Intelligent power supply system and method for controlling the same

Abstract

The application provides an intense pulsed light therapeutic instrument based on an Internet of Things and a data synchronization method thereof, relates to the technical field of therapeutic instrument data synchronization, and acquires treatment parameters of the intense pulsed light therapeutic instrument through a cloud platform; a main control module performs treatment operation according to the treatment parameters to obtain treatment parameter feedback data; the treatment instrument data is collected according to the treatment parameters combined with the treatment parameter feedback data to obtain treatment instrument collection data; data standard processing is performed on the treatment instrument collection data to obtain a standardized data frame; coding correlation analysis is performed on the standardized data frame to obtain correlation analysis data; processing speed analysis and synchronization are performed according to the correlation analysis data to obtain therapeutic instrument synchronization data; and the application can improve the flexibility, efficiency and intelligence of the therapeutic instrument data synchronization.

Description

Technical Field

[0001] This invention proposes an IoT-based intense pulsed light therapy device and its data synchronization method, relating to the field of data synchronization technology, specifically to the field of data synchronization technology for IoT-based intense pulsed light therapy devices. Background Technology

[0002] Traditional intense pulsed light (IPL) therapy devices suffer from several drawbacks. Data acquisition and treatment parameters are disconnected, resulting in insufficient targeting. Disorganized data formats lead to cross-system compatibility difficulties and low synchronization efficiency. Fixed parameter delivery sequences can easily cause asynchrony with treatment operations, posing risks. The lack of differentiated strategies for multi-source data processing results in significant real-time data delays and the loss of critical data. Furthermore, the limited dimensions of correlation analysis and poor processing speed adaptability make it difficult to meet the precision and reliability requirements of clinical treatment for data synchronization. In addition, existing synchronization solutions are insufficiently adaptable to new treatment scenarios and cannot effectively support personalized treatment and cross-hospital data sharing. Summary of the Invention

[0003] This invention provides an IoT-based intense pulsed light therapy device and its data synchronization method to solve the above-mentioned problems:

[0004] This invention proposes an IoT-based intense pulsed light therapy device and its data synchronization method, wherein the therapy device includes:

[0005] The treatment data acquisition module is used to acquire the treatment parameters of the intense pulsed light therapy device through the cloud platform. The main control module performs treatment operations based on the treatment parameters and obtains treatment parameter feedback data.

[0006] The data acquisition and processing module is used to acquire data from the therapeutic instrument based on the treatment parameters and the feedback data of the treatment parameters, obtain the data acquired by the therapeutic instrument, and perform data standard processing on the data acquired by the therapeutic instrument to obtain standardized data frames.

[0007] The correlation speed analysis module is used to perform encoding correlation analysis on standardized data frames to obtain correlation analysis data. Based on the correlation analysis data, processing speed analysis and synchronization are performed to obtain the synchronization data of the therapeutic device.

[0008] Furthermore, the method includes:

[0009] S1. The treatment parameters of the intense pulsed light therapy device are obtained through the cloud platform. The main control module performs treatment operations based on the treatment parameters and obtains treatment parameter feedback data.

[0010] S2. Collect data from the therapeutic instrument based on the treatment parameters and the feedback data of the treatment parameters, obtain the data collected by the therapeutic instrument, and perform data standardization processing on the data collected by the therapeutic instrument to obtain standardized data frames.

[0011] S3. Perform encoding correlation analysis on the standardized data frames to obtain correlation analysis data. Based on the correlation analysis data, perform processing speed analysis and synchronization to obtain the therapeutic instrument synchronization data.

[0012] Further, S1 includes:

[0013] Targeted treatment data is obtained through a cloud platform, and preset treatment parameters are matched based on the target treatment data.

[0014] Set the corresponding transmission timing for the preset treatment parameters to obtain dynamic transmission timing information;

[0015] The preset treatment parameters are sent to the main control module of the intense pulsed light therapy device based on the dynamic transmission timing information.

[0016] The main control module controls the treatment operation by setting preset treatment parameters and obtains treatment operation control data.

[0017] Treatment parameter feedback data is obtained based on treatment operation control data.

[0018] Further, the step of setting a corresponding transmission timing for the preset treatment parameters and obtaining transmission timing information includes:

[0019] Based on the preset initial transmission timing information, the preset treatment parameters are divided into timing parameters to obtain the timing transmission parameters;

[0020] Obtain preset security time domain range information, and determine the validity of timing transmission parameters based on the preset security time domain range information to obtain parameter validity determination information;

[0021] The transmission timing information is adjusted based on the parameter validity determination information to obtain the adjusted timing information;

[0022] The initial transmission timing information is updated based on the adjustment timing information to obtain dynamic transmission timing information.

[0023] Further, S2 includes:

[0024] The treatment parameters are correlated with the treatment parameter feedback data to obtain the corresponding correlated data.

[0025] Perform parameter status analysis on the associated data to obtain parameter status analysis data;

[0026] Based on the parameter state analysis data, the parameter state data is divided into multiple categories to obtain parameter state data for multiple categories.

[0027] Collect category data based on the category parameter status data to obtain category collection data;

[0028] The collected data is divided and processed into data processing regions to obtain regionally processed data.

[0029] Standardized data frames are generated based on the data processed from the region.

