent electromagnetic heating controller (1) through a terminal block, and the other end adopts a modular interface design, which can be directly connected to a set of independent electromagnetic coils or multiple sets of electromagnetic coil units connected in series or in parallel.

3. The electromagnetic induction heating equipment for the hot plate of the tire vulcanizing machine according to claim 2, characterized in that, The intelligent coil matching element (8) integrates a programmable inductor-capacitor matching network, an inductance real-time detection unit, and a microprocessor. It can automatically identify the actual inductance parameters of coils with different layouts, different number of turns, or different wire diameters. The microprocessor can quickly calculate the optimal matching parameters and dynamically adjust the inductance and capacitance values ​​of the internal matching network so that the impedance matching rate between the electromagnetic coil (5) and the intelligent electromagnetic heating controller (1) is always maintained above 95%.

4. The electromagnetic induction heating equipment for the hot plate of a tire vulcanizing machine according to any one of claims 1-3, characterized in that, The electromagnetic coil (5) is divided into different control areas according to the different structures of the hot plate (6), avoiding structures such as the mold locking hole and the hot plate fixing hole. The electromagnetic coil (5) can be divided into three different distribution patterns in different areas: radial integral distribution in one area, inner and outer distribution, and inner, middle and outer distribution in three areas, to adapt to different mold sizes. The hot plate (6) is radially distributed in one area, with the electromagnetic coil (5) wound in an arc-shaped S-shape, U-shape, or spiral shape from the outside to the inside, and controlled by the intelligent electromagnetic heating controller (1). The hot plate (6) is radially distributed in two areas, with the electromagnetic coil (5) divided into two regions, wound in an arc-shaped S-shape, and controlled by two sets of intelligent electromagnetic heating controllers (1). The hot plate (6) is radially distributed in three areas, with the electromagnetic coil (5) divided into three regions, wound in an arc-shaped S-shape, and controlled by three sets of intelligent electromagnetic heating controllers (1). The electromagnetic coil (5) can be divided into different regions, with the hot plate (6) distributed circumferentially in equal or unequal proportions, to accommodate different types of molds. The electromagnetic coils (5) are distributed proportionally into several regions along the circumference according to the position of the mold-locking holes of the vulcanizing machine. These regions are typically divided into two, four, six, or eight equal parts. The electromagnetic coils in each region are identically distributed with the same coil density. The electromagnetic coils (5) are distributed in different proportions according to the special position of the mold-locking hole of the vulcanizing machine, and are divided into several unequal regions. Therefore, the electromagnetic coils are divided into regions of different sizes along the circumference, and the coil density is different.

5. A method for intelligent temperature control using the electromagnetic induction heating equipment for the hot plate of a tire vulcanizing machine as described in claim 1, characterized in that, The method involves the intelligent electromagnetic heating controller's self-learning system starting up, with the three main input modules first completing the transmission of basic parameters to provide a clear basis for subsequent control, forming a collaborative input link: First, the operator inputs the target temperature into the system in the form of a "temperature setpoint" according to the tire vulcanization process requirements. This parameter serves as the core benchmark for the entire temperature control process and is directly transmitted to the core control unit of the intelligent electromagnetic heating controller. Simultaneously, the temperature sensor installed on the mold starts in real time, collecting initial temperature data and generating a "temperature feedback" signal, which is synchronously transmitted to the core control unit for establishing initial temperature deviation judgment. In addition, the system's built-in status monitoring unit automatically collects "operating condition data," including grid voltage fluctuations, equipment heat dissipation conditions, and the specifications and material information of the current vulcanized workpiece. This data serves as a key basis for operating condition adaptation, assisting the core control unit in accurately identifying the working environment. The three main input signals are transmitted in parallel, jointly constructing an initial working scenario with a "clear target and clear status" for the core control unit.

6. The intelligent temperature control method for electromagnetic induction heating of the hot plate of a tire vulcanizing machine according to claim 5, characterized in that, The intelligent electromagnetic heating controller incorporates an intelligent proportional-integral-derivative closed-loop control algorithm module and a self-learning module. These two modules combine to form a dynamic linkage control method: During system startup or in scenarios involving changes in operating conditions such as workpiece or mold replacement, the self-learning module combines the input "operating condition data" with the temperature response curve fed back by the intelligent closed-loop control algorithm module. It automatically identifies the system's core thermal characteristics, including the workpiece's heat capacity and the equipment's heat loss rate, and then establishes a dedicated mathematical model. Based on this model, it generates a set of optimal initial parameters for the intelligent closed-loop control algorithm module, avoiding the tedious manual debugging. During normal operation, the self-learning module continuously records the power output curve, temperature feedback curve, and operating condition change data of the intelligent closed-loop control algorithm module, comparing the actual temperature control effect with the ideal target in real time. If performance fluctuations are detected, it automatically fine-tunes the proportional-integral-derivative parameters and synchronously transmits them to the intelligent closed-loop control algorithm module, ensuring it remains in optimal operating condition. This integration achieves a unity of "real-time control" and "continuous optimization," enabling the system to cope with both immediate temperature deviations and long-term operating condition changes.

