Wearable vitamin D intelligent synthesis physiotherapy instrument based on phototherapy

Abstract

The invention discloses a wearable vitamin D intelligent synthesis physiotherapy instrument based on phototherapy, and belongs to the field of biomedicine. A wearable vitamin D intelligent synthesis physiotherapy instrument based on phototherapy comprises a wearable main body, a data sensing unit, an intelligent control unit and a safety protection unit. The problems that an existing vitamin D supplementing mode is low in absorption efficiency and large in individual difference are solved, continuous vitamin D synthesis support is provided for a user through cooperation of the wearable body and the phototherapy assembly, physiological parameters such as the vitamin D content, the skin temperature and the heart rate can be monitored in real time through the sensor array, and the user experience is improved. The method provides an accurate basis for phototherapy, ensures maximization of the phototherapy effect, dynamically adjusts the phototherapy time, intensity and frequency according to monitoring data, realizes a personalized treatment scheme, adapts to different user requirements, sets a safety threshold, automatically pauses phototherapy once the monitoring data is abnormal, and ensures the use safety of the user.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, specifically to a wearable intelligent vitamin D synthesis therapy device based on phototherapy. Background Technology

[0002] Vitamin D is essential for human health, helping with calcium absorption and bone health, and also regulating the immune system. However, many people are unable to obtain enough vitamin D through natural sun exposure due to lifestyle or geographical limitations.

[0003] Traditional vitamin D supplementation mainly relies on oral supplements, but this method has problems such as low absorption efficiency and large individual differences. Therefore, it does not meet the current needs. In response, we have proposed a wearable intelligent vitamin D synthesis therapy device based on phototherapy. Summary of the Invention

[0004] The purpose of this invention is to provide a wearable intelligent vitamin D synthesis therapy device based on phototherapy. This device uses a sensor array to monitor physiological parameters such as vitamin D content, skin temperature, and heart rate in real time, providing precise data for phototherapy and ensuring maximum effectiveness. It dynamically adjusts the phototherapy time, intensity, and frequency based on the monitoring data to achieve personalized treatment plans. Furthermore, it sets a safety threshold; if the monitoring data is abnormal, the phototherapy is automatically paused to ensure user safety, thus solving the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a wearable vitamin D intelligent synthesis therapy device based on phototherapy, comprising a wearable body, with tightening straps on both sides of the wearable body to make the wearable body fit the surface of human skin through the tightening straps, a sensor array for collecting data on the wearable body, and a phototherapy component for emitting ultraviolet light of a specific wavelength to promote the synthesis of vitamin D in human skin. The sensor array includes a vitamin D synthesis monitoring device, a skin temperature sensor, and a heart rate sensor. It also includes a data sensing unit, an intelligent control unit, and a security protection unit; The data sensing unit is configured to monitor the user's physiological parameters in real time through a sensor array, including but not limited to the synthesis content of vitamin D in the skin, skin temperature and human heart rate, and transmit the monitoring data to the intelligent control unit and the safety protection unit respectively. During transmission, the data encryption method and compression rate are automatically adjusted by analyzing historical data and the current transmission environment. The intelligent control unit is connected to the phototherapy component and is configured to control the working state of the phototherapy component based on the monitoring data of the data sensing unit, including phototherapy time, phototherapy intensity and phototherapy frequency. The safety protection unit is configured to preset a safety threshold range based on the monitoring data of the data sensing unit. If any monitoring data exceeds the safety threshold range, the phototherapy component will be automatically paused.

[0006] Furthermore, the data sensing unit includes: The data acquisition module is configured to arrange the sensor array on the wearable body in a reasonable manner, collect the user's physiological parameters through the sensor array, and perform filtering, noise reduction and calibration processing on the collected monitoring data; The data transmission module is configured to compress the transmitted monitoring data to reduce transmission bandwidth and energy consumption. At the same time, it uses encryption technology to protect the privacy and security of the monitoring data and monitors the quality of data transmission in real time, including transmission delay, packet loss rate and bit error rate. The monitoring and optimization module is configured to monitor the working status of the data acquisition module and the data transmission module in real time, including acquisition accuracy, acquisition frequency, transmission speed and energy consumption indicators, and optimize and adjust the data acquisition module and the data transmission module based on the monitoring results.

[0007] Furthermore, the monitoring and optimization module executes the following process: Performance monitoring: Real-time monitoring of sensor acquisition performance and data transmission efficiency, including acquisition accuracy, acquisition frequency, transmission speed and energy consumption indicators. By monitoring the indicators, potential performance bottlenecks can be identified in a timely manner. Fault diagnosis and early warning: Design a fault diagnosis algorithm to judge the working status of sensors and transmission links based on the above-mentioned monitored indicators, and promptly detect faults and issue early warnings; Adaptive optimization: Based on the monitoring results, the sensor's acquisition strategy and data transmission parameters are automatically adjusted.

[0008] Furthermore, the data transmission module performs the following process: Transmission protocol selection: Select the appropriate wireless or wired transmission protocol based on the device's usage scenario and data transmission requirements; Data compression and encryption: The transmitted monitoring data is compressed to reduce transmission bandwidth and energy consumption. At the same time, encryption technology is used to protect the privacy and security of the monitoring data. Transmission quality monitoring: Real-time monitoring of data transmission quality, including transmission delay, packet loss rate, and bit error rate. If a decline in transmission quality is detected, transmission parameters are adjusted or the transmission path is switched in a timely manner to ensure reliable data transmission. Data synchronization mechanism: Design a data synchronization mechanism to ensure time synchronization and data consistency among the data sensing unit, intelligent control unit and security protection unit.

[0009] Furthermore, the data transmission module further includes: The current data transmission environment status is evaluated using transmission environment assessment indicators to obtain a transmission environment assessment score. When the transmission environment evaluation score does not fall within the set evaluation threshold range, the evaluation difference amount is determined; Based on the assessed difference, an encryption method is selected from the preset encryption method list to replace the encryption method of the current data. Based on the assessed difference and the current data transmission priority, a pre-adjustment trend for the current data compression rate is determined; Extract the compression rate of historical data within a preset historical time period, perform trend analysis based on the compression level, and determine the trend of compression rate changes; When the compression change trend is a regular change trend, if the compression ratio change trend and the pre-adjustment trend are in the same direction, then the compression ratio adjustment step size in the pre-adjustment trend is reduced to obtain the actual adjustment rule. If the trend of compression ratio change and the trend of pre-adjustment are not in the same direction, then a conflict analysis is performed on the trend of compression ratio change and the trend of pre-adjustment to determine the degree of trend conflict. Anomaly analysis of the degree of trend conflict is performed to determine the reference adjustment step size; Based on the reference adjustment step size, the corresponding actual adjustment rules are generated; When the compression trend is irregular, identify the irregularity and interference factors. The current data transmission environment is matched with the irregular interference factors to determine the reference interference factors; If there are reference interference factors, the adjustment step size of the pre-adjustment trend is adjusted according to the historical mutation compression rate generated based on the reference interference factors to generate the actual adjustment rules; If there are no reference interference factors, the pre-adjustment trend will be adjusted according to the preset adjustment strategy to obtain the actual adjustment rules; The compression ratio of the current data is adjusted using the actual adjustment rules.

