Infrared lamp therapeutic instrument control system capable of automatically controlling therapeutic parameters

Abstract

The invention discloses an infrared lamp therapeutic instrument control system capable of automatically controlling therapeutic parameters, and relates to the technical field of infrared lamp therapeutic instruments. The infrared lamp therapeutic instrument control system capable of automatically controlling the therapeutic parameters comprises a data acquisition and processing unit, a skin lesion identification and severity evaluation unit and a treatment and feedback management unit, and through real-time data acquisition and processing, the system can accurately identify the type and severity of skin lesion, thereby providing a scientific basis for subsequent treatment. Meanwhile, the treatment and feedback management unit continuously updates treatment parameters according to real-time monitoring data, the working state of the infrared lamp is automatically adjusted, and the safety and effectiveness of the treatment process are ensured. Besides, the system also supports a remote monitoring function, so that doctors or family members can know the treatment progress and skin state change in real time through a mobile phone APP and make adjustments in time, and more intimate and comprehensive treatment services are provided for patients.

Description

Technical Field

[0001] The present invention relates to the technical field of infrared lamp therapeutic apparatuses, and particularly to a control system for an infrared lamp therapeutic apparatus capable of automatically controlling treatment parameters. Background Art

[0002] An infrared lamp therapeutic apparatus, also known as an infrared physiotherapy lamp, is a device that uses infrared rays for physical therapy. The infrared lamp therapeutic apparatus emits infrared rays, which act on the treatment site, and after being absorbed by tissues, a thermal effect is generated. This thermal effect has functions such as accelerating blood circulation, increasing metabolism, dilating blood vessels, promoting the subsidence of inflammation, and accelerating tissue repair, thereby relieving pain and promoting wound healing. Clinically, it is used for skin infections, erysipelas, chronic skin ulcers, chilblains, scleroderma, etc.

[0003] Common infrared lamp therapeutic apparatuses usually control the infrared lamp therapeutic apparatus to perform treatment according to a mode after manually inputting parameters. Although this method is simple, it cannot perform targeted treatment based on the actual condition of the patient's illness. At the same time, inappropriate treatment methods, such as long-term use or close-range irradiation, may cause burns to normal skin, so the best treatment effect cannot be achieved.

[0004] Publication No. CN115970178B discloses a lower limb infrared therapeutic apparatus, including: a lower limb movement training device, a thermal imager, a control module, and a plurality of infrared physiotherapy lamps; the infrared physiotherapy lamps are respectively installed at positions corresponding to the lower limb joints in the lower limb movement training device; the thermal imager is used to capture a whole-body thermal imaging image of the treatment object; the control module is used to extract the body characteristics of the treatment object from the whole-body thermal imaging image, and the body characteristics include body shape characteristics and lower limb temperature characteristics; determine an infrared treatment plan for the treatment object according to the body characteristics; trigger the plurality of infrared physiotherapy lamps to perform treatment on the treatment object according to the infrared treatment plan; wherein, the infrared treatment plan is used to specify the power irradiation curve of the plurality of infrared physiotherapy lamps changing with time.

[0005] Although the above technology can achieve the effect of intelligent treatment by determining the infrared treatment plan for the treatment object according to the body characteristics, this technology only detects the skin temperature and generates a simple treatment plan for treatment. However, there are other factors to be considered at the actual skin lesion site, and the severity of the skin lesion cannot be accurately evaluated simply by temperature. Therefore, directly treating by detecting temperature still cannot achieve the best treatment effect. Summary of the Invention

[0006] Aiming at the deficiencies of the prior art, the present invention provides a control system for an infrared lamp therapeutic apparatus capable of automatically controlling treatment parameters, and solves the problem that the existing infrared lamp therapeutic apparatus cannot accurately evaluate the condition of the patient's skin lesion for precise treatment.

[0007] To achieve the above object, the present invention is realized by the following technical solutions: An infrared lamp therapeutic apparatus control system capable of automatically controlling treatment parameters, comprising a data acquisition and processing unit, a skin lesion recognition and severity assessment unit, and a treatment and feedback management unit. The data acquisition and processing unit includes:

[0008] A real-time data acquisition module, which is used to simultaneously collect temperature, humidity, and skin resistance data, keep the timestamps of different sensor data consistent, then apply a filtering algorithm to remove noise, and identify and process abnormal data points;

[0009] A feature extraction module, which performs a two-way analysis of the temperature gradient from the perspectives of spatial gradient and temporal gradient, performs heat map segmentation on the detection area, determines the abnormal area by comparing with the temperature threshold, and evaluates the skin state considering humidity and resistance factors;

[0010] The skin lesion recognition and severity assessment unit includes:

[0011] A pattern recognition algorithm module, which constructs a neural network model and uses the extracted features as input data to train the model to form a pattern recognition algorithm for real-time skin lesion recognition;

[0012] A severity assessment module, which selects features related to severity from the recognized skin lesion types and establishes a quantitative relationship between the features and severity using a regression model (such as linear regression, support vector regression, etc.);

[0013] The treatment and feedback management unit is used to continuously update the treatment parameters according to the real-time monitoring data, automatically adjust the working state of the infrared lamp according to the treatment parameters, and display the treatment progress and changes in skin state in real time.

