Ultra-pulse thulium fiber laser therapy apparatus with radio frequency identification function

Abstract

The invention relates to the technical field of medical instruments, and discloses an ultra-pulse thulium fiber laser therapy apparatus with a radio frequency identification function, which comprises a display device, a main body and a laser fiber, a laser, an electrical device, a coupler, a control mainboard, a thulium optical fiber and a radio frequency identification device are arranged in the main body; the radio frequency identification device comprises an image acquisition unit, an electronic tag reading unit, a pre-output power generation unit and a record generation unit; the image acquisition unit acquires an image by using an industrial camera and an annular LED light source, and identifies the specification of the laser fiber through adaptive threshold binarization and semantic segmentation; the electronic tag reading unit retrieves an electronic tag based on an NFC protocol; the pre-output power generation unit analyzes the label data and drives laser power adjustment; the record generation unit automatically generates operation data. The specification of the laser fiber is comprehensively judged, the identification accuracy is improved, and the output power of the laser fiber is stable.

Description

Technical Field

[0001] This invention relates to the field of medical device technology, and more specifically, to an ultrapulse thulium fiber laser therapy machine with radio frequency identification (RFID) function. Background Technology

[0002] Thulium fiber laser therapy machines are widely used in medical aesthetics and surgical treatments. Currently, the identification of laser fiber specifications and the control of output power in existing ultrapulse thulium fiber laser therapy machines typically rely on manual operation. Operators must determine the fiber model by observing paper labels or external features on the fiber and set the laser output power based on the manually read label information. This method is not only inefficient and susceptible to human error, but also prone to identification errors or power setting deviations, potentially leading to unstable treatment results or equipment damage. Furthermore, the data generated during operation is mostly stored manually or in simple log form, lacking structured and traceable management, making it difficult to meet the requirements of equipment safety and maintenance quality.

[0003] Therefore, it is necessary to design an ultrapulse thulium fiber laser therapy machine with radio frequency identification (RFID) function to solve the problems existing in the current technology. Summary of the Invention

[0004] In view of this, the present invention proposes an ultrapulse thulium fiber laser therapy machine with radio frequency identification function, which aims to solve the problems of low efficiency in laser fiber specification identification and output power control.

[0005] This invention proposes an ultrapulse thulium fiber laser therapy machine with radio frequency identification (RFID) function, comprising: Display device, main body, and laser fiber; The main body internally includes a laser, electrical components, a coupler, a control motherboard, a thulium optical fiber, and a radio frequency identification (RFID) device. The laser and the coupler are connected via the thulium optical fiber. The electrical components are electrically connected to both the laser and the coupler. The control motherboard is electrically connected to both the laser and the electrical components. The laser optical fiber is connected to the coupler. The RFID device is electrically connected to both the control motherboard and the electrical components. The RFID device includes an image acquisition unit, an electronic tag reading unit, a pre-output power generation unit, and a recording generation unit. The image acquisition unit uses an industrial camera equipped with a ring LED light source to acquire image data of the laser fiber and transmits the image data in real time via a standard industrial vision protocol. It performs image preprocessing using adaptive threshold binarization and semantic segmentation techniques to separate the tag area from the background area. Based on the image data, it performs paper tag recognition and image comparison recognition, and determines the laser fiber specifications through a dynamic weight allocation mechanism. The electronic tag reading unit retrieves electronic tags from a central information database based on keywords, according to the laser fiber specifications determined by the image acquisition unit. The pre-output power generation unit uses NFC protocol data reading and verification technology combined with a TLV structure parsing algorithm to parse the binary stored data in the electronic tag, extracts pre-output power parameters through a parameter extraction and mapping mechanism, and drives the laser drive circuit to adjust the actual output power. The recording generation unit automatically captures the structured data of this operation.

[0006] Furthermore, when the image acquisition unit performs paper label recognition and image comparison recognition based on the image data, it includes: When the image acquisition unit performs paper label recognition, it uses an optical character recognition engine combined with a convolutional recurrent neural network model to process the text area. When performing image comparison and recognition, the image acquisition unit uses a twin neural network architecture to extract image features from the image data and performs similarity matching with standard image data in the information database.

[0007] Furthermore, when the image acquisition unit comprehensively evaluates and determines the laser fiber specifications through a dynamic weight allocation mechanism, it includes: The image acquisition unit comprehensively evaluates the recognition confidence level, image comparison similarity, and uses a dynamic weight allocation mechanism. The confidence level is generated by the internal evaluation of the recognition model, and the similarity is determined by the feature matching process. The results of the two are fused according to a preset ratio to determine the final specifications of the laser fiber.

[0008] Furthermore, when the electronic tag reading unit retrieves electronic tags from the central information database based on keywords, it includes: The electronic tag reading unit initiates a query request to the central information database based on the laser fiber specifications determined by the image acquisition unit. The central information database adopts a distributed database architecture, supports fast keyword-based retrieval, and integrates a fuzzy matching algorithm to handle specification variations. It automatically verifies the integrity of tag data, and if an anomaly is detected, it switches to a backup identification channel to ensure accurate acquisition of electronic tag information.

[0009] Furthermore, the central information database adopts a distributed database architecture, supporting rapid keyword-based retrieval, including: The central database preprocesses the stored electronic tag data, decomposing the specifications of each electronic tag into independent keyword units. An inverted index technique is used to construct a keyword index structure, with the keyword as the root node, extending downwards to a list of all electronic tag record identifiers containing that keyword, with each identifier arranged in numerical order. The query dispatcher parses the keyword sequence in the query request and standardizes it according to the semantic features of the keywords. The query dispatcher then splits the standardized keywords into independent query units and distributes each independent query unit to multiple database nodes according to the distributed database architecture. It traverses the index tree, locates the list of record identifiers corresponding to the keywords, and uses a binary search algorithm to quickly filter out the matching identifier set.

[0010] Furthermore, when the electronic tag reading unit detects an anomaly and switches to the backup identification channel, it includes: The electronic tag reading unit acquires the original tag data stream and sends it to the verification calculation module. The verification calculation module divides the original tag data stream into continuous data segments. Each data segment is divided by modulo-2 using a generator polynomial to generate a corresponding check code sequence. All check code sequences are combined into a unified original data checksum value. The verification comparison module receives the real-time data checksum value and synchronously retrieves the original data checksum value of the corresponding electronic tag record from the central information database for comparison. When all bits are completely consistent, the data is determined to be complete and error-free, the verification result is marked as passed, and the electronic tag information is confirmed to be valid. When any bit mismatch is detected, the data integrity is determined to be abnormal, and the verification result is marked as failed.