[0010] Furthermore, the real-time monitoring of data transmission quality also includes: A comprehensive analysis of the transmission delay, packet loss rate, and bit error rate obtained during real-time monitoring of data transmission is performed to obtain a transmission quality score. By comparing the transmission quality score with a preset quality threshold range, a transmission optimization method is determined to optimize data transmission quality.

[0011] Furthermore, the transmission protocol selection is as follows:

[0012] In the formula, Indicates a certain transmission protocol; Indicates the bandwidth capability of the transmission protocol; Indicates the delay of the transmission protocol; Indicates the reliability of the transmission protocol; Indicates the security of the transmission protocol; , , , This represents the weighting coefficient, which can be adjusted according to actual needs. Collect bandwidth, latency, reliability, and security metrics for each protocol, assign weights to each metric, and calculate a score for each protocol. The protocol with the highest score is selected as the transmission protocol.

[0013] Furthermore, the intelligent control unit includes: The data parsing module is configured to receive and parse the monitoring data transmitted from the data sensing unit, converting the monitoring data into understandable physiological parameters. The data analysis module is configured to establish a phototherapy model based on physiological parameters through machine learning algorithms. Based on the analyzed monitoring data, the phototherapy model calculates the most suitable phototherapy time, intensity, and frequency for the current physiological state. The instruction generation module is configured to generate corresponding control instructions based on the calculation results of the data analysis module and send them to the phototherapy component. The phototherapy component controls its working state according to the received control instructions. The feedback adjustment module is configured to receive feedback information from the phototherapy component, including but not limited to actual working status and fault information, and adjust the control strategy according to the feedback information.

[0014] Furthermore, the data parsing module performs the following process: Data receiving interface: Design a standardized data receiving interface to receive various monitoring data from the data sensing unit; Data caching mechanism: Set up a data cache area to temporarily store newly received monitoring data for subsequent processing and analysis; Data integrity check: Perform integrity checks on the received monitoring data to ensure that the monitoring data is not lost or damaged. If data anomalies are found, promptly request retransmission or mark it as invalid data. Data parsing algorithm: The data parsing algorithm transforms the integrity-checked monitoring data into understandable physiological parameters.

[0015] Furthermore, the security protection unit includes: The threshold setting module is configured to preset safe threshold ranges for various physiological parameters based on the monitoring data from the data sensing unit. The monitoring and early warning module is configured to receive physiological parameters transmitted from the data sensing unit in real time and compare them with preset safety threshold ranges. If any data exceeds the safety threshold range, an early warning signal is generated, and different early warning levels are set according to the degree to which the data exceeds the threshold. Specifically: Assuming physiological parameters are The safety threshold range is ; If any data exceeds the safety threshold, an early warning signal is generated, and the early warning process is initiated. < or > ; Warning levels are divided into low-level warnings and high-level warnings; Low-level warning: or ; High-level warning: or ; in, Thresholds indicating warning levels are used to distinguish between low-level and high-level warnings; The safety protection module is configured to automatically pause the operation of the phototherapy component when the monitored data exceeds a safety threshold, based on the comparison results from the monitoring and early warning module. It will automatically resume phototherapy once safety conditions are restored. Simultaneously, it will notify the user and record detailed information about the event when a safety incident occurs. Specifically: When the monitored data exceeds the safety threshold, a pause command is generated and immediately sent to the phototherapy component; Monitor in real time whether the phototherapy component has successfully paused operation and record the pause time; After pausing phototherapy, the physiological parameters are continuously monitored to determine whether the recovery conditions are met, i.e., whether the data have returned to the safe threshold range. When the recovery conditions are met, a recovery command is generated to restore the operation of the phototherapy components. At the same time, the recovery time and the phototherapy parameters after recovery are recorded.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention provides continuous vitamin D synthesis support to users through the combination of a wearable device and a phototherapy component. It is especially suitable for people who have difficulty engaging in outdoor activities or who lack sufficient sunlight. It can also monitor the user's physiological parameters in real time. Through a sensor array, it monitors physiological parameters such as vitamin D content, skin temperature, and heart rate in real time, providing accurate data for phototherapy. The intelligent control unit dynamically adjusts the phototherapy time, intensity, and frequency based on the monitoring data to achieve personalized treatment plans, effectively improving vitamin D synthesis efficiency and meeting the needs of different users. At the same time, the safety protection unit presets safety thresholds based on the monitoring data. If any data exceeds the safe range, it automatically stops phototherapy to ensure safe and reliable use and avoid harm to users due to excessive phototherapy or other abnormal situations. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the structure of the wearable vitamin D intelligent synthesis therapy device based on phototherapy according to the present invention; Figure 2 This is an external view of the wearable vitamin D intelligent synthesis therapy device based on phototherapy according to the present invention.