[0014] Preferably, the temperature gradient analysis is specifically: applying a numerical differentiation method to calculate the spatial gradient (i.e., the rate of temperature change) and temporal gradient (i.e., the rate of temperature change with time) of the skin temperature;

[0015] Spatial gradient:

[0016]

[0017] Among them, T(x, y, z) represents the temperature at the coordinates (x, y, z), and Δx, Δy, and Δz respectively represent the small displacements in the x, y, and z directions;

[0018] Temporal gradient:

[0019]

[0020] Among them, t represents time, and Δt represents the time interval.

[0021] Preferably, the thermal map segmentation is specifically as follows: The thermal map is segmented into different regions, and the abnormal regions are identified. The steps include:

[0022] Threshold setting: Set the temperature threshold according to the difference between the normal skin temperature and the abnormal skin temperature;

[0023] Region growing: Starting from the initial point where the temperature exceeds the threshold, gradually expand to the surrounding to form abnormal regions;

[0024] Morphological processing: Apply morphological operations (such as erosion, dilation, opening operation, closing operation) to optimize the segmentation result; The calculation formulas for erosion and dilation in morphological operations are:

[0025] Erosion:

[0026] where A is the input image, B is the structuring element, and (B) D represents the image obtained by translating B to the coordinate D;

[0027] Dilation:

[0028] where represents the empty set.

[0029] Preferably, the skin condition assessment is specifically as follows: Combining humidity and resistance data, evaluate the skin barrier function. The steps include:

[0030] Humidity and resistance measurement: Use a humidity sensor and a skin resistance sensor to measure the skin humidity and resistance;

[0031] Skin barrier function assessment: According to the humidity and resistance data, combined with the known skin barrier function standards, evaluate the skin barrier function status;

[0032] Assume that the skin resistance is inversely proportional to the skin barrier function, then the evaluation formula is expressed as:

[0033]

[0034] where Rskin represents the skin resistance, and the humidity factor is a coefficient calculated according to the humidity data.

[0035] Preferably, the construction steps of the pattern recognition algorithm specifically include:

[0036] Data preparation: Use the extracted features (such as temperature gradient, abnormal region size, skin condition assessment index, etc.) as input data;

[0037] Model training: Use a deep learning framework (such as TensorFlow or PyTorch) to train a convolutional neural network (CNN) or a recurrent neural network (RNN) model;

[0038] Model evaluation: Use the validation set to evaluate the performance of the model, including accuracy and recall metrics;

[0039] Model deployment: Deploy the trained model to an embedded system for real-time skin lesion recognition;

[0040] The convolution operation formula in deep learning is:

[0041] Sc(i, j) = (Sr * w)(i, j) + b = ∑ m ∑ n Sr(i + m, j + n)w(m, n) + b;

[0042] Where Sr is the input image, w is the convolution kernel, b is the bias term, Sc is the output image after convolution, (i, j) represents the position coordinates in the image, m represents the number of rows (i.e., height) of the image, and n represents the number of columns (i.e., width) of the image.

[0043] Preferably, the severity assessment steps include:

[0044] Feature selection: Select features related to severity from the identified skin lesion types;

[0045] Quantification model: Use a regression model (such as linear regression, support vector regression, etc.) to establish a quantitative relationship between features and severity;

[0046] Model validation: Use clinical data to validate the accuracy of the model;

[0047] The calculation formula for linear regression is:

[0048] Y^ = β 0 +β 1 X 1 +β 2 X 2 +...+β c X c ;

[0049] Where Y^ is the predicted severity, β 0 、β 1 、...、β c are regression coefficients, and X 1 、X 2 、...、X c are the selected features.

