Portable red light therapeutic instrument

Abstract

The invention discloses a portable red light therapeutic instrument, and relates to the field of red light physiotherapy, and the instrument comprises an infrared irradiation module which is used for carrying out the red light supply of the irradiation intensity and the irradiation range according to a preset adjustment scheme; the adjusting module is used for presetting an adjusting scheme containing irradiation intensity and irradiation range indexes and providing position and posture adjustment for the infrared irradiation module; the capturing unit is used for identifying the state of the current to-be-treated skin area and capturing the state data of the to-be-treated skin area according to a preset capturing index; by constructing a treatment planning model and taking historical treatment data as a sample training model, a personalized treatment plan can be generated according to the skin state of an individual, the state of a to-be-treated skin area is monitored in real time through the links of image acquisition, data cleaning and state recognition, the change of the skin area in the treatment process can be captured in time, and the treatment effect is improved. After treatment, actual feedback is generated immediately, response is made quickly, and a treatment scheme is adjusted.

Description

Technical Field

[0001] The invention relates to the technical field of red light therapy, and in particular to a portable red light therapy device. Background Art

[0002] Red light therapy is a device that uses red light of a specific wavelength for treatment and conditioning. Red light can stimulate cell metabolism and promote cell repair. It is often used for wound healing and postoperative recovery. Red light therapy can relieve pain in muscles, joints and soft tissues, and has a good effect on patients with chronic pain. In sports medicine, it is used for muscle repair of athletes and auxiliary treatment of sports injuries. With the development of technology, portable red light therapy devices have gradually entered the home use market for daily health care and pain relief. They can also be used as part of home beauty care equipment and are suitable for daily skin care and anti-aging.

[0003] Traditional portable red light therapy usually uses uniform parameters such as light intensity and treatment cycle, and provides the same treatment plan for all patients, but it cannot meet the needs of individual differences, and often lacks the ability to monitor the patient's skin condition and feedback in real time. The treatment effect can only be evaluated in subsequent diagnosis and treatment. It lacks the ability to make immediate adjustments and it is difficult to detect problems in treatment in a timely manner. Data collection and analysis are often simple, and it is impossible to comprehensively consider multiple influencing factors and effectively utilize historical data. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the above-mentioned shortcomings of the prior art, the present invention provides a portable red light therapy device, which can effectively solve the problems of the prior art.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0008] The invention discloses a portable red light therapeutic device, comprising:

[0009] Infrared irradiation module, used to supply red light with irradiation intensity and irradiation range according to a preset adjustment scheme;

[0010] An adjustment module is used to preset an adjustment scheme including irradiation intensity and irradiation range indicators, and provide position and posture adjustment to the infrared irradiation module;

[0011] A capture unit, used to identify the state of the skin area to be treated and capture the state data of the skin area to be treated according to a preset capture index;

[0012] An analysis unit is used to construct a treatment planning model. The treatment planning model takes the current skin area status data as input, outputs the treatment rounds within the preset cycle and the light index of each treatment round, and outputs the expected treatment feedback after each round, and compares the current treatment effect with the expected treatment feedback;

[0013] The alarm module is used to receive the comparison results of the analysis unit, issue an alarm prompt for abnormal conditions and terminate treatment.

[0014] Furthermore, the capture unit is deployed with submodules at the lower level, including a data collection module, a preprocessing module, a state identification module and an indicator capture module. The data collection module is interactively connected to the preprocessing module via a wireless network, the preprocessing module is interactively connected to the state identification module via a wireless network, and the state identification module is interactively connected to the indicator capture module via a wireless network, wherein:

[0015] A data collection module is used to collect images of the user's current skin, actively input skin information and historical treatment information data;

[0016] The preprocessing module is used to organize and clean the collected data, delete duplicate data, fill missing values, calibrate the data and convert it into model training data, and normalize the skin image and information data;

[0017] A state recognition module is used to recognize the state of the current skin area to be treated in real time based on the skin state image and information data normalized by the preprocessing module;

[0018] The indicator capture module is used to set standard capture indicators and automatically monitor and capture the data of the skin area to be treated based on the preset capture indicators.