[0018] In the image: 1. Wearable main body; 11. Fitting strap; 2. Sensor array; 3. Phototherapy component. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] To address the current issue that vitamin D supplementation primarily relies on oral supplements, which suffer from low absorption efficiency and significant individual variability, please refer to [the relevant documentation / reference needed]. Figures 1-2 This embodiment provides the following technical solution: The wearable vitamin D intelligent synthesis therapy device based on phototherapy includes a wearable main body 1 with tightening straps 11 on both sides. The wearable main body 1 adopts a wearable structure and is made to fit the surface of human skin through the tightening straps 11. It can be easily worn and used by users in daily activities without affecting normal life and activities, thus improving the practicality and wearing comfort of the device. The wearable main body 1 is equipped with a sensor array 2 for collecting data and a phototherapy component 3 for emitting ultraviolet rays of a specific wavelength to promote the synthesis of vitamin D in human skin. It helps users supplement vitamin D under safe and controllable conditions, and is especially suitable for people who lack sunlight exposure or those with specific needs. Among them, sensor array 2 includes a vitamin D synthesis monitoring device, a skin temperature sensor, and a heart rate sensor; The vitamin D synthesis monitoring device is used to measure the irradiated area during phototherapy to assess the vitamin D synthesis content in the skin (this technology is prior art; the vitamin D synthesis determination device, the control method of the device, and the vitamin D in vivo synthesis induction system have been disclosed in announcement number CN108886859B, so it will not be described in detail here). The skin temperature sensor is used to monitor the skin temperature, and the heart rate sensor is used to monitor the human heart rate so as to control the working state of the phototherapy component 3 based on the monitored data. It also includes a data sensing unit, an intelligent control unit, and a security protection unit; The data sensing unit is configured to monitor the user's physiological parameters in real time through the sensor array 2, including but not limited to the synthesis content of vitamin D in the skin, skin temperature and human heart rate, and transmit the monitoring data to the intelligent control unit and the safety protection unit respectively. During transmission, the data encryption method and compression rate are automatically adjusted by analyzing historical data and the current transmission environment. The intelligent control unit is connected to the phototherapy component 3 and is configured to control the working state of the phototherapy component 3 according to the monitoring data of the data sensing unit, including phototherapy time, phototherapy intensity and phototherapy frequency; The safety protection unit is configured to preset a safety threshold range based on the monitoring data of the data sensing unit. If any monitoring data exceeds the safety threshold range, the phototherapy component 3 will be automatically paused.

[0021] The technical effects of the above solution are as follows: The data sensing unit monitors physiological parameters such as vitamin D content in human blood and skin temperature in real time through the sensor array 2, providing accurate information for phototherapy and ensuring that the phototherapy effect is maximized. The intelligent control unit can accurately adjust the working status of the phototherapy component (phototherapy time, intensity, and frequency) based on these data, thereby providing users with personalized phototherapy plans to ensure that the phototherapy effect is maximized and conforms to the user's current physical condition. The safety protection unit presets a safety threshold range based on the monitoring data. When a certain physiological parameter (such as skin temperature, vitamin D content, etc.) exceeds the safety threshold, it can automatically stop the operation of the phototherapy component 3, effectively avoiding potential harm to the user due to excessive phototherapy or abnormal conditions, and ensuring the safety of the use process.

[0022] In summary, this wearable intelligent vitamin D synthesis therapy device achieves personalized, safe, and efficient vitamin D phototherapy synthesis through precise monitoring, intelligent regulation, safety protection, and convenient wearability, providing users with an intelligent and automated health management solution.

[0023] The data sensing unit includes: The data acquisition module is configured to arrange the sensor array 2 on the wearable body 1 in a reasonable manner, collect the user's physiological parameters through the sensor array 2, and perform filtering, noise reduction and calibration processing on the collected monitoring data; The data transmission module is configured to compress the transmitted monitoring data to reduce transmission bandwidth and energy consumption. Simultaneously, it employs encryption technology to protect the privacy and security of the monitoring data and monitors the quality of data transmission in real time, including transmission latency, packet loss rate, and bit error rate. Specifically, it executes the following process: Transmission protocol selection: Select the appropriate wireless or wired transmission protocol based on the device's usage scenario and data transmission requirements; The specific selection of the transmission protocol is shown in the following formula:

[0024] In the formula, It indicates a certain transmission protocol (such as Wi-Fi, Bluetooth, Ethernet, etc.). Indicates the bandwidth capability of the transmission protocol; Indicates the delay of the transmission protocol; Indicates the reliability of the transmission protocol (e.g., the reciprocal of the packet loss rate); Indicates the security of the transmission protocol (e.g., whether encryption is supported); , , , This represents the weighting coefficient, which can be adjusted according to actual needs. Collect bandwidth, latency, reliability, and security metrics for each protocol, assign weights to each metric, and calculate a score for each protocol. The protocol with the highest score is selected as the transmission protocol; Data compression and encryption: The transmitted monitoring data is compressed to reduce transmission bandwidth and energy consumption. At the same time, encryption technology is used to protect the privacy and security of the monitoring data and prevent the data from being stolen or tampered with during transmission. Transmission quality monitoring: Real-time monitoring of data transmission quality, including transmission delay, packet loss rate, and bit error rate. If a decline in transmission quality is detected, transmission parameters are adjusted or the transmission path is switched in a timely manner to ensure reliable data transmission. Data synchronization mechanism: Design a data synchronization mechanism to ensure time synchronization and data consistency between the data sensing unit, intelligent control unit and security protection unit. For example, timestamps or synchronization signals can be used to calibrate the time of data transmission. The monitoring and optimization module is configured to monitor the working status of the data acquisition module and the data transmission module in real time, including acquisition accuracy, acquisition frequency, transmission speed, and energy consumption. Based on the monitoring results, the module optimizes and adjusts itself, specifically executing the following process: Performance monitoring: Real-time monitoring of sensor acquisition performance and data transmission efficiency, including acquisition accuracy, acquisition frequency, transmission speed and energy consumption indicators. By monitoring the indicators, potential performance bottlenecks can be identified in a timely manner. Fault diagnosis and early warning: Design a fault diagnosis algorithm to judge the working status of the sensor and transmission link through the above-mentioned monitored indicators, detect faults in time and issue early warnings. For example, if the sensor data is abnormal or the transmission link is interrupted, notify the user in time and take corresponding measures. Adaptive optimization: Based on monitoring results, the sensor's acquisition strategy and data transmission parameters are automatically adjusted. For example, the acquisition frequency is dynamically adjusted based on the sensor's acquisition accuracy and energy consumption; the transmission rate and encryption strength are automatically adjusted based on the quality of the transmission link.

[0025] The technical effects of the above solution are as follows: The data acquisition module rationally arranges the sensor array, enabling accurate acquisition of users' physiological parameters. Through filtering, noise reduction, and calibration, it effectively improves data quality, providing a reliable basis for subsequent data analysis and phototherapy control. The data transmission module compresses the monitoring data, reducing transmission bandwidth and energy consumption. At the same time, it uses encryption technology to protect data privacy and security, preventing data from being stolen or tampered with during transmission, ensuring the confidentiality and integrity of user data. It also monitors indicators such as transmission delay, packet loss rate, and bit error rate in real time. If a decline in transmission quality is detected, it can adjust transmission parameters or switch transmission paths in a timely manner to ensure reliable data transmission. This mechanism effectively addresses transmission challenges in complex environments and improves the robustness of the system. The monitoring and optimization module monitors the performance indicators of sensors and transmission links in real time, promptly identifying potential performance bottlenecks and faults, thereby automatically adjusting the sensor acquisition strategy and data transmission parameters to improve overall operating efficiency and user experience.