[0050] Preferably, the steps for automatically adjusting the working state of the infrared lamp specifically include:

[0051] Set the target temperature range: According to the type and severity of the skin lesion, obtain the target skin temperature range from the treatment parameter library (for example, for muscle pain treatment, the target temperature range may be set to 38°C to 42°C);

[0052] Real-time monitor the skin temperature: Use a temperature sensor to collect the patient's skin temperature data in real time, and calculate the deviation between the current temperature and the target temperature range. Among them, the central value of the target temperature range is set to (upper limit of the target temperature range + lower limit of the target temperature range) / 2;

[0053] Dynamically adjust the power: According to the temperature deviation, use a PID controller to dynamically adjust the power of the infrared lamp; if the skin temperature is lower than the target range, increase the power, and vice versa, decrease the power; The PID formula is expressed as:

[0054]

[0055] Among them, Kp, Ki, and Kd are the proportional coefficient, integral coefficient, and differential coefficient respectively, which are debugged and optimized according to experimental data;

[0056] Limit the maximum power: To prevent overheating, set a maximum power limit. Even if the temperature deviation indicates that the power needs to be increased, it will not exceed this limit; And set an overheat protection threshold (such as 60°C). When the device temperature or skin temperature reaches or exceeds this threshold, immediately trigger the overheat protection mechanism.

[0057] Preferably, the treatment progress and skin condition change display function support remote monitoring. Doctors or family members can remotely monitor the treatment progress and skin condition through the mobile APP, and provide functions such as intelligent reminders according to the treatment progress and patient status, such as changing the treatment area, adjusting the posture, etc.

[0058] Preferably, the hardware facilities of the system include:

[0059] Sensor module:

[0060] High-precision temperature sensor: Used to accurately measure the skin temperature, support multi-point measurement to obtain a comprehensive temperature distribution;

[0061] Infrared thermal imaging camera: Provide a high-resolution skin thermal map for identifying temperature abnormal areas;

[0062] Humidity and skin resistance sensor: Evaluate skin moisture and resistance to understand the skin barrier function;

[0063] Infrared lamp therapeutic apparatus:

[0064] Multi-wavelength LED array: Support the output of infrared rays of different wavelengths to meet different treatment needs;

[0065] Intelligent power regulator: Dynamically adjusts power according to treatment parameters, achieved through the PID controller algorithm;

[0066] Irradiation area regulator: Adjusts the irradiation area mechanically or electronically to adapt to different skin lesion areas;

[0067] Microprocessor / controller:

[0068] High-performance embedded system: Supports complex data processing algorithms and real-time control;

[0069] Wireless communication module: Supports Wi-Fi and Bluetooth for remote monitoring and data transmission;

[0070] User interface:

[0071] Touch screen display: Displays treatment parameters, skin heat maps, and treatment progress information;

[0072] Voice assistant: Provides voice command input and feedback for patient voice interaction.

[0073] The present invention provides an infrared lamp therapeutic apparatus control system capable of automatically controlling treatment parameters.

[0074] Compared with the prior art, it has the following beneficial effects:

[0075] 1. The infrared lamp therapeutic apparatus control system capable of automatically controlling treatment parameters can automatically identify skin lesions and intelligently adjust infrared treatment parameters, significantly improving the accuracy and efficiency of treatment. Through real-time data collection and processing, the system can accurately identify the type and severity of skin lesions, providing a scientific basis for subsequent treatment. At the same time, the treatment and feedback management unit continuously updates treatment parameters based on real-time monitoring data, automatically adjusting the working state of the infrared lamp to ensure the safety and effectiveness of the treatment process. In addition, the system also supports the remote monitoring function, and doctors or family members can understand the treatment progress and changes in skin status in real time through the mobile phone APP, making timely adjustments to provide more considerate and comprehensive treatment services for patients. This intelligent and personalized treatment method not only improves the treatment effect but also enhances the patient's treatment experience and comfort.

[0076] 2. The infrared lamp therapeutic apparatus control system capable of automatically controlling treatment parameters further realizes the precise analysis and processing of skin temperature changes and the comprehensive assessment of skin conditions, thus significantly improving the intelligent level of the infrared lamp therapeutic apparatus control system. Through temperature gradient analysis, the system can accurately capture the spatial and temporal variation characteristics of skin temperature, providing more refined data support for skin lesion identification and severity assessment. The application of the heat map segmentation technology enables the system to automatically identify abnormal areas, providing precise positioning for subsequent treatment. At the same time, the assessment of skin conditions by combining humidity and resistance data not only considers skin temperature but also comprehensively takes into account an important factor of skin barrier function, making the adjustment of treatment parameters more scientific and reasonable.