[0019] Furthermore, the standard capture index attributes of the index capture module include: skin moisture content, oil secretion, bacterial infection degree, pigmentation, redness and swelling area and skin temperature.

[0020] Furthermore, it includes: a model construction module, a plan generation module, an expectation generation module and a feedback comparison module, wherein the model construction module is interactively connected with the plan generation module and the expectation generation module via a wireless network, and the feedback comparison module is interactively connected with the plan generation module and the expectation generation module via a wireless network, wherein:

[0021] The model building module uses machine learning algorithms to build a treatment planning model, uses historical treatment data as samples to train the model, and performs cross-validation;

[0022] A plan generation module is used to input the current skin area status data into the trained treatment planning model to generate the planned treatment rounds and the light indicators including intensity and range for each round;

[0023] An expected generation module is used to interface with the capture unit to generate actual feedback for this round according to the skin state change after treatment;

[0024] The feedback comparison module is used to analyze the actual feedback from each round of treatment with the expected feedback, calculate the difference, and determine whether the result is within the preset threshold range.

[0025] Furthermore, the feedback comparison module compares the actual feedback of each round of treatment with the expected feedback in terms of several indicators, and analyzes whether the feedback comparison threshold is met. The quantitative comparison expression is:

[0026] D i =A i -E i ;

[0027]

[0028] Where D i represents the actual feedback difference value observed after the end of the i-th round of treatment, reflecting the effectiveness of the treatment, A i represents the actual feedback value of the i-th round of treatment, which is the score of a certain skin sign, E i represents the expected effect feedback value set before the i-th round of treatment, which is the ideal effect generated based on historical data and treatment planning model. i Represents the yes or no result of the treatment feedback comparison in round i, which is used to determine whether the treatment effect is within the set range. i =1, indicating that the effect is in line with expectations; if R i =0, it means that the effect does not meet expectations, and T represents the set threshold range, which represents the maximum acceptable difference between the actual effect and the expected effect.

[0029] Furthermore, the calculation logic of the quantitative comparison expression is:

[0030] a. By calculating the feedback difference D of the i-th round of treatment i , by reducing the expected feedback E i To get actual feedback A i ;

[0031] b. Use the judgment conditions to judge the feedback difference, and use the absolute value operation | D i |, determine whether the difference is within the set threshold T;

[0032] c. Generate feedback comparison result R based on the judgment of step a and step bi ;

[0033] d. If the feedback difference is within the threshold range (|D i |≤T), then maintain the treatment plan;

[0034] e. If the feedback difference exceeds the threshold range (|D i |>T), an alarm is issued and a treatment plan adjustment prompt is sent.

[0035] Furthermore, the alarm module is interactively connected to the update module via a wireless network, and the update module is interactively connected to the analysis unit via a wireless network. After the alarm module is triggered, the treatment rounds and treatment indicators in the current cycle are retrieved, and abnormal factors in the comparison results that do not meet the preset thresholds are identified in real time, and available reference features are extracted and fed back to the analysis unit for re-training.

[0036] Furthermore, the infrared irradiation module is connected to the adjustment module through an electrical medium, the infrared irradiation module is interactively connected to the capture unit through a wireless network, the capture unit is interactively connected to the analysis unit through a wireless network, and the analysis unit is interactively connected to the alarm module through a wireless network.

[0037] (III) Beneficial effects

[0038] Compared with the known prior art, the technical solution provided by the present invention has the following beneficial effects:

[0039] 1. By constructing a treatment planning model and training the model with historical treatment data as samples, it is possible to generate personalized treatment plans based on the individual's skin condition. The data-driven method based on the user's current skin condition ensures that each user obtains the irradiation intensity and range that suits them. Through image acquisition, data cleaning and state recognition, the state of the skin area to be treated is monitored in real time. Changes in the skin area during the treatment process can be captured in time, and actual feedback is generated immediately after the treatment, helping users and medical staff to understand the treatment effect in a timely manner, and to respond quickly and adjust the treatment plan.