[0011] Furthermore, when the pre-output power generation unit extracts the pre-output power parameters through the parameter extraction and mapping mechanism, it includes: The pre-output power generation unit converts the parameter fields of the pre-output power into the upper limit of pre-output power, the lower limit of pre-output power, and the recommended working range through a parameter extraction and mapping mechanism.

[0012] Furthermore, when the pre-output power generation unit drives the laser driving circuit to adjust the actual output power, it includes: The actual output power is monitored in real time using photoelectric sensors and compared with the upper limit of the pre-output power, the lower limit of the pre-output power, and the recommended operating range; and the control signal is dynamically corrected based on the monitoring results.

[0013] Furthermore, when the pre-output power generation unit dynamically corrects the control signal based on the monitoring results, it includes: The actual output power is compared with the upper limit of the pre-output power, the lower limit of the pre-output power, and the recommended operating range to obtain the deviation; a correction amount is generated based on the deviation; digital filtering, noise reduction, and anti-integral saturation processing are applied to the correction amount, and the output change rate is limited; the processed correction amount is mapped to the drive signal of the laser drive circuit through pulse width modulation to complete the power adjustment.

[0014] Furthermore, when the record generation unit automatically captures the structured data of this operation, it includes: The storage process of the record generation unit adopts a hierarchical processing mechanism, with hot data being written to the memory buffer in real time and cold data being archived to the distributed file area periodically.

[0015] Compared with existing technologies, the advantages of this invention are as follows: It utilizes a high-resolution industrial camera combined with OCR, convolutional recurrent neural networks, and Siamese neural networks for paper label recognition and image comparison. A dynamic weight allocation mechanism comprehensively determines the laser fiber specifications, avoiding human error and improving recognition accuracy. A keyword-based distributed database retrieval and verification mechanism, supplemented by backup recognition channels and data integrity verification technology, ensures accurate and reliable acquisition of electronic tag information. The pre-output power generation unit maps electronic tag parameters to a pre-output power upper limit, lower limit, and recommended working range. A microcontroller, combined with photoelectric sensor feedback, performs closed-loop dynamic adjustment, employing PID control, adaptive gain, or filtering and noise reduction algorithms for dynamic correction. This achieves stable laser fiber output power and rapid response, ensuring treatment safety and accuracy. A hierarchical storage mechanism captures and stores structured data in real time, including timestamps, fiber specifications, target power values, actual output power, and operating status, providing reliable data for subsequent quality control, maintenance, and data analysis, achieving operational traceability and digital equipment management. Attached Figure Description

[0016] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a schematic diagram of the internal structure of an ultrapulse thulium fiber laser therapy machine with radio frequency identification function provided in an embodiment of the present invention; Figure 2 A schematic diagram of an ultrapulse thulium fiber laser therapy machine with radio frequency identification function provided in an embodiment of the present invention; Figure 3 The diagram shows the operational structure of the radio frequency identification (RFI) device for an ultrapulse thulium fiber laser therapy machine with RFI functionality, as provided in an embodiment of the present invention.

[0017] The components include: 1. Display device; 2. Main body; 21. Laser; 22. Electrical components; 23. Coupler; 24. Control motherboard; 25. Thulium fiber; 26. Radio frequency identification device; 3. Laser fiber. Detailed Implementation

[0018] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0019] Current ultrapulse thulium fiber laser therapy machines rely primarily on manual operation for laser fiber specification identification and output power control. Operators must determine specifications by observing paper labels or visual features on the fiber, a process prone to misjudgment due to factors such as lighting conditions, operator experience, and visual judgment. Since different fiber specifications correspond to different permissible output powers, operators must manually set the laser output power. Fiber usage, power settings, and operation records are mostly kept manually or in simple log format, lacking structured management.

[0020] For example, when using a traditional thulium fiber laser treatment machine at a cosmetic surgery hospital, medical staff need to manually confirm the fiber specifications and set the output power. One operator, while changing fibers, mistakenly used a low-power fiber instead of a high-power fiber due to their similar appearance, setting the laser output power incorrectly. As a result, during treatment, the actual output power exceeded the safe range, causing minor burns to the patient's skin, and some components of the laser overheated due to overload. This incident exposed the vulnerability of manually identifying fiber specifications and setting power, highlighting safety hazards and the lack of traceable data records for analyzing the cause of the accident.

[0021] In some embodiments of this application, see Figure 1-3 As shown, a superpulse thulium fiber laser therapy machine with radio frequency identification (RFID) function is proposed, comprising: Display device 1, main body 2, and laser fiber 3; display device 1 is positioned above main body 2; The main body 2 contains a laser 21, electrical components 22, a coupler 23, a control board 24, a thulium fiber 25, and a radio frequency identification (RFID) device 26. The laser 21 and the coupler 23 are connected via the thulium fiber 25. The electrical components 22 are electrically connected to both the laser 21 and the coupler 23. The control board 24 is electrically connected to both the laser 21 and the electrical components 22, and controls the operation and parameter adjustment of the laser 21. One end of the laser fiber 3 is connected to the coupler 23. The RFID device 26 is electrically connected to both the control board 24 and the electrical components 22. The RFID device 26 is used to identify the pre-output power of the laser fiber 3 and transmit it to the control board 24 to adjust the actual output power of the laser fiber 3. The RFID device 26 includes an image acquisition unit, an electronic tag reading unit, a pre-output power generation unit, and a recording generation unit. The image acquisition unit uses an industrial camera equipped with a ring LED light source to acquire image data from the laser fiber and transmits the image data in real time via a standard industrial vision protocol. It performs image preprocessing using adaptive threshold binarization and semantic segmentation techniques to separate the tag area from the background area. Based on the image data, it performs paper tag recognition and image comparison recognition, and comprehensively evaluates and determines the laser fiber specifications through a dynamic weight allocation mechanism. The electronic tag reading unit retrieves electronic tags from a central information database based on keywords according to the laser fiber specifications determined by the image acquisition unit. The pre-output power generation unit uses NFC protocol data reading and verification technology combined with a TLV structure parsing algorithm to parse the binary stored data in the electronic tag, extracts the pre-output power parameters through a parameter extraction and mapping mechanism, and drives the laser drive circuit to adjust the actual output power. The recording generation unit automatically captures the structured data of this operation.