[0077] 3. The infrared lamp therapeutic apparatus control system capable of automatically controlling treatment parameters further enhances the intelligence and precision of the infrared lamp therapeutic apparatus control system. By constructing a deep learning model, the system can efficiently process and analyze the data collected from sensors and accurately identify the types of skin lesions. At the same time, the application of the deep learning model also improves the real-time performance and accuracy of skin lesion identification, providing strong support for the timely adjustment of treatment parameters. In terms of severity assessment, by selecting relevant features and establishing a quantification model, the system can scientifically and objectively assess the severity of skin lesions, providing an important basis for formulating personalized treatment plans. In addition, the deployment and application of the deep learning model also demonstrate the broad application prospects of this system in the field of medical intelligence.

[0078] 4. The infrared lamp therapeutic apparatus control system capable of automatically controlling treatment parameters further realizes the refined adjustment and safety guarantee of the infrared lamp therapeutic apparatus control system. By setting the target temperature range and monitoring the skin temperature in real time, the system can accurately control the power of the infrared lamp to ensure that the skin temperature during the treatment process is maintained within the optimal range, thereby improving the accuracy and comfort of the treatment. At the same time, the application of the PID controller makes the power adjustment more dynamic and intelligent, capable of quickly responding to temperature changes and further enhancing the treatment effect. In addition, by setting the maximum power limit and overheat protection threshold, the system effectively prevents the occurrence of overheating phenomena, ensuring the safety and reliability of the treatment process. The realization of these functions not only improves the treatment effect but also enhances the patient's treatment confidence and comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 It is the system module block diagram of the present invention;

[0080] Figure 2 It is the step flow schematic diagram of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0081] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0082] Referring to Figure 1 - Figure 2 , the present invention provides the following four technical solutions:

[0083] The first implementation method: An infrared lamp therapeutic apparatus control system capable of automatically controlling treatment parameters, including a data acquisition and processing unit, a skin lesion recognition and severity assessment unit, and a treatment and feedback management unit. The data acquisition and processing unit includes:

[0084] A real-time data acquisition module, which is used to simultaneously collect temperature, humidity, and skin resistance data, and keep the timestamps of different sensor data consistent for subsequent analysis. Then, a filtering algorithm is applied to remove noise and identify and process abnormal data points to prevent misjudgment;

[0085] A feature extraction module, which performs bidirectional temperature gradient analysis from the perspectives of spatial gradient and time gradient, and performs heat map segmentation on the detection area. The abnormal area is determined by comparing the temperature threshold, and the skin state is evaluated considering humidity and resistance factors;

[0086] The skin lesion recognition and severity assessment unit includes:

[0087] A pattern recognition algorithm module, which constructs a neural network model and uses the extracted features as input data to train the model to form a pattern recognition algorithm for real-time skin lesion recognition;

[0088] A severity assessment module, which selects features related to severity from the recognized skin lesion types and establishes a quantitative relationship between the features and severity using a regression model (such as linear regression, support vector regression, etc.);

[0089] The treatment and feedback management unit is used to continuously update treatment parameters according to real-time monitoring data, automatically adjust the working state of the infrared lamp according to the treatment parameters, and display the treatment progress and changes in skin state in real time; the treatment progress and skin state change display function supports remote monitoring. Doctors or family members can remotely monitor the treatment progress and skin state through a mobile phone APP, and provide a function of intelligent reminder according to the treatment progress and patient state, such as changing the treatment area, adjusting the posture, etc.

[0090] This system can automatically identify the skin lesion area and intelligently adjust the infrared treatment parameters, significantly improving the accuracy and efficiency of treatment. Through real-time data collection and processing, the system can accurately identify the type and severity of the skin lesion, providing a scientific basis for subsequent treatment. At the same time, the treatment and feedback management unit continuously updates the treatment parameters according to the real-time monitoring data, automatically adjusting the working state of the infrared lamp to ensure the safety and effectiveness of the treatment process. In addition, the system also supports remote monitoring function. Doctors or family members can understand the treatment progress and skin condition changes in real time through the mobile phone APP, make timely adjustments, and provide more considerate and comprehensive treatment services for patients. This intelligent and personalized treatment method not only improves the treatment effect, but also enhances the patient's treatment experience and comfort.

[0091] The second implementation mode is mainly different from the first implementation mode in that: the temperature gradient analysis specifically is: applying the numerical differentiation method to calculate the spatial gradient (i.e., the rate of temperature change) and the time gradient (i.e., the rate of temperature change over time) of the skin temperature;

[0092] Spatial gradient:

[0093]

[0094] Among them, T(x, y, z) represents the temperature at the coordinate (x, y, z), and Δx, Δy, and Δz respectively represent the small displacements in the x, y, and z directions;

[0095] Time gradient:

[0096]

[0097] Among them, t represents time, and Δt represents the time interval.

