Intelligent control method and system for intelligent parameters of infrared physiotherapy equipment

Abstract

The invention provides an intelligent control method and system for intelligent parameters of infrared physiotherapy equipment, and is applied to the technical field of intelligent control of the infrared physiotherapy equipment. Physiological characterization data, such as a skin optical parameter change rate and a temperature change rate, of a target treatment area are collected in real time; the physiological response of an individual to thermal stimulation can be objectively and quantitatively reflected; according to the physiological characterization data, the thermal sensitivity grade of the target object is intelligently determined, and therefore accurate identification of individual differences is achieved; furthermore, PID control parameters are dynamically adjusted according to the determined thermal sensitivity level, output of the infrared radiation source is accurately controlled based on the adjusted parameters, and individuation and high efficiency of the treatment process are ensured. Therefore, intelligent self-adaptive control over the parameters of the infrared physiotherapy equipment can be achieved, the problems that traditional equipment cannot adapt to individual differences, the treatment effect is poor and potential safety hazards exist are effectively solved, and the beneficial effects of remarkably improving treatment individuation, effectiveness and safety are achieved.

Description

Technical Field

[0001] This application relates to the field of intelligent control technology for infrared physiotherapy equipment, and particularly to an intelligent control method and system for intelligent parameters of infrared physiotherapy equipment. Background Technology

[0002] Infrared therapy equipment plays a vital role in modern rehabilitation treatment. It applies precise heat energy to the human body to alleviate discomfort, accelerate tissue repair, and promote patient recovery. However, in practical clinical applications, especially when dealing with an increasingly diverse patient population, achieving precise and adaptive control of the operating parameters of infrared therapy equipment remains a critical technical challenge. Traditional infrared therapy equipment typically uses preset, fixed operating parameters. This one-size-fits-all control model is ill-suited to individual physiological differences. When there are significant differences in individual patient characteristics (such as skin type, subcutaneous fat thickness, and heat sensitivity), or when operators lack experience, this rigid control method often leads to unsatisfactory treatment results and may even pose safety hazards. To compensate for these shortcomings, manual intervention by experienced medical personnel is often required in clinical practice. However, this introduces new problems, such as reduced accuracy in recording treatment data and over-reliance on the operator's personal experience, making it difficult to standardize and quantify the treatment process.

[0003] To address the inherent inconsistencies in control and optimize the treatment process, some experienced medical professionals have developed a strategy for manual adjustments based on patients' real-time responses and subjective comfort through long-term practice. However, while such frequent manual intervention may seem to improve treatment efficiency and patient comfort in the short term, it introduces a new and more insidious problem: a significant discrepancy arises between the treatment parameters recorded internally by the device and the actual output parameters applied to the patient. The root cause lies in the fact that the internal data recording systems of many infrared therapy devices are primarily designed to record preset treatment parameters and raw data collected by sensors, rather than precisely tracking the specific values ​​and timing of each manual intervention. This data distortion severely impacts the objective evaluation of treatment effects for different patient groups, hinders the standardized management and optimization of treatment protocols, and makes clinical research or treatment protocol improvements based on historical data extremely difficult.

[0004] In light of the above, given the increasingly diverse patient population, the fluctuations in treatment efficacy and safety risks due to varying levels of operator experience, and the inherent limitations of traditional manual parameter adjustment methods, existing infrared therapy equipment urgently needs a system capable of real-time sensing of individual differences, intelligent prediction of skin thermal response, and adaptive adjustment of core control parameters. Such a system aims to ensure optimal therapeutic efficacy while maximizing patient safety in different treatment scenarios, and to simplify operation and standardize the treatment process.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] In view of the shortcomings of the prior art, this application provides an intelligent parameter control method and system for infrared physiotherapy equipment. By collecting physiological characterization data in real time and dynamically adjusting PID control parameters accordingly, this application can achieve the beneficial effect of intelligent adaptive control of infrared physiotherapy equipment parameters, effectively solving the problems of traditional equipment being unable to adapt to individual differences, poor treatment effects, and safety hazards, and significantly improving the personalization, effectiveness, and safety of treatment.