[0040] 2. By calculating the difference between actual feedback and expected feedback, the treatment effect can be directly judged by setting a threshold, providing a quantitative basis for clinical decision-making. The treatment effect can be monitored and evaluated within the set threshold range to ensure that light therapy is both safe and effective and to avoid unnecessary repeated treatments. By quantitatively comparing and analyzing the relationship between the feedback effect and the set threshold, refined management can be achieved, the treatment effect can be optimized, abnormal conditions can be identified in a timely manner, and inappropriate treatment can be terminated through an alarm mechanism, which can greatly improve the safety and reliability of use and avoid potential skin irritation or damage.

[0041] 3. Through the feedback loop mechanism, the support system has the ability of self-learning, can continuously iterate and improve, improve the scientificity and effectiveness of the treatment plan, and can extract available reference features when it is identified that the feedback does not meet the preset threshold, and feed it back to the model for retraining, so as to provide more accurate guidance for future treatment and continuously optimize the treatment planning model. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0043] Figure 1 It is a schematic diagram of the architecture of the present invention.

[0044] The numbers in the figure represent, respectively, 1. infrared irradiation module; 2. adjustment module; 3. capture unit; 31. data collection module; 32. preprocessing module; 33. state recognition module; 34. indicator capture module; 4. analysis unit; 41. model building module; 42. plan generation module; 43. expectation generation module; 44. feedback comparison module; 5. alarm module; 6. update module. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0046] The present invention will be further described below in conjunction with the embodiments.

[0047] Example 1

[0048] A portable red light therapy device according to this embodiment, such as Figure 1 As shown, including:

[0049] Infrared irradiation module 1, used to supply red light with irradiation intensity and irradiation range according to a preset adjustment scheme, providing precise lighting conditions for skin treatment, which helps to optimize the treatment effect;

[0050] The adjustment module 2 is used to preset the adjustment scheme including the irradiation intensity and irradiation range indicators, and provide the infrared irradiation module 1 with position and posture adjustment, allowing the user to customize the adjustment scheme of the irradiation intensity and range, and can also adjust the position and posture according to the user's skin condition, thereby improving the pertinence and effectiveness of the treatment;

[0051] The capturing unit 3 is used to identify the state of the skin area to be treated and capture the state data of the skin area to be treated according to the preset capturing index;

[0052] The capture unit 3 is deployed with submodules at the lower level, including a data collection module 31, a preprocessing module 32, a state identification module 33 and an indicator capture module 34. The data collection module 31 is interactively connected with the preprocessing module 32 through a wireless network, the preprocessing module 32 is interactively connected with the state identification module 33 through a wireless network, and the state identification module 33 is interactively connected with the indicator capture module 34 through a wireless network, wherein:

[0053] The data collection module 31 is used to collect the user's current skin image, actively input skin information and historical treatment information data, and comprehensively provide skin quality evaluation;

[0054] The preprocessing module 32 is used to organize and clean the collected data, delete duplicate data, fill in missing values, calibrate the data and convert it into model training data, and normalize the skin image and information data to ensure the accuracy of the data. The skin information is normalized to facilitate subsequent analysis;

[0055] The state recognition module 33 is used to recognize the state of the current skin area to be treated in real time based on the skin state image and information data normalized by the preprocessing module 32, and use the processed data to perform real-time state recognition, accurately locate the area to be treated, and improve the pertinence of treatment;

[0056] The index capture module 34 is used to set standard capture indicators, and automatically monitor and capture the skin area data to be treated according to the preset capture indicators; the standard capture indicator attributes include: skin moisture content, oil secretion, bacterial infection degree, pigmentation, redness and swelling area and skin temperature, to ensure that the treatment is based on the actual situation, and the standard capture indicators include a variety of skin characteristics to provide a comprehensive skin quality assessment;