[0098] The thermal image segmentation specifically is: dividing the thermal image into different regions and identifying the abnormal regions. The steps include:

[0099] Threshold setting: Setting the temperature threshold according to the difference between the normal skin temperature and the abnormal skin temperature;

[0100] Region growing: Starting from the initial point where the temperature exceeds the threshold, gradually expanding to the surrounding to form an abnormal region;

[0101] Morphological processing: Applying morphological operations to optimize the segmentation result; the calculation formulas for erosion and dilation in morphological operations are:

[0102] Erosion:

[0103] Among them, A is the input image, B is the structuring element, and (B) D represents the image obtained by translating B to the coordinate D;

[0104] Expansion:

[0105] where represents the empty set.

[0106] The skin condition assessment is specifically as follows: Combining humidity and resistance data to evaluate the skin barrier function. The steps include:

[0107] Humidity and resistance measurement: Measuring skin humidity and resistance using a humidity sensor and a skin resistance sensor;

[0108] Skin barrier function assessment: Evaluating the skin barrier function status based on humidity and resistance data, in combination with known skin barrier function criteria;

[0109] Assume that the skin resistance is inversely proportional to the skin barrier function, then the evaluation formula is expressed as:

[0110]

[0111] where Rskin represents the skin resistance, and the humidity factor is a coefficient calculated based on humidity data. Its specific calculation process is shown in the following example:

[0112] Assume that the relative humidity range of normal skin is between 40% and 60%, and the specific range can be adjusted according to the specific application scenario and skin type;

[0113] Calculate the deviation between the measured humidity and the midpoint of the normal humidity range. For example, if the measured humidity is 50%, the deviation is 0 (because 50% is exactly at the midpoint of the normal range). If the measured humidity is 70%, the deviation is +10%;

[0114] According to the magnitude of the humidity deviation, apply a weight coefficient to adjust the value of the humidity factor. The weight coefficient can be set according to the sensitivity of the humidity deviation. For example, set a linear weight so that the greater the humidity deviation, the greater the weight;

[0115] Multiply the humidity deviation by the weight coefficient, and then add a base value (usually 1, representing the normal state) to obtain the humidity factor. For example:

[0116] Humidity factor = 1 + (humidity deviation × weight coefficient);

[0117] If the humidity deviation is +10% and the weight coefficient is 0.5, then:

[0118] Humidity factor = 1 + (10% × 0.5) = 1 + 0.05 = 1.05;

[0119] This means that the moisture state of the skin is slightly higher than the normal level but still within the acceptable range.

[0120] This embodiment further realizes the precise analysis and processing of skin temperature changes, as well as the comprehensive evaluation of skin conditions, thus significantly improving the intelligent level of the control system of the infrared lamp therapeutic apparatus. Through temperature gradient analysis, the system can accurately capture the spatial and temporal change characteristics of skin temperature, providing more refined data support for skin lesion recognition and severity assessment. The application of the heat map segmentation technology enables the system to automatically identify abnormal areas, providing precise positioning for subsequent treatment. At the same time, evaluating the skin condition by combining humidity and resistance data not only considers skin temperature but also comprehensively takes into account an important factor of skin barrier function, making the adjustment of treatment parameters more scientific and reasonable.

[0121] The third implementation manner is mainly different from the first implementation manner in that the construction steps of the pattern recognition algorithm specifically include:

[0122] Data preparation: Taking the extracted features (such as temperature gradient, abnormal area size, skin condition evaluation index, etc.) as input data;

[0123] Model training: Using a deep learning framework (such as TensorFlow or PyTorch) to train a convolutional neural network (CNN) or a recurrent neural network (RNN) model;

[0124] Model evaluation: Using a validation set to evaluate the performance of the model, including accuracy rate, recall rate and other metrics;

[0125] Model deployment: Deploying the trained model to an embedded system for real-time skin lesion recognition;

[0126] The convolution operation formula in deep learning is:

[0127] Sc(i, j) = (Sr * w)(i, j) + b = ∑ m ∑ n Sr(i + m, j + n)w(m, n) + b;

[0128] Where, Sr is the input image, w is the convolution kernel, b is the bias term, Sc is the output image after convolution, (i, j) represents the position coordinates in the image, m represents the number of rows (i.e., height) of the image, and n represents the number of columns (i.e., width) of the image.