[0026] In summary, the data sensing unit significantly improves the performance and user experience of the entire wearable vitamin D intelligent synthesis therapy device through efficient data acquisition and processing, secure and reliable data transmission, adaptive optimization capabilities, and data synchronization mechanisms.

[0027] The data transmission module also includes: The current data transmission environment status is evaluated using transmission environment assessment indicators to obtain a transmission environment assessment score. When the transmission environment evaluation score does not fall within the set evaluation threshold range, the evaluation difference amount is determined; Based on the assessed difference, an encryption method is selected from the preset encryption method list to replace the encryption method of the current data. Based on the assessed difference and the current data transmission priority, a pre-adjustment trend for the current data compression rate is determined; Extract the compression rate of historical data within a preset historical time period, perform trend analysis based on the compression level, and determine the trend of compression rate changes; When the compression change trend is a regular change trend, if the compression ratio change trend and the pre-adjustment trend are in the same direction, then the compression ratio adjustment step size in the pre-adjustment trend is reduced to obtain the actual adjustment rule. If the trend of compression ratio change and the trend of pre-adjustment are not in the same direction, then a conflict analysis is performed on the trend of compression ratio change and the trend of pre-adjustment to determine the degree of trend conflict. Anomaly analysis of the degree of trend conflict is performed to determine the reference adjustment step size; Based on the reference adjustment step size, the corresponding actual adjustment rules are generated; When the compression trend is irregular, identify the irregularity and interference factors. The current data transmission environment is matched with the irregular interference factors to determine the reference interference factors; If there are reference interference factors, the adjustment step size of the pre-adjustment trend is adjusted according to the historical mutation compression rate generated based on the reference interference factors to generate the actual adjustment rules; If there are no reference interference factors, the pre-adjustment trend will be adjusted according to the preset adjustment strategy to obtain the actual adjustment rules; The compression ratio of the current data is adjusted using the actual adjustment rules.

[0028] In the above embodiments, the transmission environment evaluation index refers to a series of standards or parameters used to measure and evaluate the status of the data transmission environment, such as network bandwidth; the transmission environment evaluation score refers to the quantitative score obtained after evaluating the current data transmission environment status according to the transmission environment evaluation index, and the value range is (0, 1); the transmission environment evaluation score is obtained by: firstly obtaining the ratio of the actual measured value of each index to the preset ideal value (referring to the optimal and most ideal value that each transmission environment evaluation index can achieve under the data transmission scenario, which can be determined in advance based on experiments), then assigning weights to each index ratio and summing them to obtain the final transmission environment evaluation score; wherein, the weight assigned to each index ratio is obtained by solving the matrix constructed by pairwise comparison and scoring using the analytic hierarchy process, and the value range is (0, 1).

[0029] In the above embodiments, the set evaluation threshold range is a pre-set numerical range used to determine whether the evaluation score of the transmission environment is qualified. It can be determined through experimental testing according to the requirements of the data transmission environment in the actual application scenario, for example (0.5, 0.8).

[0030] In the above embodiments, the evaluation difference refers to the difference between the evaluation score and the upper or lower limit of the threshold when the transmission environment evaluation score does not fall within the set evaluation threshold range; the preset encryption method list refers to a list of a series of data encryption methods that are preset and stored. The encryption methods include AES encryption, RSA encryption, DES encryption, etc., and different evaluation difference ranges correspond to different encryption methods.

[0031] In the above embodiments, the transmission priority level refers to the transmission priority preset according to the importance of the data. For example, the data transmission priority of human heart rate is the highest and is set as first priority; the data of vitamin D content in human blood is set as second priority; and the data of skin temperature is set as third priority. The data to be transmitted includes, but is not limited to, the synthesis content of vitamin D in the skin, skin temperature, and human heart rate. The data compression rate refers to the ratio of the amount of data after compression before transmission to the amount of the original data. For example, if the original data is 100MB and the compressed data is 80MB, the compression rate is 80%.

[0032] In the above embodiments, the pre-adjustment trend is determined by inputting the evaluation difference amount and the current data transmission priority level into a pre-built trend recognition model to obtain the adjustment direction and magnitude of the current data compression rate. For example, the pre-adjustment trend is to increase the data compression rate by 5%. The construction steps of the trend recognition model include: first, collecting data compression rate adjustment information under different evaluation difference amounts and transmission priority levels in historical data as training data; then, cleaning the training data, removing outliers and missing values, and performing normalization processing; finally, using the processed data to train a neural network.

[0033] In the above embodiments, the compression ratio change trend refers to the historical trend of data compression ratio changing over time, derived from the analysis of historical data compression ratio. The specific acquisition steps are as follows: First, collect historical data compression ratio data within a preset historical time period and arrange them in chronological order; then, classify the compression ratio at each time point according to the set compression level (such as low, medium, and high); next, count the frequency and duration of compression ratio at each compression level in different time periods; finally, by observing the changes in frequency and duration, analyze the trend of compression ratio changing over time, such as showing an upward, downward, or periodic change trend.

[0034] In the above embodiments, the historical preset time period refers to a pre-set time range for extracting historical data; the historical data compression rate refers to the historical record of the data compression rate at each historical moment within the past preset time period; the compression level refers to different degrees or levels of data compression, used to classify and analyze the historical data compression rate, for example, classifying the compression rate into low (when the data compression rate is <70%), medium (when the data compression rate is <70%), and high (when the data compression rate is <70%). There are three levels: high (when the data compression rate is >80%), medium (when the data compression rate is >80%), and high (when the data compression rate is >80%).

[0035] In the above embodiments, the regular change trend refers to the compression ratio change trend exhibiting a certain regularity, such as periodic change, linear change, etc.; for example, the compression ratio increases within the same time period every day, exhibiting periodic change; the trend adjustment direction refers to the compression ratio adjustment direction set in the pre-adjustment trend, including both increasing and decreasing directions; the adjustment step size refers to the magnitude or amount of adjustment each time the compression ratio is adjusted, for example, the adjustment step size for each compression ratio adjustment is 2%.