[0007] In a first aspect, a method for intelligent control of intelligent parameters of an infrared physiotherapy device, the method comprising the following steps: S1: Real-time acquisition of physiological characterization data of the target treatment area, wherein the physiological characterization data includes at least the rate of change of skin optical parameters and the rate of change of temperature; S2: Determine the thermal sensitivity level of the target object based on the physiological characterization data; S3: Adjust the PID control parameters according to the heat sensitivity level, and control the output of the infrared radiation source based on the adjusted PID control parameters.

[0008] Furthermore, step S2 includes: S21: The rate of change of the skin optical parameters and the rate of change of temperature are used as input variables and input into a preset fuzzy logic decision tree model; S22: The input variables are fuzzified using the fuzzy logic decision tree model, and logical reasoning is performed in conjunction with a preset fuzzy rule base. S23: Defuzzify the calculation result and output the quantized value as the thermal sensitivity level of the target object.

[0009] Furthermore, in step S3, adjusting the PID control parameters according to the heat sensitivity level includes the following steps: S31: Access the pre-stored control parameter database, which contains mapping relationships between multiple heat sensitivity levels and multiple sets of PID control parameters; S32: Using the determined heat sensitivity level as the index key, retrieve and match the corresponding target PID control parameter group in the control parameter database. The target PID control parameter group includes at least the proportional coefficient, integral coefficient, and derivative coefficient. S33: Load the target PID control parameter group into the preset operation logic to replace the current control parameters.

[0010] Furthermore, in step S3, controlling the output of the infrared radiation source based on the adjusted PID control parameters includes the following steps: S34: Obtain real-time temperature data of the target treatment area; S35: Calculate the deviation between the real-time temperature data and the preset treatment target temperature; S36: Use the target PID control parameter set to perform PID calculation on the deviation value to generate a corresponding power control signal; S37: The power control signal is sent to the drive circuit of the infrared radiation source to adjust the infrared radiation power by adjusting the input voltage or duty cycle of the infrared radiation source.

[0011] Furthermore, step S2 also includes: S24: Real-time monitoring and receiving of user's subjective comfort feedback signals; S25: Real-time acquisition of ambient temperature and humidity data for the treatment environment; S26: The subjective comfort feedback signal, the environmental temperature and humidity data, the rate of change of skin optical parameters, and the rate of temperature change are jointly analyzed to determine the thermal sensitivity level of the target object.

[0012] Furthermore, step S7 includes: S71: Determine whether the subjective comfort feedback signal representing discomfort has been received; S72: When the subjective comfort feedback signal representing discomfort is received, the subjective comfort feedback signal is used as a high-priority judgment criterion, and the thermal sensitivity level is directly set to the preset high sensitivity level. S73: When no subjective comfort feedback signal representing discomfort is received, the environmental temperature and humidity data are used as an environmental correction factor, and the skin optical parameter change rate and the temperature change rate are combined and input into the fuzzy logic decision tree model to obtain the thermal sensitivity level through fuzzy inference calculation.

[0013] Furthermore, the method also includes: S4: Based on the heat sensitivity level, find the corresponding power limiting strategy and temperature safety threshold strategy; S5: Determine the initial maximum power value of infrared radiation according to the power limiting strategy; S6: Determine the upper limit of safe control for skin surface temperature based on the temperature safety threshold strategy.

[0014] Furthermore, in step S3, controlling the output of the infrared radiation source based on the adjusted PID control parameters also includes the following steps: S34: In the initial stage of the infrared radiation source activation, the output power is limited within the initial maximum power value; S35: During the operation of the infrared radiation source, the output is adjusted using the PID control parameters to ensure that the real-time temperature of the target treatment area does not exceed the safety control upper limit.

[0015] Furthermore, step S4 includes: S41: When the thermal sensitivity level is high sensitivity, a power limiting strategy that restricts the initial duty cycle of pulse width modulation is selected to achieve gentle startup; S42: When the thermal sensitivity level is high sensitivity, select a temperature safety threshold strategy that lowers the upper limit of the safety control value to increase the safety margin based on the preset treatment target temperature.

[0016] Secondly, an intelligent parameter control system for an infrared physiotherapy device, the system comprising: A physiological response monitoring unit is used to collect physiological characterization data of the target treatment area in real time, and the physiological characterization data includes at least the rate of change of skin optical parameters and the rate of change of temperature. A sensitivity assessment unit is used to determine the thermal sensitivity level of the target object based on the physiological characterization data; An adaptive control unit is used to adjust the PID control parameters according to the thermal sensitivity level, and to control the output of the infrared radiation source based on the adjusted PID control parameters.