[0129] The severity assessment steps include:

[0130] Feature selection: Selecting features related to severity from the identified skin lesion types;

[0131] Quantification model: Using a regression model (such as linear regression, support vector regression, etc.) to establish a quantitative relationship between features and severity;

[0132] Model validation: Validate the accuracy of the model using clinical data;

[0133] The calculation formula for linear regression is:

[0134] Ŷ = β 0 + β 1 X 1 + β 2 X 2 +... + β c X c ;

[0135] Among them, Ŷ is the predicted severity, β 0 , β 1 ,..., β c are regression coefficients, and X 1 , X 2 ,..., X c are the selected features.

[0136] This embodiment further enhances the intelligence and accuracy of the control system of the infrared lamp therapeutic apparatus. By constructing a deep learning model, the system can efficiently process and analyze the data collected from sensors, accurately identify the types of skin lesions. At the same time, the application of the deep learning model also improves the real-time performance and accuracy of skin lesion recognition, providing strong support for the timely adjustment of treatment parameters. In terms of severity assessment, by selecting relevant features and establishing a quantification model, the system can scientifically and objectively evaluate the severity of skin lesions, providing an important basis for formulating personalized treatment plans. In addition, the deployment and application of the deep learning model also demonstrate the broad application prospects of this system in the field of medical intelligence.

[0137] The fourth implementation method, the main difference from the first implementation method is that: the steps of automatically adjusting the working state of the infrared lamp specifically include:

[0138] Set the target temperature range: According to the type and severity of the skin lesion, obtain the target skin temperature range from the treatment parameter library (for example, for muscle pain treatment, the target temperature range may be set to 38°C to 42°C);

[0139] Real-time monitor the skin temperature: Use a temperature sensor to collect the skin temperature data of the patient in real time, and calculate the deviation between the current temperature and the target temperature range, where the central value of the target temperature range is set to (upper limit of the target temperature range + lower limit of the target temperature range) / 2;

[0140] Dynamically adjust the power: According to the temperature deviation, use a PID controller to dynamically adjust the power of the infrared lamp; if the skin temperature is lower than the target range, increase the power, otherwise, decrease the power; The PID formula is expressed as:

[0141]

[0142] Among them, Kp, Ki, and Kd are the proportional coefficient, integral coefficient, and differential coefficient respectively, which are adjusted and optimized according to experimental data;

[0143] Example:

[0144] Assume that the target temperature range is set to 32°C to 36°C, then the central value of the target temperature range is calculated as follows:

[0145] Central value of the target temperature range = (Upper limit of the target temperature range + Lower limit of the target temperature range) / 2 = (36°C + 32°C) / 2 = 34°C;

[0146] Collect skin temperature data in real time: Use a temperature sensor to collect the patient's skin temperature data in real time. The skin temperature collected at a certain moment is 33°C;

[0147] Calculate the deviation between the current temperature and the central value of the target temperature range:

[0148] Temperature deviation = Current temperature - Central value of the target temperature range = 33°C - 34°C = -1°C;

[0149] Assume that after debugging and optimizing through experimental data, the parameters of the PID controller are set as:

[0150] Proportional coefficient Kp = 1.0;

[0151] Integral coefficient Ki = 0.5;

[0152] Differential coefficient Kd = 0.1;

[0153] Then calculate the control quantity according to the PID formula:

[0154]

[0155] Let the integral part ∫ temperature deviation dt be calculated by accumulating the temperature deviation. Before this, the temperature deviation has been -1°C, and the integration time is 1 minute (60 seconds), then the integral is -1°C × 60 seconds = -60°C·seconds;

[0156] Differential part Assume that before the current moment, the change rate of the temperature deviation is 0 (i.e., the temperature remains unchanged), so the differential term is 0;

[0157] Substitute these values into the PID formula:

[0158] Control quantity = 1.0 × (-1°C) + 0.5 × (-60°C·seconds) + 0.1 × 0 = -1.0 - 30.0 = -31.0;

[0159] The control quantity here is a relative value, which is used to indicate the direction and magnitude of the power adjustment of the infrared lamp. A negative value indicates that the power needs to be increased, and a positive value indicates that the power needs to be decreased; the power of the infrared lamp is dynamically adjusted according to the calculated control quantity. When the control quantity is -31.0, the power of the infrared lamp is increased.

[0160] Limit the maximum power: To prevent overheating, a maximum power limit is set. Even if the temperature deviation indicates that the power needs to be increased, it will not exceed this limit; and an overheat protection threshold (such as 60 °C) is set. When the device temperature or skin temperature reaches or exceeds this threshold, the overheat protection mechanism is immediately triggered.