[0036] In the above embodiments, for example, there is a compression rate change trend 1: the data compression rate increases by 1% per hour, and a pre-adjustment trend 1: the data compression rate increases by 2% per hour. The two trends are adjusted in the same direction, both increasing the data compression rate. At this time, the compression rate adjustment step size in the pre-adjustment trend 1 is reduced using a preset reduction ratio (the preset reduction ratio is determined by collecting a large amount of historical data transmission scenarios in advance, when the compression rate change trend and the pre-adjustment trend are adjusted in the same direction, and analyzing the impact of different reduction ratios on data transmission stability, efficiency, etc.). If we assume the preset reduction ratio is 30%, then the adjustment step after reduction is equal to 2% × (1 - 30%) = 1.4%. Therefore, the actual adjustment rule is to increase the compression ratio by 1.4% per hour.

[0037] In the above embodiments, the degree of trend conflict refers to the degree of conflict or difference between the compression ratio change trend and the pre-adjustment trend when they are inconsistent. The step of quantifying the difference between the two trends in adjustment direction and magnitude is as follows: First, determine the difference in adjustment direction between the two trends: if the compression rate trend is increasing and the pre-adjustment trend is decreasing, or vice versa, then the difference in adjustment direction is 1; if the two trends adjust in the same direction, then the difference in adjustment direction is 0. Next, calculate the difference in adjustment magnitude between the two trends: subtract the adjustment magnitude of the compression rate change trend from the adjustment magnitude of the pre-adjustment trend, and take the absolute value as the difference in adjustment magnitude. Finally, the differences in adjustment direction and adjustment magnitude are quantified: Trend conflict degree = Difference in adjustment direction x Difference in adjustment magnitude; For example, there is a compression rate change trend 1: increasing by 0.8% per hour, and a pre-adjustment trend 1: decreasing by 1.2% per hour. The difference in adjustment direction is 1, and the difference in adjustment magnitude is |-1.2%-0.8%| = 2%. The degree of trend conflict between compression rate change trend 1 and pre-adjustment trend 1 is 1 x 2% = 2%.

[0038] In the above embodiments, anomaly judgment is made on the degree of trend conflict, and a reference adjustment step size is determined based on the anomaly judgment result. Specifically, when the degree of trend conflict is abnormal (i.e., the degree of trend conflict exceeds a preset conflict threshold, which is generally set to 20% of the maximum absolute value of the adjustment step size corresponding to the compression ratio change trend and the pre-adjustment trend), the adjustment step size in the compression ratio change trend and the pre-adjustment trend is extracted and analyzed to determine the reference adjustment step size; when the degree of trend conflict is not abnormal (i.e., the degree of trend conflict does not exceed the preset conflict threshold), the adjustment step size in the pre-adjustment trend is adjusted using the degree of trend conflict to determine the reference adjustment step size.

[0039] In the above embodiments, for example, there is a trend in the compression ratio. Data compression rate increases by 0.8% per hour, pre-adjustment trend. Data compression rate decreases by 1.5% per hour; At this point, the preset conflict threshold = |-1.5%| x 20% = 0.3%; Compression ratio trend. and pre-adjustment trend The degree of trend conflict: the difference in adjustment direction is 1, the difference in adjustment magnitude is |-1.5%-0.8%| = 2.3%, the degree of trend conflict between compression rate change trend 1 and pre-adjustment trend 1 = 1 x 2.3% = 2.3%; Determine the trend. and trends The degree of trend conflict is abnormal; At this point, the calculation reference adjustment step size = .

[0040] In the above embodiments, for example, there is a trend in the compression ratio. Data compression rate increases by 0.5% per hour, pre-adjustment trend. Data compression rate decreases by 0.6% per hour; At this point, the preset conflict threshold = |-0.6%| x 20% = 0.12%; the adjustment direction difference is 1, and the adjustment magnitude difference is |-0.6% - 0.5%| = 0.055%, showing the compression ratio change trend. and pre-adjustment trend The degree of trend conflict = 1 x 0.55% = 0.055%; Determining the trend and trends The degree of trend conflict is not abnormal; At this point, using the aforementioned trend conflict level of 0.055%, the pre-adjustment trend is... Adjusting the step size by -0.6%, we get the reference adjustment step size = -0.6% (1 - 0.055%). .

[0041] In the above embodiments, irregular change trends refer to random changes in compression ratio and changes with characteristics such as historical abrupt compression ratio changes. Here, historical abrupt compression ratio refers to the compression ratio value when the data compression ratio suddenly changes significantly due to irregular interference factors (such as network congestion, equipment failure, etc.). Irregular interference factors refer to factors that cause historical abrupt compression ratio changes, such as network congestion and equipment failure.

[0042] In the above embodiments, the reference interference factor refers to an irregular interference factor that is similar in characteristics to the current data transmission environment, and is determined based on cosine similarity analysis. The steps for determining the reference interference factor are as follows: First, the characteristics of the current data transmission environment (including network performance characteristics (such as bandwidth), device status characteristics (such as device load, device failure), and data characteristics (such as data volume) etc.) and the characteristics of the irregular interference factor are quantified; then, the cosine similarity between the feature vectors is calculated. The value range of the cosine similarity is [-1, 1]. When the cosine similarity is greater than a preset matching threshold (e.g., 0.7), it is determined that the current data transmission environment matches the irregular interference factor, and the current irregular interference factor is regarded as the reference interference factor.

[0043] In the above embodiments, for example, there is a pre-adjustment trend 2: the compression rate increases by 2% per hour. Due to network congestion at the current moment, based on the historical abrupt change in compression rate, it is determined that the average adjustment range of the historical abrupt change in compression rate during network congestion is 25% of that under normal conditions. At this point, the adjustment step size of the pre-adjustment trend 2 is adjusted by 2%, resulting in an adjusted step size of 2% × 25% = 0.5%; finally, the actual adjustment rule is determined to be an increase of 0.5% in the compression rate per hour.

[0044] In the above embodiments, for example, there is a pre-adjustment trend 3: the compression rate increases by 5% per hour. Due to network congestion and equipment failure at the current moment, based on historical abrupt changes in compression rate, it is determined that the average adjustment of the historical abrupt change compression rate during network congestion is 25% of the normal rate; and the average adjustment of the historical abrupt change compression rate during equipment failure is 15% of the normal rate. At this point, the adjustment step size of the pre-adjustment trend 2 is adjusted by 5%, resulting in an adjusted step size equal to... The final adjustment rule was determined to be an increase of 1% in the compression rate every hour.