[0057] Analysis unit 4 is used to construct a treatment planning model. The treatment planning model takes the current skin area state data as input, outputs the treatment rounds within the preset cycle and the light index of each treatment round, and outputs the expected treatment feedback after each round, and compares the current treatment effect with the expected treatment feedback;

[0058] The analysis unit 4 is deployed with submodules at the lower level, including: a model building module 41, a plan generation module 42, an expectation generation module 43 and a feedback comparison module 44. The model building module 41 is interactively connected with the plan generation module 42 and the expectation generation module 43 through a wireless network, and the feedback comparison module 44 is interactively connected with the plan generation module 42 and the expectation generation module 43 through a wireless network, wherein:

[0059] The model building module 41 uses a machine learning algorithm to build a treatment planning model, uses historical treatment data as samples to train the model, and performs cross-validation to ensure the accuracy and reliability of the model;

[0060] A plan generation module 42 is used to input the current skin area state data into the trained treatment planning model to generate the planned treatment rounds and the light indicators including intensity and range for each round;

[0061] The expected generation module 43 is used to generate actual feedback of this round according to the skin state change after treatment by docking with the capture unit 3, and to generate actual feedback in combination with the skin state change after treatment, so as to provide a basis for subsequent treatment adjustment;

[0062] Feedback comparison module 44, used to analyze the actual feedback of each round of treatment with the expected feedback, calculate the difference, and determine whether the result is within a preset threshold range to ensure that the expected effect is met;

[0063] The alarm module 5 is used to receive the comparison result of the analysis unit 4, issue an alarm prompt for abnormal status and terminate the treatment, which can ensure the safety of the patient, avoid the risk caused by incorrect treatment, and ensure the real-time monitoring and feedback mechanism during the treatment process;

[0064] The alarm module 5 is interactively connected to the update module 6 via a wireless network, and the update module 6 is interactively connected to the analysis unit 4 via a wireless network. After the alarm module 5 is triggered, the treatment rounds and treatment indicators in the current cycle are retrieved, and abnormal factors in the comparison results that do not meet the preset thresholds are identified in real time, and available reference features are extracted and fed back to the analysis unit 4 for re-training. When an abnormal situation occurs, the device can extract available reference features and feed them back to the model for retraining, providing more accurate guidance for future treatments.

[0065] As an implementation method in this embodiment, Figure 1 As shown, the infrared irradiation module 1 is connected to the adjustment module 2 through an electrical medium, the infrared irradiation module 1 is interactively connected to the capture unit 3 through a wireless network, the capture unit 3 is interactively connected to the analysis unit 4 through a wireless network, and the analysis unit 4 is interactively connected to the alarm module 5 through a wireless network.

[0066] Compared with existing technologies, the safety and effectiveness of treatment have been greatly improved through functions such as personalization, real-time monitoring, intelligent optimization and abnormal alarm. Its design concept revolves around improving user experience and treatment effects. From red light supply to real-time status monitoring, to treatment planning and feedback, a complete, closed-loop treatment system has been formed, which greatly improves the effect and safety of treatment. At the same time, it uses machine learning and automated data processing capabilities to continuously optimize treatment plans to ensure that users' treatment experience and effects are continuously improved.

[0067] Example 2

[0068] In other aspects, this embodiment also provides a calculation method for quantitative comparison analysis. The feedback comparison module 44 compares the actual feedback of each round of treatment with the expected feedback for a number of indicators to analyze whether the feedback comparison threshold is met. The quantitative comparison expression is:

[0069] D i =A i -E i ;

[0070]

[0071] Where D i represents the actual feedback difference value observed after the end of the i-th round of treatment, reflecting the effectiveness of the treatment, A i represents the actual feedback value of the i-th round of treatment, which is the score of a certain skin sign, E i represents the expected effect feedback value set before the i-th round of treatment, which is the ideal effect generated based on historical data and treatment planning model. i Represents the yes or no result of the treatment feedback comparison in round i, which is used to determine whether the treatment effect is within the set range. i =1, indicating that the effect is in line with expectations; if R i =0, it means the effect does not meet expectations, T represents the set threshold range, which represents the maximum acceptable difference between the actual effect and the expected effect;