[0030] Furthermore, the step of performing parameter state analysis on the associated corresponding data to obtain parameter state analysis data includes:

[0031] Extract key parameter fields from the associated data;

[0032] Calculate the deviation between the actual value of each key parameter field and the preset standard value of the treatment parameter to obtain the parameter deviation coefficient;

[0033] Obtain the time information of the parameter deviation coefficients to obtain the time series parameter deviation information;

[0034] The timing parameter deviation information is compared with a preset timing parameter deviation threshold to obtain parameter deviation comparison information;

[0035] Based on the parameter deviation comparison information, the associated corresponding data are classified into levels to obtain parameter level classification data;

[0036] The parameter level classification data is the parameter state analysis data.

[0037] Furthermore, the step of dividing and processing the collected data into processing regions to obtain processed region data includes:

[0038] Based on the type-collected data and its type parameter status data, the processing area is divided to obtain the processing area division data;

[0039] The processing area division data includes a real-time priority processing area, a buffer processing area, and a regular processing area.

[0040] Based on the real-time priority processing area, the collected data of different types are processed in real time to obtain real-time regional processed data.

[0041] Based on the buffer processing area, the collected data of different types is subjected to buffer data feature processing to obtain buffer area processed data;

[0042] Based on the standard processing area, the collected data of each type undergoes standard data feature processing to obtain standard area processed data;

[0043] The real-time region processing data, buffer region processing data, and regular region processing data are timestamped to obtain the region processing data.

[0044] Further, S3 includes:

[0045] Perform data correlation analysis on standardized data frames to obtain correlation analysis data;

[0046] Based on the correlation analysis data, the standardized data frames are combined and packaged to obtain combined packaged data;

[0047] Analyze the processing speed of the combined packaged data to obtain combined processing speed analysis data;

[0048] A data synchronization queue for combined packaged data is generated based on the combined processing speed analysis data.

[0049] The data is combined and packaged according to the data synchronization queue to output the synchronization operation, and the synchronization data of the treatment device is obtained.

[0050] Furthermore, the step of performing data correlation analysis on the standardized data frames to obtain correlation analysis data includes:

[0051] Metadata is extracted from standardized data frames to obtain metadata extraction information;

[0052] The extracted metadata information is processed by information encoding to obtain metadata encoded information;

[0053] Retrieve new encoded information from metadata encoding information;

[0054] Obtain the proportion of new encoding information in the metadata encoding information to get the new encoding proportion data;

[0055] The correlation degree of the standardized data frame is calculated based on the metadata encoding information to obtain the correlation degree calculation information of the data frame;

[0056] Calculate the product of the new encoding proportion data and the data frame correlation calculation information to obtain the data frame correlation coefficient;

[0057] Based on the correlation coefficient of the data frames, the standardized data frames are matched for correlation to obtain correlation analysis data.

[0058] Furthermore, the step of analyzing the processing speed of the combined packaged data to obtain combined processing speed analysis data includes:

[0059] Obtain the total data volume, data field complexity, and data transmission priority of the combined and packaged data;

[0060] Obtain the ratio of total data volume to comprehensive data volume to obtain the data volume coefficient;

[0061] Obtain the ratio of data field complexity to overall complexity to get the complexity coefficient;

[0062] Obtain the ratio of data transmission priority to overall priority to obtain the priority coefficient;

[0063] The transmission difficulty coefficient is obtained by multiplying the data volume coefficient, complexity coefficient, and priority coefficient.

[0064] Obtain the transmission difficulty coefficient corresponding to the preset transmission speed data, and obtain the speed difficulty coefficient;

[0065] Calculate the product of the transmission difficulty coefficient and the speed difficulty coefficient to obtain combined processing speed analysis data.

[0066] The beneficial effects of this invention are as follows: This invention solves the technical problems of insufficient data targeting, chaotic data formats leading to low synchronization efficiency, and fixed synchronization strategies that cannot adapt to different data characteristics in traditional data synchronization methods. It achieves deep binding between data synchronization and treatment parameters, ensuring the practicality and targeting of synchronized data; standardized processing improves data compatibility, providing support for cross-system (such as hospital HIS and EMR systems) synchronization; dynamically adaptable synchronization strategies improve the efficiency and reliability of data synchronization, avoiding synchronization delays caused by data congestion. Simultaneously, the streamlined design reduces the difficulty of managing the synchronization process, improving the repeatability and stability of synchronization operations. Attached Figure Description

[0067] Figure 1 This is a schematic diagram of an IoT-based intense pulsed light therapy device. Detailed Implementation

[0068] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0069] In one embodiment of the present invention, the present invention proposes an Internet of Things-based intense pulsed light therapy device and its data synchronization method, wherein the therapy device includes:

[0070] The treatment data acquisition module is used to acquire the treatment parameters of the intense pulsed light therapy device through the cloud platform. The main control module performs treatment operations based on the treatment parameters and obtains treatment parameter feedback data.

[0071] The data acquisition and processing module is used to acquire data from the therapeutic instrument based on the treatment parameters and the feedback data of the treatment parameters, obtain the data acquired by the therapeutic instrument, and perform data standard processing on the data acquired by the therapeutic instrument to obtain standardized data frames.

[0072] The correlation speed analysis module is used to perform encoding correlation analysis on standardized data frames to obtain correlation analysis data. Based on the correlation analysis data, processing speed analysis and synchronization are performed to obtain the synchronization data of the therapeutic device.