[0022] Specifically, the display device 1 is positioned above the main body 2, responsible for intuitively presenting operation information, power parameters, and status information to the operator, facilitating real-time monitoring and adjustment. The main body 2 integrates core components such as a laser 21, electrical components 22, a coupler 23, a control motherboard 24, a thulium fiber 25, and a radio frequency identification (RFID) device 26. These modules work collaboratively to achieve precise laser control and intelligent identification. The laser 21 is connected to the coupler 23 via the thulium fiber 25, transmitting the laser signal to one end of the laser fiber 3. The electrical components not only handle the electrical control of the laser 21 and coupler 23 but also provide a data communication interface, enabling the control motherboard 24 to acquire the device status in real time and make precise adjustments. As the core control unit, the control motherboard 24 interacts with the laser 21 and electrical components 22 via electrical connections, automatically adjusting the laser output power, running time, and mode to ensure the safe and efficient operation of the laser fiber. The RFID device 26 plays a crucial role in identifying the specifications and pre-output power of the laser fiber and feeding the data back to the control motherboard 24 for dynamic adjustment. Internally, it includes an image acquisition unit, an electronic tag reading unit, a pre-output power generation unit, and a recording generation unit. When acquiring image data from the laser fiber 3, the image acquisition unit uses a high-resolution industrial camera to obtain panoramic and detailed images of the fiber end and its tag. This industrial camera is equipped with a ring LED light source to create a uniform illumination effect and effectively eliminate ambient light interference, thereby ensuring image clarity and stability. In the data transmission stage, the image acquisition unit transmits the acquired images to the back-end processing module in real time via a standard industrial vision protocol, ensuring high bandwidth and low latency data transmission. Subsequently, the images are preprocessed. First, an adaptive threshold binarization method is used to perform grayscale analysis on the original image, which can dynamically adjust the threshold according to different lighting conditions, effectively distinguishing tag text, identifiers, and background areas. Then, semantic segmentation technology is used to perform pixel-level classification of key areas in the image, completely separating the tag area from the background area, and contour optimization is performed on the edge parts to reduce noise and false edge interference. The electronic tag reading unit retrieves relevant electronic tags from the central information database based on the image recognition results, ensuring data accuracy. The pre-output power generation unit establishes a communication link with the electronic tag via the NFC protocol and reads the binary data stream stored in the tag. To ensure that the data is not tampered with or lost during transmission, integrity verification technology is used to verify the read data, such as verifying the correctness of the data through CRC checksum or Message Authentication Code (MAC). The TLV (Tag-Length-Value) structure parsing algorithm is used to parse the binary data, splitting and identifying different tag fields according to type (Tag), length (Length), and value (Value).For example, the label may contain multiple fields such as upper power limit, lower power limit, recommended operating range, and production batch. The TLV parsing algorithm can accurately distinguish and extract the numerical information of each field. After parsing, the pre-output power generation unit converts the extracted field information into specific pre-output power parameters, including the upper pre-output power limit, lower pre-output power limit, and recommended operating range, through a parameter extraction and mapping mechanism. For example, for an optical fiber labeled "30W-A type," the parsed upper limit may be 32W, the lower limit 28W, and the recommended operating range 29W~31W. These parameters are transmitted to the control board for dynamically adjusting the actual output power of the laser fiber. The record generation unit is responsible for automatically capturing the structured data generated during the operation, including timestamps, fiber specifications, target power values, actual output power, and operating status, achieving complete data tracking and operation recording, providing a reliable basis for subsequent analysis, maintenance, and optimization.

[0023] During operation, after the laser fiber 3 is connected to the coupler 23, the RFID device 26 first activates the image acquisition unit to acquire an image of the laser fiber. It then uses paper tag identification and image comparison to determine the laser fiber's specifications using a dynamic weight allocation mechanism. Subsequently, the electronic tag reading unit retrieves the corresponding electronic tag from the central database based on the identified specifications and parses its parameter information. The pre-output power generation unit parses the electronic tag content, extracts the pre-output power value corresponding to the laser fiber, and transmits this value to the control motherboard 24. Upon receiving the pre-output power information, the control motherboard 24, in conjunction with the electrical components 22, regulates the laser 21 and coupler 23, adjusting the actual output power of the laser fiber 3 in real time to ensure it matches the target parameters. Simultaneously, the recording unit automatically captures all data from the entire operation, including timestamps, fiber specifications, target power, actual power, and operational status, achieving full traceability and data retention.

[0024] As a preferred embodiment, the solution of this application is specifically implemented as follows: In practical applications, doctors need to select specific specifications of thulium fiber lasers based on the surgical plan. The doctor selected a 30W rated power laser fiber 3. After one end was inserted into the coupler 23 of the device, it automatically entered the identification and adjustment process. The image acquisition unit photographed the external label of the laser fiber, combining paper label recognition and image comparison recognition technologies, and comprehensively analyzed the data through a dynamic weight allocation mechanism to ultimately determine that the fiber belonged to the 30W specification model. The electronic tag reading unit, based on the identified specification information, performed a keyword search in the central information database, found the electronic tag corresponding to the fiber, and successfully parsed the stored parameter information, including the fiber's preset safe output power value of 30W. The pre-output power generation unit transmitted the parsed power value to the control motherboard 24. The control motherboard, based on this parameter, adjusted the laser 21 and electrical components 22, adjusting the laser output in real time to ensure that the actual output power was stably maintained around 30W (e.g., fluctuating between 29.8W and 30.2W), avoiding excessive or insufficient power due to improper manual operation or equipment deviation. The recording unit monitors and records the entire process, automatically capturing structured data, including the surgery start time stamp, laser fiber specifications (30W), the set target power value (30W), the real-time detected actual output power value, and the operation status (such as running, paused, stopped, etc.). All data is synchronously stored in the database, providing complete traceability and safety assurance for the surgical process.