[0017] Beneficial Effects: This application proposes an intelligent parameter control method and system for infrared physiotherapy equipment. By collecting real-time physiological characterization data of the target treatment area, such as the rate of change of skin optical parameters and the rate of temperature change, it can objectively and quantitatively reflect an individual's physiological response to thermal stimulation. Based on this physiological characterization data, the thermal sensitivity level of the target object is intelligently determined, thereby achieving accurate identification of individual differences. Furthermore, based on the determined thermal sensitivity level, the PID control parameters are dynamically adjusted, and the output of the infrared radiation source is precisely controlled based on the adjusted parameters, ensuring the personalization and efficiency of the treatment process. Therefore, this application can achieve intelligent adaptive control of infrared physiotherapy equipment parameters, effectively solving the problems of traditional equipment being unable to adapt to individual differences, having poor treatment effects, and posing safety hazards, and has the beneficial effects of significantly improving the personalization, effectiveness, and safety of treatment. Attached Figure Description

[0018] Figure 1 This is a flowchart of an intelligent parameter control method for an infrared physiotherapy device proposed in this application.

[0019] Figure 2This is a structural diagram of an intelligent parameter control system for an infrared physiotherapy device proposed in this application.

[0020] Figure 3 This is a schematic diagram of an intelligent parameter control system for an infrared physiotherapy device proposed in this application.

[0021] Labeling explanation: 201, Physiological response monitoring unit; 202, Sensitivity assessment unit; 203, Adaptive control unit. Detailed Implementation

[0022] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and marked in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0023] Please refer to Figure 1 This application proposes an intelligent parameter control method for infrared physiotherapy equipment, aiming to solve the problems of poor treatment effects, safety hazards, inaccurate treatment data recording, and inability to adapt to individual differences caused by the use of fixed parameters or reliance on manual intervention in existing infrared physiotherapy equipment. This method dynamically adjusts core control parameters by sensing the user's individual physiological response in real time, thereby achieving intelligent, personalized, and highly safe treatment. The method includes the following steps: S1: Real-time acquisition of physiological characterization data of the target treatment area, including at least the rate of change of skin optical parameters and the rate of temperature change; S2: Determine the thermal sensitivity level of the target object based on physiological characterization data; S3: Adjust the PID control parameters according to the thermal sensitivity level, and control the output of the infrared radiation source based on the adjusted PID control parameters.

[0024] The method works by simulating and surpassing the judgment and operation of experienced operators through an integrated set of sensing and control logic. At the start of treatment, the device's built-in monitoring module continuously and non-invasively measures the subtle physiological changes in the user's skin after exposure to infrared radiation. These changes, particularly the dynamic variations in skin optical properties and the rate of temperature rise, objectively reflect an individual's ability to absorb and respond to thermal stimuli.

[0025] Specifically, step S1, which involves real-time acquisition of physiological data of the target treatment area, forms the data foundation for the entire intelligent control process. The rate of temperature change is acquired by integrating one or more non-contact infrared temperature sensors, such as a thermopile sensor array, into the treatment head, continuously pointing them at the skin surface of the target treatment area. The controller reads the temperature data at a high frequency, for example, ten times per second, and calculates the real-time rate of temperature change using differential calculations, expressed in degrees Celsius per second. This rate indicator reflects the efficiency of skin tissue in absorbing infrared energy more instantly than a single temperature value; a faster rate indicates stronger tissue absorption or weaker heat dissipation, and vice versa.

[0026] The acquisition of the rate of change of skin optical parameters involves more precise monitoring of physiological state. Its core lies in quantifying the changes in local microcirculation caused by infrared thermal effects, mainly the changes in blood flow in subcutaneous capillaries.

[0027] In one specific implementation, the rate of change of skin optical parameters can be acquired using multispectral photoplethysmography (PPG). The treatment head integrates a miniature optical sensor module containing multiple light-emitting diodes (LEDs) of different wavelengths, such as a 660 nm red LED and a 940 nm near-infrared LED, along with a highly sensitive photodetector, such as a photodiode. During treatment, these LEDs alternately or simultaneously emit light into the skin tissue, while the photodetector receives the light signals reflected or transmitted from the subcutaneous tissue. The underlying principle is that hemoglobin in the blood has different absorption rates for different wavelengths of light. When infrared therapy causes a rise in local tissue temperature, leading to vasodilation and increased blood flow, the hemoglobin content per unit volume of tissue also increases, resulting in a measurable change in the intensity of reflected light. By analyzing the rate of change of reflected light intensity at a specific wavelength, the controller can quantify the rate of change in subcutaneous blood perfusion, which serves as a specific representation of the rate of change of skin optical parameters.