[0073] The working principle and technical effects of the above technical solution are as follows: This invention constructs a full-link data synchronization system through the coordinated linkage of three major modules: cloud parameter distribution, terminal data acquisition, and synchronous analysis output. The treatment data acquisition module, as the starting point of data synchronization, builds a communication bridge between the cloud platform and the main control module of the treatment device. After acquiring treatment parameters adapted to the patient's condition through the cloud, it drives the main control module to execute treatment operations, while simultaneously collecting real-time feedback data of the treatment parameters, forming an initial closed loop of parameter distribution, operation execution, and feedback collection. The acquisition and processing module receives the feedback data and the original treatment parameters, first establishing a correlation mapping between the two, clarifying the correspondence between preset parameter values ​​and actual output values, and then conducting targeted data acquisition based on this correlation to ensure a strong correlation between the acquired data and the treatment parameters. Data standardization processing eliminates data format differences from different acquisition sources, generating unified standardized data frames. The correlation speed analysis module, as the core processing end of data synchronization, mines the inherent correlation between different standardized data frames through coded correlation analysis, and then combines the correlation analysis results to conduct processing speed adaptation analysis, dynamically adjusting the synchronization strategy, ultimately achieving accurate and efficient synchronous output of data. The three modules form a collaborative link for data input, data processing, and data output, ensuring that the synchronization process is deeply integrated with the treatment process and linked in real time.

[0074] This invention solves the technical problems of traditional intense pulsed light (IPL) therapy devices, such as the disconnect between data acquisition and treatment parameters, inconsistent data formats leading to synchronization difficulties, and the lack of targeted adaptation in the synchronization process. It achieves closed-loop management of the entire treatment data chain from acquisition to synchronized output, ensuring real-time matching between synchronized data and the treatment process. Through standardized processing and correlation analysis, it improves the accuracy and effectiveness of data synchronization, avoiding interference from invalid or disorganized data. Simultaneously, the modular design reduces system maintenance difficulty, allowing each module to be independently upgraded and optimized, thus improving the overall system's scalability and compatibility.

[0075] In one embodiment of the present invention, the method includes:

[0076] S1. The treatment parameters of the intense pulsed light therapy device are obtained through the cloud platform. The main control module performs treatment operations based on the treatment parameters and obtains treatment parameter feedback data.

[0077] S2. Collect data from the therapeutic instrument based on the treatment parameters and the feedback data of the treatment parameters, obtain the data collected by the therapeutic instrument, and perform data standardization processing on the data collected by the therapeutic instrument to obtain standardized data frames.

[0078] S3. Perform encoding correlation analysis on the standardized data frames to obtain correlation analysis data. Based on the correlation analysis data, perform processing speed analysis and synchronization to obtain the therapeutic instrument synchronization data.

[0079] The working principle and technical effects of the above technical solution are as follows: This method uses a cloud platform as the parameter hub, matches preset parameters based on the patient's treatment needs, and after the main control module executes the treatment operation, it collects feedback data on the actual execution of the parameters to establish a correspondence between preset parameters and actual execution. Based on the parameter correspondence, it collects targeted data on the operation of the treatment device to ensure the relevance and relevance of the collected data. Then, it eliminates data format differences and supplements metadata information through data standard processing to generate standardized data frames, solving the compatibility problem of different collected data. Through coding correlation analysis, it sorts out the inherent correlation of the standardized data frames, and then analyzes the data processing speed requirements based on the correlation results, dynamically generates a synchronization queue, and completes the data synchronization output in an orderly manner according to priority and correlation, ensuring that the synchronization process is adapted to the data characteristics.

[0080] This invention addresses the technical problems of traditional data synchronization methods, such as insufficient data targeting, inconsistent data formats leading to low synchronization efficiency, and fixed synchronization strategies failing to adapt to different data characteristics. It achieves deep binding between data synchronization and treatment parameters, ensuring the practicality and targeting of synchronized data; standardization improves data compatibility, supporting cross-system synchronization (such as hospital HIS and EMR systems); and dynamically adaptable synchronization strategies enhance the efficiency and reliability of data synchronization, avoiding synchronization delays caused by data congestion. Furthermore, the streamlined design reduces the difficulty of managing the synchronization process, improving the repeatability and stability of synchronization operations.

[0081] In one embodiment of the present invention, S1 includes:

[0082] Targeted treatment data is obtained through a cloud platform, and preset treatment parameters are matched based on the target treatment data.

[0083] Set the corresponding transmission timing for the preset treatment parameters to obtain dynamic transmission timing information;

[0084] The preset treatment parameters are sent to the main control module of the intense pulsed light therapy device based on the dynamic transmission timing information.

[0085] The main control module controls the treatment operation by setting preset treatment parameters and obtains treatment operation control data.

[0086] Treatment parameter feedback data is obtained based on treatment operation control data.

[0087] The working principle and technical effects of the above technical solution are as follows: This process focuses on the precise delivery and feedback loop of treatment parameters. The core lies in improving the timeliness and accuracy of parameter delivery through dynamic timing control. Target treatment data (including patient condition, treatment site, treatment stage, etc.) is obtained from the cloud platform. Based on this information, suitable preset treatment parameters are matched from the parameter database to ensure precise matching between parameters and patient needs. Considering the different timeliness requirements of different treatment parameters (e.g., pulse width parameters require real-time response, while cooling temperature parameters can be moderately delayed), the preset treatment parameters are dynamically sent according to the timing settings to avoid asynchronous parameter delivery and execution caused by fixed timing. Parameters are precisely delivered to the main control module according to the dynamic timing. The main control module parses the parameters and drives various components of the treatment device (such as the pulse emission module and cooling module) to perform treatment operations, while simultaneously collecting control data of the treatment operations (such as component operating status and actual parameter output values). Treatment parameter feedback data is extracted from the control data, completing the entire closed-loop process of parameter matching, timing delivery, execution, and feedback.