[0025] Through the above-mentioned solution, this application achieves automated determination of laser fiber specifications and accurate identification of pre-output power by integrating a radio frequency identification device within the main body and combining image acquisition and electronic tag dual identification mechanisms. This avoids the errors and risks associated with traditional methods that rely on manual settings or single tag identification. The control motherboard can adjust the laser output in real time based on the identification results, ensuring that the actual output power of the laser fiber is consistent with the target power, thereby guaranteeing the stability and safety of the treatment or processing process. It can automatically generate and store structured data of the operation process, including timestamps, fiber specifications, target values, and actual values, which facilitates subsequent traceability, quality analysis, and operation and maintenance management.

[0026] This application further proposes that when the image acquisition unit performs paper label recognition and image comparison recognition based on image data, it includes: When the image acquisition unit performs paper label recognition, it uses an optical character recognition engine combined with a convolutional recurrent neural network model to process the text area. When the image acquisition unit performs image comparison and recognition, it uses a twin neural network architecture to extract image features from the image data and performs similarity matching with standard image data in the database.

[0027] Specifically, when the image acquisition unit identifies the specifications of the laser fiber 3, it uses an Optical Character Recognition (OCR) engine to identify the paper label in the image. The OCR engine, combined with a Convolutional Recurrent Neural Network (CRNN) model, performs deep feature extraction and serialization processing on the text region, accurately recognizing the character information on the label. Even with slight tilting, stains, or uneven lighting on the label, it maintains high recognition accuracy. The image acquisition unit also compares and identifies the overall image of the fiber. Using a Siamese Network architecture, it extracts image features and performs similarity matching between the acquired fiber image features and standard image data in the database, thereby obtaining the matching degree between the fiber's appearance and the standard template. This process not only identifies the fiber type but also detects potential fiber damage or anomalies through feature comparison.

[0028] As a preferred embodiment, the solution of this application is specifically implemented as follows: In practical applications, during the preparation phase, staff need to install a laser fiber 3 for prostate enlargement surgery. The image acquisition unit first photographs the fiber tip and its label, then uses an OCR engine combined with a convolutional recurrent neural network to recognize the text information on the paper label, such as reading the power specification "30W". Using a Siamese neural network to extract image features, the captured image is compared with standard fiber optic images stored in the database to confirm the label's integrity and the fiber's appearance characteristics.

[0029] Through the above scheme, this application combines an optical character recognition engine and a convolutional recurrent neural network model to accurately identify character information on paper labels. Even under conditions of uneven lighting, slight label damage, or tilting, it can maintain a high recognition accuracy. By using a twin neural network for image comparison and recognition, it can extract the overall image features of the optical fiber and accurately match them with standard images in the information database to identify the appearance features and potential anomalies of the optical fiber.

[0030] This application further proposes that when the image acquisition unit comprehensively evaluates and determines the laser fiber specifications through a dynamic weight allocation mechanism, it includes: The image acquisition unit comprehensively evaluates the recognition confidence level, image comparison similarity, and uses a dynamic weight allocation mechanism. The confidence level is generated by the internal evaluation of the recognition model, and the similarity is determined by the feature matching process. The results of the two are fused according to a preset ratio to determine the final laser fiber specification.

[0031] Specifically, in the comprehensive evaluation phase, decisions are made based on two parts of information: firstly, the confidence score generated internally by the OCR recognition model, reflecting the reliability of the paper label recognition results; and secondly, the similarity score obtained from image comparison, reflecting the degree of matching between the fiber optic appearance and the standard image. Through a dynamic weight allocation mechanism, the confidence score and similarity score are fused according to a preset ratio. This allows for automatic adjustment of various weights based on actual conditions. For example, the confidence score weight is increased when the label clarity is high, while the similarity score weight is increased when the label is blurry or the fiber optic appearance features are obvious, thereby generating a comprehensive score. The specifications of the laser fiber are determined based on the comprehensive score, ensuring high-precision and high-reliability automatic recognition even in complex environments or when the label is damaged. This provides a solid data foundation for subsequent electronic tag reading, pre-output power generation, and control board adjustment.

[0032] As a preferred embodiment, the solution of this application is specifically implemented as follows: In practical applications, based on the confidence level generated within the recognition model and the similarity obtained from image comparison, a dynamic fusion evaluation is performed according to preset weights. Ultimately, the specification of the laser fiber is determined to be 30W, and the result is transmitted to the control motherboard 24 to adjust the laser output power. Through this multimodal recognition and dynamic weight-based comprehensive evaluation method, the fiber specification can be accurately identified even if the label is worn or the fiber appearance is slightly different.

[0033] Through the above scheme, this application integrates recognition confidence and image comparison similarity according to a preset ratio through a dynamic weight allocation mechanism, which can adaptively adjust the weight according to the actual situation, thereby improving the reliability and robustness of the comprehensive judgment.

[0034] This application further proposes that when the electronic tag reading unit retrieves electronic tags from a central information database based on keywords, it includes: The electronic tag reading unit initiates a query request to the central information database based on the laser fiber specifications determined by the image acquisition unit. The central information database adopts a distributed database architecture, supports fast keyword-based retrieval, and integrates a fuzzy matching algorithm to handle specification variations. It automatically verifies the integrity of tag data, and if an anomaly is detected, it switches to a backup recognition channel to ensure accurate acquisition of electronic tag information.

[0035] Specifically, once the image acquisition unit determines the specifications of the laser fiber 3, the electronic tag reading unit initiates a query request to the central information database using that specification as a keyword. The central information database employs a distributed database architecture, enabling parallel processing of query requests across multiple nodes, thus achieving rapid response to large amounts of tag data. During the query process, not only is the corresponding electronic tag found using an exact match method, but a fuzzy matching algorithm is also integrated to handle subtle variations or naming differences in fiber specifications, ensuring that retrieval does not fail due to inconsistencies in specification naming. The electronic tag reading unit performs integrity verification on the returned tag data, including verifying the data format, checking key fields, and verifying whether the tag content is consistent with the fiber specifications. If data anomalies or missing data are detected during the verification process, the system automatically switches to a backup identification channel, re-acquiring information from backup tags or a secondary database, thereby ensuring that the final read electronic tag data is accurate, complete, and usable for subsequent pre-output power generation and control motherboard adjustment.