7. The intelligent temperature control method for electromagnetic induction heating of the hot plate of a tire vulcanizing machine according to claim 5, characterized in that, The power adjustment command generated by the core control unit is converted and executed by the power drive module, and then the actual state is fed back through the feedback link, forming a complete closed loop: After receiving the electrical signal command from the core control unit, the power drive module converts it into actual power output that can drive the heating mechanism, and controls the electromagnetic coil to generate a high-frequency alternating magnetic field of corresponding intensity; the heating mechanism, i.e., the electromagnetic coil, generates heat through the eddy current effect under power drive and transfers it to the mold, pushing the mold temperature closer to the set value; during this process, the temperature sensor continuously collects the real-time temperature of the mold and continuously sends the updated "temperature feedback" signal back to the core control unit, so that the core control unit can grasp the temperature control effect in real time and adjust subsequent commands according to the new temperature deviation. This closed loop process of "command output - power execution - temperature change - status feedback" is repeated to ensure that the temperature is always stable within the set range.

8. The intelligent temperature control method for electromagnetic induction heating of the hot plate of a tire vulcanizing machine according to any one of claims 5 or 6, characterized in that, The intelligent electromagnetic heating controller (1) is used in conjunction with the intelligent thermal management optimization module (2). The intelligent thermal management optimization module (2) integrates machine learning algorithms and multi-channel sensor access units, which can realize the stable access of multiple thermal resistors (7). The thermal resistors (7) are evenly distributed inside the hot plate to form a full-area temperature sensing network. The intelligent thermal management optimization module (2) flexibly sets the sampling interval according to the actual temperature control requirements. During the heating process, the built-in machine learning algorithm drives the channel timing switching mechanism to select a single thermal resistor (7) as the current temperature control reference in sequence. The intelligent thermal management optimization module (2) continuously collects the temperature data of each sensor node and uses the algorithm to dynamically optimize the switching timing and temperature control parameters, gradually reducing the temperature deviation between each thermal resistor (7), and finally achieving high-precision temperature control and improving the uniformity of the surface temperature distribution of the hot plate (6).

9. The intelligent temperature control method for electromagnetic induction heating of the hot plate of a tire vulcanizing machine according to claim 8, characterized in that, The intelligent thermal management optimization module has a built-in programmable timer, and the cycle switching period is dynamically adjusted according to the vulcanization stage: a short cycle switching is used during the heating stage to quickly collect temperature data at each measuring point and provide real-time feedback on the heating rate of the hot plate to avoid local overheating; a long cycle switching is used during the constant temperature stage to ensure the stability of temperature data at each measuring point and accurately maintain the set temperature. Users can preset the switching period through the touch interface of the intelligent thermal management optimization module or the host computer software, or enable "automatic mode". The system automatically shortens the switching period according to the real-time temperature fluctuation range, and automatically extends the switching period when the temperature is stable within ±1℃, balancing the control response speed and stability.

10. The intelligent temperature control method for electromagnetic induction heating of the hot plate of a tire vulcanizing machine according to claim 9, characterized in that, The intelligent thermal management optimization module employs a "single-point dominant temperature control + multi-point data reference" method during heating. Instead of simply switching sequentially, the module dynamically allocates temperature control priority based on the temperature deviation of each measuring point. Specifically, the system presets a temperature threshold. When the temperature of a measuring point falls below the threshold, that point automatically gains temperature control priority. The intelligent thermal management optimization module uses the temperature of that measuring point as a feedback signal to adjust the output power of the electromagnetic heating controller in the corresponding area. If multiple measuring points simultaneously exceed the threshold, the system prioritizes the measuring point with the lowest temperature as the dominant temperature control point, while simultaneously referencing the temperature deviations of other measuring points. An algorithm compensates for the heating power of adjacent areas, preventing localized temperature imbalances caused by single-point temperature control.

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