[0088] This invention solves the technical problems of asynchronous parameters and treatment operations, mismatch between parameter matching and patient needs, and the inability to promptly detect anomalies due to a lack of parameter execution feedback caused by fixed timing in traditional parameter delivery processes. It achieves personalized and precise matching of treatment parameters and dynamic timing delivery, improving the timeliness and adaptability of parameter delivery; by establishing closed-loop control of parameter execution through feedback data collection, it can promptly detect parameter execution deviations and reduce treatment risks caused by parameter anomalies; at the same time, dynamic timing settings improve the resource utilization of the main control module and avoid execution delays caused by parameter congestion.

[0089] In one embodiment of the present invention, setting a corresponding transmission timing for preset treatment parameters and obtaining transmission timing information includes:

[0090] Based on the preset initial transmission timing information, the preset treatment parameters are divided into timing parameters to obtain the timing transmission parameters;

[0091] Obtain preset security time domain range information, and determine the validity of timing transmission parameters based on the preset security time domain range information to obtain parameter validity determination information;

[0092] The transmission timing information is adjusted based on the parameter validity determination information to obtain the adjusted timing information;

[0093] The initial transmission timing information is updated based on the adjustment timing information to obtain dynamic transmission timing information.

[0094] The working principle and technical effect of the above technical solution are as follows: Based on the preset initial transmission sequence (e.g., a default interval of 100ms), the preset treatment parameters are divided into time sequence parameters according to the characteristics of each treatment parameter (e.g., execution priority, response requirements), and the parameters are assigned to different time sequence intervals, clarifying the transmission order and time interval of each parameter; the preset safe time domain range (i.e., the time window in which the parameter can be effectively executed after transmission, and if it exceeds this window, the parameter execution may fail or cause risks) is obtained, and the validity of the divided time sequence transmission parameters is determined to determine whether the transmission sequence of each parameter is within the safe time domain and whether there are timing conflicts or delay risks; the transmission sequence is adjusted according to the validity determination result (e.g., the timing of a parameter exceeds the safe time domain or there are timing conflicts), the parameter transmission order is optimized, the time interval is adjusted, and timing risks are eliminated; the adjusted timing information is used to update the initial transmission sequence, forming dynamic transmission timing information to ensure that subsequent parameter transmission is always within a safe and efficient timing range.

[0095] This invention solves the technical problems of fixed parameter transmission timing, lack of safety constraints leading to parameter execution failure, timing conflicts causing equipment malfunctions, and the inability of timing to adapt to dynamic changes in the treatment process. It achieves safe and dynamic adjustment of parameter transmission timing, ensuring that parameters are delivered within the effective time window, thus improving the reliability of parameter execution; it eliminates conflicts through timing optimization, reducing the risk of equipment malfunctions; and its dynamic update mechanism allows timing settings to adapt to the changing needs of different treatment stages, improving the flexibility and adaptability of timing settings.

[0096] In one embodiment of the present invention, S2 includes:

[0097] The treatment parameters are correlated with the treatment parameter feedback data to obtain the corresponding correlated data.

[0098] Perform parameter status analysis on the associated data to obtain parameter status analysis data;

[0099] Based on the parameter state analysis data, the parameter state data is divided into multiple categories to obtain parameter state data for multiple categories.

[0100] Collect category data based on the category parameter status data to obtain category collection data;

[0101] The collected data is divided and processed into data processing regions to obtain regionally processed data.

[0102] Standardized data frames are generated based on the data processed from the region.

[0103] The working principle and technical effects of the above technical solution are as follows: Data association is established between the S treatment parameters and feedback data to create a one-to-one correspondence between preset parameters and actual output parameters, clarifying the target direction of data collection; parameter status analysis is performed on the associated corresponding data to determine the operating status of each parameter (normal, slightly abnormal, severely abnormal); based on the parameter status analysis results, parameter status types are classified, categorizing the data into different types (e.g., normal parameter data, abnormal parameter data); targeted collection is carried out for different types of parameter status data to ensure the complete collection of key data (e.g., abnormal parameter data); subsequently, the collected data types are divided into processing areas, allocated to different processing areas according to data characteristics (real-time requirements, importance), and differentiated processing strategies are adopted to improve processing efficiency; the processed data from each area are integrated, metadata is supplemented according to unified standards, and standardized formats are generated to generate standardized data frames, ensuring data compatibility across modules and systems.

[0104] This invention solves the technical problems of insufficient targeting, omission of key data, and difficulties in subsequent processing caused by disordered data formats in traditional data acquisition processes, as well as the impact of inconsistent data quality on synchronization effects. It achieves targeted and complete data acquisition, ensuring no key data is missed; it improves data processing efficiency and reduces processing resource consumption through partitioned processing; and the generation of standardized data frames eliminates data format barriers, improving data compatibility and portability.

[0105] In one embodiment of the present invention, the step of performing parameter state analysis on the associated corresponding data to obtain parameter state analysis data includes:

[0106] Extract key parameter fields from the associated data. The key parameter fields include at least the treatment energy value, pulse width, filter wavelength, cooling system temperature, parameter issuance timestamp, and parameter feedback timestamp.

[0107] Calculate the deviation between the actual value of each key parameter field and the preset standard value of the treatment parameter to obtain the parameter deviation coefficient;

[0108] Obtain the time information of the parameter deviation coefficients to obtain the time series parameter deviation information;

[0109] The timing parameter deviation information is compared with a preset timing parameter deviation threshold to obtain parameter deviation comparison information;

[0110] Based on the parameter deviation comparison information, the associated corresponding data are classified into levels to obtain parameter level classification data;

[0111] The parameter level classification data is the parameter state analysis data.