[0028] In another feasible implementation, the rate of change of skin optical parameters can be acquired using laser speckle contrast imaging. This technique employs a low-power coherent laser source, such as a vertical-cavity surface-emitting laser (VCSEL), to illuminate the skin surface of the target treatment area, forming a speckle pattern. A high-speed image sensor, such as a complementary metal-oxide-semiconductor (CMOS) image sensor, is used to capture this speckle pattern. The principle is that when the laser irradiates the tissue, it is scattered by moving particles within the tissue, primarily red blood cells. These moving red blood cells cause the speckle pattern to fluctuate and blur over time. By calculating the change in speckle pattern contrast over a short period, the velocity information of subcutaneous blood flow can be accurately inferred. The rate of change of blood flow velocity can also serve as an effective quantitative indicator of the rate of change of skin optical parameters.

[0029] After obtaining objective and dynamic physiological data through the above methods, the process proceeds to step S2, which involves determining the target subject's thermal sensitivity level based on this data. This step is crucial for achieving personalized treatment, as it aims to transform complex physiological signals into a quantitative indicator that can guide control strategies. Since the human body's physiological response to heat is a complex and non-linear process, difficult to describe using a simple linear model, this application preferably employs a decision-making model that more closely resembles the thinking of human experts.

[0030] Specifically, the steps for determining the thermal sensitivity level of a target object based on physiological characterization data include: S21: Input the rate of change of skin optical parameters and the rate of change of temperature as input variables into the preset fuzzy logic decision tree model; S22: The input variables are fuzzified using a fuzzy logic decision tree model, and logical reasoning is performed in conjunction with a pre-set fuzzy rule base. S23: Defuzzify the calculation result and output the quantized value as the thermal sensitivity level of the target object.

[0031] The process begins with fuzzification. The controller receives precise numerical inputs, such as a temperature change rate of 0.25 degrees Celsius per second and a skin optical parameter change rate of 3 percent per second. The fuzzification process maps these precise values ​​to predefined fuzzy sets. For example, for the temperature change rate, three fuzzy sets can be defined: slow, medium, and fast. Each set is described by a membership function, such as a triangular or trapezoidal function. An input value of 0.25 degrees Celsius per second might simultaneously belong to the medium-speed set with a membership degree of 0.6 and to the fast-speed set with a membership degree of 0.2. Similarly, the skin optical parameter change rate is also fuzzified into fuzzy sets such as low change, medium change, and high change.

[0032] The next step involves logical reasoning based on a pre-built fuzzy rule base. This rule base is built upon extensive clinical data and expert experience and contains a series of if-then rules. For example: Rule 1, if the rate of temperature change is rapid and the rate of change of skin optical parameters is high, then the thermal sensitivity level is very high; Rule 2, if the rate of temperature change is medium and the rate of change of skin optical parameters is moderate, then the thermal sensitivity level is moderate; Rule 3, if the rate of temperature change is slow and the rate of change of skin optical parameters is low, then the thermal sensitivity level is low. The controller evaluates all rules and calculates the trigger strength of each rule based on the membership degree of the input variable in each fuzzy set.

[0033] Finally, the calculation results are defuzzified. After fuzzy inference, a fuzzy output regarding the heat sensitivity level is obtained; for example, a heat sensitivity level of 0.7 is considered high, and 0.3 is considered medium. The defuzzification process, for example using the centroid method, transforms this fuzzy conclusion into a single, definite numerical value. The computer calculates the geometric center of the graph formed by all the output fuzzy sets; the x-coordinate of this center is the final quantized output. For example, a value of 7.8 on a scale of 1 to 10 represents the final determined heat sensitivity level.

[0034] The above method can transform individual differences that are difficult to describe precisely into a standardized value that can be used for subsequent control, thus laying the foundation for the realization of adaptive control.