[0036] As a preferred embodiment, the solution of this application is specifically implemented as follows: In practical applications, the image acquisition unit first identifies the fiber optic specification as "30W". The electronic tag reading unit uses "30W" as a keyword to initiate a query request to the central information database. The central information database adopts a distributed database architecture, which can process requests in parallel across multiple server nodes and quickly return the corresponding electronic tag data. Since different models or production batches of the same power fiber optic cable may exist, the built-in fuzzy matching algorithm can identify specification variations such as "30W-A type" or "30W-B type", ensuring that query results are not missed due to differences in tag naming. After the data is returned, the electronic tag reading unit automatically verifies the tag integrity, including checking whether key fields such as power value, production date, and fiber number are complete and correct. If missing or abnormal tag data is detected, it will automatically switch to the backup identification channel and retrieve data from the backup database or secondary tag source, thereby ensuring the accuracy of the electronic tag information.

[0037] Through the above scheme, the electronic tag reading unit of this application can achieve rapid and accurate identification of laser fiber specifications, improving data reliability and response efficiency. The distributed database architecture supports high-concurrency queries, ensuring that the required tag information can still be quickly obtained in multi-fiber and multi-batch environments; the fuzzy matching algorithm can effectively handle specification variations or naming differences, avoiding identification failures caused by slight differences in fiber models; the automatic verification mechanism and backup identification channel further guarantee the integrity and accuracy of tag data, reducing the risks caused by human operation and equipment malfunctions.

[0038] This application further proposes that the central information database adopts a distributed database architecture, supporting rapid keyword-based retrieval, including: The central database preprocesses the stored electronic tag data, decomposing the specifications of each electronic tag into independent keyword units. An inverted index technique is used to construct a keyword index structure, with the keyword as the root node, extending downwards to a list of all electronic tag record identifiers containing that keyword, with each identifier arranged in numerical order. The query dispatcher parses the keyword sequence in the query request and standardizes it according to the semantic features of the keywords. The query dispatcher then splits the standardized keywords into independent query units and, based on the distributed database architecture, distributes each independent query unit to multiple database nodes. It traverses the index tree to locate the list of record identifiers corresponding to the keyword and uses a binary search algorithm to quickly filter out the matching identifier set.

[0039] Specifically, the central database first preprocesses the stored electronic tag data, breaking down the specifications of each tag into independent keyword units. For example, "30W-A type-batch 2025" is split into three keywords: "30W," "A type," and "batch 2025." An inverted index technology is used to construct a keyword index structure: each keyword serves as the root node, with a list of all electronic tag record identifiers containing that keyword attached below, arranged in numerical order for rapid location and retrieval. When an electronic tag reading unit initiates a query request, the query distributor first parses the keyword sequence in the request and standardizes the keywords based on semantic features. For example, different naming formats like "30W-A type" and "30WA type" are unified into a standard form. The query distributor then breaks down each standardized keyword into an independent query unit and, based on the distributed database architecture, distributes these query units to different database nodes for parallel processing. On each node, the inverted index tree is traversed to quickly locate the list of record identifiers corresponding to the keyword, and a binary search algorithm is used to filter out matches from the sorted identifier set. The matching results returned by each node are aggregated to form a complete set of electronic tag records that meet the query conditions, enabling efficient and accurate retrieval of electronic tag data.

[0040] As a preferred embodiment, the solution of this application is specifically implemented as follows: In practical applications, when quickly obtaining the electronic tag information corresponding to a laser fiber 3 with a rated power of "30W-Type A-Batch 2025", the central information database first breaks down the tag's specifications into three independent keyword units: "30W", "Type A", and "Batch 2025". A keyword index is built using inverted indexing technology, with each keyword serving as the root node, and a list of all electronic tag record identifiers containing that keyword, arranged in numerical order. For example, the keyword "30W" might contain record IDs 1001, 1003, 1007, etc., facilitating rapid location. When a query request reaches the query distributor, the keyword sequence is standardized, for example, "30W-Type A" is standardized to the standard form "30W-Type A", and "Batch 2025" is identified as the batch keyword. The query distributor then breaks down the three keywords into independent query units and distributes them to different database nodes for parallel queries based on the distributed database architecture. At each node, the inverted index tree is traversed to quickly locate the list of record identifiers corresponding to the keyword. Then, a binary search algorithm is used to filter out the matching record IDs in the sorted list. For example, the record ID 1003 is finally located, which corresponds to the electronic tag of the target optical fiber.

[0041] Through the above scheme, the central information database of this application can achieve efficient, accurate management and rapid retrieval of electronic tag data. Preprocessing decomposes each tag specification parameter into independent keyword units. Combined with the index structure built using inverted index technology, it can quickly locate target tags in massive amounts of data, shortening query time. The query dispatcher standardizes keywords and splits them into independent query units. Combined with parallel processing by distributed database nodes, it improves the response speed and throughput of retrieval. The binary search algorithm quickly filters matching items in a sorted list of identifiers, further enhancing retrieval efficiency and accuracy.

[0042] This application further proposes that when the electronic tag reading unit detects an anomaly and switches to the backup identification channel, it includes: The electronic tag reading unit acquires the raw tag data stream and sends it to the verification calculation module. The verification calculation module divides the raw tag data stream into continuous data segments. Each data segment is divided by modulo-2 using a generator polynomial to generate a corresponding check code sequence. All check code sequences are combined into a unified raw data checksum value. The verification comparison module receives the real-time data checksum value and synchronously retrieves the raw data checksum value of the corresponding electronic tag record from the central information database for comparison. When all bits are completely consistent, the data is determined to be complete and error-free, the verification result is marked as passed, and the electronic tag information is confirmed to be valid. When any bit mismatch is detected, the data integrity is determined to be abnormal, and the verification result is marked as failed.

[0043] Specifically, after acquiring the raw tag data stream, the electronic tag reading unit sends the data stream to the verification calculation module for integrity verification. The verification calculation module divides the raw data stream into several continuous data segments. Each data segment is divided modulo-2 using a predefined generator polynomial to generate a corresponding checksum sequence. All checksum sequences are then aggregated to form a unified raw data checksum value. The verification comparison module receives the currently acquired electronic tag data checksum value in real time and simultaneously retrieves the corresponding raw data checksum value from the central information database for comparison. If the comparison result shows that all bits are completely consistent, the tag data is determined to be complete and error-free, the verification result is marked as passed, and the electronic tag information is confirmed as valid and can be used for subsequent pre-output power generation and control motherboard adjustment. If any bit mismatch is found during the verification comparison process, it indicates that an anomaly may have occurred during data transmission or reading, such as signal interference, packet loss, or tag damage. In this case, the data integrity is determined to be abnormal, and the verification result is marked as failed. A backup identification channel will be automatically activated, switching the raw tag data stream to the backup channel for re-reading and verification to ensure that correct and complete electronic tag information can be obtained.