[0112] Assume that during intense pulsed light therapy, the types of data collected include: treatment energy value, pulse width (core treatment parameter, status is normal), patient's real-time heart rate (physiological sign data, status is slight fluctuation), and treatment environment temperature and humidity (auxiliary data, status is normal).

[0113] Based on the data characteristics and status, the treatment energy value and pulse width are classified into the real-time priority processing area, the patient's real-time heart rate is classified into the buffer processing area, and the ambient temperature and humidity are classified into the routine processing area.

[0114] The energy value and pulse width data in the real-time priority area are quickly denoised to retain core numerical features; the heart rate data in the buffer area is locally cached and backed up with redundancy checks to avoid data loss; the temperature and humidity data in the regular area are formatted and standardized to use ℃ and %RH as units.

[0115] Using "2026-01-12 10:30:00.000" as the benchmark, the timestamps of the three types of processed data are uniformly calibrated to obtain well-organized regional processed data.

[0116] The working principle and technical effects of the above technical solution are as follows: Key parameter fields (including core treatment parameters and time-series parameters) are extracted from the associated data. These fields directly affect the treatment effect and safety, and are the core basis for status analysis. The deviation between the actual value and the preset standard value of each key parameter field is calculated and converted into a standardized deviation coefficient to quantify the degree of difference between the actual output of the parameter and the preset requirements. The time information corresponding to the deviation coefficient is extracted to form time-series parameter deviation information, clarifying the time node and duration of the deviation. The time-series parameter deviation information is compared with a preset threshold to determine whether the deviation exceeds the allowable range and whether the duration of the deviation is too long. Based on the comparison results, the associated data is classified into levels (such as normal, slightly abnormal, and severely abnormal), clearly defining the operating status of each parameter.

[0117] This invention solves the technical problems of traditional parameter status judgment relying on manual methods, single-dimensional deviation analysis, and vague status definitions leading to omissions or misjudgments of key data. It achieves quantitative analysis and precise level classification of parameter status, improving the objectivity and accuracy of status judgment; multi-dimensional deviation analysis (numerical deviation + temporal deviation) ensures the comprehensiveness of status judgment, avoiding misjudgments caused by single-dimensional analysis; clear status levels provide a clear basis for prioritizing subsequent data collection and processing, improving the overall process's relevance and efficiency.

[0118] In one embodiment of the present invention, the step of dividing and processing the collected data into regions to obtain processed regional data includes:

[0119] Based on the type-collected data and its type parameter status data, the processing area is divided to obtain the processing area division data;

[0120] The processing area division data includes a real-time priority processing area, a buffer processing area, and a regular processing area.

[0121] Based on the real-time priority processing area, the collected data of different types are processed in real time to obtain real-time regional processed data.

[0122] Based on the buffer processing area, the collected data of different types is subjected to buffer data feature processing to obtain buffer area processed data;

[0123] Based on the standard processing area, the collected data of each type undergoes standard data feature processing to obtain standard area processed data;

[0124] The real-time region processing data, buffer region processing data, and regular region processing data are timestamped to obtain the region processing data.

[0125] The working principle and technical effects of the above technical solution are as follows: The core of this process lies in improving data processing efficiency and synchronization consistency through differentiated processing strategies and timestamp alignment. Combining the characteristics of various types of collected data (real-time requirements, importance, data volume) and their corresponding parameter status data, the data is divided into three processing areas: a real-time priority processing area (such as core treatment parameter data, which needs to be processed quickly to avoid delays), a buffer processing area (such as physiological sign data, which needs to be cached to avoid loss), and a routine processing area (such as environmental auxiliary data, which can be processed routinely). Differentiated feature processing strategies are adopted for different areas: Real-time priority data undergoes rapid denoising and feature extraction to ensure processing speed; buffer processing data undergoes caching backup and redundancy verification to ensure data integrity; routine processing data undergoes format standardization and redundancy removal to improve processing efficiency; and the processed data in the three areas are timestamped to ensure that all data is based on a unified time benchmark, avoiding synchronization chaos caused by time deviations, ultimately forming well-organized regional processed data.

[0126] This invention solves the technical problems caused by the uniform strategy used in traditional data processing, such as real-time data delays, loss of important data, waste of processing resources, and synchronization chaos caused by inconsistent data time bases. It achieves differentiated and precise data processing, ensuring the processing speed of real-time data and the integrity of important data; the differentiated processing strategy reduces the ineffective consumption of processing resources and improves overall processing efficiency; timestamp alignment ensures the time consistency of data, providing a unified time base for subsequent correlation analysis and synchronous output, thus improving the accuracy of synchronization.

[0127] In one embodiment of the present invention, S3 includes:

[0128] Perform data correlation analysis on standardized data frames to obtain correlation analysis data;

[0129] Based on the correlation analysis data, the standardized data frames are combined and packaged to obtain combined packaged data;

[0130] Analyze the processing speed of the combined packaged data to obtain combined processing speed analysis data;

[0131] A data synchronization queue for combined packaged data is generated based on the combined processing speed analysis data.

[0132] The data is combined and packaged according to the data synchronization queue to output the synchronization operation, and the synchronization data of the treatment device is obtained.