[0035] After determining the heat sensitivity level of the target object, the further step in step S3, adjusting the PID control parameters according to the heat sensitivity level, includes the following steps: S31: Access the pre-stored control parameter database, which contains mapping relationships between multiple heat sensitivity levels and multiple sets of PID control parameters; S32: Using the determined heat sensitivity level as the index key, retrieve and match the corresponding target PID control parameter set in the control parameter database. The target PID control parameter set includes at least the proportional coefficient, integral coefficient, and derivative coefficient. S33: Load the target PID control parameter group into the preset operation logic to replace the current control parameters.

[0036] The control parameter database is obtained through extensive experimentation and simulation optimization before the equipment leaves the factory. For example, the database can be designed as a lookup table. The heat sensitivity level is divided into several intervals, each interval corresponding to a carefully tuned set of PID parameters.

[0037] In a specific example, the database might contain the following mapping relationships: When the thermal sensitivity level is between 1 and 3, it is determined to be low sensitivity. The matched PID parameter set is a high proportional gain, a medium integral gain, and a low derivative gain, for example, Kp=5.0, Ki=0.8, Kd=0.1. This parameter combination results in a very rapid temperature response, quickly reaching the target treatment temperature, because users with low sensitivity can tolerate a faster heating process. When the thermal sensitivity level is between 4 and 7, it is determined to be medium sensitivity, and the matched parameter set is more balanced, for example, Kp=3.0, Ki=0.5, Kd=0.3, aiming to achieve a faster response speed and less overshoot. When the thermal sensitivity level is between 8 and 10, it is determined to be high sensitivity, and the matched parameter set is very conservative, with a low proportional gain, a low integral gain, and a relatively high derivative gain, for example, Kp=1.5, Ki=0.2, Kd=0.4. This combination of parameters ensures a very smooth heating process, minimizing temperature overshoot and guaranteeing the comfort and safety of highly sensitive users.

[0038] Once the new PID parameter set is loaded, the closed-loop control process for controlling the infrared radiation source output based on these adjusted parameters begins. This process specifically includes: S34: Obtain real-time temperature data of the target treatment area; S35: Calculate the deviation between real-time temperature data and the preset treatment target temperature; S36: Use the target PID control parameter set to perform PID calculation on the deviation value and generate the corresponding power control signal; S37: Sends a power control signal to the drive circuit of the infrared radiation source, and adjusts the infrared radiation power by adjusting the input voltage or duty cycle of the infrared radiation source.

[0039] This process is a classic feedback control loop. For example, if the target treatment temperature is set at 42 degrees Celsius, and the real-time monitored skin temperature is 40 degrees Celsius, the deviation is 2 degrees Celsius. The PID logic calculates an output value based on this deviation and the current Kp, Ki, and Kd parameters. The proportional term determines the output power based on the magnitude of the current deviation; the larger the deviation, the greater the output power. The integral term eliminates steady-state error; if the temperature remains below the target value for an extended period, the integral term gradually accumulates, increasing the output power until the temperature reaches the target value. The derivative term predicts the trend of deviation changes; if the temperature rises too quickly, the derivative term generates a counter-force to suppress overshoot and make the temperature change more stable.

[0040] The generated power control signal is a digital quantity that needs to be converted into actual control of the infrared radiation source. In one implementation, this digital quantity is converted into an analog voltage signal via a digital-to-analog converter (DAC). This voltage signal controls a linear power supply, thereby regulating the voltage applied to the infrared lamp; the higher the voltage, the greater the power. In a more efficient implementation, this digital quantity is used to set the duty cycle of a pulse-width modulation (PWM) signal. For example, a power control signal ranging from 0 to 1023 can be directly mapped to a duty cycle from 0 to 100%. This PWM signal controls a power switch, such as a metal-oxide-semiconductor (MOSFET), to frequently switch the current supplied to the infrared radiation source, thus precisely controlling its average output power.

[0041] To further improve the accuracy and robustness of heat sensitivity level assessment, making it not only reliant on objective physiological data but also taking into account the user's subjective feelings and the influence of the external environment, the method of this application can further include the following steps in determining the heat sensitivity level: S24: Real-time monitoring and receiving of user's subjective comfort feedback signals; S25: Real-time acquisition of ambient temperature and humidity data for the treatment environment; S26: Combine subjective comfort feedback signals, environmental temperature and humidity data with skin optical parameter change rate and temperature change rate for joint analysis to determine the thermal sensitivity level of the target object.