[0044] As a preferred embodiment, the solution of this application is specifically implemented as follows: In practical applications, when preparing to use a laser fiber 3 for surgery, the electronic tag reading unit acquires the raw tag data stream on the fiber and sends it to the verification calculation module for integrity verification. Assuming the fiber tag contains the specification parameter "30W-A type-batch 2025", the verification calculation module divides the entire data stream into several continuous data segments. Each data segment is processed by modulo-2 division using a generator polynomial to generate a corresponding checksum sequence, which is then aggregated to generate a complete raw data checksum value. The verification comparison module compares this real-time calculated checksum value with the raw data checksum value of the "30W-A type-batch 2025" electronic tag stored in the central information database. If the two are completely identical, the data is deemed complete and error-free, the verification result is marked as passed, the electronic tag information is confirmed as valid, and the control motherboard can safely adjust the output power of the fiber optic cable based on this information. If any data mismatch is found during the comparison process, such as due to damage to the fiber tag or signal interference during reading causing partial data loss, the data integrity is deemed abnormal, and the verification result is marked as failed. The electronic tag reading unit will automatically switch to the backup identification channel, re-collect tag data from the backup reading module, recalculate the verification value and compare it with the central information database to ensure that the final obtained electronic tag information is complete and accurate, thereby ensuring the safe use of laser fiber during surgery.

[0045] Through the above scheme, the electronic tag reading unit of this application can effectively ensure the integrity and accuracy of laser fiber optic electronic tag data. The verification calculation module performs segmented calculations on the raw data stream and generates a unified checksum value, which can accurately detect the integrity of each data bit; the verification comparison module compares the real-time data with the raw data in the central information database, which can quickly identify data anomalies, thereby avoiding power adjustment errors caused by reading errors or signal interference. When an anomaly is detected, it automatically switches to the backup identification channel, ensuring that the electronic tag information can be accurately acquired and verified as valid.

[0046] This application further proposes that when the pre-output power generation unit extracts pre-output power parameters through a parameter extraction and mapping mechanism, it includes: The pre-output power generation unit converts the parameter fields of the pre-output power into the upper limit of the pre-output power, the lower limit of the pre-output power, and the recommended operating range through a parameter extraction and mapping mechanism.

[0047] Specifically, the pre-output power generation unit uses a parameter extraction and mapping mechanism to convert the extracted field information into specific pre-output power parameters, including the upper limit, lower limit, and recommended operating range of the pre-output power. For example, for an optical fiber labeled "30W-A type," the upper limit might be 32W, the lower limit 28W, and the recommended operating range 29W~31W. Ultimately, these parameters are transmitted to the main control board for dynamically adjusting the actual output power of the laser fiber.

[0048] As a preferred embodiment, the solution of this application is specifically implemented as follows: In practical applications, when a laser fiber with a nominal power of "30W-A type" is inserted into the device, the pre-output power generation unit first establishes a communication link with the electronic tag on the fiber via the NFC protocol and reads the binary data stream stored in the tag. To ensure that the data has not been tampered with or lost, the read data is verified for integrity, for example, by using a CRC checksum. Only after confirming that the data is correct is it parsed. Using a TLV (Tag-Length-Value) structure parsing algorithm, the read binary data is split into different fields. For example, Tag=01 represents the upper power limit, Tag=02 represents the lower power limit, and Tag=03 represents the recommended operating range. After parsing the length and corresponding value of each field, the pre-output power generation unit converts these fields into specific parameters through a parameter extraction and mapping mechanism: the upper power limit is 32W, the lower power limit is 28W, and the recommended operating range is 29W~31W. These parsed parameters are transmitted to the control motherboard, which dynamically adjusts the actual output power of the laser fiber according to the pre-output power upper and lower limits and the recommended operating range, ensuring that the device operates within a safe and optimal power range.

[0049] Through the above scheme, this application enables the control motherboard to dynamically adjust the actual output power of the laser fiber based on precise power parameters, ensuring that the fiber operates within a safe and optimal power range.

[0050] This application further proposes that when the pre-output power generation unit drives the laser driving circuit to adjust the actual output power, it includes: The actual output power is monitored in real time using photoelectric sensors and compared with the upper limit of the pre-output power, the lower limit of the pre-output power, and the recommended operating range; and the control signal is dynamically corrected based on the monitoring results.

[0051] Specifically, during the operation of the laser fiber, photoelectric sensors are used to monitor the actual output power in real time and feed the monitoring data back to the microcontroller. The microcontroller compares the real-time power with the upper and lower limits of the pre-output power and the recommended operating range. If the power is detected to deviate from the recommended range or approach the upper / lower limit boundary, the microcontroller will dynamically correct the control signal, such as adjusting the drive current or output regulation parameters, so that the actual output power is stabilized again within a safe and optimal operating range.

[0052] As a preferred embodiment, the solution of this application is specifically implemented as follows: In practical applications, the operator installs a laser fiber 3 with a nominal power of "30W-A type" onto the laser therapy machine. The pre-output power generation unit first reads the binary data in the fiber optic tag and parses the pre-output power parameters via the NFC protocol: an upper limit of 32W, a lower limit of 28W, and a recommended operating range of 29W~31W. The microcontroller generates a control signal based on these parameters, driving the laser drive circuit to adjust the actual output power to the initial target value within the recommended operating range, for example, set to 30W. The photoelectric sensor monitors the laser output power in real time. When it detects that the actual power has dropped to 28.5W due to fluctuations in the light source or changes in circuit temperature, the microcontroller automatically corrects the control signal, increasing the drive current to bring the power back to around 30W within the recommended range. If the power unexpectedly rises to 31.5W, the microcontroller similarly reduces the control signal to stabilize the power below 31W.

[0053] Through the above scheme, the photoelectric sensor of the pre-output power generation unit in this application monitors the actual output power in real time and feeds the data back to the microcontroller. Based on the real-time monitoring results, the control signal can be automatically adjusted, thereby correcting the output power deviation and achieving precise and stable power control. This prevents excessive power from causing tissue damage or equipment wear, and prevents insufficient power from affecting treatment or processing results.