[0133] The working principle and technical effects of the above technical solution are as follows: This process focuses on the accuracy and efficiency of data synchronization, with the core being the optimization of synchronization strategies through correlation analysis and speed adaptation. Data correlation analysis is performed on standardized data frames to uncover the inherent connections between them (such as connections between the same patient, the same treatment period, or the same parameter type), clarifying the data's ownership and association logic. Based on the correlation analysis results, highly correlated data frames are combined and packaged to reduce the number of synchronization operations and improve synchronization efficiency. Processing speed analysis is performed on the combined and packaged data, considering factors such as data volume, complexity, and transmission priority to determine the data processing speed requirements under the current processing resources and transmission environment. A data synchronization queue is generated based on the processing speed analysis results, sorted according to data priority and the degree of correlation to ensure that important data is synchronized first. The output synchronization operation of the combined and packaged data is executed in an orderly manner according to the synchronization queue, synchronizing the data to the cloud platform or relevant hospital systems, completing the entire data synchronization process.

[0134] This invention solves the technical problems of disordered synchronization caused by chaotic data correlation, low efficiency due to excessively large or small data volumes in a single synchronization, and the inability of fixed synchronization strategies to adapt to changes in processing speed during traditional data synchronization processes. It achieves correlated combination and ordered synchronization of data, improving synchronization efficiency and data integrity; dynamically generated synchronization queues ensure priority synchronization of important data, reducing the risk of synchronization delays for critical data; and processing speed analysis allows the synchronization strategy to adapt to different processing environments, improving the stability and adaptability of synchronization.

[0135] In one embodiment of the present invention, the step of performing data association analysis on standardized data frames to obtain association analysis data includes:

[0136] Metadata is extracted from standardized data frames to obtain metadata extraction information;

[0137] The extracted metadata information is processed by information encoding to obtain metadata encoded information;

[0138] Retrieve new encoded information from metadata encoding information; the new encoded information is encoded data that does not exist in all encoded information.

[0139] Obtain the proportion of new encoding information in the metadata encoding information to get the new encoding proportion data;

[0140] The correlation degree of the standardized data frame is calculated based on the metadata encoding information to obtain the correlation degree calculation information of the data frame;

[0141] Calculate the product of the new encoding proportion data and the data frame correlation calculation information to obtain the data frame correlation coefficient;

[0142] Based on the correlation coefficient of the data frames, the standardized data frames are matched for correlation to obtain correlation analysis data.

[0143] Assuming that an intense pulsed light (IPL) therapy device completes two adjacent pigmentation removal treatments, generating two standardized data frames (frame A and frame B), the following is an example of the correlation analysis process:

[0144] Metadata was extracted from two frames of data. Frame A metadata: Patient ID=P001, Treatment Time=2026-01-12 10:00, Acquisition Module Number=M01, Treatment Site=Cheek; Frame B metadata: Patient ID=P001, Treatment Time=2026-01-12 10:05, Acquisition Module Number=M02, Treatment Site=Cheek

[0145] The text metadata is converted into a unified encoding format using Base64 encoding to obtain the metadata encoding information (Frame A: P001→UEAwMDE=, M01→TTAx=; Frame B: P001→UEAwMDE=, M02→TTAy=).

[0146] Retrieving the historical metadata encoding library, it was found that "M02→TTAy=" did not exist in the historical encoding (the acquisition module M02 is a newly added module), therefore "TTAy=" in frame B is new encoding information;

[0147] The frame B metadata encoding information consists of 4 items, including 1 new encoding information item. The proportion of new encoding data is 1 / 4 = 0.25.

[0148] Based on the correlation calculation of patient ID, treatment site, and treatment time, if two data frames have the same patient ID, the same treatment site, and a treatment time interval of 5 minutes (belonging to the same treatment cycle), the data frame correlation calculation information is 0.9 (the preset correlation is 0-1, and the larger the value, the stronger the correlation).

[0149] Correlation coefficient = new coding percentage data × correlation calculation information = 0.25 × 0.9 = 0.225;

[0150] The preset correlation coefficient threshold is 0.2. Since 0.225 > 0.2, frame A and frame B are determined to be associated. The correlation analysis data is "frame A - frame B (associated with the same patient in the same treatment cycle, including 1 newly added coded information)".

[0151] The working principle and technical effects of the above technical solution are as follows: The core of this process lies in accurately mining the correlation relationships of standardized data frames through encoding processing and multi-dimensional quantitative analysis. Metadata information (including patient ID, treatment time, acquisition module number, etc.) is extracted from the standardized data frames. This information is the core basis for judging data correlation. The extracted metadata information is encoded to convert different types of metadata, such as text and time, into a unified encoding format, improving the efficiency and accuracy of correlation analysis. New encoded information (i.e., encoded data that does not exist completely in the historical encoding, corresponding to newly added treatment scenarios or patient information) is identified in the metadata encoding information. Its proportion in the overall metadata encoding information is calculated to quantify the impact of new information on correlation analysis. The correlation degree between different data frames is calculated based on the metadata encoding information to measure the matching degree of data frames. Then, the correlation coefficient of the data frames is obtained by multiplying the proportion of new encoded data with the correlation degree. Taking into account the basic correlation degree and the impact of new information, the correlation strength of the data frames is accurately characterized. Correlation matching is performed according to the correlation coefficient of the data frames. Data frames with correlation coefficients higher than the threshold are classified into one category to form correlation analysis data.

[0152] This invention addresses the technical problems of traditional data association analysis, such as reliance on a single dimension, insufficient adaptation to new information leading to inaccurate associations, missing associated data, and vague association judgment criteria. It achieves multi-dimensional and accurate quantification of data frame association relationships, improving the accuracy and comprehensiveness of association analysis. The introduction of a new coding ratio allows association analysis to adapt to new treatment scenarios and patient information, enhancing the flexibility and scalability of the analysis. Clearly defined association coefficient thresholds make association matching more objective, avoiding errors caused by subjective judgment.