[0072] The calculation logic of the quantitative comparison expression is:

[0073] a. By calculating the feedback difference D of the i-th round of treatment i , by reducing the expected feedback E i To get actual feedback A i ;

[0074] b. Use the judgment conditions to judge the feedback difference, and use the absolute value operation | D i |, determine whether the difference is within the set threshold T;

[0075] c. Generate feedback comparison result R based on the judgment of step a and step b i ;

[0076] d. If the feedback difference is within the threshold range |D i |≤T, then maintain the treatment plan;

[0077] e. If the feedback difference exceeds the threshold range |D i |>T, an alarm is issued and a treatment plan adjustment prompt is sent;

[0078] Compared with the existing technology, it provides an effective and standardized method to analyze the difference between actual treatment feedback and expected feedback. In clinical practice, it can help users or medical personnel judge the treatment effect and promptly discover and correct situations that do not meet expectations.

[0079] Working principle: the present invention constructs a treatment planning model through a model building module 41, and trains the treatment planning model with historical treatment data as samples, identifies the state of the current skin treatment area through a data collection module 31, and filters the collected data through a preprocessing module 32. The state recognition module 33 captures the skin state data to be treated according to a preset capture index. The plan generation module 42 uses the current skin state data as an input to the treatment planning model, outputs the treatment rounds within a preset cycle and the light index (range and intensity) of each treatment round, and outputs the treatment expectation feedback after each round through the expectation generation module 43. The adjustment module 2 receives the planned light index and submits it to the infrared irradiation module 1 to perform treatment;

[0080] The feedback comparison module 44 compares each round of treatment with the preset expected feedback of the treatment round after the end of each round of treatment. If the comparison result meets the threshold, the treatment will continue to be performed according to the preset rounds and light indicators. If the comparison result does not meet the threshold, the alarm module 5 will alarm and terminate the treatment. Based on the current skin condition, the treatment planning model will re-formulate future treatment rounds and treatment indicators in the current cycle, and identify abnormal factors that cause the comparison result to not meet the threshold. The update module 6 extracts available reference features to the treatment planning model for training;

[0081] Through multi-level modular design, the present invention realizes functions such as intelligent identification of skin condition, formulation of personalized treatment plans, real-time effect feedback and abnormal state warning, providing users with an efficient and safe red light treatment experience. It not only focuses on improving the treatment effect, but also pays more attention to data feedback and continuous optimization, making it highly practical and intelligent, and able to meet the needs of different users.

[0082] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A portable red light therapy device, characterized in that: include: An infrared irradiation module (1) is used to supply red light with an irradiation intensity and an irradiation range according to a preset adjustment scheme; An adjustment module (2) is used to preset an adjustment scheme including irradiation intensity and irradiation range indicators, and to provide position and posture adjustment for the infrared irradiation module (1); A capturing unit (3) is used to identify the state of the skin area to be treated and capture the state data of the skin area to be treated according to a preset capturing index; An analysis unit (4) is used to construct a treatment planning model, which takes the current skin area state data as input, outputs the treatment rounds within a preset period and the light index of each treatment round, and outputs the expected treatment feedback after each round, and compares the current treatment effect with the expected treatment feedback; The alarm module (5) is used to receive the comparison result of the analysis unit (4), issue an alarm prompt for abnormal conditions and terminate treatment.

2. A portable red light therapy device according to claim 1, characterized in that: The capture unit (3) is provided with submodules at a lower level, including a data collection module (31), a preprocessing module (32), a state recognition module (33) and an indicator capture module (34). The data collection module (31) is interactively connected to the preprocessing module (32) via a wireless network, the preprocessing module (32) is interactively connected to the state recognition module (33) via a wireless network, and the state recognition module (33) is interactively connected to the indicator capture module (34) via a wireless network, wherein: A data collection module (31) is used to collect the user's current skin image, actively input skin information and historical treatment information data; A preprocessing module (32) is used to organize and clean the collected data, delete duplicate data, fill in missing values, calibrate the data and convert it into model training data, and normalize the skin image and information data; A state recognition module (33) is used to recognize the state of the current skin area to be treated in real time based on the skin state image and information data normalized by the preprocessing module (32); The index capturing module (34) is used to set standard capturing indexes and automatically monitor and capture the data of the skin area to be treated according to the preset capturing indexes.