[0054] This application further proposes a method for a microcontroller to dynamically correct control signals based on monitoring results, including: The actual output power is compared with the upper limit of the pre-output power, the lower limit of the pre-output power, and the recommended operating range to obtain the deviation; a correction amount is generated based on the deviation; digital filtering, noise reduction, and anti-integral saturation processing are applied to the correction amount, and the output change rate is limited; the processed correction amount is mapped to the drive signal of the laser drive circuit through pulse width modulation to complete the power adjustment.

[0055] Specifically, the microcontroller first compares the actual output power collected in real time by the photoelectric sensor with the upper and lower limits of the pre-output power and the recommended operating range obtained from the electronic tag analysis, and calculates the power deviation. For example, if the actual power is 30.5W and the recommended operating range is 29W~31W, the deviation is +0.5W, indicating that the output power is slightly higher than the target median. Based on this deviation, the microcontroller generates a power correction amount to adjust the output of the laser drive circuit. To ensure a smooth and reliable adjustment process, the microcontroller performs digital filtering on the correction amount to remove the interference of instantaneous noise signals on the control; at the same time, it implements anti-integral saturation processing to avoid power overshoot or undershoot caused by long-term accumulated errors; and it limits the output change rate to ensure that the power adjustment is not too fast and causes laser fluctuations or equipment damage. The correction amount after the above processing is then converted into the actual drive signal of the laser drive circuit through pulse width modulation (PWM). For example, if the correction amount is +0.5W, the duty cycle of the PWM signal will be increased accordingly, increasing the drive current, and the actual output power gradually returns to the target value within the recommended range. The entire process forms a closed-loop feedback control, ensuring that the output power of the laser fiber can be dynamically stabilized within the preset safety range, achieving precise, safe, and efficient power regulation.

[0056] As a preferred embodiment, the solution of this application is specifically implemented as follows: In practical applications, the electronic tag for the laser fiber 3 specifies an upper limit of 32W and a lower limit of 28W for the pre-output power, with a recommended operating range of 29W to 31W. When the photoelectric sensor detects an actual output power of 31.5W during treatment, the microcontroller first calculates the deviation: 31.5W - 30W (target median) = +1.5W, indicating that the output power is slightly higher than the target. The microcontroller then generates a correction based on this deviation and performs digital filtering and denoising to eliminate the interference of instantaneous signal fluctuations on power adjustment. Simultaneously, anti-integral saturation processing prevents power overshoot due to accumulated errors, and rate limiting ensures a smooth power adjustment process, avoiding sudden increases or decreases that could affect the laser or the patient. The processed correction is mapped to the control signal of the laser drive circuit via pulse width modulation (PWM). For example, the PWM duty cycle is fine-tuned to reduce the drive current, causing the actual output power to gradually decrease from 31.5W to the recommended midpoint of 30W.

[0057] Through the above scheme, the microcontroller of this application can achieve real-time, accurate, and safe control of the laser fiber output power. The actual output power is compared with the upper and lower limits of the pre-output power and the recommended operating range. After obtaining the deviation, a correction value is generated. Through digital filtering, noise reduction, and anti-integral saturation processing, instantaneous interference and accumulated errors are eliminated, while the output change rate is limited, ensuring smooth and reliable power adjustment. The processed correction value is mapped to the control signal of the laser drive circuit through pulse width modulation, realizing dynamic adjustment of the actual output power.

[0058] This application further proposes that when the record generation unit automatically captures the structured data of this operation, it includes: The record generation unit storage process adopts a hierarchical processing mechanism, with hot data being written to the memory buffer in real time and cold data being archived to the distributed file area periodically.

[0059] Specifically, the record generation unit employs a hierarchical processing mechanism to achieve efficient and reliable data capture and storage during laser fiber operation. Hot data, including real-time acquired key parameters such as laser fiber specifications, target power value, actual output power, operating status, and timestamps, is immediately written to a memory buffer. This memory buffer supports high-speed read / write and circular storage, ensuring data integrity even during rapid laser changes or continuous operation, while also enabling real-time querying and analysis. Cold data, such as historical operation records, statistical logs, and structured reports, is periodically archived from the memory buffer to a distributed file area. This distributed file area utilizes multi-node redundant storage and a block-based mechanism to ensure long-term data reliability and high availability, while also supporting large-scale concurrent access and batch analysis.

[0060] Through the above scheme, this application employs a hierarchical processing mechanism, enabling the record generation unit to achieve efficient data management while ensuring data integrity and security. Hot data is written to the memory buffer in real time, allowing key operating parameters such as laser fiber specifications, target power values, actual output power, and operating status to be recorded and retrieved instantly, thus supporting real-time monitoring, rapid feedback, and closed-loop control. Cold data is periodically archived to a distributed file area, ensuring the long-term reliable storage of historical operation records and statistical information, and supporting large-scale concurrent access and subsequent analysis.

[0061] In summary, a high-resolution industrial camera combined with OCR, convolutional recurrent neural networks, and Siamese neural networks is used for paper label recognition and image comparison. A dynamic weight allocation mechanism comprehensively determines the laser fiber specifications, avoiding human error and improving recognition accuracy. A keyword-based distributed database retrieval and verification mechanism, supplemented by backup recognition channels and data integrity verification technology, ensures accurate and reliable acquisition of electronic tag information. The electronic tag parameters are mapped to the upper and lower limits of the pre-output power and the recommended working range through a pre-output power generation unit. A microcontroller, combined with feedback from photoelectric sensors, performs closed-loop dynamic adjustment, employing PID control, adaptive gain, or filtering and noise reduction algorithms for dynamic correction. This achieves stable laser fiber output power and rapid response, ensuring treatment safety and accuracy. A hierarchical storage mechanism captures and stores structured data in real time, including timestamps, fiber specifications, target power values, actual output power, and operating status, providing reliable data for subsequent quality control, maintenance, and data analysis, achieving operational traceability and digital equipment management.