[0153] In one embodiment of the present invention, the step of analyzing the processing speed of the combined packaged data to obtain combined processing speed analysis data includes:

[0154] Obtain the total data volume, data field complexity, and data transmission priority of the combined and packaged data;

[0155] Obtain the ratio of total data volume to comprehensive data volume to obtain the data volume coefficient;

[0156] Obtain the ratio of data field complexity to overall complexity to get the complexity coefficient;

[0157] Obtain the ratio of data transmission priority to overall priority to obtain the priority coefficient;

[0158] The transmission difficulty coefficient is obtained by multiplying the data volume coefficient, complexity coefficient, and priority coefficient.

[0159] Obtain the transmission difficulty coefficient corresponding to the preset transmission speed data, and obtain the speed difficulty coefficient;

[0160] Calculate the product of the transmission difficulty coefficient and the speed difficulty coefficient to obtain combined processing speed analysis data.

[0161] Assuming that after a single treatment for freckle removal is completed by an intense pulsed light therapy device, the packaged data includes core parameters such as treatment energy and pulse width, as well as physiological data such as the patient's heart rate.

[0162] The total data size is 8KB, the data field complexity is 6 (preset from 1 to 10, the larger the value, the more complex), and the data transmission priority is 3 (preset from 1 to 5, with level 1 being the lowest and level 5 being the highest).

[0163] The total data size is 10KB, the total complexity is 8, and the total priority is 5.

[0164] Data volume coefficient = 8KB / 10KB = 0.8, complexity coefficient = 6 / 8 = 0.75, priority coefficient = 3 / 5 = 0.6; 4. Transmission difficulty coefficient = 0.8 × 0.75 × 0.6 = 0.36;

[0165] When the preset transmission speed is 100KB / s, the corresponding speed difficulty coefficient is 1.2 (representing the transmission difficulty threshold coefficient that this speed can bear).

[0166] The combined processing speed analysis data is 0.36 × 1.2 = 0.43, which indicates that the current processing speed requirement for combined packaged data is well-suited to the 100KB / s transmission capacity.

[0167] The working principle and technical effects of the above technical solution are as follows: The core of this process lies in accurately analyzing the processing speed requirements of combined and packaged data through multi-dimensional coefficient quantification. Three core parameters of the combined and packaged data are extracted: total data volume (reflecting the volume pressure of data processing), data field complexity (reflecting the difficulty of data parsing), and data transmission priority (reflecting the urgency of data synchronization). Coefficients corresponding to each core parameter are calculated: data volume coefficient (the ratio of total data volume to the preset comprehensive data volume, quantifying the relative pressure of data volume), complexity coefficient (the ratio of data field complexity to the preset comprehensive complexity, quantifying the relative level of parsing difficulty), and priority coefficient (the ratio of data transmission priority to the preset comprehensive priority, quantifying the relative urgency of synchronization). The three coefficients are multiplied to obtain the transmission difficulty coefficient, comprehensively quantifying the overall transmission difficulty of the combined and packaged data. The speed difficulty coefficient corresponding to the preset transmission speed data is matched (i.e., the transmission difficulty threshold that can be carried at different transmission speeds). The transmission difficulty coefficient is multiplied by the speed difficulty coefficient to obtain the combined processing speed analysis data, accurately representing the degree of compatibility between the processing speed required for the current combined and packaged data and the existing transmission capacity.

[0168] This invention solves the technical problem of traditional data processing speed analysis being limited to a single dimension and failing to comprehensively consider data characteristics and transmission environment, leading to mismatches between synchronization strategies and processing speeds, resulting in data congestion or resource waste. It achieves multi-dimensional and precise quantification of combined data processing speed requirements, improving the comprehensiveness and accuracy of speed analysis; through adaptation analysis with preset transmission speeds, it provides precise speed criteria for the generation of synchronization queues, enabling synchronization strategies to dynamically adapt to data processing difficulty and transmission capacity; it avoids synchronization delays or resource waste caused by processing speed mismatches, improving the efficiency and stability of data synchronization.

[0169] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An IoT-based intense pulsed light therapy device, characterized in that, The therapeutic device includes: The treatment data acquisition module is used to acquire the treatment parameters of the intense pulsed light therapy device through the cloud platform. The main control module performs treatment operations based on the treatment parameters and obtains treatment parameter feedback data. The data acquisition and processing module is used to acquire data from the therapeutic instrument based on the treatment parameters and the feedback data of the treatment parameters, obtain the data acquired by the therapeutic instrument, and perform data standard processing on the data acquired by the therapeutic instrument to obtain standardized data frames. The correlation speed analysis module is used to perform encoding correlation analysis on standardized data frames to obtain correlation analysis data. Based on the correlation analysis data, processing speed analysis and synchronization are performed to obtain the synchronization data of the therapeutic device.

2. A data synchronization method for an IoT-based intense pulsed light therapy device, characterized in that, The method includes: S1. The treatment parameters of the intense pulsed light therapy device are obtained through the cloud platform. The main control module performs treatment operations based on the treatment parameters and obtains treatment parameter feedback data. S2. Collect data from the therapeutic instrument based on the treatment parameters and the feedback data of the treatment parameters, obtain the data collected by the therapeutic instrument, and perform data standardization processing on the data collected by the therapeutic instrument to obtain standardized data frames. S3. Perform encoding correlation analysis on the standardized data frames to obtain correlation analysis data. Based on the correlation analysis data, perform processing speed analysis and synchronization to obtain the therapeutic instrument synchronization data.