[0045] In the above embodiments, the preset adjustment strategy refers to a method for adjusting the data compression rate that is pre-set based on historical data statistical patterns and system resource status. The adjustment is applied when there are no reference interference factors. For example, the adjustment step size of the pre-adjustment trend is adjusted according to a fixed adjustment amount. For example, if the pre-adjustment trend is to increase the compression rate by 2% per hour and the fixed adjustment amount is to decrease it by 0.15% each time, then the actual adjustment rule after adjustment is to increase the compression rate by 1.85% per hour. The actual adjustment rule refers to the specific compression rate adjustment scheme obtained after analyzing the compression rate change trend and the pre-adjustment trend. For example, the actual adjustment rule is to increase the compression rate by 3%.

[0046] The beneficial effects of the above technical solution are: by analyzing historical data and the current transmission environment, the data encryption method and compression rate are automatically adjusted, so that the data can maintain high security and efficiency in different transmission environments, while avoiding excessive compression that wastes computing resources or insufficient compression that leads to excessive data volume and excessive bandwidth consumption, thereby optimizing the utilization of system resources.

[0047] The working principle of the above technical solution is as follows: First, the transmission environment of the current data is evaluated using transmission environment assessment indicators to obtain a transmission environment assessment score. Next, when the transmission environment assessment score does not fall within the set assessment threshold range, the assessment difference is determined. Based on the assessment difference, a suitable encryption method is selected from the preset encryption methods to replace the current data encryption method. Furthermore, based on the assessment difference and the transmission priority level of the current data, a pre-built trend recognition model is input to derive the pre-adjustment trend of the current data compression rate. Then, by analyzing historical data compression rates, the compression rate change trend is determined. Next, the compression rate change trend and the pre-adjustment trend are analyzed for trend changes, trend adjustment consistency, and trend conflict degree to obtain the actual adjustment rules. Finally, the generated actual adjustment rules are used to adjust the current data compression rate to adapt to different transmission environments and data transmission requirements.

[0048] Real-time monitoring of data transmission quality also includes: A comprehensive analysis of the transmission delay, packet loss rate, and bit error rate obtained during real-time monitoring of data transmission is performed to obtain a transmission quality score. By comparing the transmission quality score with a preset quality threshold range, a transmission optimization method is determined to optimize data transmission quality.

[0049] In the above embodiments, the formula for calculating the transmission quality score is as follows:

[0050] In the formula, G represents the transmission quality score; This represents the weight of the impact of transmission delay on the quality of data transmission in the analysis. This represents the transmission delay for the current data transmission; This is represented as a preset transmission delay threshold; This is expressed as the bit error rate of the current data transmission; This is represented as the weight of the impact of bit error rate on the quality of data transmission in the analysis. This is represented as the preset error threshold; This represents the packet loss rate of the current data transmission; This is represented as the preset packet loss threshold; This represents the weight of the impact of packet loss rate on the quality of data transmission.

[0051] In the above embodiments, the weights assigned to transmission delay, packet loss rate, and bit error rate are obtained by solving the matrix constructed by pairwise comparison and scoring using the analytic hierarchy process, and the values ​​range from (0, 1). The preset transmission delay threshold, preset bit error rate threshold, and preset packet loss threshold are determined in advance based on experiments, taking into account the tolerance of data transmission to delay, bit error, and packet loss under different scenarios.

[0052] In the above embodiments, for example, when the transmission quality score is within a preset quality threshold range, data transmission is optimized by adjusting the transmission parameters; when the transmission quality score is less than the lower limit of the preset quality threshold range, data transmission is optimized by switching the transmission path.

[0053] In the above embodiments, the preset quality threshold range is determined based on the actual service requirements for data transmission quality through a large number of experimental tests and feedback from practical applications, for example (0.4, 0.7).

[0054] The beneficial effects of the above technical solution are: by monitoring the transmission delay, packet loss rate and bit error rate in real time during the data transmission process, and by comprehensively analyzing the transmission quality score, it is possible to comprehensively and accurately assess the current data transmission quality status, and provide an effective basis for subsequent transmission optimization by adjusting transmission parameters or switching transmission paths.

[0055] The intelligent control unit includes: The data parsing module is configured to receive and parse the monitoring data transmitted from the data sensing unit, converting the monitoring data into understandable physiological parameters. Specifically, it executes the following process: Data receiving interface: Design a standardized data receiving interface to receive various monitoring data from the data sensing unit; Data caching mechanism: Set up a data cache area to temporarily store newly received monitoring data for subsequent processing and analysis; Data integrity check: Perform integrity checks on the received monitoring data to ensure that the monitoring data is not lost or damaged. If data abnormalities are found, promptly request retransmission or mark it as invalid data. Data parsing algorithm: The data parsing algorithm converts the integrity-checked monitoring data into understandable physiological parameters. The data analysis module is configured to establish a phototherapy model based on physiological parameters through machine learning algorithms. Based on the analyzed monitoring data, the phototherapy model calculates the most suitable phototherapy time, intensity, and frequency for the current physiological state. The instruction generation module is configured to generate corresponding control instructions based on the calculation results of the data analysis module and send them to the phototherapy component 3. The phototherapy component 3 controls its working state according to the received control instructions. The feedback adjustment module is configured to receive feedback information from the phototherapy component 3, including but not limited to actual working status and fault information, and adjust the control strategy according to the feedback information.

[0056] The technical effects of the above solution are as follows: The data parsing module, through a data parsing algorithm, converts the monitoring data, after integrity checks, into understandable physiological parameters, providing an accurate data foundation for subsequent phototherapy model analysis and ensuring the scientific validity and effectiveness of the phototherapy plan. Before parsing, the received monitoring data undergoes rigorous checks to ensure its integrity and accuracy, preventing misjudgments or erroneous control due to data issues, thus improving the system's reliability and stability. The data analysis module, through machine learning algorithms, establishes a phototherapy model based on physiological parameters, dynamically calculating the most suitable phototherapy time, intensity, and frequency according to the user's real-time physiological state. This personalized phototherapy plan maximizes the phototherapy effect while avoiding potential risks to the user from excessive or insufficient phototherapy. The instruction generation module automatically generates control instructions based on the parsed monitoring data and the calculation results of the phototherapy model and sends them to the phototherapy component 3 without manual intervention, greatly improving the system's intelligence and ease of use. The feedback adjustment module's real-time feedback mechanism enables the system to adjust the control strategy promptly based on the actual operation of the phototherapy component 3, ensuring the stability and reliability of the phototherapy process.