[0161] This embodiment further realizes the refined adjustment and safety guarantee of the infrared lamp therapeutic apparatus control system. By setting the target temperature range and monitoring the skin temperature in real time, the system can accurately control the power of the infrared lamp to ensure that the skin temperature during the treatment is maintained within the optimal range, thereby improving the accuracy and comfort of the treatment. At the same time, the application of the PID controller makes the power adjustment more dynamic and intelligent, and can quickly respond to temperature changes, further improving the treatment effect. In addition, by setting the maximum power limit and the overheat protection threshold, the system effectively prevents the occurrence of overheating phenomena, ensuring the safety and reliability of the treatment process. The realization of these functions not only improves the treatment quality, but also enhances the patient's treatment confidence and comfort.

[0162] To implement the above system operation content, the system also provides the following hardware facilities, including:

[0163] Sensor module:

[0164] High-precision temperature sensor: Used to accurately measure the skin temperature, supporting multi-point measurement to obtain a comprehensive temperature distribution;

[0165] Infrared thermal imaging camera: Provides a high-resolution skin thermal map for identifying temperature abnormal areas;

[0166] Humidity and skin resistance sensor: Evaluates skin moisture and resistance to understand the skin barrier function;

[0167] Infrared lamp therapeutic apparatus:

[0168] Multi-wavelength LED array: Supports the output of infrared rays of different wavelengths to meet different treatment needs;

[0169] Intelligent power regulator: Dynamically adjusts the power according to the treatment parameters, implemented through the PID controller algorithm;

[0170] Irradiation area regulator: Adjusts the irradiation area mechanically or electronically to adapt to different skin lesion areas;

[0171] Microprocessor / Controller:

[0172] High-performance embedded system: supporting complex data processing algorithms and real-time control;

[0173] Wireless communication module: supporting Wi-Fi and Bluetooth for remote monitoring and data transmission;

[0174] User interface:

[0175] Touch screen display: displaying treatment parameters, skin heat maps, and treatment progress information;

[0176] Voice assistant: providing voice command input and feedback for patient voice interaction.

[0177] Meanwhile, the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0178] It should be noted that, in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0179] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An infrared lamp therapeutic device control system capable of automatically controlling treatment parameters, characterized in that: It includes a data acquisition and processing unit, a skin lesion identification and severity assessment unit, and a treatment and feedback management unit. The data acquisition and processing unit includes: Real-time data acquisition module, which is used to collect temperature, humidity, and skin resistance data at the same time, keep the timestamps of different sensor data consistent, and then apply filtering algorithms to remove noise and identify and process abnormal data points; The feature extraction module performs bidirectional temperature gradient analysis from the perspectives of spatial gradient and temporal gradient, and performs heat map segmentation on the detection area. It determines the abnormal area by comparing the temperature threshold and evaluates the skin condition by considering humidity and resistance factors. The skin lesion identification and severity assessment unit comprises: The pattern recognition algorithm module builds a neural network model and uses the extracted features as input data to train the model, forming a pattern recognition algorithm for real-time skin lesion recognition; The severity assessment module selects features related to severity from the identified lesion types and uses a regression model to establish a quantitative relationship between the features and severity; The treatment and feedback management unit is used to continuously update treatment parameters according to real-time monitoring data, automatically adjust the working state of the infrared lamp according to the treatment parameters, and display the treatment progress and skin condition changes in real time.

2. The infrared lamp therapeutic apparatus control system capable of automatically controlling treatment parameters according to claim 1, characterized in that: The temperature gradient analysis is as follows: the spatial and temporal gradients of skin temperature are calculated using numerical differentiation methods; Spatial gradient: Where T(x, y, z) represents the temperature at the coordinate (x, y, z), Δx, Δy, Δz represent the small displacements in the x, y, z directions respectively; Time gradient: Wherein, t represents time and Δt represents the time interval.

3. The infrared lamp therapeutic apparatus control system capable of automatically controlling therapeutic parameters according to claim 1, characterized in that: Heat map segmentation is to segment the heat map into different areas and identify abnormal areas. The steps include: Threshold setting: Set the temperature threshold according to the difference between normal skin temperature and abnormal skin temperature; Regional growth: Starting from the initial point where the temperature exceeds the threshold, it gradually expands to the surrounding area to form an abnormal area; Morphological processing: Apply morphological operations to optimize segmentation results; the calculation formulas for erosion and expansion in morphological operations are: corrosion: Where A is the input image, B is the structural element, (B) D Represents the image after B is translated to coordinate D; Expansion: in Represents the empty set.