[0042] Subjective comfort feedback signals can be received through a simple user interface on the device, such as a touch button with options for overheat, comfortable, and cool, or a handheld remote control. When the user feels discomfort and presses the overheat button, a high-priority signal is sent to the controller. Ambient temperature and humidity data are acquired by integrating a temperature and humidity sensor into the device casing, which continuously monitors the environmental conditions in the therapy room.

[0043] When performing joint analysis, a priority-based decision logic is employed. Further, step S7 includes: S71: Determine whether a subjective comfort feedback signal representing discomfort has been received; S72: When a subjective comfort feedback signal indicating discomfort is received, the subjective comfort feedback signal is used as a high-priority judgment basis, and the thermal sensitivity level is directly set to the preset high sensitivity level. S73: When no subjective comfort feedback signal representing discomfort is received, the ambient temperature and humidity data are used as an environmental correction factor. Combined with the rate of change of skin optical parameters and the rate of temperature change, the data are input into the fuzzy logic decision tree model, and the thermal sensitivity level is obtained through fuzzy inference calculation.

[0044] Specifically, the system first determines whether a subjective comfort feedback signal indicating discomfort has been received. When such a signal is received, for example, if the user presses an overheat button, this subjective comfort feedback signal will be used as the highest priority criterion. At this point, the controller bypasses complex fuzzy logic reasoning and directly sets the heat sensitivity level to a preset high sensitivity level, such as a value of 10. This design follows the principle of safety first, ensuring the fastest and most conservative response to clearly expressed discomfort from the user, immediately switching to the gentlest treatment mode.

[0045] When no subjective comfort feedback signal indicating discomfort is received, the collected ambient temperature and humidity data are used as environmental correction factors. These factors, along with the rate of change of skin optical parameters and the rate of temperature change, are input into the aforementioned fuzzy logic decision tree model. Fuzzy inference calculations then yield the thermal sensitivity level. The role of the environmental correction factor is to fine-tune the interpretation of physiological data. For example, a correction algorithm can be designed to multiply the original rate of temperature change by a coefficient related to ambient temperature before inputting it into the fuzzy logic model. If the ambient temperature is higher than a standard value, such as 25 degrees Celsius, this coefficient is greater than 1, thus amplifying the rate of temperature change in the calculation and causing the model to tend to output a higher thermal sensitivity level. Similarly, higher ambient humidity affects the skin's evaporative heat dissipation and can also be used as a factor to improve the final sensitivity assessment. In this way, the sensitivity assessment becomes more comprehensive and can adapt to individual differences in perception under different treatment environments.

[0046] In addition to dynamically adjusting the PID control parameters, this application's method also includes the following to construct a more comprehensive safety assurance system: Furthermore, the methods also include: S4: Based on the heat sensitivity level, find the corresponding power limiting strategy and temperature safety threshold strategy; S5: Determine the initial maximum power value of infrared radiation based on the power limiting strategy; S6: Determine the upper limit of safe control for skin surface temperature based on the temperature safety threshold strategy.

[0047] This step means that, in addition to the PID parameter database, the device also stores another safety policy database. This database is also indexed by thermal sensitivity level, but its content consists of hard-line rules for power and temperature limits. For example, for low-sensitivity users, the initial maximum power can be set to 90% of the device's rated power, and the upper limit of temperature safety can be set to 1.5 degrees Celsius above the target treatment temperature. For high-sensitivity users, the initial maximum power may be limited to 40% of the rated power, and the upper limit of temperature safety may be only 0.5 degrees Celsius above the target treatment temperature.

[0048] Furthermore, in step S3, controlling the output of the infrared radiation source based on the adjusted PID control parameters also includes the following steps: S34: During the initial stage of infrared radiation source startup, limit the output power to within the initial maximum power value; S35: During the operation of the infrared radiation source, the output is adjusted using PID control parameters to ensure that the real-time temperature of the target treatment area does not exceed the upper limit of the safety control value.

[0049] Specifically, in the initial stage of infrared radiation source activation, such as the first 30 seconds of treatment, its output power will be strictly limited to the initial maximum power value determined according to the power limiting strategy. Even if the PID controller calculates a high power demand due to a large initial deviation, this hard upper limit ensures that the device starts up gently, avoiding sudden thermal shock to sensitive skin. Throughout the operation of the infrared radiation source, in addition to dynamic adjustment using PID control parameters, the controller also has a parallel, higher-priority monitoring task: continuously comparing the real-time skin temperature with the safe control upper limit. Once the real-time temperature reaches or exceeds this upper limit, regardless of the PID calculation result, the controller will immediately force a reduction or even cut off the power of the infrared radiation source, thus establishing a last line of defense against low-temperature burns.