[0057] The security protection unit includes: The threshold setting module is configured to preset safe threshold ranges for various physiological parameters based on the monitoring data from the data sensing unit. The monitoring and early warning module is configured to receive physiological parameters transmitted from the data sensing unit in real time and compare them with preset safety threshold ranges. If any data exceeds the safety threshold range, an early warning signal is generated, and different early warning levels are set according to the degree to which the data exceeds the threshold. Specifically: Assuming physiological parameters are The safety threshold range is ; If any data exceeds the safety threshold, an early warning signal is generated, and the early warning process is initiated. < or > ; Warning levels are divided into low-level warnings and high-level warnings; Low-level warning: or ; High-level warning: or ; in, Thresholds indicating warning levels are used to distinguish between low-level and high-level warnings; The safety protection module is configured to automatically pause the operation of the phototherapy component 3 when the monitored data exceeds a safety threshold, based on the comparison results from the monitoring and early warning module. It will automatically resume phototherapy once safety conditions are restored. Simultaneously, it will notify the user and record detailed information about the event when a safety incident occurs. Specifically: Pause command generation: When the monitored data exceeds the safety threshold, a pause command is generated and immediately sent to the phototherapy component 3; Pause Status Monitoring: Monitors in real time whether the phototherapy component 3 has successfully paused operation and records the pause time; Recovery condition assessment: After phototherapy is paused, the changes in physiological parameters are continuously monitored to determine whether the recovery conditions are met, i.e., whether the data have returned to the safe threshold range; Recovery command generation: When the recovery conditions are met, a recovery command is generated to restore the operation of phototherapy component 3. At the same time, the recovery time and the phototherapy parameters after recovery are recorded.

[0058] The technical effects of the above solution are as follows: The threshold setting module ensures the targetedness and effectiveness of safety protection by preset safety threshold ranges for various physiological parameters. The monitoring and early warning module compares the monitored data with the preset safety threshold range. If any data exceeds the safety threshold, an early warning signal is generated, and different early warning levels are set according to the degree to which the data exceeds the threshold, allowing users to understand their current physiological state and potential risks in a timely manner, thus enhancing the system's early warning function and user experience. When the monitored data exceeds the safety threshold, the safety protection module can immediately generate a pause command and send it to the phototherapy component 3, ensuring that the phototherapy process stops quickly when a safety risk occurs, effectively avoiding potential harm to the user. At the same time, after the safety conditions are restored, the phototherapy can be automatically resumed, ensuring the continuity and effectiveness of the phototherapy process. Through the collaborative work of the monitoring and early warning module and the safety protection module, a complete safety protection closed loop is formed. The early warning signal can alert the user to potential risks in advance, while the safety protection module takes measures quickly when a risk occurs to ensure user safety. This collaborative mechanism significantly improves the system's safety performance.

[0059] Working principle: The wearable main body 1 adopts a wearable structure and is made to fit the surface of the human skin by the tightening strap 11, which can be easily worn by users in daily activities. The sensor array 2 monitors physiological parameters such as vitamin D content in human blood and skin temperature in real time, which can provide accurate data for phototherapy and thus ensure the maximization of phototherapy effect. The working state of the phototherapy component is precisely adjusted according to the monitoring data, thereby providing users with personalized phototherapy solutions to ensure that the phototherapy effect is maximized and conforms to the user's current physical condition. Based on the monitoring data, a preset safety threshold range is set. When a certain physiological parameter is detected to exceed the safety threshold, the operation of the phototherapy component 3 can be automatically stopped, which can effectively avoid potential harm to users due to excessive phototherapy or abnormal conditions and ensure the safety of use.

[0060] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0061] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. A wearable intelligent vitamin D synthesis therapy device based on phototherapy, comprising a wearable main body (1), characterized in that, The wearable body (1) is provided with a tightening strap (11) on both sides. The tightening strap (11) makes the wearable body (1) fit the surface of the human skin. The wearable body (1) is provided with a sensor array (2) for collecting data, and a phototherapy component (3) for emitting ultraviolet light of a specific wavelength to promote the synthesis of vitamin D in the human skin. Among them, the sensor array (2) includes a vitamin D synthesis content monitoring device, a skin temperature sensor and a heart rate sensor; It also includes a data sensing unit, an intelligent control unit, and a security protection unit; The data sensing unit is configured to monitor the user's physiological parameters in real time through the sensor array (2), including but not limited to the synthesis content of vitamin D in the skin, skin temperature and human heart rate, and transmit the monitoring data to the intelligent control unit and the safety protection unit respectively; During transmission, the data encryption method and compression rate are automatically adjusted by analyzing historical data and the current transmission environment. The intelligent control unit is connected to the phototherapy component (3) and is configured to control the working state of the phototherapy component (3) according to the monitoring data of the data sensing unit, including phototherapy time, phototherapy intensity and phototherapy frequency; The safety protection unit is configured to preset a safety threshold range based on the monitoring data of the data sensing unit. If any monitoring data exceeds the safety threshold range, the phototherapy component will be automatically paused (3).

2. The wearable intelligent vitamin D synthesis therapy device based on phototherapy according to claim 1, characterized in that, The data sensing unit includes: The data acquisition module is configured to arrange the sensor array (2) reasonably on the wearable body (1), collect the user's physiological parameters through the sensor array (2), and perform filtering, noise reduction and calibration processing on the collected monitoring data; The data transmission module is configured to compress the transmitted monitoring data to reduce transmission bandwidth and energy consumption. At the same time, it uses encryption technology to protect the privacy and security of the monitoring data and monitors the quality of data transmission in real time, including transmission delay, packet loss rate and bit error rate. The monitoring and optimization module is configured to monitor the working status of the data acquisition module and the data transmission module in real time, including acquisition accuracy, acquisition frequency, transmission speed and energy consumption indicators, and optimize and adjust the data acquisition module and the data transmission module based on the monitoring results.

3. The wearable intelligent vitamin D synthesis therapy device based on phototherapy according to claim 2, characterized in that, The monitoring and optimization module executes the following process: Real-time monitoring of sensor acquisition performance and data transmission efficiency, including acquisition accuracy, acquisition frequency, transmission speed and energy consumption, allows for timely identification of potential performance bottlenecks based on the monitored indicators. Design a fault diagnosis algorithm to determine the working status of sensors and transmission links based on the above-mentioned monitored indicators, and promptly detect faults and issue early warnings; Based on the monitoring results, the sensor's acquisition strategy and data transmission parameters are automatically adjusted.