[0062] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0063] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0064] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0065] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A superpulse thulium fiber laser therapy machine with radio frequency identification (RFID) function, characterized in that, include: Display device, main body, and laser fiber; The main body internally includes a laser, electrical components, a coupler, a control motherboard, a thulium optical fiber, and a radio frequency identification (RFID) device. The laser and the coupler are connected via the thulium optical fiber. The electrical components are electrically connected to both the laser and the coupler. The control motherboard is electrically connected to both the laser and the electrical components. The laser optical fiber is connected to the coupler. The RFID device is electrically connected to both the control motherboard and the electrical components. The radio frequency identification device includes an image acquisition unit, an electronic tag reading unit, a pre-output power generation unit, and a recording generation unit; the image acquisition unit is used to acquire image data of the laser fiber using an industrial camera, the industrial camera is equipped with a ring LED light source, and the image data is transmitted in real time through a standard industrial vision protocol. Adaptive threshold binarization and semantic segmentation techniques are used for image preprocessing to separate the label region from the background region. The image data is used for paper label recognition and image comparison recognition, and the laser fiber specifications are determined through a dynamic weight allocation mechanism. The electronic tag reading unit is used to retrieve electronic tags from the central information database based on keywords according to the laser fiber specifications determined by the image acquisition unit. The pre-output power generation unit is used to parse the binary stored data in the electronic tag using NFC protocol data reading and verification technology combined with TLV structure parsing algorithm, extract the pre-output power parameters through parameter extraction and mapping mechanism, and drive the laser drive circuit to adjust the actual output power. The record generation unit is used to automatically capture the structured data of this operation.

2. The ultrapulse thulium fiber laser therapy machine with radio frequency identification function according to claim 1, characterized in that, When the image acquisition unit performs paper label recognition and image comparison recognition based on the image data, it includes: When the image acquisition unit performs paper label recognition, it uses an optical character recognition engine combined with a convolutional recurrent neural network model to process the text area. When performing image comparison and recognition, the image acquisition unit uses a twin neural network architecture to extract image features from the image data and performs similarity matching with standard image data in the information database.

3. The ultrapulse thulium fiber laser therapy machine with radio frequency identification function according to claim 2, characterized in that, When the image acquisition unit comprehensively evaluates and determines the laser fiber specifications through a dynamic weight allocation mechanism, it includes: The image acquisition unit comprehensively evaluates the recognition confidence level, image comparison similarity, and uses a dynamic weight allocation mechanism. The confidence level is generated by the internal evaluation of the recognition model, and the similarity is determined by the feature matching process. The results of the two are fused according to a preset ratio to determine the final specifications of the laser fiber.

4. The ultrapulse thulium fiber laser therapy machine with radio frequency identification function according to claim 3, characterized in that, When the electronic tag reading unit retrieves electronic tags from the central information database based on keywords, it includes: The electronic tag reading unit initiates a query request to the central information database based on the laser fiber specifications determined by the image acquisition unit. The central information database adopts a distributed database architecture, supports fast keyword-based retrieval, and integrates a fuzzy matching algorithm to handle specification variations. It automatically verifies the integrity of tag data, and if an anomaly is detected, it switches to a backup identification channel to ensure accurate acquisition of electronic tag information.

5. The ultrapulse thulium fiber laser therapy machine with radio frequency identification function according to claim 4, characterized in that, The central information database adopts a distributed database architecture and supports fast keyword-based retrieval, including: The central database preprocesses the stored electronic tag data, decomposing the specifications of each electronic tag into independent keyword units. An inverted index technique is used to construct a keyword index structure, with the keyword as the root node, extending downwards to a list of all electronic tag record identifiers containing that keyword, with each identifier arranged in numerical order. The query dispatcher parses the keyword sequence in the query request and standardizes it according to the semantic features of the keywords. The query dispatcher then splits the standardized keywords into independent query units and distributes each independent query unit to multiple database nodes according to the distributed database architecture. It traverses the index tree, locates the list of record identifiers corresponding to the keywords, and uses a binary search algorithm to quickly filter out the matching identifier set.

6. The ultrapulse thulium fiber laser therapy machine with radio frequency identification function according to claim 5, characterized in that, When the electronic tag reading unit detects an anomaly and switches to the backup identification channel, it includes: The electronic tag reading unit acquires the original tag data stream and sends it to the verification calculation module. The verification calculation module divides the original tag data stream into continuous data segments. Each data segment is divided by modulo-2 using a generator polynomial to generate a corresponding check code sequence. All check code sequences are combined into a unified original data checksum value. The verification comparison module receives the real-time data checksum value and synchronously retrieves the original data checksum value of the corresponding electronic tag record from the central information database for comparison. When all bits are completely consistent, the data is determined to be complete and error-free, the verification result is marked as passed, and the electronic tag information is confirmed to be valid. When any bit mismatch is detected, the data integrity is determined to be abnormal, and the verification result is marked as failed.

7. A superpulse thulium fiber laser therapy machine with radio frequency identification function according to claim 6, characterized in that, When the pre-output power generation unit extracts the pre-output power parameters through the parameter extraction and mapping mechanism, it includes: The pre-output power generation unit converts the parameter fields of the pre-output power into the upper limit of pre-output power, the lower limit of pre-output power, and the recommended working range through a parameter extraction and mapping mechanism.

8. The ultrapulse thulium fiber laser therapy machine with radio frequency identification function according to claim 7, characterized in that, When the pre-output power generation unit drives the laser driving circuit to adjust the actual output power, it includes: The actual output power is monitored in real time using photoelectric sensors and compared with the upper limit of the pre-output power, the lower limit of the pre-output power, and the recommended operating range; and the control signal is dynamically corrected based on the monitoring results.

9. A superpulse thulium fiber laser therapy machine with radio frequency identification function according to claim 8, characterized in that, When the pre-output power generation unit dynamically corrects the control signal based on the monitoring results, it includes: The actual output power is compared with the upper limit of the pre-output power, the lower limit of the pre-output power, and the recommended operating range to obtain the deviation; a correction amount is generated based on the deviation; digital filtering, noise reduction, and anti-integral saturation processing are applied to the correction amount, and the output change rate is limited; the processed correction amount is mapped to the drive signal of the laser drive circuit through pulse width modulation to complete the power adjustment.

10. A superpulse thulium fiber laser therapy machine with radio frequency identification function according to claim 9, characterized in that, When the record generation unit automatically captures the structured data of this operation, it includes: The storage process of the record generation unit adopts a hierarchical processing mechanism, with hot data being written to the memory buffer in real time and cold data being archived to the distributed file area periodically.

Images