[0050] Furthermore, step S4 includes: S41: When the thermal sensitivity level is high, select a power limiting strategy that limits the initial duty cycle of pulse width modulation to achieve gentle startup; S42: When the thermal sensitivity level is high, select the strategy of lowering the upper limit of the safety control temperature safety threshold to increase the safety margin on the basis of the preset treatment target temperature.

[0051] Specifically, for users identified as highly sensitive, the power limiting strategy and temperature safety threshold strategy have more concrete implementation methods. When the thermal sensitivity level is high, the system will select a power limiting strategy that restricts the initial duty cycle of pulse width modulation to achieve a gentle start-up. This means that for devices using pulse width modulation to control power, the initial duty cycle will be directly limited to a low value, such as 40%, ensuring a gradual increase in energy output. Simultaneously, when the thermal sensitivity level is high, a temperature safety threshold strategy that lowers the upper limit of the safety control value will be selected to increase the safety margin based on the preset treatment target temperature. For example, if the target temperature is 42 degrees Celsius, the safety upper limit set for highly sensitive users may be 42.5 degrees Celsius, while it may be 43.5 degrees Celsius set for ordinary users. This smaller safety margin provides the highest level of safety protection for the user group most in need of protection.

[0052] Please refer to Figure 2 , Figure 3 To achieve the above method, this application also provides an intelligent parameter control system for an infrared physiotherapy device, the system comprising: The physiological response monitoring unit 201 is used to collect physiological characterization data of the target treatment area in real time. The physiological characterization data includes at least the rate of change of skin optical parameters and the rate of change of temperature. Sensitivity assessment unit 202 is used to determine the thermal sensitivity level of the target object based on physiological characterization data; The adaptive control unit 203 is used to adjust the PID control parameters according to the thermal sensitivity level, and control the output of the infrared radiation source based on the adjusted PID control parameters.

[0053] The physiological response monitoring unit 201 is a sensor assembly integrated into the physiotherapy head, responsible for real-time acquisition of physiological characterization data of the target treatment area. This data includes at least the rate of change of skin optical parameters and the rate of temperature change. Specifically, this unit may consist of a non-contact infrared thermometer, a multi-wavelength optical reflectance measurement module or a laser speckle imaging module, and corresponding signal amplification, filtering, and analog-to-digital conversion circuits.

[0054] The sensitivity assessment unit 202 is typically firmware running on the device's main controller, such as a microcontroller or digital signal processor. This unit receives raw data from the physiological response monitoring unit, executes the aforementioned fuzzy logic decision tree algorithm, and integrates subjective feedback and environmental data to ultimately calculate the quantified thermal sensitivity level of the target object.

[0055] The adaptive control unit 203 is also a core software module running on the main controller. Based on the thermal sensitivity level output by the sensitivity assessment unit, this unit searches for and loads the optimal PID control parameter set and corresponding safety policies from its internal database. Subsequently, this unit executes the PID closed-loop control algorithm to generate control commands for the infrared radiation source, while strictly enforcing power limits and temperature upper limit protection. Finally, it precisely controls the output of the infrared radiation source through the drive circuit. These three units work together to form a complete intelligent control closed loop of perception, decision-making, and execution, thereby achieving a high degree of automation, personalization, and safety in the infrared therapy process.

[0056] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for intelligent parameter control of an infrared physiotherapy device, characterized in that, The method includes the following steps: S1: Real-time acquisition of physiological characterization data of the target treatment area, wherein the physiological characterization data includes at least the rate of change of skin optical parameters and the rate of change of temperature; S2: Determine the thermal sensitivity level of the target object based on the physiological characterization data; S3: Adjust the PID control parameters according to the heat sensitivity level, and control the output of the infrared radiation source based on the adjusted PID control parameters.

2. The intelligent parameter control method for an infrared physiotherapy device according to claim 1, characterized in that, Step S2 includes: S21: The rate of change of the skin optical parameters and the rate of change of temperature are used as input variables and input into a preset fuzzy logic decision tree model; S22: The input variables are fuzzified using the fuzzy logic decision tree model, and logical reasoning is performed in conjunction with a preset fuzzy rule base. S23: Defuzzify the calculation result and output the quantized value as the thermal sensitivity level of the target object.