4. The wearable intelligent vitamin D synthesis therapy device based on phototherapy according to claim 2, characterized in that, The data transmission module performs the following process: Choose the appropriate wireless or wired transmission protocol based on the device's usage scenario and data transmission requirements; The transmitted monitoring data is compressed to reduce transmission bandwidth and energy consumption. At the same time, encryption technology is used to protect the privacy and security of the monitoring data. Real-time monitoring of data transmission quality, including transmission delay, packet loss rate, and bit error rate. If a decline in transmission quality is detected, timely adjustment of transmission parameters or switching of transmission paths is performed to ensure reliable data transmission. Design a data synchronization mechanism to ensure time synchronization and data consistency among the data sensing unit, intelligent control unit, and security protection unit.

5. The wearable intelligent vitamin D synthesis therapy device based on phototherapy according to claim 4, characterized in that, The data transmission module further includes: The current data transmission environment status is evaluated using transmission environment assessment indicators to obtain a transmission environment assessment score. When the transmission environment evaluation score does not fall within the set evaluation threshold range, the evaluation difference amount is determined; Based on the assessed difference, an encryption method is selected from the preset encryption method list to replace the encryption method of the current data. Based on the assessed difference and the current data transmission priority, a pre-adjustment trend for the current data compression rate is determined; Extract the compression rate of historical data within a preset historical time period, perform trend analysis based on the compression level, and determine the trend of compression rate changes; When the compression change trend is a regular change trend, if the compression ratio change trend and the pre-adjustment trend are in the same direction, then the compression ratio adjustment step size in the pre-adjustment trend is reduced to obtain the actual adjustment rule. If the trend of compression ratio change and the trend of pre-adjustment are not in the same direction, then a conflict analysis is performed on the trend of compression ratio change and the trend of pre-adjustment to determine the degree of trend conflict. Anomaly analysis of the degree of trend conflict is performed to determine the reference adjustment step size; Based on the reference adjustment step size, the corresponding actual adjustment rules are generated; When the compression trend is irregular, identify the irregularity and interference factors. The current data transmission environment is matched with the irregular interference factors to determine the reference interference factors; If there are reference interference factors, the adjustment step size of the pre-adjustment trend is adjusted according to the historical mutation compression rate generated based on the reference interference factors to generate the actual adjustment rules; If there are no reference interference factors, the pre-adjustment trend will be adjusted according to the preset adjustment strategy to obtain the actual adjustment rules; The compression ratio of the current data is adjusted using the actual adjustment rules.

6. The wearable intelligent vitamin D synthesis therapy device based on phototherapy according to claim 4, characterized in that, The real-time monitoring of data transmission quality also includes: A comprehensive analysis of the transmission delay, packet loss rate, and bit error rate obtained during real-time monitoring of data transmission is performed to obtain a transmission quality score. By comparing the transmission quality score with a preset quality threshold range, a transmission optimization method is determined to optimize data transmission quality.

7. The wearable intelligent vitamin D synthesis therapy device based on phototherapy according to claim 4, characterized in that, Choose the appropriate wireless or wired transmission protocol, as shown in the following formula: In the formula, This indicates the selected transmission protocol, i.e., whether to select a wireless transmission protocol or a wired transmission protocol; Indicates the bandwidth capability of the transmission protocol; Indicates the delay of the transmission protocol; Indicates the reliability of the transmission protocol; Indicates the security of the transmission protocol; , , , This represents the weighting coefficient, which can be adjusted according to actual needs. Collect bandwidth capacity, latency, reliability, and security metrics for each transport protocol, assign weights to each metric, and calculate a score for each transport protocol. The protocol with the highest score is selected as the transmission protocol.

8. The wearable intelligent vitamin D synthesis therapy device based on phototherapy according to claim 1, characterized in that, The intelligent control unit includes: The data parsing module is configured to receive and parse the monitoring data transmitted from the data sensing unit, converting the monitoring data into understandable physiological parameters. The data analysis module is configured to establish a phototherapy model based on physiological parameters through machine learning algorithms. Based on the analyzed monitoring data, the phototherapy model calculates the most suitable phototherapy time, intensity, and frequency for the current physiological state. The instruction generation module is configured to generate corresponding control instructions based on the calculation results of the data analysis module and send them to the phototherapy component (3). The phototherapy component (3) controls the working state of the phototherapy component (3) according to the received control instructions. The feedback adjustment module is configured to receive feedback information from the phototherapy component (3), including but not limited to actual working status and fault information, and adjust the control strategy according to the feedback information.

9. The wearable intelligent vitamin D synthesis therapy device based on phototherapy according to claim 8, characterized in that, The data parsing module performs the following process: Design a standardized data receiving interface to receive various monitoring data from the data sensing unit; Set up a data cache area to temporarily store newly received monitoring data for subsequent processing and analysis; Perform integrity checks on the received monitoring data to ensure that the monitoring data is not lost or damaged. If any abnormal data is found, promptly request retransmission or mark it as invalid data. Through data parsing algorithms, the monitoring data that has undergone integrity checks is converted into understandable physiological parameters.

10. The wearable intelligent vitamin D synthesis therapy device based on phototherapy according to claim 1, characterized in that, The security protection unit includes: The threshold setting module is configured to preset safe threshold ranges for various physiological parameters based on the monitoring data from the data sensing unit. The monitoring and early warning module is configured to receive physiological parameters transmitted from the data sensing unit in real time and compare them with preset safety threshold ranges. If any data exceeds the safety threshold range, an early warning signal is generated, and different early warning levels are set according to the degree to which the data exceeds the threshold. Specifically: Assuming physiological parameters are The safety threshold range is ; If any data exceeds the safety threshold, an early warning signal is generated, and the early warning process is initiated. < or > ; Warning levels are divided into low-level warnings and high-level warnings; Low-level warning: or ; High-level warning: or ; in, Thresholds indicating warning levels are used to distinguish between low-level and high-level warnings; The safety protection module is configured to automatically pause the operation of the phototherapy component (3) when the monitoring data exceeds the safety threshold based on the comparison results of the monitoring and early warning module, and automatically resume phototherapy after the safety conditions are restored. At the same time, when a safety event occurs, the user is notified and the event details are recorded, specifically: When the monitored data exceeds the safety threshold, a pause command is generated and immediately sent to the phototherapy component (3). Monitor in real time whether the phototherapy component (3) has successfully paused operation and record the pause time; After pausing phototherapy, the physiological parameters are continuously monitored to determine whether the recovery conditions are met, i.e., whether the data have returned to the safe threshold range. When the recovery conditions are met, a recovery command is generated to restore the operation of the phototherapy component (3). At the same time, the recovery time and the phototherapy parameters after recovery are recorded.

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