3. A portable red light therapy device according to claim 2, characterized in that: The standard capture index attributes of the index capture module (34) include: skin moisture content, oil secretion, bacterial infection degree, pigmentation, redness and swelling area and skin temperature.

4. A portable red light therapy device according to claim 1, characterized in that: The analysis unit (4) is provided with submodules at a lower level, including: a model construction module (41), a plan generation module (42), an expectation generation module (43) and a feedback comparison module (44); the model construction module (41) is interactively connected with the plan generation module (42) and the expectation generation module (43) via a wireless network; the feedback comparison module (44) is interactively connected with the plan generation module (42) and the expectation generation module (43) via a wireless network, wherein: The model building module (41) uses a machine learning algorithm to build a treatment planning model, uses historical treatment data as samples to train the model, and performs cross-validation; A plan generation module (42) is used to input the current skin area state data into the trained treatment planning model to generate the planned treatment rounds and the light indicators including intensity and range of each round; An expected generation module (43) is used to interface with the capture unit (3) to generate actual feedback for this round according to the change in skin condition after treatment; The feedback comparison module (44) is used to analyze the actual feedback of each round of treatment with the expected feedback, calculate the difference, and determine whether the result is within a preset threshold range.

5. A portable red light therapy device according to claim 4, characterized in that: The feedback comparison module (44) performs a quantitative comparison of several indicators between the actual feedback and the expected feedback of each round of treatment, and analyzes whether the feedback comparison threshold is met. The quantitative comparison expression is: D i =A i -E i ; Where D i represents the actual feedback difference value observed after the end of the i-th round of treatment, reflecting the effectiveness of the treatment, A i represents the actual feedback value of the i-th round of treatment, which is the score of a certain skin sign, E i represents the expected effect feedback value set before the i-th round of treatment, which is the ideal effect generated based on historical data and treatment planning model. i Represents the yes or no result of the treatment feedback comparison in round i, which is used to determine whether the treatment effect is within the set range. i =1, indicating that the effect is in line with expectations; if R i =0, it means that the effect does not meet expectations, and T represents the set threshold range, which represents the maximum acceptable difference between the actual effect and the expected effect.

6. A portable red light therapy device according to claim 5, characterized in that: The calculation logic of the quantitative comparison expression is: a. By calculating the feedback difference D of the i-th round of treatment i , by reducing the expected feedback E i To get actual feedback A i ; b. Use the judgment conditions to judge the feedback difference, and use the absolute value operation | D i |, determine whether the difference is within the set threshold T; c. Generate feedback comparison result R based on the judgment of step a and step b i ; d. If the feedback difference is within the threshold range (|D i |≤T), then maintain the treatment plan; e. If the feedback difference exceeds the threshold range (|D i |>T), an alarm is issued and a treatment plan adjustment prompt is sent.

7. The portable red light therapy device according to claim 1, characterized in that: The alarm module (5) is interactively connected to the update module (6) via a wireless network, and the update module (6) is interactively connected to the analysis unit (4) via a wireless network. After the alarm module (5) is triggered, the treatment rounds and treatment indicators in the current cycle are retrieved, and abnormal factors in the comparison results that do not meet the preset thresholds are identified in real time, and available reference features are extracted and fed back to the analysis unit (4) for re-training.

8. The portable red light therapy device according to claim 1, characterized in that: The infrared irradiation module (1) is connected to the adjustment module (2) via an electrical medium, the infrared irradiation module (1) is interactively connected to the capture unit (3) via a wireless network, the capture unit (3) is interactively connected to the analysis unit (4) via a wireless network, and the analysis unit (4) is interactively connected to the alarm module (5) via a wireless network.

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