3. The data synchronization method for the IoT-based intense pulsed light therapy device according to claim 2, characterized in that, S1 includes: Targeted treatment data is obtained through a cloud platform, and preset treatment parameters are matched based on the target treatment data. Set the corresponding transmission timing for the preset treatment parameters to obtain dynamic transmission timing information; The preset treatment parameters are sent to the main control module of the intense pulsed light therapy device based on the dynamic transmission timing information. The main control module controls the treatment operation by setting preset treatment parameters and obtains treatment operation control data. Treatment parameter feedback data is obtained based on treatment operation control data.

4. The data synchronization method for the IoT-based intense pulsed light therapy device according to claim 3, characterized in that, The step of setting a corresponding transmission timing for preset treatment parameters and obtaining transmission timing information includes: Based on the preset initial transmission timing information, the preset treatment parameters are divided into timing parameters to obtain the timing transmission parameters; Obtain preset security time domain range information, and determine the validity of timing transmission parameters based on the preset security time domain range information to obtain parameter validity determination information; The transmission timing information is adjusted based on the parameter validity determination information to obtain the adjusted timing information; The initial transmission timing information is updated based on the adjustment timing information to obtain dynamic transmission timing information.

5. The data synchronization method for the IoT-based intense pulsed light therapy device according to claim 2, characterized in that, S2 includes: The treatment parameters are correlated with the treatment parameter feedback data to obtain the corresponding correlated data. Perform parameter status analysis on the associated data to obtain parameter status analysis data; Based on the parameter state analysis data, the parameter state data is divided into multiple categories to obtain parameter state data for multiple categories. Collect category data based on the category parameter status data to obtain category collection data; The collected data is divided and processed into data processing regions to obtain regionally processed data. Standardized data frames are generated based on the data processed from the region.

6. The data synchronization method for the IoT-based intense pulsed light therapy device according to claim 5, characterized in that, The step of performing parameter state analysis on the associated corresponding data to obtain parameter state analysis data includes: Extract key parameter fields from the associated data; Calculate the deviation between the actual value of each key parameter field and the preset standard value of the treatment parameter to obtain the parameter deviation coefficient; Obtain the time information of the parameter deviation coefficients to obtain the time series parameter deviation information; The timing parameter deviation information is compared with a preset timing parameter deviation threshold to obtain parameter deviation comparison information; Based on the parameter deviation comparison information, the associated corresponding data are classified into levels to obtain parameter level classification data; The parameter level classification data is the parameter state analysis data.

7. The data synchronization method for an IoT-based intense pulsed light therapy device according to claim 5, characterized in that, The process of dividing and processing the collected data into regions to obtain processed region data includes: Based on the type-collected data and its type parameter status data, the processing area is divided to obtain the processing area division data; The processing area division data includes a real-time priority processing area, a buffer processing area, and a regular processing area. Based on the real-time priority processing area, the collected data of different types are processed in real time to obtain real-time regional processed data. Based on the buffer processing area, the collected data of different types is subjected to buffer data feature processing to obtain buffer area processed data; Based on the standard processing area, the collected data of each type undergoes standard data feature processing to obtain standard area processed data; The real-time region processing data, buffer region processing data, and regular region processing data are timestamped to obtain the region processing data.

8. The data synchronization method for an IoT-based intense pulsed light therapy device according to claim 2, characterized in that, S3 includes: Perform data correlation analysis on standardized data frames to obtain correlation analysis data; Based on the correlation analysis data, the standardized data frames are combined and packaged to obtain combined packaged data; Analyze the processing speed of the combined packaged data to obtain combined processing speed analysis data; A data synchronization queue for combined packaged data is generated based on the combined processing speed analysis data. The data is combined and packaged according to the data synchronization queue to output the synchronization operation, and the synchronization data of the treatment device is obtained.

9. The data synchronization method for an IoT-based intense pulsed light therapy device according to claim 8, characterized in that, The step of performing data correlation analysis on standardized data frames to obtain correlation analysis data includes: Metadata is extracted from standardized data frames to obtain metadata extraction information; The extracted metadata information is processed by information encoding to obtain metadata encoded information; Retrieve new encoded information from metadata encoding information; Obtain the proportion of new encoding information in the metadata encoding information to get the new encoding proportion data; The correlation degree of the standardized data frame is calculated based on the metadata encoding information to obtain the correlation degree calculation information of the data frame; Calculate the product of the new encoding proportion data and the data frame correlation calculation information to obtain the data frame correlation coefficient; Based on the correlation coefficient of the data frames, the standardized data frames are matched for correlation to obtain correlation analysis data.

10. The data synchronization method for the Internet of Things-based intense pulsed light therapy device according to claim 8, characterized in that, The process of analyzing the combined packaged data to obtain combined processing speed analysis data includes: Obtain the total data volume, data field complexity, and data transmission priority of the combined and packaged data; Obtain the ratio of total data volume to comprehensive data volume to obtain the data volume coefficient; Obtain the ratio of data field complexity to overall complexity to get the complexity coefficient; Obtain the ratio of data transmission priority to overall priority to obtain the priority coefficient; The transmission difficulty coefficient is obtained by multiplying the data volume coefficient, complexity coefficient, and priority coefficient. Obtain the transmission difficulty coefficient corresponding to the preset transmission speed data, and obtain the speed difficulty coefficient; Calculate the product of the transmission difficulty coefficient and the speed difficulty coefficient to obtain combined processing speed analysis data.

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