4. The infrared lamp therapeutic apparatus control system capable of automatically controlling therapeutic parameters according to claim 1, characterized in that: Skin condition assessment is specifically: combining humidity and resistance data to assess skin barrier function. The steps include: Humidity and resistance measurement: Use humidity sensor and skin resistance sensor to measure skin humidity and resistance; Skin barrier function assessment: Based on humidity and resistance data, combined with known skin barrier function standards, the skin barrier function status is assessed; Assuming that skin resistance is inversely proportional to skin barrier function, the evaluation formula is expressed as: Among them, Rskin represents skin resistance, and the humidity factor is a coefficient calculated based on humidity data.

5. The infrared lamp therapeutic apparatus control system capable of automatically controlling therapeutic parameters according to claim 1, characterized in that: The steps of constructing the pattern recognition algorithm specifically include: Data preparation: Use the extracted features as input data; Model training: Use deep learning frameworks to train convolutional neural network or recurrent neural network models; Model evaluation: Use the validation set to evaluate the performance of the model, including accuracy and recall indicators; Model deployment: Deploy the trained model to an embedded system for real-time skin lesion recognition; The convolution operation formula in deep learning is: Sc(i,j)=(Sr*w)(i,j)+b=Σ m ∑ n Sr(i+m,j+n)w(m,n)+b; Among them, Sr is the input image, w is the convolution kernel, b is the bias term, Sc is the output image after convolution, (i, j) represents the position coordinates in the image, m represents the number of rows or height of the image, and n represents the number of columns or width of the image.

6. The infrared lamp therapeutic apparatus control system capable of automatically controlling therapeutic parameters according to claim 1, characterized in that: The severity assessment steps include: Feature selection: Select features associated with severity from the identified lesion types; Quantitative model: Use regression model to establish quantitative relationship between features and severity; Model validation: Use clinical data to verify the accuracy of the model; The linear regression formula is: Y^=β0+β1X1+β2X2+...+β c X c ; Where Y^ is the predicted severity, β0, β1, ..., β c are regression coefficients, X1, X2, ..., X c is the selected feature.

7. The infrared lamp therapeutic apparatus control system capable of automatically controlling therapeutic parameters according to claim 1, characterized in that: The steps of automatically adjusting the working state of the infrared lamp specifically include: Set target temperature range: Obtain target skin temperature range from treatment parameter library according to skin lesion type and severity; Real-time monitoring of skin temperature: Use a temperature sensor to collect the patient's skin temperature data in real time and calculate the deviation between the current temperature and the target temperature range, where the center value of the target temperature range is set to (the upper limit of the target temperature range + the lower limit of the target temperature range) / 2; Dynamically adjust power: Use a PID controller to dynamically adjust the power of the infrared lamp based on the temperature deviation; if the skin temperature is below the target range, increase the power, otherwise, reduce the power; the PID formula is: Among them, Kp, Ki, and Kd are the proportional coefficient, integral coefficient, and differential coefficient, respectively, and are debugged and optimized according to experimental data; Limit maximum power: To prevent overheating, set a maximum power limit that will not be exceeded even if temperature deviations indicate a need for increased power; and set an overheat protection threshold that will immediately trigger the overheat protection mechanism when the device temperature or skin temperature reaches or exceeds this threshold.

8. The infrared lamp therapeutic apparatus control system capable of automatically controlling therapeutic parameters according to claim 1, characterized in that: The treatment progress and skin condition change display function supports remote monitoring. Doctors or family members can remotely monitor the treatment progress and skin condition through the mobile phone APP, and provide intelligent reminder functions based on the treatment progress and patient status.

9. The infrared lamp therapeutic apparatus control system capable of automatically controlling therapeutic parameters according to claim 1, characterized in that: The system's hardware facilities include: Sensor Module: High-precision temperature sensor: used to accurately measure skin temperature and support multi-point measurement to obtain comprehensive temperature distribution; Infrared thermal imaging camera: provides high-resolution skin thermal maps for identifying areas of abnormal temperature; Humidity and skin resistance sensor: Evaluate skin moisture and resistance to understand skin barrier function; Infrared lamp therapy device: Multi-wavelength LED array: supports infrared output of different wavelengths to meet different treatment needs; Intelligent power regulator: dynamically adjusts power according to treatment parameters, achieved through PID controller algorithm; Irradiation area regulator: adjusts the irradiation area mechanically or electronically to suit different skin lesion areas; Microprocessor / Controller: High-performance embedded system: supports complex data processing algorithms and real-time control; Wireless communication module: supports Wi-Fi and Bluetooth for remote monitoring and data transmission; user interface: Touch screen display: displays treatment parameters, skin thermal map, and treatment progress information; Voice Assistant: Provides voice command input and feedback for patient voice interaction.

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