3. The intelligent parameter control method for an infrared physiotherapy device according to claim 1, characterized in that, In step S3, adjusting the PID control parameters according to the heat sensitivity level includes the following steps: S31: Access the pre-stored control parameter database, which contains mapping relationships between multiple heat sensitivity levels and multiple sets of PID control parameters; S32: Using the determined heat sensitivity level as the index key, retrieve and match the corresponding target PID control parameter group in the control parameter database. The target PID control parameter group includes at least the proportional coefficient, integral coefficient, and derivative coefficient. S33: Load the target PID control parameter group into the preset operation logic to replace the current control parameters.

4. The intelligent parameter control method for an infrared physiotherapy device according to claim 3, characterized in that, In step S3, controlling the output of the infrared radiation source based on the adjusted PID control parameters includes the following steps: S34: Obtain real-time temperature data of the target treatment area; S35: Calculate the deviation between the real-time temperature data and the preset treatment target temperature; S36: Use the target PID control parameter set to perform PID calculation on the deviation value to generate a corresponding power control signal; S37: The power control signal is sent to the drive circuit of the infrared radiation source to adjust the infrared radiation power by adjusting the input voltage or duty cycle of the infrared radiation source.

5. The intelligent parameter control method for an infrared physiotherapy device according to claim 1, characterized in that, Step S2 also includes: S24: Real-time monitoring and receiving of user's subjective comfort feedback signals; S25: Real-time acquisition of ambient temperature and humidity data for the treatment environment; S26: The subjective comfort feedback signal, the environmental temperature and humidity data, the rate of change of skin optical parameters, and the rate of temperature change are jointly analyzed to determine the thermal sensitivity level of the target object.

6. The intelligent parameter control method for an infrared physiotherapy device according to claim 5, characterized in that, Step S7 includes: S71: Determine whether the subjective comfort feedback signal representing discomfort has been received; S72: When the subjective comfort feedback signal representing discomfort is received, the subjective comfort feedback signal is used as a high-priority judgment criterion, and the thermal sensitivity level is directly set to the preset high sensitivity level. S73: When no subjective comfort feedback signal representing discomfort is received, the environmental temperature and humidity data are used as an environmental correction factor, and the skin optical parameter change rate and the temperature change rate are combined and input into the fuzzy logic decision tree model to obtain the thermal sensitivity level through fuzzy inference calculation.

7. The intelligent parameter control method for an infrared physiotherapy device according to claim 1, characterized in that, The method further includes: S4: Based on the heat sensitivity level, find the corresponding power limiting strategy and temperature safety threshold strategy; S5: Determine the initial maximum power value of infrared radiation according to the power limiting strategy; S6: Determine the upper limit of safe control for skin surface temperature based on the temperature safety threshold strategy.

8. The intelligent parameter control method for an infrared physiotherapy device according to claim 7, characterized in that, In step S3, controlling the output of the infrared radiation source based on the adjusted PID control parameters further includes the following steps: S34: In the initial stage of the infrared radiation source activation, the output power is limited within the initial maximum power value; S35: During the operation of the infrared radiation source, the output is adjusted using the PID control parameters to ensure that the real-time temperature of the target treatment area does not exceed the safety control upper limit.

9. The intelligent parameter control method for an infrared physiotherapy device according to claim 7, characterized in that, Step S4 includes: S41: When the thermal sensitivity level is high sensitivity, a power limiting strategy that restricts the initial duty cycle of pulse width modulation is selected to achieve gentle startup; S42: When the thermal sensitivity level is high sensitivity, select a temperature safety threshold strategy that lowers the upper limit of the safety control value to increase the safety margin based on the preset treatment target temperature.

10. An intelligent parameter control system for an infrared physiotherapy device, characterized in that, The system includes: A physiological response monitoring unit is used to collect physiological characterization data of the target treatment area in real time, and the physiological characterization data includes at least the rate of change of skin optical parameters and the rate of change of temperature. A sensitivity assessment unit is used to determine the thermal sensitivity level of the target object based on the physiological characterization data; An adaptive control unit is used to adjust the PID control parameters according to the thermal sensitivity level, and to control the output of the infrared radiation source based on the adjusted PID control parameters.

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